| 1 | import _asap
|
|---|
| 2 | from asap import rcParams
|
|---|
| 3 |
|
|---|
| 4 | class fitter:
|
|---|
| 5 | """
|
|---|
| 6 | The fitting class for ASAP.
|
|---|
| 7 | """
|
|---|
| 8 | def _verbose(self, *args):
|
|---|
| 9 | """
|
|---|
| 10 | Set stdout output.
|
|---|
| 11 | """
|
|---|
| 12 | if type(args[0]) is bool:
|
|---|
| 13 | self._vb = args[0]
|
|---|
| 14 | return
|
|---|
| 15 | elif len(args) == 0:
|
|---|
| 16 | return self._vb
|
|---|
| 17 |
|
|---|
| 18 | def __init__(self):
|
|---|
| 19 | """
|
|---|
| 20 | Create a fitter object. No state is set.
|
|---|
| 21 | """
|
|---|
| 22 | self.fitter = _asap.fitter()
|
|---|
| 23 | self.x = None
|
|---|
| 24 | self.y = None
|
|---|
| 25 | self.mask = None
|
|---|
| 26 | self.fitfunc = None
|
|---|
| 27 | self.fitfuncs = None
|
|---|
| 28 | self.fitted = False
|
|---|
| 29 | self.data = None
|
|---|
| 30 | self.components = 0
|
|---|
| 31 | self._fittedrow = 0
|
|---|
| 32 | self._p = None
|
|---|
| 33 | self._vb = True
|
|---|
| 34 | self._selection = None
|
|---|
| 35 |
|
|---|
| 36 | def set_data(self, xdat, ydat, mask=None):
|
|---|
| 37 | """
|
|---|
| 38 | Set the absissa and ordinate for the fit. Also set the mask
|
|---|
| 39 | indicationg valid points.
|
|---|
| 40 | This can be used for data vectors retrieved from a scantable.
|
|---|
| 41 | For scantable fitting use 'fitter.set_scan(scan, mask)'.
|
|---|
| 42 | Parameters:
|
|---|
| 43 | xdat: the abcissa values
|
|---|
| 44 | ydat: the ordinate values
|
|---|
| 45 | mask: an optional mask
|
|---|
| 46 |
|
|---|
| 47 | """
|
|---|
| 48 | self.fitted = False
|
|---|
| 49 | self.x = xdat
|
|---|
| 50 | self.y = ydat
|
|---|
| 51 | if mask == None:
|
|---|
| 52 | from numarray import ones
|
|---|
| 53 | self.mask = ones(len(xdat))
|
|---|
| 54 | else:
|
|---|
| 55 | self.mask = mask
|
|---|
| 56 | return
|
|---|
| 57 |
|
|---|
| 58 | def set_scan(self, thescan=None, mask=None):
|
|---|
| 59 | """
|
|---|
| 60 | Set the 'data' (a scantable) of the fitter.
|
|---|
| 61 | Parameters:
|
|---|
| 62 | thescan: a scantable
|
|---|
| 63 | mask: a msk retireved from the scantable
|
|---|
| 64 | """
|
|---|
| 65 | if not thescan:
|
|---|
| 66 | print "Please give a correct scan"
|
|---|
| 67 | self.fitted = False
|
|---|
| 68 | self.data = thescan
|
|---|
| 69 | if mask is None:
|
|---|
| 70 | from numarray import ones
|
|---|
| 71 | self.mask = ones(self.data.nchan())
|
|---|
| 72 | else:
|
|---|
| 73 | self.mask = mask
|
|---|
| 74 | return
|
|---|
| 75 |
|
|---|
| 76 | def set_function(self, **kwargs):
|
|---|
| 77 | """
|
|---|
| 78 | Set the function to be fit.
|
|---|
| 79 | Parameters:
|
|---|
| 80 | poly: use a polynomial of the order given
|
|---|
| 81 | gauss: fit the number of gaussian specified
|
|---|
| 82 | Example:
|
|---|
| 83 | fitter.set_function(gauss=2) # will fit two gaussians
|
|---|
| 84 | fitter.set_function(poly=3) # will fit a 3rd order polynomial
|
|---|
| 85 | """
|
|---|
| 86 | #default poly order 0
|
|---|
| 87 | n=0
|
|---|
| 88 | if kwargs.has_key('poly'):
|
|---|
| 89 | self.fitfunc = 'poly'
|
|---|
| 90 | n = kwargs.get('poly')
|
|---|
| 91 | self.components = [n]
|
|---|
| 92 | elif kwargs.has_key('gauss'):
|
|---|
| 93 | n = kwargs.get('gauss')
|
|---|
| 94 | self.fitfunc = 'gauss'
|
|---|
| 95 | self.fitfuncs = [ 'gauss' for i in range(n) ]
|
|---|
| 96 | self.components = [ 3 for i in range(n) ]
|
|---|
| 97 | else:
|
|---|
| 98 | print "Invalid function type."
|
|---|
| 99 | return
|
|---|
| 100 | self.fitter.setexpression(self.fitfunc,n)
|
|---|
| 101 | return
|
|---|
| 102 |
|
|---|
| 103 | def fit(self, row=0):
|
|---|
| 104 | """
|
|---|
| 105 | Execute the actual fitting process. All the state has to be set.
|
|---|
| 106 | Parameters:
|
|---|
| 107 | row: specify the row in the scantable
|
|---|
| 108 | Example:
|
|---|
| 109 | s = scantable('myscan.asap')
|
|---|
| 110 | s.set_cursor(thepol=1) # select second pol
|
|---|
| 111 | f = fitter()
|
|---|
| 112 | f.set_scan(s)
|
|---|
| 113 | f.set_function(poly=0)
|
|---|
| 114 | f.fit(row=0) # fit first row
|
|---|
| 115 | """
|
|---|
| 116 | if ((self.x is None or self.y is None) and self.data is None) \
|
|---|
| 117 | or self.fitfunc is None:
|
|---|
| 118 | print "Fitter not yet initialised. Please set data & fit function"
|
|---|
| 119 | return
|
|---|
| 120 | else:
|
|---|
| 121 | if self.data is not None:
|
|---|
| 122 | self.x = self.data._getabcissa(row)
|
|---|
| 123 | self.y = self.data._getspectrum(row)
|
|---|
| 124 | print "Fitting:"
|
|---|
| 125 | vb = self.data._vb
|
|---|
| 126 | self.data._vb = True
|
|---|
| 127 | self.selection = self.data.get_cursor()
|
|---|
| 128 | self.data._vb = vb
|
|---|
| 129 | self.fitter.setdata(self.x, self.y, self.mask)
|
|---|
| 130 | if self.fitfunc == 'gauss':
|
|---|
| 131 | ps = self.fitter.getparameters()
|
|---|
| 132 | if len(ps) == 0:
|
|---|
| 133 | self.fitter.estimate()
|
|---|
| 134 | try:
|
|---|
| 135 | self.fitter.fit()
|
|---|
| 136 | except RuntimeError, msg:
|
|---|
| 137 | print msg
|
|---|
| 138 | self._fittedrow = row
|
|---|
| 139 | self.fitted = True
|
|---|
| 140 | return
|
|---|
| 141 |
|
|---|
| 142 | def store_fit(self):
|
|---|
| 143 | """
|
|---|
| 144 | Store the fit parameters in the scantable.
|
|---|
| 145 | """
|
|---|
| 146 | if self.fitted and self.data is not None:
|
|---|
| 147 | pars = list(self.fitter.getparameters())
|
|---|
| 148 | fixed = list(self.fitter.getfixedparameters())
|
|---|
| 149 | self.data._addfit(self._fittedrow, pars, fixed,
|
|---|
| 150 | self.fitfuncs, self.components)
|
|---|
| 151 |
|
|---|
| 152 | def set_parameters(self, params, fixed=None, component=None):
|
|---|
| 153 | """
|
|---|
| 154 | Set the parameters to be fitted.
|
|---|
| 155 | Parameters:
|
|---|
| 156 | params: a vector of parameters
|
|---|
| 157 | fixed: a vector of which parameters are to be held fixed
|
|---|
| 158 | (default is none)
|
|---|
| 159 | component: in case of multiple gaussians, the index of the
|
|---|
| 160 | component
|
|---|
| 161 | """
|
|---|
| 162 | if self.fitfunc is None:
|
|---|
| 163 | print "Please specify a fitting function first."
|
|---|
| 164 | return
|
|---|
| 165 | if self.fitfunc == "gauss" and component is not None:
|
|---|
| 166 | if not self.fitted:
|
|---|
| 167 | from numarray import zeros
|
|---|
| 168 | pars = list(zeros(len(self.components)*3))
|
|---|
| 169 | fxd = list(zeros(len(pars)))
|
|---|
| 170 | else:
|
|---|
| 171 | pars = list(self.fitter.getparameters())
|
|---|
| 172 | fxd = list(self.fitter.getfixedparameters())
|
|---|
| 173 | i = 3*component
|
|---|
| 174 | pars[i:i+3] = params
|
|---|
| 175 | fxd[i:i+3] = fixed
|
|---|
| 176 | params = pars
|
|---|
| 177 | fixed = fxd
|
|---|
| 178 | self.fitter.setparameters(params)
|
|---|
| 179 | if fixed is not None:
|
|---|
| 180 | self.fitter.setfixedparameters(fixed)
|
|---|
| 181 | return
|
|---|
| 182 |
|
|---|
| 183 | def set_gauss_parameters(self, peak, centre, fhwm,
|
|---|
| 184 | peakfixed=False, centerfixed=False,
|
|---|
| 185 | fhwmfixed=False,
|
|---|
| 186 | component=0):
|
|---|
| 187 | """
|
|---|
| 188 | Set the Parameters of a 'Gaussian' component, set with set_function.
|
|---|
| 189 | Parameters:
|
|---|
| 190 | peak, centre, fhwm: The gaussian parameters
|
|---|
| 191 | peakfixed,
|
|---|
| 192 | centerfixed,
|
|---|
| 193 | fhwmfixed: Optional parameters to indicate if
|
|---|
| 194 | the paramters should be held fixed during
|
|---|
| 195 | the fitting process. The default is to keep
|
|---|
| 196 | all parameters flexible.
|
|---|
| 197 | component: The number of the component (Default is the
|
|---|
| 198 | component 0)
|
|---|
| 199 | """
|
|---|
| 200 | if self.fitfunc != "gauss":
|
|---|
| 201 | print "Function only operates on Gaussian components."
|
|---|
| 202 | return
|
|---|
| 203 | if 0 <= component < len(self.components):
|
|---|
| 204 | self.set_parameters([peak, centre, fhwm],
|
|---|
| 205 | [peakfixed, centerfixed, fhwmfixed],
|
|---|
| 206 | component)
|
|---|
| 207 | else:
|
|---|
| 208 | print "Please select a valid component."
|
|---|
| 209 | return
|
|---|
| 210 |
|
|---|
| 211 | def get_parameters(self, component=None):
|
|---|
| 212 | """
|
|---|
| 213 | Return the fit paramters.
|
|---|
| 214 | Parameters:
|
|---|
| 215 | component: get the parameters for the specified component
|
|---|
| 216 | only, default is all components
|
|---|
| 217 | """
|
|---|
| 218 | if not self.fitted:
|
|---|
| 219 | print "Not yet fitted."
|
|---|
| 220 | pars = list(self.fitter.getparameters())
|
|---|
| 221 | fixed = list(self.fitter.getfixedparameters())
|
|---|
| 222 | if component is not None:
|
|---|
| 223 | if self.fitfunc == "gauss":
|
|---|
| 224 | i = 3*component
|
|---|
| 225 | cpars = pars[i:i+3]
|
|---|
| 226 | cfixed = fixed[i:i+3]
|
|---|
| 227 | else:
|
|---|
| 228 | cpars = pars
|
|---|
| 229 | cfixed = fixed
|
|---|
| 230 | else:
|
|---|
| 231 | cpars = pars
|
|---|
| 232 | cfixed = fixed
|
|---|
| 233 | fpars = self._format_pars(cpars, cfixed)
|
|---|
| 234 | if self._vb:
|
|---|
| 235 | print fpars
|
|---|
| 236 | return cpars, cfixed, fpars
|
|---|
| 237 |
|
|---|
| 238 | def _format_pars(self, pars, fixed):
|
|---|
| 239 | out = ''
|
|---|
| 240 | if self.fitfunc == 'poly':
|
|---|
| 241 | c = 0
|
|---|
| 242 | for i in range(len(pars)):
|
|---|
| 243 | fix = ""
|
|---|
| 244 | if fixed[i]: fix = "(fixed)"
|
|---|
| 245 | out += ' p%d%s= %3.3f,' % (c,fix,pars[i])
|
|---|
| 246 | c+=1
|
|---|
| 247 | out = out[:-1] # remove trailing ','
|
|---|
| 248 | elif self.fitfunc == 'gauss':
|
|---|
| 249 | i = 0
|
|---|
| 250 | c = 0
|
|---|
| 251 | aunit = ''
|
|---|
| 252 | ounit = ''
|
|---|
| 253 | if self.data:
|
|---|
| 254 | aunit = self.data.get_unit()
|
|---|
| 255 | ounit = self.data.get_fluxunit()
|
|---|
| 256 | while i < len(pars):
|
|---|
| 257 | out += ' %d: peak = %3.3f %s , centre = %3.3f %s, FWHM = %3.3f %s \n' % (c,pars[i],ounit,pars[i+1],aunit,pars[i+2],aunit)
|
|---|
| 258 | c+=1
|
|---|
| 259 | i+=3
|
|---|
| 260 | return out
|
|---|
| 261 |
|
|---|
| 262 | def get_estimate(self):
|
|---|
| 263 | """
|
|---|
| 264 | Return the parameter estimates (for non-linear functions).
|
|---|
| 265 | """
|
|---|
| 266 | pars = self.fitter.getestimate()
|
|---|
| 267 | if self._vb:
|
|---|
| 268 | print self._format_pars(pars)
|
|---|
| 269 | return pars
|
|---|
| 270 |
|
|---|
| 271 |
|
|---|
| 272 | def get_residual(self):
|
|---|
| 273 | """
|
|---|
| 274 | Return the residual of the fit.
|
|---|
| 275 | """
|
|---|
| 276 | if not self.fitted:
|
|---|
| 277 | print "Not yet fitted."
|
|---|
| 278 | return self.fitter.getresidual()
|
|---|
| 279 |
|
|---|
| 280 | def get_chi2(self):
|
|---|
| 281 | """
|
|---|
| 282 | Return chi^2.
|
|---|
| 283 | """
|
|---|
| 284 | if not self.fitted:
|
|---|
| 285 | print "Not yet fitted."
|
|---|
| 286 | ch2 = self.fitter.getchi2()
|
|---|
| 287 | if self._vb:
|
|---|
| 288 | print 'Chi^2 = %3.3f' % (ch2)
|
|---|
| 289 | return ch2
|
|---|
| 290 |
|
|---|
| 291 | def get_fit(self):
|
|---|
| 292 | """
|
|---|
| 293 | Return the fitted ordinate values.
|
|---|
| 294 | """
|
|---|
| 295 | if not self.fitted:
|
|---|
| 296 | print "Not yet fitted."
|
|---|
| 297 | return self.fitter.getfit()
|
|---|
| 298 |
|
|---|
| 299 | def commit(self):
|
|---|
| 300 | """
|
|---|
| 301 | Return a new scan where the fits have been commited (subtracted)
|
|---|
| 302 | """
|
|---|
| 303 | if not self.fitted:
|
|---|
| 304 | print "Not yet fitted."
|
|---|
| 305 | if self.data is not scantable:
|
|---|
| 306 | print "Only works with scantables"
|
|---|
| 307 | return
|
|---|
| 308 | scan = self.data.copy()
|
|---|
| 309 | scan._setspectrum(self.fitter.getresidual())
|
|---|
| 310 |
|
|---|
| 311 | def plot(self, residual=False, components=None, plotparms=False):
|
|---|
| 312 | """
|
|---|
| 313 | Plot the last fit.
|
|---|
| 314 | Parameters:
|
|---|
| 315 | residual: an optional parameter indicating if the residual
|
|---|
| 316 | should be plotted (default 'False')
|
|---|
| 317 | components: a list of components to plot, e.g [0,1],
|
|---|
| 318 | -1 plots the total fit. Default is to only
|
|---|
| 319 | plot the total fit.
|
|---|
| 320 | plotparms: Inidicates if the parameter values should be present
|
|---|
| 321 | on the plot
|
|---|
| 322 | """
|
|---|
| 323 | if not self.fitted:
|
|---|
| 324 | return
|
|---|
| 325 | if not self._p:
|
|---|
| 326 | from asap.asaplot import ASAPlot
|
|---|
| 327 | self._p = ASAPlot()
|
|---|
| 328 | if self._p.is_dead:
|
|---|
| 329 | from asap.asaplot import ASAPlot
|
|---|
| 330 | self._p = ASAPlot()
|
|---|
| 331 | self._p.clear()
|
|---|
| 332 | self._p.set_panels()
|
|---|
| 333 | self._p.palette(0)
|
|---|
| 334 | tlab = 'Spectrum'
|
|---|
| 335 | xlab = 'Abcissa'
|
|---|
| 336 | m = ()
|
|---|
| 337 | if self.data:
|
|---|
| 338 | tlab = self.data._getsourcename(self._fittedrow)
|
|---|
| 339 | xlab = self.data._getabcissalabel(self._fittedrow)
|
|---|
| 340 | m = self.data._getmask(self._fittedrow)
|
|---|
| 341 | ylab = self.data._get_ordinate_label()
|
|---|
| 342 |
|
|---|
| 343 | colours = ["#777777","#bbbbbb","red","orange","purple","green","magenta", "cyan"]
|
|---|
| 344 | self._p.palette(0,colours)
|
|---|
| 345 | self._p.set_line(label='Spectrum')
|
|---|
| 346 | self._p.plot(self.x, self.y, m)
|
|---|
| 347 | if residual:
|
|---|
| 348 | self._p.palette(1)
|
|---|
| 349 | self._p.set_line(label='Residual')
|
|---|
| 350 | self._p.plot(self.x, self.get_residual(), m)
|
|---|
| 351 | self._p.palette(2)
|
|---|
| 352 | if components is not None:
|
|---|
| 353 | cs = components
|
|---|
| 354 | if isinstance(components,int): cs = [components]
|
|---|
| 355 | if plotparms:
|
|---|
| 356 | self._p.text(0.15,0.15,str(self.get_parameters()[2]),size=8)
|
|---|
| 357 | n = len(self.components)
|
|---|
| 358 | self._p.palette(3)
|
|---|
| 359 | for c in cs:
|
|---|
| 360 | if 0 <= c < n:
|
|---|
| 361 | lab = self.fitfuncs[c]+str(c)
|
|---|
| 362 | self._p.set_line(label=lab)
|
|---|
| 363 | self._p.plot(self.x, self.fitter.evaluate(c), m)
|
|---|
| 364 | elif c == -1:
|
|---|
| 365 | self._p.palette(2)
|
|---|
| 366 | self._p.set_line(label="Total Fit")
|
|---|
| 367 | self._p.plot(self.x, self.get_fit(), m)
|
|---|
| 368 | else:
|
|---|
| 369 | self._p.palette(2)
|
|---|
| 370 | self._p.set_line(label='Fit')
|
|---|
| 371 | self._p.plot(self.x, self.get_fit(), m)
|
|---|
| 372 | self._p.set_axes('xlabel',xlab)
|
|---|
| 373 | self._p.set_axes('ylabel',ylab)
|
|---|
| 374 | self._p.set_axes('title',tlab)
|
|---|
| 375 | self._p.release()
|
|---|
| 376 |
|
|---|
| 377 | def auto_fit(self, insitu=None):
|
|---|
| 378 | """
|
|---|
| 379 | Return a scan where the function is applied to all rows for
|
|---|
| 380 | all Beams/IFs/Pols.
|
|---|
| 381 |
|
|---|
| 382 | """
|
|---|
| 383 | from asap import scantable
|
|---|
| 384 | if not isinstance(self.data, scantable) :
|
|---|
| 385 | print "Only works with scantables"
|
|---|
| 386 | return
|
|---|
| 387 | if insitu is None: insitu = rcParams['insitu']
|
|---|
| 388 | if not insitu:
|
|---|
| 389 | scan = self.data.copy()
|
|---|
| 390 | else:
|
|---|
| 391 | scan = self.data
|
|---|
| 392 | vb = scan._vb
|
|---|
| 393 | scan._vb = False
|
|---|
| 394 | sel = scan.get_cursor()
|
|---|
| 395 | rows = range(scan.nrow())
|
|---|
| 396 | for i in range(scan.nbeam()):
|
|---|
| 397 | scan.setbeam(i)
|
|---|
| 398 | for j in range(scan.nif()):
|
|---|
| 399 | scan.setif(j)
|
|---|
| 400 | for k in range(scan.npol()):
|
|---|
| 401 | scan.setpol(k)
|
|---|
| 402 | if self._vb:
|
|---|
| 403 | print "Fitting:"
|
|---|
| 404 | print 'Beam[%d], IF[%d], Pol[%d]' % (i,j,k)
|
|---|
| 405 | for iRow in rows:
|
|---|
| 406 | self.x = scan._getabcissa(iRow)
|
|---|
| 407 | self.y = scan._getspectrum(iRow)
|
|---|
| 408 | self.data = None
|
|---|
| 409 | self.fit()
|
|---|
| 410 | x = self.get_parameters()
|
|---|
| 411 | scan._setspectrum(self.fitter.getresidual(),iRow)
|
|---|
| 412 | scan.set_cursor(sel[0],sel[1],sel[2])
|
|---|
| 413 | scan._vb = vb
|
|---|
| 414 | if not insitu:
|
|---|
| 415 | return scan
|
|---|
