| 1 | |
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| 2 | from sans.models.BaseComponent import BaseComponent |
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| 3 | from sans.models.SphereSLDModel import SphereSLDModel |
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| 4 | from copy import deepcopy |
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| 5 | from math import floor |
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| 6 | #from scipy.special import erf |
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| 7 | func_list = {'Erf(|nu|*z)':0, 'RPower(z^|nu|)':1, 'LPower(z^|nu|)':2, \ |
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| 8 | 'RExp(-|nu|*z)':3, 'LExp(-|nu|*z)':4} |
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| 9 | max_nshells = 10 |
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| 10 | class SphericalSLDModel(BaseComponent): |
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| 11 | """ |
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| 12 | This multi-model is based on Parratt formalism and provides the capability |
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| 13 | of changing the number of layers between 0 and 10. |
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| 14 | """ |
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| 15 | def __init__(self, multfactor=1): |
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| 16 | BaseComponent.__init__(self) |
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| 17 | """ |
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| 18 | :param multfactor: number of layers in the model, |
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| 19 | assumes 0<= n_shells <=10. |
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| 20 | """ |
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| 21 | |
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| 22 | ## Setting model name model description |
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| 23 | self.description="" |
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| 24 | model = SphereSLDModel() |
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| 25 | self.model = model |
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| 26 | self.name = "SphericalSLDModel" |
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| 27 | self.description=model.description |
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| 28 | self.n_shells = multfactor |
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| 29 | ## Define parameters |
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| 30 | self.params = {} |
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| 31 | |
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| 32 | ## Parameter details [units, min, max] |
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| 33 | self.details = {} |
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| 34 | |
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| 35 | # non-fittable parameters |
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| 36 | self.non_fittable = model.non_fittable |
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| 37 | |
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| 38 | # list of function in order of the function number |
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| 39 | self.fun_list = self._get_func_list() |
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| 40 | ## dispersion |
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| 41 | self._set_dispersion() |
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| 42 | ## Define parameters |
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| 43 | self._set_params() |
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| 44 | |
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| 45 | ## Parameter details [units, min, max] |
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| 46 | self._set_details() |
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| 47 | |
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| 48 | #list of parameter that can be fitted |
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| 49 | self._set_fixed_params() |
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| 50 | self.model.params['n_shells'] = self.n_shells |
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| 51 | |
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| 52 | ## functional multiplicity info of the model |
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| 53 | # [int(maximum no. of functionality),"str(Titl), |
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| 54 | # [str(name of function0),...], [str(x-asix name of sld),...]] |
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| 55 | self.multiplicity_info = [max_nshells,"No. of Shells:",[],['Radius']] |
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| 56 | |
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| 57 | |
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| 58 | def _clone(self, obj): |
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| 59 | """ |
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| 60 | Internal utility function to copy the internal |
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| 61 | data members to a fresh copy. |
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| 62 | """ |
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| 63 | obj.params = deepcopy(self.params) |
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| 64 | obj.non_fittable = deepcopy(self.non_fittable) |
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| 65 | obj.description = deepcopy(self.description) |
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| 66 | obj.details = deepcopy(self.details) |
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| 67 | obj.dispersion = deepcopy(self.dispersion) |
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| 68 | obj.model = self.model.clone() |
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| 69 | |
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| 70 | return obj |
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| 71 | |
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| 72 | |
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| 73 | def _set_dispersion(self): |
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| 74 | """ |
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| 75 | model dispersions |
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| 76 | """ |
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| 77 | ##set dispersion from model |
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| 78 | self.dispersion = {} |
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| 79 | |
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| 80 | |
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| 81 | def _set_params(self): |
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| 82 | """ |
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| 83 | Concatenate the parameters of the model to create |
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| 84 | this model parameters |
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| 85 | """ |
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| 86 | # rearrange the parameters for the given # of shells |
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| 87 | for name , value in self.model.params.iteritems(): |
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| 88 | n = 0 |
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| 89 | pos = len(name.split('_'))-1 |
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| 90 | first_name = name.split('_')[0] |
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| 91 | last_name = name.split('_')[pos] |
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| 92 | if first_name == 'npts': |
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| 93 | self.params[name]=value |
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| 94 | continue |
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| 95 | elif first_name == 'func': |
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| 96 | n= -1 |
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| 97 | while n<self.n_shells: |
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| 98 | n += 1 |
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| 99 | if last_name == 'inter%s' % str(n): |
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| 100 | self.params[name]=value |
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| 101 | continue |
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| 102 | elif last_name[0:5] == 'inter': |
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| 103 | n= -1 |
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| 104 | while n<self.n_shells: |
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| 105 | n += 1 |
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| 106 | if last_name == 'inter%s' % str(n): |
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| 107 | self.params[name]= value |
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| 108 | continue |
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| 109 | elif last_name[0:4] == 'flat': |
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| 110 | while n<self.n_shells: |
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| 111 | n += 1 |
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| 112 | if last_name == 'flat%s' % str(n): |
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| 113 | self.params[name]= value |
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| 114 | continue |
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| 115 | elif name == 'n_shells': |
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| 116 | continue |
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| 117 | else: |
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| 118 | self.params[name]= value |
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| 119 | |
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| 120 | self.model.params['n_shells'] = self.n_shells |
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| 121 | |
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| 122 | # set constrained values for the original model params |
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| 123 | self._set_xtra_model_param() |
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| 124 | |
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| 125 | def _set_details(self): |
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| 126 | """ |
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| 127 | Concatenate details of the original model to create |
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| 128 | this model details |
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| 129 | """ |
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| 130 | for name ,detail in self.model.details.iteritems(): |
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| 131 | if name in self.params.iterkeys(): |
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| 132 | self.details[name]= detail |
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| 133 | |
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| 134 | |
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| 135 | def _set_xtra_model_param(self): |
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| 136 | """ |
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| 137 | Set params of original model that are hidden from this model |
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| 138 | """ |
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| 139 | # look for the model parameters that are not in param list |
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| 140 | for key in self.model.params.iterkeys(): |
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| 141 | if key not in self.params.keys(): |
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| 142 | if key.split('_')[0] == 'thick': |
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| 143 | self.model.setParam(key, 0) |
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| 144 | continue |
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| 145 | if key.split('_')[0] == 'func': |
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| 146 | self.model.setParam(key, 0) |
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| 147 | continue |
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| 148 | |
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| 149 | for nshell in range(self.n_shells,max_nshells): |
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| 150 | if key.split('_')[1] == 'flat%s' % str(nshell+1): |
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| 151 | try: |
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| 152 | if key.split('_')[0] == 'sld': |
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| 153 | value = self.model.params['sld_solv'] |
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| 154 | self.model.setParam(key, value) |
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| 155 | except: pass |
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| 156 | |
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| 157 | def _get_func_list(self): |
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| 158 | """ |
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| 159 | Get the list of functions in each layer (shell) |
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| 160 | """ |
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| 161 | #func_list = {} |
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| 162 | return func_list |
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| 163 | |
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| 164 | def getProfile(self): |
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| 165 | """ |
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| 166 | Get SLD profile |
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| 167 | |
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| 168 | : return: (z, beta) where z is a list of depth of the transition points |
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| 169 | beta is a list of the corresponding SLD values |
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| 170 | """ |
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| 171 | # max_pts for each layers |
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| 172 | n_sub = self.params['npts_inter'] |
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| 173 | z = [] |
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| 174 | beta = [] |
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| 175 | z0 = 0 |
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| 176 | sub_range = floor(n_sub/2.0) |
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| 177 | # two sld points for core |
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| 178 | z.append(0) |
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| 179 | beta.append(self.params['sld_core0']) |
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| 180 | z.append(self.params['rad_core0']) |
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| 181 | beta.append(self.params['sld_core0']) |
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| 182 | z0 += self.params['rad_core0'] |
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| 183 | # for layers from the core |
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| 184 | for n in range(1,self.n_shells+2): |
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| 185 | i = n |
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| 186 | # j=0 for interface, j=1 for flat layer |
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| 187 | for j in range(0,2): |
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| 188 | # interation for sub-layers |
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| 189 | for n_s in range(0,n_sub+1): |
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| 190 | if j==1: |
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| 191 | if i==self.n_shells+1: |
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| 192 | break |
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| 193 | # shift half sub thickness for the first point |
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| 194 | z0 -= dz#/2.0 |
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| 195 | z.append(z0) |
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| 196 | #z0 -= dz/2.0 |
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| 197 | z0 += self.params['thick_flat%s'% str(i)] |
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| 198 | |
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| 199 | sld_i = self.params['sld_flat%s'% str(i)] |
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| 200 | beta.append(self.params['sld_flat%s'% str(i)]) |
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| 201 | dz = 0 |
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| 202 | else: |
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| 203 | dz = self.params['thick_inter%s'% str(i-1)]/n_sub |
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| 204 | nu = self.params['nu_inter%s'% str(i-1)] |
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| 205 | # decide which sld is which, sld_r or sld_l |
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| 206 | if i == 1: |
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| 207 | sld_l = self.params['sld_core0'] |
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| 208 | else: |
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| 209 | sld_l = self.params['sld_flat%s'% str(i-1)] |
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| 210 | if i == self.n_shells+1: |
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| 211 | sld_r = self.params['sld_solv'] |
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| 212 | else: |
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| 213 | sld_r = self.params['sld_flat%s'% str(i)] |
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| 214 | # get function type |
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| 215 | func_idx = self.params['func_inter%s'% str(i-1)] |
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| 216 | # calculate the sld |
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| 217 | sld_i = self._get_sld(func_idx, n_sub, n_s, nu, |
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| 218 | sld_l, sld_r) |
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| 219 | # append to the list |
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| 220 | z.append(z0) |
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| 221 | beta.append(sld_i) |
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| 222 | z0 += dz |
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| 223 | if j==1: break |
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| 224 | # put sld of solvent |
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| 225 | z.append(z0) |
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| 226 | beta.append(self.params['sld_solv']) |
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| 227 | z_ext = z0/5.0 |
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| 228 | z.append(z0+z_ext) |
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| 229 | beta.append(self.params['sld_solv']) |
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| 230 | # return sld profile (r, beta) |
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| 231 | return z, beta |
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| 232 | |
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| 233 | def _get_sld(self, func_idx, n_sub, n_s, nu, sld_l, sld_r): |
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| 234 | """ |
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| 235 | Get the function asked to build sld profile |
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| 236 | : param func_idx: func type number |
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| 237 | : param n_sub: total number of sub_layer |
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| 238 | : param n_s: index of sub_layer |
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| 239 | : param nu: coefficient of the function |
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| 240 | : param sld_l: sld on the left side |
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| 241 | : param sld_r: sld on the right side |
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| 242 | : return: sld value, float |
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| 243 | """ |
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| 244 | from sans.models.SLDCalFunc import SLDCalFunc |
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| 245 | # sld_cal init |
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| 246 | sld_cal = SLDCalFunc() |
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| 247 | # set params |
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| 248 | sld_cal.setParam('fun_type',func_idx) |
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| 249 | sld_cal.setParam('npts_inter',n_sub) |
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| 250 | sld_cal.setParam('shell_num',n_s) |
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| 251 | sld_cal.setParam('nu_inter',nu) |
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| 252 | sld_cal.setParam('sld_left',sld_l) |
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| 253 | sld_cal.setParam('sld_right',sld_r) |
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| 254 | # return sld value |
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| 255 | return sld_cal.run() |
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| 256 | |
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| 257 | def setParam(self, name, value): |
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| 258 | """ |
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| 259 | Set the value of a model parameter |
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| 260 | |
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| 261 | : param name: name of the parameter |
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| 262 | : param value: value of the parameter |
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| 263 | """ |
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| 264 | # set param to new model |
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| 265 | self._setParamHelper( name, value) |
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| 266 | |
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| 267 | ## setParam to model |
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| 268 | if name=='sld_solv': |
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| 269 | # the sld_*** model.params not in params must set to |
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| 270 | # value of sld_solv |
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| 271 | for key in self.model.params.iterkeys(): |
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| 272 | if key not in self.params.keys()and key.split('_')[0] == 'sld': |
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| 273 | self.model.setParam(key, value) |
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| 274 | |
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| 275 | self.model.setParam( name, value) |
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| 276 | |
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| 277 | def _setParamHelper(self, name, value): |
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| 278 | """ |
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| 279 | Helper function to setParam |
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| 280 | """ |
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| 281 | |
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| 282 | # Look for standard parameter |
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| 283 | for item in self.params.keys(): |
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| 284 | if item.lower()==name.lower(): |
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| 285 | self.params[item] = value |
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| 286 | return |
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| 287 | |
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| 288 | raise ValueError, "Model does not contain parameter %s" % name |
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| 289 | |
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| 290 | |
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| 291 | def _set_fixed_params(self): |
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| 292 | """ |
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| 293 | Fill the self.fixed list with the model fixed list |
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| 294 | """ |
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| 295 | pass |
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| 296 | |
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| 297 | def run(self, x = 0.0): |
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| 298 | """ |
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| 299 | Evaluate the model |
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| 300 | |
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| 301 | :param x: input q, or [q,phi] |
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| 302 | |
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| 303 | :return: scattering function P(q) |
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| 304 | |
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| 305 | """ |
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| 306 | |
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| 307 | return self.model.run(x) |
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| 308 | |
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| 309 | def runXY(self, x = 0.0): |
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| 310 | """ |
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| 311 | Evaluate the model |
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| 312 | |
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| 313 | : param x: input q-value (float or [float, float] as [qx, qy]) |
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| 314 | : return: scattering function value |
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| 315 | """ |
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| 316 | |
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| 317 | return self.model.runXY(x) |
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| 318 | |
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| 319 | ## Now (May27,10) directly uses the model eval function |
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| 320 | ## instead of the for-loop in Base Component. |
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| 321 | def evalDistribution(self, x = []): |
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| 322 | """ |
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| 323 | Evaluate the model in cartesian coordinates |
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| 324 | |
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| 325 | : param x: input q[], or [qx[], qy[]] |
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| 326 | : return: scattering function P(q[]) |
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| 327 | """ |
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| 328 | # set effective radius and scaling factor before run |
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| 329 | return self.model.evalDistribution(x) |
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| 330 | def calculate_ER(self): |
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| 331 | """ |
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| 332 | """ |
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| 333 | return self.model.calculate_ER() |
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| 334 | def set_dispersion(self, parameter, dispersion): |
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| 335 | """ |
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| 336 | Set the dispersion object for a model parameter |
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| 337 | |
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| 338 | : param parameter: name of the parameter [string] |
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| 339 | :dispersion: dispersion object of type DispersionModel |
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| 340 | """ |
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| 341 | pass |
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