|
casa
$Rev:20696$
|
|
casa
$Rev:20696$
|
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('_imager', [dirname(__file__)]) 00017 except ImportError: 00018 import _imager 00019 return _imager 00020 if fp is not None: 00021 try: 00022 _mod = imp.load_module('_imager', fp, pathname, description) 00023 finally: 00024 fp.close() 00025 return _mod 00026 _imager = swig_import_helper() 00027 del swig_import_helper 00028 else: 00029 import _imager 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 imager(_object): 00071 """Proxy of C++ casac::imager class""" 00072 __swig_setmethods__ = {} 00073 __setattr__ = lambda self, name, value: _swig_setattr(self, imager, name, value) 00074 __swig_getmethods__ = {} 00075 __getattr__ = lambda self, name: _swig_getattr(self, imager, name) 00076 __repr__ = _swig_repr 00077 __swig_setmethods__["_pixels_advise"] = _imager.imager__pixels_advise_set 00078 __swig_getmethods__["_pixels_advise"] = _imager.imager__pixels_advise_get 00079 if _newclass:_pixels_advise = _swig_property(_imager.imager__pixels_advise_get, _imager.imager__pixels_advise_set) 00080 __swig_setmethods__["_cell_advise"] = _imager.imager__cell_advise_set 00081 __swig_getmethods__["_cell_advise"] = _imager.imager__cell_advise_get 00082 if _newclass:_cell_advise = _swig_property(_imager.imager__cell_advise_get, _imager.imager__cell_advise_set) 00083 __swig_setmethods__["_facets_advise"] = _imager.imager__facets_advise_set 00084 __swig_getmethods__["_facets_advise"] = _imager.imager__facets_advise_get 00085 if _newclass:_facets_advise = _swig_property(_imager.imager__facets_advise_get, _imager.imager__facets_advise_set) 00086 __swig_setmethods__["_phasecenter_advise"] = _imager.imager__phasecenter_advise_set 00087 __swig_getmethods__["_phasecenter_advise"] = _imager.imager__phasecenter_advise_get 00088 if _newclass:_phasecenter_advise = _swig_property(_imager.imager__phasecenter_advise_get, _imager.imager__phasecenter_advise_set) 00089 __swig_setmethods__["_bmaj_fitpsf"] = _imager.imager__bmaj_fitpsf_set 00090 __swig_getmethods__["_bmaj_fitpsf"] = _imager.imager__bmaj_fitpsf_get 00091 if _newclass:_bmaj_fitpsf = _swig_property(_imager.imager__bmaj_fitpsf_get, _imager.imager__bmaj_fitpsf_set) 00092 __swig_setmethods__["_bmin_fitpsf"] = _imager.imager__bmin_fitpsf_set 00093 __swig_getmethods__["_bmin_fitpsf"] = _imager.imager__bmin_fitpsf_get 00094 if _newclass:_bmin_fitpsf = _swig_property(_imager.imager__bmin_fitpsf_get, _imager.imager__bmin_fitpsf_set) 00095 __swig_setmethods__["_bpa_fitpsf"] = _imager.imager__bpa_fitpsf_set 00096 __swig_getmethods__["_bpa_fitpsf"] = _imager.imager__bpa_fitpsf_get 00097 if _newclass:_bpa_fitpsf = _swig_property(_imager.imager__bpa_fitpsf_get, _imager.imager__bpa_fitpsf_set) 00098 __swig_setmethods__["_pointsource_sensitivity"] = _imager.imager__pointsource_sensitivity_set 00099 __swig_getmethods__["_pointsource_sensitivity"] = _imager.imager__pointsource_sensitivity_get 00100 if _newclass:_pointsource_sensitivity = _swig_property(_imager.imager__pointsource_sensitivity_get, _imager.imager__pointsource_sensitivity_set) 00101 __swig_setmethods__["_relative_sensitivity"] = _imager.imager__relative_sensitivity_set 00102 __swig_getmethods__["_relative_sensitivity"] = _imager.imager__relative_sensitivity_get 00103 if _newclass:_relative_sensitivity = _swig_property(_imager.imager__relative_sensitivity_get, _imager.imager__relative_sensitivity_set) 00104 __swig_setmethods__["_sumweights_sensitivity"] = _imager.imager__sumweights_sensitivity_set 00105 __swig_getmethods__["_sumweights_sensitivity"] = _imager.imager__sumweights_sensitivity_get 00106 if _newclass:_sumweights_sensitivity = _swig_property(_imager.imager__sumweights_sensitivity_get, _imager.imager__sumweights_sensitivity_set) 00107 def __init__(self): 00108 """__init__(self) -> imager""" 00109 this = _imager.new_imager() 00110 try: self.this.append(this) 00111 except: self.this = this 00112 __swig_destroy__ = _imager.delete_imager 00113 __del__ = lambda self : None; 00114 def advise(self, *args, **kwargs): 00115 """ 00116 advise(self, takeadvice=True, amplitudeloss=0.05, fieldofview=initialize_variant("1.0deg"), _pixels=_pixels_advise, 00117 _cell=_cell_advise, _facets=_facets_advise, _phasecenter=_phasecenter_advise) -> bool 00118 00119 Summary 00120 Advise (and optionally use) parameter values 00121 00122 Input Parameters: 00123 takeadvice Use the advised values? true 00124 amplitudeloss Maximum fractional amplitude loss due to faceting 0.05 00125 fieldofview Desired field of view 1.0deg 00126 00127 00128 Output Parameters: 00129 _pixels Number of pixels on a side 00130 _cell Recommended maximum cellsize 00131 _facets Recommended number of facets on one axis 00132 _phasecenter Direction of phase center as a measure 00133 00134 -------------------------------------------------------------------------------- 00135 00136 """ 00137 return _imager.imager_advise(self, *args, **kwargs) 00138 00139 def advisechansel(self, *args, **kwargs): 00140 """ 00141 advisechansel(self, freqstart=1.0e6, freqend=1.1e6, freqstep=100.0, freqframe=string("LSRK"), msname=string(""), 00142 fieldid=0, getfreqrange=False, spwselection=string("")) -> record * 00143 00144 Summary 00145 Advise on spw and chan selection optimal for the image frequency range wanted 00146 00147 Input Parameters: 00148 freqstart Begining of frequency range in Hz 1.0e6 00149 freqend End of frequency range in Hz 1.1e6 00150 freqstep spectral channel resolution of intended image in Hz 100.0 00151 freqframe frame in which frequency is being expressed in other parameters LSRK 00152 msname name of an ms, if empty string it will use the ms's used in selectvis 00153 fieldid fieldid to use when msname is not empty otherwise ignored and field selected in selectvis is used 0 00154 getfreqrange if set then freqrange is returned in the frame requested for the data selected false 00155 spwselection if getfreqrange=True then this is needed to find the range of frequency in the frame requested 00156 00157 -------------------------------------------------------------------------------- 00158 00159 """ 00160 return _imager.imager_advisechansel(self, *args, **kwargs) 00161 00162 def approximatepsf(self, *args, **kwargs): 00163 """ 00164 approximatepsf(self, psf=string(""), async=False) -> bool 00165 00166 Summary 00167 Calculate approximate point spread functions 00168 00169 Input Parameters: 00170 psf Name of output point spread function 00171 async Run asynchronously in the background false 00172 00173 -------------------------------------------------------------------------------- 00174 00175 """ 00176 return _imager.imager_approximatepsf(self, *args, **kwargs) 00177 00178 def boxmask(self, *args, **kwargs): 00179 """ 00180 boxmask(self, mask=string(""), blc=initialize_vector(4,(int)0, (int)0, (int)0, (int)0), trc=std::vector< int >(), 00181 value=1.0) -> bool 00182 00183 Summary 00184 Construct a mask image from blc, trc 00185 00186 Input Parameters: 00187 mask name of mask image 00188 blc Bottom left corner 0 0 0 0 00189 trc Top right corner, should be image shape 00190 value Value to fill in 1.0 00191 00192 -------------------------------------------------------------------------------- 00193 00194 """ 00195 return _imager.imager_boxmask(self, *args, **kwargs) 00196 00197 def calcuvw(self, *args, **kwargs): 00198 """ 00199 calcuvw(self, fields=initialize_vector(1, (int)-1), refcode=string(""), reuse=True) -> bool 00200 00201 Summary 00202 Calculates (u, v, w) coordinates for the ms. 00203 00204 Input Parameters: 00205 fields Field IDs (numbered relative to 0) to operate on. Blank = all. -1 00206 refcode Reference frame to use for the generated (u, v, w)s. WARNING: clean and the im tool ignore the reference frame claimed by the UVW column (it is often mislabelled as ITRF when it is really J2000) and instead assume the (u, v, w)s are in the same frame as the phase tracking center. calcuvw does not yet force the UVW column and field centers to use the same reference frame! Blank = use the phase tracking frame of vis. 00207 reuse Start from the UVWs in vis (True) or calculate them from the antenna positions? true 00208 00209 -------------------------------------------------------------------------------- 00210 00211 """ 00212 return _imager.imager_calcuvw(self, *args, **kwargs) 00213 00214 def clean(self, *args, **kwargs): 00215 """ 00216 clean(self, algorithm=string("clark"), niter=1000, gain=0.1, threshold=initialize_variant("0.0Jy"), 00217 displayprogress=False, model=std::vector< string >(1, ""), keepfixed=initialize_vector(1, (bool)false), 00218 complist=string(""), mask=std::vector< string >(1, ""), 00219 image=std::vector< string >(1, ""), residual=std::vector< string >(1, ""), 00220 psfimage=std::vector< string >(1, ""), interactive=False, 00221 npercycle=100, masktemplate=string(""), async=False) -> record * 00222 00223 Summary 00224 Calculate a deconvolved image with selected clean algorithm 00225 00226 Input Parameters: 00227 algorithm Algorithm to use clark clarkstokes hogbom multiscale mfclark mfclarkstokes csclean csfast mfhogbom mfmultiscale wfclark wfhogbom clark 00228 niter Number of Iterations, set to zero for no CLEANing 1000 00229 gain Loop Gain for CLEANing 0.1 00230 threshold Flux level at which to stop CLEANing 0.0Jy 00231 displayprogress Display the progress of the cleaning? false 00232 model Names of clean model images 00233 keepfixed Keep one or more models fixed false 00234 complist Name of component list 00235 mask Names of mask images used for CLEANing 00236 image Names of restored images 00237 residual Names of residual images 00238 psfimage Names of psfs if they are needed 00239 interactive whether to stop clean and interactively mask false 00240 npercycle If interactive is 'T', then no of iter of clean before stopping, usually a fraction of niter 100 00241 masktemplate If non empty then will use this image to make the mask the first time 00242 async Run asynchronously in the background? false 00243 00244 -------------------------------------------------------------------------------- 00245 00246 """ 00247 return _imager.imager_clean(self, *args, **kwargs) 00248 00249 def clipimage(self, *args, **kwargs): 00250 """ 00251 clipimage(self, image=string(""), threshold=initialize_variant("0.0Jy")) -> bool 00252 00253 Summary 00254 Zero all pixels where Stokes I is below 00255 a threshold 00256 00257 Input Parameters: 00258 image name of image 00259 threshold Threshold 0.0Jy 00260 00261 -------------------------------------------------------------------------------- 00262 00263 """ 00264 return _imager.imager_clipimage(self, *args, **kwargs) 00265 00266 def clipvis(self, *args, **kwargs): 00267 """ 00268 clipvis(self, threshold=initialize_variant("0.0Jy")) -> bool 00269 00270 Summary 00271 Flag visibilities where residual exceeds 00272 a threshold 00273 00274 Input Parameters: 00275 threshold Threshold 0.0Jy 00276 00277 -------------------------------------------------------------------------------- 00278 00279 """ 00280 return _imager.imager_clipvis(self, *args, **kwargs) 00281 00282 def close(self): 00283 """ 00284 close(self) -> bool 00285 00286 Summary 00287 Close the imager tool, with data written on disk, keeping imager process running for future use 00288 -------------------------------------------------------------------------------- 00289 00290 """ 00291 return _imager.imager_close(self) 00292 00293 def defineimage(self, *args, **kwargs): 00294 """ 00295 defineimage(self, nx=128, ny=-1, cellx=initialize_variant("1.0"), celly=initialize_variant(""), stokes=string("I"), 00296 phasecenter=initialize_variant("0"), mode=string("mfs"), nchan=-1, 00297 start=initialize_variant("0"), step=initialize_variant("1"), spw=initialize_vector(1, (int)0), 00298 restfreq=initialize_variant(""), outframe=string("LSRK"), 00299 veltype=string("radio"), facets=1, movingsource=initialize_variant(""), 00300 distance=initialize_variant("0.0")) -> bool 00301 00302 Summary 00303 Set the image parameters for subsequent processing 00304 00305 Input Parameters: 00306 nx Total number of spatial pixels in x 128 00307 ny Total number of spatial pixels in y -1 00308 cellx Cellsize in x (e.g. '1arcsec') 1.0 00309 celly Cellsize in y (e.g. '1arcsec') 00310 stokes Stokes parameters to image (e.g. 'IQUV') IV IQU IQUV I 00311 phasecenter Direction of phase center as a diretion measure or a field id 0 00312 mode Type of processing (velocity =radiovelocity) frequency radiovelocity opticalvelocity truevelocity mfs 00313 nchan Number of channels; a -1 (default) means all the channels as selected in selectvis and combined into one continuum channel -1 00314 start Start channel; A 0-relative channel number of the spwid or a frequency quantity or a velocity quantity or radial velocity measure 0 00315 step Step in channel; integer for number of channels or frequency quantity or velocity quantity or radial velocity measure 1 00316 spw Spectral Window Id (0 relative) that defines center of image 0 00317 restfreq rest frequency to use; default => use the one available in ms 00318 outframe frequency frame of output image (default LSRK, '' => as input ms or LSRK in case of multiple ms's), options are LSRK, LSRD, BARY, GALACTO, LGROUP, CMB LSRK LSRK LSRD BARY GALACTO LGROUP CMB 00319 veltype velocity definition ('radio' or 'optical' or 'relativistic') optical relativistic radio 00320 facets Number of facets on each axis 1 00321 movingsource Name of moving source, e.g planet or moon, to keep fixed in image 00322 distance Distance to object: usually ignore this! (m) 0.0 00323 00324 -------------------------------------------------------------------------------- 00325 00326 """ 00327 return _imager.imager_defineimage(self, *args, **kwargs) 00328 00329 def done(self): 00330 """ 00331 done(self) -> bool 00332 00333 Summary 00334 Terminate the imager process 00335 -------------------------------------------------------------------------------- 00336 00337 """ 00338 return _imager.imager_done(self) 00339 00340 def drawmask(self, *args, **kwargs): 00341 """ 00342 drawmask(self, image=string(""), mask=string(""), niter=0, npercycle=0, threshold=string("0 mJy")) -> record * 00343 00344 Summary 00345 Allows you do draw mask using the viewer 00346 00347 Input Parameters: 00348 image name of template image 00349 mask name of image to save mask in 00350 niter Total number of iteration to display in box; just for display or python packaging 0 00351 npercycle npercycle value to display in box; just for display or python packaging 0 00352 threshold threshold to display in box ; just for display or python packaging 0 mJy 00353 00354 -------------------------------------------------------------------------------- 00355 00356 """ 00357 return _imager.imager_drawmask(self, *args, **kwargs) 00358 00359 def exprmask(self, *args, **kwargs): 00360 """ 00361 exprmask(self, mask=string(""), expr=1.0) -> bool 00362 00363 Summary 00364 Construct a mask image from a LEL expression 00365 00366 Input Parameters: 00367 mask name of mask image 00368 expr Value to set the mask to. Any scalar or LEL expression 1.0 00369 00370 -------------------------------------------------------------------------------- 00371 00372 """ 00373 return _imager.imager_exprmask(self, *args, **kwargs) 00374 00375 def feather(self, *args, **kwargs): 00376 """ 00377 feather(self, image=string(""), highres=string(""), lowres=string(""), lowpsf=string(""), effdishdiam=-1.0, 00378 showplot=False, async=False) -> bool 00379 00380 Summary 00381 Feather together an interferometer and a single dish image 00382 in the Fourier plane 00383 00384 Input Parameters: 00385 image Name of output feathered image 00386 highres Name of high resolution (interferometer) image 00387 lowres Name of low resolution (single dish) image 00388 lowpsf Name of optional low resolution point spread function 00389 effdishdiam Optional new SD dish diameter in m to use in feathering; can be smaller than true dish size -1.0 00390 showplot Plot the feather function false 00391 async Run asynchronously in the background false 00392 00393 -------------------------------------------------------------------------------- 00394 00395 """ 00396 return _imager.imager_feather(self, *args, **kwargs) 00397 00398 def filter(self, *args, **kwargs): 00399 """ 00400 filter(self, type=string("gaussian"), bmaj=initialize_variant("1arcsec"), bmin=initialize_variant("1arcsec"), 00401 bpa=initialize_variant("0deg"), async=False) -> bool 00402 00403 Summary 00404 Apply additional weighting by filtering (u-v taper) 00405 00406 Input Parameters: 00407 type Type of filtering or u-v tapering gaussian 00408 bmaj Major axis of filter 1arcsec 00409 bmin Minor axis of filter 1arcsec 00410 bpa Position angle of filter 0deg 00411 async Run asynchronously in the background false 00412 00413 -------------------------------------------------------------------------------- 00414 00415 """ 00416 return _imager.imager_filter(self, *args, **kwargs) 00417 00418 def fitpsf(self, *args, **kwargs): 00419 """ 00420 fitpsf(self, psf, async=False, _bmaj=_bmaj_fitpsf, _bmin=_bmin_fitpsf, _bpa=_bpa_fitpsf) -> bool 00421 00422 Summary 00423 Fit the point spread function, making psf image first if needed 00424 00425 Input Parameters: 00426 psf Name of input psf 00427 async Run asynchronously in the background false 00428 00429 00430 Output Parameters: 00431 _bmaj Major axis of beam 00432 _bmin Minor axis of beam 00433 _bpa Position angle of beam 00434 00435 -------------------------------------------------------------------------------- 00436 00437 """ 00438 return _imager.imager_fitpsf(self, *args, **kwargs) 00439 00440 def fixvis(self, *args, **kwargs): 00441 """ 00442 fixvis(self, fields=initialize_vector(1, (int)-1), phasedirs=std::vector< string >(1, ""), refcode=string(""), 00443 distances=initialize_vector(1, (double)0.0), datacolumn=string("all")) -> bool 00444 00445 Summary 00446 Performs visibility adjustments. 00447 00448 Input Parameters: 00449 fields Field IDs (numbered relative to 0) to operate on. Blank = all. -1 00450 phasedirs Phase tracking centers for each field in fields, in the same order. 00451 refcode Reference frame to use for the generated UVWs. WARNING: clean and the im tool ignore the reference frame claimed by the UVW column (it is often mislabelled as ITRF when it is really J2000) and instead assume the (u, v, w)s are in the same frame as the phase tracking center. calcuvw does not yet force the UVW column and field centers to use the same reference frame! Blank = use the phase tracking frame of vis. 00452 distances A list of distances (in m) for the fields listed in fields. 0 = infinity. 0.0 00453 datacolumn Which of DATA, MODEL_DATA, and/or CORRECTED_DATA to operate on. Default: 'all'. all 00454 00455 -------------------------------------------------------------------------------- 00456 00457 """ 00458 return _imager.imager_fixvis(self, *args, **kwargs) 00459 00460 def ft(self, *args, **kwargs): 00461 """ 00462 ft(self, model=std::vector< string >(1, ""), complist=string(""), incremental=False, async=False) -> bool 00463 00464 Summary 00465 Fourier transform the specified model and componentlist 00466 00467 Input Parameters: 00468 model Name of image 00469 complist Name of component list 00470 incremental Add to the existing MODEL\_DATA column? false 00471 async Run asynchronously in the background? false 00472 00473 -------------------------------------------------------------------------------- 00474 00475 """ 00476 return _imager.imager_ft(self, *args, **kwargs) 00477 00478 def getweightgrid(self, *args, **kwargs): 00479 """ 00480 getweightgrid(self, type=string("imaging"), wgtimages=std::vector< string >(1, "")) -> variant * 00481 00482 Summary 00483 get the requested weight grids 00484 00485 Input Parameters: 00486 type Type of weight requested (imaging, ftweight) imaging imaging ftweight 00487 wgtimages names of weightimages to save 00488 00489 -------------------------------------------------------------------------------- 00490 00491 """ 00492 return _imager.imager_getweightgrid(self, *args, **kwargs) 00493 00494 def linearmosaic(self, *args, **kwargs): 00495 """ 00496 linearmosaic(self, images=std::vector< string >(1, ""), mosaic=string(""), fluxscale=string(""), sensitivity=string(""), 00497 fieldids=initialize_vector(1, (int)0), usedefaultvp=True, 00498 vptable=string(""), async=False) -> bool 00499 00500 Summary 00501 Make a linear mosaic of several images 00502 00503 Input Parameters: 00504 images Input images to be mosaiced 00505 mosaic Output mosaic image 00506 fluxscale Fluxscale image 00507 sensitivity Sensitivity image 00508 fieldids List of field ids that correspond each of the images,used to center the PB of each image. (0-based list) 0 00509 usedefaultvp Use the default vp type? true 00510 vptable Voltage pattern table from the vpmanager for detailed specification 00511 async Run asynchronously in the background false 00512 00513 -------------------------------------------------------------------------------- 00514 00515 """ 00516 return _imager.imager_linearmosaic(self, *args, **kwargs) 00517 00518 def make(self, *args, **kwargs): 00519 """ 00520 make(self, image=string(""), async=False) -> bool 00521 00522 Summary 00523 Make an empty (i.e. blank) image 00524 00525 Input Parameters: 00526 image name of output image 00527 async Run asynchronously in the background false 00528 00529 -------------------------------------------------------------------------------- 00530 00531 """ 00532 return _imager.imager_make(self, *args, **kwargs) 00533 00534 def predictcomp(self, *args, **kwargs): 00535 """ 00536 predictcomp(self, objname=string(""), standard=string(""), epoch=initialize_variant("55555.0d"), freqs=initialize_vector(1,(double)1.0e11), 00537 pfx=string("predictcomp")) -> string 00538 00539 Summary 00540 Make a component list for a known object 00541 00542 Input Parameters: 00543 objname Name of the object 00544 standard Name of the flux standard 00545 epoch Time to use, as an epoch measure, e.g. me.epoch('UTC', '55555d'), for Solar System objects 55555.0d epoch measure 00546 freqs The frequencies to use, in Hz 1.0e11 00547 pfx Prefix for the name of the component list predictcomp 00548 00549 -------------------------------------------------------------------------------- 00550 00551 """ 00552 return _imager.imager_predictcomp(self, *args, **kwargs) 00553 00554 def makeimage(self, *args, **kwargs): 00555 """ 00556 makeimage(self, type=string("observed"), image=string(""), compleximage=string(""), verbose=True, 00557 async=False) -> bool 00558 00559 Summary 00560 Calculate images by gridding, etc. 00561 00562 Input Parameters: 00563 type Type of output image observed 00564 image Name of output image 00565 compleximage Name of output complex image 00566 verbose Report things like the center frequency to the logger true 00567 async Run asynchronously in the background false 00568 00569 -------------------------------------------------------------------------------- 00570 00571 """ 00572 return _imager.imager_makeimage(self, *args, **kwargs) 00573 00574 def makemodelfromsd(self, *args, **kwargs): 00575 """ 00576 makemodelfromsd(self, sdimage=string(""), modelimage=string(""), sdpsf=string(""), maskimage=string("")) -> bool 00577 00578 Summary 00579 Make an initial model image from a 00580 Single Dish image 00581 00582 Input Parameters: 00583 sdimage Single Dish image 00584 modelimage Name of output image to be used as model 00585 sdpsf PSF of Single Dish if needed 00586 maskimage mask image 00587 00588 -------------------------------------------------------------------------------- 00589 00590 """ 00591 return _imager.imager_makemodelfromsd(self, *args, **kwargs) 00592 00593 def mask(self, *args, **kwargs): 00594 """ 00595 mask(self, image=string(""), mask=string(""), threshold=initialize_variant("0.0Jy"), async=False) -> bool 00596 00597 Summary 00598 Construct a mask image by thresholding an image 00599 00600 Input Parameters: 00601 image name of template image 00602 mask name of mask image 00603 threshold threshold for mask 0.0Jy 00604 async Run asynchronously in the background false 00605 00606 -------------------------------------------------------------------------------- 00607 00608 """ 00609 return _imager.imager_mask(self, *args, **kwargs) 00610 00611 def mem(self, *args, **kwargs): 00612 """ 00613 mem(self, algorithm=string("entropy"), niter=20, sigma=initialize_variant("0.001Jy"), targetflux=initialize_variant("1.0Jy"), 00614 constrainflux=False, displayprogress=False, 00615 model=std::vector< string >(1, ""), keepfixed=initialize_vector(1, (bool)false), 00616 complist=string(""), prior=std::vector< string >(1, ""), mask=std::vector< string >(1, ""), 00617 image=std::vector< string >(1, ""), residual=std::vector< string >(1, ""), 00618 async=False) -> bool 00619 00620 Summary 00621 Calculate a deconvolved image with selected mem (maximum entropy) algorithm 00622 00623 Input Parameters: 00624 algorithm Algorithm to use entropy emptiness mfentropy mfemptiness entropy entropy 00625 niter Number of Iterations 20 00626 sigma Image sigma to try to achieve 0.001Jy 00627 targetflux Target flux for final image 1.0Jy 00628 constrainflux Constrain image to match target flux? else targetflux used only to initialize model false 00629 displayprogress Display the progress of the cleaning? false 00630 model Names of model images 00631 keepfixed Keep model fixed false 00632 complist Name of component list 00633 prior Names of mem prior images 00634 mask Names of mask images (0=>no emission, 1=>emission permitted 00635 image Names of restored images 00636 residual Names of residual images 00637 async Run asynchronously in the background? false 00638 00639 -------------------------------------------------------------------------------- 00640 00641 """ 00642 return _imager.imager_mem(self, *args, **kwargs) 00643 00644 def nnls(self, *args, **kwargs): 00645 """ 00646 nnls(self, model=std::vector< string >(1, ""), keepfixed=initialize_vector(1, (bool)false), 00647 complist=string(""), niter=0, tolerance=1e-06, fluxmask=std::vector< string >(1, ""), 00648 datamask=std::vector< string >(1, ""), image=std::vector< string >(1, ""), 00649 residual=std::vector< string >(1, ""), async=False) -> bool 00650 00651 Summary 00652 Calculate a deconvolved image using the 00653 NNLS algorithm 00654 00655 Input Parameters: 00656 model Name of image 00657 keepfixed Keep model fixed false 00658 complist Name of component list 00659 niter Number of Iterations, set to zero for no NNLS 0 00660 tolerance Tolerance for solution 1e-06 00661 fluxmask Name of mask for allowed flux 00662 datamask Name of mask for constraint pixels in dirty image 00663 image Names of restored images 00664 residual Names of restored images 00665 async Run asynchronously in the background false 00666 00667 -------------------------------------------------------------------------------- 00668 00669 """ 00670 return _imager.imager_nnls(self, *args, **kwargs) 00671 00672 def open(self, *args, **kwargs): 00673 """ 00674 open(self, thems=string(""), compress=False, usescratch=False) -> bool 00675 00676 Summary 00677 Open a new MeasurementSet, for processing, closing current MeasurementSet 00678 00679 Input Parameters: 00680 thems New MeasurementSet to be processed 00681 compress Compress calibration columns? false 00682 usescratch If true: Imager will use corrected data column and make scratch columns of they donot exist false 00683 00684 -------------------------------------------------------------------------------- 00685 00686 """ 00687 return _imager.imager_open(self, *args, **kwargs) 00688 00689 def pb(self, *args, **kwargs): 00690 """ 00691 pb(self, inimage=string(""), outimage=string(""), incomps=string(""), outcomps=string(""), 00692 operation=string("apply"), pointingcenter=string("N. pole"), parangle=initialize_variant("0.0deg"), 00693 pborvp=string("pb"), async=False) -> bool 00694 00695 Summary 00696 Applies or corrects for a primary beam 00697 00698 Input Parameters: 00699 inimage Input image to apply beam to 00700 outimage Output image after beam is applied 00701 incomps Input Componentlist table name 00702 outcomps Output Componentlist table name 00703 operation Operation correct apply 00704 pointingcenter Pointing center for primary beam application false N. pole 00705 parangle Parallactic angle for calculation 0.0deg 00706 pborvp Primary Beam or Voltage Pattern vb pb 00707 async Run asynchronously in the background false 00708 00709 -------------------------------------------------------------------------------- 00710 00711 """ 00712 return _imager.imager_pb(self, *args, **kwargs) 00713 00714 def plotsummary(self): 00715 """ 00716 plotsummary(self) -> bool 00717 00718 Summary 00719 Plot a summary of field and spectral window ids 00720 -------------------------------------------------------------------------------- 00721 00722 """ 00723 return _imager.imager_plotsummary(self) 00724 00725 def plotuv(self, rotate=False): 00726 """ 00727 plotuv(self, rotate=False) -> bool 00728 00729 Summary 00730 Plot the uv coverage 00731 00732 Input Parameters: 00733 rotate Rotate uvw coordinates to specified phase center? false 00734 00735 -------------------------------------------------------------------------------- 00736 00737 """ 00738 return _imager.imager_plotuv(self, rotate) 00739 00740 def plotvis(self, *args, **kwargs): 00741 """ 00742 plotvis(self, type=string("all"), increment=1) -> bool 00743 00744 Summary 00745 Plot the visibility amplitudes as a function of u-v radius (also, see visplot tool 00746 00747 Input Parameters: 00748 type Type of plot: can contain all, observed, corrected, model, residual all observed corrected model residual all 00749 increment Increment in points to plot 1 00750 00751 -------------------------------------------------------------------------------- 00752 00753 """ 00754 return _imager.imager_plotvis(self, *args, **kwargs) 00755 00756 def plotweights(self, gridded=False, increment=1): 00757 """ 00758 plotweights(self, gridded=False, increment=1) -> bool 00759 00760 Summary 00761 Plot the visibility weights as a function of u-v radius 00762 00763 Input Parameters: 00764 gridded Do gridded plot? false 00765 increment Increment in points to plot 1 00766 00767 -------------------------------------------------------------------------------- 00768 00769 """ 00770 return _imager.imager_plotweights(self, gridded, increment) 00771 00772 def regionmask(self, *args, **kwargs): 00773 """ 00774 regionmask(self, mask=string(""), region=initialize_record(""), boxes=initialize_variant(""), circles=initialize_variant(""), 00775 value=1.0) -> bool 00776 00777 Summary 00778 Construct a mask image from a region 00779 00780 Input Parameters: 00781 mask name of mask image 00782 region Region record usually from regionmanager unset 00783 boxes list of 4 elements lists e.g [[xblc1, yblc1, xtrc1, ytrc1], [[xblc2, yblc2, xtrc2, ytrc2]] 00784 circles list of 3 elements lists e.g [[rad0, xcen0, ycen0], [rad1,xcen1, ycen1], .....] 00785 value Value to set the mask to 1.0 00786 00787 -------------------------------------------------------------------------------- 00788 00789 """ 00790 return _imager.imager_regionmask(self, *args, **kwargs) 00791 00792 def regiontoimagemask(self, *args, **kwargs): 00793 """ 00794 regiontoimagemask(self, mask=string(""), region=initialize_record(""), boxes=initialize_variant(""), circles=initialize_variant(""), 00795 value=1.0) -> bool 00796 00797 Summary 00798 union a mask image with various regions 00799 00800 Input Parameters: 00801 mask name of mask image 00802 region Region record usually from regionmanager unset 00803 boxes list of 4 elements lists e.g [[xblc1, yblc1, xtrc1, ytrc1], [[xblc2, yblc2, xtrc2, ytrc2]] 00804 circles list of 3 elements lists e.g [[rad0, xcen0, ycen0], [rad1,xcen1, ycen1], .....] 00805 value Value to set the mask to 1.0 00806 00807 -------------------------------------------------------------------------------- 00808 00809 """ 00810 return _imager.imager_regiontoimagemask(self, *args, **kwargs) 00811 00812 def residual(self, *args, **kwargs): 00813 """ 00814 residual(self, model=std::vector< string >(1, ""), complist=string(""), image=std::vector< string >(1, ""), 00815 async=False) -> bool 00816 00817 Summary 00818 Calculate the residual image with respect to current model and component list 00819 00820 Input Parameters: 00821 model Names of input models 00822 complist Name of component list 00823 image Names of output residual images 00824 async Run asynchronously in the background false 00825 00826 -------------------------------------------------------------------------------- 00827 00828 """ 00829 return _imager.imager_residual(self, *args, **kwargs) 00830 00831 def restore(self, *args, **kwargs): 00832 """ 00833 restore(self, model=std::vector< string >(1, ""), complist=string(""), image=std::vector< string >(1, ""), 00834 residual=std::vector< string >(1, ""), async=False) -> bool 00835 00836 Summary 00837 Calculate the restored image with restored model, component list, and residuals 00838 00839 Input Parameters: 00840 model Names of input model 00841 complist Name of component list 00842 image Names of output restored images 00843 residual Names of residual images 00844 async Run asynchronously in the background false 00845 00846 -------------------------------------------------------------------------------- 00847 00848 """ 00849 return _imager.imager_restore(self, *args, **kwargs) 00850 00851 def updateresidual(self, *args, **kwargs): 00852 """ 00853 updateresidual(self, model=std::vector< string >(1, ""), complist=string(""), image=std::vector< string >(1, ""), 00854 residual=std::vector< string >(1, "")) -> bool 00855 00856 Summary 00857 Calculate the residual and restored images with new modified model, component list, 00858 00859 Input Parameters: 00860 model Names of input model 00861 complist Name of component list 00862 image Names of output restored images 00863 residual Names of residual images 00864 00865 -------------------------------------------------------------------------------- 00866 00867 """ 00868 return _imager.imager_updateresidual(self, *args, **kwargs) 00869 00870 def sensitivity(self, *args, **kwargs): 00871 """ 00872 sensitivity(self, async=False, _pointsource=_pointsource_sensitivity, _relative=_relative_sensitivity, 00873 _sumweights=_sumweights_sensitivity) -> bool 00874 00875 Summary 00876 Calculate rms sensitivity 00877 00878 Input Parameters: 00879 async Run asynchronously in the background false 00880 00881 00882 Output Parameters: 00883 _pointsource Calculated point source sensitivity (Jy/beam) 00884 _relative Calculated relative sensitivity 00885 _sumweights Calculated sum of weights 00886 00887 -------------------------------------------------------------------------------- 00888 00889 """ 00890 return _imager.imager_sensitivity(self, *args, **kwargs) 00891 00892 def setbeam(self, *args, **kwargs): 00893 """ 00894 setbeam(self, bmaj=initialize_variant("1.0arcsec"), bmin=initialize_variant("1.0arcsec"), bpa=initialize_variant("0deg"), 00895 async=False) -> bool 00896 00897 Summary 00898 Set the beam parameters for clean restoration 00899 00900 Input Parameters: 00901 bmaj Major axis of beam 1.0arcsec 00902 bmin Minor axis of beam 1.0arcsec 00903 bpa Position angle of beam 0deg 00904 async Run asynchronously in the background false 00905 00906 -------------------------------------------------------------------------------- 00907 00908 """ 00909 return _imager.imager_setbeam(self, *args, **kwargs) 00910 00911 def selectvis(self, *args, **kwargs): 00912 """ 00913 selectvis(self, vis=string(""), nchan=initialize_vector(1, (int)-1), start=initialize_vector(1, (int)0), 00914 step=initialize_vector(1, (int)1), spw=initialize_variant("-1"), field=initialize_variant("-1"), 00915 baseline=initialize_variant("-1"), time=initialize_variant(""), 00916 scan=initialize_variant(""), observation=initialize_variant(""), 00917 uvrange=initialize_variant(""), taql=string(""), usescratch=False, 00918 datainmemory=False, writeaccess=True) -> bool 00919 00920 Summary 00921 Select visibilities for subsequent processing 00922 00923 Input Parameters: 00924 vis Measurementset for which this selection applies; an empty string '' implies that it is to be applied in ms used in open 00925 nchan Number of channels to select -1 00926 start Start channels (0-relative) 0 00927 step Step in channel number 1 00928 spw Spectral Window Ids (0 relative) to select; -1 interpreted as all -1 00929 field Field Ids (0 relative) or Field names (msselection syntax and wilcards are used) to select -1 00930 baseline Antenna Ids (0 relative) or Antenna names (msselection syntax and wilcards are used) to select -1 00931 time Limit data selected to be within a given time range. The syntax is defined in the msselection link 00932 scan Limit data selected on scan numbers. The syntax is defined in the msselection link 00933 observation Limit data using observation IDs. The syntax is defined in the msselection link 00934 uvrange Limit data selected on uv distance. The syntax is defined in the msselection link 00935 taql For the TAQL experts, flexible data selection using the TAQL syntax 00936 usescratch If True: imager will use CORRECTED_DATA column and will make scratch columns and store Model visibilities after deconvolution false 00937 datainmemory If True: imager will load the selected ms memory; useful if imaging a few channels that fit in memory false 00938 writeaccess If False: imager will open the ms in readmode; useful for multiple process access to the MS true 00939 00940 -------------------------------------------------------------------------------- 00941 00942 """ 00943 return _imager.imager_selectvis(self, *args, **kwargs) 00944 00945 def setjy(self, *args, **kwargs): 00946 """ 00947 setjy(self, field=initialize_variant(""), spw=initialize_variant(""), modimage=string(""), fluxdensity=initialize_vector(4,(double)0.0, (double)0.0, (double)0.0, (double)0.0), 00948 standard=string("SOURCE"), 00949 scalebychan=False, spix=0.0, reffreq=initialize_variant("1GHz"), 00950 time=string(""), scan=string(""), observation=string("")) -> bool 00951 00952 Summary 00953 Compute the model visibility for a specified source flux density 00954 00955 Input Parameters: 00956 field Field Id (0-relative) or name 00957 spw Spectral Window Id. (0-relative) 00958 modimage A model image 00959 fluxdensity Specified flux density (I,Q,U,V) in Jy (lookup the value; use 1.0 if not found) 0.0 0.0 0.0 0.0 00960 standard Flux density standard Baars Perley 90 Perley-Taylor 95 Perley-Taylor 99 Perley-Butler 2010 SOURCE 00961 scalebychan Do the flux scaling on a per channel basis else on a spw basis. Effectively True if fluxdensity is specified. false 00962 spix Spectral index for fluxdensity. S = fluxdensity * (freq/reffreq)**spix 0.0 00963 reffreq Reference frequency for spix. 1GHz 00964 time Time range to operate on 00965 scan Scan(s) to operate on 00966 observation Observation ID(s) to operate on 00967 00968 -------------------------------------------------------------------------------- 00969 00970 """ 00971 return _imager.imager_setjy(self, *args, **kwargs) 00972 00973 def ssoflux(self): 00974 """ 00975 ssoflux(self) -> bool 00976 00977 Summary 00978 Use setjy instead. 00979 -------------------------------------------------------------------------------- 00980 00981 """ 00982 return _imager.imager_ssoflux(self) 00983 00984 def setmfcontrol(self, *args, **kwargs): 00985 """ 00986 setmfcontrol(self, cyclefactor=1.5, cyclespeedup=-1, cyclemaxpsffraction=0.8, stoplargenegatives=2, 00987 stoppointmode=-1, minpb=0.1, scaletype=string("NONE"), constpb=0.4, fluxscale=std::vector< string >(1, ""), 00988 flatnoise=True) -> bool 00989 00990 Summary 00991 Set various cycle control parameters for 00992 multi-field and wide-field imageing. 00993 00994 Input Parameters: 00995 cyclefactor Cycle threshold = max_resid * min(cyclemaxpsffraction, this * max_sidelobe) 1.5 00996 cyclespeedup Cycle threshold doubles in this number of iterations -1 00997 cyclemaxpsffraction Cycle threshold = max_resid * min(this, cyclefactor * max_sidelobe) 0.8 00998 stoplargenegatives Stop the multiscale cycle for the first n cycles when a negative comp is found on the largest scale 2 00999 stoppointmode Stop multiscale altogether if the smallest scale recieves this many consecutive components -1 01000 minpb Minimum PB level to use 0.1 01001 scaletype Image plane flux scale type SAULT NONE NONE 01002 constpb In Sault weighting the flux scale is constant above this PB level 0.4 01003 fluxscale Names of flux scale images for mosaicing 01004 flatnoise Set to false if clean component search is to be done in an optimal signal/noise residual image if true will clean in a constant noise image true 01005 01006 -------------------------------------------------------------------------------- 01007 01008 """ 01009 return _imager.imager_setmfcontrol(self, *args, **kwargs) 01010 01011 def setoptions(self, *args, **kwargs): 01012 """ 01013 setoptions(self, ftmachine=string("ft"), cache=-1, tile=16, gridfunction=string("SF"), location=initialize_variant(""), 01014 padding=1.0, freqinterp=string("nearest"), wprojplanes=1, 01015 epjtablename=string(""), applypointingoffsets=False, dopbgriddingcorrections=True, 01016 cfcachedirname=string(""), pastep=1.0, pblimit=0.05, imagetilevol=0, 01017 singleprecisiononly=False, numthreads=-1, psterm=True, aterm=True, wbawp=False) -> bool 01018 01019 Summary 01020 Set some general options for subsequent processing 01021 01022 Input Parameters: 01023 ftmachine Fourier transform machine ft ft sd both wproject mosaic 01024 cache Size of gridding cache in complex pixels; default use half the memory available on computer -1 01025 tile Size of a gridding tile in pixels (in 1 dimension) 16 01026 gridfunction Gridding function SF SF BOX PB GAUSS GJINC 01027 location Location used in phase rotations 01028 padding Padding factor in image plane (>=1.0) 1.0 01029 freqinterp interpolation mode in frequency;options:- nearest, linear, cubic, spline nearest nearest linear cubic spline 01030 wprojplanes No of gridding convolution functions used in wproject-ft machine 1 01031 epjtablename E-Jones table name. This is used if applypointingoffsets is set to True. 01032 applypointingoffsets Apply pointing offset corrections during deconvolution. false 01033 dopbgriddingcorrections Correct for PB gridding before prediction of visibilities. This should be True when doing deconvolution. This should be False when predicting visibilities for model sky with no primary beam attenuation in the model. true 01034 cfcachedirname Directory where convolution functions are to be (or are being ) cached on the disk. 01035 pastep The number of steps in Parallactic Angle used to compute the average power pattern. Since a linear interpolation is used to interpolate convolution functions during gridding, this can be different from PA increment to compute new convolution functions. This parameter is however not used yet. 1.0 01036 pblimit Primary beam limit when using PBWProjection 0.05 01037 imagetilevol Tile size on for image on disk (in pixel, multiply by 4 to get the byte size). It is safe to leave this as default, meant for usage on filesystem like Lustre, the default implies 32x32x4x32 tile shape 0 01038 singleprecisiononly Set this value to True to force single precision all the time. Otherwise imager may use double precision gridding (ft and wproject only for now) when it can and deems it fit. Setting to True can be handy on low memory machines false 01039 numthreads Limit the number of threads used in this run (openmp enabled only) -1 01040 psterm Switch-on the PS-Term? true 01041 aterm Switch-on the A-Term? true 01042 wbawp Trigger the WB A-Projection algorithm? false 01043 01044 -------------------------------------------------------------------------------- 01045 01046 """ 01047 return _imager.imager_setoptions(self, *args, **kwargs) 01048 01049 def setscales(self, *args, **kwargs): 01050 """ 01051 setscales(self, scalemethod=string("nscales"), nscales=5, uservector=initialize_vector(3,(double)0.0, (double)3.0, (double)10.0)) -> bool 01052 01053 Summary 01054 Set the scale sizes for MultiScale Clean 01055 01056 Input Parameters: 01057 scalemethod Method by which scales are set nscales uservector nscales 01058 nscales Number of scales 5 01059 uservector Vector of scale sizes in pixels to use, defaults should be 0,3,10 0.0 3.0 10.0 01060 01061 -------------------------------------------------------------------------------- 01062 01063 """ 01064 return _imager.imager_setscales(self, *args, **kwargs) 01065 01066 def setsmallscalebias(self, inbias=0.6): 01067 """ 01068 setsmallscalebias(self, inbias=0.6) -> bool 01069 01070 Summary 01071 Set bias toward smaller scales for MultiScale Clean 01072 01073 Input Parameters: 01074 inbias small scale bias 0.6 01075 01076 -------------------------------------------------------------------------------- 01077 01078 """ 01079 return _imager.imager_setsmallscalebias(self, inbias) 01080 01081 def settaylorterms(self, ntaylorterms=2, reffreq=0.0): 01082 """ 01083 settaylorterms(self, ntaylorterms=2, reffreq=0.0) -> bool 01084 01085 Summary 01086 Set the number of Taylor series terms for Multi-Frequency Clean 01087 01088 Input Parameters: 01089 ntaylorterms Number of Taylor terms 2 01090 reffreq Reference Frequency 0.0 01091 01092 -------------------------------------------------------------------------------- 01093 01094 """ 01095 return _imager.imager_settaylorterms(self, ntaylorterms, reffreq) 01096 01097 def setsdoptions(self, *args, **kwargs): 01098 """ 01099 setsdoptions(self, scale=1.0, weight=1.0, convsupport=-1, pointingcolumntouse=string("DIRECTION"), truncate=initialize_variant("-1pixel"), 01100 gwidth=initialize_variant("-1pixel"), 01101 jwidth=initialize_variant("-1pixel")) -> bool 01102 01103 Summary 01104 Set some options for single dish processing 01105 01106 Input Parameters: 01107 scale Scaling applied to single dish data 1.0 01108 weight Weights applied to single dish data 1.0 01109 convsupport number of pixel for convolution support -1 01110 pointingcolumntouse Which Pointing Table column to use to get direction DIRECTION DIRECTION TARGET ENCODER POINTING_OFFSET SOURCE_OFFSET 01111 truncate truncation radius (effective only for 'GAUSS' or 'GJINC') -1pixel 01112 gwidth radius of half maximum for gaussian (effective only for 'GAUSS' or 'GJINC') -1pixel 01113 jwidth c-parameter for jinc function (effective only for 'GJINC') -1pixel 01114 01115 -------------------------------------------------------------------------------- 01116 01117 """ 01118 return _imager.imager_setsdoptions(self, *args, **kwargs) 01119 01120 def setvp(self, *args, **kwargs): 01121 """ 01122 setvp(self, dovp=False, usedefaultvp=True, vptable=string(""), dosquint=False, parangleinc=initialize_variant("360deg"), 01123 skyposthreshold=initialize_variant("180deg"), telescope=string(""), 01124 verbose=True) -> bool 01125 01126 Summary 01127 Set the voltage pattern model for subsequent processing 01128 01129 Input Parameters: 01130 dovp Do voltage pattern (ie, primary beam) correction false 01131 usedefaultvp Look up the default VP for this telescope and frequency? true 01132 vptable If usedefaultvp is False, provide a VP Table made with vpmanager 01133 dosquint Activate the beam squint in the VP model false 01134 parangleinc Parallactice angle increment for squint application 360deg 01135 skyposthreshold Sky position threshold 180deg 01136 telescope Which default telescope to use; if empty use the one in encoded in MS 01137 verbose If false, suppress some messages from being sent to the logger. true 01138 01139 -------------------------------------------------------------------------------- 01140 01141 """ 01142 return _imager.imager_setvp(self, *args, **kwargs) 01143 01144 def setweightgrid(self, *args, **kwargs): 01145 """ 01146 setweightgrid(self, weight=initialize_variant(""), type=string("imaging")) -> bool 01147 01148 Summary 01149 set the requested weight grids 01150 01151 Input Parameters: 01152 weight Numeric array. Required input. 01153 type Type of weight requested imaging 01154 01155 -------------------------------------------------------------------------------- 01156 01157 """ 01158 return _imager.imager_setweightgrid(self, *args, **kwargs) 01159 01160 def smooth(self, *args, **kwargs): 01161 """ 01162 smooth(self, model=std::vector< string >(1, ""), image=std::vector< string >(1, ""), usefit=True, 01163 bmaj=initialize_variant("5.arcsec"), bmin=initialize_variant("5.arcsec"), 01164 bpa=initialize_variant("0deg"), normalize=True, async=False) -> bool 01165 01166 Summary 01167 Calculate an image smoothed with a Gaussian beam 01168 01169 Input Parameters: 01170 model Name of input model 01171 image Name of output smoothed images 01172 usefit Use the fitted value (rather than that specified true 01173 bmaj Major axis of beam 5.arcsec 01174 bmin Minor axis of beam 5.arcsec 01175 bpa Position angle of beam 0deg 01176 normalize Normalize volume of psf to unity true 01177 async Run asynchronously in the background false 01178 01179 -------------------------------------------------------------------------------- 01180 01181 """ 01182 return _imager.imager_smooth(self, *args, **kwargs) 01183 01184 def stop(self): 01185 """ 01186 stop(self) -> bool 01187 01188 Summary 01189 stop the currently executing function asap 01190 -------------------------------------------------------------------------------- 01191 01192 """ 01193 return _imager.imager_stop(self) 01194 01195 def summary(self): 01196 """ 01197 summary(self) -> bool 01198 01199 Summary 01200 Summarize the current state of the imager tool 01201 -------------------------------------------------------------------------------- 01202 01203 """ 01204 return _imager.imager_summary(self) 01205 01206 def uvrange(self, uvmin=0.0, uvmax=0.0): 01207 """ 01208 uvrange(self, uvmin=0.0, uvmax=0.0) -> bool 01209 01210 Summary 01211 Select data within the limit of a given range 01212 01213 Input Parameters: 01214 uvmin Minimum uv distance allowed (wavelengths) 0.0 01215 uvmax Maximum uv distance allowed (wavelengths) 0.0 01216 01217 -------------------------------------------------------------------------------- 01218 01219 """ 01220 return _imager.imager_uvrange(self, uvmin, uvmax) 01221 01222 def weight(self, *args, **kwargs): 01223 """ 01224 weight(self, type=string("natural"), rmode=string("none"), noise=initialize_variant("0.0Jy"), 01225 robust=0.0, fieldofview=initialize_variant("0.0arcsec"), npixels=0, mosaic=False, 01226 async=False) -> bool 01227 01228 Summary 01229 Apply additional weighting to the visibility weights 01230 01231 Input Parameters: 01232 type Type of weighting uniform natural briggs radial natural 01233 rmode Mode of briggs weighting norm abs none 01234 noise Noise used in absolute briggs weighting 0.0Jy 01235 robust Parameter in briggs weighting 0.0 01236 fieldofview Field of view for uniform weighting 0.0arcsec 01237 npixels Number of pixels in the u and v directions 0 01238 mosaic Individually weight the fields of a mosaic false 01239 async Run asynchronously in the background false 01240 01241 -------------------------------------------------------------------------------- 01242 01243 """ 01244 return _imager.imager_weight(self, *args, **kwargs) 01245 01246 imager_swigregister = _imager.imager_swigregister 01247 imager_swigregister(imager) 01248 cvar = _imager.cvar 01249 01250 # This file is compatible with both classic and new-style classes. 01251 01252
1.8.0
