| 1 | import _asap
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| 2 | from asap import rcParams
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| 3 | from asap import print_log_dec
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| 4 | from asap import _n_bools
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| 5 | from asap import mask_and
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| 6 |
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| 7 | class fitter:
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| 8 | """
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| 9 | The fitting class for ASAP.
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| 10 | """
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| 11 |
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| 12 | def __init__(self):
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| 13 | """
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| 14 | Create a fitter object. No state is set.
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| 15 | """
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| 16 | self.fitter = _asap.fitter()
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| 17 | self.x = None
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| 18 | self.y = None
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| 19 | self.mask = None
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| 20 | self.fitfunc = None
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| 21 | self.fitfuncs = None
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| 22 | self.fitted = False
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| 23 | self.data = None
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| 24 | self.components = 0
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| 25 | self._fittedrow = 0
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| 26 | self._p = None
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| 27 | self._selection = None
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| 28 | self.uselinear = False
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| 29 |
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| 30 | def set_data(self, xdat, ydat, mask=None):
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| 31 | """
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| 32 | Set the absissa and ordinate for the fit. Also set the mask
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| 33 | indicationg valid points.
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| 34 | This can be used for data vectors retrieved from a scantable.
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| 35 | For scantable fitting use 'fitter.set_scan(scan, mask)'.
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| 36 | Parameters:
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| 37 | xdat: the abcissa values
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| 38 | ydat: the ordinate values
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| 39 | mask: an optional mask
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| 40 |
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| 41 | """
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| 42 | self.fitted = False
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| 43 | self.x = xdat
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| 44 | self.y = ydat
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| 45 | if mask == None:
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| 46 | self.mask = _n_bools(len(xdat), True)
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| 47 | else:
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| 48 | self.mask = mask
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| 49 | return
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| 50 |
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| 51 | def set_scan(self, thescan=None, mask=None):
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| 52 | """
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| 53 | Set the 'data' (a scantable) of the fitter.
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| 54 | Parameters:
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| 55 | thescan: a scantable
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| 56 | mask: a msk retrieved from the scantable
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| 57 | """
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| 58 | if not thescan:
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| 59 | msg = "Please give a correct scan"
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| 60 | if rcParams['verbose']:
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| 61 | print msg
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| 62 | return
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| 63 | else:
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| 64 | raise TypeError(msg)
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| 65 | self.fitted = False
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| 66 | self.data = thescan
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| 67 | self.mask = None
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| 68 | if mask is None:
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| 69 | self.mask = _n_bools(self.data.nchan(), True)
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| 70 | else:
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| 71 | self.mask = mask
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| 72 | return
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| 73 |
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| 74 | def set_function(self, **kwargs):
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| 75 | """
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| 76 | Set the function to be fit.
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| 77 | Parameters:
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| 78 | poly: use a polynomial of the order given with nonlinear least squares fit
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| 79 | lpoly: use polynomial of the order given with linear least squares fit
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| 80 | gauss: fit the number of gaussian specified
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| 81 | Example:
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| 82 | fitter.set_function(gauss=2) # will fit two gaussians
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| 83 | fitter.set_function(poly=3) # will fit a 3rd order polynomial via nonlinear method
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| 84 | fitter.set_function(lpoly=3) # will fit a 3rd order polynomial via linear method
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| 85 | """
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| 86 | #default poly order 0
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| 87 | n=0
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| 88 | if kwargs.has_key('poly'):
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| 89 | self.fitfunc = 'poly'
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| 90 | n = kwargs.get('poly')
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| 91 | self.components = [n]
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| 92 | self.uselinear = False
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| 93 | elif kwargs.has_key('lpoly'):
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| 94 | self.fitfunc = 'poly'
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| 95 | n = kwargs.get('lpoly')
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| 96 | self.components = [n]
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| 97 | self.uselinear = True
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| 98 | elif kwargs.has_key('gauss'):
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| 99 | n = kwargs.get('gauss')
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| 100 | self.fitfunc = 'gauss'
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| 101 | self.fitfuncs = [ 'gauss' for i in range(n) ]
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| 102 | self.components = [ 3 for i in range(n) ]
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| 103 | self.uselinear = False
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| 104 | else:
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| 105 | msg = "Invalid function type."
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| 106 | if rcParams['verbose']:
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| 107 | print msg
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| 108 | return
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| 109 | else:
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| 110 | raise TypeError(msg)
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| 111 |
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| 112 | self.fitter.setexpression(self.fitfunc,n)
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| 113 | self.fitted = False
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| 114 | return
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| 115 |
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| 116 | @print_log_dec
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| 117 | def fit(self, row=0, estimate=False):
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| 118 | """
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| 119 | Execute the actual fitting process. All the state has to be set.
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| 120 | Parameters:
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| 121 | row: specify the row in the scantable
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| 122 | estimate: auto-compute an initial parameter set (default False)
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| 123 | This can be used to compute estimates even if fit was
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| 124 | called before.
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| 125 | Example:
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| 126 | s = scantable('myscan.asap')
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| 127 | s.set_cursor(thepol=1) # select second pol
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| 128 | f = fitter()
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| 129 | f.set_scan(s)
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| 130 | f.set_function(poly=0)
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| 131 | f.fit(row=0) # fit first row
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| 132 | """
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| 133 | if ((self.x is None or self.y is None) and self.data is None) \
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| 134 | or self.fitfunc is None:
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| 135 | msg = "Fitter not yet initialised. Please set data & fit function"
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| 136 | if rcParams['verbose']:
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| 137 | print msg
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| 138 | return
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| 139 | else:
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| 140 | raise RuntimeError(msg)
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| 141 |
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| 142 | else:
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| 143 | if self.data is not None:
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| 144 | self.x = self.data._getabcissa(row)
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| 145 | self.y = self.data._getspectrum(row)
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| 146 | self.mask = mask_and(self.mask, self.data._getmask(row))
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| 147 | from asap import asaplog
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| 148 | asaplog.push("Fitting:")
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| 149 | i = row
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| 150 | out = "Scan[%d] Beam[%d] IF[%d] Pol[%d] Cycle[%d]" % (self.data.getscan(i),
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| 151 | self.data.getbeam(i),
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| 152 | self.data.getif(i),
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| 153 | self.data.getpol(i),
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| 154 | self.data.getcycle(i))
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| 155 | asaplog.push(out,False)
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| 156 | self.fitter.setdata(self.x, self.y, self.mask)
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| 157 | if self.fitfunc == 'gauss':
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| 158 | ps = self.fitter.getparameters()
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| 159 | if len(ps) == 0 or estimate:
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| 160 | self.fitter.estimate()
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| 161 | try:
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| 162 | fxdpar = list(self.fitter.getfixedparameters())
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| 163 | if len(fxdpar) and fxdpar.count(0) == 0:
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| 164 | raise RuntimeError,"No point fitting, if all parameters are fixed."
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| 165 | if self.uselinear:
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| 166 | converged = self.fitter.lfit()
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| 167 | else:
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| 168 | converged = self.fitter.fit()
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| 169 | if not converged:
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| 170 | raise RuntimeError,"Fit didn't converge."
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| 171 | except RuntimeError, msg:
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| 172 | if rcParams['verbose']:
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| 173 | print msg
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| 174 | else:
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| 175 | raise
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| 176 | self._fittedrow = row
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| 177 | self.fitted = True
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| 178 | return
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| 179 |
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| 180 | def store_fit(self, filename=None):
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| 181 | """
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| 182 | Save the fit parameters.
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| 183 | Parameters:
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| 184 | filename: if specified save as an ASCII file, if None (default)
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| 185 | store it in the scnatable
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| 186 | """
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| 187 | if self.fitted and self.data is not None:
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| 188 | pars = list(self.fitter.getparameters())
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| 189 | fixed = list(self.fitter.getfixedparameters())
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| 190 | from asap.asapfit import asapfit
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| 191 | fit = asapfit()
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| 192 | fit.setparameters(pars)
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| 193 | fit.setfixedparameters(fixed)
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| 194 | fit.setfunctions(self.fitfuncs)
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| 195 | fit.setcomponents(self.components)
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| 196 | fit.setframeinfo(self.data._getcoordinfo())
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| 197 | if filename is not None:
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| 198 | import os
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| 199 | filename = os.path.expandvars(os.path.expanduser(filename))
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| 200 | if os.path.exists(filename):
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| 201 | raise IOError("File '%s' exists." % filename)
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| 202 | fit.save(filename)
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| 203 | else:
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| 204 | self.data._addfit(fit,self._fittedrow)
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| 205 |
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| 206 | @print_log_dec
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| 207 | def set_parameters(self,*args,**kwargs):
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| 208 | """
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| 209 | Set the parameters to be fitted.
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| 210 | Parameters:
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| 211 | params: a vector of parameters
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| 212 | fixed: a vector of which parameters are to be held fixed
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| 213 | (default is none)
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| 214 | component: in case of multiple gaussians, the index of the
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| 215 | component
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| 216 | """
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| 217 | component = None
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| 218 | fixed = None
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| 219 | params = None
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| 220 |
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| 221 | if len(args) and isinstance(args[0],dict):
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| 222 | kwargs = args[0]
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| 223 | if kwargs.has_key("fixed"): fixed = kwargs["fixed"]
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| 224 | if kwargs.has_key("params"): params = kwargs["params"]
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| 225 | if len(args) == 2 and isinstance(args[1], int):
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| 226 | component = args[1]
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| 227 | if self.fitfunc is None:
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| 228 | msg = "Please specify a fitting function first."
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| 229 | if rcParams['verbose']:
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| 230 | print msg
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| 231 | return
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| 232 | else:
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| 233 | raise RuntimeError(msg)
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| 234 | if self.fitfunc == "gauss" and component is not None:
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| 235 | if not self.fitted and sum(self.fitter.getparameters()) == 0:
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| 236 | pars = _n_bools(len(self.components)*3, False)
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| 237 | fxd = _n_bools(len(pars), False)
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| 238 | else:
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| 239 | pars = list(self.fitter.getparameters())
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| 240 | fxd = list(self.fitter.getfixedparameters())
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| 241 | i = 3*component
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| 242 | pars[i:i+3] = params
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| 243 | fxd[i:i+3] = fixed
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| 244 | params = pars
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| 245 | fixed = fxd
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| 246 | self.fitter.setparameters(params)
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| 247 | if fixed is not None:
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| 248 | self.fitter.setfixedparameters(fixed)
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| 249 | return
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| 250 |
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| 251 | def set_gauss_parameters(self, peak, centre, fwhm,
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| 252 | peakfixed=0, centrefixed=0,
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| 253 | fwhmfixed=0,
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| 254 | component=0):
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| 255 | """
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| 256 | Set the Parameters of a 'Gaussian' component, set with set_function.
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| 257 | Parameters:
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| 258 | peak, centre, fwhm: The gaussian parameters
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| 259 | peakfixed,
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| 260 | centrefixed,
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| 261 | fwhmfixed: Optional parameters to indicate if
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| 262 | the paramters should be held fixed during
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| 263 | the fitting process. The default is to keep
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| 264 | all parameters flexible.
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| 265 | component: The number of the component (Default is the
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| 266 | component 0)
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| 267 | """
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| 268 | if self.fitfunc != "gauss":
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| 269 | msg = "Function only operates on Gaussian components."
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| 270 | if rcParams['verbose']:
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| 271 | print msg
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| 272 | return
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| 273 | else:
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| 274 | raise ValueError(msg)
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| 275 | if 0 <= component < len(self.components):
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| 276 | d = {'params':[peak, centre, fwhm],
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| 277 | 'fixed':[peakfixed, centrefixed, fwhmfixed]}
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| 278 | self.set_parameters(d, component)
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| 279 | else:
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| 280 | msg = "Please select a valid component."
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| 281 | if rcParams['verbose']:
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| 282 | print msg
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| 283 | return
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| 284 | else:
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| 285 | raise ValueError(msg)
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| 286 |
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| 287 | def get_area(self, component=None):
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| 288 | """
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| 289 | Return the area under the fitted gaussian component.
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| 290 | Parameters:
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| 291 | component: the gaussian component selection,
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| 292 | default (None) is the sum of all components
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| 293 | Note:
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| 294 | This will only work for gaussian fits.
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| 295 | """
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| 296 | if not self.fitted: return
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| 297 | if self.fitfunc == "gauss":
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| 298 | pars = list(self.fitter.getparameters())
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| 299 | from math import log,pi,sqrt
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| 300 | fac = sqrt(pi/log(16.0))
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| 301 | areas = []
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| 302 | for i in range(len(self.components)):
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| 303 | j = i*3
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| 304 | cpars = pars[j:j+3]
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| 305 | areas.append(fac * cpars[0] * cpars[2])
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| 306 | else:
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| 307 | return None
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| 308 | if component is not None:
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| 309 | return areas[component]
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| 310 | else:
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| 311 | return sum(areas)
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| 312 |
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| 313 | def get_errors(self, component=None):
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| 314 | """
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| 315 | Return the errors in the parameters.
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| 316 | Parameters:
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| 317 | component: get the errors for the specified component
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| 318 | only, default is all components
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| 319 | """
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| 320 | if not self.fitted:
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| 321 | msg = "Not yet fitted."
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| 322 | if rcParams['verbose']:
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| 323 | print msg
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| 324 | return
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| 325 | else:
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| 326 | raise RuntimeError(msg)
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| 327 | errs = list(self.fitter.geterrors())
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| 328 | cerrs = errs
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| 329 | if component is not None:
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| 330 | if self.fitfunc == "gauss":
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| 331 | i = 3*component
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| 332 | if i < len(errs):
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| 333 | cerrs = errs[i:i+3]
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| 334 | return cerrs
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| 335 |
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| 336 | def get_parameters(self, component=None, errors=False):
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| 337 | """
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| 338 | Return the fit paramters.
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| 339 | Parameters:
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| 340 | component: get the parameters for the specified component
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| 341 | only, default is all components
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| 342 | """
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| 343 | if not self.fitted:
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| 344 | msg = "Not yet fitted."
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| 345 | if rcParams['verbose']:
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| 346 | print msg
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| 347 | return
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| 348 | else:
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| 349 | raise RuntimeError(msg)
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| 350 | pars = list(self.fitter.getparameters())
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| 351 | fixed = list(self.fitter.getfixedparameters())
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| 352 | errs = list(self.fitter.geterrors())
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| 353 | area = []
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| 354 | if component is not None:
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| 355 | if self.fitfunc == "gauss":
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| 356 | i = 3*component
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| 357 | cpars = pars[i:i+3]
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| 358 | cfixed = fixed[i:i+3]
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| 359 | cerrs = errs[i:i+3]
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| 360 | a = self.get_area(component)
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| 361 | area = [a for i in range(3)]
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| 362 | else:
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| 363 | cpars = pars
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| 364 | cfixed = fixed
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| 365 | cerrs = errs
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| 366 | else:
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| 367 | cpars = pars
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| 368 | cfixed = fixed
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| 369 | cerrs = errs
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| 370 | if self.fitfunc == "gauss":
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| 371 | for c in range(len(self.components)):
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| 372 | a = self.get_area(c)
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| 373 | area += [a for i in range(3)]
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| 374 | fpars = self._format_pars(cpars, cfixed, errors and cerrs, area)
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| 375 | if rcParams['verbose']:
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| 376 | print fpars
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| 377 | return {'params':cpars, 'fixed':cfixed, 'formatted': fpars,
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| 378 | 'errors':cerrs}
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| 379 |
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| 380 | def _format_pars(self, pars, fixed, errors, area):
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| 381 | out = ''
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| 382 | if self.fitfunc == 'poly':
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| 383 | c = 0
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| 384 | for i in range(len(pars)):
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| 385 | fix = ""
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| 386 | if len(fixed) and fixed[i]: fix = "(fixed)"
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| 387 | if errors :
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| 388 | out += ' p%d%s= %3.6f (%1.6f),' % (c,fix,pars[i], errors[i])
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| 389 | else:
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| 390 | out += ' p%d%s= %3.6f,' % (c,fix,pars[i])
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| 391 | c+=1
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| 392 | out = out[:-1] # remove trailing ','
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| 393 | elif self.fitfunc == 'gauss':
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| 394 | i = 0
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| 395 | c = 0
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| 396 | aunit = ''
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| 397 | ounit = ''
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| 398 | if self.data:
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| 399 | aunit = self.data.get_unit()
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| 400 | ounit = self.data.get_fluxunit()
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| 401 | while i < len(pars):
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| 402 | if len(area):
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| 403 | out += ' %2d: peak = %3.3f %s , centre = %3.3f %s, FWHM = %3.3f %s\n area = %3.3f %s %s\n' % (c,pars[i],ounit,pars[i+1],aunit,pars[i+2],aunit, area[i],ounit,aunit)
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| 404 | else:
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| 405 | out += ' %2d: peak = %3.3f %s , centre = %3.3f %s, FWHM = %3.3f %s\n' % (c,pars[i],ounit,pars[i+1],aunit,pars[i+2],aunit,ounit,aunit)
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| 406 | c+=1
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| 407 | i+=3
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| 408 | return out
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| 409 |
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| 410 | def get_estimate(self):
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| 411 | """
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| 412 | Return the parameter estimates (for non-linear functions).
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| 413 | """
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| 414 | pars = self.fitter.getestimate()
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| 415 | fixed = self.fitter.getfixedparameters()
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| 416 | if rcParams['verbose']:
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| 417 | print self._format_pars(pars,fixed,None)
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| 418 | return pars
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| 419 |
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| 420 | def get_residual(self):
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| 421 | """
|
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| 422 | Return the residual of the fit.
|
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| 423 | """
|
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| 424 | if not self.fitted:
|
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| 425 | msg = "Not yet fitted."
|
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| 426 | if rcParams['verbose']:
|
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| 427 | print msg
|
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| 428 | return
|
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| 429 | else:
|
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| 430 | raise RuntimeError(msg)
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| 431 | return self.fitter.getresidual()
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| 432 |
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| 433 | def get_chi2(self):
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| 434 | """
|
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| 435 | Return chi^2.
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| 436 | """
|
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| 437 | if not self.fitted:
|
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| 438 | msg = "Not yet fitted."
|
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| 439 | if rcParams['verbose']:
|
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| 440 | print msg
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| 441 | return
|
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| 442 | else:
|
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| 443 | raise RuntimeError(msg)
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| 444 | ch2 = self.fitter.getchi2()
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| 445 | if rcParams['verbose']:
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| 446 | print 'Chi^2 = %3.3f' % (ch2)
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| 447 | return ch2
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| 448 |
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| 449 | def get_fit(self):
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| 450 | """
|
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| 451 | Return the fitted ordinate values.
|
|---|
| 452 | """
|
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| 453 | if not self.fitted:
|
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| 454 | msg = "Not yet fitted."
|
|---|
| 455 | if rcParams['verbose']:
|
|---|
| 456 | print msg
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|---|
| 457 | return
|
|---|
| 458 | else:
|
|---|
| 459 | raise RuntimeError(msg)
|
|---|
| 460 | return self.fitter.getfit()
|
|---|
| 461 |
|
|---|
| 462 | @print_log_dec
|
|---|
| 463 | def commit(self):
|
|---|
| 464 | """
|
|---|
| 465 | Return a new scan where the fits have been commited (subtracted)
|
|---|
| 466 | """
|
|---|
| 467 | if not self.fitted:
|
|---|
| 468 | msg = "Not yet fitted."
|
|---|
| 469 | if rcParams['verbose']:
|
|---|
| 470 | print msg
|
|---|
| 471 | return
|
|---|
| 472 | else:
|
|---|
| 473 | raise RuntimeError(msg)
|
|---|
| 474 | from asap import scantable
|
|---|
| 475 | if not isinstance(self.data, scantable):
|
|---|
| 476 | msg = "Not a scantable"
|
|---|
| 477 | if rcParams['verbose']:
|
|---|
| 478 | print msg
|
|---|
| 479 | return
|
|---|
| 480 | else:
|
|---|
| 481 | raise TypeError(msg)
|
|---|
| 482 | scan = self.data.copy()
|
|---|
| 483 | scan._setspectrum(self.fitter.getresidual())
|
|---|
| 484 | return scan
|
|---|
| 485 |
|
|---|
| 486 | @print_log_dec
|
|---|
| 487 | def plot(self, residual=False, components=None, plotparms=False, filename=None):
|
|---|
| 488 | """
|
|---|
| 489 | Plot the last fit.
|
|---|
| 490 | Parameters:
|
|---|
| 491 | residual: an optional parameter indicating if the residual
|
|---|
| 492 | should be plotted (default 'False')
|
|---|
| 493 | components: a list of components to plot, e.g [0,1],
|
|---|
| 494 | -1 plots the total fit. Default is to only
|
|---|
| 495 | plot the total fit.
|
|---|
| 496 | plotparms: Inidicates if the parameter values should be present
|
|---|
| 497 | on the plot
|
|---|
| 498 | """
|
|---|
| 499 | if not self.fitted:
|
|---|
| 500 | return
|
|---|
| 501 | if not self._p or self._p.is_dead:
|
|---|
| 502 | if rcParams['plotter.gui']:
|
|---|
| 503 | from asap.asaplotgui import asaplotgui as asaplot
|
|---|
| 504 | else:
|
|---|
| 505 | from asap.asaplot import asaplot
|
|---|
| 506 | self._p = asaplot()
|
|---|
| 507 | self._p.hold()
|
|---|
| 508 | self._p.clear()
|
|---|
| 509 | self._p.set_panels()
|
|---|
| 510 | self._p.palette(0)
|
|---|
| 511 | tlab = 'Spectrum'
|
|---|
| 512 | xlab = 'Abcissa'
|
|---|
| 513 | ylab = 'Ordinate'
|
|---|
| 514 | from matplotlib.numerix import ma,logical_not,logical_and,array
|
|---|
| 515 | m = self.mask
|
|---|
| 516 | if self.data:
|
|---|
| 517 | tlab = self.data._getsourcename(self._fittedrow)
|
|---|
| 518 | xlab = self.data._getabcissalabel(self._fittedrow)
|
|---|
| 519 | m = logical_and(self.mask,
|
|---|
| 520 | array(self.data._getmask(self._fittedrow),
|
|---|
| 521 | copy=False))
|
|---|
| 522 |
|
|---|
| 523 | ylab = self.data._get_ordinate_label()
|
|---|
| 524 |
|
|---|
| 525 | colours = ["#777777","#dddddd","red","orange","purple","green","magenta", "cyan"]
|
|---|
| 526 | self._p.palette(0,colours)
|
|---|
| 527 | self._p.set_line(label='Spectrum')
|
|---|
| 528 | y = ma.masked_array(self.y,mask=logical_not(m))
|
|---|
| 529 | self._p.plot(self.x, y)
|
|---|
| 530 | if residual:
|
|---|
| 531 | self._p.palette(1)
|
|---|
| 532 | self._p.set_line(label='Residual')
|
|---|
| 533 | y = ma.masked_array(self.get_residual(),
|
|---|
| 534 | mask=logical_not(m))
|
|---|
| 535 | self._p.plot(self.x, y)
|
|---|
| 536 | self._p.palette(2)
|
|---|
| 537 | if components is not None:
|
|---|
| 538 | cs = components
|
|---|
| 539 | if isinstance(components,int): cs = [components]
|
|---|
| 540 | if plotparms:
|
|---|
| 541 | self._p.text(0.15,0.15,str(self.get_parameters()['formatted']),size=8)
|
|---|
| 542 | n = len(self.components)
|
|---|
| 543 | self._p.palette(3)
|
|---|
| 544 | for c in cs:
|
|---|
| 545 | if 0 <= c < n:
|
|---|
| 546 | lab = self.fitfuncs[c]+str(c)
|
|---|
| 547 | self._p.set_line(label=lab)
|
|---|
| 548 | y = ma.masked_array(self.fitter.evaluate(c),
|
|---|
| 549 | mask=logical_not(m))
|
|---|
| 550 |
|
|---|
| 551 | self._p.plot(self.x, y)
|
|---|
| 552 | elif c == -1:
|
|---|
| 553 | self._p.palette(2)
|
|---|
| 554 | self._p.set_line(label="Total Fit")
|
|---|
| 555 | y = ma.masked_array(self.fitter.getfit(),
|
|---|
| 556 | mask=logical_not(m))
|
|---|
| 557 | self._p.plot(self.x, y)
|
|---|
| 558 | else:
|
|---|
| 559 | self._p.palette(2)
|
|---|
| 560 | self._p.set_line(label='Fit')
|
|---|
| 561 | y = ma.masked_array(self.fitter.getfit(),
|
|---|
| 562 | mask=logical_not(m))
|
|---|
| 563 | self._p.plot(self.x, y)
|
|---|
| 564 | xlim=[min(self.x),max(self.x)]
|
|---|
| 565 | self._p.axes.set_xlim(xlim)
|
|---|
| 566 | self._p.set_axes('xlabel',xlab)
|
|---|
| 567 | self._p.set_axes('ylabel',ylab)
|
|---|
| 568 | self._p.set_axes('title',tlab)
|
|---|
| 569 | self._p.release()
|
|---|
| 570 | if (not rcParams['plotter.gui']):
|
|---|
| 571 | self._p.save(filename)
|
|---|
| 572 |
|
|---|
| 573 | @print_log_dec
|
|---|
| 574 | def auto_fit(self, insitu=None, plot=False):
|
|---|
| 575 | """
|
|---|
| 576 | Return a scan where the function is applied to all rows for
|
|---|
| 577 | all Beams/IFs/Pols.
|
|---|
| 578 |
|
|---|
| 579 | """
|
|---|
| 580 | from asap import scantable
|
|---|
| 581 | if not isinstance(self.data, scantable) :
|
|---|
| 582 | msg = "Data is not a scantable"
|
|---|
| 583 | if rcParams['verbose']:
|
|---|
| 584 | print msg
|
|---|
| 585 | return
|
|---|
| 586 | else:
|
|---|
| 587 | raise TypeError(msg)
|
|---|
| 588 | if insitu is None: insitu = rcParams['insitu']
|
|---|
| 589 | if not insitu:
|
|---|
| 590 | scan = self.data.copy()
|
|---|
| 591 | else:
|
|---|
| 592 | scan = self.data
|
|---|
| 593 | rows = xrange(scan.nrow())
|
|---|
| 594 | from asap import asaplog
|
|---|
| 595 | asaplog.push("Fitting:")
|
|---|
| 596 | for r in rows:
|
|---|
| 597 | out = " Scan[%d] Beam[%d] IF[%d] Pol[%d] Cycle[%d]" % (scan.getscan(r),
|
|---|
| 598 | scan.getbeam(r),
|
|---|
| 599 | scan.getif(r),
|
|---|
| 600 | scan.getpol(r),
|
|---|
| 601 | scan.getcycle(r))
|
|---|
| 602 | asaplog.push(out, False)
|
|---|
| 603 | self.x = scan._getabcissa(r)
|
|---|
| 604 | self.y = scan._getspectrum(r)
|
|---|
| 605 | self.mask = mask_and(self.mask, scan._getmask(r))
|
|---|
| 606 | self.data = None
|
|---|
| 607 | self.fit()
|
|---|
| 608 | x = self.get_parameters()
|
|---|
| 609 | if plot:
|
|---|
| 610 | self.plot(residual=True)
|
|---|
| 611 | x = raw_input("Accept fit ([y]/n): ")
|
|---|
| 612 | if x.upper() == 'N':
|
|---|
| 613 | continue
|
|---|
| 614 | scan._setspectrum(self.fitter.getresidual(), r)
|
|---|
| 615 | if plot:
|
|---|
| 616 | self._p.unmap()
|
|---|
| 617 | self._p = None
|
|---|
| 618 | return scan
|
|---|
