| [8f20419d] | 1 | |
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| 2 | from sans.models.BaseComponent import BaseComponent |
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| 3 | from sans.models.ReflModel import ReflModel |
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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':0, 'Linear':1, 'RParabolic':2, \ |
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| 8 | 'LParabola':3, 'RCubic':4, 'LCubic':5} |
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| 9 | max_nshells = 10 |
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| 10 | class ReflectivityModel(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, assumes 0<= n_shells <=10. |
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| 19 | """ |
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| 20 | |
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| 21 | ## Setting model name model description |
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| 22 | self.description="" |
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| 23 | model = ReflModel() |
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| 24 | self.model = model |
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| 25 | self.name = "ReflectivityModel" |
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| 26 | self.description=model.description |
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| 27 | self.n_layers = multfactor |
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| 28 | ## Define parameters |
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| 29 | self.params = {} |
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| 30 | |
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| 31 | ## Parameter details [units, min, max] |
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| 32 | self.details = {} |
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| 33 | |
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| 34 | # non-fittable parameters |
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| 35 | self.non_fittable = model.non_fittable |
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| 36 | |
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| 37 | # list of function in order of the function number |
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| 38 | self.fun_list = self._get_func_list() |
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| 39 | ## dispersion |
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| 40 | self._set_dispersion() |
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| 41 | ## Define parameters |
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| 42 | self._set_params() |
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| 43 | |
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| 44 | ## Parameter details [units, min, max] |
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| 45 | self._set_details() |
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| 46 | |
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| 47 | #list of parameter that can be fitted |
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| 48 | self._set_fixed_params() |
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| 49 | self.model.params['n_layers'] = self.n_layers |
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| 50 | |
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| 51 | ## functional multiplicity info of the model |
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| 52 | # [int(maximum no. of functionality),"str(Titl), |
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| 53 | # [str(name of function0),...], [str(x-asix name of sld),...]] |
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| 54 | self.multiplicity_info = [max_nshells,"No. of Layers:",[],['Depth']] |
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| 55 | |
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| 56 | |
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| 57 | def _clone(self, obj): |
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| 58 | """ |
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| 59 | Internal utility function to copy the internal |
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| 60 | data members to a fresh copy. |
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| 61 | """ |
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| 62 | obj.params = deepcopy(self.params) |
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| 63 | obj.non_fittable = deepcopy(self.non_fittable) |
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| 64 | obj.description = deepcopy(self.description) |
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| 65 | obj.details = deepcopy(self.details) |
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| 66 | obj.dispersion = deepcopy(self.dispersion) |
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| 67 | obj.model = self.model.clone() |
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| 68 | |
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| 69 | return obj |
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| 70 | |
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| 71 | |
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| 72 | def _set_dispersion(self): |
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| 73 | """ |
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| 74 | model dispersions |
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| 75 | """ |
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| 76 | ##set dispersion from model |
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| 77 | self.dispersion = {} |
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| 78 | |
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| 79 | |
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| 80 | def _set_params(self): |
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| 81 | """ |
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| 82 | Concatenate the parameters of the model to create |
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| 83 | this model parameters |
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| 84 | """ |
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| 85 | # rearrange the parameters for the given # of shells |
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| 86 | for name , value in self.model.params.iteritems(): |
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| 87 | n = 0 |
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| 88 | pos = len(name.split('_'))-1 |
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| 89 | if name.split('_')[0] == 'sldIM': |
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| 90 | continue |
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| 91 | elif name.split('_')[0] == 'func': |
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| 92 | n= -1 |
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| 93 | while n<self.n_layers: |
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| 94 | n += 1 |
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| 95 | if name.split('_')[pos] == 'inter%s' % str(n): |
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| 96 | self.params[name]=value |
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| 97 | continue |
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| 98 | #continue |
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| 99 | elif name.split('_')[pos][0:5] == 'inter': |
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| 100 | n= -1 |
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| 101 | while n<self.n_layers: |
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| 102 | n += 1 |
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| 103 | if name.split('_')[pos] == 'inter%s' % str(n): |
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| 104 | self.params[name]= value |
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| 105 | continue |
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| 106 | elif name.split('_')[pos][0:4] == 'flat': |
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| 107 | while n<self.n_layers: |
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| 108 | n += 1 |
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| 109 | if name.split('_')[pos] == 'flat%s' % str(n): |
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| 110 | self.params[name]= value |
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| 111 | continue |
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| 112 | elif name == 'n_layers': |
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| 113 | continue |
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| 114 | else: |
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| 115 | self.params[name]= value |
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| 116 | |
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| 117 | self.model.params['n_layers'] = self.n_layers |
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| 118 | |
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| 119 | # set constrained values for the original model params |
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| 120 | self._set_xtra_model_param() |
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| 121 | |
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| 122 | def _set_details(self): |
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| 123 | """ |
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| 124 | Concatenate details of the original model to create |
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| 125 | this model details |
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| 126 | """ |
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| 127 | for name ,detail in self.model.details.iteritems(): |
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| 128 | if name in self.params.iterkeys(): |
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| 129 | self.details[name]= detail |
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| 130 | |
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| 131 | |
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| 132 | def _set_xtra_model_param(self): |
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| 133 | """ |
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| 134 | Set params of original model that are hidden from this model |
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| 135 | """ |
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| 136 | # look for the model parameters that are not in param list |
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| 137 | for key in self.model.params.iterkeys(): |
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| 138 | if key not in self.params.keys(): |
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| 139 | if key.split('_')[0] == 'thick': |
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| 140 | self.model.setParam(key, 0) |
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| 141 | continue |
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| 142 | if key.split('_')[0] == 'func': |
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| 143 | self.model.setParam(key, 0) |
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| 144 | continue |
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| 145 | for nshell in range(self.n_layers,max_nshells): |
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| 146 | if key.split('_')[1] == 'flat%s' % str(nshell+1): |
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| 147 | try: |
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| 148 | if key.split('_')[0] == 'sld': |
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| 149 | value = self.model.params['sld_medium'] |
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| 150 | elif key.split('_')[0] == 'sldIM': |
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| 151 | value = self.model.params['sldIM_medium'] |
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| 152 | self.model.setParam(key, value) |
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| 153 | except: pass |
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| 154 | |
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| 155 | def _get_func_list(self): |
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| 156 | """ |
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| 157 | Get the list of functions in each layer (shell) |
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| 158 | """ |
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| 159 | #func_list = {} |
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| 160 | return func_list |
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| 161 | |
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| 162 | def getProfile(self): |
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| 163 | """ |
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| 164 | Get SLD profile |
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| 165 | |
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| 166 | : return: (z, beta) where z is a list of depth of the transition points |
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| 167 | beta is a list of the corresponding SLD values |
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| 168 | """ |
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| 169 | # max_pts for each layers |
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| 170 | n_sub = 21 |
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| 171 | z = [] |
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| 172 | beta = [] |
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| 173 | sub_range = floor(n_sub/2.0) |
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| 174 | z.append(0) |
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| 175 | beta.append(self.params['sld_sub0']) |
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| 176 | |
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| 177 | z0 = 0 |
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| 178 | # for layers from the top |
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| 179 | for n in range(1,self.n_layers+2): |
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| 180 | i = n |
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| 181 | |
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| 182 | for j in range(0,2): |
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| 183 | for n_s in range(-sub_range,sub_range+1): |
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| 184 | if j==1: |
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| 185 | if i==self.n_layers+1: |
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| 186 | break |
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| 187 | # shift half sub thickness for the first point |
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| 188 | z0 += dz/2.0 |
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| 189 | z.append(z0) |
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| 190 | #z0 -= dz/2.0 |
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| 191 | z0 += self.params['thick_flat%s'% str(i)] |
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| 192 | |
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| 193 | sld_i = self.params['sld_flat%s'% str(i)] |
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| 194 | beta.append(self.params['sld_flat%s'% str(i)]) |
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| 195 | else: |
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| 196 | |
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| 197 | dz = self.params['thick_inter%s'% str(i-1)]/n_sub |
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| 198 | |
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| 199 | if n_s == -sub_range: |
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| 200 | # shift half sub thickness for the first point |
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| 201 | z0 -= dz/2.0 |
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| 202 | #exec "dz = self.params['thick_inter[%s-1]'% str(i)]/9" |
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| 203 | #print "%d = %g \n"% (i,self.params['thick_inter3']) |
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| 204 | z0 += dz |
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| 205 | |
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| 206 | if i == 1: |
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| 207 | sld_l = self.params['sld_sub0'] |
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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_layers+1: |
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| 211 | sld_r = self.params['sld_medium'] |
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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 | func_idx = self.params['func_inter%s'% str(i-1)] |
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| 215 | func = self._get_func(n_s, n_sub, func_idx) |
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| 216 | if sld_r>sld_l: |
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| 217 | sld_i = (sld_r-sld_l)*func+sld_l |
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| 218 | elif sld_r<sld_l: |
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| 219 | sld_i = (sld_l-sld_r)*(1-func)+sld_r |
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| 220 | else: |
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| 221 | sld_i = sld_r |
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| 222 | z.append(z0) |
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| 223 | beta.append(sld_i) |
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| 224 | if j==1: break |
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| 225 | # put substrate and superstrate profile |
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| 226 | # shift half sub thickness for the first point |
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| 227 | z0 += dz/2.0 |
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| 228 | z.append(z0) |
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| 229 | beta.append(self.params['sld_medium']) |
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| 230 | z_ext = z0/6.0 |
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| 231 | |
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| 232 | # put the extra points for the substrate |
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| 233 | # and superstrate |
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| 234 | z.append(z0+z_ext) |
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| 235 | beta.append(self.params['sld_medium']) |
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| 236 | z.insert(0,-z_ext) |
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| 237 | beta.insert(0,self.params['sld_sub0']) |
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| 238 | z = [z0 - x for x in z] |
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| 239 | z.reverse() |
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| 240 | beta.reverse() |
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| 241 | return z, beta |
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| 242 | |
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| 243 | def _get_func(self, index, n_sub, func_idx): |
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| 244 | """ |
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| 245 | Get the function asked to buil sld profile |
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| 246 | : param index: index of sub_layer |
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| 247 | : param n_sub: total number of sub_layer |
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| 248 | : param func_idx: an integer to identify a function |
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| 249 | |
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| 250 | : return out: the output from the function, float |
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| 251 | """ |
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| 252 | # cal bin_size |
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| 253 | bin_size = 1.0/n_sub |
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| 254 | # erf |
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| 255 | if func_idx == 0: |
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| 256 | out = erf(index/(n_sub/5.0))/2.0 + 0.5 |
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| 257 | return out |
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| 258 | else: |
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| 259 | index += 0.5 |
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| 260 | # linear |
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| 261 | if func_idx == 1: |
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| 262 | out = ((index + floor(n_sub/2.0))*bin_size) |
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| 263 | # r_parabolic |
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| 264 | elif func_idx == 2: |
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| 265 | out = ((index + floor(n_sub/2.0))*bin_size)* \ |
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| 266 | ((index + floor(n_sub/2.0))*bin_size) |
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| 267 | # l_parabolic |
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| 268 | elif func_idx == 3: |
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| 269 | out = 1.0-(((index + floor(n_sub/2.0))*bin_size) - 1.0) *\ |
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| 270 | (((index + floor(n_sub/2.0))*bin_size) - 1.0) |
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| 271 | # r_cubic |
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| 272 | elif func_idx == 4: |
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| 273 | out = ((index + floor(n_sub/2.0))*bin_size)* \ |
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| 274 | ((index + floor(n_sub/2.0))*bin_size)* \ |
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| 275 | ((index + floor(n_sub/2.0))*bin_size) |
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| 276 | # l_cubic |
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| 277 | elif func_idx == 5: |
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| 278 | out = 1.0+(((index + floor(n_sub/2.0)))*bin_size - 1.0) *\ |
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| 279 | (((index + floor(n_sub/2.0)))*bin_size - 1.0) *\ |
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| 280 | (((index + floor(n_sub/2.0)))*bin_size - 1.0) |
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| 281 | # return output |
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| 282 | return out |
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| 283 | |
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| 284 | def setParam(self, name, value): |
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| 285 | """ |
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| 286 | Set the value of a model parameter |
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| 287 | |
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| 288 | : param name: name of the parameter |
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| 289 | : param value: value of the parameter |
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| 290 | """ |
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| 291 | # set param to new model |
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| 292 | self._setParamHelper( name, value) |
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| 293 | |
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| 294 | ## setParam to model |
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| 295 | if name=='sld_medium': |
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| 296 | # the sld_*** model.params not in params must set to value of sld_solv |
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| 297 | for key in self.model.params.iterkeys(): |
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| 298 | if key not in self.params.keys()and key.split('_')[0] == 'sld': |
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| 299 | self.model.setParam(key, value) |
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| 300 | |
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| 301 | self.model.setParam( name, value) |
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| 302 | |
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| 303 | def _setParamHelper(self, name, value): |
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| 304 | """ |
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| 305 | Helper function to setParam |
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| 306 | """ |
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| 307 | |
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| 308 | # Look for standard parameter |
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| 309 | for item in self.params.keys(): |
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| 310 | if item.lower()==name.lower(): |
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| 311 | self.params[item] = value |
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| 312 | return |
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| 313 | |
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| 314 | raise ValueError, "Model does not contain parameter %s" % name |
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| 315 | |
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| 316 | |
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| 317 | def _set_fixed_params(self): |
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| 318 | """ |
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| 319 | Fill the self.fixed list with the model fixed list |
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| 320 | """ |
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| 321 | pass |
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| 322 | |
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| 323 | def run(self, x = 0.0): |
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| 324 | """ |
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| 325 | Evaluate the model |
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| 326 | |
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| 327 | :param x: input q, or [q,phi] |
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| 328 | |
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| 329 | :return: scattering function P(q) |
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| 330 | |
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| 331 | """ |
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| 332 | |
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| 333 | return self.model.run(x) |
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| 334 | |
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| 335 | def runXY(self, x = 0.0): |
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| 336 | """ |
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| 337 | Evaluate the model |
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| 338 | |
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| 339 | : param x: input q-value (float or [float, float] as [qx, qy]) |
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| 340 | : return: scattering function value |
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| 341 | """ |
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| 342 | |
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| 343 | return self.model.runXY(x) |
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| 344 | |
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| 345 | ## Now (May27,10) directly uses the model eval function |
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| 346 | ## instead of the for-loop in Base Component. |
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| 347 | def evalDistribution(self, x = []): |
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| 348 | """ |
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| 349 | Evaluate the model in cartesian coordinates |
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| 350 | |
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| 351 | : param x: input q[], or [qx[], qy[]] |
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| 352 | : return: scattering function P(q[]) |
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| 353 | """ |
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| 354 | # set effective radius and scaling factor before run |
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| 355 | return self.model.evalDistribution(x) |
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| 356 | def calculate_ER(self): |
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| 357 | """ |
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| 358 | """ |
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| 359 | return self.model.calculate_ER() |
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| 360 | def set_dispersion(self, parameter, dispersion): |
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| 361 | """ |
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| 362 | Set the dispersion object for a model parameter |
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| 363 | |
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| 364 | : param parameter: name of the parameter [string] |
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| 365 | :dispersion: dispersion object of type DispersionModel |
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| 366 | """ |
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| 367 | pass |
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