from sas.models.BaseComponent import BaseComponent from sas.models.ReflAdvModel import ReflAdvModel from copy import deepcopy from math import floor from math import fabs 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 class ReflectivityIIModel(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_layers <=10. """ BaseComponent.__init__(self) ## Setting model name model description self.description = "" model = ReflAdvModel() self.model = model self.name = "ReflectivityIIModel" 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 first_name = name.split('_')[0] last_name = name.split('_')[pos] if first_name == 'npts': self.params[name]=value continue elif first_name == 'sldIM': continue elif first_name == 'func': n = -1 while n < self.n_layers: n += 1 if last_name == 'inter%s' % str(n): self.params[name] = value continue #continue elif last_name[0:5] == 'inter': n = -1 while n < self.n_layers: n += 1 if last_name == 'inter%s' % str(n): self.params[name] = value continue elif last_name[0:4] == 'flat': while n < self.n_layers: n += 1 if last_name == '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: message = "ReflectivityIIModel evaluation problem" raise RuntimeError, message def _get_func_list(self): """ Get the list of functions in each layer (shell) """ return func_list 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 = [] z.append(0) beta.append(self.params['sld_bottom0']) z0 = 0.0 dz = 0.0 # for layers from the top for n_lyr in range(1, self.n_layers+2): i = n_lyr # 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): # for flat layer 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) sld_i = self.params['sld_flat%s' % str(i)] beta.append(sld_i) dz = self.params['thick_flat%s' % str(i)] z0 += dz else: dz = self.params['thick_inter%s' % str(i-1)]/n_sub nu = fabs(self.params['nu_inter%s' % str(i-1)]) if n_s == 0: # shift half sub thickness for the point z0 += dz/2.0 # decide which sld is which, sld_r or sld_l 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)] if sld_r == sld_l: sld_i = sld_r else: func_idx = self.params['func_inter%s' % str(i-1)] # calculate the sld sld_i = self._get_sld(func_idx, n_sub, n_s+0.5, nu, sld_l, sld_r) # append to the list z.append(z0) beta.append(sld_i) if j == 1: break else: z0 += dz # put substrate and superstrate profile z.append(z0) beta.append(self.params['sld_medium']) z_ext = z0/5.0 # 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']) # rearrange the profile for NR sld profile style z = [z0 - x for x in z] z.reverse() beta.reverse() 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() 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 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) 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. 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) def calculate_ER(self): """ """ return self.model.calculate_ER() 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