1 | #class Fitting |
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2 | from sans.guitools.plottables import Data1D |
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3 | from Loader import Load |
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4 | from scipy import optimize |
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5 | #from Fitting import Fit |
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6 | |
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7 | class FitArrange: |
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8 | def __init__(self): |
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9 | """ |
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10 | Store a set of data for a given model to perform the Fit |
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11 | @param model: the model selected by the user |
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12 | @param Ldata: a list of data what the user want to fit |
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13 | """ |
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14 | self.model = None |
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15 | self.dList =[] |
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16 | |
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17 | def set_model(self,model): |
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18 | """ set the model """ |
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19 | self.model = model |
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20 | |
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21 | def add_data(self,data): |
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22 | """ |
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23 | @param data: Data to add in the list |
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24 | fill a self.dataList with data to fit |
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25 | """ |
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26 | if not data in self.dList: |
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27 | self.dList.append(data) |
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28 | |
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29 | def get_model(self): |
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30 | """ Return the model""" |
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31 | return self.model |
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32 | |
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33 | def get_data(self): |
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34 | """ Return list of data""" |
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35 | return self.dList |
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36 | |
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37 | def remove_data(self,data): |
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38 | """ |
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39 | Remove one element from the list |
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40 | @param data: Data to remove from the the lsit of data |
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41 | """ |
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42 | if data in self.dList: |
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43 | self.dList.remove(data) |
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44 | |
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45 | class ScipyFit: |
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46 | """ |
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47 | Performs the Fit.he user determine what kind of data |
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48 | """ |
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49 | def __init__(self,data=[]): |
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50 | #this is a dictionary of FitArrange elements |
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51 | self.fitArrangeList={} |
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52 | #the constraint of the Fit |
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53 | self.constraint =None |
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54 | #Specify the use of scipy or park fit |
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55 | self.fitType =None |
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56 | |
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57 | |
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58 | |
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59 | def fit(self,pars, qmin=None, qmax=None): |
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60 | """ |
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61 | Do the fit |
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62 | """ |
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63 | #for item in self.fitArrangeList.: |
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64 | |
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65 | fitproblem=self.fitArrangeList.values()[0] |
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66 | listdata=[] |
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67 | model = fitproblem.get_model() |
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68 | listdata = fitproblem.get_data() |
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69 | |
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70 | parameters = self.set_param(model,model.name,pars) |
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71 | |
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72 | # Do the fit with data set (contains one or more data) and one model |
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73 | xtemp,ytemp,dytemp=self._concatenateData( listdata) |
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74 | print "dytemp",dytemp |
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75 | if qmin==None: |
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76 | qmin= min(xtemp) |
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77 | if qmax==None: |
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78 | qmax= max(xtemp) |
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79 | chisqr, out, cov = fitHelper(model,parameters, xtemp,ytemp, dytemp ,qmin,qmax) |
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80 | return chisqr, out, cov |
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81 | |
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82 | def _concatenateData(self, listdata=[]): |
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83 | """ concatenate each fields of all data contains ins listdata""" |
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84 | if listdata==[]: |
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85 | raise ValueError, " data list missing" |
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86 | else: |
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87 | xtemp=[] |
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88 | ytemp=[] |
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89 | dytemp=[] |
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90 | |
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91 | for data in listdata: |
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92 | for i in range(len(data.x)): |
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93 | if not data.x[i] in xtemp: |
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94 | xtemp.append(data.x[i]) |
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95 | |
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96 | if not data.y[i] in ytemp: |
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97 | ytemp.append(data.y[i]) |
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98 | |
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99 | if not data.dy[i] in dytemp: |
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100 | dytemp.append(data.dy[i]) |
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101 | return xtemp, ytemp,dytemp |
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102 | |
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103 | def set_model(self,model,Uid): |
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104 | """ Set model """ |
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105 | if self.fitArrangeList.has_key(Uid): |
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106 | self.fitArrangeList[Uid].set_model(model) |
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107 | else: |
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108 | fitproblem= FitArrange() |
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109 | fitproblem.set_model(model) |
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110 | self.fitArrangeList[Uid]=fitproblem |
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111 | |
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112 | def set_data(self,data,Uid): |
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113 | """ Receive plottable and create a list of data to fit""" |
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114 | |
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115 | if self.fitArrangeList.has_key(Uid): |
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116 | self.fitArrangeList[Uid].add_data(data) |
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117 | else: |
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118 | fitproblem= FitArrange() |
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119 | fitproblem.add_data(data) |
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120 | self.fitArrangeList[Uid]=fitproblem |
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121 | |
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122 | def get_model(self,Uid): |
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123 | """ return list of data""" |
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124 | return self.fitArrangeList[Uid] |
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125 | |
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126 | def set_param(self,model,name, pars): |
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127 | """ Recieve a dictionary of parameter and save it """ |
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128 | parameters=[] |
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129 | if model==None: |
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130 | raise ValueError, "Cannot set parameters for empty model" |
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131 | else: |
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132 | model.name=name |
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133 | for key, value in pars.iteritems(): |
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134 | param = Parameter(model, key, value) |
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135 | parameters.append(param) |
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136 | return parameters |
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137 | |
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138 | def add_constraint(self, constraint): |
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139 | """ User specify contraint to fit """ |
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140 | self.constraint = str(constraint) |
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141 | |
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142 | def get_constraint(self): |
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143 | """ return the contraint value """ |
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144 | return self.constraint |
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145 | |
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146 | def set_constraint(self,constraint): |
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147 | """ |
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148 | receive a string as a constraint |
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149 | @param constraint: a string used to constraint some parameters to get a |
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150 | specific value |
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151 | """ |
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152 | self.constraint= constraint |
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153 | |
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154 | def createProblem(self): |
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155 | """ |
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156 | Check the contraint value and specify what kind of fit to use |
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157 | """ |
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158 | mylist=[] |
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159 | for k,value in self.fitArrangeList.iteritems(): |
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160 | couple=() |
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161 | model=value.get_model() |
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162 | data=value.get_data() |
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163 | couple=(model,data) |
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164 | mylist.append(couple) |
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165 | #print mylist |
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166 | return mylist |
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167 | def remove_data(self,Uid,data=None): |
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168 | """ remove one or all data""" |
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169 | if data==None:# remove all element in data list |
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170 | if self.fitArrangeList.has_key(Uid): |
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171 | self.fitArrangeList[Uid].remove_datalist() |
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172 | else: |
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173 | if self.fitArrangeList.has_key(Uid): |
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174 | self.fitArrangeList[Uid].remove_data(data) |
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175 | |
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176 | def remove_model(self,Uid): |
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177 | """ remove model """ |
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178 | if self.fitArrangeList.has_key(Uid): |
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179 | self.fitArrangeList[Uid].remove_model() |
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180 | |
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181 | |
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182 | class Parameter: |
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183 | """ |
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184 | Class to handle model parameters |
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185 | """ |
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186 | def __init__(self, model, name, value=None): |
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187 | self.model = model |
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188 | self.name = name |
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189 | if not value==None: |
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190 | self.model.setParam(self.name, value) |
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191 | |
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192 | def set(self, value): |
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193 | """ |
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194 | Set the value of the parameter |
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195 | """ |
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196 | self.model.setParam(self.name, value) |
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197 | |
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198 | def __call__(self): |
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199 | """ |
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200 | Return the current value of the parameter |
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201 | """ |
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202 | return self.model.getParam(self.name) |
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203 | |
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204 | def fitHelper(model, pars, x, y, err_y ,qmin=None, qmax=None): |
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205 | """ |
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206 | Fit function |
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207 | @param model: sans model object |
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208 | @param pars: list of parameters |
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209 | @param x: vector of x data |
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210 | @param y: vector of y data |
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211 | @param err_y: vector of y errors |
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212 | """ |
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213 | def f(params): |
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214 | """ |
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215 | Calculates the vector of residuals for each point |
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216 | in y for a given set of input parameters. |
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217 | @param params: list of parameter values |
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218 | @return: vector of residuals |
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219 | """ |
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220 | i = 0 |
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221 | for p in pars: |
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222 | p.set(params[i]) |
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223 | i += 1 |
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224 | |
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225 | residuals = [] |
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226 | for j in range(len(x)): |
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227 | if x[j]>qmin and x[j]<qmax: |
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228 | residuals.append( ( y[j] - model.runXY(x[j]) ) / err_y[j] ) |
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229 | |
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230 | return residuals |
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231 | |
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232 | def chi2(params): |
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233 | """ |
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234 | Calculates chi^2 |
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235 | @param params: list of parameter values |
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236 | @return: chi^2 |
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237 | """ |
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238 | sum = 0 |
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239 | res = f(params) |
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240 | for item in res: |
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241 | sum += item*item |
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242 | return sum |
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243 | |
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244 | p = [param() for param in pars] |
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245 | out, cov_x, info, mesg, success = optimize.leastsq(f, p, full_output=1, warning=True) |
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246 | print info, mesg, success |
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247 | # Calculate chi squared |
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248 | if len(pars)>1: |
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249 | chisqr = chi2(out) |
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250 | elif len(pars)==1: |
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251 | chisqr = chi2([out]) |
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252 | |
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253 | return chisqr, out, cov_x |
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254 | |
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