1 | import _asap |
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2 | |
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3 | class fitter: |
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4 | """ |
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5 | The fitting class for ASAP. |
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6 | """ |
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7 | def _verbose(self, *args): |
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8 | """ |
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9 | Set stdout output. |
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10 | """ |
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11 | if type(args[0]) is bool: |
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12 | self._vb = args[0] |
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13 | return |
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14 | elif len(args) == 0: |
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15 | return self._vb |
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16 | |
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17 | def __init__(self): |
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18 | """ |
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19 | Create a fitter object. No state is set. |
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20 | """ |
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21 | self.fitter = _asap.fitter() |
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22 | self.x = None |
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23 | self.y = None |
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24 | self.mask = None |
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25 | self.fitfunc = None |
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26 | self.fitted = False |
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27 | self.data = None |
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28 | self._p = None |
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29 | self._vb = True |
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30 | |
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31 | def set_data(self, xdat, ydat, mask=None): |
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32 | """ |
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33 | Set the absissa and ordinate for the fit. Also set the mask |
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34 | indicationg valid points. |
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35 | This can be used for data vectors retrieved from a scantable. |
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36 | For scantable fitting use 'fitter.set_scan(scan, mask)'. |
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37 | Parameters: |
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38 | xdat: the abcissa values |
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39 | ydat: the ordinate values |
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40 | mask: an optional mask |
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41 | |
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42 | """ |
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43 | self.fitted = False |
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44 | self.x = xdat |
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45 | self.y = ydat |
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46 | if mask == None: |
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47 | from numarray import ones |
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48 | self.mask = ones(len(xdat)) |
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49 | else: |
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50 | self.mask = mask |
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51 | return |
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52 | |
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53 | def set_scan(self, thescan=None, mask=None): |
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54 | """ |
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55 | Set the 'data' (a scantable) of the fitter. |
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56 | Parameters: |
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57 | thescan: a scantable |
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58 | mask: a msk retireved from the scantable |
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59 | """ |
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60 | if not thescan: |
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61 | print "Please give a correct scan" |
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62 | self.fitted = False |
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63 | self.data = thescan |
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64 | if mask is None: |
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65 | from numarray import ones |
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66 | self.mask = ones(self.data.nchan()) |
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67 | else: |
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68 | self.mask = mask |
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69 | return |
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70 | |
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71 | def set_function(self, **kwargs): |
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72 | """ |
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73 | Set the function to be fit. |
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74 | Parameters: |
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75 | poly: use a polynomial of the order given |
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76 | gauss: fit the number of gaussian specified |
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77 | Example: |
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78 | fitter.set_function(gauss=2) # will fit two gaussians |
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79 | fitter.set_function(poly=3) # will fit a 3rd order polynomial |
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80 | """ |
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81 | #default poly order 0 |
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82 | self.fitfunc = 'poly' |
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83 | n=0 |
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84 | if kwargs.has_key('poly'): |
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85 | self.fitfunc = 'poly' |
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86 | n = kwargs.get('poly') |
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87 | elif kwargs.has_key('gauss'): |
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88 | n = kwargs.get('gauss') |
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89 | self.fitfunc = 'gauss' |
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90 | |
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91 | self.fitter.setexpression(self.fitfunc,n) |
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92 | return |
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93 | |
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94 | def fit(self): |
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95 | """ |
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96 | Execute the actual fitting process. All the state has to be set. |
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97 | Parameters: |
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98 | none |
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99 | Example: |
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100 | s= scantable('myscan.asap') |
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101 | f = fitter() |
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102 | f.set_scan(s) |
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103 | f.set_function(poly=0) |
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104 | f.fit() |
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105 | """ |
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106 | if ((self.x is None or self.y is None) and self.data is None) \ |
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107 | or self.fitfunc is None: |
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108 | print "Fitter not yet initialised. Please set data & fit function" |
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109 | return |
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110 | else: |
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111 | if self.data is not None: |
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112 | self.x = self.data.getabcissa() |
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113 | self.y = self.data.getspectrum() |
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114 | print "Fitting:" |
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115 | vb = self.data._verbose |
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116 | self.data._verbose(True) |
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117 | s = self.data.get_selection() |
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118 | self.data._verbose(vb) |
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119 | |
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120 | self.fitter.setdata(self.x,self.y,self.mask) |
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121 | if self.fitfunc == 'gauss': |
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122 | ps = self.fitter.getparameters() |
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123 | if len(ps) == 0: |
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124 | self.fitter.estimate() |
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125 | self.fitter.fit() |
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126 | self.fitted = True |
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127 | return |
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128 | |
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129 | def set_parameters(self, params, fixed=None): |
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130 | self.fitter.setparameters(params) |
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131 | if fixed is not None: |
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132 | self.fitter.setfixedparameters(fixed) |
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133 | return |
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134 | |
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135 | def get_parameters(self): |
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136 | """ |
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137 | Return the fit paramters. |
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138 | |
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139 | """ |
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140 | if not self.fitted: |
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141 | print "Not yet fitted." |
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142 | pars = list(self.fitter.getparameters()) |
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143 | fixed = list(self.fitter.getfixedparameters()) |
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144 | if self._vb: |
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145 | print self._format_pars(pars) |
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146 | return pars,fixed |
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147 | |
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148 | def _format_pars(self, pars): |
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149 | out = '' |
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150 | if self.fitfunc == 'poly': |
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151 | c = 0 |
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152 | for i in pars: |
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153 | out += ' p%d = %3.3f, ' % (c,i) |
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154 | c+=1 |
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155 | elif self.fitfunc == 'gauss': |
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156 | i = 0 |
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157 | c = 0 |
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158 | unit = '' |
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159 | if self.data: |
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160 | unit = self.data.get_unit() |
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161 | while i < len(pars): |
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162 | out += ' %d: peak = %3.3f , centre = %3.3f %s, FWHM = %3.3f %s \n' % (c,pars[i],pars[i+1],unit,pars[i+2],unit) |
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163 | c+=1 |
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164 | i+=3 |
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165 | return out |
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166 | |
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167 | def get_estimate(self): |
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168 | """ |
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169 | Return the paramter estimates (for non-linear functions). |
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170 | """ |
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171 | pars = self.fitter.getestimate() |
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172 | if self._vb: |
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173 | print self._format_pars(pars) |
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174 | return pars |
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175 | |
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176 | |
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177 | def get_residual(self): |
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178 | """ |
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179 | Return the residual of the fit. |
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180 | """ |
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181 | if not self.fitted: |
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182 | print "Not yet fitted." |
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183 | return self.fitter.getresidual() |
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184 | |
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185 | def get_chi2(self): |
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186 | """ |
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187 | Return chi^2. |
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188 | """ |
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189 | |
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190 | if not self.fitted: |
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191 | print "Not yet fitted." |
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192 | ch2 = self.fitter.getchi2() |
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193 | if self._vb: |
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194 | print 'Chi^2 = %3.3f' % (ch2) |
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195 | return ch2 |
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196 | |
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197 | def get_fit(self): |
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198 | """ |
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199 | Return the fitted ordinate values. |
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200 | """ |
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201 | if not self.fitted: |
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202 | print "Not yet fitted." |
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203 | return self.fitter.getfit() |
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204 | |
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205 | def commit(self): |
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206 | """ |
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207 | Return a new scan where teh fits have been commited. |
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208 | """ |
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209 | if not self.fitted: |
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210 | print "Not yet fitted." |
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211 | if self.data is not scantable: |
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212 | print "Only works with scantables" |
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213 | return |
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214 | scan = self.data.copy() |
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215 | scan.setspectrum(self.fitter.getresidual()) |
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216 | |
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217 | def plot(self, residual=False): |
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218 | """ |
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219 | Plot the last fit. |
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220 | Parameters: |
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221 | residual: an optional parameter indicating if the residual |
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222 | should be plotted (default 'False') |
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223 | """ |
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224 | if not self.fitted: |
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225 | return |
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226 | if not self._p: |
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227 | from asap.asaplot import ASAPlot |
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228 | self._p = ASAPlot() |
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229 | if self._.is_dead: |
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230 | from asap.asaplot import ASAPlot |
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231 | self._p = ASAPlot() |
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232 | self._p.clear() |
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233 | tlab = 'Spectrum' |
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234 | xlab = 'Abcissa' |
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235 | if self.data: |
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236 | tlab = self.data._getsourcename(0) |
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237 | xlab = self.data.getabcissalabel(0) |
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238 | ylab = r'Flux' |
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239 | m = self.data.getmask(0) |
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240 | self._p.set_line(colour='blue',label='Spectrum') |
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241 | self._p.plot(self.x, self.y, m) |
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242 | if residual: |
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243 | self._p.set_line(colour='green',label='Residual') |
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244 | self._p.plot(self.x, self.get_residual(), m) |
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245 | self._p.set_line(colour='red',label='Fit') |
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246 | self._p.plot(self.x, self.get_fit(), m) |
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247 | |
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248 | self._p.set_axes('xlabel',xlab) |
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249 | self._p.set_axes('ylabel',ylab) |
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250 | self._p.set_axes('title',tlab) |
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251 | self._p.release() |
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252 | |
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253 | |
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254 | def auto_fit(self): |
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255 | """ |
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256 | Return a scan where the function is applied to all rows for all Beams/IFs/Pols. |
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257 | |
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258 | """ |
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259 | from asap import scantable |
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260 | if not isinstance(self.data,scantable) : |
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261 | print "Only works with scantables" |
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262 | return |
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263 | scan = self.data.copy() |
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264 | vb = scan._verbose |
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265 | scan._verbose(False) |
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266 | sel = scan.get_selection() |
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267 | rows = range(scan.nrow()) |
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268 | for i in range(scan.nbeam()): |
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269 | scan.setbeam(i) |
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270 | for j in range(scan.nif()): |
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271 | scan.setif(j) |
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272 | for k in range(scan.npol()): |
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273 | scan.setpol(k) |
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274 | if self._vb: |
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275 | print "Fitting:" |
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276 | print 'Beam[%d], IF[%d], Pol[%d]' % (i,j,k) |
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277 | for iRow in rows: |
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278 | self.x = scan.getabcissa(iRow) |
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279 | self.y = scan.getspectrum(iRow) |
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280 | self.data = None |
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281 | self.fit() |
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282 | x = self.get_parameters() |
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283 | scan.setspectrum(self.fitter.getresidual(),iRow) |
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284 | scan.set_selection(sel[0],sel[1],sel[2]) |
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285 | scan._verbose(vb) |
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286 | return scan |
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