AbstractFitEngine.py @ e0072082

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 e0072082 was e0072082, checked in by Gervaise Alina <gervyh@…>, 14 years ago
displaying result on status bar for single fit
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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.res_err_image=[]
260	self.index_model=[]
261	self.qmin= None
262	self.qmax= None
263	self.set_data(sans_data2d )
264
265	def set_data(self, sans_data2d, qmin=None, qmax=None ):
266	"""
267	Determine the correct qx_data and qy_data within range to fit
268	"""
269	self.data = sans_data2d.data
270	self.err_data = sans_data2d.err_data
271	self.qx_data = sans_data2d.qx_data
272	self.qy_data = sans_data2d.qy_data
273	self.mask = sans_data2d.mask
274
275	x_max = max(math.fabs(sans_data2d.xmin), math.fabs(sans_data2d.xmax))
276	y_max = max(math.fabs(sans_data2d.ymin), math.fabs(sans_data2d.ymax))
277
278	## fitting range
279	if qmin == None:
280	self.qmin = 1e-16
281	if qmax == None:
282	self.qmax = math.sqrt(x_maxx_max +y_maxy_max)
283	## new error image for fitting purpose
284	if self.err_data== None or self.err_data ==[]:
285	self.res_err_data= numpy.zeros(len(self.data))
286	else:
287	self.res_err_data = copy.deepcopy(self.err_data)
288	self.res_err_data[self.res_err_data==0]=numpy.sqrt(self.data[self.res_err_data==0])
289
290	self.radius= numpy.sqrt(self.qx_data2 + self.qy_data2)
291
292	# Note: mask = True: for MASK while mask = False for NOT to mask
293	self.index_model = ((self.qmin <= self.radius)&(self.radius<= self.qmax))
294	self.mask = (self.index_model) & (self.mask)
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	self.radius= numpy.sqrt(self.qx_data2 + self.qy_data2)
305	self.index_model = ((self.qmin <= self.radius)&(self.radius<= self.qmax))
306	self.mask = (self.index_model) &(self.mask)
307
308
309	def getFitRange(self):
310	"""
311	@return the range of data.x to fit
312	"""
313	return self.qmin, self.qmax
314
315	def residuals(self, fn):
316	"""
317	@return the residuals
318	"""
319	# use only the data point within ROI range
320	res=(self.data[self.mask] - fn([self.qx_data[self.mask],
321	self.qy_data[self.mask]]))/self.res_err_data[self.mask]
322	return res
323
324
325	def residuals_deriv(self, model, pars=[]):
326	"""
327	@return residuals derivatives .
328	@note: in this case just return empty array
329	"""
330	return []
331
332	class FitAbort(Exception):
333	"""
334	Exception raise to stop the fit
335	"""
336	print"Creating fit abort Exception"
337
338
339	class SansAssembly:
340	"""
341	Sans Assembly class a class wrapper to be call in optimizer.leastsq method
342	"""
343	def __init__(self, paramlist, model=None , data=None, fitresult=None,
344	handler=None, curr_thread=None):
345	"""
346	@param Model: the model wrapper fro sans -model
347	@param Data: the data wrapper for sans data
348	"""
349	self.model = model
350	self.data = data
351	self.paramlist = paramlist
352	self.curr_thread = curr_thread
353	self.handler = handler
354	self.fitresult = fitresult
355	self.res = []
356	self.func_name = "Functor"
357
358	def chisq(self, params):
359	"""
360	Calculates chi^2
361	@param params: list of parameter values
362	@return: chi^2
363	"""
364	sum = 0
365	for item in self.res:
366	sum += item*item
367	if len(self.res)==0:
368	return None
369	return sum/ len(self.res)
370
371	def __call__(self,params):
372	"""
373	Compute residuals
374	@param params: value of parameters to fit
375	"""
376	#import thread
377	self.model.setParams(self.paramlist,params)
378	self.res= self.data.residuals(self.model.eval)
379	if self.fitresult is not None and self.handler is not None:
380	self.fitresult.set_model(model=self.model)
381	self.handler.set_result(result=self.fitresult)
382	self.handler.update_fit()
383
384	#if self.curr_thread != None :
385	# try:
386	# self.curr_thread.isquit()
387	# except:
388	# raise FitAbort,"stop leastsqr optimizer"
389	return self.res
390
391	class FitEngine:
392	def __init__(self):
393	"""
394	Base class for scipy and park fit engine
395	"""
396	#List of parameter names to fit
397	self.paramList=[]
398	#Dictionnary of fitArrange element (fit problems)
399	self.fitArrangeDict={}
400
401	def _concatenateData(self, listdata=[]):
402	"""
403	_concatenateData method concatenates each fields of all data contains ins listdata.
404	@param listdata: list of data
405	@return Data: Data is wrapper class for sans plottable. it is created with all parameters
406	of data concatenanted
407	@raise: if listdata is empty will return None
408	@raise: if data in listdata don't contain dy field ,will create an error
409	during fitting
410	"""
411	#TODO: we have to refactor the way we handle data.
412	# We should move away from plottables and move towards the Data1D objects
413	# defined in DataLoader. Data1D allows data manipulations, which should be
414	# used to concatenate.
415	# In the meantime we should switch off the concatenation.
416	#if len(listdata)>1:
417	# raise RuntimeError, "FitEngine._concatenateData: Multiple data files is not currently supported"
418	#return listdata[0]
419
420	if listdata==[]:
421	raise ValueError, " data list missing"
422	else:
423	xtemp=[]
424	ytemp=[]
425	dytemp=[]
426	self.mini=None
427	self.maxi=None
428
429	for item in listdata:
430	data=item.data
431	mini,maxi=data.getFitRange()
432	if self.mini==None and self.maxi==None:
433	self.mini=mini
434	self.maxi=maxi
435	else:
436	if mini < self.mini:
437	self.mini=mini
438	if self.maxi < maxi:
439	self.maxi=maxi
440
441
442	for i in range(len(data.x)):
443	xtemp.append(data.x[i])
444	ytemp.append(data.y[i])
445	if data.dy is not None and len(data.dy)==len(data.y):
446	dytemp.append(data.dy[i])
447	else:
448	raise RuntimeError, "Fit._concatenateData: y-errors missing"
449	data= Data(x=xtemp,y=ytemp,dy=dytemp)
450	data.setFitRange(self.mini, self.maxi)
451	return data
452
453
454	def set_model(self,model,Uid,pars=[], constraints=[]):
455	"""
456	set a model on a given uid in the fit engine.
457	@param model: sans.models type
458	@param Uid :is the key of the fitArrange dictionnary where model is saved as a value
459	@param pars: the list of parameters to fit
460	@param constraints: list of
461	tuple (name of parameter, value of parameters)
462	the value of parameter must be a string to constraint 2 different
463	parameters.
464	Example:
465	we want to fit 2 model M1 and M2 both have parameters A and B.
466	constraints can be:
467	constraints = [(M1.A, M2.B+2), (M1.B= M2.A *5),...,]
468	@note : pars must contains only name of existing model's paramaters
469	"""
470	if model == None:
471	raise ValueError, "AbstractFitEngine: Need to set model to fit"
472
473	new_model= model
474	if not issubclass(model.__class__, Model):
475	new_model= Model(model)
476
477	if len(constraints)>0:
478	for constraint in constraints:
479	name, value = constraint
480	try:
481	new_model.parameterset[ str(name)].set( str(value) )
482	except:
483	msg= "Fit Engine: Error occurs when setting the constraint"
484	msg += " %s for parameter %s "%(value, name)
485	raise ValueError, msg
486
487	if len(pars) >0:
488	temp=[]
489	for item in pars:
490	if item in new_model.model.getParamList():
491	temp.append(item)
492	self.paramList.append(item)
493	else:
494
495	msg = "wrong parameter %s used"%str(item)
496	msg += "to set model %s. Choose"%str(new_model.model.name)
497	msg += "parameter name within %s"%str(new_model.model.getParamList())
498	raise ValueError,msg
499
500	#A fitArrange is already created but contains dList only at Uid
501	if self.fitArrangeDict.has_key(Uid):
502	self.fitArrangeDict[Uid].set_model(new_model)
503	self.fitArrangeDict[Uid].pars= pars
504	else:
505	#no fitArrange object has been create with this Uid
506	fitproblem = FitArrange()
507	fitproblem.set_model(new_model)
508	fitproblem.pars= pars
509	self.fitArrangeDict[Uid] = fitproblem
510
511	else:
512	raise ValueError, "park_integration:missing parameters"
513
514	def set_data(self,data,Uid,smearer=None,qmin=None,qmax=None):
515	""" Receives plottable, creates a list of data to fit,set data
516	in a FitArrange object and adds that object in a dictionary
517	with key Uid.
518	@param data: data added
519	@param Uid: unique key corresponding to a fitArrange object with data
520	"""
521	if data.__class__.__name__=='Data2D':
522	fitdata=FitData2D(sans_data2d=data, data=data.data, err_data= data.err_data)
523	else:
524	fitdata=FitData1D(x=data.x, y=data.y , dx= data.dx,dy=data.dy,smearer=smearer)
525
526	fitdata.setFitRange(qmin=qmin,qmax=qmax)
527	#A fitArrange is already created but contains model only at Uid
528	if self.fitArrangeDict.has_key(Uid):
529	self.fitArrangeDict[Uid].add_data(fitdata)
530	else:
531	#no fitArrange object has been create with this Uid
532	fitproblem= FitArrange()
533	fitproblem.add_data(fitdata)
534	self.fitArrangeDict[Uid]=fitproblem
535
536	def get_model(self,Uid):
537	"""
538	@param Uid: Uid is key in the dictionary containing the model to return
539	@return a model at this uid or None if no FitArrange element was created
540	with this Uid
541	"""
542	if self.fitArrangeDict.has_key(Uid):
543	return self.fitArrangeDict[Uid].get_model()
544	else:
545	return None
546
547	def remove_Fit_Problem(self,Uid):
548	"""remove fitarrange in Uid"""
549	if self.fitArrangeDict.has_key(Uid):
550	del self.fitArrangeDict[Uid]
551
552	def select_problem_for_fit(self,Uid,value):
553	"""
554	select a couple of model and data at the Uid position in dictionary
555	and set in self.selected value to value
556	@param value: the value to allow fitting. can only have the value one or zero
557	"""
558	if self.fitArrangeDict.has_key(Uid):
559	self.fitArrangeDict[Uid].set_to_fit( value)
560
561
562	def get_problem_to_fit(self,Uid):
563	"""
564	return the self.selected value of the fit problem of Uid
565	@param Uid: the Uid of the problem
566	"""
567	if self.fitArrangeDict.has_key(Uid):
568	self.fitArrangeDict[Uid].get_to_fit()
569
570	class FitArrange:
571	def __init__(self):
572	"""
573	Class FitArrange contains a set of data for a given model
574	to perform the Fit.FitArrange must contain exactly one model
575	and at least one data for the fit to be performed.
576	model: the model selected by the user
577	Ldata: a list of data what the user wants to fit
578
579	"""
580	self.model = None
581	self.dList =[]
582	self.pars=[]
583	#self.selected is zero when this fit problem is not schedule to fit
584	#self.selected is 1 when schedule to fit
585	self.selected = 0
586
587	def set_model(self,model):
588	"""
589	set_model save a copy of the model
590	@param model: the model being set
591	"""
592	self.model = model
593
594	def add_data(self,data):
595	"""
596	add_data fill a self.dList with data to fit
597	@param data: Data to add in the list
598	"""
599	if not data in self.dList:
600	self.dList.append(data)
601
602	def get_model(self):
603	""" @return: saved model """
604	return self.model
605
606	def get_data(self):
607	""" @return: list of data dList"""
608	#return self.dList
609	return self.dList[0]
610
611	def remove_data(self,data):
612	"""
613	Remove one element from the list
614	@param data: Data to remove from dList
615	"""
616	if data in self.dList:
617	self.dList.remove(data)
618	def set_to_fit (self, value=0):
619	"""
620	set self.selected to 0 or 1 for other values raise an exception
621	@param value: integer between 0 or 1
622	"""
623	self.selected= value
624
625	def get_to_fit(self):
626	"""
627	@return self.selected value
628	"""
629	return self.selected

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