| 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 abscissa 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 abscissa 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.getabscissa()
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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 | self._p.clear()
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| 230 | tlab = 'Spectrum'
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| 231 | xlab = 'Abscissa'
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| 232 | if self.data:
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| 233 | tlab = self.data._getsourcename(0)
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| 234 | xlab = self.data.getabscissalabel(0)
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| 235 | ylab = r'Flux'
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| 236 | m = self.data.getmask(0)
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| 237 | self._p.set_line(colour='blue',label='Spectrum')
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| 238 | self._p.plot(self.x, self.y, m)
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| 239 | if residual:
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| 240 | self._p.set_line(colour='green',label='Residual')
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| 241 | self._p.plot(self.x, self.get_residual(), m)
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| 242 | self._p.set_line(colour='red',label='Fit')
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| 243 | self._p.plot(self.x, self.get_fit(), m)
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| 244 |
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| 245 | self._p.set_axes('xlabel',xlab)
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| 246 | self._p.set_axes('ylabel',ylab)
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| 247 | self._p.set_axes('title',tlab)
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| 248 | self._p.release()
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| 249 |
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| 250 |
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| 251 | def auto_fit(self):
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| 252 | """
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| 253 | Return a scan where the function is applied to all Beams/IFs/Pols.
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| 254 |
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| 255 | """
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| 256 | from asap import scantable
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| 257 | if not isinstance(self.data,scantable) :
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| 258 | print "Only works with scantables"
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| 259 | return
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| 260 | scan = self.data.copy()
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| 261 | vb = scan._verbose
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| 262 | scan._verbose(False)
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| 263 | sel = scan.get_selection()
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| 264 | for i in range(scan.nbeam()):
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| 265 | scan.setbeam(i)
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| 266 | for j in range(scan.nif()):
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| 267 | scan.setif(j)
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| 268 | for k in range(scan.npol()):
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| 269 | scan.setpol(k)
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| 270 | self.x = scan.getabscissa()
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| 271 | self.y = scan.getspectrum()
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| 272 | self.data = None
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| 273 | self.fit()
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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 | x = self.get_parameters()
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| 278 | scan.setspectrum(self.fitter.getresidual())
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| 279 | scan.set_selection(sel[0],sel[1],sel[2])
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| 280 | scan._verbose(vb)
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| 281 | return scan
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| 282 |
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