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casa
$Rev:20696$
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casa
$Rev:20696$
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00001 # 00002 # This file was generated using xslt from its XML file 00003 # 00004 # Copyright 2008, Associated Universities Inc., Washington DC 00005 # 00006 import sys 00007 import os 00008 from casac import * 00009 import string 00010 import time 00011 import inspect 00012 import gc 00013 import numpy 00014 from odict import odict 00015 from task_deconvolve import deconvolve 00016 from task_deconvolve import casalog 00017 00018 class deconvolve_pg_: 00019 __name__ = "deconvolve" 00020 00021 def __init__(self) : 00022 self.__bases__ = (deconvolve_pg_,) 00023 self.__doc__ = self.__call__.__doc__ 00024 00025 00026 def __call__(self, imagename=None, model=None, psf=None, alg=None, niter=None, gain=None, threshold=None, mask=None, scales=None, sigma=None, targetflux=None, prior=None, async=None): 00027 00028 """Image based deconvolver 00029 00030 Several algorithms are available to deconvolve an image with a 00031 known psf (dirty beam), or a Gaussian beam. The algorithms 00032 available are clark and hogbom clean, a multiscale clean and a 00033 mem clean. For more deconvolution control, use clean. 00034 00035 Keyword arguments: 00036 imagename -- Name of input image to be deconvolved 00037 model -- Name of output image containing the clean components 00038 psf -- Name of psf image (dirty beam) to use 00039 example: psf='casaxmlf.image' . 00040 If the psf has 3 parameter, then a Gaussian 00041 psf is assumed with the values representing 00042 the major , minor and position angle values 00043 e.g psf=['3arcsec', '2.5arcsec', '10deg'] 00044 alg -- algorithm to use: default = 'clark' 00045 options: clark, hogbom, multiscale or mem. 00046 niter -- Maximum number of iterations 00047 gain -- CLEAN gain parameter; fraction to remove from peak 00048 threshold -- Halt deconvolution if the maximum residual image is 00049 below this threshold. 00050 default = '0.0Jy' 00051 mask -- mask image (same shape as image and psf) to limit region 00052 where deconvoltion is to occur 00053 00054 ------parameters useful for multiscale only 00055 scales -- in pixel numbers; the size of component to deconvolve. 00056 default value [0,3,10] 00057 recommended sizes are 0 (point), 3 (points per clean beam), and 00058 10 (about a factor of three lower resolution) 00059 ------parameters useful for mem only 00060 sigma -- Estimated noise for image 00061 targetflux -- Target total flux in image 00062 prior -- Prior image to guide mem 00063 00064 00065 00066 """ 00067 a=inspect.stack() 00068 stacklevel=0 00069 for k in range(len(a)): 00070 if (string.find(a[k][1], 'ipython console') > 0) or (string.find(a[k][1], '<string>') >= 0): 00071 stacklevel=k 00072 break 00073 myf=sys._getframe(stacklevel).f_globals 00074 myf['__last_task'] = 'deconvolve' 00075 myf['taskname'] = 'deconvolve' 00076 ### 00077 myf['update_params'](func=myf['taskname'],printtext=False) 00078 ### 00079 ### 00080 #Handle globals or user over-ride of arguments 00081 # 00082 function_signature_defaults=dict(zip(self.__call__.func_code.co_varnames,self.__call__.func_defaults)) 00083 useLocalDefaults = False 00084 00085 for item in function_signature_defaults.iteritems(): 00086 key,val = item 00087 keyVal = eval(key) 00088 if (keyVal == None): 00089 #user hasn't set it - use global/default 00090 pass 00091 else: 00092 #user has set it - use over-ride 00093 if (key != 'self') : 00094 useLocalDefaults = True 00095 #myf[key]=keyVal 00096 00097 myparams = {} 00098 if useLocalDefaults : 00099 for item in function_signature_defaults.iteritems(): 00100 key,val = item 00101 keyVal = eval(key) 00102 exec('myparams[key] = keyVal') 00103 if (keyVal == None): 00104 exec('myparams[key] = '+ key + ' = self.itsdefault(key)') 00105 keyVal = eval(key) 00106 if(type(keyVal) == dict) : 00107 exec('myparams[key] = ' + key + ' = keyVal[len(keyVal)-1][\'value\']') 00108 00109 else : 00110 uselessvariable = None 00111 myparams['imagename'] = imagename = myf['imagename'] 00112 myparams['model'] = model = myf['model'] 00113 myparams['psf'] = psf = myf['psf'] 00114 myparams['alg'] = alg = myf['alg'] 00115 myparams['niter'] = niter = myf['niter'] 00116 myparams['gain'] = gain = myf['gain'] 00117 myparams['threshold'] = threshold = myf['threshold'] 00118 myparams['mask'] = mask = myf['mask'] 00119 myparams['scales'] = scales = myf['scales'] 00120 myparams['sigma'] = sigma = myf['sigma'] 00121 myparams['targetflux'] = targetflux = myf['targetflux'] 00122 myparams['prior'] = prior = myf['prior'] 00123 00124 if type(psf)==str: psf=[psf] 00125 if type(scales)==int: scales=[scales] 00126 00127 result = None 00128 00129 # 00130 # The following is work around to avoid a bug with current python translation 00131 # 00132 mytmp = {} 00133 00134 mytmp['imagename'] = imagename 00135 mytmp['model'] = model 00136 mytmp['psf'] = psf 00137 mytmp['alg'] = alg 00138 mytmp['niter'] = niter 00139 mytmp['gain'] = gain 00140 if type(threshold) == str : 00141 mytmp['threshold'] = casac.quanta().quantity(threshold) 00142 else : 00143 mytmp['threshold'] = threshold 00144 mytmp['mask'] = mask 00145 mytmp['scales'] = scales 00146 if type(sigma) == str : 00147 mytmp['sigma'] = casac.quanta().quantity(sigma) 00148 else : 00149 mytmp['sigma'] = sigma 00150 if type(targetflux) == str : 00151 mytmp['targetflux'] = casac.quanta().quantity(targetflux) 00152 else : 00153 mytmp['targetflux'] = targetflux 00154 mytmp['prior'] = prior 00155 pathname='file:///'+os.environ.get('CASAPATH').split()[0]+'/share/xml/' 00156 trec = casac.utils().torecord(pathname+'deconvolve.xml') 00157 00158 casalog.origin('deconvolve') 00159 if not trec.has_key('deconvolve') or not casac.utils().verify(mytmp, trec['deconvolve']) : 00160 return False 00161 00162 00163 try : 00164 casalog.post('') 00165 casalog.post('##########################################') 00166 casalog.post('##### Begin Task: deconvolve #####') 00167 casalog.post('') 00168 result = deconvolve(imagename, model, psf, alg, niter, gain, threshold, mask, scales, sigma, targetflux, prior) 00169 casalog.post('') 00170 casalog.post('##### End Task: deconvolve #####') 00171 casalog.post('##########################################') 00172 00173 00174 # saveinputs for individule engine has no use 00175 # saveinputs should alos be removed from casa_in_py.py 00176 # 00177 # 00178 # saveinputs = myf['saveinputs'] 00179 # saveinputs('deconvolve', 'deconvolve.last', myparams) 00180 # 00181 # 00182 except Exception, instance: 00183 #print '**** Error **** ',instance 00184 pass 00185 00186 gc.collect() 00187 return result 00188 # 00189 # 00190 ## 00191 # def paramgui(self, useGlobals=True): 00192 # """ 00193 # Opens a parameter GUI for this task. If useGlobals is true, then any relevant global parameter settings are used. 00194 # """ 00195 # import paramgui 00196 # 00197 # a=inspect.stack() 00198 # stacklevel=0 00199 # for k in range(len(a)): 00200 # if (string.find(a[k][1], 'ipython console') > 0) or (string.find(a[k][1], '<string>') >= 0): 00201 # stacklevel=k 00202 # break 00203 # myf = sys._getframe(stacklevel).f_globals 00204 # 00205 # if useGlobals: 00206 # paramgui.setGlobals(myf) 00207 # else: 00208 # paramgui.setGlobals({}) 00209 # 00210 # paramgui.runTask('deconvolve', myf['_ip']) 00211 # paramgui.setGlobals({}) 00212 # 00213 # 00214 # 00215 # 00216 def defaults(self, param=None): 00217 a=inspect.stack() 00218 stacklevel=0 00219 for k in range(len(a)): 00220 if (string.find(a[k][1], 'ipython console') > 0) or (string.find(a[k][1], '<string>') >= 0): 00221 stacklevel=k 00222 break 00223 myf=sys._getframe(stacklevel).f_globals 00224 a = odict() 00225 a['imagename'] = '' 00226 a['model'] = '' 00227 a['psf'] = [''] 00228 a['alg'] = 'clark' 00229 a['niter'] = 10 00230 a['gain'] = 0.1 00231 a['threshold'] = '0.0mJy' 00232 a['mask'] = '' 00233 00234 a['async']=False 00235 a['alg'] = { 00236 0:{'value':'clark'}, 00237 1:{'value':'hogbom'}, 00238 2:odict([{'value':'multiscale'}, {'scales':[0, 3, 10]}]), 00239 3:odict([{'value':'mem'}, {'sigma':1.0}, {'targetflux':1.0}, {'prior':''}])} 00240 00241 ### This function sets the default values but also will return the list of 00242 ### parameters or the default value of a given parameter 00243 if(param == None): 00244 myf['__set_default_parameters'](a) 00245 elif(param == 'paramkeys'): 00246 return a.keys() 00247 else: 00248 if(a.has_key(param)): 00249 #if(type(a[param]) == dict) : 00250 # return a[param][len(a[param])-1]['value'] 00251 #else : 00252 return a[param] 00253 00254 00255 # 00256 # 00257 def check_params(self, param=None, value=None): 00258 a=inspect.stack() 00259 stacklevel=0 00260 for k in range(len(a)): 00261 if (string.find(a[k][1], 'ipython console') > 0) or (string.find(a[k][1], '<string>') >= 0): 00262 stacklevel=k 00263 break 00264 myf=sys._getframe(stacklevel).f_globals 00265 00266 # print 'param:', param, 'value:', value 00267 try : 00268 if str(type(value)) != "<type 'instance'>" : 00269 value0 = value 00270 value = myf['cu'].expandparam(param, value) 00271 matchtype = False 00272 if(type(value) == numpy.ndarray): 00273 if(type(value) == type(value0)): 00274 myf[param] = value.tolist() 00275 else: 00276 #print 'value:', value, 'value0:', value0 00277 #print 'type(value):', type(value), 'type(value0):', type(value0) 00278 myf[param] = value0 00279 if type(value0) != list : 00280 matchtype = True 00281 else : 00282 myf[param] = value 00283 value = myf['cu'].verifyparam({param:value}) 00284 if matchtype: 00285 value = False 00286 except Exception, instance: 00287 #ignore the exception and just return it unchecked 00288 myf[param] = value 00289 return value 00290 00291 # 00292 # 00293 def description(self, key='deconvolve', subkey=None): 00294 desc={'deconvolve': 'Image based deconvolver', 00295 'imagename': 'Input image to deconvolve', 00296 'model': 'Output image containing deconvolved point model', 00297 'psf': 'Point spread function (dirty beam)', 00298 'alg': 'Algorithm to use (clark, hogbom, multiscale, mem) ', 00299 'niter': 'number of iteration in deconvolution process', 00300 'gain': 'CLEAN gain parameter', 00301 'threshold': 'level below which sources will not be deconvolved', 00302 'mask': 'image mask to limit region of deconvolution', 00303 'scales': 'scale sizes (pixels) to deconvolve', 00304 'sigma': 'mem parameter: Expected noise in image', 00305 'targetflux': 'mem parameter: Estimated total flux in image', 00306 'prior': 'mem parameter: prior image for mem search', 00307 00308 'async': 'If true the taskname must be started using deconvolve(...)' 00309 } 00310 00311 # 00312 # Set subfields defaults if needed 00313 # 00314 00315 if(desc.has_key(key)) : 00316 return desc[key] 00317 00318 def itsdefault(self, paramname) : 00319 a = {} 00320 a['imagename'] = '' 00321 a['model'] = '' 00322 a['psf'] = [''] 00323 a['alg'] = 'clark' 00324 a['niter'] = 10 00325 a['gain'] = 0.1 00326 a['threshold'] = '0.0mJy' 00327 a['mask'] = '' 00328 a['scales'] = [0, 3, 10] 00329 a['sigma'] = '0.0mJy' 00330 a['targetflux'] = '1.0Jy' 00331 a['prior'] = '' 00332 00333 if a.has_key(paramname) : 00334 return a[paramname] 00335 deconvolve_pg = deconvolve_pg_()
1.8.0
