| 1 | """ |
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| 2 | Spherical SLD model |
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| 3 | """ |
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| 4 | from sans.models.BaseComponent import BaseComponent |
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| 5 | from sans.models.SphereSLDModel import SphereSLDModel |
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| 6 | from copy import deepcopy |
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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 | |
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| 11 | class SphericalSLDModel(BaseComponent): |
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| 12 | """ |
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| 13 | This multi-model is based on Parratt formalism and provides the capability |
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| 14 | of changing the number of layers between 0 and 10. |
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| 15 | """ |
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| 16 | def __init__(self, multfactor=1): |
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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 | BaseComponent.__init__(self) |
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| 22 | |
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| 23 | ## Setting model name model description |
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| 24 | self.description = "" |
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| 25 | model = SphereSLDModel() |
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| 26 | self.model = model |
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| 27 | self.name = "SphericalSLDModel" |
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| 28 | self.description = model.description |
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| 29 | self.n_shells = multfactor |
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| 30 | ## Define parameters |
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| 31 | self.params = {} |
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| 32 | |
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| 33 | ## Parameter details [units, min, max] |
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| 34 | self.details = {} |
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| 35 | |
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| 36 | # non-fittable parameters |
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| 37 | self.non_fittable = model.non_fittable |
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| 38 | |
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| 39 | # list of function in order of the function number |
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| 40 | self.fun_list = self._get_func_list() |
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| 41 | ## dispersion |
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| 42 | self._set_dispersion() |
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| 43 | ## Define parameters |
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| 44 | self._set_params() |
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| 45 | |
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| 46 | ## Parameter details [units, min, max] |
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| 47 | self._set_details() |
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| 48 | |
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| 49 | #list of parameter that can be fitted |
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| 50 | self._set_fixed_params() |
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| 51 | self.model.params['n_shells'] = self.n_shells |
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| 52 | |
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| 53 | ## functional multiplicity info of the model |
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| 54 | # [int(maximum no. of functionality),"str(Titl), |
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| 55 | # [str(name of function0),...], [str(x-asix name of sld),...]] |
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| 56 | self.multiplicity_info = [max_nshells, "No. of Shells:", [], ['Radius']] |
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| 57 | |
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| 58 | |
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| 59 | def _clone(self, obj): |
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| 60 | """ |
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| 61 | Internal utility function to copy the internal |
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| 62 | data members to a fresh copy. |
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| 63 | """ |
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| 64 | obj.params = deepcopy(self.params) |
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| 65 | obj.non_fittable = deepcopy(self.non_fittable) |
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| 66 | obj.description = deepcopy(self.description) |
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| 67 | obj.details = deepcopy(self.details) |
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| 68 | obj.dispersion = deepcopy(self.dispersion) |
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| 69 | obj.model = self.model.clone() |
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| 70 | |
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| 71 | return obj |
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| 72 | |
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| 73 | |
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| 74 | def _set_dispersion(self): |
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| 75 | """ |
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| 76 | model dispersions |
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| 77 | """ |
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| 78 | ##set dispersion from model |
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| 79 | for name , value in self.model.dispersion.iteritems(): |
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| 80 | |
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| 81 | nshell = -1 |
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| 82 | if name.split('_')[0] == 'thick': |
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| 83 | while nshell < 1: |
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| 84 | nshell += 1 |
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| 85 | if name.split('_')[1] == 'inter%s' % str(nshell): |
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| 86 | self.dispersion[name] = value |
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| 87 | else: |
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| 88 | continue |
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| 89 | else: |
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| 90 | self.dispersion[name] = value |
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| 91 | |
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| 92 | def _set_params(self): |
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| 93 | """ |
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| 94 | Concatenate the parameters of the model to create |
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| 95 | this model parameters |
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| 96 | """ |
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| 97 | # rearrange the parameters for the given # of shells |
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| 98 | for name , value in self.model.params.iteritems(): |
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| 99 | n = 0 |
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| 100 | pos = len(name.split('_'))-1 |
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| 101 | first_name = name.split('_')[0] |
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| 102 | last_name = name.split('_')[pos] |
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| 103 | if first_name == 'npts': |
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| 104 | self.params[name] = value |
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| 105 | continue |
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| 106 | elif first_name == 'func': |
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| 107 | n = -1 |
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| 108 | while n < self.n_shells: |
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| 109 | n += 1 |
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| 110 | if last_name == 'inter%s' % str(n): |
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| 111 | self.params[name] = value |
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| 112 | continue |
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| 113 | elif last_name[0:5] == 'inter': |
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| 114 | n = -1 |
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| 115 | while n < self.n_shells: |
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| 116 | n += 1 |
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| 117 | if last_name == 'inter%s' % str(n): |
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| 118 | self.params[name] = value |
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| 119 | continue |
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| 120 | elif last_name[0:4] == 'flat': |
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| 121 | while n < self.n_shells: |
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| 122 | n += 1 |
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| 123 | if last_name == 'flat%s' % str(n): |
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| 124 | self.params[name] = value |
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| 125 | continue |
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| 126 | elif name == 'n_shells': |
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| 127 | continue |
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| 128 | else: |
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| 129 | self.params[name] = value |
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| 130 | |
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| 131 | self.model.params['n_shells'] = self.n_shells |
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| 132 | |
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| 133 | # set constrained values for the original model params |
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| 134 | self._set_xtra_model_param() |
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| 135 | |
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| 136 | def _set_details(self): |
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| 137 | """ |
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| 138 | Concatenate details of the original model to create |
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| 139 | this model details |
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| 140 | """ |
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| 141 | for name, detail in self.model.details.iteritems(): |
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| 142 | if name in self.params.iterkeys(): |
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| 143 | self.details[name] = detail |
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| 144 | |
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| 145 | |
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| 146 | def _set_xtra_model_param(self): |
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| 147 | """ |
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| 148 | Set params of original model that are hidden from this model |
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| 149 | """ |
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| 150 | # look for the model parameters that are not in param list |
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| 151 | for key in self.model.params.iterkeys(): |
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| 152 | if key not in self.params.keys(): |
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| 153 | if key.split('_')[0] == 'thick': |
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| 154 | self.model.setParam(key, 0) |
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| 155 | continue |
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| 156 | if key.split('_')[0] == 'func': |
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| 157 | self.model.setParam(key, 0) |
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| 158 | continue |
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| 159 | |
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| 160 | for nshell in range(self.n_shells, max_nshells): |
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| 161 | if key.split('_')[1] == 'flat%s' % str(nshell+1): |
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| 162 | try: |
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| 163 | if key.split('_')[0] == 'sld': |
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| 164 | value = self.model.params['sld_solv'] |
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| 165 | self.model.setParam(key, value) |
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| 166 | except: |
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| 167 | raise RuntimeError, "SphericalSLD model problem" |
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| 168 | |
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| 169 | def _get_func_list(self): |
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| 170 | """ |
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| 171 | Get the list of functions in each layer (shell) |
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| 172 | """ |
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| 173 | return func_list |
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| 174 | |
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| 175 | def getProfile(self): |
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| 176 | """ |
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| 177 | Get SLD profile |
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| 178 | |
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| 179 | : return: (z, beta) where z is a list of depth of the transition points |
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| 180 | beta is a list of the corresponding SLD values |
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| 181 | """ |
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| 182 | # max_pts for each layers |
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| 183 | n_sub = int(self.params['npts_inter']) |
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| 184 | z = [] |
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| 185 | beta = [] |
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| 186 | z0 = 0 |
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| 187 | # two sld points for core |
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| 188 | z.append(0) |
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| 189 | beta.append(self.params['sld_core0']) |
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| 190 | z.append(self.params['rad_core0']) |
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| 191 | beta.append(self.params['sld_core0']) |
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| 192 | z0 += self.params['rad_core0'] |
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| 193 | # for layers from the core |
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| 194 | for i in range(1, self.n_shells+2): |
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| 195 | dz = self.params['thick_inter%s' % str(i-1)]/n_sub |
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| 196 | # j=0 for interface, j=1 for flat layer |
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| 197 | for j in range(0, 2): |
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| 198 | # interation for sub-layers |
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| 199 | for n_s in range(0, n_sub+1): |
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| 200 | if j == 1: |
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| 201 | if i == self.n_shells+1: |
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| 202 | break |
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| 203 | # shift half sub thickness for the first point |
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| 204 | z0 -= dz#/2.0 |
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| 205 | z.append(z0) |
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| 206 | #z0 -= dz/2.0 |
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| 207 | z0 += self.params['thick_flat%s' % str(i)] |
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| 208 | |
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| 209 | sld_i = self.params['sld_flat%s' % str(i)] |
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| 210 | beta.append(self.params['sld_flat%s' % str(i)]) |
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| 211 | dz = 0 |
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| 212 | else: |
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| 213 | nu = self.params['nu_inter%s' % str(i-1)] |
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| 214 | # decide which sld is which, sld_r or sld_l |
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| 215 | if i == 1: |
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| 216 | sld_l = self.params['sld_core0'] |
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| 217 | else: |
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| 218 | sld_l = self.params['sld_flat%s' % str(i-1)] |
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| 219 | if i == self.n_shells+1: |
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| 220 | sld_r = self.params['sld_solv'] |
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| 221 | else: |
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| 222 | sld_r = self.params['sld_flat%s' % str(i)] |
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| 223 | # get function type |
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| 224 | func_idx = self.params['func_inter%s' % str(i-1)] |
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| 225 | # calculate the sld |
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| 226 | sld_i = self._get_sld(func_idx, n_sub, n_s, nu, |
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| 227 | sld_l, sld_r) |
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| 228 | # append to the list |
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| 229 | z.append(z0) |
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| 230 | beta.append(sld_i) |
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| 231 | z0 += dz |
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| 232 | if j == 1: |
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| 233 | break |
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| 234 | # put sld of solvent |
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| 235 | z.append(z0) |
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| 236 | beta.append(self.params['sld_solv']) |
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| 237 | z_ext = z0/5.0 |
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| 238 | z.append(z0+z_ext) |
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| 239 | beta.append(self.params['sld_solv']) |
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| 240 | # return sld profile (r, beta) |
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| 241 | return z, beta |
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| 242 | |
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| 243 | def _get_sld(self, func_idx, n_sub, n_s, nu, sld_l, sld_r): |
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| 244 | """ |
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| 245 | Get the function asked to build sld profile |
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| 246 | : param func_idx: func type number |
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| 247 | : param n_sub: total number of sub_layer |
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| 248 | : param n_s: index of sub_layer |
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| 249 | : param nu: coefficient of the function |
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| 250 | : param sld_l: sld on the left side |
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| 251 | : param sld_r: sld on the right side |
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| 252 | : return: sld value, float |
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| 253 | """ |
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| 254 | from sans.models.SLDCalFunc import SLDCalFunc |
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| 255 | # sld_cal init |
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| 256 | sld_cal = SLDCalFunc() |
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| 257 | # set params |
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| 258 | sld_cal.setParam('fun_type', func_idx) |
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| 259 | sld_cal.setParam('npts_inter', n_sub) |
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| 260 | sld_cal.setParam('shell_num', n_s) |
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| 261 | sld_cal.setParam('nu_inter', nu) |
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| 262 | sld_cal.setParam('sld_left', sld_l) |
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| 263 | sld_cal.setParam('sld_right', sld_r) |
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| 264 | # return sld value |
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| 265 | return sld_cal.run() |
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| 266 | |
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| 267 | def setParam(self, name, value): |
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| 268 | """ |
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| 269 | Set the value of a model parameter |
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| 270 | |
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| 271 | : param name: name of the parameter |
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| 272 | : param value: value of the parameter |
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| 273 | """ |
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| 274 | # set param to new model |
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| 275 | self._setParamHelper(name, value) |
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| 276 | |
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| 277 | ## setParam to model |
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| 278 | if name == 'sld_solv': |
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| 279 | # the sld_*** model.params not in params must set to |
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| 280 | # value of sld_solv |
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| 281 | for key in self.model.params.iterkeys(): |
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| 282 | if key not in self.params.keys() and key.split('_')[0] == 'sld': |
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| 283 | self.model.setParam(key, value) |
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| 284 | |
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| 285 | self.model.setParam(name, value) |
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| 286 | |
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| 287 | def _setParamHelper(self, name, value): |
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| 288 | """ |
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| 289 | Helper function to setParam |
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| 290 | """ |
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| 291 | toks = name.split('.') |
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| 292 | if len(toks) == 2: |
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| 293 | for item in self.dispersion.keys(): |
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| 294 | if item.lower() == toks[0].lower(): |
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| 295 | for par in self.dispersion[item]: |
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| 296 | if par.lower() == toks[1].lower(): |
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| 297 | self.dispersion[item][par] = value |
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| 298 | return |
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| 299 | # Look for standard parameter |
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| 300 | for item in self.params.keys(): |
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| 301 | if item.lower() == name.lower(): |
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| 302 | self.params[item] = value |
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| 303 | return |
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| 304 | |
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| 305 | raise ValueError, "Model does not contain parameter %s" % name |
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| 306 | |
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| 307 | |
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| 308 | def _set_fixed_params(self): |
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| 309 | """ |
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| 310 | Fill the self.fixed list with the model fixed list |
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| 311 | """ |
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| 312 | for item in self.model.fixed: |
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| 313 | if item.split('.')[0] in self.params.keys(): |
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| 314 | self.fixed.append(item) |
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| 315 | |
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| 316 | self.fixed.sort() |
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| 317 | |
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| 318 | def run(self, x = 0.0): |
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| 319 | """ |
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| 320 | Evaluate the model |
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| 321 | |
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| 322 | :param x: input q, or [q,phi] |
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| 323 | |
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| 324 | :return: scattering function P(q) |
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| 325 | |
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| 326 | """ |
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| 327 | |
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| 328 | return self.model.run(x) |
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| 329 | |
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| 330 | def runXY(self, x = 0.0): |
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| 331 | """ |
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| 332 | Evaluate the model |
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| 333 | |
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| 334 | : param x: input q-value (float or [float, float] as [qx, qy]) |
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| 335 | : return: scattering function value |
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| 336 | """ |
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| 337 | |
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| 338 | return self.model.runXY(x) |
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| 339 | |
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| 340 | ## Now (May27,10) directly uses the model eval function |
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| 341 | ## instead of the for-loop in Base Component. |
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| 342 | def evalDistribution(self, x): |
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| 343 | """ |
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| 344 | Evaluate the model in cartesian coordinates |
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| 345 | |
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| 346 | : param x: input q[], or [qx[], qy[]] |
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| 347 | : return: scattering function P(q[]) |
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| 348 | """ |
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| 349 | # set effective radius and scaling factor before run |
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| 350 | return self.model.evalDistribution(x) |
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| 351 | |
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| 352 | def calculate_ER(self): |
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| 353 | """ |
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| 354 | """ |
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| 355 | return self.model.calculate_ER() |
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| 356 | |
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| 357 | def set_dispersion(self, parameter, dispersion): |
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| 358 | """ |
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| 359 | Set the dispersion object for a model parameter |
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| 360 | |
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| 361 | : param parameter: name of the parameter [string] |
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| 362 | :dispersion: dispersion object of type DispersionModel |
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| 363 | """ |
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| 364 | value = None |
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| 365 | if parameter in self.model.dispersion.keys(): |
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| 366 | value = self.model.set_dispersion(parameter, dispersion) |
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| 367 | self._set_dispersion() |
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| 368 | return value |
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