import park,numpy,math class SansParameter(park.Parameter): """ SANS model parameters for use in the PARK fitting service. The parameter attribute value is redirected to the underlying parameter value in the SANS model. """ def __init__(self, name, model): """ @param name: the name of the model parameter @param model: the sans model to wrap as a park model """ self._model, self._name = model,name #set the value for the parameter of the given name self.set(model.getParam(name)) def _getvalue(self): """ override the _getvalue of park parameter @return value the parameter associates with self.name """ return self._model.getParam(self.name) def _setvalue(self,value): """ override the _setvalue pf park parameter @param value: the value to set on a given parameter """ self._model.setParam(self.name, value) value = property(_getvalue,_setvalue) def _getrange(self): """ Override _getrange of park parameter return the range of parameter """ lo,hi = self._model.details[self.name][1:] if lo is None: lo = -numpy.inf if hi is None: hi = numpy.inf return lo,hi def _setrange(self,r): """ override _setrange of park parameter @param r: the value of the range to set """ self._model.details[self.name][1:] = r range = property(_getrange,_setrange) class Model(park.Model): """ PARK wrapper for SANS models. """ def __init__(self, sans_model, **kw): """ @param sans_model: the sans model to wrap using park interface """ park.Model.__init__(self, **kw) self.model = sans_model self.name = sans_model.name #list of parameters names self.sansp = sans_model.getParamList() #list of park parameter self.parkp = [SansParameter(p,sans_model) for p in self.sansp] #list of parameterset self.parameterset = park.ParameterSet(sans_model.name,pars=self.parkp) self.pars=[] def getParams(self,fitparams): """ return a list of value of paramter to fit @param fitparams: list of paramaters name to fit """ list=[] self.pars=[] self.pars=fitparams for item in fitparams: for element in self.parkp: if element.name ==str(item): list.append(element.value) return list def setParams(self,paramlist, params): """ Set value for parameters to fit @param params: list of value for parameters to fit """ try: for i in range(len(self.parkp)): for j in range(len(paramlist)): if self.parkp[i].name==paramlist[j]: self.parkp[i].value = params[j] self.model.setParam(self.parkp[i].name,params[j]) except: raise def eval(self,x): """ override eval method of park model. @param x: the x value used to compute a function """ return self.model.runXY(x) class Data(object): """ Wrapper class for SANS data """ def __init__(self,x=None,y=None,dy=None,dx=None,sans_data=None): """ Data can be initital with a data (sans plottable) or with vectors. """ if sans_data !=None: self.x= sans_data.x self.y= sans_data.y self.dx= sans_data.dx self.dy= sans_data.dy elif (x!=None and y!=None and dy!=None): self.x=x self.y=y self.dx=dx self.dy=dy else: raise ValueError,\ "Data is missing x, y or dy, impossible to compute residuals later on" self.qmin=None self.qmax=None def setFitRange(self,mini=None,maxi=None): """ to set the fit range""" self.qmin=mini self.qmax=maxi def getFitRange(self): """ @return the range of data.x to fit """ return self.qmin, self.qmax def residuals(self, fn): """ @param fn: function that return model value @return residuals """ x,y,dy = [numpy.asarray(v) for v in (self.x,self.y,self.dy)] if self.qmin==None and self.qmax==None: fx =numpy.asarray([fn(v) for v in x]) return (y - fx)/dy else: idx = (x>=self.qmin) & (x <= self.qmax) fx = numpy.asarray([fn(item)for item in x[idx ]]) return (y[idx] - fx)/dy[idx] def residuals_deriv(self, model, pars=[]): """ @return residuals derivatives . @note: in this case just return empty array """ return [] class FitData1D(object): """ Wrapper class for SANS data """ def __init__(self,sans_data1d, smearer=None): """ Data can be initital with a data (sans plottable) or with vectors. self.smearer is an object of class QSmearer or SlitSmearer that will smear the theory data (slit smearing or resolution smearing) when set. The proper way to set the smearing object would be to do the following: from DataLoader.qsmearing import smear_selection fitdata1d = FitData1D(some_data) fitdata1d.smearer = smear_selection(some_data) Note that some_data _HAS_ to be of class DataLoader.data_info.Data1D Setting it back to None will turn smearing off. """ self.smearer = smearer # Initialize from Data1D object self.data=sans_data1d self.x= sans_data1d.x self.y= sans_data1d.y self.dx= sans_data1d.dx self.dy= sans_data1d.dy ## Min Q-value self.qmin=None ## Max Q-value self.qmax=None def setFitRange(self,qmin=None,qmax=None,ymin=None,ymax=None,): """ to set the fit range""" self.qmin=qmin self.qmax=qmax def getFitRange(self): """ @return the range of data.x to fit """ return self.qmin, self.qmax def residuals(self, fn): """ Compute residuals. If self.smearer has been set, use if to smear the data before computing chi squared. @param fn: function that return model value @return residuals """ x,y,dy = [numpy.asarray(v) for v in (self.x,self.y,self.dy)] # Find entries to consider if self.qmin==None and self.qmax==None: idx = Ellipsis else: idx = (x>=self.qmin) & (x <= self.qmax) # Compute theory data f(x) fx = numpy.zeros(len(x)) fx[idx] = numpy.asarray([fn(v) for v in x[idx]]) # Smear theory data if self.smearer is not None: fx = self.smearer(fx) # Sanity check if numpy.size(dy) < numpy.size(x): raise RuntimeError, "FitData1D: invalid error array" return (y[idx] - fx[idx])/dy[idx] def residuals_old(self, fn): """ @param fn: function that return model value @return residuals """ x,y,dy = [numpy.asarray(v) for v in (self.x,self.y,self.dy)] if self.qmin==None and self.qmax==None: fx =numpy.asarray([fn(v) for v in x]) return (y - fx)/dy else: idx = (x>=self.qmin) & (x <= self.qmax) fx = numpy.asarray([fn(item)for item in x[idx ]]) return (y[idx] - fx)/dy[idx] def residuals_deriv(self, model, pars=[]): """ @return residuals derivatives . @note: in this case just return empty array """ return [] class FitData2D(object): """ Wrapper class for SANS data """ def __init__(self,sans_data2d): """ Data can be initital with a data (sans plottable) or with vectors. """ self.data=sans_data2d self.image = sans_data2d.data self.err_image = sans_data2d.err_data self.x_bins= sans_data2d.x_bins self.y_bins= sans_data2d.y_bins self.xmin= self.data.xmin self.xmax= self.data.xmax self.ymin= self.data.ymin self.ymax= self.data.ymax def setFitRange(self,qmin=None,qmax=None,ymin=None,ymax=None): """ to set the fit range""" self.xmin= qmin self.xmax= qmax self.ymin= ymin self.ymax= ymax def getFitRange(self): """ @return the range of data.x to fit """ return self.xmin, self.xmax,self.ymin, self.ymax def residuals(self, fn): """ @param fn: function that return model value @return residuals """ res=[] if self.xmin==None: #Here we define that xmin = qmin >=0 and xmax=qmax>=qmain self.xmin= 0 #self.data.xmin if self.xmax==None: self.xmax= self.data.xmax if self.ymin==None: self.ymin= self.data.ymin if self.ymax==None: self.ymax= self.data.ymax for i in range(len(self.y_bins)): #if self.y_bins[i]>= self.ymin and self.y_bins[i]<= self.ymax: for j in range(len(self.x_bins)): if math.pow(self.data.x_bins[i],2)+math.pow(self.data.y_bins[j],2)>=math.pow(self.xmin,2): if math.pow(self.data.x_bins[i],2)+math.pow(self.data.y_bins[j],2)<=math.pow(self.xmax,2): #if self.x_bins[j]>= self.xmin and self.x_bins[j]<= self.xmax: res.append( (self.image[j][i]- fn([self.x_bins[i],self.y_bins[j]]))\ /self.err_image[j][i] ) return numpy.array(res) def residuals_deriv(self, model, pars=[]): """ @return residuals derivatives . @note: in this case just return empty array """ return [] class sansAssembly: """ Sans Assembly class a class wrapper to be call in optimizer.leastsq method """ def __init__(self,paramlist,Model=None , Data=None): """ @param Model: the model wrapper fro sans -model @param Data: the data wrapper for sans data """ self.model = Model self.data = Data self.paramlist=paramlist self.res=[] def chisq(self, params): """ Calculates chi^2 @param params: list of parameter values @return: chi^2 """ sum = 0 for item in self.res: sum += item*item #print "length of data =",len(self.res) return sum/ len(self.res) def __call__(self,params): """ Compute residuals @param params: value of parameters to fit """ #import thread self.model.setParams(self.paramlist,params) self.res= self.data.residuals(self.model.eval) #print "residuals",thread.get_ident() return self.res class FitEngine: def __init__(self): """ Base class for scipy and park fit engine """ #List of parameter names to fit self.paramList=[] #Dictionnary of fitArrange element (fit problems) self.fitArrangeDict={} def _concatenateData(self, listdata=[]): """ _concatenateData method concatenates each fields of all data contains ins listdata. @param listdata: list of data @return Data: Data is wrapper class for sans plottable. it is created with all parameters of data concatenanted @raise: if listdata is empty will return None @raise: if data in listdata don't contain dy field ,will create an error during fitting """ #TODO: we have to refactor the way we handle data. # We should move away from plottables and move towards the Data1D objects # defined in DataLoader. Data1D allows data manipulations, which should be # used to concatenate. # In the meantime we should switch off the concatenation. #if len(listdata)>1: # raise RuntimeError, "FitEngine._concatenateData: Multiple data files is not currently supported" #return listdata[0] if listdata==[]: raise ValueError, " data list missing" else: xtemp=[] ytemp=[] dytemp=[] self.mini=None self.maxi=None for item in listdata: data=item.data mini,maxi=data.getFitRange() if self.mini==None and self.maxi==None: self.mini=mini self.maxi=maxi else: if mini < self.mini: self.mini=mini if self.maxi < maxi: self.maxi=maxi for i in range(len(data.x)): xtemp.append(data.x[i]) ytemp.append(data.y[i]) if data.dy is not None and len(data.dy)==len(data.y): dytemp.append(data.dy[i]) else: raise RuntimeError, "Fit._concatenateData: y-errors missing" data= Data(x=xtemp,y=ytemp,dy=dytemp) data.setFitRange(self.mini, self.maxi) return data def set_model(self,model,Uid,pars=[]): """ set a model on a given uid in the fit engine. @param model: the model to fit @param Uid :is the key of the fitArrange dictionnary where model is saved as a value @param pars: the list of parameters to fit @note : pars must contains only name of existing model's paramaters """ if len(pars) >0: if model==None: raise ValueError, "AbstractFitEngine: Specify parameters to fit" else: for item in pars: if item in model.model.getParamList(): self.paramList.append(item) else: raise ValueError,"wrong paramter %s used to set model %s. Choose\ parameter name within %s"%(item, model.model.name,str(model.model.getParamList())) return #A fitArrange is already created but contains dList only at Uid if self.fitArrangeDict.has_key(Uid): self.fitArrangeDict[Uid].set_model(model) else: #no fitArrange object has been create with this Uid fitproblem = FitArrange() fitproblem.set_model(model) self.fitArrangeDict[Uid] = fitproblem else: raise ValueError, "park_integration:missing parameters" def set_data(self,data,Uid,smearer=None,qmin=None,qmax=None,ymin=None,ymax=None): """ Receives plottable, creates a list of data to fit,set data in a FitArrange object and adds that object in a dictionary with key Uid. @param data: data added @param Uid: unique key corresponding to a fitArrange object with data """ if data.__class__.__name__=='Data2D': fitdata=FitData2D(data) else: fitdata=FitData1D(data, smearer) fitdata.setFitRange(qmin=qmin,qmax=qmax, ymin=ymin,ymax=ymax) #A fitArrange is already created but contains model only at Uid if self.fitArrangeDict.has_key(Uid): self.fitArrangeDict[Uid].add_data(fitdata) else: #no fitArrange object has been create with this Uid fitproblem= FitArrange() fitproblem.add_data(fitdata) self.fitArrangeDict[Uid]=fitproblem def get_model(self,Uid): """ @param Uid: Uid is key in the dictionary containing the model to return @return a model at this uid or None if no FitArrange element was created with this Uid """ if self.fitArrangeDict.has_key(Uid): return self.fitArrangeDict[Uid].get_model() else: return None def remove_Fit_Problem(self,Uid): """remove fitarrange in Uid""" if self.fitArrangeDict.has_key(Uid): del self.fitArrangeDict[Uid] def select_problem_for_fit(self,Uid,value): """ select a couple of model and data at the Uid position in dictionary and set in self.selected value to value @param value: the value to allow fitting. can only have the value one or zero """ if self.fitArrangeDict.has_key(Uid): self.fitArrangeDict[Uid].set_to_fit( value) def get_problem_to_fit(self,Uid): """ return the self.selected value of the fit problem of Uid @param Uid: the Uid of the problem """ if self.fitArrangeDict.has_key(Uid): self.fitArrangeDict[Uid].get_to_fit() class FitArrange: def __init__(self): """ Class FitArrange contains a set of data for a given model to perform the Fit.FitArrange must contain exactly one model and at least one data for the fit to be performed. model: the model selected by the user Ldata: a list of data what the user wants to fit """ self.model = None self.dList =[] #self.selected is zero when this fit problem is not schedule to fit #self.selected is 1 when schedule to fit self.selected = 0 def set_model(self,model): """ set_model save a copy of the model @param model: the model being set """ self.model = model def add_data(self,data): """ add_data fill a self.dList with data to fit @param data: Data to add in the list """ if not data in self.dList: self.dList.append(data) def get_model(self): """ @return: saved model """ return self.model def get_data(self): """ @return: list of data dList""" #return self.dList return self.dList[0] def remove_data(self,data): """ Remove one element from the list @param data: Data to remove from dList """ if data in self.dList: self.dList.remove(data) def set_to_fit (self, value=0): """ set self.selected to 0 or 1 for other values raise an exception @param value: integer between 0 or 1 """ self.selected= value def get_to_fit(self): """ @return self.selected value """ return self.selected