| 1 | import _asap
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| 2 | from asap.parameters import rcParams
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| 3 | from asap.logging import asaplog, asaplog_post_dec
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| 4 | from asap.utils import _n_bools, mask_and
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| 5 |
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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 | def __init__(self):
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| 12 | """
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| 13 | Create a fitter object. No state is set.
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| 14 | """
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| 15 | self.fitter = _asap.fitter()
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| 16 | self.x = None
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| 17 | self.y = None
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| 18 | self.mask = None
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| 19 | self.fitfunc = None
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| 20 | self.fitfuncs = None
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| 21 | self.fitted = False
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| 22 | self.data = None
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| 23 | self.components = 0
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| 24 | self._fittedrow = 0
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| 25 | self._p = None
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| 26 | self._selection = None
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| 27 | self.uselinear = False
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| 28 |
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| 29 | def set_data(self, xdat, ydat, mask=None):
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| 30 | """
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| 31 | Set the absissa and ordinate for the fit. Also set the mask
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| 32 | indicationg valid points.
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| 33 | This can be used for data vectors retrieved from a scantable.
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| 34 | For scantable fitting use 'fitter.set_scan(scan, mask)'.
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| 35 | Parameters:
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| 36 | xdat: the abcissa values
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| 37 | ydat: the ordinate values
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| 38 | mask: an optional mask
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| 39 |
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| 40 | """
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| 41 | self.fitted = False
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| 42 | self.x = xdat
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| 43 | self.y = ydat
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| 44 | if mask == None:
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| 45 | self.mask = _n_bools(len(xdat), True)
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| 46 | else:
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| 47 | self.mask = mask
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| 48 | return
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| 49 |
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| 50 | @asaplog_post_dec
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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 | raise TypeError(msg)
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| 61 | self.fitted = False
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| 62 | self.data = thescan
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| 63 | self.mask = None
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| 64 | if mask is None:
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| 65 | self.mask = _n_bools(self.data.nchan(), True)
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| 66 | else:
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| 67 | self.mask = mask
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| 68 | return
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| 69 |
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| 70 | @asaplog_post_dec
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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 with nonlinear least squares fit
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| 76 | lpoly: use polynomial of the order given with linear least squares fit
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| 77 | gauss: fit the number of gaussian specified
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| 78 | lorentz: fit the number of lorentzian specified
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| 79 | Example:
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| 80 | fitter.set_function(poly=3) # will fit a 3rd order polynomial via nonlinear method
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| 81 | fitter.set_function(lpoly=3) # will fit a 3rd order polynomial via linear method
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| 82 | fitter.set_function(gauss=2) # will fit two gaussians
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| 83 | fitter.set_function(lorentz=2) # will fit two lorentzians
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| 84 | """
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| 85 | #default poly order 0
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| 86 | n=0
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| 87 | if kwargs.has_key('poly'):
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| 88 | self.fitfunc = 'poly'
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| 89 | self.fitfuncs = ['poly']
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| 90 | n = kwargs.get('poly')
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| 91 | self.components = [n+1]
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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 | self.fitfuncs = ['lpoly']
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| 96 | n = kwargs.get('lpoly')
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| 97 | self.components = [n+1]
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| 98 | self.uselinear = True
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| 99 | elif kwargs.has_key('gauss'):
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| 100 | n = kwargs.get('gauss')
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| 101 | self.fitfunc = 'gauss'
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| 102 | self.fitfuncs = [ 'gauss' for i in range(n) ]
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| 103 | self.components = [ 3 for i in range(n) ]
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| 104 | self.uselinear = False
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| 105 | elif kwargs.has_key('lorentz'):
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| 106 | n = kwargs.get('lorentz')
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| 107 | self.fitfunc = 'lorentz'
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| 108 | self.fitfuncs = [ 'lorentz' for i in range(n) ]
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| 109 | self.components = [ 3 for i in range(n) ]
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| 110 | self.uselinear = False
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| 111 | else:
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| 112 | msg = "Invalid function type."
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| 113 | raise TypeError(msg)
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| 114 |
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| 115 | self.fitter.setexpression(self.fitfunc,n)
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| 116 | self.fitted = False
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| 117 | return
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| 118 |
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| 119 | @asaplog_post_dec
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| 120 | def fit(self, row=0, estimate=False):
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| 121 | """
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| 122 | Execute the actual fitting process. All the state has to be set.
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| 123 | Parameters:
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| 124 | row: specify the row in the scantable
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| 125 | estimate: auto-compute an initial parameter set (default False)
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| 126 | This can be used to compute estimates even if fit was
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| 127 | called before.
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| 128 | Example:
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| 129 | s = scantable('myscan.asap')
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| 130 | s.set_cursor(thepol=1) # select second pol
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| 131 | f = fitter()
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| 132 | f.set_scan(s)
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| 133 | f.set_function(poly=0)
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| 134 | f.fit(row=0) # fit first row
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| 135 | """
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| 136 | if ((self.x is None or self.y is None) and self.data is None) \
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| 137 | or self.fitfunc is None:
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| 138 | msg = "Fitter not yet initialised. Please set data & fit function"
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| 139 | raise RuntimeError(msg)
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| 140 |
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| 141 | else:
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| 142 | if self.data is not None:
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| 143 | self.x = self.data._getabcissa(row)
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| 144 | self.y = self.data._getspectrum(row)
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| 145 | self.mask = mask_and(self.mask, self.data._getmask(row))
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| 146 | asaplog.push("Fitting:")
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| 147 | i = row
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| 148 | out = "Scan[%d] Beam[%d] IF[%d] Pol[%d] Cycle[%d]" % (self.data.getscan(i),
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| 149 | self.data.getbeam(i),
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| 150 | self.data.getif(i),
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| 151 | self.data.getpol(i),
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| 152 | self.data.getcycle(i))
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| 153 | asaplog.push(out,False)
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| 154 | self.fitter.setdata(self.x, self.y, self.mask)
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| 155 | if self.fitfunc == 'gauss' or self.fitfunc == 'lorentz':
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| 156 | ps = self.fitter.getparameters()
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| 157 | if len(ps) == 0 or estimate:
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| 158 | self.fitter.estimate()
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| 159 | fxdpar = list(self.fitter.getfixedparameters())
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| 160 | if len(fxdpar) and fxdpar.count(0) == 0:
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| 161 | raise RuntimeError,"No point fitting, if all parameters are fixed."
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| 162 | if self.uselinear:
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| 163 | converged = self.fitter.lfit()
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| 164 | else:
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| 165 | converged = self.fitter.fit()
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| 166 | if not converged:
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| 167 | raise RuntimeError,"Fit didn't converge."
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| 168 | self._fittedrow = row
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| 169 | self.fitted = True
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| 170 | return
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| 171 |
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| 172 | def store_fit(self, filename=None):
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| 173 | """
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| 174 | Save the fit parameters.
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| 175 | Parameters:
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| 176 | filename: if specified save as an ASCII file, if None (default)
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| 177 | store it in the scnatable
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| 178 | """
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| 179 | if self.fitted and self.data is not None:
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| 180 | pars = list(self.fitter.getparameters())
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| 181 | fixed = list(self.fitter.getfixedparameters())
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| 182 | from asap.asapfit import asapfit
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| 183 | fit = asapfit()
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| 184 | fit.setparameters(pars)
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| 185 | fit.setfixedparameters(fixed)
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| 186 | fit.setfunctions(self.fitfuncs)
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| 187 | fit.setcomponents(self.components)
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| 188 | fit.setframeinfo(self.data._getcoordinfo())
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| 189 | if filename is not None:
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| 190 | import os
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| 191 | filename = os.path.expandvars(os.path.expanduser(filename))
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| 192 | if os.path.exists(filename):
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| 193 | raise IOError("File '%s' exists." % filename)
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| 194 | fit.save(filename)
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| 195 | else:
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| 196 | self.data._addfit(fit,self._fittedrow)
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| 197 |
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| 198 | @asaplog_post_dec
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| 199 | def set_parameters(self,*args,**kwargs):
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| 200 | """
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| 201 | Set the parameters to be fitted.
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| 202 | Parameters:
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| 203 | params: a vector of parameters
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| 204 | fixed: a vector of which parameters are to be held fixed
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| 205 | (default is none)
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| 206 | component: in case of multiple gaussians/lorentzians,
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| 207 | the index of the component
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| 208 | """
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| 209 | component = None
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| 210 | fixed = None
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| 211 | params = None
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| 212 |
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| 213 | if len(args) and isinstance(args[0],dict):
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| 214 | kwargs = args[0]
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| 215 | if kwargs.has_key("fixed"): fixed = kwargs["fixed"]
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| 216 | if kwargs.has_key("params"): params = kwargs["params"]
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| 217 | if len(args) == 2 and isinstance(args[1], int):
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| 218 | component = args[1]
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| 219 | if self.fitfunc is None:
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| 220 | msg = "Please specify a fitting function first."
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| 221 | raise RuntimeError(msg)
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| 222 | if (self.fitfunc == "gauss" or self.fitfunc == 'lorentz') and component is not None:
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| 223 | if not self.fitted and sum(self.fitter.getparameters()) == 0:
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| 224 | pars = _n_bools(len(self.components)*3, False)
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| 225 | fxd = _n_bools(len(pars), False)
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| 226 | else:
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| 227 | pars = list(self.fitter.getparameters())
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| 228 | fxd = list(self.fitter.getfixedparameters())
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| 229 | i = 3*component
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| 230 | pars[i:i+3] = params
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| 231 | fxd[i:i+3] = fixed
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| 232 | params = pars
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| 233 | fixed = fxd
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| 234 | self.fitter.setparameters(params)
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| 235 | if fixed is not None:
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| 236 | self.fitter.setfixedparameters(fixed)
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| 237 | return
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| 238 |
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| 239 | @asaplog_post_dec
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| 240 | def set_gauss_parameters(self, peak, centre, fwhm,
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| 241 | peakfixed=0, centrefixed=0,
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| 242 | fwhmfixed=0,
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| 243 | component=0):
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| 244 | """
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| 245 | Set the Parameters of a 'Gaussian' component, set with set_function.
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| 246 | Parameters:
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| 247 | peak, centre, fwhm: The gaussian parameters
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| 248 | peakfixed,
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| 249 | centrefixed,
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| 250 | fwhmfixed: Optional parameters to indicate if
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| 251 | the paramters should be held fixed during
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| 252 | the fitting process. The default is to keep
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| 253 | all parameters flexible.
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| 254 | component: The number of the component (Default is the
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| 255 | component 0)
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| 256 | """
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| 257 | if self.fitfunc != "gauss":
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| 258 | msg = "Function only operates on Gaussian components."
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| 259 | raise ValueError(msg)
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| 260 | if 0 <= component < len(self.components):
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| 261 | d = {'params':[peak, centre, fwhm],
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| 262 | 'fixed':[peakfixed, centrefixed, fwhmfixed]}
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| 263 | self.set_parameters(d, component)
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| 264 | else:
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| 265 | msg = "Please select a valid component."
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| 266 | raise ValueError(msg)
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| 267 |
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| 268 | @asaplog_post_dec
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| 269 | def set_lorentz_parameters(self, peak, centre, fwhm,
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| 270 | peakfixed=0, centrefixed=0,
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| 271 | fwhmfixed=0,
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| 272 | component=0):
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| 273 | """
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| 274 | Set the Parameters of a 'Lorentzian' component, set with set_function.
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| 275 | Parameters:
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| 276 | peak, centre, fwhm: The lorentzian parameters
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| 277 | peakfixed,
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| 278 | centrefixed,
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| 279 | fwhmfixed: Optional parameters to indicate if
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| 280 | the paramters should be held fixed during
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| 281 | the fitting process. The default is to keep
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| 282 | all parameters flexible.
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| 283 | component: The number of the component (Default is the
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| 284 | component 0)
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| 285 | """
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| 286 | if self.fitfunc != "lorentz":
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| 287 | msg = "Function only operates on Lorentzian components."
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| 288 | raise ValueError(msg)
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| 289 | if 0 <= component < len(self.components):
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| 290 | d = {'params':[peak, centre, fwhm],
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| 291 | 'fixed':[peakfixed, centrefixed, fwhmfixed]}
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| 292 | self.set_parameters(d, component)
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| 293 | else:
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| 294 | msg = "Please select a valid component."
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| 295 | raise ValueError(msg)
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| 296 |
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| 297 | def get_area(self, component=None):
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| 298 | """
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| 299 | Return the area under the fitted gaussian/lorentzian component.
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| 300 | Parameters:
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| 301 | component: the gaussian/lorentzian component selection,
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| 302 | default (None) is the sum of all components
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| 303 | Note:
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| 304 | This will only work for gaussian/lorentzian fits.
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| 305 | """
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| 306 | if not self.fitted: return
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| 307 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz":
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| 308 | pars = list(self.fitter.getparameters())
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| 309 | from math import log,pi,sqrt
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| 310 | if self.fitfunc == "gauss":
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| 311 | fac = sqrt(pi/log(16.0))
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| 312 | elif self.fitfunc == "lorentz":
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| 313 | fac = pi/2.0
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| 314 | areas = []
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| 315 | for i in range(len(self.components)):
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| 316 | j = i*3
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| 317 | cpars = pars[j:j+3]
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| 318 | areas.append(fac * cpars[0] * cpars[2])
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| 319 | else:
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| 320 | return None
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| 321 | if component is not None:
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| 322 | return areas[component]
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| 323 | else:
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| 324 | return sum(areas)
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| 325 |
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| 326 | @asaplog_post_dec
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| 327 | def get_errors(self, component=None):
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| 328 | """
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| 329 | Return the errors in the parameters.
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| 330 | Parameters:
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| 331 | component: get the errors for the specified component
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| 332 | only, default is all components
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| 333 | """
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| 334 | if not self.fitted:
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| 335 | msg = "Not yet fitted."
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| 336 | raise RuntimeError(msg)
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| 337 | errs = list(self.fitter.geterrors())
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| 338 | cerrs = errs
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| 339 | if component is not None:
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| 340 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz":
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| 341 | i = 3*component
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| 342 | if i < len(errs):
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| 343 | cerrs = errs[i:i+3]
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| 344 | return cerrs
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| 345 |
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| 346 |
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| 347 | @asaplog_post_dec
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| 348 | def get_parameters(self, component=None, errors=False):
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| 349 | """
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| 350 | Return the fit paramters.
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| 351 | Parameters:
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| 352 | component: get the parameters for the specified component
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| 353 | only, default is all components
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| 354 | """
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| 355 | if not self.fitted:
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| 356 | msg = "Not yet fitted."
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| 357 | raise RuntimeError(msg)
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| 358 | pars = list(self.fitter.getparameters())
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| 359 | fixed = list(self.fitter.getfixedparameters())
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| 360 | errs = list(self.fitter.geterrors())
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| 361 | area = []
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| 362 | if component is not None:
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| 363 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz":
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| 364 | i = 3*component
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| 365 | cpars = pars[i:i+3]
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| 366 | cfixed = fixed[i:i+3]
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| 367 | cerrs = errs[i:i+3]
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| 368 | a = self.get_area(component)
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| 369 | area = [a for i in range(3)]
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| 370 | else:
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| 371 | cpars = pars
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| 372 | cfixed = fixed
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| 373 | cerrs = errs
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| 374 | else:
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| 375 | cpars = pars
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| 376 | cfixed = fixed
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| 377 | cerrs = errs
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| 378 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz":
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| 379 | for c in range(len(self.components)):
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| 380 | a = self.get_area(c)
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| 381 | area += [a for i in range(3)]
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| 382 | fpars = self._format_pars(cpars, cfixed, errors and cerrs, area)
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| 383 | asaplog.push(fpars)
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| 384 | return {'params':cpars, 'fixed':cfixed, 'formatted': fpars,
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| 385 | 'errors':cerrs}
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| 386 |
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| 387 | def _format_pars(self, pars, fixed, errors, area):
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| 388 | out = ''
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| 389 | if self.fitfunc == 'poly':
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| 390 | c = 0
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| 391 | for i in range(len(pars)):
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| 392 | fix = ""
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| 393 | if len(fixed) and fixed[i]: fix = "(fixed)"
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| 394 | if errors :
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| 395 | out += ' p%d%s= %3.6f (%1.6f),' % (c,fix,pars[i], errors[i])
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| 396 | else:
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| 397 | out += ' p%d%s= %3.6f,' % (c,fix,pars[i])
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| 398 | c+=1
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| 399 | out = out[:-1] # remove trailing ','
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| 400 | elif self.fitfunc == 'gauss' or self.fitfunc == 'lorentz':
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| 401 | i = 0
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| 402 | c = 0
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| 403 | aunit = ''
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| 404 | ounit = ''
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| 405 | if self.data:
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| 406 | aunit = self.data.get_unit()
|
|---|
| 407 | ounit = self.data.get_fluxunit()
|
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| 408 | while i < len(pars):
|
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| 409 | if len(area):
|
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| 410 | 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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| 411 | else:
|
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| 412 | 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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| 413 | c+=1
|
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| 414 | i+=3
|
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| 415 | return out
|
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| 416 |
|
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| 417 |
|
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| 418 | @asaplog_post_dec
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| 419 | def get_estimate(self):
|
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| 420 | """
|
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| 421 | Return the parameter estimates (for non-linear functions).
|
|---|
| 422 | """
|
|---|
| 423 | pars = self.fitter.getestimate()
|
|---|
| 424 | fixed = self.fitter.getfixedparameters()
|
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| 425 | asaplog.push(self._format_pars(pars,fixed,None,None))
|
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| 426 | return pars
|
|---|
| 427 |
|
|---|
| 428 | @asaplog_post_dec
|
|---|
| 429 | def get_residual(self):
|
|---|
| 430 | """
|
|---|
| 431 | Return the residual of the fit.
|
|---|
| 432 | """
|
|---|
| 433 | if not self.fitted:
|
|---|
| 434 | msg = "Not yet fitted."
|
|---|
| 435 | raise RuntimeError(msg)
|
|---|
| 436 | return self.fitter.getresidual()
|
|---|
| 437 |
|
|---|
| 438 | @asaplog_post_dec
|
|---|
| 439 | def get_chi2(self):
|
|---|
| 440 | """
|
|---|
| 441 | Return chi^2.
|
|---|
| 442 | """
|
|---|
| 443 | if not self.fitted:
|
|---|
| 444 | msg = "Not yet fitted."
|
|---|
| 445 | raise RuntimeError(msg)
|
|---|
| 446 | ch2 = self.fitter.getchi2()
|
|---|
| 447 | asaplog.push( 'Chi^2 = %3.3f' % (ch2) )
|
|---|
| 448 | return ch2
|
|---|
| 449 |
|
|---|
| 450 | @asaplog_post_dec
|
|---|
| 451 | def get_fit(self):
|
|---|
| 452 | """
|
|---|
| 453 | Return the fitted ordinate values.
|
|---|
| 454 | """
|
|---|
| 455 | if not self.fitted:
|
|---|
| 456 | msg = "Not yet fitted."
|
|---|
| 457 | raise RuntimeError(msg)
|
|---|
| 458 | return self.fitter.getfit()
|
|---|
| 459 |
|
|---|
| 460 | @asaplog_post_dec
|
|---|
| 461 | def commit(self):
|
|---|
| 462 | """
|
|---|
| 463 | Return a new scan where the fits have been commited (subtracted)
|
|---|
| 464 | """
|
|---|
| 465 | if not self.fitted:
|
|---|
| 466 | msg = "Not yet fitted."
|
|---|
| 467 | raise RuntimeError(msg)
|
|---|
| 468 | from asap import scantable
|
|---|
| 469 | if not isinstance(self.data, scantable):
|
|---|
| 470 | msg = "Not a scantable"
|
|---|
| 471 | raise TypeError(msg)
|
|---|
| 472 | scan = self.data.copy()
|
|---|
| 473 | scan._setspectrum(self.fitter.getresidual())
|
|---|
| 474 | return scan
|
|---|
| 475 |
|
|---|
| 476 | @asaplog_post_dec
|
|---|
| 477 | def plot(self, residual=False, components=None, plotparms=False,
|
|---|
| 478 | filename=None):
|
|---|
| 479 | """
|
|---|
| 480 | Plot the last fit.
|
|---|
| 481 | Parameters:
|
|---|
| 482 | residual: an optional parameter indicating if the residual
|
|---|
| 483 | should be plotted (default 'False')
|
|---|
| 484 | components: a list of components to plot, e.g [0,1],
|
|---|
| 485 | -1 plots the total fit. Default is to only
|
|---|
| 486 | plot the total fit.
|
|---|
| 487 | plotparms: Inidicates if the parameter values should be present
|
|---|
| 488 | on the plot
|
|---|
| 489 | """
|
|---|
| 490 | if not self.fitted:
|
|---|
| 491 | return
|
|---|
| 492 | if not self._p or self._p.is_dead:
|
|---|
| 493 | if rcParams['plotter.gui']:
|
|---|
| 494 | from asap.asaplotgui import asaplotgui as asaplot
|
|---|
| 495 | else:
|
|---|
| 496 | from asap.asaplot import asaplot
|
|---|
| 497 | self._p = asaplot()
|
|---|
| 498 | self._p.hold()
|
|---|
| 499 | self._p.clear()
|
|---|
| 500 | self._p.set_panels()
|
|---|
| 501 | self._p.palette(0)
|
|---|
| 502 | tlab = 'Spectrum'
|
|---|
| 503 | xlab = 'Abcissa'
|
|---|
| 504 | ylab = 'Ordinate'
|
|---|
| 505 | from numpy import ma,logical_not,logical_and,array
|
|---|
| 506 | m = self.mask
|
|---|
| 507 | if self.data:
|
|---|
| 508 | tlab = self.data._getsourcename(self._fittedrow)
|
|---|
| 509 | xlab = self.data._getabcissalabel(self._fittedrow)
|
|---|
| 510 | m = logical_and(self.mask,
|
|---|
| 511 | array(self.data._getmask(self._fittedrow),
|
|---|
| 512 | copy=False))
|
|---|
| 513 |
|
|---|
| 514 | ylab = self.data._get_ordinate_label()
|
|---|
| 515 |
|
|---|
| 516 | colours = ["#777777","#dddddd","red","orange","purple","green","magenta", "cyan"]
|
|---|
| 517 | nomask=True
|
|---|
| 518 | for i in range(len(m)):
|
|---|
| 519 | nomask = nomask and m[i]
|
|---|
| 520 | label0='Masked Region'
|
|---|
| 521 | label1='Spectrum'
|
|---|
| 522 | if ( nomask ):
|
|---|
| 523 | label0=label1
|
|---|
| 524 | else:
|
|---|
| 525 | y = ma.masked_array( self.y, mask = m )
|
|---|
| 526 | self._p.palette(1,colours)
|
|---|
| 527 | self._p.set_line( label = label1 )
|
|---|
| 528 | self._p.plot( self.x, y )
|
|---|
| 529 | self._p.palette(0,colours)
|
|---|
| 530 | self._p.set_line(label=label0)
|
|---|
| 531 | y = ma.masked_array(self.y,mask=logical_not(m))
|
|---|
| 532 | self._p.plot(self.x, y)
|
|---|
| 533 | if residual:
|
|---|
| 534 | self._p.palette(7)
|
|---|
| 535 | self._p.set_line(label='Residual')
|
|---|
| 536 | y = ma.masked_array(self.get_residual(),
|
|---|
| 537 | mask=logical_not(m))
|
|---|
| 538 | self._p.plot(self.x, y)
|
|---|
| 539 | self._p.palette(2)
|
|---|
| 540 | if components is not None:
|
|---|
| 541 | cs = components
|
|---|
| 542 | if isinstance(components,int): cs = [components]
|
|---|
| 543 | if plotparms:
|
|---|
| 544 | self._p.text(0.15,0.15,str(self.get_parameters()['formatted']),size=8)
|
|---|
| 545 | n = len(self.components)
|
|---|
| 546 | self._p.palette(3)
|
|---|
| 547 | for c in cs:
|
|---|
| 548 | if 0 <= c < n:
|
|---|
| 549 | lab = self.fitfuncs[c]+str(c)
|
|---|
| 550 | self._p.set_line(label=lab)
|
|---|
| 551 | y = ma.masked_array(self.fitter.evaluate(c),
|
|---|
| 552 | mask=logical_not(m))
|
|---|
| 553 |
|
|---|
| 554 | self._p.plot(self.x, y)
|
|---|
| 555 | elif c == -1:
|
|---|
| 556 | self._p.palette(2)
|
|---|
| 557 | self._p.set_line(label="Total Fit")
|
|---|
| 558 | y = ma.masked_array(self.fitter.getfit(),
|
|---|
| 559 | mask=logical_not(m))
|
|---|
| 560 | self._p.plot(self.x, y)
|
|---|
| 561 | else:
|
|---|
| 562 | self._p.palette(2)
|
|---|
| 563 | self._p.set_line(label='Fit')
|
|---|
| 564 | y = ma.masked_array(self.fitter.getfit(),
|
|---|
| 565 | mask=logical_not(m))
|
|---|
| 566 | self._p.plot(self.x, y)
|
|---|
| 567 | xlim=[min(self.x),max(self.x)]
|
|---|
| 568 | self._p.axes.set_xlim(xlim)
|
|---|
| 569 | self._p.set_axes('xlabel',xlab)
|
|---|
| 570 | self._p.set_axes('ylabel',ylab)
|
|---|
| 571 | self._p.set_axes('title',tlab)
|
|---|
| 572 | self._p.release()
|
|---|
| 573 | if (not rcParams['plotter.gui']):
|
|---|
| 574 | self._p.save(filename)
|
|---|
| 575 |
|
|---|
| 576 | @asaplog_post_dec
|
|---|
| 577 | def auto_fit(self, insitu=None, plot=False):
|
|---|
| 578 | """
|
|---|
| 579 | Return a scan where the function is applied to all rows for
|
|---|
| 580 | all Beams/IFs/Pols.
|
|---|
| 581 |
|
|---|
| 582 | """
|
|---|
| 583 | from asap import scantable
|
|---|
| 584 | if not isinstance(self.data, scantable) :
|
|---|
| 585 | msg = "Data is not a scantable"
|
|---|
| 586 | raise TypeError(msg)
|
|---|
| 587 | if insitu is None: insitu = rcParams['insitu']
|
|---|
| 588 | if not insitu:
|
|---|
| 589 | scan = self.data.copy()
|
|---|
| 590 | else:
|
|---|
| 591 | scan = self.data
|
|---|
| 592 | rows = xrange(scan.nrow())
|
|---|
| 593 | # Save parameters of baseline fits as a class attribute.
|
|---|
| 594 | # NOTICE: This does not reflect changes in scantable!
|
|---|
| 595 | if len(rows) > 0: self.blpars=[]
|
|---|
| 596 | asaplog.push("Fitting:")
|
|---|
| 597 | for r in rows:
|
|---|
| 598 | out = " Scan[%d] Beam[%d] IF[%d] Pol[%d] Cycle[%d]" % (scan.getscan(r),
|
|---|
| 599 | scan.getbeam(r),
|
|---|
| 600 | scan.getif(r),
|
|---|
| 601 | scan.getpol(r),
|
|---|
| 602 | scan.getcycle(r))
|
|---|
| 603 | asaplog.push(out, False)
|
|---|
| 604 | self.x = scan._getabcissa(r)
|
|---|
| 605 | self.y = scan._getspectrum(r)
|
|---|
| 606 | self.mask = mask_and(self.mask, scan._getmask(r))
|
|---|
| 607 | self.data = None
|
|---|
| 608 | self.fit()
|
|---|
| 609 | x = self.get_parameters()
|
|---|
| 610 | fpar = self.get_parameters()
|
|---|
| 611 | if plot:
|
|---|
| 612 | self.plot(residual=True)
|
|---|
| 613 | x = raw_input("Accept fit ([y]/n): ")
|
|---|
| 614 | if x.upper() == 'N':
|
|---|
| 615 | self.blpars.append(None)
|
|---|
| 616 | continue
|
|---|
| 617 | scan._setspectrum(self.fitter.getresidual(), r)
|
|---|
| 618 | self.blpars.append(fpar)
|
|---|
| 619 | if plot:
|
|---|
| 620 | self._p.unmap()
|
|---|
| 621 | self._p = None
|
|---|
| 622 | return scan
|
|---|
