from sas.models.BaseComponent import BaseComponent
#import numpy, math
import copy
#from sas.models.pluginmodel import Model1DPlugin
class MultiplicationModel(BaseComponent):
[docs] """
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
then P*S is multiplied by a new param, 'scale_factor'.
The polydispersion is applicable only to P(Q), not to S(Q).
Note: P(Q) refers to 'form factor' model while S(Q) does to 'structure factor'.
"""
def __init__(self, p_model, s_model ):
BaseComponent.__init__(self)
"""
:param p_model: form factor, P(Q)
:param s_model: structure factor, S(Q)
"""
## Setting model name model description
self.description = ""
self.name = p_model.name +" * "+ s_model.name
self.description= self.name + "\n"
self.fill_description(p_model, s_model)
## Define parameters
self.params = {}
## Parameter details [units, min, max]
self.details = {}
##models
self.p_model = p_model
self.s_model = s_model
self.magnetic_params = []
## 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_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.s_model.orientation_params:
if not item in self.orientation_params:
self.orientation_params.append(item)
# get multiplicity if model provide it, else 1.
try:
multiplicity = p_model.multiplicity
except:
multiplicity = 1
## functional multiplicity of the model
self.multiplicity = multiplicity
# non-fittable parameters
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.fun_list = p_model.fun_list
except:
pass
else:
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_model = self.p_model.clone()
obj.s_model = self.s_model.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_model.dispersion.iteritems():
self.dispersion[name] = value
def getProfile(self):
[docs] """
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).
"""
try:
x, y = self.p_model.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_model.params.iteritems():
if not name in self.params.keys() and name != 'scale':
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':
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_scale_factor()
self._set_effect_radius()
def _set_details(self):
"""
Concatenate details of the two models to create
this model details
"""
for name, detail in self.p_model.details.iteritems():
if name != 'scale':
self.details[name] = detail
for name , detail in self.s_model.details.iteritems():
if not name in self.details.keys() or name != 'effect_radius':
self.details[name] = detail
def _set_scale_factor(self):
"""
Set scale=volfraction to P model
"""
value = self.params['volfraction']
if value != None:
factor = self.p_model.calculate_VR()
if factor == None or factor == NotImplemented or factor == 0.0:
val = value
else:
val = value / factor
self.p_model.setParam('scale', value)
self.s_model.setParam('volfraction', val)
def _set_effect_radius(self):
"""
Set effective radius to S(Q) model
"""
if not 'effect_radius' 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)
def setParam(self, name, value):
[docs] """
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
if name == 'volfraction':
self._set_scale_factor()
elif name in self.p_model.getParamList():
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():
if name != 'volfraction':
self.s_model.setParam( name, value)
#self._setParamHelper( name, value)
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():
for par in self.dispersion[item]:
if par.lower() == toks[1].lower():
self.dispersion[item][par] = value
return
else:
# Look for standard parameter
for item in self.params.keys():
if item.lower() == name.lower():
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_model.fixed:
self.fixed.append(item)
self.fixed.sort()
def run(self, x = 0.0):
[docs] """
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_scale_factor()
return self.params['scale_factor'] * self.p_model.run(x) * \
self.s_model.run(x)
def runXY(self, x = 0.0):
[docs] """ 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_scale_factor()
out = self.params['scale_factor'] * self.p_model.runXY(x) * \
self.s_model.runXY(x)
return out
## Now (May27,10) directly uses the model eval function
## instead of the for-loop in Base Component.
def evalDistribution(self, x = []):
[docs] """
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_scale_factor()
out = self.params['scale_factor'] * self.p_model.evalDistribution(x) * \
self.s_model.evalDistribution(x)
return out
def set_dispersion(self, parameter, dispersion):
[docs] """
Set the dispersion object for a model parameter
:param parameter: name of the parameter [string]
:dispersion: dispersion object of type DispersionModel
"""
value = None
try:
if parameter in self.p_model.dispersion.keys():
value = self.p_model.set_dispersion(parameter, dispersion)
self._set_dispersion()
return value
except:
raise
def fill_description(self, p_model, s_model):
[docs] """
Fill the description for P(Q)*S(Q)
"""
description = ""
description += "Note:1) The effect_radius (effective radius) of %s \n"%\
(s_model.name)
description += " is automatically calculated "
description += "from size parameters (radius...).\n"
description += " 2) For non-spherical shape, "
description += "this approximation is valid \n"
description += " only for limited systems. "
description += "Thus, use it at your own risk.\n"
description += "See %s description and %s description \n"% \
( p_model.name, s_model.name )
description += " for details of individual models."
self.description += description