import park,numpy

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):
        """
            Data can be initital with a data (sans plottable)
            or with vectors.
        """
        self.data=sans_data1d
        self.x= sans_data1d.x
        self.y= sans_data1d.y
        self.dx= sans_data1d.dx
        self.dy= sans_data1d.dy
        self.qmin=None
        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):
        """ @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.image
        self.err_image = sans_data2d.err_image
        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:
            self.xmin= 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 self.x_bins[j]>= self.xmin and self.x_bins[j]<= self.xmax:
                res.append( (self.image[j][i]- fn([self.x_bins[j],self.y_bins[i]]))\
                            /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
        return sum
    def __call__(self,params):
        """
            Compute residuals
            @param params: value of parameters to fit
        """
        self.model.setParams(self.paramlist,params)
        self.res= self.data.residuals(self.model.eval)
        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
        """
        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,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__=='MetaData2D':
            fitdata=FitData2D(data)
        else:
            fitdata=FitData1D(data)
       
        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