| 1 | import _asap
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| 2 | from asap import rcParams
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| 3 |
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| 4 | class fitter:
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| 5 | """
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| 6 | The fitting class for ASAP.
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| 7 | """
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| 8 | def _verbose(self, *args):
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| 9 | """
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| 10 | Set stdout output.
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| 11 | """
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| 12 | if type(args[0]) is bool:
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| 13 | self._vb = args[0]
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| 14 | return
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| 15 | elif len(args) == 0:
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| 16 | return self._vb
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| 17 |
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| 18 | def __init__(self):
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| 19 | """
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| 20 | Create a fitter object. No state is set.
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| 21 | """
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| 22 | self.fitter = _asap.fitter()
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| 23 | self.x = None
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| 24 | self.y = None
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| 25 | self.mask = None
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| 26 | self.fitfunc = None
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| 27 | self.fitfuncs = None
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| 28 | self.fitted = False
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| 29 | self.data = None
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| 30 | self.components = 0
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| 31 | self._fittedrow = 0
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| 32 | self._p = None
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| 33 | self._vb = True
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| 34 | self._selection = None
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| 35 |
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| 36 | def set_data(self, xdat, ydat, mask=None):
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| 37 | """
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| 38 | Set the absissa and ordinate for the fit. Also set the mask
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| 39 | indicationg valid points.
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| 40 | This can be used for data vectors retrieved from a scantable.
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| 41 | For scantable fitting use 'fitter.set_scan(scan, mask)'.
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| 42 | Parameters:
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| 43 | xdat: the abcissa values
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| 44 | ydat: the ordinate values
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| 45 | mask: an optional mask
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| 46 |
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| 47 | """
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| 48 | self.fitted = False
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| 49 | self.x = xdat
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| 50 | self.y = ydat
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| 51 | if mask == None:
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| 52 | from numarray import ones
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| 53 | self.mask = ones(len(xdat))
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| 54 | else:
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| 55 | self.mask = mask
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| 56 | return
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| 57 |
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| 58 | def set_scan(self, thescan=None, mask=None):
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| 59 | """
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| 60 | Set the 'data' (a scantable) of the fitter.
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| 61 | Parameters:
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| 62 | thescan: a scantable
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| 63 | mask: a msk retireved from the scantable
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| 64 | """
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| 65 | if not thescan:
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| 66 | print "Please give a correct scan"
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| 67 | self.fitted = False
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| 68 | self.data = thescan
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| 69 | if mask is None:
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| 70 | from numarray import ones
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| 71 | self.mask = ones(self.data.nchan())
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| 72 | else:
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| 73 | self.mask = mask
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| 74 | return
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| 75 |
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| 76 | def set_function(self, **kwargs):
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| 77 | """
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| 78 | Set the function to be fit.
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| 79 | Parameters:
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| 80 | poly: use a polynomial of the order given
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| 81 | gauss: fit the number of gaussian specified
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| 82 | Example:
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| 83 | fitter.set_function(gauss=2) # will fit two gaussians
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| 84 | fitter.set_function(poly=3) # will fit a 3rd order polynomial
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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 | elif kwargs.has_key('gauss'):
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| 93 | n = kwargs.get('gauss')
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| 94 | self.fitfunc = 'gauss'
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| 95 | self.fitfuncs = [ 'gauss' for i in range(n) ]
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| 96 | self.components = [ 3 for i in range(n) ]
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| 97 | else:
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| 98 | print "Invalid function type."
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| 99 | return
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| 100 | self.fitter.setexpression(self.fitfunc,n)
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| 101 | return
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| 102 |
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| 103 | def fit(self, row=0):
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| 104 | """
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| 105 | Execute the actual fitting process. All the state has to be set.
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| 106 | Parameters:
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| 107 | none
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| 108 | Example:
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| 109 | s = scantable('myscan.asap')
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| 110 | s.set_cursor(thepol=1) # select second pol
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| 111 | f = fitter()
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| 112 | f.set_scan(s)
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| 113 | f.set_function(poly=0)
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| 114 | f.fit(row=0) # fit first row
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| 115 | """
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| 116 | if ((self.x is None or self.y is None) and self.data is None) \
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| 117 | or self.fitfunc is None:
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| 118 | print "Fitter not yet initialised. Please set data & fit function"
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| 119 | return
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| 120 | else:
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| 121 | if self.data is not None:
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| 122 | self.x = self.data._getabcissa(row)
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| 123 | self.y = self.data._getspectrum(row)
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| 124 | print "Fitting:"
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| 125 | vb = self.data._vb
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| 126 | self.data._vb = True
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| 127 | self.selection = self.data.get_cursor()
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| 128 | self.data._vb = vb
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| 129 | self.fitter.setdata(self.x, self.y, self.mask)
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| 130 | if self.fitfunc == 'gauss':
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| 131 | ps = self.fitter.getparameters()
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| 132 | if len(ps) == 0:
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| 133 | self.fitter.estimate()
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| 134 | self.fitter.fit()
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| 135 | self._fittedrow = row
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| 136 | self.fitted = True
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| 137 | return
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| 138 |
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| 139 | def store_fit(self):
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| 140 | if self.fitted and self.data is not None:
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| 141 | pars = list(self.fitter.getparameters())
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| 142 | fixed = list(self.fitter.getfixedparameters())
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| 143 | self.data._addfit(self._fittedrow, pars, fixed,
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| 144 | self.fitfuncs, self.components)
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| 145 |
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| 146 | def set_parameters(self, params, fixed=None, component=None):
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| 147 | if self.fitfunc is None:
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| 148 | print "Please specify a fitting function first."
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| 149 | return
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| 150 | if self.fitfunc == "gauss" and component is not None:
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| 151 | if not self.fitted:
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| 152 | from numarray import zeros
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| 153 | pars = list(zeros(len(self.components)*3))
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| 154 | fxd = list(zeros(len(pars)))
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| 155 | else:
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| 156 | pars = list(self.fitter.getparameters())
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| 157 | fxd = list(self.fitter.getfixedparameters())
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| 158 | i = 3*component
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| 159 | pars[i:i+3] = params
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| 160 | fxd[i:i+3] = fixed
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| 161 | params = pars
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| 162 | fixed = fxd
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| 163 | self.fitter.setparameters(params)
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| 164 | if fixed is not None:
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| 165 | self.fitter.setfixedparameters(fixed)
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| 166 | return
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| 167 |
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| 168 | def set_gauss_parameters(self, peak, centre, fhwm,
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| 169 | peakfixed=False, centerfixed=False,
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| 170 | fhwmfixed=False,
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| 171 | component=0):
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| 172 | """
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| 173 | Set the Parameters of a 'Gaussian' component, set with set_function.
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| 174 | Parameters:
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| 175 | component: The number of the component (Default is the
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| 176 | first component.
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| 177 | peak, centre, fhwm: The gaussian parameters
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| 178 | peakfixed,
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| 179 | centerfixed,
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| 180 | fhwmfixed: Optional parameters to indicate if
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| 181 | the paramters should be held fixed during
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| 182 | the fitting process. The default is to keep
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| 183 | all parameters flexible.
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| 184 | """
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| 185 | if self.fitfunc != "gauss":
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| 186 | print "Function only operates on Gaussian components."
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| 187 | return
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| 188 | if 0 <= component < len(self.components):
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| 189 | self.set_parameters([peak, centre, fhwm],
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| 190 | [peakfixed, centerfixed, fhwmfixed],
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| 191 | component)
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| 192 | else:
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| 193 | print "Please select a valid component."
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| 194 | return
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| 195 |
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| 196 | def get_parameters(self, component=None):
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| 197 | """
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| 198 | Return the fit paramters.
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| 199 |
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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 | pars = list(self.fitter.getparameters())
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| 204 | fixed = list(self.fitter.getfixedparameters())
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| 205 | if component is not None:
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| 206 | if self.fitfunc == "gauss":
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| 207 | i = 3*component
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| 208 | cpars = pars[i:i+3]
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| 209 | cfixed = fixed[i:i+3]
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| 210 | else:
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| 211 | cpars = pars
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| 212 | cfixed = fixed
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| 213 | else:
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| 214 | cpars = pars
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| 215 | cfixed = fixed
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| 216 | fpars = self._format_pars(cpars, cfixed)
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| 217 | if self._vb:
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| 218 | print fpars
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| 219 | return cpars, cfixed, fpars
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| 220 |
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| 221 | def _format_pars(self, pars, fixed):
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| 222 | out = ''
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| 223 | if self.fitfunc == 'poly':
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| 224 | c = 0
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| 225 | for i in range(len(pars)):
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| 226 | fix = ""
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| 227 | if fixed[i]: fix = "(fixed)"
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| 228 | out += ' p%d%s= %3.3f,' % (c,fix,pars[i])
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| 229 | c+=1
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| 230 | out = out[:-1] # remove trailing ','
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| 231 | elif self.fitfunc == 'gauss':
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| 232 | i = 0
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| 233 | c = 0
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| 234 | aunit = ''
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| 235 | ounit = ''
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| 236 | if self.data:
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| 237 | aunit = self.data.get_unit()
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| 238 | ounit = self.data.get_fluxunit()
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| 239 | while i < len(pars):
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| 240 | out += ' %d: 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)
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| 241 | c+=1
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| 242 | i+=3
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| 243 | return out
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| 244 |
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| 245 | def get_estimate(self):
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| 246 | """
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| 247 | Return the parameter estimates (for non-linear functions).
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| 248 | """
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| 249 | pars = self.fitter.getestimate()
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| 250 | if self._vb:
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| 251 | print self._format_pars(pars)
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| 252 | return pars
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| 253 |
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| 254 |
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| 255 | def get_residual(self):
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| 256 | """
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| 257 | Return the residual of the fit.
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| 258 | """
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| 259 | if not self.fitted:
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| 260 | print "Not yet fitted."
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| 261 | return self.fitter.getresidual()
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| 262 |
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| 263 | def get_chi2(self):
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| 264 | """
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| 265 | Return chi^2.
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| 266 | """
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| 267 | if not self.fitted:
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| 268 | print "Not yet fitted."
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| 269 | ch2 = self.fitter.getchi2()
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| 270 | if self._vb:
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| 271 | print 'Chi^2 = %3.3f' % (ch2)
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| 272 | return ch2
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| 273 |
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| 274 | def get_fit(self):
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| 275 | """
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| 276 | Return the fitted ordinate values.
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| 277 | """
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| 278 | if not self.fitted:
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| 279 | print "Not yet fitted."
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| 280 | return self.fitter.getfit()
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| 281 |
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| 282 | def commit(self):
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| 283 | """
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| 284 | Return a new scan where the fits have been commited.
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| 285 | """
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| 286 | if not self.fitted:
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| 287 | print "Not yet fitted."
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| 288 | if self.data is not scantable:
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| 289 | print "Only works with scantables"
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| 290 | return
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| 291 | scan = self.data.copy()
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| 292 | scan._setspectrum(self.fitter.getresidual())
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| 293 |
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| 294 | def plot(self, residual=False, components=None, plotparms=False,
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| 295 | plotrange=None):
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| 296 | """
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| 297 | Plot the last fit.
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| 298 | Parameters:
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| 299 | residual: an optional parameter indicating if the residual
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| 300 | should be plotted (default 'False')
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| 301 | """
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| 302 | if not self.fitted:
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| 303 | return
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| 304 | if not self._p:
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| 305 | from asap.asaplot import ASAPlot
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| 306 | self._p = ASAPlot()
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| 307 | if self._p.is_dead:
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| 308 | from asap.asaplot import ASAPlot
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| 309 | self._p = ASAPlot()
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| 310 | self._p.clear()
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| 311 | self._p.set_panels()
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| 312 | self._p.palette(1)
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| 313 | tlab = 'Spectrum'
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| 314 | xlab = 'Abcissa'
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| 315 | m = ()
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| 316 | if self.data:
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| 317 | tlab = self.data._getsourcename(self._fittedrow)
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| 318 | xlab = self.data._getabcissalabel(self._fittedrow)
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| 319 | m = self.data._getmask(self._fittedrow)
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| 320 | ylab = r'Flux'
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| 321 |
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| 322 | colours = ["grey60","grey80","red","orange","purple","yellow","magenta", "cyan"]
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| 323 | self._p.palette(1,colours)
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| 324 | self._p.set_line(label='Spectrum')
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| 325 | self._p.plot(self.x, self.y, m)
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| 326 | if residual:
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| 327 | self._p.palette(2)
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| 328 | self._p.set_line(label='Residual')
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| 329 | self._p.plot(self.x, self.get_residual(), m)
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| 330 | self._p.palette(3)
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| 331 | if components is not None:
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| 332 | cs = components
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| 333 | if isinstance(components,int): cs = [components]
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| 334 | self._p.text(0.15,0.15,str(self.get_parameters()[2]),size=8)
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| 335 | n = len(self.components)
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| 336 | self._p.palette(4)
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| 337 | for c in cs:
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| 338 | if 0 <= c < n:
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| 339 | lab = self.fitfuncs[c]+str(c)
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| 340 | self._p.set_line(label=lab)
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| 341 | self._p.plot(self.x, self.fitter.evaluate(c), m)
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| 342 | elif c == -1:
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| 343 | self._p.palette(3)
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| 344 | self._p.set_line(label="Total Fit")
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| 345 | self._p.plot(self.x, self.get_fit(), m)
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| 346 | else:
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| 347 | self._p.palette(3)
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| 348 | self._p.set_line(label='Fit')
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| 349 | self._p.plot(self.x, self.get_fit(), m)
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| 350 | self._p.set_axes('xlabel',xlab)
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| 351 | self._p.set_axes('ylabel',ylab)
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| 352 | self._p.set_axes('title',tlab)
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| 353 | self._p.release()
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| 354 |
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| 355 | def auto_fit(self, insitu=None):
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| 356 | """
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| 357 | Return a scan where the function is applied to all rows for
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| 358 | all Beams/IFs/Pols.
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| 359 |
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| 360 | """
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| 361 | from asap import scantable
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| 362 | if not isinstance(self.data, scantable) :
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| 363 | print "Only works with scantables"
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| 364 | return
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| 365 | if insitu is None: insitu = rcParams['insitu']
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| 366 | if not insitu:
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| 367 | scan = self.data.copy()
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| 368 | else:
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| 369 | scan = self.data
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| 370 | vb = scan._vb
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| 371 | scan._vb = False
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| 372 | sel = scan.get_cursor()
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| 373 | rows = range(scan.nrow())
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| 374 | for i in range(scan.nbeam()):
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| 375 | scan.setbeam(i)
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| 376 | for j in range(scan.nif()):
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| 377 | scan.setif(j)
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| 378 | for k in range(scan.npol()):
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| 379 | scan.setpol(k)
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| 380 | if self._vb:
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| 381 | print "Fitting:"
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| 382 | print 'Beam[%d], IF[%d], Pol[%d]' % (i,j,k)
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| 383 | for iRow in rows:
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| 384 | self.x = scan._getabcissa(iRow)
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| 385 | self.y = scan._getspectrum(iRow)
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| 386 | self.data = None
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| 387 | self.fit()
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| 388 | x = self.get_parameters()
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| 389 | scan._setspectrum(self.fitter.getresidual(),iRow)
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| 390 | scan.set_cursor(sel[0],sel[1],sel[2])
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| 391 | scan._vb = vb
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| 392 | if not insitu:
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| 393 | return scan
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