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_long_c", 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("long_c", 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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