AbstractFitEngine.py @ c6036f5

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 c6036f5 was 83195f7, checked in by Jae Cho <jhjcho@…>, 15 years ago
updated according to the changes in 2d inputs
Property mode set to `100644`
File size: 22.6 KB

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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, curr_thread= None):
344	"""
345	@param Model: the model wrapper fro sans -model
346	@param Data: the data wrapper for sans data
347	"""
348	self.model = Model
349	self.data = Data
350	self.paramlist=paramlist
351	self.curr_thread= curr_thread
352	self.res=[]
353	self.func_name="Functor"
354	def chisq(self, params):
355	"""
356	Calculates chi^2
357	@param params: list of parameter values
358	@return: chi^2
359	"""
360	sum = 0
361	for item in self.res:
362	sum += item*item
363	if len(self.res)==0:
364	return None
365	return sum/ len(self.res)
366
367	def __call__(self,params):
368	"""
369	Compute residuals
370	@param params: value of parameters to fit
371	"""
372	#import thread
373	self.model.setParams(self.paramlist,params)
374	self.res= self.data.residuals(self.model.eval)
375	#if self.curr_thread != None :
376	# try:
377	# self.curr_thread.isquit()
378	# except:
379	# raise FitAbort,"stop leastsqr optimizer"
380	return self.res
381
382	class FitEngine:
383	def __init__(self):
384	"""
385	Base class for scipy and park fit engine
386	"""
387	#List of parameter names to fit
388	self.paramList=[]
389	#Dictionnary of fitArrange element (fit problems)
390	self.fitArrangeDict={}
391
392	def _concatenateData(self, listdata=[]):
393	"""
394	_concatenateData method concatenates each fields of all data contains ins listdata.
395	@param listdata: list of data
396	@return Data: Data is wrapper class for sans plottable. it is created with all parameters
397	of data concatenanted
398	@raise: if listdata is empty will return None
399	@raise: if data in listdata don't contain dy field ,will create an error
400	during fitting
401	"""
402	#TODO: we have to refactor the way we handle data.
403	# We should move away from plottables and move towards the Data1D objects
404	# defined in DataLoader. Data1D allows data manipulations, which should be
405	# used to concatenate.
406	# In the meantime we should switch off the concatenation.
407	#if len(listdata)>1:
408	# raise RuntimeError, "FitEngine._concatenateData: Multiple data files is not currently supported"
409	#return listdata[0]
410
411	if listdata==[]:
412	raise ValueError, " data list missing"
413	else:
414	xtemp=[]
415	ytemp=[]
416	dytemp=[]
417	self.mini=None
418	self.maxi=None
419
420	for item in listdata:
421	data=item.data
422	mini,maxi=data.getFitRange()
423	if self.mini==None and self.maxi==None:
424	self.mini=mini
425	self.maxi=maxi
426	else:
427	if mini < self.mini:
428	self.mini=mini
429	if self.maxi < maxi:
430	self.maxi=maxi
431
432
433	for i in range(len(data.x)):
434	xtemp.append(data.x[i])
435	ytemp.append(data.y[i])
436	if data.dy is not None and len(data.dy)==len(data.y):
437	dytemp.append(data.dy[i])
438	else:
439	raise RuntimeError, "Fit._concatenateData: y-errors missing"
440	data= Data(x=xtemp,y=ytemp,dy=dytemp)
441	data.setFitRange(self.mini, self.maxi)
442	return data
443
444
445	def set_model(self,model,Uid,pars=[], constraints=[]):
446	"""
447	set a model on a given uid in the fit engine.
448	@param model: sans.models type
449	@param Uid :is the key of the fitArrange dictionnary where model is saved as a value
450	@param pars: the list of parameters to fit
451	@param constraints: list of
452	tuple (name of parameter, value of parameters)
453	the value of parameter must be a string to constraint 2 different
454	parameters.
455	Example:
456	we want to fit 2 model M1 and M2 both have parameters A and B.
457	constraints can be:
458	constraints = [(M1.A, M2.B+2), (M1.B= M2.A *5),...,]
459	@note : pars must contains only name of existing model's paramaters
460	"""
461	if model == None:
462	raise ValueError, "AbstractFitEngine: Need to set model to fit"
463
464	new_model= model
465	if not issubclass(model.__class__, Model):
466	new_model= Model(model)
467
468	if len(constraints)>0:
469	for constraint in constraints:
470	name, value = constraint
471	try:
472	new_model.parameterset[ str(name)].set( str(value) )
473	except:
474	msg= "Fit Engine: Error occurs when setting the constraint"
475	msg += " %s for parameter %s "%(value, name)
476	raise ValueError, msg
477
478	if len(pars) >0:
479	temp=[]
480	for item in pars:
481	if item in new_model.model.getParamList():
482	temp.append(item)
483	self.paramList.append(item)
484	else:
485
486	msg = "wrong parameter %s used"%str(item)
487	msg += "to set model %s. Choose"%str(new_model.model.name)
488	msg += "parameter name within %s"%str(new_model.model.getParamList())
489	raise ValueError,msg
490
491	#A fitArrange is already created but contains dList only at Uid
492	if self.fitArrangeDict.has_key(Uid):
493	self.fitArrangeDict[Uid].set_model(new_model)
494	self.fitArrangeDict[Uid].pars= pars
495	else:
496	#no fitArrange object has been create with this Uid
497	fitproblem = FitArrange()
498	fitproblem.set_model(new_model)
499	fitproblem.pars= pars
500	self.fitArrangeDict[Uid] = fitproblem
501
502	else:
503	raise ValueError, "park_integration:missing parameters"
504
505	def set_data(self,data,Uid,smearer=None,qmin=None,qmax=None):
506	""" Receives plottable, creates a list of data to fit,set data
507	in a FitArrange object and adds that object in a dictionary
508	with key Uid.
509	@param data: data added
510	@param Uid: unique key corresponding to a fitArrange object with data
511	"""
512	if data.__class__.__name__=='Data2D':
513	fitdata=FitData2D(sans_data2d=data, data=data.data, err_data= data.err_data)
514	else:
515	fitdata=FitData1D(x=data.x, y=data.y , dx= data.dx,dy=data.dy,smearer=smearer)
516
517	fitdata.setFitRange(qmin=qmin,qmax=qmax)
518	#A fitArrange is already created but contains model only at Uid
519	if self.fitArrangeDict.has_key(Uid):
520	self.fitArrangeDict[Uid].add_data(fitdata)
521	else:
522	#no fitArrange object has been create with this Uid
523	fitproblem= FitArrange()
524	fitproblem.add_data(fitdata)
525	self.fitArrangeDict[Uid]=fitproblem
526
527	def get_model(self,Uid):
528	"""
529	@param Uid: Uid is key in the dictionary containing the model to return
530	@return a model at this uid or None if no FitArrange element was created
531	with this Uid
532	"""
533	if self.fitArrangeDict.has_key(Uid):
534	return self.fitArrangeDict[Uid].get_model()
535	else:
536	return None
537
538	def remove_Fit_Problem(self,Uid):
539	"""remove fitarrange in Uid"""
540	if self.fitArrangeDict.has_key(Uid):
541	del self.fitArrangeDict[Uid]
542
543	def select_problem_for_fit(self,Uid,value):
544	"""
545	select a couple of model and data at the Uid position in dictionary
546	and set in self.selected value to value
547	@param value: the value to allow fitting. can only have the value one or zero
548	"""
549	if self.fitArrangeDict.has_key(Uid):
550	self.fitArrangeDict[Uid].set_to_fit( value)
551
552
553	def get_problem_to_fit(self,Uid):
554	"""
555	return the self.selected value of the fit problem of Uid
556	@param Uid: the Uid of the problem
557	"""
558	if self.fitArrangeDict.has_key(Uid):
559	self.fitArrangeDict[Uid].get_to_fit()
560
561	class FitArrange:
562	def __init__(self):
563	"""
564	Class FitArrange contains a set of data for a given model
565	to perform the Fit.FitArrange must contain exactly one model
566	and at least one data for the fit to be performed.
567	model: the model selected by the user
568	Ldata: a list of data what the user wants to fit
569
570	"""
571	self.model = None
572	self.dList =[]
573	self.pars=[]
574	#self.selected is zero when this fit problem is not schedule to fit
575	#self.selected is 1 when schedule to fit
576	self.selected = 0
577
578	def set_model(self,model):
579	"""
580	set_model save a copy of the model
581	@param model: the model being set
582	"""
583	self.model = model
584
585	def add_data(self,data):
586	"""
587	add_data fill a self.dList with data to fit
588	@param data: Data to add in the list
589	"""
590	if not data in self.dList:
591	self.dList.append(data)
592
593	def get_model(self):
594	""" @return: saved model """
595	return self.model
596
597	def get_data(self):
598	""" @return: list of data dList"""
599	#return self.dList
600	return self.dList[0]
601
602	def remove_data(self,data):
603	"""
604	Remove one element from the list
605	@param data: Data to remove from dList
606	"""
607	if data in self.dList:
608	self.dList.remove(data)
609	def set_to_fit (self, value=0):
610	"""
611	set self.selected to 0 or 1 for other values raise an exception
612	@param value: integer between 0 or 1
613	"""
614	self.selected= value
615
616	def get_to_fit(self):
617	"""
618	@return self.selected value
619	"""
620	return self.selected

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