| 1 | |
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| 2 | from sans.models.BaseComponent import BaseComponent |
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| 3 | import numpy, math |
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| 4 | import copy |
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| 5 | from sans.models.pluginmodel import Model1DPlugin |
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| 6 | class MultiplicationModel(BaseComponent): |
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| 7 | """ |
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| 8 | Use for P(Q)*S(Q); function call must be in the order of P(Q) and then S(Q): |
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| 9 | The model parameters are combined from both models, P(Q) and S(Q), except 1) 'effect_radius' of S(Q) |
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| 10 | which will be calculated from P(Q) via calculate_ER(), |
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| 11 | and 2) 'scale' in P model which is synchronized w/ volfraction in S |
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| 12 | then P*S is multiplied by a new param, 'scale_factor'. |
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| 13 | The polydispersion is applicable only to P(Q), not to S(Q). |
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| 14 | Note: P(Q) refers to 'form factor' model while S(Q) does to 'structure factor'. |
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| 15 | """ |
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| 16 | def __init__(self, p_model, s_model ): |
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| 17 | BaseComponent.__init__(self) |
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| 18 | """ |
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| 19 | @param p_model: form factor, P(Q) |
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| 20 | @param s_model: structure factor, S(Q) |
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| 21 | """ |
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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 | self.name = p_model.name +" * "+ s_model.name |
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| 26 | self.description= self.name+"\n" |
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| 27 | self.fill_description(p_model, s_model) |
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| 28 | |
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| 29 | ## Define parameters |
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| 30 | self.params = {} |
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| 31 | |
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| 32 | ## Parameter details [units, min, max] |
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| 33 | self.details = {} |
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| 34 | |
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| 35 | ##models |
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| 36 | self.p_model= p_model |
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| 37 | self.s_model= s_model |
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| 38 | |
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| 39 | |
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| 40 | ## dispersion |
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| 41 | self._set_dispersion() |
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| 42 | ## Define parameters |
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| 43 | self._set_params() |
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| 44 | ## New parameter:Scaling factor |
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| 45 | self.params['scale_factor'] = 1 |
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| 46 | |
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| 47 | ## Parameter details [units, min, max] |
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| 48 | self._set_details() |
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| 49 | self.details['scale_factor'] = ['', None, None] |
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| 50 | |
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| 51 | #list of parameter that can be fitted |
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| 52 | self._set_fixed_params() |
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| 53 | ## parameters with orientation |
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| 54 | for item in self.p_model.orientation_params: |
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| 55 | self.orientation_params.append(item) |
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| 56 | |
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| 57 | for item in self.s_model.orientation_params: |
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| 58 | if not item in self.orientation_params: |
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| 59 | self.orientation_params.append(item) |
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| 60 | |
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| 61 | |
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| 62 | def _clone(self, obj): |
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| 63 | """ |
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| 64 | Internal utility function to copy the internal |
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| 65 | data members to a fresh copy. |
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| 66 | """ |
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| 67 | obj.params = copy.deepcopy(self.params) |
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| 68 | obj.description = copy.deepcopy(self.description) |
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| 69 | obj.details = copy.deepcopy(self.details) |
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| 70 | obj.dispersion = copy.deepcopy(self.dispersion) |
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| 71 | obj.p_model = self.p_model.clone() |
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| 72 | obj.s_model = self.s_model.clone() |
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| 73 | #obj = copy.deepcopy(self) |
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| 74 | return obj |
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| 75 | |
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| 76 | |
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| 77 | def _set_dispersion(self): |
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| 78 | """ |
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| 79 | combined the two models dispersions |
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| 80 | Polydispersion should not be applied to s_model |
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| 81 | """ |
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| 82 | ##set dispersion only from p_model |
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| 83 | for name , value in self.p_model.dispersion.iteritems(): |
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| 84 | self.dispersion[name]= value |
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| 85 | |
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| 86 | def getProfile(self): |
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| 87 | """ |
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| 88 | Get SLD profile of p_model if exists |
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| 89 | |
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| 90 | : return: (r, beta) where r is a list of radius of the transition points |
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| 91 | beta is a list of the corresponding SLD values |
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| 92 | : Note: This works only for func_shell# = 2 (exp function). |
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| 93 | """ |
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| 94 | try: |
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| 95 | x,y = self.p_model.getProfile() |
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| 96 | except: |
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| 97 | x = None |
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| 98 | y = None |
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| 99 | |
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| 100 | return x, y |
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| 101 | |
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| 102 | def _set_params(self): |
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| 103 | """ |
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| 104 | Concatenate the parameters of the two models to create |
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| 105 | this model parameters |
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| 106 | """ |
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| 107 | |
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| 108 | for name , value in self.p_model.params.iteritems(): |
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| 109 | if not name in self.params.keys() and name != 'scale': |
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| 110 | self.params[name]= value |
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| 111 | |
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| 112 | for name , value in self.s_model.params.iteritems(): |
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| 113 | #Remove the effect_radius from the (P*S) model parameters. |
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| 114 | if not name in self.params.keys() and name != 'effect_radius': |
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| 115 | self.params[name]= value |
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| 116 | |
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| 117 | # Set "scale and effec_radius to P and S model as initializing |
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| 118 | # since run P*S comes from P and S separately. |
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| 119 | self._set_scale_factor() |
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| 120 | self._set_effect_radius() |
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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 two models 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.p_model.details.iteritems(): |
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| 128 | if name != 'scale': |
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| 129 | self.details[name]= detail |
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| 130 | |
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| 131 | for name , detail in self.s_model.details.iteritems(): |
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| 132 | if not name in self.details.keys() or name != 'effect_radius': |
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| 133 | self.details[name]= detail |
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| 134 | |
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| 135 | def _set_scale_factor(self): |
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| 136 | """ |
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| 137 | Set scale=volfraction to P model |
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| 138 | """ |
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| 139 | value = self.params['volfraction'] |
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| 140 | if value != None: |
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| 141 | self.p_model.setParam( 'scale', value) |
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| 142 | |
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| 143 | |
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| 144 | def _set_effect_radius(self): |
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| 145 | """ |
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| 146 | Set effective radius to S(Q) model |
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| 147 | """ |
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| 148 | effective_radius = self.p_model.calculate_ER() |
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| 149 | #Reset the effective_radius of s_model just before the run |
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| 150 | if effective_radius != None and effective_radius != NotImplemented: |
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| 151 | self.s_model.setParam('effect_radius',effective_radius) |
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| 152 | |
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| 153 | def setParam(self, name, value): |
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| 154 | """ |
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| 155 | Set the value of a model parameter |
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| 156 | |
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| 157 | @param name: name of the parameter |
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| 158 | @param value: value of the parameter |
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| 159 | """ |
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| 160 | # set param to P*S model |
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| 161 | self._setParamHelper( name, value) |
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| 162 | |
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| 163 | ## setParam to p model |
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| 164 | # set 'scale' in P(Q) equal to volfraction |
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| 165 | if name == 'volfraction': |
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| 166 | self._set_scale_factor() |
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| 167 | elif name in self.p_model.getParamList(): |
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| 168 | self.p_model.setParam( name, value) |
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| 169 | |
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| 170 | ## setParam to s model |
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| 171 | # This is a little bit abundant: Todo: find better way |
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| 172 | self._set_effect_radius() |
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| 173 | if name in self.s_model.getParamList(): |
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| 174 | self.s_model.setParam( name, value) |
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| 175 | |
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| 176 | |
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| 177 | #self._setParamHelper( name, value) |
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| 178 | |
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| 179 | def _setParamHelper(self, name, value): |
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| 180 | """ |
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| 181 | Helper function to setparam |
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| 182 | """ |
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| 183 | # Look for dispersion parameters |
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| 184 | toks = name.split('.') |
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| 185 | if len(toks)==2: |
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| 186 | for item in self.dispersion.keys(): |
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| 187 | if item.lower()==toks[0].lower(): |
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| 188 | for par in self.dispersion[item]: |
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| 189 | if par.lower() == toks[1].lower(): |
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| 190 | self.dispersion[item][par] = value |
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| 191 | return |
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| 192 | else: |
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| 193 | # Look for standard parameter |
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| 194 | for item in self.params.keys(): |
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| 195 | if item.lower()==name.lower(): |
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| 196 | self.params[item] = value |
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| 197 | return |
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| 198 | |
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| 199 | raise ValueError, "Model does not contain parameter %s" % name |
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| 200 | |
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| 201 | |
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| 202 | def _set_fixed_params(self): |
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| 203 | """ |
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| 204 | fill the self.fixed list with the p_model fixed list |
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| 205 | """ |
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| 206 | for item in self.p_model.fixed: |
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| 207 | self.fixed.append(item) |
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| 208 | |
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| 209 | self.fixed.sort() |
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| 210 | |
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| 211 | |
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| 212 | def run(self, x = 0.0): |
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| 213 | """ Evaluate the model |
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| 214 | @param x: input q-value (float or [float, float] as [r, theta]) |
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| 215 | @return: (DAB value) |
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| 216 | """ |
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| 217 | # set effective radius and scaling factor before run |
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| 218 | self._set_effect_radius() |
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| 219 | self._set_scale_factor() |
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| 220 | return self.params['scale_factor']*self.p_model.run(x)*self.s_model.run(x) |
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| 221 | |
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| 222 | def runXY(self, x = 0.0): |
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| 223 | """ Evaluate the model |
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| 224 | @param x: input q-value (float or [float, float] as [qx, qy]) |
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| 225 | @return: DAB value |
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| 226 | """ |
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| 227 | # set effective radius and scaling factor before run |
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| 228 | self._set_effect_radius() |
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| 229 | self._set_scale_factor() |
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| 230 | return self.params['scale_factor']*self.p_model.runXY(x)* self.s_model.runXY(x) |
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| 231 | |
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| 232 | ## Now (May27,10) directly uses the model eval function |
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| 233 | ## instead of the for-loop in Base Component. |
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| 234 | def evalDistribution(self, x = []): |
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| 235 | """ Evaluate the model in cartesian coordinates |
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| 236 | @param x: input q[], or [qx[], qy[]] |
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| 237 | @return: scattering function P(q[]) |
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| 238 | """ |
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| 239 | # set effective radius and scaling factor before run |
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| 240 | self._set_effect_radius() |
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| 241 | self._set_scale_factor() |
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| 242 | return self.params['scale_factor']*self.p_model.evalDistribution(x)* self.s_model.evalDistribution(x) |
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| 243 | |
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| 244 | def set_dispersion(self, parameter, dispersion): |
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| 245 | """ |
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| 246 | Set the dispersion object for a model parameter |
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| 247 | @param parameter: name of the parameter [string] |
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| 248 | @dispersion: dispersion object of type DispersionModel |
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| 249 | """ |
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| 250 | value= None |
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| 251 | try: |
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| 252 | if parameter in self.p_model.dispersion.keys(): |
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| 253 | value= self.p_model.set_dispersion(parameter, dispersion) |
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| 254 | self._set_dispersion() |
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| 255 | return value |
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| 256 | except: |
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| 257 | raise |
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| 258 | |
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| 259 | def fill_description(self, p_model, s_model): |
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| 260 | """ |
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| 261 | Fill the description for P(Q)*S(Q) |
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| 262 | """ |
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| 263 | description = "" |
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| 264 | description += "Note:1) The effect_radius (effective radius) of %s \n"% (s_model.name) |
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| 265 | description +=" is automatically calculated from size parameters (radius...).\n" |
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| 266 | description += " 2) For non-spherical shape, this approximation is valid \n" |
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| 267 | description += " only for limited systems. Thus, use it at your own risk.\n" |
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| 268 | description +="See %s description and %s description \n"%( p_model.name, s_model.name ) |
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| 269 | description += " for details of individual models." |
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| 270 | self.description += description |
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| 271 | |
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