"""
Spherical SLD model
"""
from sas.models.BaseComponent import BaseComponent
from sas.models.SphereSLDModel import SphereSLDModel
from copy import deepcopy
func_list = {'Erf(|nu|*z)':0, 'RPower(z^|nu|)':1, 'LPower(z^|nu|)':2, \
'RExp(-|nu|*z)':3, 'LExp(-|nu|*z)':4}
max_nshells = 10
[docs]class SphericalSLDModel(BaseComponent):
"""
This multi-model is based on Parratt formalism and provides the capability
of changing the number of layers between 0 and 10.
"""
def __init__(self, multfactor=1):
"""
:param multfactor: number of layers in the model,
assumes 0<= n_shells <=10.
"""
BaseComponent.__init__(self)
## Setting model name model description
self.description = ""
model = SphereSLDModel()
self.model = model
self.name = "SphericalSLDModel"
self.description = model.description
self.n_shells = multfactor
## Define parameters
self.params = {}
## Parameter details [units, min, max]
self.details = {}
# non-fittable parameters
self.non_fittable = model.non_fittable
# list of function in order of the function number
self.fun_list = self._get_func_list()
## dispersion
self._set_dispersion()
## Define parameters
self._set_params()
## Parameter details [units, min, max]
self._set_details()
#list of parameter that can be fitted
self._set_fixed_params()
self.model.params['n_shells'] = self.n_shells
## functional multiplicity info of the model
# [int(maximum no. of functionality),"str(Titl),
# [str(name of function0),...], [str(x-asix name of sld),...]]
self.multiplicity_info = [max_nshells, "No. of Shells:", [], ['Radius']]
def _clone(self, obj):
"""
Internal utility function to copy the internal
data members to a fresh copy.
"""
obj.params = deepcopy(self.params)
obj.non_fittable = deepcopy(self.non_fittable)
obj.description = deepcopy(self.description)
obj.details = deepcopy(self.details)
obj.dispersion = deepcopy(self.dispersion)
obj.model = self.model.clone()
return obj
def _set_dispersion(self):
"""
model dispersions
"""
##set dispersion from model
for name , value in self.model.dispersion.iteritems():
nshell = -1
if name.split('_')[0] == 'thick':
while nshell < 1:
nshell += 1
if name.split('_')[1] == 'inter%s' % str(nshell):
self.dispersion[name] = value
else:
continue
else:
self.dispersion[name] = value
def _set_params(self):
"""
Concatenate the parameters of the model to create
this model parameters
"""
# rearrange the parameters for the given # of shells
for name , value in self.model.params.iteritems():
n = 0
pos = len(name.split('_'))-1
first_name = name.split('_')[0]
last_name = name.split('_')[pos]
if first_name == 'npts':
self.params[name] = value
continue
elif first_name == 'func':
n = -1
while n < self.n_shells:
n += 1
if last_name == 'inter%s' % str(n):
self.params[name] = value
continue
elif last_name[0:5] == 'inter':
n = -1
while n < self.n_shells:
n += 1
if last_name == 'inter%s' % str(n):
self.params[name] = value
continue
elif last_name[0:4] == 'flat':
while n < self.n_shells:
n += 1
if last_name == 'flat%s' % str(n):
self.params[name] = value
continue
elif name == 'n_shells':
continue
else:
self.params[name] = value
self.model.params['n_shells'] = self.n_shells
# set constrained values for the original model params
self._set_xtra_model_param()
def _set_details(self):
"""
Concatenate details of the original model to create
this model details
"""
for name, detail in self.model.details.iteritems():
if name in self.params.iterkeys():
self.details[name] = detail
def _set_xtra_model_param(self):
"""
Set params of original model that are hidden from this model
"""
# look for the model parameters that are not in param list
for key in self.model.params.iterkeys():
if key not in self.params.keys():
if key.split('_')[0] == 'thick':
self.model.setParam(key, 0)
continue
if key.split('_')[0] == 'func':
self.model.setParam(key, 0)
continue
for nshell in range(self.n_shells, max_nshells):
if key.split('_')[1] == 'flat%s' % str(nshell+1):
try:
if key.split('_')[0] == 'sld':
value = self.model.params['sld_solv']
self.model.setParam(key, value)
except:
raise RuntimeError, "SphericalSLD model problem"
def _get_func_list(self):
"""
Get the list of functions in each layer (shell)
"""
return func_list
[docs] def getProfile(self):
"""
Get SLD profile
: return: (z, beta) where z is a list of depth of the transition points
beta is a list of the corresponding SLD values
"""
# max_pts for each layers
n_sub = int(self.params['npts_inter'])
z = []
beta = []
z0 = 0
# two sld points for core
z.append(0)
beta.append(self.params['sld_core0'])
z.append(self.params['rad_core0'])
beta.append(self.params['sld_core0'])
z0 += self.params['rad_core0']
# for layers from the core
for i in range(1, self.n_shells+2):
dz = self.params['thick_inter%s' % str(i-1)]/n_sub
# j=0 for interface, j=1 for flat layer
for j in range(0, 2):
# interation for sub-layers
for n_s in range(0, n_sub+1):
if j == 1:
if i == self.n_shells+1:
break
# shift half sub thickness for the first point
z0 -= dz#/2.0
z.append(z0)
#z0 -= dz/2.0
z0 += self.params['thick_flat%s' % str(i)]
sld_i = self.params['sld_flat%s' % str(i)]
beta.append(self.params['sld_flat%s' % str(i)])
dz = 0
else:
nu = self.params['nu_inter%s' % str(i-1)]
# decide which sld is which, sld_r or sld_l
if i == 1:
sld_l = self.params['sld_core0']
else:
sld_l = self.params['sld_flat%s' % str(i-1)]
if i == self.n_shells+1:
sld_r = self.params['sld_solv']
else:
sld_r = self.params['sld_flat%s' % str(i)]
# get function type
func_idx = self.params['func_inter%s' % str(i-1)]
# calculate the sld
sld_i = self._get_sld(func_idx, n_sub, n_s, nu,
sld_l, sld_r)
# append to the list
z.append(z0)
beta.append(sld_i)
z0 += dz
if j == 1:
break
# put sld of solvent
z.append(z0)
beta.append(self.params['sld_solv'])
z_ext = z0/5.0
z.append(z0+z_ext)
beta.append(self.params['sld_solv'])
# return sld profile (r, beta)
return z, beta
def _get_sld(self, func_idx, n_sub, n_s, nu, sld_l, sld_r):
"""
Get the function asked to build sld profile
: param func_idx: func type number
: param n_sub: total number of sub_layer
: param n_s: index of sub_layer
: param nu: coefficient of the function
: param sld_l: sld on the left side
: param sld_r: sld on the right side
: return: sld value, float
"""
from sas.models.SLDCalFunc import SLDCalFunc
# sld_cal init
sld_cal = SLDCalFunc()
# set params
sld_cal.setParam('fun_type', func_idx)
sld_cal.setParam('npts_inter', n_sub)
sld_cal.setParam('shell_num', n_s)
sld_cal.setParam('nu_inter', nu)
sld_cal.setParam('sld_left', sld_l)
sld_cal.setParam('sld_right', sld_r)
# return sld value
return sld_cal.run()
[docs] 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 new model
self._setParamHelper(name, value)
## setParam to model
if name == 'sld_solv':
# the sld_*** model.params not in params must set to
# value of sld_solv
for key in self.model.params.iterkeys():
if key not in self.params.keys() and key.split('_')[0] == 'sld':
self.model.setParam(key, value)
self.model.setParam(name, value)
def _setParamHelper(self, name, value):
"""
Helper function to setParam
"""
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
# 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 model fixed list
"""
for item in self.model.fixed:
if item.split('.')[0] in self.params.keys():
self.fixed.append(item)
self.fixed.sort()
[docs] def run(self, x = 0.0):
"""
Evaluate the model
:param x: input q, or [q,phi]
:return: scattering function P(q)
"""
return self.model.run(x)
[docs] 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
"""
return self.model.runXY(x)
## Now (May27,10) directly uses the model eval function
## instead of the for-loop in Base Component.
[docs] 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
return self.model.evalDistribution(x)
[docs] def calculate_ER(self):
"""
"""
return self.model.calculate_ER()
[docs] 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
if parameter in self.model.dispersion.keys():
value = self.model.set_dispersion(parameter, dispersion)
self._set_dispersion()
return value