source: trunk/python/asapfitter.py@ 2702

Last change on this file since 2702 was 2681, checked in by Malte Marquarding, 12 years ago

add better description to add_constraint

  • Property svn:eol-style set to native
  • Property svn:keywords set to Author Date Id Revision
File size: 27.1 KB
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1import _asap
2from asap.parameters import rcParams
3from asap.logging import asaplog, asaplog_post_dec
4from asap.utils import _n_bools, mask_and
5from numpy import ndarray
6
7class 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 self._constraints = []
29
30 def set_data(self, xdat, ydat, mask=None):
31 """
32 Set the absissa and ordinate for the fit. Also set the mask
33 indicating valid points.
34 This can be used for data vectors retrieved from a scantable.
35 For scantable fitting use 'fitter.set_scan(scan, mask)'.
36 Parameters:
37 xdat: the abcissa values
38 ydat: the ordinate values
39 mask: an optional mask
40
41 """
42 self.fitted = False
43 self.x = xdat
44 self.y = ydat
45 if mask == None:
46 self.mask = _n_bools(len(xdat), True)
47 else:
48 self.mask = mask
49 return
50
51 @asaplog_post_dec
52 def set_scan(self, thescan=None, mask=None):
53 """
54 Set the 'data' (a scantable) of the fitter.
55 Parameters:
56 thescan: a scantable
57 mask: a msk retrieved from the scantable
58 """
59 if not thescan:
60 msg = "Please give a correct scan"
61 raise TypeError(msg)
62 self.fitted = False
63 self.data = thescan
64 self.mask = None
65 if mask is None:
66 self.mask = _n_bools(self.data.nchan(), True)
67 else:
68 self.mask = mask
69 return
70
71 @asaplog_post_dec
72 def set_function(self, **kwargs):
73 """
74 Set the function to be fit.
75 Parameters:
76 poly: use a polynomial of the order given with nonlinear
77 least squares fit
78 lpoly: use polynomial of the order given with linear least
79 squares fit
80 gauss: fit the number of gaussian specified
81 lorentz: fit the number of lorentzian specified
82 sinusoid: fit the number of sinusoid specified
83 Example:
84 fitter.set_function(poly=3) # will fit a 3rd order polynomial
85 # via nonlinear method
86 fitter.set_function(lpoly=3) # will fit a 3rd order polynomial
87 # via linear method
88 fitter.set_function(gauss=2) # will fit two gaussians
89 fitter.set_function(lorentz=2) # will fit two lorentzians
90 fitter.set_function(sinusoid=3) # will fit three sinusoids
91 """
92 #default poly order 0
93 n=0
94 if kwargs.has_key('poly'):
95 self.fitfunc = 'poly'
96 self.fitfuncs = ['poly']
97 n = kwargs.get('poly')
98 self.components = [n+1]
99 self.uselinear = False
100 elif kwargs.has_key('lpoly'):
101 self.fitfunc = 'poly'
102 self.fitfuncs = ['lpoly']
103 n = kwargs.get('lpoly')
104 self.components = [n+1]
105 self.uselinear = True
106 elif kwargs.has_key('gauss'):
107 n = kwargs.get('gauss')
108 self.fitfunc = 'gauss'
109 self.fitfuncs = [ 'gauss' for i in range(n) ]
110 self.components = [ 3 for i in range(n) ]
111 self.uselinear = False
112 elif kwargs.has_key('lorentz'):
113 n = kwargs.get('lorentz')
114 self.fitfunc = 'lorentz'
115 self.fitfuncs = [ 'lorentz' for i in range(n) ]
116 self.components = [ 3 for i in range(n) ]
117 self.uselinear = False
118 elif kwargs.has_key('sinusoid'):
119 n = kwargs.get('sinusoid')
120 self.fitfunc = 'sinusoid'
121 self.fitfuncs = [ 'sinusoid' for i in range(n) ]
122 self.components = [ 3 for i in range(n) ]
123 self.uselinear = False
124 elif kwargs.has_key('expression'):
125 self.uselinear = False
126 raise RuntimeError("Not yet implemented")
127 else:
128 msg = "Invalid function type."
129 raise TypeError(msg)
130
131 self.fitter.setexpression(self.fitfunc,n)
132 self._constraints = []
133 self.fitted = False
134 return
135
136 @asaplog_post_dec
137 def fit(self, row=0, estimate=False):
138 """
139 Execute the actual fitting process. All the state has to be set.
140 Parameters:
141 row: specify the row in the scantable
142 estimate: auto-compute an initial parameter set (default False)
143 This can be used to compute estimates even if fit was
144 called before.
145 Example:
146 s = scantable('myscan.asap')
147 s.set_cursor(thepol=1) # select second pol
148 f = fitter()
149 f.set_scan(s)
150 f.set_function(poly=0)
151 f.fit(row=0) # fit first row
152 """
153 if ((self.x is None or self.y is None) and self.data is None) \
154 or self.fitfunc is None:
155 msg = "Fitter not yet initialised. Please set data & fit function"
156 raise RuntimeError(msg)
157
158 if self.data is not None:
159 self.x = self.data._getabcissa(row)
160 self.y = self.data._getspectrum(row)
161 #self.mask = mask_and(self.mask, self.data._getmask(row))
162 if len(self.x) == len(self.mask):
163 self.mask = mask_and(self.mask, self.data._getmask(row))
164 else:
165 asaplog.push('lengths of data and mask are not the same. '
166 'preset mask will be ignored')
167 asaplog.post('WARN','asapfit.fit')
168 self.mask=self.data._getmask(row)
169 asaplog.push("Fitting:")
170 i = row
171 out = "Scan[%d] Beam[%d] IF[%d] Pol[%d] Cycle[%d]" % (
172 self.data.getscan(i),
173 self.data.getbeam(i),
174 self.data.getif(i),
175 self.data.getpol(i),
176 self.data.getcycle(i))
177
178 asaplog.push(out, False)
179
180 self.fitter.setdata(self.x, self.y, self.mask)
181 if self.fitfunc == 'gauss' or self.fitfunc == 'lorentz':
182 ps = self.fitter.getparameters()
183 if len(ps) == 0 or estimate:
184 self.fitter.estimate()
185 fxdpar = list(self.fitter.getfixedparameters())
186 if len(fxdpar) and fxdpar.count(0) == 0:
187 raise RuntimeError,"No point fitting, if all parameters are fixed."
188 if self._constraints:
189 for c in self._constraints:
190 self.fitter.addconstraint(c[0]+[c[-1]])
191 if self.uselinear:
192 converged = self.fitter.lfit()
193 else:
194 converged = self.fitter.fit()
195 if not converged:
196 raise RuntimeError,"Fit didn't converge."
197 self._fittedrow = row
198 self.fitted = True
199 return
200
201 def store_fit(self, filename=None):
202 """
203 Save the fit parameters.
204 Parameters:
205 filename: if specified save as an ASCII file, if None (default)
206 store it in the scnatable
207 """
208 if self.fitted and self.data is not None:
209 pars = list(self.fitter.getparameters())
210 fixed = list(self.fitter.getfixedparameters())
211 from asap.asapfit import asapfit
212 fit = asapfit()
213 fit.setparameters(pars)
214 fit.setfixedparameters(fixed)
215 fit.setfunctions(self.fitfuncs)
216 fit.setcomponents(self.components)
217 fit.setframeinfo(self.data._getcoordinfo())
218 if filename is not None:
219 import os
220 filename = os.path.expandvars(os.path.expanduser(filename))
221 if os.path.exists(filename):
222 raise IOError("File '%s' exists." % filename)
223 fit.save(filename)
224 else:
225 self.data._addfit(fit,self._fittedrow)
226
227 @asaplog_post_dec
228 def set_parameters(self,*args,**kwargs):
229 """
230 Set the parameters to be fitted.
231 Parameters:
232 params: a vector of parameters
233 fixed: a vector of which parameters are to be held fixed
234 (default is none)
235 component: in case of multiple gaussians/lorentzians/sinusoidals,
236 the index of the target component
237 """
238 component = None
239 fixed = None
240 params = None
241
242 if len(args) and isinstance(args[0],dict):
243 kwargs = args[0]
244 if kwargs.has_key("fixed"): fixed = kwargs["fixed"]
245 if kwargs.has_key("params"): params = kwargs["params"]
246 if len(args) == 2 and isinstance(args[1], int):
247 component = args[1]
248 if self.fitfunc is None:
249 msg = "Please specify a fitting function first."
250 raise RuntimeError(msg)
251 if (self.fitfunc == "gauss" or self.fitfunc == "lorentz"
252 or self.fitfunc == "sinusoid") and component is not None:
253 if not self.fitted and sum(self.fitter.getparameters()) == 0:
254 pars = _n_bools(len(self.components)*3, False)
255 fxd = _n_bools(len(pars), False)
256 else:
257 pars = list(self.fitter.getparameters())
258 fxd = list(self.fitter.getfixedparameters())
259 i = 3*component
260 pars[i:i+3] = params
261 fxd[i:i+3] = fixed
262 params = pars
263 fixed = fxd
264 self.fitter.setparameters(params)
265 if fixed is not None:
266 self.fitter.setfixedparameters(fixed)
267 return
268
269 @asaplog_post_dec
270 def set_gauss_parameters(self, peak, centre, fwhm,
271 peakfixed=0, centrefixed=0,
272 fwhmfixed=0,
273 component=0):
274 """
275 Set the Parameters of a 'Gaussian' component, set with set_function.
276 Parameters:
277 peak, centre, fwhm: The gaussian parameters
278 peakfixed,
279 centrefixed,
280 fwhmfixed: Optional parameters to indicate if
281 the paramters should be held fixed during
282 the fitting process. The default is to keep
283 all parameters flexible.
284 component: The number of the component (Default is the
285 component 0)
286 """
287 if self.fitfunc != "gauss":
288 msg = "Function only operates on Gaussian components."
289 raise ValueError(msg)
290 if 0 <= component < len(self.components):
291 d = {'params':[peak, centre, fwhm],
292 'fixed':[peakfixed, centrefixed, fwhmfixed]}
293 self.set_parameters(d, component)
294 else:
295 msg = "Please select a valid component."
296 raise ValueError(msg)
297
298 @asaplog_post_dec
299 def set_lorentz_parameters(self, peak, centre, fwhm,
300 peakfixed=0, centrefixed=0,
301 fwhmfixed=0,
302 component=0):
303 """
304 Set the Parameters of a 'Lorentzian' component, set with set_function.
305 Parameters:
306 peak, centre, fwhm: The lorentzian parameters
307 peakfixed,
308 centrefixed,
309 fwhmfixed: Optional parameters to indicate if
310 the paramters should be held fixed during
311 the fitting process. The default is to keep
312 all parameters flexible.
313 component: The number of the component (Default is the
314 component 0)
315 """
316 if self.fitfunc != "lorentz":
317 msg = "Function only operates on Lorentzian components."
318 raise ValueError(msg)
319 if 0 <= component < len(self.components):
320 d = {'params':[peak, centre, fwhm],
321 'fixed':[peakfixed, centrefixed, fwhmfixed]}
322 self.set_parameters(d, component)
323 else:
324 msg = "Please select a valid component."
325 raise ValueError(msg)
326
327 @asaplog_post_dec
328 def set_sinusoid_parameters(self, ampl, period, x0,
329 amplfixed=0, periodfixed=0,
330 x0fixed=0,
331 component=0):
332 """
333 Set the Parameters of a 'Sinusoidal' component, set with set_function.
334 Parameters:
335 ampl, period, x0: The sinusoidal parameters
336 amplfixed,
337 periodfixed,
338 x0fixed: Optional parameters to indicate if
339 the paramters should be held fixed during
340 the fitting process. The default is to keep
341 all parameters flexible.
342 component: The number of the component (Default is the
343 component 0)
344 """
345 if self.fitfunc != "sinusoid":
346 msg = "Function only operates on Sinusoidal components."
347 raise ValueError(msg)
348 if 0 <= component < len(self.components):
349 d = {'params':[ampl, period, x0],
350 'fixed': [amplfixed, periodfixed, x0fixed]}
351 self.set_parameters(d, component)
352 else:
353 msg = "Please select a valid component."
354 raise ValueError(msg)
355
356
357 def add_constraint(self, xpar, y):
358 """Add parameter constraints to the fit. This is done by setting up
359 linear equations for the related parameters.
360
361 For example a two component gaussian fit where the amplitudes are
362 constraint by amp1 = 2*amp2 and paramaters for the two components
363 in the order [amp1,peakv1,sigma1,amp2,peakv2,sigma2]
364 needs a constraint
365
366 add_constraint([1, 0, 0, -2, 0, 0], 0)
367
368 as the linear equation is
369
370 1*amp1 + 0*peakv1 + 0*sigma1 -2*amp2 + 0*peakv2 + 0*sigma2 = 0
371
372 and similarly for a velocity difference of v2-v1=17
373
374 add_constraint([0.,-1.,0.,0.,1.,0.], 17.)
375
376 """
377 self._constraints.append((xpar, y))
378
379
380 def get_area(self, component=None):
381 """
382 Return the area under the fitted gaussian/lorentzian component.
383 Parameters:
384 component: the gaussian/lorentzian component selection,
385 default (None) is the sum of all components
386 Note:
387 This will only work for gaussian/lorentzian fits.
388 """
389 if not self.fitted: return
390 if self.fitfunc == "gauss" or self.fitfunc == "lorentz":
391 pars = list(self.fitter.getparameters())
392 from math import log,pi,sqrt
393 if self.fitfunc == "gauss":
394 fac = sqrt(pi/log(16.0))
395 elif self.fitfunc == "lorentz":
396 fac = pi/2.0
397 areas = []
398 for i in range(len(self.components)):
399 j = i*3
400 cpars = pars[j:j+3]
401 areas.append(fac * cpars[0] * cpars[2])
402 else:
403 return None
404 if component is not None:
405 return areas[component]
406 else:
407 return sum(areas)
408
409 @asaplog_post_dec
410 def get_errors(self, component=None):
411 """
412 Return the errors in the parameters.
413 Parameters:
414 component: get the errors for the specified component
415 only, default is all components
416 """
417 if not self.fitted:
418 msg = "Not yet fitted."
419 raise RuntimeError(msg)
420 errs = list(self.fitter.geterrors())
421 cerrs = errs
422 if component is not None:
423 if self.fitfunc == "gauss" or self.fitfunc == "lorentz" \
424 or self.fitfunc == "sinusoid":
425 i = 3*component
426 if i < len(errs):
427 cerrs = errs[i:i+3]
428 return cerrs
429
430
431 @asaplog_post_dec
432 def get_parameters(self, component=None, errors=False):
433 """
434 Return the fit paramters.
435 Parameters:
436 component: get the parameters for the specified component
437 only, default is all components
438 """
439 if not self.fitted:
440 msg = "Not yet fitted."
441 raise RuntimeError(msg)
442 pars = list(self.fitter.getparameters())
443 fixed = list(self.fitter.getfixedparameters())
444 errs = list(self.fitter.geterrors())
445 area = []
446 if component is not None:
447 if self.fitfunc == "poly" or self.fitfunc == "lpoly":
448 cpars = pars
449 cfixed = fixed
450 cerrs = errs
451 else:
452 i = 3*component
453 cpars = pars[i:i+3]
454 cfixed = fixed[i:i+3]
455 cerrs = errs[i:i+3]
456 if self.fitfunc == "gauss" or self.fitfunc == "lorentz":
457 a = self.get_area(component)
458 area = [a for i in range(3)]
459 else:
460 cpars = pars
461 cfixed = fixed
462 cerrs = errs
463 if self.fitfunc == "gauss" or self.fitfunc == "lorentz":
464 for c in range(len(self.components)):
465 a = self.get_area(c)
466 area += [a for i in range(3)]
467 fpars = self._format_pars(cpars, cfixed, errors and cerrs, area)
468 asaplog.push(fpars)
469 return {'params':cpars, 'fixed':cfixed, 'formatted': fpars,
470 'errors':cerrs}
471
472 def _format_pars(self, pars, fixed, errors, area):
473 out = ''
474 if self.fitfunc == "poly" or self.fitfunc == "lpoly":
475 c = 0
476 for i in range(len(pars)):
477 fix = ""
478 if len(fixed) and fixed[i]: fix = "(fixed)"
479 out += " p%d%s= %3.6f" % (c, fix, pars[i])
480 if errors : out += " (%1.6f)" % errors[i]
481 out += ","
482 c+=1
483 out = out[:-1] # remove trailing ','
484 else:
485 i = 0
486 c = 0
487 if self.fitfunc == "gauss" or self.fitfunc == "lorentz":
488 pnam = ["peak", "centre", "FWHM"]
489 elif self.fitfunc == "sinusoid":
490 pnam = ["amplitude", "period", "x0"]
491 aunit = ""
492 ounit = ""
493 if self.data:
494 aunit = self.data.get_unit()
495 ounit = self.data.get_fluxunit()
496 while i < len(pars):
497 fix0 = fix1 = fix2 = ""
498 if i < len(fixed)-2:
499 if fixed[i]: fix0 = "(fixed)"
500 if fixed[i+1]: fix1 = "(fixed)"
501 if fixed[i+2]: fix2 = "(fixed)"
502 out += " %2d: " % c
503 out += "%s%s = %3.3f %s, " % (pnam[0], fix0, pars[i], ounit)
504 out += "%s%s = %3.3f %s, " % (pnam[1], fix1, pars[i+1], aunit)
505 out += "%s%s = %3.3f %s\n" % (pnam[2], fix2, pars[i+2], aunit)
506 if len(area): out += " area = %3.3f %s %s\n" % (area[i],
507 ounit,
508 aunit)
509 c+=1
510 i+=3
511 return out
512
513
514 @asaplog_post_dec
515 def get_estimate(self):
516 """
517 Return the parameter estimates (for non-linear functions).
518 """
519 pars = self.fitter.getestimate()
520 fixed = self.fitter.getfixedparameters()
521 asaplog.push(self._format_pars(pars,fixed,None,None))
522 return pars
523
524 @asaplog_post_dec
525 def get_residual(self):
526 """
527 Return the residual of the fit.
528 """
529 if not self.fitted:
530 msg = "Not yet fitted."
531 raise RuntimeError(msg)
532 return self.fitter.getresidual()
533
534 @asaplog_post_dec
535 def get_chi2(self):
536 """
537 Return chi^2.
538 """
539 if not self.fitted:
540 msg = "Not yet fitted."
541 raise RuntimeError(msg)
542 ch2 = self.fitter.getchi2()
543 asaplog.push( 'Chi^2 = %3.3f' % (ch2) )
544 return ch2
545
546 @asaplog_post_dec
547 def get_fit(self):
548 """
549 Return the fitted ordinate values.
550 """
551 if not self.fitted:
552 msg = "Not yet fitted."
553 raise RuntimeError(msg)
554 return self.fitter.getfit()
555
556 @asaplog_post_dec
557 def commit(self):
558 """
559 Return a new scan where the fits have been commited (subtracted)
560 """
561 if not self.fitted:
562 msg = "Not yet fitted."
563 raise RuntimeError(msg)
564 from asap import scantable
565 if not isinstance(self.data, scantable):
566 msg = "Not a scantable"
567 raise TypeError(msg)
568 scan = self.data.copy()
569 scan._setspectrum(self.fitter.getresidual())
570 return scan
571
572 @asaplog_post_dec
573 def plot(self, residual=False, components=None, plotparms=False,
574 filename=None):
575 """
576 Plot the last fit.
577 Parameters:
578 residual: an optional parameter indicating if the residual
579 should be plotted (default 'False')
580 components: a list of components to plot, e.g [0,1],
581 -1 plots the total fit. Default is to only
582 plot the total fit.
583 plotparms: Inidicates if the parameter values should be present
584 on the plot
585 """
586 from matplotlib import rc as rcp
587 if not self.fitted:
588 return
589 #if not self._p or self._p.is_dead:
590 if not (self._p and self._p._alive()):
591 from asap.asapplotter import new_asaplot
592 del self._p
593 self._p = new_asaplot(rcParams['plotter.gui'])
594 self._p.hold()
595 self._p.clear()
596 rcp('lines', linewidth=1)
597 self._p.set_panels()
598 self._p.palette(0)
599 tlab = 'Spectrum'
600 xlab = 'Abcissa'
601 ylab = 'Ordinate'
602 from numpy import ma,logical_not,logical_and,array
603 m = self.mask
604 if self.data:
605 tlab = self.data._getsourcename(self._fittedrow)
606 xlab = self.data._getabcissalabel(self._fittedrow)
607 if self.data._getflagrow(self._fittedrow):
608 m = [False]
609 else:
610 m = logical_and(self.mask,
611 array(self.data._getmask(self._fittedrow),
612 copy=False))
613
614 ylab = self.data._get_ordinate_label()
615
616 colours = ["#777777","#dddddd","red","orange","purple","green",
617 "magenta", "cyan"]
618 nomask=True
619 for i in range(len(m)):
620 nomask = nomask and m[i]
621 if len(m) == 1:
622 m = m[0]
623 invm = (not m)
624 else:
625 invm = logical_not(m)
626 label0='Masked Region'
627 label1='Spectrum'
628 if ( nomask ):
629 label0=label1
630 else:
631 y = ma.masked_array( self.y, mask = m )
632 self._p.palette(1,colours)
633 self._p.set_line( label = label1 )
634 self._p.plot( self.x, y )
635 self._p.palette(0,colours)
636 self._p.set_line(label=label0)
637 y = ma.masked_array(self.y,mask=invm)
638 self._p.plot(self.x, y)
639 if residual:
640 self._p.palette(7)
641 self._p.set_line(label='Residual')
642 y = ma.masked_array(self.get_residual(),
643 mask=invm)
644 self._p.plot(self.x, y)
645 self._p.palette(2)
646 if components is not None:
647 cs = components
648 if isinstance(components,int): cs = [components]
649 if plotparms:
650 self._p.text(0.15,0.15,
651 str(self.get_parameters()['formatted']),size=8)
652 n = len(self.components)
653 self._p.palette(3)
654 for c in cs:
655 if 0 <= c < n:
656 lab = self.fitfuncs[c]+str(c)
657 self._p.set_line(label=lab)
658 y = ma.masked_array(self.fitter.evaluate(c), mask=invm)
659
660 self._p.plot(self.x, y)
661 elif c == -1:
662 self._p.palette(2)
663 self._p.set_line(label="Total Fit")
664 y = ma.masked_array(self.fitter.getfit(),
665 mask=invm)
666 self._p.plot(self.x, y)
667 else:
668 self._p.palette(2)
669 self._p.set_line(label='Fit')
670 y = ma.masked_array(self.fitter.getfit(),mask=invm)
671 self._p.plot(self.x, y)
672 xlim=[min(self.x),max(self.x)]
673 self._p.axes.set_xlim(xlim)
674 self._p.set_axes('xlabel',xlab)
675 self._p.set_axes('ylabel',ylab)
676 self._p.set_axes('title',tlab)
677 self._p.release()
678 if (not rcParams['plotter.gui']):
679 self._p.save(filename)
680
681 @asaplog_post_dec
682 def auto_fit(self, insitu=None, plot=False):
683 """
684 Return a scan where the function is applied to all rows for
685 all Beams/IFs/Pols.
686
687 """
688 from asap import scantable
689 if not isinstance(self.data, scantable) :
690 msg = "Data is not a scantable"
691 raise TypeError(msg)
692 if insitu is None: insitu = rcParams['insitu']
693 if not insitu:
694 scan = self.data.copy()
695 else:
696 scan = self.data
697 rows = xrange(scan.nrow())
698 # Save parameters of baseline fits as a class attribute.
699 # NOTICE: This does not reflect changes in scantable!
700 if len(rows) > 0: self.blpars=[]
701 asaplog.push("Fitting:")
702 for r in rows:
703 out = " Scan[%d] Beam[%d] IF[%d] Pol[%d] Cycle[%d]" % (
704 scan.getscan(r),
705 scan.getbeam(r),
706 scan.getif(r),
707 scan.getpol(r),
708 scan.getcycle(r)
709 )
710 asaplog.push(out, False)
711 self.x = scan._getabcissa(r)
712 self.y = scan._getspectrum(r)
713 #self.mask = mask_and(self.mask, scan._getmask(r))
714 if len(self.x) == len(self.mask):
715 self.mask = mask_and(self.mask, self.data._getmask(row))
716 else:
717 asaplog.push('lengths of data and mask are not the same. '
718 'preset mask will be ignored')
719 asaplog.post('WARN','asapfit.fit')
720 self.mask=self.data._getmask(row)
721 self.data = None
722 self.fit()
723 x = self.get_parameters()
724 fpar = self.get_parameters()
725 if plot:
726 self.plot(residual=True)
727 x = raw_input("Accept fit ([y]/n): ")
728 if x.upper() == 'N':
729 self.blpars.append(None)
730 continue
731 scan._setspectrum(self.fitter.getresidual(), r)
732 self.blpars.append(fpar)
733 if plot:
734 self._p.quit()
735 del self._p
736 self._p = None
737 return scan
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