# A sample of an experimental model function for Sum(Pmodel1,Pmodel2)
import copy
from sans.models.pluginmodel import Model1DPlugin
# User can change the name of the model (only with single functional model)
from sans.models.CylinderModel import CylinderModel as P1
from sans.models.PolymerExclVolume import PolymerExclVolume as P2


class Model(Model1DPlugin):
    """
    Use for p1(Q)+p2(Q); 
    Note: P(Q) refers to 'form factor' model.
    """
    name = ""
    def __init__(self):
        Model1DPlugin.__init__(self, name='')
        """
        :param p_model1: a form factor, P(Q)
        :param p_model2: another form factor, P(Q)
        """
        p_model1 = P1()
        p_model2 = P2()
        ## Setting  model name model description
        self.description=""
        self.name = "Sum[" + "P1(Cyl)"  +", "+ "P2(PEV)" + "]"
        self.description = p_model1.name+"\n"
        self.description += p_model2.name+"\n"
        self.fill_description(p_model1, p_model2)

        ## Define parameters
        self.params = {}

        ## Parameter details [units, min, max]
        self.details = {}
        
        # non-fittable parameters
        self.non_fittable = p_model1.non_fittable  
        self.non_fittable += p_model2.non_fittable  
            
        ##models 
        self.p_model1= p_model1
        self.p_model2= p_model2
        
       
        ## dispersion
        self._set_dispersion()
        ## Define parameters
        self._set_params()
        ## New parameter:Scaling factor
        self.params['scale_factor'] = 1
        
        ## Parameter details [units, min, max]
        self._set_details()
        self.details['scale_factor'] = ['',     None, None]

        
        #list of parameter that can be fitted
        self._set_fixed_params()  
        ## parameters with orientation
        for item in self.p_model1.orientation_params:
            new_item = "p1_" + item
            if not new_item in self.orientation_params:
                self.orientation_params.append(new_item)
            
        for item in self.p_model2.orientation_params:
            new_item = "p2_" + item
            if not new_item in self.orientation_params:
                self.orientation_params.append(new_item)
        # get multiplicity if model provide it, else 1.
        try:
            multiplicity1 = p_model1.multiplicity
            try:
                multiplicity2 = p_model2.multiplicity
            except:
                multiplicity2 = 1
        except:
            multiplicity1 = 1
            multiplicity2 = 1
        ## functional multiplicity of the model
        self.multiplicity1 = multiplicity1  
        self.multiplicity2 = multiplicity2    
        self.multiplicity_info = []   
        
    def _clone(self, obj):
        """
            Internal utility function to copy the internal
            data members to a fresh copy.
        """
        obj.params     = copy.deepcopy(self.params)
        obj.description     = copy.deepcopy(self.description)
        obj.details    = copy.deepcopy(self.details)
        obj.dispersion = copy.deepcopy(self.dispersion)
        obj.p_model1  = self.p_model1.clone()
        obj.p_model2  = self.p_model2.clone()
        #obj = copy.deepcopy(self)
        return obj
    
    
    def _set_dispersion(self):
        """
           combined the two models dispersions
           Polydispersion should not be applied to s_model
        """
        ##set dispersion only from p_model 
        for name , value in self.p_model1.dispersion.iteritems():
            #if name.lower() not in self.p_model1.orientation_params:
            new_name = "p1_" + name
            self.dispersion[new_name]= value 
        for name , value in self.p_model2.dispersion.iteritems():
            #if name.lower() not in self.p_model2.orientation_params:
            new_name = "p2_" + name
            self.dispersion[new_name]= value 
            
    def function(self, x=0.0): 
        """
        """
        return 0
                               
    def getProfile(self):
        """
        Get SLD profile of p_model if exists
        
        : return: (r, beta) where r is a list of radius of the transition points
                beta is a list of the corresponding SLD values 
        : Note: This works only for func_shell# = 2 (exp function)
                and is not supporting for p2
        """
        try:
            x,y = self.p_model1.getProfile()
        except:
            x = None
            y = None
            
        return x, y
    
    def _set_params(self):
        """
            Concatenate the parameters of the two models to create
            this model parameters 
        """

        for name , value in self.p_model1.params.iteritems():
            # No 2D-supported
            #if name not in self.p_model1.orientation_params:
            new_name = "p1_" + name
            self.params[new_name]= value
            
        for name , value in self.p_model2.params.iteritems():
            # No 2D-supported
            #if name not in self.p_model2.orientation_params:
            new_name = "p2_" + name
            self.params[new_name]= value
                
        # Set "scale" as initializing
        self._set_scale_factor()
      
            
    def _set_details(self):
        """
            Concatenate details of the two models to create
            this model details 
        """
        for name ,detail in self.p_model1.details.iteritems():
            new_name = "p1_" + name
            #if new_name not in self.orientation_params:
            self.details[new_name]= detail
            
        for name ,detail in self.p_model2.details.iteritems():
            new_name = "p2_" + name
            #if new_name not in self.orientation_params:
            self.details[new_name]= detail
    
    def _set_scale_factor(self):
        """
        Not implemented
        """
        pass
        
                
    def setParam(self, name, value):
        """ 
        Set the value of a model parameter
    
        :param name: name of the parameter
        :param value: value of the parameter
        """
        # set param to p1+p2 model
        self._setParamHelper(name, value)
        
        ## setParam to p model 
        model_pre = name.split('_', 1)[0]
        new_name = name.split('_', 1)[1]
        if model_pre == "p1":
            if new_name in self.p_model1.getParamList():
                self.p_model1.setParam(new_name, value)
        elif model_pre == "p2":
             if new_name in self.p_model2.getParamList():
                self.p_model2.setParam(new_name, value)
        elif name.lower() == 'scale_factor':
            self.params['scale_factor'] = value
        else:
            raise ValueError, "Model does not contain parameter %s" % name
            
    def getParam(self, name):
        """ 
        Set the value of a model parameter

        :param name: name of the parameter
        
        """
        # Look for dispersion parameters
        toks = name.split('.')
        if len(toks)==2:
            for item in self.dispersion.keys():
                # 2D not supported
                if item.lower()==toks[0].lower():# and \
                            #item.lower() not in self.orientation_params \
                            #and toks[0].lower() not in self.orientation_params:
                    for par in self.dispersion[item]:
                        if par.lower() == toks[1].lower():
                            return self.dispersion[item][par]
        else:
            # Look for standard parameter
            for item in self.params.keys():
                if item.lower()==name.lower():#and \
                            #item.lower() not in self.orientation_params \
                            #and toks[0].lower() not in self.orientation_params:
                    return self.params[item]
        return  
        #raise ValueError, "Model does not contain parameter %s" % name
       
    def _setParamHelper(self, name, value):
        """
        Helper function to setparam
        """
        # Look for dispersion parameters
        toks = name.split('.')
        if len(toks)== 2:
            for item in self.dispersion.keys():
                if item.lower()== toks[0].lower():# and \
                            #item.lower() not in self.orientation_params:
                    for par in self.dispersion[item]:
                        if par.lower() == toks[1].lower():#and \
                                #item.lower() not in self.orientation_params:
                            self.dispersion[item][par] = value
                            return
        else:
            # Look for standard parameter
            for item in self.params.keys():
                if item.lower()== name.lower():#and \
                            #item.lower() not in self.orientation_params:
                    self.params[item] = value
                    return
            
        raise ValueError, "Model does not contain parameter %s" % name
             
   
    def _set_fixed_params(self):
        """
             fill the self.fixed list with the p_model fixed list
        """
        for item in self.p_model1.fixed:
            new_item = "p1" + item
            self.fixed.append(new_item)
        for item in self.p_model2.fixed:
            new_item = "p2" + item
            self.fixed.append(new_item)

        self.fixed.sort()
                
                    
    def run(self, x = 0.0):
        """ 
        Evaluate the model
        
        :param x: input q-value (float or [float, float] as [r, theta])
        :return: (scattering function value)
        """
        self._set_scale_factor()
        return self.params['scale_factor'] * \
                (self.p_model1.run(x) + self.p_model2.run(x))
    
    def runXY(self, x = 0.0):
        """ 
        Evaluate the model
        
        :param x: input q-value (float or [float, float] as [qx, qy])
        :return: scattering function value
        """  
        self._set_scale_factor()
        return self.params['scale_factor'] * \
                (self.p_model1.runXY(x) + self.p_model2.runXY(x))
    
    ## Now (May27,10) directly uses the model eval function 
    ## instead of the for-loop in Base Component.
    def evalDistribution(self, x = []):
        """ 
        Evaluate the model in cartesian coordinates
        
        :param x: input q[], or [qx[], qy[]]
        :return: scattering function P(q[])
        """
        self._set_scale_factor()
        return self.params['scale_factor'] * \
                    (self.p_model1.evalDistribution(x) + \
                                 self.p_model2.evalDistribution(x))

    def set_dispersion(self, parameter, dispersion):
        """
        Set the dispersion object for a model parameter
        
        :param parameter: name of the parameter [string]
        :dispersion: dispersion object of type DispersionModel
        """
        value= None
        new_pre = parameter.split("_", 1)[0]
        new_parameter = parameter.split("_", 1)[1]
        try:
            if new_pre == 'p1' and \
                            new_parameter in self.p_model1.dispersion.keys():
                value= self.p_model1.set_dispersion(new_parameter, dispersion)
            if new_pre == 'p2' and \
                             new_parameter in self.p_model2.dispersion.keys():
                value= self.p_model2.set_dispersion(new_parameter, dispersion)
            self._set_dispersion()
            return value
        except:
            raise 

    def fill_description(self, p_model1, p_model2):
        """
        Fill the description for P(Q)+P(Q)
        """
        description = ""
        description +="This model gives the summation of  %s and %s.\n"% \
                                        ( p_model1.name, p_model2.name )
        self.description += description
        
if __name__ == "__main__": 
    m1= Model() 
    m1.setParam("p1_scale", 25)  
    m1.setParam("p1_long_c", 1000)
    m1.setParam("p2_scale", 100) 
    m1.setParam("p2_rg", 100) 
    out1 = m1.runXY(0.01)

    m2= Model()
    m2.p_model1.setParam("scale", 25) 
    m2.p_model1.setParam("long_c", 1000) 
    m2.p_model2.setParam("scale", 100)
    m2.p_model2.setParam("rg", 100)
    out2 = m2.p_model1.runXY(0.01) + m2.p_model2.runXY(0.01)
    print out1, " = ", out2