| 1 | # A sample of an experimental model function for Sum(Pmodel1,Pmodel2) |
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| 2 | import copy |
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| 3 | from sans.models.pluginmodel import Model1DPlugin |
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| 4 | # User can change the name of the model (only with single functional model) |
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| 5 | from sans.models.CylinderModel import CylinderModel as P1 |
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| 6 | from sans.models.PolymerExclVolume import PolymerExclVolume as P2 |
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| 7 | |
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| 8 | |
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| 9 | class Model(Model1DPlugin): |
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| 10 | """ |
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| 11 | Use for p1(Q)+p2(Q); |
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| 12 | Note: P(Q) refers to 'form factor' model. |
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| 13 | """ |
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| 14 | name = "" |
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| 15 | def __init__(self): |
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| 16 | Model1DPlugin.__init__(self, name='') |
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| 17 | """ |
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| 18 | :param p_model1: a form factor, P(Q) |
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| 19 | :param p_model2: another form factor, P(Q) |
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| 20 | """ |
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| 21 | p_model1 = P1() |
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| 22 | p_model2 = P2() |
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| 23 | ## Setting model name model description |
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| 24 | self.description="" |
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| 25 | self.name = "Sum[" + "P1(Cyl)" +", "+ "P2(PEV)" + "]" |
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| 26 | self.description = p_model1.name+"\n" |
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| 27 | self.description += p_model2.name+"\n" |
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| 28 | self.fill_description(p_model1, p_model2) |
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| 29 | |
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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 = p_model1.non_fittable |
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| 38 | self.non_fittable += p_model2.non_fittable |
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| 39 | |
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| 40 | ##models |
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| 41 | self.p_model1= p_model1 |
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| 42 | self.p_model2= p_model2 |
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| 43 | |
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| 44 | |
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| 45 | ## dispersion |
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| 46 | self._set_dispersion() |
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| 47 | ## Define parameters |
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| 48 | self._set_params() |
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| 49 | ## New parameter:Scaling factor |
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| 50 | self.params['scale_factor'] = 1 |
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| 51 | |
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| 52 | ## Parameter details [units, min, max] |
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| 53 | self._set_details() |
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| 54 | self.details['scale_factor'] = ['', None, None] |
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| 55 | |
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| 56 | |
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| 57 | #list of parameter that can be fitted |
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| 58 | self._set_fixed_params() |
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| 59 | ## parameters with orientation |
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| 60 | for item in self.p_model1.orientation_params: |
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| 61 | new_item = "p1_" + item |
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| 62 | if not new_item in self.orientation_params: |
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| 63 | self.orientation_params.append(new_item) |
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| 64 | |
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| 65 | for item in self.p_model2.orientation_params: |
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| 66 | new_item = "p2_" + item |
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| 67 | if not new_item in self.orientation_params: |
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| 68 | self.orientation_params.append(new_item) |
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| 69 | # get multiplicity if model provide it, else 1. |
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| 70 | try: |
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| 71 | multiplicity1 = p_model1.multiplicity |
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| 72 | try: |
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| 73 | multiplicity2 = p_model2.multiplicity |
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| 74 | except: |
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| 75 | multiplicity2 = 1 |
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| 76 | except: |
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| 77 | multiplicity1 = 1 |
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| 78 | multiplicity2 = 1 |
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| 79 | ## functional multiplicity of the model |
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| 80 | self.multiplicity1 = multiplicity1 |
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| 81 | self.multiplicity2 = multiplicity2 |
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| 82 | self.multiplicity_info = [] |
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| 83 | |
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| 84 | def _clone(self, obj): |
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| 85 | """ |
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| 86 | Internal utility function to copy the internal |
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| 87 | data members to a fresh copy. |
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| 88 | """ |
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| 89 | obj.params = copy.deepcopy(self.params) |
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| 90 | obj.description = copy.deepcopy(self.description) |
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| 91 | obj.details = copy.deepcopy(self.details) |
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| 92 | obj.dispersion = copy.deepcopy(self.dispersion) |
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| 93 | obj.p_model1 = self.p_model1.clone() |
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| 94 | obj.p_model2 = self.p_model2.clone() |
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| 95 | #obj = copy.deepcopy(self) |
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| 96 | return obj |
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| 97 | |
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| 98 | |
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| 99 | def _set_dispersion(self): |
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| 100 | """ |
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| 101 | combined the two models dispersions |
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| 102 | Polydispersion should not be applied to s_model |
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| 103 | """ |
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| 104 | ##set dispersion only from p_model |
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| 105 | for name , value in self.p_model1.dispersion.iteritems(): |
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| 106 | #if name.lower() not in self.p_model1.orientation_params: |
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| 107 | new_name = "p1_" + name |
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| 108 | self.dispersion[new_name]= value |
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| 109 | for name , value in self.p_model2.dispersion.iteritems(): |
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| 110 | #if name.lower() not in self.p_model2.orientation_params: |
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| 111 | new_name = "p2_" + name |
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| 112 | self.dispersion[new_name]= value |
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| 113 | |
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| 114 | def function(self, x=0.0): |
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| 115 | """ |
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| 116 | """ |
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| 117 | return 0 |
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| 118 | |
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| 119 | def getProfile(self): |
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| 120 | """ |
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| 121 | Get SLD profile of p_model if exists |
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| 122 | |
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| 123 | : return: (r, beta) where r is a list of radius of the transition points |
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| 124 | beta is a list of the corresponding SLD values |
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| 125 | : Note: This works only for func_shell# = 2 (exp function) |
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| 126 | and is not supporting for p2 |
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| 127 | """ |
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| 128 | try: |
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| 129 | x,y = self.p_model1.getProfile() |
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| 130 | except: |
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| 131 | x = None |
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| 132 | y = None |
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| 133 | |
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| 134 | return x, y |
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| 135 | |
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| 136 | def _set_params(self): |
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| 137 | """ |
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| 138 | Concatenate the parameters of the two models to create |
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| 139 | this model parameters |
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| 140 | """ |
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| 141 | |
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| 142 | for name , value in self.p_model1.params.iteritems(): |
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| 143 | # No 2D-supported |
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| 144 | #if name not in self.p_model1.orientation_params: |
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| 145 | new_name = "p1_" + name |
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| 146 | self.params[new_name]= value |
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| 147 | |
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| 148 | for name , value in self.p_model2.params.iteritems(): |
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| 149 | # No 2D-supported |
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| 150 | #if name not in self.p_model2.orientation_params: |
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| 151 | new_name = "p2_" + name |
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| 152 | self.params[new_name]= value |
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| 153 | |
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| 154 | # Set "scale" as initializing |
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| 155 | self._set_scale_factor() |
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| 156 | |
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| 157 | |
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| 158 | def _set_details(self): |
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| 159 | """ |
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| 160 | Concatenate details of the two models to create |
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| 161 | this model details |
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| 162 | """ |
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| 163 | for name ,detail in self.p_model1.details.iteritems(): |
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| 164 | new_name = "p1_" + name |
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| 165 | #if new_name not in self.orientation_params: |
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| 166 | self.details[new_name]= detail |
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| 167 | |
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| 168 | for name ,detail in self.p_model2.details.iteritems(): |
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| 169 | new_name = "p2_" + name |
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| 170 | #if new_name not in self.orientation_params: |
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| 171 | self.details[new_name]= detail |
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| 172 | |
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| 173 | def _set_scale_factor(self): |
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| 174 | """ |
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| 175 | Not implemented |
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| 176 | """ |
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| 177 | pass |
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| 178 | |
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| 179 | |
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| 180 | def setParam(self, name, value): |
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| 181 | """ |
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| 182 | Set the value of a model parameter |
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| 183 | |
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| 184 | :param name: name of the parameter |
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| 185 | :param value: value of the parameter |
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| 186 | """ |
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| 187 | # set param to p1+p2 model |
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| 188 | self._setParamHelper(name, value) |
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| 189 | |
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| 190 | ## setParam to p model |
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| 191 | model_pre = name.split('_', 1)[0] |
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| 192 | new_name = name.split('_', 1)[1] |
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| 193 | if model_pre == "p1": |
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| 194 | if new_name in self.p_model1.getParamList(): |
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| 195 | self.p_model1.setParam(new_name, value) |
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| 196 | elif model_pre == "p2": |
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| 197 | if new_name in self.p_model2.getParamList(): |
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| 198 | self.p_model2.setParam(new_name, value) |
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| 199 | elif name.lower() == 'scale_factor': |
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| 200 | self.params['scale_factor'] = value |
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| 201 | else: |
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| 202 | raise ValueError, "Model does not contain parameter %s" % name |
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| 203 | |
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| 204 | def getParam(self, name): |
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| 205 | """ |
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| 206 | Set the value of a model parameter |
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| 207 | |
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| 208 | :param name: name of the parameter |
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| 209 | |
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| 210 | """ |
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| 211 | # Look for dispersion parameters |
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| 212 | toks = name.split('.') |
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| 213 | if len(toks)==2: |
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| 214 | for item in self.dispersion.keys(): |
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| 215 | # 2D not supported |
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| 216 | if item.lower()==toks[0].lower():# and \ |
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| 217 | #item.lower() not in self.orientation_params \ |
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| 218 | #and toks[0].lower() not in self.orientation_params: |
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| 219 | for par in self.dispersion[item]: |
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| 220 | if par.lower() == toks[1].lower(): |
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| 221 | return self.dispersion[item][par] |
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| 222 | else: |
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| 223 | # Look for standard parameter |
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| 224 | for item in self.params.keys(): |
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| 225 | if item.lower()==name.lower():#and \ |
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| 226 | #item.lower() not in self.orientation_params \ |
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| 227 | #and toks[0].lower() not in self.orientation_params: |
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| 228 | return self.params[item] |
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| 229 | return |
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| 230 | #raise ValueError, "Model does not contain parameter %s" % name |
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| 231 | |
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| 232 | def _setParamHelper(self, name, value): |
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| 233 | """ |
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| 234 | Helper function to setparam |
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| 235 | """ |
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| 236 | # Look for dispersion parameters |
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| 237 | toks = name.split('.') |
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| 238 | if len(toks)== 2: |
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| 239 | for item in self.dispersion.keys(): |
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| 240 | if item.lower()== toks[0].lower():# and \ |
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| 241 | #item.lower() not in self.orientation_params: |
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| 242 | for par in self.dispersion[item]: |
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| 243 | if par.lower() == toks[1].lower():#and \ |
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| 244 | #item.lower() not in self.orientation_params: |
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| 245 | self.dispersion[item][par] = value |
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| 246 | return |
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| 247 | else: |
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| 248 | # Look for standard parameter |
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| 249 | for item in self.params.keys(): |
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| 250 | if item.lower()== name.lower():#and \ |
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| 251 | #item.lower() not in self.orientation_params: |
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| 252 | self.params[item] = value |
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| 253 | return |
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| 254 | |
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| 255 | raise ValueError, "Model does not contain parameter %s" % name |
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| 256 | |
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| 257 | |
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| 258 | def _set_fixed_params(self): |
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| 259 | """ |
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| 260 | fill the self.fixed list with the p_model fixed list |
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| 261 | """ |
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| 262 | for item in self.p_model1.fixed: |
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| 263 | new_item = "p1" + item |
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| 264 | self.fixed.append(new_item) |
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| 265 | for item in self.p_model2.fixed: |
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| 266 | new_item = "p2" + item |
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| 267 | self.fixed.append(new_item) |
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| 268 | |
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| 269 | self.fixed.sort() |
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| 270 | |
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| 271 | |
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| 272 | def run(self, x = 0.0): |
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| 273 | """ |
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| 274 | Evaluate the model |
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| 275 | |
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| 276 | :param x: input q-value (float or [float, float] as [r, theta]) |
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| 277 | :return: (scattering function value) |
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| 278 | """ |
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| 279 | self._set_scale_factor() |
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| 280 | return self.params['scale_factor'] * \ |
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| 281 | (self.p_model1.run(x) + self.p_model2.run(x)) |
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| 282 | |
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| 283 | def runXY(self, x = 0.0): |
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| 284 | """ |
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| 285 | Evaluate the model |
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| 286 | |
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| 287 | :param x: input q-value (float or [float, float] as [qx, qy]) |
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| 288 | :return: scattering function value |
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| 289 | """ |
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| 290 | self._set_scale_factor() |
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| 291 | return self.params['scale_factor'] * \ |
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| 292 | (self.p_model1.runXY(x) + self.p_model2.runXY(x)) |
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| 293 | |
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| 294 | ## Now (May27,10) directly uses the model eval function |
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| 295 | ## instead of the for-loop in Base Component. |
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| 296 | def evalDistribution(self, x = []): |
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| 297 | """ |
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| 298 | Evaluate the model in cartesian coordinates |
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| 299 | |
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| 300 | :param x: input q[], or [qx[], qy[]] |
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| 301 | :return: scattering function P(q[]) |
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| 302 | """ |
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| 303 | self._set_scale_factor() |
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| 304 | return self.params['scale_factor'] * \ |
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| 305 | (self.p_model1.evalDistribution(x) + \ |
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| 306 | self.p_model2.evalDistribution(x)) |
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| 307 | |
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| 308 | def set_dispersion(self, parameter, dispersion): |
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| 309 | """ |
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| 310 | Set the dispersion object for a model parameter |
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| 311 | |
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| 312 | :param parameter: name of the parameter [string] |
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| 313 | :dispersion: dispersion object of type DispersionModel |
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| 314 | """ |
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| 315 | value= None |
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| 316 | new_pre = parameter.split("_", 1)[0] |
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| 317 | new_parameter = parameter.split("_", 1)[1] |
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| 318 | try: |
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| 319 | if new_pre == 'p1' and \ |
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| 320 | new_parameter in self.p_model1.dispersion.keys(): |
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| 321 | value= self.p_model1.set_dispersion(new_parameter, dispersion) |
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| 322 | if new_pre == 'p2' and \ |
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| 323 | new_parameter in self.p_model2.dispersion.keys(): |
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| 324 | value= self.p_model2.set_dispersion(new_parameter, dispersion) |
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| 325 | self._set_dispersion() |
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| 326 | return value |
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| 327 | except: |
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| 328 | raise |
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| 329 | |
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| 330 | def fill_description(self, p_model1, p_model2): |
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| 331 | """ |
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| 332 | Fill the description for P(Q)+P(Q) |
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| 333 | """ |
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| 334 | description = "" |
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| 335 | description +="This model gives the summation of %s and %s.\n"% \ |
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| 336 | ( p_model1.name, p_model2.name ) |
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| 337 | self.description += description |
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| 338 | |
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| 339 | if __name__ == "__main__": |
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| 340 | m1= Model() |
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| 341 | m1.setParam("p1_scale", 25) |
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| 342 | m1.setParam("p1_length", 1000) |
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| 343 | m1.setParam("p2_scale", 100) |
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| 344 | m1.setParam("p2_rg", 100) |
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| 345 | out1 = m1.runXY(0.01) |
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| 346 | |
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| 347 | m2= Model() |
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| 348 | m2.p_model1.setParam("scale", 25) |
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| 349 | m2.p_model1.setParam("length", 1000) |
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| 350 | m2.p_model2.setParam("scale", 100) |
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| 351 | m2.p_model2.setParam("rg", 100) |
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| 352 | out2 = m2.p_model1.runXY(0.01) + m2.p_model2.runXY(0.01) |
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| 353 | print out1, " = ", out2 |
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| 354 | |
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| 355 | |
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