1 | |
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2 | |
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3 | """ |
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4 | ScipyFitting module contains FitArrange , ScipyFit, |
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5 | Parameter classes.All listed classes work together to perform a |
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6 | simple fit with scipy optimizer. |
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7 | """ |
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8 | |
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9 | import numpy |
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10 | import sys |
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11 | |
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12 | |
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13 | from sans.fit.AbstractFitEngine import FitEngine |
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14 | from sans.fit.AbstractFitEngine import SansAssembly |
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15 | from sans.fit.AbstractFitEngine import FitAbort |
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16 | from sans.fit.AbstractFitEngine import Model |
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17 | from sans.fit.AbstractFitEngine import FResult |
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18 | |
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19 | class ScipyFit(FitEngine): |
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20 | """ |
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21 | ScipyFit performs the Fit.This class can be used as follow: |
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22 | #Do the fit SCIPY |
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23 | create an engine: engine = ScipyFit() |
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24 | Use data must be of type plottable |
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25 | Use a sans model |
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26 | |
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27 | Add data with a dictionnary of FitArrangeDict where Uid is a key and data |
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28 | is saved in FitArrange object. |
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29 | engine.set_data(data,Uid) |
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30 | |
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31 | Set model parameter "M1"= model.name add {model.parameter.name:value}. |
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32 | |
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33 | :note: Set_param() if used must always preceded set_model() |
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34 | for the fit to be performed.In case of Scipyfit set_param is called in |
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35 | fit () automatically. |
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36 | |
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37 | engine.set_param( model,"M1", {'A':2,'B':4}) |
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38 | |
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39 | Add model with a dictionnary of FitArrangeDict{} where Uid is a key and model |
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40 | is save in FitArrange object. |
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41 | engine.set_model(model,Uid) |
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42 | |
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43 | engine.fit return chisqr,[model.parameter 1,2,..],[[err1....][..err2...]] |
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44 | chisqr1, out1, cov1=engine.fit({model.parameter.name:value},qmin,qmax) |
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45 | """ |
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46 | def __init__(self): |
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47 | """ |
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48 | Creates a dictionary (self.fit_arrange_dict={})of FitArrange elements |
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49 | with Uid as keys |
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50 | """ |
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51 | FitEngine.__init__(self) |
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52 | self.fit_arrange_dict = {} |
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53 | self.param_list = [] |
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54 | self.curr_thread = None |
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55 | #def fit(self, *args, **kw): |
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56 | # return profile(self._fit, *args, **kw) |
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57 | |
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58 | def fit(self, q=None, handler=None, curr_thread=None, ftol=1.49012e-8): |
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59 | """ |
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60 | """ |
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61 | fitproblem = [] |
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62 | for fproblem in self.fit_arrange_dict.itervalues(): |
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63 | if fproblem.get_to_fit() == 1: |
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64 | fitproblem.append(fproblem) |
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65 | if len(fitproblem) > 1 : |
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66 | msg = "Scipy can't fit more than a single fit problem at a time." |
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67 | raise RuntimeError, msg |
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68 | return |
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69 | elif len(fitproblem) == 0 : |
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70 | raise RuntimeError, "No Assembly scheduled for Scipy fitting." |
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71 | return |
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72 | |
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73 | listdata = [] |
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74 | model = fitproblem[0].get_model() |
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75 | listdata = fitproblem[0].get_data() |
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76 | # Concatenate dList set (contains one or more data)before fitting |
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77 | data = listdata |
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78 | |
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79 | self.curr_thread = curr_thread |
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80 | ftol = ftol |
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81 | |
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82 | # Check the initial value if it is within range |
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83 | self._check_param_range(model) |
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84 | |
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85 | result = FResult(model=model, data=data, param_list=self.param_list) |
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86 | if handler is not None: |
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87 | handler.set_result(result=result) |
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88 | try: |
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89 | # This import must be here; otherwise it will be confused when more |
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90 | # than one thread exist. |
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91 | from scipy import optimize |
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92 | |
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93 | functor = SansAssembly(self.param_list, model, data, handler=handler,\ |
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94 | fitresult=result, curr_thread= curr_thread) |
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95 | out, cov_x, _, mesg, success = optimize.leastsq(functor, |
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96 | model.get_params(self.param_list), |
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97 | ftol=ftol, |
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98 | full_output=1, |
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99 | warning=True) |
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100 | |
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101 | except KeyboardInterrupt: |
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102 | msg = "Fitting: Terminated!!!" |
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103 | handler.error(msg) |
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104 | raise KeyboardInterrupt, msg #<= more stable |
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105 | #less stable below |
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106 | """ |
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107 | if hasattr(sys, 'last_type') and sys.last_type == KeyboardInterrupt: |
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108 | if handler is not None: |
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109 | msg = "Fitting: Terminated!!!" |
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110 | handler.error(msg) |
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111 | result = handler.get_result() |
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112 | return result |
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113 | else: |
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114 | raise |
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115 | """ |
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116 | except: |
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117 | raise |
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118 | chisqr = functor.chisq() |
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119 | if cov_x is not None and numpy.isfinite(cov_x).all(): |
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120 | stderr = numpy.sqrt(numpy.diag(cov_x)) |
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121 | else: |
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122 | stderr = None |
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123 | |
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124 | if not (numpy.isnan(out).any()) and (cov_x != None): |
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125 | result.fitness = chisqr |
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126 | result.stderr = stderr |
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127 | result.pvec = out |
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128 | result.success = success |
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129 | result.theory = functor.theory |
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130 | #print "scipy", result.inputs |
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131 | if q is not None: |
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132 | q.put(result) |
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133 | return q |
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134 | if success < 1 or success > 5: |
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135 | result = None |
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136 | return [result] |
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137 | """ |
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138 | else: |
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139 | return None |
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140 | """ |
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141 | # Error will be present to the client, not here |
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142 | #else: |
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143 | # raise ValueError, "SVD did not converge" + str(mesg) |
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144 | |
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145 | def _check_param_range(self, model): |
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146 | """ |
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147 | Check parameter range and set the initial value inside |
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148 | if it is out of range. |
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149 | |
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150 | : model: park model object |
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151 | """ |
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152 | is_outofbound = False |
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153 | # loop through parameterset |
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154 | for p in model.parameterset: |
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155 | param_name = p.get_name() |
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156 | # proceed only if the parameter name is in the list of fitting |
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157 | if param_name in self.param_list: |
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158 | # if the range was defined, check the range |
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159 | if numpy.isfinite(p.range[0]): |
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160 | if p.value <= p.range[0]: |
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161 | # 10 % backing up from the border if not zero |
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162 | # for Scipy engine to work properly. |
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163 | shift = self._get_zero_shift(p.range[0]) |
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164 | new_value = p.range[0] + shift |
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165 | p.value = new_value |
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166 | is_outofbound = True |
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167 | if numpy.isfinite(p.range[1]): |
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168 | if p.value >= p.range[1]: |
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169 | shift = self._get_zero_shift(p.range[1]) |
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170 | # 10 % backing up from the border if not zero |
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171 | # for Scipy engine to work properly. |
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172 | new_value = p.range[1] - shift |
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173 | # Check one more time if the new value goes below |
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174 | # the low bound, If so, re-evaluate the value |
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175 | # with the mean of the range. |
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176 | if numpy.isfinite(p.range[0]): |
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177 | if new_value < p.range[0]: |
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178 | new_value = (p.range[0] + p.range[1]) / 2.0 |
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179 | # Todo: |
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180 | # Need to think about when both min and max are same. |
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181 | p.value = new_value |
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182 | is_outofbound = True |
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183 | |
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184 | return is_outofbound |
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185 | |
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186 | def _get_zero_shift(self, range): |
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187 | """ |
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188 | Get 10% shift of the param value = 0 based on the range value |
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189 | |
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190 | : param range: min or max value of the bounds |
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191 | """ |
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192 | if range == 0: |
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193 | shift = 0.1 |
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194 | else: |
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195 | shift = 0.1 * range |
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196 | |
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197 | return shift |
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198 | |
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199 | |
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200 | #def profile(fn, *args, **kw): |
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201 | # import cProfile, pstats, os |
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202 | # global call_result |
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203 | # def call(): |
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204 | # global call_result |
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205 | # call_result = fn(*args, **kw) |
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206 | # cProfile.runctx('call()', dict(call=call), {}, 'profile.out') |
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207 | # stats = pstats.Stats('profile.out') |
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208 | # stats.sort_stats('time') |
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209 | # stats.sort_stats('calls') |
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210 | # stats.print_stats() |
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211 | # os.unlink('profile.out') |
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212 | # return call_result |
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213 | |
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214 | |
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