AbstractFitEngine.py @ b345c45

ESS_GUIESS_GUI_DocsESS_GUI_batch_fittingESS_GUI_bumps_abstractionESS_GUI_iss1116ESS_GUI_iss879ESS_GUI_iss959ESS_GUI_openclESS_GUI_orderingESS_GUI_sync_sascalccostrafo411magnetic_scattrelease-4.1.1release-4.1.2release-4.2.2release_4.0.1ticket-1009ticket-1094-headlessticket-1242-2d-resolutionticket-1243ticket-1249ticket885unittest-saveload

Last change on this file since b345c45 was 027e8f2, checked in by Jae Cho <jhjcho@…>, 15 years ago
Fixed a 2D problem of not properly (calculating chi2 and fitting 2Ddata) with bounded Q range.
Property mode set to `100644`
File size: 22.4 KB

Line
1	import logging, sys
2	import park,numpy,math, copy
3	from DataLoader.data_info import Data1D
4	from DataLoader.data_info import Data2D
5	class SansParameter(park.Parameter):
6	"""
7	SANS model parameters for use in the PARK fitting service.
8	The parameter attribute value is redirected to the underlying
9	parameter value in the SANS model.
10	"""
11	def __init__(self, name, model):
12	"""
13	@param name: the name of the model parameter
14	@param model: the sans model to wrap as a park model
15	"""
16	self._model, self._name = model,name
17	#set the value for the parameter of the given name
18	self.set(model.getParam(name))
19
20	def _getvalue(self):
21	"""
22	override the _getvalue of park parameter
23	@return value the parameter associates with self.name
24	"""
25	return self._model.getParam(self.name)
26
27	def _setvalue(self,value):
28	"""
29	override the _setvalue pf park parameter
30	@param value: the value to set on a given parameter
31	"""
32	self._model.setParam(self.name, value)
33
34	value = property(_getvalue,_setvalue)
35
36	def _getrange(self):
37	"""
38	Override _getrange of park parameter
39	return the range of parameter
40	"""
41	#if not self.name in self._model.getDispParamList():
42	lo,hi = self._model.details[self.name][1:3]
43	if lo is None: lo = -numpy.inf
44	if hi is None: hi = numpy.inf
45	#else:
46	#lo,hi = self._model.details[self.name][1:]
47	#if lo is None: lo = -numpy.inf
48	#if hi is None: hi = numpy.inf
49	if lo >= hi:
50	raise ValueError,"wrong fit range for parameters"
51
52	return lo,hi
53
54	def _setrange(self,r):
55	"""
56	override _setrange of park parameter
57	@param r: the value of the range to set
58	"""
59	self._model.details[self.name][1:3] = r
60	range = property(_getrange,_setrange)
61
62	class Model(park.Model):
63	"""
64	PARK wrapper for SANS models.
65	"""
66	def __init__(self, sans_model, **kw):
67	"""
68	@param sans_model: the sans model to wrap using park interface
69	"""
70	park.Model.__init__(self, **kw)
71	self.model = sans_model
72	self.name = sans_model.name
73	#list of parameters names
74	self.sansp = sans_model.getParamList()
75	#list of park parameter
76	self.parkp = [SansParameter(p,sans_model) for p in self.sansp]
77	#list of parameterset
78	self.parameterset = park.ParameterSet(sans_model.name,pars=self.parkp)
79	self.pars=[]
80
81
82	def getParams(self,fitparams):
83	"""
84	return a list of value of paramter to fit
85	@param fitparams: list of paramaters name to fit
86	"""
87	list=[]
88	self.pars=[]
89	self.pars=fitparams
90	for item in fitparams:
91	for element in self.parkp:
92	if element.name ==str(item):
93	list.append(element.value)
94	return list
95
96
97	def setParams(self,paramlist, params):
98	"""
99	Set value for parameters to fit
100	@param params: list of value for parameters to fit
101	"""
102	try:
103	for i in range(len(self.parkp)):
104	for j in range(len(paramlist)):
105	if self.parkp[i].name==paramlist[j]:
106	self.parkp[i].value = params[j]
107	self.model.setParam(self.parkp[i].name,params[j])
108	except:
109	raise
110
111	def eval(self,x):
112	"""
113	override eval method of park model.
114	@param x: the x value used to compute a function
115	"""
116	try:
117	return self.model.evalDistribution(x)
118	except:
119	raise
120
121
122	class FitData1D(Data1D):
123	"""
124	Wrapper class for SANS data
125	FitData1D inherits from DataLoader.data_info.Data1D. Implements
126	a way to get residuals from data.
127	"""
128	def __init__(self,x, y,dx= None, dy=None, smearer=None):
129	Data1D.__init__(self, x=numpy.array(x), y=numpy.array(y), dx=dx, dy=dy)
130	"""
131	@param smearer: is an object of class QSmearer or SlitSmearer
132	that will smear the theory data (slit smearing or resolution
133	smearing) when set.
134
135	The proper way to set the smearing object would be to
136	do the following:
137
138	from DataLoader.qsmearing import smear_selection
139	smearer = smear_selection(some_data)
140	fitdata1d = FitData1D( x= [1,3,..,],
141	y= [3,4,..,8],
142	dx=None,
143	dy=[1,2...], smearer= smearer)
144
145	Note that some_data _HAS_ to be of class DataLoader.data_info.Data1D
146
147	Setting it back to None will turn smearing off.
148
149	"""
150
151	self.smearer = smearer
152	if dy ==None or dy==[]:
153	self.dy= numpy.zeros(len(self.y))
154	else:
155	self.dy= numpy.asarray(dy)
156
157	# For fitting purposes, replace zero errors by 1
158	#TODO: check validity for the rare case where only
159	# a few points have zero errors
160	self.dy[self.dy==0]=1
161
162	## Min Q-value
163	#Skip the Q=0 point, especially when y(q=0)=None at x[0].
164	if min (self.x) ==0.0 and self.x[0]==0 and not numpy.isfinite(self.y[0]):
165	self.qmin = min(self.x[self.x!=0])
166	else:
167	self.qmin= min (self.x)
168	## Max Q-value
169	self.qmax = max (self.x)
170
171	# Range used for input to smearing
172	self._qmin_unsmeared = self.qmin
173	self._qmax_unsmeared = self.qmax
174	# Identify the bin range for the unsmeared and smeared spaces
175	self.idx = (self.x>=self.qmin) & (self.x <= self.qmax)
176	self.idx_unsmeared = (self.x>=self._qmin_unsmeared) & (self.x <= self._qmax_unsmeared)
177
178
179
180	def setFitRange(self,qmin=None,qmax=None):
181	""" to set the fit range"""
182	# Skip Q=0 point, (especially for y(q=0)=None at x[0]).
183	#ToDo: Fix this.
184	if qmin==0.0 and not numpy.isfinite(self.y[qmin]):
185	self.qmin = min(self.x[self.x!=0])
186	elif qmin!=None:
187	self.qmin = qmin
188
189	if qmax !=None:
190	self.qmax = qmax
191
192	# Determine the range needed in unsmeared-Q to cover
193	# the smeared Q range
194	self._qmin_unsmeared = self.qmin
195	self._qmax_unsmeared = self.qmax
196
197	self._first_unsmeared_bin = 0
198	self._last_unsmeared_bin = len(self.x)-1
199
200	if self.smearer!=None:
201	self._first_unsmeared_bin, self._last_unsmeared_bin = self.smearer.get_bin_range(self.qmin, self.qmax)
202	self._qmin_unsmeared = self.x[self._first_unsmeared_bin]
203	self._qmax_unsmeared = self.x[self._last_unsmeared_bin]
204
205	# Identify the bin range for the unsmeared and smeared spaces
206	self.idx = (self.x>=self.qmin) & (self.x <= self.qmax)
207	self.idx_unsmeared = (self.x>=self._qmin_unsmeared) & (self.x <= self._qmax_unsmeared)
208
209
210	def getFitRange(self):
211	"""
212	@return the range of data.x to fit
213	"""
214	return self.qmin, self.qmax
215
216	def residuals(self, fn):
217	"""
218	Compute residuals.
219
220	If self.smearer has been set, use if to smear
221	the data before computing chi squared.
222
223	@param fn: function that return model value
224	@return residuals
225	"""
226	# Compute theory data f(x)
227	fx= numpy.zeros(len(self.x))
228	fx[self.idx_unsmeared] = fn(self.x[self.idx_unsmeared])
229
230	## Smear theory data
231	if self.smearer is not None:
232	fx = self.smearer(fx, self._first_unsmeared_bin, self._last_unsmeared_bin)
233
234	## Sanity check
235	if numpy.size(self.dy)!= numpy.size(fx):
236	raise RuntimeError, "FitData1D: invalid error array %d <> %d" % (numpy.shape(self.dy),
237	numpy.size(fx))
238
239	return (self.y[self.idx]-fx[self.idx])/self.dy[self.idx]
240
241
242
243	def residuals_deriv(self, model, pars=[]):
244	"""
245	@return residuals derivatives .
246	@note: in this case just return empty array
247	"""
248	return []
249
250
251	class FitData2D(Data2D):
252	""" Wrapper class for SANS data """
253	def __init__(self,sans_data2d ,data=None, err_data=None):
254	Data2D.__init__(self, data= data, err_data= err_data)
255	"""
256	Data can be initital with a data (sans plottable)
257	or with vectors.
258	"""
259	self.x_bins_array = []
260	self.y_bins_array = []
261	self.res_err_image=[]
262	self.index_model=[]
263	self.qmin= None
264	self.qmax= None
265	self.set_data(sans_data2d )
266
267
268	def set_data(self, sans_data2d, qmin=None, qmax=None ):
269	"""
270	Determine the correct x_bin and y_bin to fit
271	"""
272	self.x_bins_array= numpy.reshape(sans_data2d.x_bins,
273	[1,len(sans_data2d.x_bins)])
274	self.y_bins_array = numpy.reshape(sans_data2d.y_bins,
275	[len(sans_data2d.y_bins),1])
276
277	x_max = max(sans_data2d.xmin, sans_data2d.xmax)
278	y_max = max(sans_data2d.ymin, sans_data2d.ymax)
279
280	## fitting range
281	if qmin == None:
282	self.qmin = 1e-16
283	if qmax == None:
284	self.qmax = math.sqrt(x_maxx_max +y_maxy_max)
285	## new error image for fitting purpose
286	if self.err_data== None or self.err_data ==[]:
287	self.res_err_data= numpy.zeros(len(self.y_bins),len(self.x_bins))
288	else:
289	self.res_err_data = copy.deepcopy(self.err_data)
290	self.res_err_data[self.res_err_data==0]=1
291
292	self.radius= numpy.sqrt(self.x_bins_array2 + self.y_bins_array2)
293	self.index_model = (self.qmin <= self.radius)&(self.radius<= self.qmax)
294
295
296	def setFitRange(self,qmin=None,qmax=None):
297	""" to set the fit range"""
298	if qmin==0.0:
299	self.qmin = 1e-16
300	elif qmin!=None:
301	self.qmin = qmin
302	if qmax!=None:
303	self.qmax= qmax
304
305	self.radius= numpy.sqrt(self.x_bins_array2 + self.y_bins_array2)
306	self.index_model = (self.qmin <= self.radius)&(self.radius<= self.qmax)
307
308	def getFitRange(self):
309	"""
310	@return the range of data.x to fit
311	"""
312	return self.qmin, self.qmax
313
314	def residuals(self, fn):
315
316	res=self.index_model*(self.data - fn([self.x_bins_array,
317	self.y_bins_array]))/self.res_err_data
318	return res.ravel()
319
320
321	def residuals_deriv(self, model, pars=[]):
322	"""
323	@return residuals derivatives .
324	@note: in this case just return empty array
325	"""
326	return []
327
328	class FitAbort(Exception):
329	"""
330	Exception raise to stop the fit
331	"""
332	print"Creating fit abort Exception"
333
334
335	class SansAssembly:
336	"""
337	Sans Assembly class a class wrapper to be call in optimizer.leastsq method
338	"""
339	def __init__(self,paramlist,Model=None , Data=None, curr_thread= None):
340	"""
341	@param Model: the model wrapper fro sans -model
342	@param Data: the data wrapper for sans data
343	"""
344	self.model = Model
345	self.data = Data
346	self.paramlist=paramlist
347	self.curr_thread= curr_thread
348	self.res=[]
349	self.func_name="Functor"
350	def chisq(self, params):
351	"""
352	Calculates chi^2
353	@param params: list of parameter values
354	@return: chi^2
355	"""
356	sum = 0
357	for item in self.res:
358	sum += item*item
359	if len(self.res)==0:
360	return None
361	return sum/ len(self.res)
362
363	def __call__(self,params):
364	"""
365	Compute residuals
366	@param params: value of parameters to fit
367	"""
368	#import thread
369	self.model.setParams(self.paramlist,params)
370	self.res= self.data.residuals(self.model.eval)
371	#if self.curr_thread != None :
372	# try:
373	# self.curr_thread.isquit()
374	# except:
375	# raise FitAbort,"stop leastsqr optimizer"
376	return self.res
377
378	class FitEngine:
379	def __init__(self):
380	"""
381	Base class for scipy and park fit engine
382	"""
383	#List of parameter names to fit
384	self.paramList=[]
385	#Dictionnary of fitArrange element (fit problems)
386	self.fitArrangeDict={}
387
388	def _concatenateData(self, listdata=[]):
389	"""
390	_concatenateData method concatenates each fields of all data contains ins listdata.
391	@param listdata: list of data
392	@return Data: Data is wrapper class for sans plottable. it is created with all parameters
393	of data concatenanted
394	@raise: if listdata is empty will return None
395	@raise: if data in listdata don't contain dy field ,will create an error
396	during fitting
397	"""
398	#TODO: we have to refactor the way we handle data.
399	# We should move away from plottables and move towards the Data1D objects
400	# defined in DataLoader. Data1D allows data manipulations, which should be
401	# used to concatenate.
402	# In the meantime we should switch off the concatenation.
403	#if len(listdata)>1:
404	# raise RuntimeError, "FitEngine._concatenateData: Multiple data files is not currently supported"
405	#return listdata[0]
406
407	if listdata==[]:
408	raise ValueError, " data list missing"
409	else:
410	xtemp=[]
411	ytemp=[]
412	dytemp=[]
413	self.mini=None
414	self.maxi=None
415
416	for item in listdata:
417	data=item.data
418	mini,maxi=data.getFitRange()
419	if self.mini==None and self.maxi==None:
420	self.mini=mini
421	self.maxi=maxi
422	else:
423	if mini < self.mini:
424	self.mini=mini
425	if self.maxi < maxi:
426	self.maxi=maxi
427
428
429	for i in range(len(data.x)):
430	xtemp.append(data.x[i])
431	ytemp.append(data.y[i])
432	if data.dy is not None and len(data.dy)==len(data.y):
433	dytemp.append(data.dy[i])
434	else:
435	raise RuntimeError, "Fit._concatenateData: y-errors missing"
436	data= Data(x=xtemp,y=ytemp,dy=dytemp)
437	data.setFitRange(self.mini, self.maxi)
438	return data
439
440
441	def set_model(self,model,Uid,pars=[], constraints=[]):
442	"""
443	set a model on a given uid in the fit engine.
444	@param model: sans.models type
445	@param Uid :is the key of the fitArrange dictionnary where model is saved as a value
446	@param pars: the list of parameters to fit
447	@param constraints: list of
448	tuple (name of parameter, value of parameters)
449	the value of parameter must be a string to constraint 2 different
450	parameters.
451	Example:
452	we want to fit 2 model M1 and M2 both have parameters A and B.
453	constraints can be:
454	constraints = [(M1.A, M2.B+2), (M1.B= M2.A *5),...,]
455	@note : pars must contains only name of existing model's paramaters
456	"""
457	if model == None:
458	raise ValueError, "AbstractFitEngine: Need to set model to fit"
459
460	new_model= model
461	if not issubclass(model.__class__, Model):
462	new_model= Model(model)
463
464	if len(constraints)>0:
465	for constraint in constraints:
466	name, value = constraint
467	try:
468	new_model.parameterset[ str(name)].set( str(value) )
469	except:
470	msg= "Fit Engine: Error occurs when setting the constraint"
471	msg += " %s for parameter %s "%(value, name)
472	raise ValueError, msg
473
474	if len(pars) >0:
475	temp=[]
476	for item in pars:
477	if item in new_model.model.getParamList():
478	temp.append(item)
479	self.paramList.append(item)
480	else:
481
482	msg = "wrong parameter %s used"%str(item)
483	msg += "to set model %s. Choose"%str(new_model.model.name)
484	msg += "parameter name within %s"%str(new_model.model.getParamList())
485	raise ValueError,msg
486
487	#A fitArrange is already created but contains dList only at Uid
488	if self.fitArrangeDict.has_key(Uid):
489	self.fitArrangeDict[Uid].set_model(new_model)
490	self.fitArrangeDict[Uid].pars= pars
491	else:
492	#no fitArrange object has been create with this Uid
493	fitproblem = FitArrange()
494	fitproblem.set_model(new_model)
495	fitproblem.pars= pars
496	self.fitArrangeDict[Uid] = fitproblem
497
498	else:
499	raise ValueError, "park_integration:missing parameters"
500
501	def set_data(self,data,Uid,smearer=None,qmin=None,qmax=None):
502	""" Receives plottable, creates a list of data to fit,set data
503	in a FitArrange object and adds that object in a dictionary
504	with key Uid.
505	@param data: data added
506	@param Uid: unique key corresponding to a fitArrange object with data
507	"""
508	if data.__class__.__name__=='Data2D':
509	fitdata=FitData2D(sans_data2d=data, data=data.data, err_data= data.err_data)
510	else:
511	fitdata=FitData1D(x=data.x, y=data.y , dx= data.dx,dy=data.dy,smearer=smearer)
512
513	fitdata.setFitRange(qmin=qmin,qmax=qmax)
514	#A fitArrange is already created but contains model only at Uid
515	if self.fitArrangeDict.has_key(Uid):
516	self.fitArrangeDict[Uid].add_data(fitdata)
517	else:
518	#no fitArrange object has been create with this Uid
519	fitproblem= FitArrange()
520	fitproblem.add_data(fitdata)
521	self.fitArrangeDict[Uid]=fitproblem
522
523	def get_model(self,Uid):
524	"""
525	@param Uid: Uid is key in the dictionary containing the model to return
526	@return a model at this uid or None if no FitArrange element was created
527	with this Uid
528	"""
529	if self.fitArrangeDict.has_key(Uid):
530	return self.fitArrangeDict[Uid].get_model()
531	else:
532	return None
533
534	def remove_Fit_Problem(self,Uid):
535	"""remove fitarrange in Uid"""
536	if self.fitArrangeDict.has_key(Uid):
537	del self.fitArrangeDict[Uid]
538
539	def select_problem_for_fit(self,Uid,value):
540	"""
541	select a couple of model and data at the Uid position in dictionary
542	and set in self.selected value to value
543	@param value: the value to allow fitting. can only have the value one or zero
544	"""
545	if self.fitArrangeDict.has_key(Uid):
546	self.fitArrangeDict[Uid].set_to_fit( value)
547
548
549	def get_problem_to_fit(self,Uid):
550	"""
551	return the self.selected value of the fit problem of Uid
552	@param Uid: the Uid of the problem
553	"""
554	if self.fitArrangeDict.has_key(Uid):
555	self.fitArrangeDict[Uid].get_to_fit()
556
557	class FitArrange:
558	def __init__(self):
559	"""
560	Class FitArrange contains a set of data for a given model
561	to perform the Fit.FitArrange must contain exactly one model
562	and at least one data for the fit to be performed.
563	model: the model selected by the user
564	Ldata: a list of data what the user wants to fit
565
566	"""
567	self.model = None
568	self.dList =[]
569	self.pars=[]
570	#self.selected is zero when this fit problem is not schedule to fit
571	#self.selected is 1 when schedule to fit
572	self.selected = 0
573
574	def set_model(self,model):
575	"""
576	set_model save a copy of the model
577	@param model: the model being set
578	"""
579	self.model = model
580
581	def add_data(self,data):
582	"""
583	add_data fill a self.dList with data to fit
584	@param data: Data to add in the list
585	"""
586	if not data in self.dList:
587	self.dList.append(data)
588
589	def get_model(self):
590	""" @return: saved model """
591	return self.model
592
593	def get_data(self):
594	""" @return: list of data dList"""
595	#return self.dList
596	return self.dList[0]
597
598	def remove_data(self,data):
599	"""
600	Remove one element from the list
601	@param data: Data to remove from dList
602	"""
603	if data in self.dList:
604	self.dList.remove(data)
605	def set_to_fit (self, value=0):
606	"""
607	set self.selected to 0 or 1 for other values raise an exception
608	@param value: integer between 0 or 1
609	"""
610	self.selected= value
611
612	def get_to_fit(self):
613	"""
614	@return self.selected value
615	"""
616	return self.selected

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