| 1 | import _asap
|
|---|
| 2 | from asap.parameters import rcParams
|
|---|
| 3 | from asap.logging import asaplog, asaplog_post_dec
|
|---|
| 4 | from asap.utils import _n_bools, mask_and
|
|---|
| 5 |
|
|---|
| 6 |
|
|---|
| 7 | class fitter:
|
|---|
| 8 | """
|
|---|
| 9 | The fitting class for ASAP.
|
|---|
| 10 | """
|
|---|
| 11 | def __init__(self):
|
|---|
| 12 | """
|
|---|
| 13 | Create a fitter object. No state is set.
|
|---|
| 14 | """
|
|---|
| 15 | self.fitter = _asap.fitter()
|
|---|
| 16 | self.x = None
|
|---|
| 17 | self.y = None
|
|---|
| 18 | self.mask = None
|
|---|
| 19 | self.fitfunc = None
|
|---|
| 20 | self.fitfuncs = None
|
|---|
| 21 | self.fitted = False
|
|---|
| 22 | self.data = None
|
|---|
| 23 | self.components = 0
|
|---|
| 24 | self._fittedrow = 0
|
|---|
| 25 | self._p = None
|
|---|
| 26 | self._selection = None
|
|---|
| 27 | self.uselinear = False
|
|---|
| 28 |
|
|---|
| 29 | def set_data(self, xdat, ydat, mask=None):
|
|---|
| 30 | """
|
|---|
| 31 | Set the absissa and ordinate for the fit. Also set the mask
|
|---|
| 32 | indicating valid points.
|
|---|
| 33 | This can be used for data vectors retrieved from a scantable.
|
|---|
| 34 | For scantable fitting use 'fitter.set_scan(scan, mask)'.
|
|---|
| 35 | Parameters:
|
|---|
| 36 | xdat: the abcissa values
|
|---|
| 37 | ydat: the ordinate values
|
|---|
| 38 | mask: an optional mask
|
|---|
| 39 |
|
|---|
| 40 | """
|
|---|
| 41 | self.fitted = False
|
|---|
| 42 | self.x = xdat
|
|---|
| 43 | self.y = ydat
|
|---|
| 44 | if mask == None:
|
|---|
| 45 | self.mask = _n_bools(len(xdat), True)
|
|---|
| 46 | else:
|
|---|
| 47 | self.mask = mask
|
|---|
| 48 | return
|
|---|
| 49 |
|
|---|
| 50 | @asaplog_post_dec
|
|---|
| 51 | def set_scan(self, thescan=None, mask=None):
|
|---|
| 52 | """
|
|---|
| 53 | Set the 'data' (a scantable) of the fitter.
|
|---|
| 54 | Parameters:
|
|---|
| 55 | thescan: a scantable
|
|---|
| 56 | mask: a msk retrieved from the scantable
|
|---|
| 57 | """
|
|---|
| 58 | if not thescan:
|
|---|
| 59 | msg = "Please give a correct scan"
|
|---|
| 60 | raise TypeError(msg)
|
|---|
| 61 | self.fitted = False
|
|---|
| 62 | self.data = thescan
|
|---|
| 63 | self.mask = None
|
|---|
| 64 | if mask is None:
|
|---|
| 65 | self.mask = _n_bools(self.data.nchan(), True)
|
|---|
| 66 | else:
|
|---|
| 67 | self.mask = mask
|
|---|
| 68 | return
|
|---|
| 69 |
|
|---|
| 70 | @asaplog_post_dec
|
|---|
| 71 | def set_function(self, **kwargs):
|
|---|
| 72 | """
|
|---|
| 73 | Set the function to be fit.
|
|---|
| 74 | Parameters:
|
|---|
| 75 | poly: use a polynomial of the order given with nonlinear least squares fit
|
|---|
| 76 | lpoly: use polynomial of the order given with linear least squares fit
|
|---|
| 77 | gauss: fit the number of gaussian specified
|
|---|
| 78 | lorentz: fit the number of lorentzian specified
|
|---|
| 79 | sinusoid: fit the number of sinusoid specified
|
|---|
| 80 | Example:
|
|---|
| 81 | fitter.set_function(poly=3) # will fit a 3rd order polynomial via nonlinear method
|
|---|
| 82 | fitter.set_function(lpoly=3) # will fit a 3rd order polynomial via linear method
|
|---|
| 83 | fitter.set_function(gauss=2) # will fit two gaussians
|
|---|
| 84 | fitter.set_function(lorentz=2) # will fit two lorentzians
|
|---|
| 85 | fitter.set_function(sinusoid=3) # will fit three sinusoids
|
|---|
| 86 | """
|
|---|
| 87 | #default poly order 0
|
|---|
| 88 | n=0
|
|---|
| 89 | if kwargs.has_key('poly'):
|
|---|
| 90 | self.fitfunc = 'poly'
|
|---|
| 91 | self.fitfuncs = ['poly']
|
|---|
| 92 | n = kwargs.get('poly')
|
|---|
| 93 | self.components = [n+1]
|
|---|
| 94 | self.uselinear = False
|
|---|
| 95 | elif kwargs.has_key('lpoly'):
|
|---|
| 96 | self.fitfunc = 'poly'
|
|---|
| 97 | self.fitfuncs = ['lpoly']
|
|---|
| 98 | n = kwargs.get('lpoly')
|
|---|
| 99 | self.components = [n+1]
|
|---|
| 100 | self.uselinear = True
|
|---|
| 101 | elif kwargs.has_key('gauss'):
|
|---|
| 102 | n = kwargs.get('gauss')
|
|---|
| 103 | self.fitfunc = 'gauss'
|
|---|
| 104 | self.fitfuncs = [ 'gauss' for i in range(n) ]
|
|---|
| 105 | self.components = [ 3 for i in range(n) ]
|
|---|
| 106 | self.uselinear = False
|
|---|
| 107 | elif kwargs.has_key('lorentz'):
|
|---|
| 108 | n = kwargs.get('lorentz')
|
|---|
| 109 | self.fitfunc = 'lorentz'
|
|---|
| 110 | self.fitfuncs = [ 'lorentz' for i in range(n) ]
|
|---|
| 111 | self.components = [ 3 for i in range(n) ]
|
|---|
| 112 | self.uselinear = False
|
|---|
| 113 | elif kwargs.has_key('sinusoid'):
|
|---|
| 114 | n = kwargs.get('sinusoid')
|
|---|
| 115 | self.fitfunc = 'sinusoid'
|
|---|
| 116 | self.fitfuncs = [ 'sinusoid' for i in range(n) ]
|
|---|
| 117 | self.components = [ 3 for i in range(n) ]
|
|---|
| 118 | self.uselinear = False
|
|---|
| 119 | else:
|
|---|
| 120 | msg = "Invalid function type."
|
|---|
| 121 | raise TypeError(msg)
|
|---|
| 122 |
|
|---|
| 123 | self.fitter.setexpression(self.fitfunc,n)
|
|---|
| 124 | self.fitted = False
|
|---|
| 125 | return
|
|---|
| 126 |
|
|---|
| 127 | @asaplog_post_dec
|
|---|
| 128 | def fit(self, row=0, estimate=False):
|
|---|
| 129 | """
|
|---|
| 130 | Execute the actual fitting process. All the state has to be set.
|
|---|
| 131 | Parameters:
|
|---|
| 132 | row: specify the row in the scantable
|
|---|
| 133 | estimate: auto-compute an initial parameter set (default False)
|
|---|
| 134 | This can be used to compute estimates even if fit was
|
|---|
| 135 | called before.
|
|---|
| 136 | Example:
|
|---|
| 137 | s = scantable('myscan.asap')
|
|---|
| 138 | s.set_cursor(thepol=1) # select second pol
|
|---|
| 139 | f = fitter()
|
|---|
| 140 | f.set_scan(s)
|
|---|
| 141 | f.set_function(poly=0)
|
|---|
| 142 | f.fit(row=0) # fit first row
|
|---|
| 143 | """
|
|---|
| 144 | if ((self.x is None or self.y is None) and self.data is None) \
|
|---|
| 145 | or self.fitfunc is None:
|
|---|
| 146 | msg = "Fitter not yet initialised. Please set data & fit function"
|
|---|
| 147 | raise RuntimeError(msg)
|
|---|
| 148 |
|
|---|
| 149 | else:
|
|---|
| 150 | if self.data is not None:
|
|---|
| 151 | self.x = self.data._getabcissa(row)
|
|---|
| 152 | self.y = self.data._getspectrum(row)
|
|---|
| 153 | #self.mask = mask_and(self.mask, self.data._getmask(row))
|
|---|
| 154 | if len(self.x) == len(self.mask):
|
|---|
| 155 | self.mask = mask_and(self.mask, self.data._getmask(row))
|
|---|
| 156 | else:
|
|---|
| 157 | asaplog.push('lengths of data and mask are not the same. preset mask will be ignored')
|
|---|
| 158 | asaplog.post('WARN','asapfit.fit')
|
|---|
| 159 | self.mask=self.data._getmask(row)
|
|---|
| 160 | asaplog.push("Fitting:")
|
|---|
| 161 | i = row
|
|---|
| 162 | out = "Scan[%d] Beam[%d] IF[%d] Pol[%d] Cycle[%d]" % (self.data.getscan(i),
|
|---|
| 163 | self.data.getbeam(i),
|
|---|
| 164 | self.data.getif(i),
|
|---|
| 165 | self.data.getpol(i),
|
|---|
| 166 | self.data.getcycle(i))
|
|---|
| 167 | asaplog.push(out,False)
|
|---|
| 168 | self.fitter.setdata(self.x, self.y, self.mask)
|
|---|
| 169 | if self.fitfunc == 'gauss' or self.fitfunc == 'lorentz':
|
|---|
| 170 | ps = self.fitter.getparameters()
|
|---|
| 171 | if len(ps) == 0 or estimate:
|
|---|
| 172 | self.fitter.estimate()
|
|---|
| 173 | fxdpar = list(self.fitter.getfixedparameters())
|
|---|
| 174 | if len(fxdpar) and fxdpar.count(0) == 0:
|
|---|
| 175 | raise RuntimeError,"No point fitting, if all parameters are fixed."
|
|---|
| 176 | if self.uselinear:
|
|---|
| 177 | converged = self.fitter.lfit()
|
|---|
| 178 | else:
|
|---|
| 179 | converged = self.fitter.fit()
|
|---|
| 180 | if not converged:
|
|---|
| 181 | raise RuntimeError,"Fit didn't converge."
|
|---|
| 182 | self._fittedrow = row
|
|---|
| 183 | self.fitted = True
|
|---|
| 184 | return
|
|---|
| 185 |
|
|---|
| 186 | def store_fit(self, filename=None):
|
|---|
| 187 | """
|
|---|
| 188 | Save the fit parameters.
|
|---|
| 189 | Parameters:
|
|---|
| 190 | filename: if specified save as an ASCII file, if None (default)
|
|---|
| 191 | store it in the scnatable
|
|---|
| 192 | """
|
|---|
| 193 | if self.fitted and self.data is not None:
|
|---|
| 194 | pars = list(self.fitter.getparameters())
|
|---|
| 195 | fixed = list(self.fitter.getfixedparameters())
|
|---|
| 196 | from asap.asapfit import asapfit
|
|---|
| 197 | fit = asapfit()
|
|---|
| 198 | fit.setparameters(pars)
|
|---|
| 199 | fit.setfixedparameters(fixed)
|
|---|
| 200 | fit.setfunctions(self.fitfuncs)
|
|---|
| 201 | fit.setcomponents(self.components)
|
|---|
| 202 | fit.setframeinfo(self.data._getcoordinfo())
|
|---|
| 203 | if filename is not None:
|
|---|
| 204 | import os
|
|---|
| 205 | filename = os.path.expandvars(os.path.expanduser(filename))
|
|---|
| 206 | if os.path.exists(filename):
|
|---|
| 207 | raise IOError("File '%s' exists." % filename)
|
|---|
| 208 | fit.save(filename)
|
|---|
| 209 | else:
|
|---|
| 210 | self.data._addfit(fit,self._fittedrow)
|
|---|
| 211 |
|
|---|
| 212 | @asaplog_post_dec
|
|---|
| 213 | def set_parameters(self,*args,**kwargs):
|
|---|
| 214 | """
|
|---|
| 215 | Set the parameters to be fitted.
|
|---|
| 216 | Parameters:
|
|---|
| 217 | params: a vector of parameters
|
|---|
| 218 | fixed: a vector of which parameters are to be held fixed
|
|---|
| 219 | (default is none)
|
|---|
| 220 | component: in case of multiple gaussians/lorentzians/sinusoidals,
|
|---|
| 221 | the index of the target component
|
|---|
| 222 | """
|
|---|
| 223 | component = None
|
|---|
| 224 | fixed = None
|
|---|
| 225 | params = None
|
|---|
| 226 |
|
|---|
| 227 | if len(args) and isinstance(args[0],dict):
|
|---|
| 228 | kwargs = args[0]
|
|---|
| 229 | if kwargs.has_key("fixed"): fixed = kwargs["fixed"]
|
|---|
| 230 | if kwargs.has_key("params"): params = kwargs["params"]
|
|---|
| 231 | if len(args) == 2 and isinstance(args[1], int):
|
|---|
| 232 | component = args[1]
|
|---|
| 233 | if self.fitfunc is None:
|
|---|
| 234 | msg = "Please specify a fitting function first."
|
|---|
| 235 | raise RuntimeError(msg)
|
|---|
| 236 | if (self.fitfunc == "gauss" or self.fitfunc == "lorentz" or self.fitfunc == "sinusoid") and component is not None:
|
|---|
| 237 | if not self.fitted and sum(self.fitter.getparameters()) == 0:
|
|---|
| 238 | pars = _n_bools(len(self.components)*3, False)
|
|---|
| 239 | fxd = _n_bools(len(pars), False)
|
|---|
| 240 | else:
|
|---|
| 241 | pars = list(self.fitter.getparameters())
|
|---|
| 242 | fxd = list(self.fitter.getfixedparameters())
|
|---|
| 243 | i = 3*component
|
|---|
| 244 | pars[i:i+3] = params
|
|---|
| 245 | fxd[i:i+3] = fixed
|
|---|
| 246 | params = pars
|
|---|
| 247 | fixed = fxd
|
|---|
| 248 | self.fitter.setparameters(params)
|
|---|
| 249 | if fixed is not None:
|
|---|
| 250 | self.fitter.setfixedparameters(fixed)
|
|---|
| 251 | return
|
|---|
| 252 |
|
|---|
| 253 | @asaplog_post_dec
|
|---|
| 254 | def set_gauss_parameters(self, peak, centre, fwhm,
|
|---|
| 255 | peakfixed=0, centrefixed=0,
|
|---|
| 256 | fwhmfixed=0,
|
|---|
| 257 | component=0):
|
|---|
| 258 | """
|
|---|
| 259 | Set the Parameters of a 'Gaussian' component, set with set_function.
|
|---|
| 260 | Parameters:
|
|---|
| 261 | peak, centre, fwhm: The gaussian parameters
|
|---|
| 262 | peakfixed,
|
|---|
| 263 | centrefixed,
|
|---|
| 264 | fwhmfixed: Optional parameters to indicate if
|
|---|
| 265 | the paramters should be held fixed during
|
|---|
| 266 | the fitting process. The default is to keep
|
|---|
| 267 | all parameters flexible.
|
|---|
| 268 | component: The number of the component (Default is the
|
|---|
| 269 | component 0)
|
|---|
| 270 | """
|
|---|
| 271 | if self.fitfunc != "gauss":
|
|---|
| 272 | msg = "Function only operates on Gaussian components."
|
|---|
| 273 | raise ValueError(msg)
|
|---|
| 274 | if 0 <= component < len(self.components):
|
|---|
| 275 | d = {'params':[peak, centre, fwhm],
|
|---|
| 276 | 'fixed':[peakfixed, centrefixed, fwhmfixed]}
|
|---|
| 277 | self.set_parameters(d, component)
|
|---|
| 278 | else:
|
|---|
| 279 | msg = "Please select a valid component."
|
|---|
| 280 | raise ValueError(msg)
|
|---|
| 281 |
|
|---|
| 282 | @asaplog_post_dec
|
|---|
| 283 | def set_lorentz_parameters(self, peak, centre, fwhm,
|
|---|
| 284 | peakfixed=0, centrefixed=0,
|
|---|
| 285 | fwhmfixed=0,
|
|---|
| 286 | component=0):
|
|---|
| 287 | """
|
|---|
| 288 | Set the Parameters of a 'Lorentzian' component, set with set_function.
|
|---|
| 289 | Parameters:
|
|---|
| 290 | peak, centre, fwhm: The lorentzian parameters
|
|---|
| 291 | peakfixed,
|
|---|
| 292 | centrefixed,
|
|---|
| 293 | fwhmfixed: Optional parameters to indicate if
|
|---|
| 294 | the paramters should be held fixed during
|
|---|
| 295 | the fitting process. The default is to keep
|
|---|
| 296 | all parameters flexible.
|
|---|
| 297 | component: The number of the component (Default is the
|
|---|
| 298 | component 0)
|
|---|
| 299 | """
|
|---|
| 300 | if self.fitfunc != "lorentz":
|
|---|
| 301 | msg = "Function only operates on Lorentzian components."
|
|---|
| 302 | raise ValueError(msg)
|
|---|
| 303 | if 0 <= component < len(self.components):
|
|---|
| 304 | d = {'params':[peak, centre, fwhm],
|
|---|
| 305 | 'fixed':[peakfixed, centrefixed, fwhmfixed]}
|
|---|
| 306 | self.set_parameters(d, component)
|
|---|
| 307 | else:
|
|---|
| 308 | msg = "Please select a valid component."
|
|---|
| 309 | raise ValueError(msg)
|
|---|
| 310 |
|
|---|
| 311 | @asaplog_post_dec
|
|---|
| 312 | def set_sinusoid_parameters(self, ampl, period, x0,
|
|---|
| 313 | amplfixed=0, periodfixed=0,
|
|---|
| 314 | x0fixed=0,
|
|---|
| 315 | component=0):
|
|---|
| 316 | """
|
|---|
| 317 | Set the Parameters of a 'Sinusoidal' component, set with set_function.
|
|---|
| 318 | Parameters:
|
|---|
| 319 | ampl, period, x0: The sinusoidal parameters
|
|---|
| 320 | amplfixed,
|
|---|
| 321 | periodfixed,
|
|---|
| 322 | x0fixed: Optional parameters to indicate if
|
|---|
| 323 | the paramters should be held fixed during
|
|---|
| 324 | the fitting process. The default is to keep
|
|---|
| 325 | all parameters flexible.
|
|---|
| 326 | component: The number of the component (Default is the
|
|---|
| 327 | component 0)
|
|---|
| 328 | """
|
|---|
| 329 | if self.fitfunc != "sinusoid":
|
|---|
| 330 | msg = "Function only operates on Sinusoidal components."
|
|---|
| 331 | raise ValueError(msg)
|
|---|
| 332 | if 0 <= component < len(self.components):
|
|---|
| 333 | d = {'params':[ampl, period, x0],
|
|---|
| 334 | 'fixed': [amplfixed, periodfixed, x0fixed]}
|
|---|
| 335 | self.set_parameters(d, component)
|
|---|
| 336 | else:
|
|---|
| 337 | msg = "Please select a valid component."
|
|---|
| 338 | raise ValueError(msg)
|
|---|
| 339 |
|
|---|
| 340 | def get_area(self, component=None):
|
|---|
| 341 | """
|
|---|
| 342 | Return the area under the fitted gaussian/lorentzian component.
|
|---|
| 343 | Parameters:
|
|---|
| 344 | component: the gaussian/lorentzian component selection,
|
|---|
| 345 | default (None) is the sum of all components
|
|---|
| 346 | Note:
|
|---|
| 347 | This will only work for gaussian/lorentzian fits.
|
|---|
| 348 | """
|
|---|
| 349 | if not self.fitted: return
|
|---|
| 350 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz":
|
|---|
| 351 | pars = list(self.fitter.getparameters())
|
|---|
| 352 | from math import log,pi,sqrt
|
|---|
| 353 | if self.fitfunc == "gauss":
|
|---|
| 354 | fac = sqrt(pi/log(16.0))
|
|---|
| 355 | elif self.fitfunc == "lorentz":
|
|---|
| 356 | fac = pi/2.0
|
|---|
| 357 | areas = []
|
|---|
| 358 | for i in range(len(self.components)):
|
|---|
| 359 | j = i*3
|
|---|
| 360 | cpars = pars[j:j+3]
|
|---|
| 361 | areas.append(fac * cpars[0] * cpars[2])
|
|---|
| 362 | else:
|
|---|
| 363 | return None
|
|---|
| 364 | if component is not None:
|
|---|
| 365 | return areas[component]
|
|---|
| 366 | else:
|
|---|
| 367 | return sum(areas)
|
|---|
| 368 |
|
|---|
| 369 | @asaplog_post_dec
|
|---|
| 370 | def get_errors(self, component=None):
|
|---|
| 371 | """
|
|---|
| 372 | Return the errors in the parameters.
|
|---|
| 373 | Parameters:
|
|---|
| 374 | component: get the errors for the specified component
|
|---|
| 375 | only, default is all components
|
|---|
| 376 | """
|
|---|
| 377 | if not self.fitted:
|
|---|
| 378 | msg = "Not yet fitted."
|
|---|
| 379 | raise RuntimeError(msg)
|
|---|
| 380 | errs = list(self.fitter.geterrors())
|
|---|
| 381 | cerrs = errs
|
|---|
| 382 | if component is not None:
|
|---|
| 383 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz" or self.fitfunc == "sinusoid":
|
|---|
| 384 | i = 3*component
|
|---|
| 385 | if i < len(errs):
|
|---|
| 386 | cerrs = errs[i:i+3]
|
|---|
| 387 | return cerrs
|
|---|
| 388 |
|
|---|
| 389 |
|
|---|
| 390 | @asaplog_post_dec
|
|---|
| 391 | def get_parameters(self, component=None, errors=False):
|
|---|
| 392 | """
|
|---|
| 393 | Return the fit paramters.
|
|---|
| 394 | Parameters:
|
|---|
| 395 | component: get the parameters for the specified component
|
|---|
| 396 | only, default is all components
|
|---|
| 397 | """
|
|---|
| 398 | if not self.fitted:
|
|---|
| 399 | msg = "Not yet fitted."
|
|---|
| 400 | raise RuntimeError(msg)
|
|---|
| 401 | pars = list(self.fitter.getparameters())
|
|---|
| 402 | fixed = list(self.fitter.getfixedparameters())
|
|---|
| 403 | errs = list(self.fitter.geterrors())
|
|---|
| 404 | area = []
|
|---|
| 405 | if component is not None:
|
|---|
| 406 | if self.fitfunc == "poly" or self.fitfunc == "lpoly":
|
|---|
| 407 | cpars = pars
|
|---|
| 408 | cfixed = fixed
|
|---|
| 409 | cerrs = errs
|
|---|
| 410 | else:
|
|---|
| 411 | i = 3*component
|
|---|
| 412 | cpars = pars[i:i+3]
|
|---|
| 413 | cfixed = fixed[i:i+3]
|
|---|
| 414 | cerrs = errs[i:i+3]
|
|---|
| 415 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz":
|
|---|
| 416 | a = self.get_area(component)
|
|---|
| 417 | area = [a for i in range(3)]
|
|---|
| 418 | else:
|
|---|
| 419 | cpars = pars
|
|---|
| 420 | cfixed = fixed
|
|---|
| 421 | cerrs = errs
|
|---|
| 422 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz":
|
|---|
| 423 | for c in range(len(self.components)):
|
|---|
| 424 | a = self.get_area(c)
|
|---|
| 425 | area += [a for i in range(3)]
|
|---|
| 426 | fpars = self._format_pars(cpars, cfixed, errors and cerrs, area)
|
|---|
| 427 | asaplog.push(fpars)
|
|---|
| 428 | return {'params':cpars, 'fixed':cfixed, 'formatted': fpars,
|
|---|
| 429 | 'errors':cerrs}
|
|---|
| 430 |
|
|---|
| 431 | def _format_pars(self, pars, fixed, errors, area):
|
|---|
| 432 | out = ''
|
|---|
| 433 | if self.fitfunc == "poly" or self.fitfunc == "lpoly":
|
|---|
| 434 | c = 0
|
|---|
| 435 | for i in range(len(pars)):
|
|---|
| 436 | fix = ""
|
|---|
| 437 | if len(fixed) and fixed[i]: fix = "(fixed)"
|
|---|
| 438 | out += " p%d%s= %3.6f" % (c, fix, pars[i])
|
|---|
| 439 | if errors : out += " (%1.6f)" % errors[i]
|
|---|
| 440 | out += ","
|
|---|
| 441 | c+=1
|
|---|
| 442 | out = out[:-1] # remove trailing ','
|
|---|
| 443 | else:
|
|---|
| 444 | i = 0
|
|---|
| 445 | c = 0
|
|---|
| 446 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz":
|
|---|
| 447 | pnam = ["peak", "centre", "FWHM"]
|
|---|
| 448 | elif self.fitfunc == "sinusoid":
|
|---|
| 449 | pnam = ["amplitude", "period", "x0"]
|
|---|
| 450 | aunit = ""
|
|---|
| 451 | ounit = ""
|
|---|
| 452 | if self.data:
|
|---|
| 453 | aunit = self.data.get_unit()
|
|---|
| 454 | ounit = self.data.get_fluxunit()
|
|---|
| 455 | while i < len(pars):
|
|---|
| 456 | fix0 = fix1 = fix2 = ""
|
|---|
| 457 | if i < len(fixed)-2:
|
|---|
| 458 | if fixed[i]: fix0 = "(fixed)"
|
|---|
| 459 | if fixed[i+1]: fix1 = "(fixed)"
|
|---|
| 460 | if fixed[i+2]: fix2 = "(fixed)"
|
|---|
| 461 | out += " %2d: " % c
|
|---|
| 462 | out += "%s%s = %3.3f %s, " % (pnam[0], fix0, pars[i], ounit)
|
|---|
| 463 | out += "%s%s = %3.3f %s, " % (pnam[1], fix1, pars[i+1], aunit)
|
|---|
| 464 | out += "%s%s = %3.3f %s\n" % (pnam[2], fix2, pars[i+2], aunit)
|
|---|
| 465 | if len(area): out += " area = %3.3f %s %s\n" % (area[i], ounit, aunit)
|
|---|
| 466 | c+=1
|
|---|
| 467 | i+=3
|
|---|
| 468 | return out
|
|---|
| 469 |
|
|---|
| 470 |
|
|---|
| 471 | @asaplog_post_dec
|
|---|
| 472 | def get_estimate(self):
|
|---|
| 473 | """
|
|---|
| 474 | Return the parameter estimates (for non-linear functions).
|
|---|
| 475 | """
|
|---|
| 476 | pars = self.fitter.getestimate()
|
|---|
| 477 | fixed = self.fitter.getfixedparameters()
|
|---|
| 478 | asaplog.push(self._format_pars(pars,fixed,None,None))
|
|---|
| 479 | return pars
|
|---|
| 480 |
|
|---|
| 481 | @asaplog_post_dec
|
|---|
| 482 | def get_residual(self):
|
|---|
| 483 | """
|
|---|
| 484 | Return the residual of the fit.
|
|---|
| 485 | """
|
|---|
| 486 | if not self.fitted:
|
|---|
| 487 | msg = "Not yet fitted."
|
|---|
| 488 | raise RuntimeError(msg)
|
|---|
| 489 | return self.fitter.getresidual()
|
|---|
| 490 |
|
|---|
| 491 | @asaplog_post_dec
|
|---|
| 492 | def get_chi2(self):
|
|---|
| 493 | """
|
|---|
| 494 | Return chi^2.
|
|---|
| 495 | """
|
|---|
| 496 | if not self.fitted:
|
|---|
| 497 | msg = "Not yet fitted."
|
|---|
| 498 | raise RuntimeError(msg)
|
|---|
| 499 | ch2 = self.fitter.getchi2()
|
|---|
| 500 | asaplog.push( 'Chi^2 = %3.3f' % (ch2) )
|
|---|
| 501 | return ch2
|
|---|
| 502 |
|
|---|
| 503 | @asaplog_post_dec
|
|---|
| 504 | def get_fit(self):
|
|---|
| 505 | """
|
|---|
| 506 | Return the fitted ordinate values.
|
|---|
| 507 | """
|
|---|
| 508 | if not self.fitted:
|
|---|
| 509 | msg = "Not yet fitted."
|
|---|
| 510 | raise RuntimeError(msg)
|
|---|
| 511 | return self.fitter.getfit()
|
|---|
| 512 |
|
|---|
| 513 | @asaplog_post_dec
|
|---|
| 514 | def commit(self):
|
|---|
| 515 | """
|
|---|
| 516 | Return a new scan where the fits have been commited (subtracted)
|
|---|
| 517 | """
|
|---|
| 518 | if not self.fitted:
|
|---|
| 519 | msg = "Not yet fitted."
|
|---|
| 520 | raise RuntimeError(msg)
|
|---|
| 521 | from asap import scantable
|
|---|
| 522 | if not isinstance(self.data, scantable):
|
|---|
| 523 | msg = "Not a scantable"
|
|---|
| 524 | raise TypeError(msg)
|
|---|
| 525 | scan = self.data.copy()
|
|---|
| 526 | scan._setspectrum(self.fitter.getresidual())
|
|---|
| 527 | return scan
|
|---|
| 528 |
|
|---|
| 529 | @asaplog_post_dec
|
|---|
| 530 | def plot(self, residual=False, components=None, plotparms=False,
|
|---|
| 531 | filename=None):
|
|---|
| 532 | """
|
|---|
| 533 | Plot the last fit.
|
|---|
| 534 | Parameters:
|
|---|
| 535 | residual: an optional parameter indicating if the residual
|
|---|
| 536 | should be plotted (default 'False')
|
|---|
| 537 | components: a list of components to plot, e.g [0,1],
|
|---|
| 538 | -1 plots the total fit. Default is to only
|
|---|
| 539 | plot the total fit.
|
|---|
| 540 | plotparms: Inidicates if the parameter values should be present
|
|---|
| 541 | on the plot
|
|---|
| 542 | """
|
|---|
| 543 | if not self.fitted:
|
|---|
| 544 | return
|
|---|
| 545 | if not self._p or self._p.is_dead:
|
|---|
| 546 | #if rcParams['plotter.gui']:
|
|---|
| 547 | # from asap.asaplotgui import asaplotgui as asaplot
|
|---|
| 548 | #else:
|
|---|
| 549 | # from asap.asaplot import asaplot
|
|---|
| 550 | #self._p = asaplot()
|
|---|
| 551 | from asap.asapplotter import new_asaplot
|
|---|
| 552 | self._p = new_asaplot(rcParams['plotter.gui'])
|
|---|
| 553 | self._p.hold()
|
|---|
| 554 | self._p.clear()
|
|---|
| 555 | self._p.set_panels()
|
|---|
| 556 | self._p.palette(0)
|
|---|
| 557 | tlab = 'Spectrum'
|
|---|
| 558 | xlab = 'Abcissa'
|
|---|
| 559 | ylab = 'Ordinate'
|
|---|
| 560 | from numpy import ma,logical_not,logical_and,array
|
|---|
| 561 | m = self.mask
|
|---|
| 562 | if self.data:
|
|---|
| 563 | tlab = self.data._getsourcename(self._fittedrow)
|
|---|
| 564 | xlab = self.data._getabcissalabel(self._fittedrow)
|
|---|
| 565 | if self.data._getflagrow(self._fittedrow):
|
|---|
| 566 | m = [False]
|
|---|
| 567 | else:
|
|---|
| 568 | m = logical_and(self.mask,
|
|---|
| 569 | array(self.data._getmask(self._fittedrow),
|
|---|
| 570 | copy=False))
|
|---|
| 571 |
|
|---|
| 572 | ylab = self.data._get_ordinate_label()
|
|---|
| 573 |
|
|---|
| 574 | colours = ["#777777","#dddddd","red","orange","purple","green","magenta", "cyan"]
|
|---|
| 575 | nomask=True
|
|---|
| 576 | for i in range(len(m)):
|
|---|
| 577 | nomask = nomask and m[i]
|
|---|
| 578 | if len(m) == 1:
|
|---|
| 579 | m = m[0]
|
|---|
| 580 | invm = (not m)
|
|---|
| 581 | else:
|
|---|
| 582 | invm = logical_not(m)
|
|---|
| 583 | label0='Masked Region'
|
|---|
| 584 | label1='Spectrum'
|
|---|
| 585 | if ( nomask ):
|
|---|
| 586 | label0=label1
|
|---|
| 587 | else:
|
|---|
| 588 | y = ma.masked_array( self.y, mask = m )
|
|---|
| 589 | self._p.palette(1,colours)
|
|---|
| 590 | self._p.set_line( label = label1 )
|
|---|
| 591 | self._p.plot( self.x, y )
|
|---|
| 592 | self._p.palette(0,colours)
|
|---|
| 593 | self._p.set_line(label=label0)
|
|---|
| 594 | y = ma.masked_array(self.y,mask=invm)
|
|---|
| 595 | self._p.plot(self.x, y)
|
|---|
| 596 | if residual:
|
|---|
| 597 | self._p.palette(7)
|
|---|
| 598 | self._p.set_line(label='Residual')
|
|---|
| 599 | y = ma.masked_array(self.get_residual(),
|
|---|
| 600 | mask=invm)
|
|---|
| 601 | self._p.plot(self.x, y)
|
|---|
| 602 | self._p.palette(2)
|
|---|
| 603 | if components is not None:
|
|---|
| 604 | cs = components
|
|---|
| 605 | if isinstance(components,int): cs = [components]
|
|---|
| 606 | if plotparms:
|
|---|
| 607 | self._p.text(0.15,0.15,str(self.get_parameters()['formatted']),size=8)
|
|---|
| 608 | n = len(self.components)
|
|---|
| 609 | self._p.palette(3)
|
|---|
| 610 | for c in cs:
|
|---|
| 611 | if 0 <= c < n:
|
|---|
| 612 | lab = self.fitfuncs[c]+str(c)
|
|---|
| 613 | self._p.set_line(label=lab)
|
|---|
| 614 | y = ma.masked_array(self.fitter.evaluate(c), mask=invm)
|
|---|
| 615 |
|
|---|
| 616 | self._p.plot(self.x, y)
|
|---|
| 617 | elif c == -1:
|
|---|
| 618 | self._p.palette(2)
|
|---|
| 619 | self._p.set_line(label="Total Fit")
|
|---|
| 620 | y = ma.masked_array(self.fitter.getfit(),
|
|---|
| 621 | mask=invm)
|
|---|
| 622 | self._p.plot(self.x, y)
|
|---|
| 623 | else:
|
|---|
| 624 | self._p.palette(2)
|
|---|
| 625 | self._p.set_line(label='Fit')
|
|---|
| 626 | y = ma.masked_array(self.fitter.getfit(),mask=invm)
|
|---|
| 627 | self._p.plot(self.x, y)
|
|---|
| 628 | xlim=[min(self.x),max(self.x)]
|
|---|
| 629 | self._p.axes.set_xlim(xlim)
|
|---|
| 630 | self._p.set_axes('xlabel',xlab)
|
|---|
| 631 | self._p.set_axes('ylabel',ylab)
|
|---|
| 632 | self._p.set_axes('title',tlab)
|
|---|
| 633 | self._p.release()
|
|---|
| 634 | if (not rcParams['plotter.gui']):
|
|---|
| 635 | self._p.save(filename)
|
|---|
| 636 |
|
|---|
| 637 | @asaplog_post_dec
|
|---|
| 638 | def auto_fit(self, insitu=None, plot=False):
|
|---|
| 639 | """
|
|---|
| 640 | Return a scan where the function is applied to all rows for
|
|---|
| 641 | all Beams/IFs/Pols.
|
|---|
| 642 |
|
|---|
| 643 | """
|
|---|
| 644 | from asap import scantable
|
|---|
| 645 | if not isinstance(self.data, scantable) :
|
|---|
| 646 | msg = "Data is not a scantable"
|
|---|
| 647 | raise TypeError(msg)
|
|---|
| 648 | if insitu is None: insitu = rcParams['insitu']
|
|---|
| 649 | if not insitu:
|
|---|
| 650 | scan = self.data.copy()
|
|---|
| 651 | else:
|
|---|
| 652 | scan = self.data
|
|---|
| 653 | rows = xrange(scan.nrow())
|
|---|
| 654 | # Save parameters of baseline fits as a class attribute.
|
|---|
| 655 | # NOTICE: This does not reflect changes in scantable!
|
|---|
| 656 | if len(rows) > 0: self.blpars=[]
|
|---|
| 657 | asaplog.push("Fitting:")
|
|---|
| 658 | for r in rows:
|
|---|
| 659 | out = " Scan[%d] Beam[%d] IF[%d] Pol[%d] Cycle[%d]" % (scan.getscan(r),
|
|---|
| 660 | scan.getbeam(r),
|
|---|
| 661 | scan.getif(r),
|
|---|
| 662 | scan.getpol(r),
|
|---|
| 663 | scan.getcycle(r))
|
|---|
| 664 | asaplog.push(out, False)
|
|---|
| 665 | self.x = scan._getabcissa(r)
|
|---|
| 666 | self.y = scan._getspectrum(r)
|
|---|
| 667 | #self.mask = mask_and(self.mask, scan._getmask(r))
|
|---|
| 668 | if len(self.x) == len(self.mask):
|
|---|
| 669 | self.mask = mask_and(self.mask, self.data._getmask(row))
|
|---|
| 670 | else:
|
|---|
| 671 | asaplog.push('lengths of data and mask are not the same. preset mask will be ignored')
|
|---|
| 672 | asaplog.post('WARN','asapfit.fit')
|
|---|
| 673 | self.mask=self.data._getmask(row)
|
|---|
| 674 | self.data = None
|
|---|
| 675 | self.fit()
|
|---|
| 676 | x = self.get_parameters()
|
|---|
| 677 | fpar = self.get_parameters()
|
|---|
| 678 | if plot:
|
|---|
| 679 | self.plot(residual=True)
|
|---|
| 680 | x = raw_input("Accept fit ([y]/n): ")
|
|---|
| 681 | if x.upper() == 'N':
|
|---|
| 682 | self.blpars.append(None)
|
|---|
| 683 | continue
|
|---|
| 684 | scan._setspectrum(self.fitter.getresidual(), r)
|
|---|
| 685 | self.blpars.append(fpar)
|
|---|
| 686 | if plot:
|
|---|
| 687 | self._p.quit()
|
|---|
| 688 | del self._p
|
|---|
| 689 | self._p = None
|
|---|
| 690 | return scan
|
|---|
