Source code for sas.models.ReflectivityModel

   
from sas.models.BaseComponent import BaseComponent
from sas.models.ReflModel import ReflModel
from copy import deepcopy
from math import floor
from scipy.special import erf
func_list = {'Erf':0, 'Linear':1}
max_nshells = 10

[docs]class ReflectivityModel(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 = ReflModel() self.model = model self.name = "ReflectivityModel" self.description = model.description self.n_layers = int(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_layers'] = self.n_layers ## 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 Layers:", [], ['Depth']] ## independent parameter name and unit [string] self.input_name = "Q" self.input_unit = "A^{-1}" ## output name and unit [string] self.output_name = "Reflectivity" self.output_unit = "" 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 self.dispersion = {} 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 if name.split('_')[0] == 'sldIM': continue elif name.split('_')[0] == 'func': n = -1 while n < self.n_layers: n += 1 if name.split('_')[pos] == 'inter%s' % str(n): self.params[name] = value continue #continue elif name.split('_')[pos][0:5] == 'inter': n = -1 while n < self.n_layers: n += 1 if name.split('_')[pos] == 'inter%s' % str(n): self.params[name] = value continue elif name.split('_')[pos][0:4] == 'flat': while n < self.n_layers: n += 1 if name.split('_')[pos] == 'flat%s' % str(n): self.params[name] = value continue elif name == 'n_layers': continue else: self.params[name] = value self.model.params['n_layers'] = self.n_layers # 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_layers,max_nshells): if key.split('_')[1] == 'flat%s' % str(nshell+1): try: if key.split('_')[0] == 'sld': value = self.model.params['sld_medium'] elif key.split('_')[0] == 'sldIM': value = self.model.params['sldIM_medium'] self.model.setParam(key, value) except: raise RuntimeError, "ReflectivityModel problem" def _get_func_list(self): """ Get the list of functions in each layer (shell) """ #func_list = {} 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 = 21 z = [] beta = [] sub_range = int(floor(n_sub/2.0)) z.append(0) beta.append(self.params['sld_bottom0']) z0 = 0 # for layers from the top for n in range(1, self.n_layers+2): i = n for j in range(0, 2): for n_s in range(-sub_range, sub_range+1): dz = self.params['thick_inter%s' % str(i-1)]/n_sub if j == 1: if i == self.n_layers+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)]) else: if n_s == -sub_range: # shift half sub thickness for the first point z0 -= dz/2.0 #exec "dz = self.params['thick_inter[%s-1]'% str(i)]/9" #print "%d = %g \n"% (i,self.params['thick_inter3']) z0 += dz if i == 1: sld_l = self.params['sld_bottom0'] else: sld_l = self.params['sld_flat%s' % str(i-1)] if i == self.n_layers+1: sld_r = self.params['sld_medium'] else: sld_r = self.params['sld_flat%s' % str(i)] func_idx = self.params['func_inter%s' % str(i-1)] func = self._get_func(n_s, n_sub, func_idx) if sld_r > sld_l: sld_i = (sld_r-sld_l)*func+sld_l elif sld_r < sld_l: sld_i = (sld_l-sld_r)*(1-func)+sld_r else: sld_i = sld_r z.append(z0) beta.append(sld_i) if j == 1: break # put substrate and superstrate profile # shift half sub thickness for the first point z0 += dz/2.0 z.append(z0) beta.append(self.params['sld_medium']) z_ext = z0/6.0 # put the extra points for the substrate # and superstrate z.append(z0+z_ext) beta.append(self.params['sld_medium']) z.insert(0, -z_ext) beta.insert(0, self.params['sld_bottom0']) z = [z0 - x for x in z] z.reverse() beta.reverse() return z, beta
def _get_func(self, index, n_sub, func_idx): """ Get the function asked to buil sld profile : param index: index of sub_layer : param n_sub: total number of sub_layer : param func_idx: an integer to identify a function : return out: the output from the function, float """ # cal bin_size bin_size = 1.0/n_sub # erf if func_idx == 0: out = erf(index/(n_sub/5.0))/2.0 + 0.5 return out else: index += 0.5 # linear if func_idx == 1: out = ((index + floor(n_sub/2.0))*bin_size) # r_parabolic elif func_idx == 2: out = ((index + floor(n_sub/2.0))*bin_size)* \ ((index + floor(n_sub/2.0))*bin_size) # l_parabolic elif func_idx == 3: out = 1.0-(((index + floor(n_sub/2.0))*bin_size) - 1.0) *\ (((index + floor(n_sub/2.0))*bin_size) - 1.0) # r_cubic elif func_idx == 4: out = ((index + floor(n_sub/2.0))*bin_size)* \ ((index + floor(n_sub/2.0))*bin_size)* \ ((index + floor(n_sub/2.0))*bin_size) # l_cubic elif func_idx == 5: out = 1.0+(((index + floor(n_sub/2.0)))*bin_size - 1.0) *\ (((index + floor(n_sub/2.0)))*bin_size - 1.0) *\ (((index + floor(n_sub/2.0)))*bin_size - 1.0) # return output return out
[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_medium': # 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 """ # 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 """ pass
[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 """ pass