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source: trunk/python/asapfitter.py @ 2830

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Last change on this file since 2830 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

Rev	Line
[113]	1	import _asap
[1826]	2	from asap.parameters import rcParams
[1862]	3	from asap.logging import asaplog, asaplog_post_dec
[1826]	4	from asap.utils import _n_bools, mask_and
[2666]	5	from numpy import ndarray
[113]	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
[515]	20	self.fitfuncs = None
[113]	21	self.fitted = False
	22	self.data = None
[515]	23	self.components = 0
	24	self._fittedrow = 0
[113]	25	self._p = None
[515]	26	self._selection = None
[1391]	27	self.uselinear = False
[2666]	28	self._constraints = []
[113]	29
	30	def set_data(self, xdat, ydat, mask=None):
	31	"""
[158]	32	Set the absissa and ordinate for the fit. Also set the mask
[2153]	33	indicating valid points.
[113]	34	This can be used for data vectors retrieved from a scantable.
	35	For scantable fitting use 'fitter.set_scan(scan, mask)'.
	36	Parameters:
[158]	37	xdat: the abcissa values
[113]	38	ydat: the ordinate values
	39	mask: an optional mask
[723]	40
[113]	41	"""
	42	self.fitted = False
	43	self.x = xdat
	44	self.y = ydat
	45	if mask == None:
[1295]	46	self.mask = _n_bools(len(xdat), True)
[113]	47	else:
	48	self.mask = mask
	49	return
	50
[1862]	51	@asaplog_post_dec
[113]	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
[1420]	57	mask: a msk retrieved from the scantable
[113]	58	"""
	59	if not thescan:
[723]	60	msg = "Please give a correct scan"
[1859]	61	raise TypeError(msg)
[113]	62	self.fitted = False
	63	self.data = thescan
[1075]	64	self.mask = None
[113]	65	if mask is None:
[1295]	66	self.mask = _n_bools(self.data.nchan(), True)
[113]	67	else:
	68	self.mask = mask
	69	return
	70
[1862]	71	@asaplog_post_dec
[113]	72	def set_function(self, **kwargs):
	73	"""
	74	Set the function to be fit.
	75	Parameters:
[2666]	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
[2047]	80	gauss: fit the number of gaussian specified
	81	lorentz: fit the number of lorentzian specified
	82	sinusoid: fit the number of sinusoid specified
[113]	83	Example:
[2666]	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
[1819]	88	fitter.set_function(gauss=2) # will fit two gaussians
	89	fitter.set_function(lorentz=2) # will fit two lorentzians
[2047]	90	fitter.set_function(sinusoid=3) # will fit three sinusoids
[113]	91	"""
[723]	92	#default poly order 0
[515]	93	n=0
[113]	94	if kwargs.has_key('poly'):
	95	self.fitfunc = 'poly'
[1938]	96	self.fitfuncs = ['poly']
[113]	97	n = kwargs.get('poly')
[1938]	98	self.components = [n+1]
[1589]	99	self.uselinear = False
[1391]	100	elif kwargs.has_key('lpoly'):
	101	self.fitfunc = 'poly'
[1938]	102	self.fitfuncs = ['lpoly']
[1391]	103	n = kwargs.get('lpoly')
[1938]	104	self.components = [n+1]
[1391]	105	self.uselinear = True
[113]	106	elif kwargs.has_key('gauss'):
	107	n = kwargs.get('gauss')
	108	self.fitfunc = 'gauss'
[515]	109	self.fitfuncs = [ 'gauss' for i in range(n) ]
	110	self.components = [ 3 for i in range(n) ]
[1589]	111	self.uselinear = False
[1819]	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
[2047]	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
[2666]	124	elif kwargs.has_key('expression'):
	125	self.uselinear = False
	126	raise RuntimeError("Not yet implemented")
[515]	127	else:
[723]	128	msg = "Invalid function type."
[1859]	129	raise TypeError(msg)
[723]	130
[113]	131	self.fitter.setexpression(self.fitfunc,n)
[2666]	132	self._constraints = []
[1232]	133	self.fitted = False
[113]	134	return
[723]	135
[1862]	136	@asaplog_post_dec
[1075]	137	def fit(self, row=0, estimate=False):
[113]	138	"""
	139	Execute the actual fitting process. All the state has to be set.
	140	Parameters:
[1075]	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.
[113]	145	Example:
[515]	146	s = scantable('myscan.asap')
	147	s.set_cursor(thepol=1) # select second pol
[113]	148	f = fitter()
	149	f.set_scan(s)
	150	f.set_function(poly=0)
[723]	151	f.fit(row=0) # fit first row
[113]	152	"""
	153	if ((self.x is None or self.y is None) and self.data is None) \
	154	or self.fitfunc is None:
[723]	155	msg = "Fitter not yet initialised. Please set data & fit function"
[1859]	156	raise RuntimeError(msg)
[723]	157
[2666]	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
[515]	180	self.fitter.setdata(self.x, self.y, self.mask)
[1819]	181	if self.fitfunc == 'gauss' or self.fitfunc == 'lorentz':
[113]	182	ps = self.fitter.getparameters()
[1075]	183	if len(ps) == 0 or estimate:
[113]	184	self.fitter.estimate()
[1859]	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."
[2666]	188	if self._constraints:
	189	for c in self._constraints:
	190	self.fitter.addconstraint(c[0]+[c[-1]])
[1859]	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."
[515]	197	self._fittedrow = row
[113]	198	self.fitted = True
	199	return
	200
[1232]	201	def store_fit(self, filename=None):
[526]	202	"""
[1232]	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
[526]	207	"""
[515]	208	if self.fitted and self.data is not None:
	209	pars = list(self.fitter.getparameters())
	210	fixed = list(self.fitter.getfixedparameters())
[975]	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())
[1232]	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)
[515]	226
[1862]	227	@asaplog_post_dec
[1017]	228	def set_parameters(self,args,*kwargs):
[526]	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)
[2047]	235	component: in case of multiple gaussians/lorentzians/sinusoidals,
	236	the index of the target component
[1017]	237	"""
	238	component = None
	239	fixed = None
	240	params = None
[1031]	241
[1017]	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]
[515]	248	if self.fitfunc is None:
[723]	249	msg = "Please specify a fitting function first."
[1859]	250	raise RuntimeError(msg)
[2666]	251	if (self.fitfunc == "gauss" or self.fitfunc == "lorentz"
	252	or self.fitfunc == "sinusoid") and component is not None:
[1017]	253	if not self.fitted and sum(self.fitter.getparameters()) == 0:
[1295]	254	pars = _n_bools(len(self.components)*3, False)
[2047]	255	fxd = _n_bools(len(pars), False)
[515]	256	else:
[723]	257	pars = list(self.fitter.getparameters())
[2047]	258	fxd = list(self.fitter.getfixedparameters())
[515]	259	i = 3*component
	260	pars[i:i+3] = params
[2047]	261	fxd[i:i+3] = fixed
[515]	262	params = pars
[2047]	263	fixed = fxd
[113]	264	self.fitter.setparameters(params)
	265	if fixed is not None:
	266	self.fitter.setfixedparameters(fixed)
	267	return
[515]	268
[1862]	269	@asaplog_post_dec
[1217]	270	def set_gauss_parameters(self, peak, centre, fwhm,
[1409]	271	peakfixed=0, centrefixed=0,
[1217]	272	fwhmfixed=0,
[515]	273	component=0):
[113]	274	"""
[515]	275	Set the Parameters of a 'Gaussian' component, set with set_function.
	276	Parameters:
[1232]	277	peak, centre, fwhm: The gaussian parameters
[515]	278	peakfixed,
[1409]	279	centrefixed,
[1217]	280	fwhmfixed: Optional parameters to indicate if
[515]	281	the paramters should be held fixed during
	282	the fitting process. The default is to keep
	283	all parameters flexible.
[526]	284	component: The number of the component (Default is the
	285	component 0)
[515]	286	"""
	287	if self.fitfunc != "gauss":
[723]	288	msg = "Function only operates on Gaussian components."
[1859]	289	raise ValueError(msg)
[515]	290	if 0 <= component < len(self.components):
[1217]	291	d = {'params':[peak, centre, fwhm],
[1409]	292	'fixed':[peakfixed, centrefixed, fwhmfixed]}
[1017]	293	self.set_parameters(d, component)
[515]	294	else:
[723]	295	msg = "Please select a valid component."
[1859]	296	raise ValueError(msg)
[723]	297
[1862]	298	@asaplog_post_dec
[1819]	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:
[1927]	306	peak, centre, fwhm: The lorentzian parameters
[1819]	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."
[1859]	318	raise ValueError(msg)
[1819]	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."
[1859]	325	raise ValueError(msg)
[1819]	326
[2047]	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
[2666]	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
[2681]	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
[2666]	365
[2681]	366	add_constraint([1, 0, 0, -2, 0, 0], 0)
[2666]	367
[2681]	368	as the linear equation is
	369
	370	1amp1 + 0peakv1 + 0sigma1 -2amp2 + 0peakv2 + 0sigma2 = 0
	371
	372	and similarly for a velocity difference of v2-v1=17
[2666]	373
	374	add_constraint([0.,-1.,0.,0.,1.,0.], 17.)
	375
	376	"""
	377	self._constraints.append((xpar, y))
	378
	379
[975]	380	def get_area(self, component=None):
	381	"""
[1819]	382	Return the area under the fitted gaussian/lorentzian component.
[975]	383	Parameters:
[1819]	384	component: the gaussian/lorentzian component selection,
[975]	385	default (None) is the sum of all components
	386	Note:
[1819]	387	This will only work for gaussian/lorentzian fits.
[975]	388	"""
	389	if not self.fitted: return
[1819]	390	if self.fitfunc == "gauss" or self.fitfunc == "lorentz":
[975]	391	pars = list(self.fitter.getparameters())
	392	from math import log,pi,sqrt
[1819]	393	if self.fitfunc == "gauss":
	394	fac = sqrt(pi/log(16.0))
	395	elif self.fitfunc == "lorentz":
	396	fac = pi/2.0
[975]	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
[1862]	409	@asaplog_post_dec
[1075]	410	def get_errors(self, component=None):
[515]	411	"""
[1075]	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."
[1859]	419	raise RuntimeError(msg)
[1075]	420	errs = list(self.fitter.geterrors())
	421	cerrs = errs
	422	if component is not None:
[2666]	423	if self.fitfunc == "gauss" or self.fitfunc == "lorentz" \
	424	or self.fitfunc == "sinusoid":
[1075]	425	i = 3*component
	426	if i < len(errs):
	427	cerrs = errs[i:i+3]
	428	return cerrs
	429
[1859]	430
[1862]	431	@asaplog_post_dec
[1075]	432	def get_parameters(self, component=None, errors=False):
	433	"""
[113]	434	Return the fit paramters.
[526]	435	Parameters:
	436	component: get the parameters for the specified component
	437	only, default is all components
[113]	438	"""
	439	if not self.fitted:
[723]	440	msg = "Not yet fitted."
[1859]	441	raise RuntimeError(msg)
[113]	442	pars = list(self.fitter.getparameters())
	443	fixed = list(self.fitter.getfixedparameters())
[1075]	444	errs = list(self.fitter.geterrors())
[1039]	445	area = []
[723]	446	if component is not None:
[2047]	447	if self.fitfunc == "poly" or self.fitfunc == "lpoly":
	448	cpars = pars
	449	cfixed = fixed
	450	cerrs = errs
	451	else:
[515]	452	i = 3*component
	453	cpars = pars[i:i+3]
	454	cfixed = fixed[i:i+3]
[1075]	455	cerrs = errs[i:i+3]
[2047]	456	if self.fitfunc == "gauss" or self.fitfunc == "lorentz":
	457	a = self.get_area(component)
	458	area = [a for i in range(3)]
[515]	459	else:
	460	cpars = pars
	461	cfixed = fixed
[1075]	462	cerrs = errs
[1819]	463	if self.fitfunc == "gauss" or self.fitfunc == "lorentz":
[1039]	464	for c in range(len(self.components)):
[2047]	465	a = self.get_area(c)
	466	area += [a for i in range(3)]
[1088]	467	fpars = self._format_pars(cpars, cfixed, errors and cerrs, area)
[1859]	468	asaplog.push(fpars)
[1075]	469	return {'params':cpars, 'fixed':cfixed, 'formatted': fpars,
	470	'errors':cerrs}
[723]	471
[1075]	472	def _format_pars(self, pars, fixed, errors, area):
[113]	473	out = ''
[2047]	474	if self.fitfunc == "poly" or self.fitfunc == "lpoly":
[113]	475	c = 0
[515]	476	for i in range(len(pars)):
	477	fix = ""
[1232]	478	if len(fixed) and fixed[i]: fix = "(fixed)"
[2047]	479	out += " p%d%s= %3.6f" % (c, fix, pars[i])
	480	if errors : out += " (%1.6f)" % errors[i]
	481	out += ","
[113]	482	c+=1
[515]	483	out = out[:-1] # remove trailing ','
[2047]	484	else:
[113]	485	i = 0
	486	c = 0
[2047]	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 = ""
[113]	493	if self.data:
[515]	494	aunit = self.data.get_unit()
	495	ounit = self.data.get_fluxunit()
[113]	496	while i < len(pars):
[2047]	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)
[2666]	506	if len(area): out += " area = %3.3f %s %s\n" % (area[i],
	507	ounit,
	508	aunit)
[113]	509	c+=1
	510	i+=3
	511	return out
[723]	512
[1859]	513
[1862]	514	@asaplog_post_dec
[113]	515	def get_estimate(self):
	516	"""
[515]	517	Return the parameter estimates (for non-linear functions).
[113]	518	"""
	519	pars = self.fitter.getestimate()
[943]	520	fixed = self.fitter.getfixedparameters()
[1927]	521	asaplog.push(self._format_pars(pars,fixed,None,None))
[113]	522	return pars
	523
[1862]	524	@asaplog_post_dec
[113]	525	def get_residual(self):
	526	"""
	527	Return the residual of the fit.
	528	"""
	529	if not self.fitted:
[723]	530	msg = "Not yet fitted."
[1859]	531	raise RuntimeError(msg)
[113]	532	return self.fitter.getresidual()
	533
[1862]	534	@asaplog_post_dec
[113]	535	def get_chi2(self):
	536	"""
	537	Return chi^2.
	538	"""
	539	if not self.fitted:
[723]	540	msg = "Not yet fitted."
[1859]	541	raise RuntimeError(msg)
[113]	542	ch2 = self.fitter.getchi2()
[1859]	543	asaplog.push( 'Chi^2 = %3.3f' % (ch2) )
[723]	544	return ch2
[113]	545
[1862]	546	@asaplog_post_dec
[113]	547	def get_fit(self):
	548	"""
	549	Return the fitted ordinate values.
	550	"""
	551	if not self.fitted:
[723]	552	msg = "Not yet fitted."
[1859]	553	raise RuntimeError(msg)
[113]	554	return self.fitter.getfit()
	555
[1862]	556	@asaplog_post_dec
[113]	557	def commit(self):
	558	"""
[526]	559	Return a new scan where the fits have been commited (subtracted)
[113]	560	"""
	561	if not self.fitted:
[723]	562	msg = "Not yet fitted."
[1859]	563	raise RuntimeError(msg)
[975]	564	from asap import scantable
	565	if not isinstance(self.data, scantable):
[723]	566	msg = "Not a scantable"
[1859]	567	raise TypeError(msg)
[113]	568	scan = self.data.copy()
[259]	569	scan._setspectrum(self.fitter.getresidual())
[1092]	570	return scan
[113]	571
[1862]	572	@asaplog_post_dec
[1689]	573	def plot(self, residual=False, components=None, plotparms=False,
	574	filename=None):
[113]	575	"""
	576	Plot the last fit.
	577	Parameters:
	578	residual: an optional parameter indicating if the residual
	579	should be plotted (default 'False')
[526]	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
[113]	585	"""
[2538]	586	from matplotlib import rc as rcp
[113]	587	if not self.fitted:
	588	return
[2541]	589	#if not self._p or self._p.is_dead:
	590	if not (self._p and self._p._alive()):
[2150]	591	from asap.asapplotter import new_asaplot
[2451]	592	del self._p
[2150]	593	self._p = new_asaplot(rcParams['plotter.gui'])
[723]	594	self._p.hold()
[113]	595	self._p.clear()
[2535]	596	rcp('lines', linewidth=1)
[515]	597	self._p.set_panels()
[652]	598	self._p.palette(0)
[113]	599	tlab = 'Spectrum'
[723]	600	xlab = 'Abcissa'
[1017]	601	ylab = 'Ordinate'
[1739]	602	from numpy import ma,logical_not,logical_and,array
[1273]	603	m = self.mask
[113]	604	if self.data:
[515]	605	tlab = self.data._getsourcename(self._fittedrow)
	606	xlab = self.data._getabcissalabel(self._fittedrow)
[2153]	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))
[1589]	613
[626]	614	ylab = self.data._get_ordinate_label()
[515]	615
[2666]	616	colours = ["#777777","#dddddd","red","orange","purple","green",
	617	"magenta", "cyan"]
[1819]	618	nomask=True
	619	for i in range(len(m)):
	620	nomask = nomask and m[i]
[2153]	621	if len(m) == 1:
	622	m = m[0]
	623	invm = (not m)
	624	else:
	625	invm = logical_not(m)
[1819]	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 )
[652]	635	self._p.palette(0,colours)
[1819]	636	self._p.set_line(label=label0)
[2153]	637	y = ma.masked_array(self.y,mask=invm)
[1088]	638	self._p.plot(self.x, y)
[113]	639	if residual:
[1819]	640	self._p.palette(7)
[515]	641	self._p.set_line(label='Residual')
[1116]	642	y = ma.masked_array(self.get_residual(),
[2153]	643	mask=invm)
[1088]	644	self._p.plot(self.x, y)
[652]	645	self._p.palette(2)
[515]	646	if components is not None:
	647	cs = components
	648	if isinstance(components,int): cs = [components]
[526]	649	if plotparms:
[2666]	650	self._p.text(0.15,0.15,
	651	str(self.get_parameters()['formatted']),size=8)
[515]	652	n = len(self.components)
[652]	653	self._p.palette(3)
[515]	654	for c in cs:
	655	if 0 <= c < n:
	656	lab = self.fitfuncs[c]+str(c)
	657	self._p.set_line(label=lab)
[2153]	658	y = ma.masked_array(self.fitter.evaluate(c), mask=invm)
[1088]	659
	660	self._p.plot(self.x, y)
[515]	661	elif c == -1:
[652]	662	self._p.palette(2)
[515]	663	self._p.set_line(label="Total Fit")
[1116]	664	y = ma.masked_array(self.fitter.getfit(),
[2153]	665	mask=invm)
[1088]	666	self._p.plot(self.x, y)
[515]	667	else:
[652]	668	self._p.palette(2)
[515]	669	self._p.set_line(label='Fit')
[2153]	670	y = ma.masked_array(self.fitter.getfit(),mask=invm)
[1088]	671	self._p.plot(self.x, y)
[723]	672	xlim=[min(self.x),max(self.x)]
	673	self._p.axes.set_xlim(xlim)
[113]	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()
[723]	678	if (not rcParams['plotter.gui']):
	679	self._p.save(filename)
[113]	680
[1862]	681	@asaplog_post_dec
[1061]	682	def auto_fit(self, insitu=None, plot=False):
[113]	683	"""
[515]	684	Return a scan where the function is applied to all rows for
	685	all Beams/IFs/Pols.
[723]	686
[113]	687	"""
	688	from asap import scantable
[515]	689	if not isinstance(self.data, scantable) :
[723]	690	msg = "Data is not a scantable"
[1859]	691	raise TypeError(msg)
[259]	692	if insitu is None: insitu = rcParams['insitu']
	693	if not insitu:
	694	scan = self.data.copy()
	695	else:
	696	scan = self.data
[880]	697	rows = xrange(scan.nrow())
[1826]	698	# Save parameters of baseline fits as a class attribute.
[1819]	699	# NOTICE: This does not reflect changes in scantable!
	700	if len(rows) > 0: self.blpars=[]
[876]	701	asaplog.push("Fitting:")
	702	for r in rows:
[2666]	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	)
[880]	710	asaplog.push(out, False)
[876]	711	self.x = scan._getabcissa(r)
	712	self.y = scan._getspectrum(r)
[2409]	713	#self.mask = mask_and(self.mask, scan._getmask(r))
[2408]	714	if len(self.x) == len(self.mask):
	715	self.mask = mask_and(self.mask, self.data._getmask(row))
	716	else:
[2666]	717	asaplog.push('lengths of data and mask are not the same. '
	718	'preset mask will be ignored')
[2408]	719	asaplog.post('WARN','asapfit.fit')
[2409]	720	self.mask=self.data._getmask(row)
[876]	721	self.data = None
	722	self.fit()
	723	x = self.get_parameters()
[1819]	724	fpar = self.get_parameters()
[1061]	725	if plot:
	726	self.plot(residual=True)
	727	x = raw_input("Accept fit ([y]/n): ")
	728	if x.upper() == 'N':
[1819]	729	self.blpars.append(None)
[1061]	730	continue
[880]	731	scan._setspectrum(self.fitter.getresidual(), r)
[1819]	732	self.blpars.append(fpar)
[1061]	733	if plot:
[2151]	734	self._p.quit()
	735	del self._p
[1061]	736	self._p = None
[876]	737	return scan

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