1 | |
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2 | # A sample of an experimental model function for Sum/Multiply(Pmodel1,Pmodel2) |
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3 | import os |
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4 | import sys |
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5 | import copy |
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6 | import collections |
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7 | |
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8 | import numpy |
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9 | |
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10 | from sas.sascalc.fit.pluginmodel import Model1DPlugin |
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11 | from sasmodels.sasview_model import find_model |
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12 | |
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13 | class Model(Model1DPlugin): |
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14 | name = os.path.splitext(os.path.basename(__file__))[0] |
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15 | is_multiplicity_model = False |
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16 | def __init__(self, multiplicity=1): |
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17 | Model1DPlugin.__init__(self, name='') |
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18 | P1 = find_model('lamellar') |
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19 | P2 = find_model('gaussian_peak') |
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20 | p_model1 = P1() |
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21 | p_model2 = P2() |
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22 | ## Setting model name model description |
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23 | self.description = 'lamellar+gaussian_peak' |
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24 | if self.name.rstrip().lstrip() == '': |
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25 | self.name = self._get_name(p_model1.name, p_model2.name) |
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26 | if self.description.rstrip().lstrip() == '': |
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27 | self.description = p_model1.name |
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28 | self.description += p_model2.name |
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29 | self.fill_description(p_model1, p_model2) |
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30 | |
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31 | ## Define parameters |
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32 | self.params = collections.OrderedDict() |
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33 | |
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34 | ## Parameter details [units, min, max] |
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35 | self.details = {} |
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36 | ## Magnetic Panrameters |
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37 | self.magnetic_params = [] |
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38 | # non-fittable parameters |
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39 | self.non_fittable = p_model1.non_fittable |
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40 | self.non_fittable += p_model2.non_fittable |
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41 | |
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42 | ##models |
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43 | self.p_model1= p_model1 |
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44 | self.p_model2= p_model2 |
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45 | |
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46 | |
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47 | ## dispersion |
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48 | self._set_dispersion() |
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49 | ## Define parameters |
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50 | self._set_params() |
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51 | ## New parameter:scaling_factor |
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52 | self.params['scale_factor'] = 1.0 |
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53 | |
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54 | ## Parameter details [units, min, max] |
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55 | self._set_details() |
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56 | self.details['scale_factor'] = ['', 0.0, numpy.inf] |
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57 | |
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58 | |
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59 | #list of parameter that can be fitted |
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60 | self._set_fixed_params() |
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61 | |
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62 | ## parameters with orientation |
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63 | self.orientation_params = [] |
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64 | for item in self.p_model1.orientation_params: |
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65 | new_item = "p1_" + item |
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66 | if not new_item in self.orientation_params: |
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67 | self.orientation_params.append(new_item) |
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68 | |
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69 | for item in self.p_model2.orientation_params: |
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70 | new_item = "p2_" + item |
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71 | if not new_item in self.orientation_params: |
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72 | self.orientation_params.append(new_item) |
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73 | ## magnetic params |
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74 | self.magnetic_params = [] |
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75 | for item in self.p_model1.magnetic_params: |
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76 | new_item = "p1_" + item |
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77 | if not new_item in self.magnetic_params: |
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78 | self.magnetic_params.append(new_item) |
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79 | |
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80 | for item in self.p_model2.magnetic_params: |
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81 | new_item = "p2_" + item |
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82 | if not new_item in self.magnetic_params: |
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83 | self.magnetic_params.append(new_item) |
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84 | # get multiplicity if model provide it, else 1. |
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85 | try: |
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86 | multiplicity1 = p_model1.multiplicity |
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87 | try: |
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88 | multiplicity2 = p_model2.multiplicity |
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89 | except: |
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90 | multiplicity2 = 1 |
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91 | except: |
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92 | multiplicity1 = 1 |
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93 | multiplicity2 = 1 |
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94 | ## functional multiplicity of the model |
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95 | self.multiplicity1 = multiplicity1 |
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96 | self.multiplicity2 = multiplicity2 |
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97 | self.multiplicity_info = [] |
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98 | |
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99 | def _clone(self, obj): |
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100 | obj.params = copy.deepcopy(self.params) |
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101 | obj.description = copy.deepcopy(self.description) |
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102 | obj.details = copy.deepcopy(self.details) |
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103 | obj.dispersion = copy.deepcopy(self.dispersion) |
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104 | obj.p_model1 = self.p_model1.clone() |
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105 | obj.p_model2 = self.p_model2.clone() |
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106 | #obj = copy.deepcopy(self) |
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107 | return obj |
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108 | |
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109 | def _get_name(self, name1, name2): |
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110 | p1_name = self._get_upper_name(name1) |
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111 | if not p1_name: |
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112 | p1_name = name1 |
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113 | name = p1_name |
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114 | name += "_and_" |
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115 | p2_name = self._get_upper_name(name2) |
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116 | if not p2_name: |
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117 | p2_name = name2 |
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118 | name += p2_name |
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119 | return name |
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120 | |
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121 | def _get_upper_name(self, name=None): |
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122 | if name == None: |
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123 | return "" |
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124 | upper_name = "" |
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125 | str_name = str(name) |
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126 | for index in range(len(str_name)): |
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127 | if str_name[index].isupper(): |
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128 | upper_name += str_name[index] |
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129 | return upper_name |
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130 | |
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131 | def _set_dispersion(self): |
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132 | self.dispersion = collections.OrderedDict() |
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133 | ##set dispersion only from p_model |
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134 | for name , value in self.p_model1.dispersion.iteritems(): |
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135 | #if name.lower() not in self.p_model1.orientation_params: |
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136 | new_name = "p1_" + name |
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137 | self.dispersion[new_name]= value |
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138 | for name , value in self.p_model2.dispersion.iteritems(): |
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139 | #if name.lower() not in self.p_model2.orientation_params: |
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140 | new_name = "p2_" + name |
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141 | self.dispersion[new_name]= value |
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142 | |
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143 | def function(self, x=0.0): |
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144 | return 0 |
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145 | |
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146 | def getProfile(self): |
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147 | try: |
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148 | x,y = self.p_model1.getProfile() |
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149 | except: |
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150 | x = None |
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151 | y = None |
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152 | |
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153 | return x, y |
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154 | |
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155 | def _set_params(self): |
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156 | for name , value in self.p_model1.params.iteritems(): |
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157 | # No 2D-supported |
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158 | #if name not in self.p_model1.orientation_params: |
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159 | new_name = "p1_" + name |
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160 | self.params[new_name]= value |
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161 | |
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162 | for name , value in self.p_model2.params.iteritems(): |
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163 | # No 2D-supported |
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164 | #if name not in self.p_model2.orientation_params: |
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165 | new_name = "p2_" + name |
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166 | self.params[new_name]= value |
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167 | |
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168 | # Set "scale" as initializing |
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169 | self._set_scale_factor() |
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170 | |
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171 | |
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172 | def _set_details(self): |
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173 | for name ,detail in self.p_model1.details.iteritems(): |
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174 | new_name = "p1_" + name |
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175 | #if new_name not in self.orientation_params: |
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176 | self.details[new_name]= detail |
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177 | |
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178 | for name ,detail in self.p_model2.details.iteritems(): |
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179 | new_name = "p2_" + name |
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180 | #if new_name not in self.orientation_params: |
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181 | self.details[new_name]= detail |
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182 | |
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183 | def _set_scale_factor(self): |
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184 | pass |
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185 | |
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186 | |
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187 | def setParam(self, name, value): |
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188 | # set param to this (p1, p2) model |
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189 | self._setParamHelper(name, value) |
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190 | |
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191 | ## setParam to p model |
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192 | model_pre = '' |
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193 | new_name = '' |
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194 | name_split = name.split('_', 1) |
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195 | if len(name_split) == 2: |
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196 | model_pre = name.split('_', 1)[0] |
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197 | new_name = name.split('_', 1)[1] |
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198 | if model_pre == "p1": |
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199 | if new_name in self.p_model1.getParamList(): |
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200 | self.p_model1.setParam(new_name, value) |
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201 | elif model_pre == "p2": |
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202 | if new_name in self.p_model2.getParamList(): |
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203 | self.p_model2.setParam(new_name, value) |
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204 | elif name == 'scale_factor': |
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205 | self.params['scale_factor'] = value |
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206 | else: |
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207 | raise ValueError, "Model does not contain parameter %s" % name |
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208 | |
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209 | def getParam(self, name): |
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210 | # Look for dispersion parameters |
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211 | toks = name.split('.') |
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212 | if len(toks)==2: |
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213 | for item in self.dispersion.keys(): |
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214 | # 2D not supported |
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215 | if item.lower()==toks[0].lower(): |
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216 | for par in self.dispersion[item]: |
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217 | if par.lower() == toks[1].lower(): |
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218 | return self.dispersion[item][par] |
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219 | else: |
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220 | # Look for standard parameter |
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221 | for item in self.params.keys(): |
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222 | if item.lower()==name.lower(): |
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223 | return self.params[item] |
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224 | return |
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225 | #raise ValueError, "Model does not contain parameter %s" % name |
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226 | |
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227 | def _setParamHelper(self, name, value): |
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228 | # Look for dispersion parameters |
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229 | toks = name.split('.') |
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230 | if len(toks)== 2: |
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231 | for item in self.dispersion.keys(): |
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232 | if item.lower()== toks[0].lower(): |
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233 | for par in self.dispersion[item]: |
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234 | if par.lower() == toks[1].lower(): |
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235 | self.dispersion[item][par] = value |
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236 | return |
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237 | else: |
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238 | # Look for standard parameter |
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239 | for item in self.params.keys(): |
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240 | if item.lower()== name.lower(): |
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241 | self.params[item] = value |
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242 | return |
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243 | |
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244 | raise ValueError, "Model does not contain parameter %s" % name |
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245 | |
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246 | |
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247 | def _set_fixed_params(self): |
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248 | self.fixed = [] |
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249 | for item in self.p_model1.fixed: |
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250 | new_item = "p1" + item |
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251 | self.fixed.append(new_item) |
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252 | for item in self.p_model2.fixed: |
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253 | new_item = "p2" + item |
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254 | self.fixed.append(new_item) |
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255 | |
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256 | self.fixed.sort() |
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257 | |
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258 | |
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259 | def run(self, x = 0.0): |
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260 | self._set_scale_factor() |
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261 | return self.params['scale_factor'] * (self.p_model1.run(x) + self.p_model2.run(x)) |
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262 | |
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263 | def runXY(self, x = 0.0): |
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264 | self._set_scale_factor() |
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265 | return self.params['scale_factor'] * (self.p_model1.runXY(x) + self.p_model2.runXY(x)) |
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266 | |
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267 | ## Now (May27,10) directly uses the model eval function |
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268 | ## instead of the for-loop in Base Component. |
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269 | def evalDistribution(self, x = []): |
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270 | self._set_scale_factor() |
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271 | return self.params['scale_factor'] * (self.p_model1.evalDistribution(x) + self.p_model2.evalDistribution(x)) |
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272 | |
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273 | def set_dispersion(self, parameter, dispersion): |
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274 | value= None |
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275 | new_pre = parameter.split("_", 1)[0] |
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276 | new_parameter = parameter.split("_", 1)[1] |
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277 | try: |
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278 | if new_pre == 'p1' and new_parameter in self.p_model1.dispersion.keys(): |
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279 | value= self.p_model1.set_dispersion(new_parameter, dispersion) |
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280 | if new_pre == 'p2' and new_parameter in self.p_model2.dispersion.keys(): |
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281 | value= self.p_model2.set_dispersion(new_parameter, dispersion) |
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282 | self._set_dispersion() |
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283 | return value |
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284 | except: |
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285 | raise |
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286 | |
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287 | def fill_description(self, p_model1, p_model2): |
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288 | description = "" |
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289 | description += "This model gives the summation or multiplication of" |
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290 | description += "%s and %s. "% ( p_model1.name, p_model2.name ) |
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291 | self.description += description |
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292 | |
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293 | if __name__ == "__main__": |
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294 | m1= Model() |
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295 | #m1.setParam("p1_scale", 25) |
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296 | #m1.setParam("p1_length", 1000) |
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297 | #m1.setParam("p2_scale", 100) |
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298 | #m1.setParam("p2_rg", 100) |
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299 | out1 = m1.runXY(0.01) |
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300 | |
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301 | m2= Model() |
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302 | #m2.p_model1.setParam("scale", 25) |
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303 | #m2.p_model1.setParam("length", 1000) |
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304 | #m2.p_model2.setParam("scale", 100) |
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305 | #m2.p_model2.setParam("rg", 100) |
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306 | out2 = m2.p_model1.runXY(0.01) + m2.p_model2.runXY(0.01) |
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307 | |
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308 | print "My name is %s."% m1.name |
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309 | print out1, " = ", out2 |
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310 | if out1 == out2: |
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311 | print "===> Simple Test: Passed!" |
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312 | else: |
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313 | print "===> Simple Test: Failed!" |
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314 | |
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