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

2##################### 78593ac558383844ca5bf3587a31cf5b ############################## 

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_imfit import imfit as _imfit_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 _imfit: 

15 """ 

16 imfit ---- Fit one or more elliptical Gaussian components on an image region(s) 

17 

18 --------- parameter descriptions --------------------------------------------- 

19 

20 imagename Name of the input image 

21 box Rectangular region(s) to select in direction plane. Default is to use the entire direction plane. 

22 region Region selection. Default is to use the full image. 

23 chans Channels to use. Default is to use all channels. 

24 stokes Stokes planes to use. Default is to use first Stokes plane. 

25 mask Mask to use. Default is none. 

26 includepix Range of pixel values to include for fitting. 

27 excludepix Range of pixel values to exclude for fitting. 

28 residual Name of output residual image. 

29 model Name of output model image. 

30 estimates Name of file containing initial estimates of component parameters. 

31 logfile Name of file to write fit results. 

32 append If logfile exists, append to it if True or overwrite it if False 

33 newestimates File to write fit results which can be used as initial estimates for next run. 

34 complist Name of output component list table. 

35 overwrite Overwrite component list table if it exists? 

36 dooff Also fit a zero level offset? Default is False 

37 offset Initial estimate of zero-level offset. Only used if doff is True. Default is 0.0 

38 fixoffset Keep the zero level offset fixed during fit? Default is False 

39 stretch Stretch the mask if necessary and possible? 

40 rms RMS to use in calculation of uncertainties. Numeric or valid quantity (record or string). If numeric, it is given units of the input image. If quantity, units must conform to image units. If not positive, the rms of the residual image, in the region of the fit, is used. 

41 noisefwhm Noise correlation beam FWHM. If numeric value, interpreted as pixel widths. If quantity (dictionary, string), it must have angular units. 

42 summary File name to which to write table of fit parameters. 

43 RETURNS void 

44 

45 --------- examples ----------------------------------------------------------- 

46 

47  

48 PARAMETER SUMMARY 

49 imagename Name of the input image 

50 box Rectangular region(s) to select in direction plane. 

51 for details. Default is to use the entire direction plane. 

52 eg "100, 120, 200, 220, 300, 300, 400, 400" to use two boxes. 

53 region Region selection. Default is to use 

54 the full image. 

55 chans Channels to use. Default is to use all 

56 channels. 

57 stokes Stokes planes to use. Default is to 

58 use first Stokes plane. 

59 mask Mask to use. Default is none. 

60 includepix Range of pixel values to include for fitting. Array of two numeric 

61 values assumed to have same units as image pixel values. Only one 

62 of includepix or excludepix can be specified. 

63 excludepix Range of pixel values to exclude for fitting. Array of two numeric 

64 values assumed to have same units as image pixel values. Only one 

65 of includepix or excludepix can be specified. 

66 residual Name of the residual image to write. 

67 model Name of the model image to write. 

68 estimates Name of file containing initial estimates of component parameters 

69 (see below for formatting details). 

70 logfile Name of file to write fit results. 

71 append If logfile exists, append to it (True) or overwrite it (False). 

72 newestimates File to write fit results which can be used as initial estimates 

73 for next run. 

74 complist Name of output component list table. 

75 overwrite Overwrite component list table if it exists? 

76 dooff Simultaneously fit a zero-level offset? 

77 offset Initial estimate for the zero-level offset. Only used if dooff is True. 

78 fixoffset Hold zero-level offset constant during fit? Only used if dooff is True. 

79 stretch Stretch the input mask if necessary and possible. Only used if a mask is specified. 

80  

81 rms RMS to use in calculation of various uncertainties, assumed to have units of the input 

82 image. If not positve, the rms of the residual image is used. 

83 noisefwhm Noise correlation beam FWHM. If numeric value, interpreted as pixel widths. If 

84 quantity (dictionary, string), it must have angular units. 

85 summary File name to which to write table of fit parameters. 

86  

87 OVERVIEW 

88 This application is used to fit one or more two dimensional gaussians to sources in an image as 

89 well as an optional zero-level offset. Fitting is limited to a single polarization 

90 but can be performed over several contiguous spectral channels. 

91 If the image has a clean beam, the report and returned dictionary will contain both the convolved 

92 and the deconvolved fit results. 

93  

94 When dooff is False, the method returns a dictionary with three keys, 'converged', 'results', 

95 and 'deconvolved'. The value of 'converged' is a boolean array which indicates if the fit 

96 converged on a channel by channel basis. The value of 'results' is a dictionary representing 

97 a component list reflecting the fit results. In the case of an image containing beam information, 

98 the sizes and position angles in the 'results' dictionary are those of the source(s) convolved 

99 with the restoring beam, while the same parameters in the 'deconvolved' dictionary represent the 

100 source sizes deconvolved from the beam. In the case where the image does not contain a beam, 

101 'deconvolved' will be absent. Both the 'results' and 'deconvolved' dictionaries can 

102 be read into a component list tool (default tool is named cl) using the fromrecord() method 

103 for easier inspection using tool methods, eg 

104  

105 cl.fromrecord(res['results']) 

106  

107 although this currently only works if the flux density units are conformant with Jy. 

108  

109 There are also values in each component subdictionary not used by cl.fromrecord() but meant to 

110 supply additional information. There is a 'peak' subdictionary for each component that provides the 

111 peak intensity of the component. It is present for both 'results' and 'deconvolved' components. 

112 There is also a 'sum' subdictionary for each component indicated the simple sum of pixel values in 

113 the the original image enclosed by the fitted ellipse. There is a 'channel' entry in the 'spectrum' 

114 subdictionary which provides the zero-based channel number in the input image for which the solution 

115 applies. In addtion, if the image has a beam(s), then there will be a 'beam' subdictionary associated 

116 with each component in both the 'results' and 'deconvolved' dictionaries. This subdictionary will 

117 have three keys: 'beamarcsec' will be a subdictionary giving the beam dimensions in arcsec, 

118 'beampixels' will have the value of the beam area expressed in pixels, and 'beamster' will have the 

119 value of the beam area epressed in steradians. Also, if the image has a beam(s), in the component level 

120 dictionaries will be an 'ispoint' entry with an associated boolean value describing if the component 

121 is consistent with a point source. 

122  

123 If dooff is True, in addtion to the specified number of 

124 gaussians, a zero-level offset will also be fit. The initial estimate for this 

125 offset is specified using the offset parameter. Units are assumed to be the 

126 same as the image brightness units. The zero level offset can be held constant during 

127 the fit by specifying fixoffset=True. In the case of dooff=True, the returned 

128 dictionary contains two additional keys, 'zerooff' and 'zeroofferr', which are both 

129 dictionaries containing 'unit' and 'value' keys. The values associated with the 'value' 

130 keys are arrays containing the the fitted zero level offset value and its error, respectively, 

131 for each channel. In cases where the fit did not converge, these values are set to NaN. 

132 The value associated with 'unit' is just the i`mage brightness unit. 

133  

134 The region can either be specified by a box(es) or a region. 

135 Ranges of pixel values can be included or excluded from the fit. If specified using 

136 the box parameter, multiple boxes can be given using the format 

137 box="blcx1, blcy1, trcx1, trcy1, blcx2, blcy2, trcx2, trcy2, ... , blcxN, blcyN, trcxN, trcyN" 

138 where N is the number of boxes. In this case, the union of the specified boxes will be used. 

139  

140 If specified, the residual and/or model images for successful fits will be written. 

141  

142 If an estimates file is not specified, an attempt is made to estimate 

143 initial parameters and fit a single Gaussian. If a multiple Gaussian fit 

144 is desired, the user must specify initial estimates via a text file 

145 (see below for details). 

146  

147 The user has the option of writing the result of the fit to a log file, 

148 and has the option of either appending to or overwriting an existing file. 

149  

150 The user has the option of writing the (convolved) parameters of a successful 

151 fit to a file which can be fed back to fitcomponents() as the estimates file for a 

152 subsequent run. 

153  

154 The user has the option of writing the fit results in tabular format to a file whose 

155 name is specified using the summary parameter. 

156  

157 If specified and positive, the value of rms is used to calculate the parameter uncertainties, 

158 otherwise, the rms in the selected region in the relevant channel is used for these calculations. 

159  

160 The noisefwhm parameter represents the noise-correlation beam FWHM. If specified as a quantity, 

161 it should have angular units. If specified as a numerical value, it is set equal to that number 

162 of pixels. If specified and greater than or equal to the pixel size, it is used to calculate 

163 parameter uncertainties using the correlated noise equations (see below). If it is specified but 

164 less than a pixel width, the the uncorrelated noise equations (see below) are used to 

165 compute the parameter uncertainties. If it is not specified and the image has a restoring beam(s), 

166 the the correlated noise equations are used to compute parameter uncertainties using the 

167 geometric mean of the relevant beam major and minor axes as the noise-correlation beam FWHM. If 

168 noisefwhm is not specified and the image does not have a restoring beam, then the uncorrelated 

169 noise equations are used to compute the parameter uncertainties. 

170  

171 SUPPORTED UNITS 

172  

173 Currently only images with brightness units conformant with Jy/beam, Jy.km/s/beam, and K are fully 

174 supported for fitting. If your image has some other base brightness unit, that unit will be assumed 

175 to be equivalent to Jy/pixel and results will be calculated accordingly. In particular, 

176 the flux density (reported as Integrated Flux in the logger and associated with the "flux" key 

177 in the returned component subdictionary(ies)) for such a case represents the sum of pixel values. 

178  

179 Note also that converting the returned results subdictionary to a component list via cl.fromrecord() currently 

180 only works properly if the flux density units in the results dictionary are conformant with Jy. 

181 If you need to be able to run cl.fromrecord() on the resulting dictionary you can first modify the 

182 flux density units by hand to be (some prefix)Jy and then run cl.fromrecord() on that dictionary, 

183 bearing in mind your unit conversion. 

184  

185 If the input image has units of K, the flux density of components will be reported in units 

186 of [prefix]K*rad*rad, where prefix is an SI prefix used so that the numerical value is between 

187 1 and 1000. To convert to units of K*beam, determine the area of the appropriate beam, 

188 which is given by pi/(4*ln(2))*bmaj*bmin, where bmaj and bmin are the major and minor axes 

189 of the beam, and convert to steradians (=rad*rad). This value is included in the beam portion 

190 of the component subdictionary (key 'beamster'). Then divide the numerical value of the 

191 logged flux density by the beam area in steradians. So, for example 

192  

193 begin{verbatim} 

194 # run on an image with K brightness units 

195 res = imfit(...) 

196 # get the I flux density in K*beam of component 0 

197 comp = res['results']['component0'] 

198 flux_density_kbeam = comp['flux']['value'][0]/comp['beam']['beamster'] 

199 end{verbatim} 

200  

201 FITTING OVER MULTIPLE CHANNELS 

202  

203 For fitting over multiple channels, the result of the previous successful fit is used as 

204 the estimate for the next channel. The number of gaussians fit cannot be varied on a channel 

205 by channel basis. Thus the variation of source structure should be reasonably smooth in 

206 frequency to produce reliable fit results. 

207  

208 MASK SPECIFICATION 

209  

210 Mask specification can be done using an LEL expression. For example 

211  

212 mask = '"myimage">5' will use only pixels with values greater than 5. 

213  

214 INCLUDING AND EXCLUDING PIXELS 

215  

216 Pixels can be included or excluded from the fit based on their values 

217 using these parameters. Note that specifying both is not permitted and 

218 will cause an error. If specified, both take an array of two numeric 

219 values. 

220  

221 ESTIMATES 

222  

223 Initial estimates of fit parameters may be specified via an estimates 

224 text file. Each line of this file should contain a set of parameters for 

225 a single gaussian. Optionally, some of these parameters can be fixed during 

226 the fit. The format of each line is 

227  

228 peak intensity, peak x-pixel value, peak y-pixel value, major axis, minor axis, position angle, fixed 

229  

230 The fixed parameter is optional. The peak intensity is assumed to be in the 

231 same units as the image pixel values (eg Jy/beam). The peak coordinates are specified 

232 in pixel coordinates. The major and minor axes and the position angle are the convolved 

233 parameters if the image has been convolved with a clean beam and are specified as quantities. 

234 The fixed parameter is optional and is a string. It may contain any combination of the 

235 following characters 'f' (peak intensity), 'x' (peak x position), 'y' (peak y position), 

236 'a' (major axis), 'b' (minor axis), 'p' (position angle). 

237  

238 In addition, lines in the file starting with a # are considered comments. 

239  

240 An example of such a file is: 

241  

242 begin{verbatim} 

243 # peak intensity must be in map units 

244 120, 150, 110, 23.5arcsec, 18.9arcsec, 120deg 

245 90, 60, 200, 46arcsec, 23arcsec, 140deg, fxp 

246 end{verbatim} 

247  

248  

249 This is a file which specifies that two gaussians are to be simultaneously fit, 

250 and for the second gaussian the specified peak intensity, x position, and position angle 

251 are to be held fixed during the fit. 

252  

253 ERROR ESTIMATES 

254  

255 Error estimates are based on the work of Condon 1997, PASP, 109, 166. Key assumptions made are: 

256 * The given model (elliptical Gaussian, or elliptical Gaussian plus constant offset) is an 

257 adequate representation of the data 

258 * An accurate estimate of the pixel noise is provided or can be derived (see above). For the 

259 case of correlated noise (e.g., a CLEAN map), the fit region should contain many "beams" or 

260 an independent value of rms should be provided. 

261 * The signal-to-noise ratio (SNR) or the Gaussian component is large. This is necessary because 

262 a Taylor series is used to linearize the problem. Condon (1997) states that the fractional 

263 bias in the fitted amplitude due to this assumption is of order 1/(S*S), where S is the overall 

264 SNR of the Gaussian with respect to the given data set (defined more precisely below). For a 5 

265 sigma "detection" of the Gaussian, this is a 4% effect. 

266 * All (or practically all) of the flux in the component being fit falls within the selected region. 

267 If a constant offset term is simultaneously fit and not fixed, the region of interest should be 

268 even larger. The derivations of the expressions summarized in this note assume an effectively 

269 infinite region. 

270  

271 Two sets of equations are used to calculate the parameter uncertainties, based on if 

272 the noise is correlated or uncorrelated. The rules governing which set of equations are 

273 used have been described above in the description of the noisefwhm parameter. 

274  

275 In the case of uncorrelated noise, the equations used are 

276  

277 f(A) = f(I) = f(M) = f(m) = k*s(x)/M = k*s(y)/m = (s(p)/sqrt(2))*((M*M - m*m)/(M*m)) 

278 = sqrt(2)/S 

279  

280 where s(z) is the uncertainty associated with parameter z, f(z) = s(z)/abs(z) is the 

281 fractional uncertainty associated with parameter z, A is the peak intensity, I is the flux 

282 density, M and m are the FWHM major and minor axes, p is the position angle of the 

283 component, and k = sqrt(8*ln(2)). s(x) and s(y) are the direction 

284 uncertainties of the component measured along the major and minor axes; the resulting 

285 uncertainties measured along the principle axes of the image direction coordinate are 

286 calculated by propagation of errors using the 2D rotation matrix which enacts the rotation through 

287 the position angle plus 90 degrees. S is the overall signal to noise ratio of the component, 

288 which, for the uncorrelated noise case is given by 

289  

290 S = (A/(k*h*r))*sqrt(pi*M*m) 

291  

292 where h is the pixel width of the direction coordinate and r is the rms noise (see the 

293 discussion above for the rules governing how the value of r is determined). 

294  

295 For the correlated noise case, the same equations are used to determine the uncertainties 

296 as in the uncorrelated noise case, except for the uncertainty in I (see below). However, 

297 S is given by 

298  

299 S = (A/(2*r*N)) * sqrt(M*m) * (1 + ((N*N/(M*M)))**(a/2)) * (1 + ((N*N/(m*m)))**(b/2)) 

300  

301 where N is the noise-correlation beam FWHM (see discussion of the noisefwhm parameter for 

302 rules governing how this value is determined). "**" indicates exponentiation and a and b 

303 depend on which uncertainty is being calculated. For sigma(A), a = b = 3/2. For M and x, 

304 a = 5/2 and b = 1/2. For m, y, and p, a = 1/2 and b = 5/2. f(I) is calculated in the 

305 correlated noise case according to 

306  

307 f(I) = sqrt( f(A)*f(A) + (N*N/(M*m))*(f(M*f(M) + f(m)*f(m))) ) 

308  

309 Note well the following caveats: 

310 * Fixing Gaussian component parameters will tend to cause the parameter uncertainties reported for free 

311 parameters to be overestimated. 

312 * Fitting a zero level offset that is not fixed will tend to cause the reported parameter 

313 uncertainties to be slightly underestimated. 

314 * The parameter uncertainties will be inaccurate at low SNR (a ~10% for SNR = 3). 

315 * If the fitted region is not considerably larger than the largest component that is fit, 

316 parameter uncertainties may be mis-estimated. 

317 * An accurate rms noise measurement, r, for the region in question must be supplied. 

318 Alternatively, a sufficiently large signal-free region must be present in the selected region 

319 (at least about 25 noise beams in area) to auto-derive such an estimate. 

320 * If the image noise is not statistically independent from pixel to pixel, a reasonably accurate noise 

321 correlation scale, N, must be provided. If the noise correlation function is not approximately Gaussian, 

322 the correlation length can be estimated using 

323  

324 N = sqrt(2*ln(2)/pi)* double-integral(dx dy C(x,y))/sqrt(double-integral(dx dy C(x, y) * C(x,y))) 

325  

326 where C(x,y) is the associated noise-smoothing function 

327 * If fitted model components have significan spatial overlap, the parameter uncertainties are likely to 

328 be mis-estimated (i.e., correlations between the parameters of separate components are not accounted 

329 for). 

330 * If the image being analyzed is an interferometric image with poor uv sampling, the parameter 

331 uncertainties may be significantly underestimated. 

332  

333 The deconvolved size and position angle errors are computed by taking the maximum of the absolute values of the 

334 differences of the best fit deconvolved value of the given parameter and the deconvolved size of the eight 

335 possible combinations of (FWHM major axis +/- major axis error), (FWHM minor axis +/- minor axis error), 

336 and (position andle +/- position angle error). If the source cannot be deconvolved from the beam (if the best 

337 fit convolved source size cannot be deconvolved from the beam), upper limits on the deconvolved source size 

338 are sometimes reported. These limits simply come from the maximum major and minor axes of the deconvolved 

339 gaussians taken from trying all eight of the aforementioned combinations. In the case none of these combinations 

340 produces a deconvolved size, no upper limit is reported. 

341  

342 EXAMPLE: 

343  

344 Here is how one might fit two gaussians to multiple channels of a cube using the fit 

345 from the previous channel as the initial estimate for the next. It also illustrates 

346 how one can specify a region in the associated continuum image as the region to use 

347 as the fit for the channel. 

348  

349  

350 begin{verbatim} 

351 default imfit 

352 imagename = "co_cube.im" 

353 # specify region using region from continuum 

354 region = "continuum.im:source.rgn" 

355 chans = "2~20" 

356 # only use pixels with positive values in the fit 

357 excludepix = [-1e10,0] 

358 # estimates file contains initial parameters for two Gaussians in channel 2 

359 estimates = "initial_estimates.txt" 

360 logfile = "co_fit.log" 

361 # append results to the log file for all the channels 

362 append = "True" 

363 imfit() 

364 

365 

366 """ 

367 

368 _info_group_ = """analysis""" 

369 _info_desc_ = """Fit one or more elliptical Gaussian components on an image region(s)""" 

370 

371 def __call__( self, imagename='', box='', region='', chans='', stokes='', mask='', includepix=[ ], excludepix=[ ], residual='', model='', estimates='', logfile='', append=True, newestimates='', complist='', overwrite=False, dooff=False, offset=float(0.0), fixoffset=False, stretch=False, rms=int(0), noisefwhm='', summary='' ): 

372 schema = {'imagename': {'type': 'cReqPath', 'coerce': _coerce.expand_path}, 'box': {'type': 'cStr', 'coerce': _coerce.to_str}, 'region': {'type': 'cVariant', 'coerce': [_coerce.to_variant]}, 'chans': {'type': 'cVariant', 'coerce': [_coerce.to_variant]}, 'stokes': {'type': 'cStr', 'coerce': _coerce.to_str}, 'mask': {'type': 'cStr', 'coerce': _coerce.to_str}, 'includepix': {'type': 'cFloatVec', 'coerce': [_coerce.to_list,_coerce.to_floatvec]}, 'excludepix': {'type': 'cFloatVec', 'coerce': [_coerce.to_list,_coerce.to_floatvec]}, 'residual': {'type': 'cStr', 'coerce': _coerce.to_str}, 'model': {'type': 'cStr', 'coerce': _coerce.to_str}, 'estimates': {'type': 'cStr', 'coerce': _coerce.to_str}, 'logfile': {'type': 'cStr', 'coerce': _coerce.to_str}, 'append': {'type': 'cBool'}, 'newestimates': {'type': 'cStr', 'coerce': _coerce.to_str}, 'complist': {'type': 'cStr', 'coerce': _coerce.to_str}, 'overwrite': {'type': 'cBool'}, 'dooff': {'type': 'cBool'}, 'offset': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'fixoffset': {'type': 'cBool'}, 'stretch': {'type': 'cBool'}, 'rms': {'anyof': [{'type': 'cInt'}, {'type': 'cFloat', 'coerce': _coerce.to_float}, {'type': 'cDict'}, {'type': 'cStr', 'coerce': _coerce.to_str}]}, 'noisefwhm': {'anyof': [{'type': 'cInt'}, {'type': 'cFloat', 'coerce': _coerce.to_float}, {'type': 'cDict'}, {'type': 'cStr', 'coerce': _coerce.to_str}]}, 'summary': {'type': 'cStr', 'coerce': _coerce.to_str}} 

373 doc = {'imagename': imagename, 'box': box, 'region': region, 'chans': chans, 'stokes': stokes, 'mask': mask, 'includepix': includepix, 'excludepix': excludepix, 'residual': residual, 'model': model, 'estimates': estimates, 'logfile': logfile, 'append': append, 'newestimates': newestimates, 'complist': complist, 'overwrite': overwrite, 'dooff': dooff, 'offset': offset, 'fixoffset': fixoffset, 'stretch': stretch, 'rms': rms, 'noisefwhm': noisefwhm, 'summary': summary} 

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

375 _logging_state_ = _start_log( 'imfit', [ 'imagename=' + repr(_pc.document['imagename']), 'box=' + repr(_pc.document['box']), 'region=' + repr(_pc.document['region']), 'chans=' + repr(_pc.document['chans']), 'stokes=' + repr(_pc.document['stokes']), 'mask=' + repr(_pc.document['mask']), 'includepix=' + repr(_pc.document['includepix']), 'excludepix=' + repr(_pc.document['excludepix']), 'residual=' + repr(_pc.document['residual']), 'model=' + repr(_pc.document['model']), 'estimates=' + repr(_pc.document['estimates']), 'logfile=' + repr(_pc.document['logfile']), 'append=' + repr(_pc.document['append']), 'newestimates=' + repr(_pc.document['newestimates']), 'complist=' + repr(_pc.document['complist']), 'overwrite=' + repr(_pc.document['overwrite']), 'dooff=' + repr(_pc.document['dooff']), 'offset=' + repr(_pc.document['offset']), 'fixoffset=' + repr(_pc.document['fixoffset']), 'stretch=' + repr(_pc.document['stretch']), 'rms=' + repr(_pc.document['rms']), 'noisefwhm=' + repr(_pc.document['noisefwhm']), 'summary=' + repr(_pc.document['summary']) ] ) 

376 task_result = None 

377 try: 

378 task_result = _imfit_t( _pc.document['imagename'], _pc.document['box'], _pc.document['region'], _pc.document['chans'], _pc.document['stokes'], _pc.document['mask'], _pc.document['includepix'], _pc.document['excludepix'], _pc.document['residual'], _pc.document['model'], _pc.document['estimates'], _pc.document['logfile'], _pc.document['append'], _pc.document['newestimates'], _pc.document['complist'], _pc.document['overwrite'], _pc.document['dooff'], _pc.document['offset'], _pc.document['fixoffset'], _pc.document['stretch'], _pc.document['rms'], _pc.document['noisefwhm'], _pc.document['summary'] ) 

379 except Exception as exc: 

380 _except_log('imfit', exc) 

381 raise 

382 finally: 

383 task_result = _end_log( _logging_state_, 'imfit', task_result ) 

384 return task_result 

385 

386imfit = _imfit( ) 

387