| 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 | import copy |
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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 of the model |
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| 52 | self.multiplicity = max_nshells |
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| 53 | |
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| 54 | def _clone(self, obj): |
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| 55 | """ |
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| 56 | Internal utility function to copy the internal |
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| 57 | data members to a fresh copy. |
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| 58 | """ |
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| 59 | obj.params = copy.deepcopy(self.params) |
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| 60 | obj.non_fittable = copy.deepcopy(self.non_fittable) |
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| 61 | obj.description = copy.deepcopy(self.description) |
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| 62 | obj.details = copy.deepcopy(self.details) |
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| 63 | obj.dispersion = copy.deepcopy(self.dispersion) |
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| 64 | obj.model = self.model.clone() |
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| 65 | |
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| 66 | return obj |
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| 67 | |
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| 68 | |
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| 69 | def _set_dispersion(self): |
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| 70 | """ |
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| 71 | model dispersions |
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| 72 | """ |
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| 73 | ##set dispersion from model |
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| 74 | self.dispersion = {} |
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| 75 | |
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| 76 | |
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| 77 | def _set_params(self): |
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| 78 | """ |
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| 79 | Concatenate the parameters of the model to create |
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| 80 | this model parameters |
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| 81 | """ |
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| 82 | # rearrange the parameters for the given # of shells |
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| 83 | for name , value in self.model.params.iteritems(): |
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| 84 | n = 0 |
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| 85 | pos = len(name.split('_'))-1 |
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| 86 | if name.split('_')[0] == 'func': |
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| 87 | n= -1 |
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| 88 | while n<self.n_layers: |
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| 89 | n += 1 |
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| 90 | if name.split('_')[pos] == 'inter%s' % str(n): |
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| 91 | self.params[name]=value |
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| 92 | continue |
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| 93 | #continue |
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| 94 | elif name.split('_')[pos][0:5] == 'inter': |
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| 95 | n= -1 |
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| 96 | while n<self.n_layers: |
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| 97 | n += 1 |
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| 98 | if name.split('_')[pos] == 'inter%s' % str(n): |
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| 99 | self.params[name]= value |
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| 100 | continue |
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| 101 | elif name.split('_')[pos][0:4] == 'flat': |
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| 102 | while n<self.n_layers: |
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| 103 | n += 1 |
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| 104 | if name.split('_')[pos] == 'flat%s' % str(n): |
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| 105 | self.params[name]= value |
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| 106 | continue |
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| 107 | elif name == 'n_layers': |
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| 108 | continue |
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| 109 | else: |
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| 110 | self.params[name]= value |
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| 111 | |
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| 112 | self.model.params['n_layers'] = self.n_layers |
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| 113 | |
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| 114 | # set constrained values for the original model params |
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| 115 | self._set_xtra_model_param() |
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| 116 | |
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| 117 | def _set_details(self): |
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| 118 | """ |
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| 119 | Concatenate details of the original model to create |
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| 120 | this model details |
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| 121 | """ |
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| 122 | for name ,detail in self.model.details.iteritems(): |
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| 123 | if name in self.params.iterkeys(): |
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| 124 | self.details[name]= detail |
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| 125 | |
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| 126 | |
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| 127 | def _set_xtra_model_param(self): |
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| 128 | """ |
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| 129 | Set params of original model that are hidden from this model |
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| 130 | """ |
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| 131 | # look for the model parameters that are not in param list |
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| 132 | for key in self.model.params.iterkeys(): |
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| 133 | if key not in self.params.keys(): |
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| 134 | if key.split('_')[0] == 'thick': |
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| 135 | self.model.setParam(key, 0) |
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| 136 | continue |
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| 137 | if key.split('_')[0] == 'func': |
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| 138 | self.model.setParam(key, 0) |
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| 139 | continue |
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| 140 | for nshell in range(self.n_layers,max_nshells): |
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| 141 | if key.split('_')[1] == 'flat%s' % str(nshell+1): |
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| 142 | try: |
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| 143 | value = self.model.params['sld_medium'] |
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| 144 | self.model.setParam(key, value) |
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| 145 | except: pass |
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| 146 | |
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| 147 | def _get_func_list(self): |
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| 148 | """ |
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| 149 | Get the list of functions in each layer (shell) |
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| 150 | """ |
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| 151 | #func_list = {} |
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| 152 | return func_list |
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| 153 | |
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| 154 | def getProfile(self): |
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| 155 | """ |
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| 156 | Get SLD profile |
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| 157 | |
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| 158 | : return: (z, beta) where z is a list of depth of the transition points |
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| 159 | beta is a list of the corresponding SLD values |
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| 160 | """ |
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| 161 | # max_pts for each layers |
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| 162 | n_sub = 21 |
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| 163 | z = [] |
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| 164 | beta = [] |
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| 165 | sub_range = floor(n_sub/2.0) |
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| 166 | z.append(0) |
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| 167 | beta.append(self.params['sld_sub0']) |
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| 168 | |
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| 169 | z0 = 0 |
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| 170 | # for layers from the top |
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| 171 | for n in range(1,self.n_layers+2): |
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| 172 | i = n |
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| 173 | |
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| 174 | for j in range(0,2): |
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| 175 | for n_s in range(-sub_range,sub_range+1): |
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| 176 | if j==1: |
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| 177 | if i==self.n_layers+1: |
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| 178 | break |
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| 179 | # shift half sub thickness for the first point |
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| 180 | z0 += dz/2.0 |
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| 181 | z.append(z0) |
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| 182 | #z0 -= dz/2.0 |
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| 183 | z0 += self.params['thick_flat%s'% str(i)] |
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| 184 | |
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| 185 | sld_i = self.params['sld_flat%s'% str(i)] |
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| 186 | beta.append(self.params['sld_flat%s'% str(i)]) |
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| 187 | else: |
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| 188 | |
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| 189 | dz = self.params['thick_inter%s'% str(i-1)]/n_sub |
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| 190 | |
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| 191 | if n_s == -sub_range: |
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| 192 | # shift half sub thickness for the first point |
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| 193 | z0 -= dz/2.0 |
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| 194 | #exec "dz = self.params['thick_inter[%s-1]'% str(i)]/9" |
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| 195 | #print "%d = %g \n"% (i,self.params['thick_inter3']) |
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| 196 | z0 += dz |
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| 197 | |
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| 198 | if i == 1: |
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| 199 | sld_l = self.params['sld_sub0'] |
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| 200 | else: |
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| 201 | sld_l = self.params['sld_flat%s'% str(i-1)] |
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| 202 | if i == self.n_layers+1: |
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| 203 | sld_r = self.params['sld_medium'] |
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| 204 | else: |
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| 205 | sld_r = self.params['sld_flat%s'% str(i)] |
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| 206 | func_idx = self.params['func_inter%s'% str(i-1)] |
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| 207 | func = self._get_func(n_s, n_sub, func_idx) |
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| 208 | if sld_r>sld_l: |
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| 209 | sld_i = (sld_r-sld_l)*func+sld_l |
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| 210 | elif sld_r<sld_l: |
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| 211 | sld_i = (sld_l-sld_r)*(1-func)+sld_r |
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| 212 | else: |
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| 213 | sld_i = sld_r |
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| 214 | z.append(z0) |
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| 215 | beta.append(sld_i) |
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| 216 | if j==1: break |
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| 217 | # put substrate and superstrate profile |
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| 218 | # shift half sub thickness for the first point |
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| 219 | z0 += dz/2.0 |
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| 220 | z.append(z0) |
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| 221 | beta.append(self.params['sld_medium']) |
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| 222 | z_ext = z0/6.0 |
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| 223 | |
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| 224 | # put the extra points for the substrate |
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| 225 | # and superstrate |
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| 226 | z.append(z0+z_ext) |
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| 227 | beta.append(self.params['sld_medium']) |
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| 228 | z.insert(0,-z_ext) |
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| 229 | beta.insert(0,self.params['sld_sub0']) |
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| 230 | z = [z0 - x for x in z] |
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| 231 | z.reverse() |
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| 232 | beta.reverse() |
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| 233 | return z, beta |
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| 234 | |
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| 235 | def _get_func(self, index, n_sub, func_idx): |
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| 236 | """ |
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| 237 | Get the function asked to buil sld profile |
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| 238 | : param index: index of sub_layer |
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| 239 | : param n_sub: total number of sub_layer |
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| 240 | : param func_idx: an integer to identify a function |
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| 241 | |
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| 242 | : return out: the output from the function, float |
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| 243 | """ |
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| 244 | # cal bin_size |
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| 245 | bin_size = 1.0/n_sub |
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| 246 | # erf |
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| 247 | if func_idx == 0: |
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| 248 | out = erf(index/(n_sub/5.0))/2.0 + 0.5 |
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| 249 | return out |
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| 250 | else: |
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| 251 | index += 0.5 |
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| 252 | # linear |
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| 253 | if func_idx == 1: |
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| 254 | out = ((index + floor(n_sub/2.0))*bin_size) |
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| 255 | # r_parabolic |
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| 256 | elif func_idx == 2: |
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| 257 | out = ((index + floor(n_sub/2.0))*bin_size)* \ |
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| 258 | ((index + floor(n_sub/2.0))*bin_size) |
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| 259 | # l_parabolic |
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| 260 | elif func_idx == 3: |
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| 261 | out = 1.0-(((index + floor(n_sub/2.0))*bin_size) - 1.0) *\ |
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| 262 | (((index + floor(n_sub/2.0))*bin_size) - 1.0) |
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| 263 | # r_cubic |
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| 264 | elif func_idx == 4: |
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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 | ((index + floor(n_sub/2.0))*bin_size) |
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| 268 | # l_cubic |
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| 269 | elif func_idx == 5: |
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| 270 | out = 1.0+(((index + floor(n_sub/2.0)))*bin_size - 1.0) *\ |
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| 271 | (((index + floor(n_sub/2.0)))*bin_size - 1.0) *\ |
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| 272 | (((index + floor(n_sub/2.0)))*bin_size - 1.0) |
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| 273 | # return output |
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| 274 | return out |
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| 275 | |
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| 276 | def setParam(self, name, value): |
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| 277 | """ |
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| 278 | Set the value of a model parameter |
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| 279 | |
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| 280 | : param name: name of the parameter |
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| 281 | : param value: value of the parameter |
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| 282 | """ |
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| 283 | # set param to new model |
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| 284 | self._setParamHelper( name, value) |
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| 285 | |
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| 286 | ## setParam to model |
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| 287 | if name=='sld_medium': |
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| 288 | # the sld_*** model.params not in params must set to value of sld_solv |
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| 289 | for key in self.model.params.iterkeys(): |
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| 290 | if key not in self.params.keys()and key.split('_')[0] == 'sld': |
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| 291 | self.model.setParam(key, value) |
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| 292 | |
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| 293 | self.model.setParam( name, value) |
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| 294 | |
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| 295 | def _setParamHelper(self, name, value): |
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| 296 | """ |
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| 297 | Helper function to setParam |
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| 298 | """ |
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| 299 | |
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| 300 | # Look for standard parameter |
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| 301 | for item in self.params.keys(): |
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| 302 | if item.lower()==name.lower(): |
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| 303 | self.params[item] = value |
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| 304 | return |
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| 305 | |
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| 306 | raise ValueError, "Model does not contain parameter %s" % name |
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| 307 | |
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| 308 | |
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| 309 | def _set_fixed_params(self): |
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| 310 | """ |
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| 311 | Fill the self.fixed list with the model fixed list |
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| 312 | """ |
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| 313 | pass |
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| 314 | |
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| 315 | def run(self, x = 0.0): |
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| 316 | """ |
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| 317 | Evaluate the model |
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| 318 | |
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| 319 | :param x: input q, or [q,phi] |
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| 320 | |
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| 321 | :return: scattering function P(q) |
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| 322 | |
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| 323 | """ |
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| 324 | |
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| 325 | return self.model.run(x) |
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| 326 | |
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| 327 | def runXY(self, x = 0.0): |
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| 328 | """ |
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| 329 | Evaluate the model |
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| 330 | |
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| 331 | : param x: input q-value (float or [float, float] as [qx, qy]) |
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| 332 | : return: scattering function value |
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| 333 | """ |
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| 334 | |
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| 335 | return self.model.runXY(x) |
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| 336 | |
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| 337 | ## Now (May27,10) directly uses the model eval function |
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| 338 | ## instead of the for-loop in Base Component. |
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| 339 | def evalDistribution(self, x = []): |
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| 340 | """ |
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| 341 | Evaluate the model in cartesian coordinates |
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| 342 | |
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| 343 | : param x: input q[], or [qx[], qy[]] |
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| 344 | : return: scattering function P(q[]) |
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| 345 | """ |
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| 346 | # set effective radius and scaling factor before run |
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| 347 | return self.model.evalDistribution(x) |
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| 348 | def calculate_ER(self): |
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| 349 | """ |
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| 350 | """ |
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| 351 | return self.model.calculate_ER() |
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| 352 | def set_dispersion(self, parameter, dispersion): |
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| 353 | """ |
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| 354 | Set the dispersion object for a model parameter |
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| 355 | |
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| 356 | : param parameter: name of the parameter [string] |
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| 357 | :dispersion: dispersion object of type DispersionModel |
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| 358 | """ |
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| 359 | pass |
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