-                      rcb4ef58
+                      rfd62331
     r"""
         Use for P(Q)\*S(Q); function call must be in the order of P(Q) and then S(Q):
         The model parameters are combined from both models, P(Q) and S(Q), except 1) 'effect_radius' of S(Q)
         which will be calculated from P(Q) via calculate_ER(),
         and 2) 'scale' in P model which is synchronized w/ volfraction in S
+        The model parameters are combined from both models, P(Q) and S(Q), except 1) 'radius_effective' of S(Q)
+        which will be calculated from P(Q) via calculate_ER(),
+        and 2) 'scale' in P model which is synchronized w/ volfraction in S
         then P*S is multiplied by a new parameter, 'scale_factor'.
         The polydispersion is applicable only to P(Q), not to S(Q).
 …
         ## Parameter details [units, min, max]
         self.details = {}
+        ##models
+        ## Define parameters to exclude from multiplication model
+        self.excluded_params={'radius_effective','scale','background'}
+        ##models
         self.p_model = p_model
         self.s_model = s_model
+        self.s_model = s_model
         self.magnetic_params = []
         ## dispersion
 …
         ## New parameter:Scaling factor
         self.params['scale_factor'] = 1
+        self.params['background']  = 0
         ## Parameter details [units, min, max]
         self._set_details()
         self.details['scale_factor'] = ['', 0.0, numpy.inf]
+        self.details['background'] = ['',-numpy.inf,numpy.inf]
         #list of parameter that can be fitted
         self._set_fixed_params()
+        self._set_fixed_params()
         ## parameters with orientation
         for item in self.p_model.orientation_params:
             self.orientation_params.append(item)
         for item in self.p_model.magnetic_params:
             self.magnetic_params.append(item)
+        for item in self.p_model.magnetic_params:
+            self.magnetic_params.append(item)
         for item in self.s_model.orientation_params:
             if not item in self.orientation_params:
 …
             multiplicity = 1
         ## functional multiplicity of the model
         self.multiplicity = multiplicity
+        self.multiplicity = multiplicity
         # non-fittable parameters
         self.non_fittable = p_model.non_fittable
         self.multiplicity_info = []
+        self.non_fittable = p_model.non_fittable
+        self.multiplicity_info = []
         self.fun_list = {}
         if self.non_fittable > 1:
             try:
                 self.multiplicity_info = p_model.multiplicity_info
+                self.multiplicity_info = p_model.multiplicity_info
                 self.fun_list = p_model.fun_list
                 self.is_multiplicity_model = True
 …
             self.is_multiplicity_model = False
             self.multiplicity_info = [0]
     def _clone(self, obj):
         """
 …
         #obj = copy.deepcopy(self)
         return obj
     def _set_dispersion(self):
         """
 …
         applied to s_model
         """
         ##set dispersion only from p_model
+        ##set dispersion only from p_model
         for name , value in self.p_model.dispersion.iteritems():
             self.dispersion[name] = value
+            self.dispersion[name] = value
     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
 …
             x = None
             y = None
         return x, y
     def _set_params(self):
         """
         Concatenate the parameters of the two models to create
         these model parameters
+        these model parameters
         """
         for name , value in self.p_model.params.iteritems():
             if not name in self.params.keys() and name != 'scale':
+            if not name in self.params.keys() and name not in self.excluded_params:
                 self.params[name] = value
         for name , value in self.s_model.params.iteritems():
             #Remove the effect_radius from the (P*S) model parameters.
             if not name in self.params.keys() and name != 'effect_radius':
+            #Remove the radius_effective from the (P*S) model parameters.
+            if not name in self.params.keys() and name not in self.excluded_params:
                 self.params[name] = value
         # Set "scale and effec_radius to P and S model as initializing
         # since run P*S comes from P and S separately.
+        self._set_backgrounds()
         self._set_scale_factor()
         self._set_effect_radius()
+        self._set_radius_effective()
     def _set_details(self):
         """
         Concatenate details of the two models to create
         this model's details
+        this model's details
         """
         for name, detail in self.p_model.details.iteritems():
             if name != 'scale':
+            if name not in self.excluded_params:
                 self.details[name] = detail
         for name , detail in self.s_model.details.iteritems():
             if not name in self.details.keys() or name != 'effect_radius':
+            if not name in self.details.keys() or name not in self.exluded_params:
                 self.details[name] = detail
+    def _set_backgrounds(self):
+        """
+        Set component backgrounds to zero
+        """
+        self.p_model.setParam('background',0)
+        self.s_model.setParam('background',0)
     def _set_scale_factor(self):
         """
 …
         """
         value = self.params['volfraction']
         if value != None:
+        if value != None:
             factor = self.p_model.calculate_VR()
             if factor == None or factor == NotImplemented or factor == 0.0:
 …
             self.p_model.setParam('scale', value)
             self.s_model.setParam('volfraction', val)
     def _set_effect_radius(self):
+    def _set_radius_effective(self):
         """
         Set effective radius to S(Q) model
         """
         if not 'effect_radius' in self.s_model.params.keys():
+        if not 'radius_effective' in self.s_model.params.keys():
             return
         effective_radius = self.p_model.calculate_ER()
         #Reset the effective_radius of s_model just before the run
         if effective_radius != None and effective_radius != NotImplemented:
             self.s_model.setParam('effect_radius', effective_radius)
+            self.s_model.setParam('radius_effective', effective_radius)
     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 P*S model
         self._setParamHelper( name, value)
         ## setParam to p model
         # set 'scale' in P(Q) equal to volfraction
+        ## setParam to p model
+        # set 'scale' in P(Q) equal to volfraction
         if name == 'volfraction':
             self._set_scale_factor()
         elif name in self.p_model.getParamList():
+        elif name in self.p_model.getParamList() and name not in self.excluded_params:
             self.p_model.setParam( name, value)
         ## setParam to s model
         # This is a little bit abundant: Todo: find better way
         self._set_effect_radius()
         if name in self.s_model.getParamList():
+        ## setParam to s model
+        # This is a little bit abundant: Todo: find better way
+        self._set_radius_effective()
+        if name in self.s_model.getParamList() and name not in self.excluded_params:
             if name != 'volfraction':
                 self.s_model.setParam( name, value)
         #self._setParamHelper( name, value)
     def _setParamHelper(self, name, value):
         """
 …
                     self.params[item] = value
                     return
         raise ValueError, "Model does not contain parameter %s" % name
     def _set_fixed_params(self):
         """
 …
         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)
         """
         # set effective radius and scaling factor before run
         self._set_effect_radius()
+        self._set_radius_effective()
         self._set_scale_factor()
         return self.params['scale_factor'] * self.p_model.run(x) * \
                             self.s_model.run(x)
+                            self.s_model.run(x) + self.params['background']
     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
         """
+        """
         # set effective radius and scaling factor before run
         self._set_effect_radius()
+        self._set_radius_effective()
         self._set_scale_factor()
         out = self.params['scale_factor'] * self.p_model.runXY(x) * \
                         self.s_model.runXY(x)
+                        self.s_model.runXY(x) + self.params['background']
         return out
     ## Now (May27,10) directly uses the model eval function
+    ## 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[])
         """
         # set effective radius and scaling factor before run
         self._set_effect_radius()
+        self._set_radius_effective()
         self._set_scale_factor()
         out = self.params['scale_factor'] * self.p_model.evalDistribution(x) * \
                         self.s_model.evalDistribution(x)
+                        self.s_model.evalDistribution(x) + self.params['background']
         return out
 …
         """
         Set the dispersion object for a model parameter
         :param parameter: name of the parameter [string]
         :dispersion: dispersion object of type DispersionModel
 …
             return value
         except:
             raise
+            raise
     def fill_description(self, p_model, s_model):
 …
         """
         description = ""
         description += "Note:1) The effect_radius (effective radius) of %s \n"%\
+        description += "Note:1) The radius_effective (effective radius) of %s \n"%\
                                                                 (s_model.name)
         description += "             is automatically calculated "
 …
         description += "        for details of individual models."
         self.description += description

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SasView

Changeset fd62331 in sasview

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src/sas/sascalc/fit/MultiplicationModel.py

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