[113] | 1 | import _asap |
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[259] | 2 | from asap import rcParams |
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[723] | 3 | from asap import print_log |
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[1295] | 4 | from asap import _n_bools |
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[1603] | 5 | from asap import mask_and |
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[1612] | 6 | from taskinit import * |
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[113] | 7 | |
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| 8 | class fitter: |
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| 9 | """ |
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| 10 | The fitting class for ASAP. |
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| 11 | """ |
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[723] | 12 | |
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[113] | 13 | def __init__(self): |
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| 14 | """ |
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| 15 | Create a fitter object. No state is set. |
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| 16 | """ |
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| 17 | self.fitter = _asap.fitter() |
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| 18 | self.x = None |
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| 19 | self.y = None |
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| 20 | self.mask = None |
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| 21 | self.fitfunc = None |
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[515] | 22 | self.fitfuncs = None |
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[113] | 23 | self.fitted = False |
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| 24 | self.data = None |
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[515] | 25 | self.components = 0 |
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| 26 | self._fittedrow = 0 |
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[113] | 27 | self._p = None |
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[515] | 28 | self._selection = None |
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[1389] | 29 | self.uselinear = False |
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[113] | 30 | |
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| 31 | def set_data(self, xdat, ydat, mask=None): |
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| 32 | """ |
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[158] | 33 | Set the absissa and ordinate for the fit. Also set the mask |
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[113] | 34 | indicationg valid points. |
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| 35 | This can be used for data vectors retrieved from a scantable. |
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| 36 | For scantable fitting use 'fitter.set_scan(scan, mask)'. |
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| 37 | Parameters: |
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[158] | 38 | xdat: the abcissa values |
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[113] | 39 | ydat: the ordinate values |
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| 40 | mask: an optional mask |
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[723] | 41 | |
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[113] | 42 | """ |
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| 43 | self.fitted = False |
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| 44 | self.x = xdat |
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| 45 | self.y = ydat |
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| 46 | if mask == None: |
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[1295] | 47 | self.mask = _n_bools(len(xdat), True) |
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[113] | 48 | else: |
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| 49 | self.mask = mask |
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| 50 | return |
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| 51 | |
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| 52 | def set_scan(self, thescan=None, mask=None): |
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| 53 | """ |
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| 54 | Set the 'data' (a scantable) of the fitter. |
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| 55 | Parameters: |
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| 56 | thescan: a scantable |
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[1603] | 57 | mask: a msk retrieved from the scantable |
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[113] | 58 | """ |
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| 59 | if not thescan: |
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[723] | 60 | msg = "Please give a correct scan" |
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| 61 | if rcParams['verbose']: |
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[1612] | 62 | #print msg |
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| 63 | casalog.post( msg, 'WARN' ) |
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[723] | 64 | return |
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| 65 | else: |
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| 66 | raise TypeError(msg) |
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[113] | 67 | self.fitted = False |
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| 68 | self.data = thescan |
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[1075] | 69 | self.mask = None |
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[113] | 70 | if mask is None: |
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[1295] | 71 | self.mask = _n_bools(self.data.nchan(), True) |
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[113] | 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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[1389] | 80 | poly: use a polynomial of the order given with nonlinear least squares fit |
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| 81 | lpoly: use polynomial of the order given with linear least squares fit |
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[113] | 82 | gauss: fit the number of gaussian specified |
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| 83 | Example: |
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| 84 | fitter.set_function(gauss=2) # will fit two gaussians |
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[1389] | 85 | fitter.set_function(poly=3) # will fit a 3rd order polynomial via nonlinear method |
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| 86 | fitter.set_function(lpoly=3) # will fit a 3rd order polynomial via linear method |
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[113] | 87 | """ |
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[723] | 88 | #default poly order 0 |
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[515] | 89 | n=0 |
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[113] | 90 | if kwargs.has_key('poly'): |
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| 91 | self.fitfunc = 'poly' |
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| 92 | n = kwargs.get('poly') |
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[515] | 93 | self.components = [n] |
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[1389] | 94 | self.uselinear = False |
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| 95 | elif kwargs.has_key('lpoly'): |
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| 96 | self.fitfunc = 'poly' |
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| 97 | n = kwargs.get('lpoly') |
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| 98 | self.components = [n] |
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| 99 | self.uselinear = True |
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[113] | 100 | elif kwargs.has_key('gauss'): |
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| 101 | n = kwargs.get('gauss') |
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| 102 | self.fitfunc = 'gauss' |
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[515] | 103 | self.fitfuncs = [ 'gauss' for i in range(n) ] |
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| 104 | self.components = [ 3 for i in range(n) ] |
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[1389] | 105 | self.uselinear = False |
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[515] | 106 | else: |
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[723] | 107 | msg = "Invalid function type." |
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| 108 | if rcParams['verbose']: |
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[1612] | 109 | #print msg |
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| 110 | casalog.post( msg, 'WARN' ) |
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[723] | 111 | return |
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| 112 | else: |
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| 113 | raise TypeError(msg) |
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| 114 | |
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[113] | 115 | self.fitter.setexpression(self.fitfunc,n) |
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[1232] | 116 | self.fitted = False |
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[113] | 117 | return |
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[723] | 118 | |
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[1075] | 119 | def fit(self, row=0, estimate=False): |
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[113] | 120 | """ |
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| 121 | Execute the actual fitting process. All the state has to be set. |
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| 122 | Parameters: |
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[1075] | 123 | row: specify the row in the scantable |
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| 124 | estimate: auto-compute an initial parameter set (default False) |
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| 125 | This can be used to compute estimates even if fit was |
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| 126 | called before. |
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[113] | 127 | Example: |
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[515] | 128 | s = scantable('myscan.asap') |
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| 129 | s.set_cursor(thepol=1) # select second pol |
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[113] | 130 | f = fitter() |
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| 131 | f.set_scan(s) |
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| 132 | f.set_function(poly=0) |
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[723] | 133 | f.fit(row=0) # fit first row |
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[113] | 134 | """ |
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| 135 | if ((self.x is None or self.y is None) and self.data is None) \ |
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| 136 | or self.fitfunc is None: |
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[723] | 137 | msg = "Fitter not yet initialised. Please set data & fit function" |
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| 138 | if rcParams['verbose']: |
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[1612] | 139 | #print msg |
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| 140 | casalog.post( msg, 'WARN' ) |
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[723] | 141 | return |
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| 142 | else: |
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| 143 | raise RuntimeError(msg) |
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| 144 | |
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[113] | 145 | else: |
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| 146 | if self.data is not None: |
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[515] | 147 | self.x = self.data._getabcissa(row) |
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| 148 | self.y = self.data._getspectrum(row) |
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[1603] | 149 | self.mask = mask_and(self.mask, self.data._getmask(row)) |
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[723] | 150 | from asap import asaplog |
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| 151 | asaplog.push("Fitting:") |
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[943] | 152 | i = row |
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[1603] | 153 | out = "Scan[%d] Beam[%d] IF[%d] Pol[%d] Cycle[%d]" % (self.data.getscan(i), |
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| 154 | self.data.getbeam(i), |
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| 155 | self.data.getif(i), |
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| 156 | self.data.getpol(i), |
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| 157 | self.data.getcycle(i)) |
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[1075] | 158 | asaplog.push(out,False) |
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[515] | 159 | self.fitter.setdata(self.x, self.y, self.mask) |
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[113] | 160 | if self.fitfunc == 'gauss': |
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| 161 | ps = self.fitter.getparameters() |
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[1075] | 162 | if len(ps) == 0 or estimate: |
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[113] | 163 | self.fitter.estimate() |
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[626] | 164 | try: |
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[1232] | 165 | fxdpar = list(self.fitter.getfixedparameters()) |
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| 166 | if len(fxdpar) and fxdpar.count(0) == 0: |
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| 167 | raise RuntimeError,"No point fitting, if all parameters are fixed." |
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[1389] | 168 | if self.uselinear: |
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| 169 | converged = self.fitter.lfit() |
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| 170 | else: |
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| 171 | converged = self.fitter.fit() |
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[1075] | 172 | if not converged: |
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| 173 | raise RuntimeError,"Fit didn't converge." |
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[626] | 174 | except RuntimeError, msg: |
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[723] | 175 | if rcParams['verbose']: |
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[1612] | 176 | #print msg |
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| 177 | casalog.post( msg, 'WARN' ) |
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[723] | 178 | else: |
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| 179 | raise |
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[515] | 180 | self._fittedrow = row |
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[113] | 181 | self.fitted = True |
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[723] | 182 | print_log() |
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[113] | 183 | return |
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| 184 | |
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[1232] | 185 | def store_fit(self, filename=None): |
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[526] | 186 | """ |
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[1232] | 187 | Save the fit parameters. |
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| 188 | Parameters: |
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| 189 | filename: if specified save as an ASCII file, if None (default) |
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| 190 | store it in the scnatable |
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[526] | 191 | """ |
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[515] | 192 | if self.fitted and self.data is not None: |
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| 193 | pars = list(self.fitter.getparameters()) |
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| 194 | fixed = list(self.fitter.getfixedparameters()) |
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[975] | 195 | from asap.asapfit import asapfit |
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| 196 | fit = asapfit() |
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| 197 | fit.setparameters(pars) |
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| 198 | fit.setfixedparameters(fixed) |
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| 199 | fit.setfunctions(self.fitfuncs) |
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| 200 | fit.setcomponents(self.components) |
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| 201 | fit.setframeinfo(self.data._getcoordinfo()) |
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[1232] | 202 | if filename is not None: |
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| 203 | import os |
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| 204 | filename = os.path.expandvars(os.path.expanduser(filename)) |
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| 205 | if os.path.exists(filename): |
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| 206 | raise IOError("File '%s' exists." % filename) |
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| 207 | fit.save(filename) |
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| 208 | else: |
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| 209 | self.data._addfit(fit,self._fittedrow) |
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[515] | 210 | |
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[1017] | 211 | #def set_parameters(self, params, fixed=None, component=None): |
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| 212 | def set_parameters(self,*args,**kwargs): |
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[526] | 213 | """ |
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| 214 | Set the parameters to be fitted. |
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| 215 | Parameters: |
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| 216 | params: a vector of parameters |
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| 217 | fixed: a vector of which parameters are to be held fixed |
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| 218 | (default is none) |
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| 219 | component: in case of multiple gaussians, the index of the |
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| 220 | component |
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[1017] | 221 | """ |
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| 222 | component = None |
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| 223 | fixed = None |
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| 224 | params = None |
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[1031] | 225 | |
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[1017] | 226 | if len(args) and isinstance(args[0],dict): |
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| 227 | kwargs = args[0] |
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| 228 | if kwargs.has_key("fixed"): fixed = kwargs["fixed"] |
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| 229 | if kwargs.has_key("params"): params = kwargs["params"] |
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| 230 | if len(args) == 2 and isinstance(args[1], int): |
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| 231 | component = args[1] |
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[515] | 232 | if self.fitfunc is None: |
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[723] | 233 | msg = "Please specify a fitting function first." |
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| 234 | if rcParams['verbose']: |
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[1612] | 235 | #print msg |
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| 236 | casalog.post( msg, 'WARN' ) |
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[723] | 237 | return |
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| 238 | else: |
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| 239 | raise RuntimeError(msg) |
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[515] | 240 | if self.fitfunc == "gauss" and component is not None: |
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[1017] | 241 | if not self.fitted and sum(self.fitter.getparameters()) == 0: |
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[1295] | 242 | pars = _n_bools(len(self.components)*3, False) |
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| 243 | fxd = _n_bools(len(pars), False) |
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[515] | 244 | else: |
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[723] | 245 | pars = list(self.fitter.getparameters()) |
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[515] | 246 | fxd = list(self.fitter.getfixedparameters()) |
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| 247 | i = 3*component |
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| 248 | pars[i:i+3] = params |
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| 249 | fxd[i:i+3] = fixed |
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| 250 | params = pars |
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[723] | 251 | fixed = fxd |
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[113] | 252 | self.fitter.setparameters(params) |
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| 253 | if fixed is not None: |
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| 254 | self.fitter.setfixedparameters(fixed) |
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[723] | 255 | print_log() |
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[113] | 256 | return |
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[515] | 257 | |
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[1217] | 258 | def set_gauss_parameters(self, peak, centre, fwhm, |
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[1603] | 259 | peakfixed=0, centrefixed=0, |
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[1217] | 260 | fwhmfixed=0, |
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[515] | 261 | component=0): |
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[113] | 262 | """ |
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[515] | 263 | Set the Parameters of a 'Gaussian' component, set with set_function. |
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| 264 | Parameters: |
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[1232] | 265 | peak, centre, fwhm: The gaussian parameters |
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[515] | 266 | peakfixed, |
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[1603] | 267 | centrefixed, |
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[1217] | 268 | fwhmfixed: Optional parameters to indicate if |
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[515] | 269 | the paramters should be held fixed during |
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| 270 | the fitting process. The default is to keep |
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| 271 | all parameters flexible. |
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[526] | 272 | component: The number of the component (Default is the |
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| 273 | component 0) |
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[515] | 274 | """ |
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| 275 | if self.fitfunc != "gauss": |
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[723] | 276 | msg = "Function only operates on Gaussian components." |
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| 277 | if rcParams['verbose']: |
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[1612] | 278 | #print msg |
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| 279 | casalog.post( msg, 'WARN' ) |
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[723] | 280 | return |
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| 281 | else: |
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| 282 | raise ValueError(msg) |
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[515] | 283 | if 0 <= component < len(self.components): |
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[1217] | 284 | d = {'params':[peak, centre, fwhm], |
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[1603] | 285 | 'fixed':[peakfixed, centrefixed, fwhmfixed]} |
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[1017] | 286 | self.set_parameters(d, component) |
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[515] | 287 | else: |
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[723] | 288 | msg = "Please select a valid component." |
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| 289 | if rcParams['verbose']: |
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[1612] | 290 | #print msg |
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| 291 | casalog.post( msg, 'WARN' ) |
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[723] | 292 | return |
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| 293 | else: |
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| 294 | raise ValueError(msg) |
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| 295 | |
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[975] | 296 | def get_area(self, component=None): |
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| 297 | """ |
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| 298 | Return the area under the fitted gaussian component. |
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| 299 | Parameters: |
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| 300 | component: the gaussian component selection, |
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| 301 | default (None) is the sum of all components |
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| 302 | Note: |
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| 303 | This will only work for gaussian fits. |
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| 304 | """ |
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| 305 | if not self.fitted: return |
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| 306 | if self.fitfunc == "gauss": |
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| 307 | pars = list(self.fitter.getparameters()) |
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| 308 | from math import log,pi,sqrt |
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| 309 | fac = sqrt(pi/log(16.0)) |
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| 310 | areas = [] |
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| 311 | for i in range(len(self.components)): |
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| 312 | j = i*3 |
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| 313 | cpars = pars[j:j+3] |
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| 314 | areas.append(fac * cpars[0] * cpars[2]) |
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| 315 | else: |
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| 316 | return None |
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| 317 | if component is not None: |
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| 318 | return areas[component] |
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| 319 | else: |
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| 320 | return sum(areas) |
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| 321 | |
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[1075] | 322 | def get_errors(self, component=None): |
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[515] | 323 | """ |
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[1075] | 324 | Return the errors in the parameters. |
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| 325 | Parameters: |
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| 326 | component: get the errors for the specified component |
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| 327 | only, default is all components |
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| 328 | """ |
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| 329 | if not self.fitted: |
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| 330 | msg = "Not yet fitted." |
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| 331 | if rcParams['verbose']: |
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[1612] | 332 | #print msg |
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| 333 | casalog.post( msg, 'WARN' ) |
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[1075] | 334 | return |
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| 335 | else: |
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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": |
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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 | def get_parameters(self, component=None, errors=False): |
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| 347 | """ |
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[113] | 348 | Return the fit paramters. |
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[526] | 349 | Parameters: |
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| 350 | component: get the parameters for the specified component |
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| 351 | only, default is all components |
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[113] | 352 | """ |
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| 353 | if not self.fitted: |
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[723] | 354 | msg = "Not yet fitted." |
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| 355 | if rcParams['verbose']: |
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[1612] | 356 | #print msg |
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| 357 | casalog.post( msg, 'WARN' ) |
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[723] | 358 | return |
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| 359 | else: |
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| 360 | raise RuntimeError(msg) |
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[113] | 361 | pars = list(self.fitter.getparameters()) |
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| 362 | fixed = list(self.fitter.getfixedparameters()) |
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[1075] | 363 | errs = list(self.fitter.geterrors()) |
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[1039] | 364 | area = [] |
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[723] | 365 | if component is not None: |
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[515] | 366 | if self.fitfunc == "gauss": |
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| 367 | i = 3*component |
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| 368 | cpars = pars[i:i+3] |
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| 369 | cfixed = fixed[i:i+3] |
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[1075] | 370 | cerrs = errs[i:i+3] |
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[1039] | 371 | a = self.get_area(component) |
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| 372 | area = [a for i in range(3)] |
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[515] | 373 | else: |
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| 374 | cpars = pars |
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[723] | 375 | cfixed = fixed |
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[1075] | 376 | cerrs = errs |
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[515] | 377 | else: |
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| 378 | cpars = pars |
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| 379 | cfixed = fixed |
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[1075] | 380 | cerrs = errs |
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[1039] | 381 | if self.fitfunc == "gauss": |
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| 382 | for c in range(len(self.components)): |
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| 383 | a = self.get_area(c) |
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| 384 | area += [a for i in range(3)] |
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[1088] | 385 | fpars = self._format_pars(cpars, cfixed, errors and cerrs, area) |
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[723] | 386 | if rcParams['verbose']: |
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[1612] | 387 | #print fpars |
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| 388 | casalog.post( fpars ) |
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[1075] | 389 | return {'params':cpars, 'fixed':cfixed, 'formatted': fpars, |
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| 390 | 'errors':cerrs} |
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[723] | 391 | |
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[1075] | 392 | def _format_pars(self, pars, fixed, errors, area): |
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[113] | 393 | out = '' |
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| 394 | if self.fitfunc == 'poly': |
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| 395 | c = 0 |
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[515] | 396 | for i in range(len(pars)): |
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| 397 | fix = "" |
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[1232] | 398 | if len(fixed) and fixed[i]: fix = "(fixed)" |
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[1088] | 399 | if errors : |
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| 400 | out += ' p%d%s= %3.6f (%1.6f),' % (c,fix,pars[i], errors[i]) |
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| 401 | else: |
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| 402 | out += ' p%d%s= %3.6f,' % (c,fix,pars[i]) |
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[113] | 403 | c+=1 |
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[515] | 404 | out = out[:-1] # remove trailing ',' |
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[113] | 405 | elif self.fitfunc == 'gauss': |
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| 406 | i = 0 |
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| 407 | c = 0 |
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[515] | 408 | aunit = '' |
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| 409 | ounit = '' |
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[113] | 410 | if self.data: |
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[515] | 411 | aunit = self.data.get_unit() |
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| 412 | ounit = self.data.get_fluxunit() |
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[113] | 413 | while i < len(pars): |
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[1039] | 414 | if len(area): |
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| 415 | 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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[1017] | 416 | else: |
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| 417 | 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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[113] | 418 | c+=1 |
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| 419 | i+=3 |
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| 420 | return out |
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[723] | 421 | |
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[113] | 422 | def get_estimate(self): |
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| 423 | """ |
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[515] | 424 | Return the parameter estimates (for non-linear functions). |
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[113] | 425 | """ |
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| 426 | pars = self.fitter.getestimate() |
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[943] | 427 | fixed = self.fitter.getfixedparameters() |
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[723] | 428 | if rcParams['verbose']: |
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[1612] | 429 | #print self._format_pars(pars,fixed,None) |
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| 430 | casalog.post( self._format_pars(pars,fixed,None) ) |
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[113] | 431 | return pars |
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| 432 | |
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| 433 | def get_residual(self): |
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| 434 | """ |
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| 435 | Return the residual of the fit. |
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| 436 | """ |
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| 437 | if not self.fitted: |
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[723] | 438 | msg = "Not yet fitted." |
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| 439 | if rcParams['verbose']: |
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[1612] | 440 | #print msg |
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| 441 | casalog.post( msg, 'WARN' ) |
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[723] | 442 | return |
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| 443 | else: |
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| 444 | raise RuntimeError(msg) |
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[113] | 445 | return self.fitter.getresidual() |
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| 446 | |
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| 447 | def get_chi2(self): |
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| 448 | """ |
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| 449 | Return chi^2. |
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| 450 | """ |
---|
| 451 | if not self.fitted: |
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[723] | 452 | msg = "Not yet fitted." |
---|
| 453 | if rcParams['verbose']: |
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[1612] | 454 | #print msg |
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| 455 | casalog.post( msg, 'WARN' ) |
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[723] | 456 | return |
---|
| 457 | else: |
---|
| 458 | raise RuntimeError(msg) |
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[113] | 459 | ch2 = self.fitter.getchi2() |
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[723] | 460 | if rcParams['verbose']: |
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[1612] | 461 | #print 'Chi^2 = %3.3f' % (ch2) |
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| 462 | casalog.post( 'Chi^2 = %3.3f' % (ch2) ) |
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[723] | 463 | return ch2 |
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[113] | 464 | |
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| 465 | def get_fit(self): |
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| 466 | """ |
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| 467 | Return the fitted ordinate values. |
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| 468 | """ |
---|
| 469 | if not self.fitted: |
---|
[723] | 470 | msg = "Not yet fitted." |
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| 471 | if rcParams['verbose']: |
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[1612] | 472 | #print msg |
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| 473 | casalog.post( msg, 'WARN' ) |
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[723] | 474 | return |
---|
| 475 | else: |
---|
| 476 | raise RuntimeError(msg) |
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[113] | 477 | return self.fitter.getfit() |
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| 478 | |
---|
| 479 | def commit(self): |
---|
| 480 | """ |
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[526] | 481 | Return a new scan where the fits have been commited (subtracted) |
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[113] | 482 | """ |
---|
| 483 | if not self.fitted: |
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[723] | 484 | msg = "Not yet fitted." |
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| 485 | if rcParams['verbose']: |
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[1612] | 486 | #print msg |
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| 487 | casalog.post( msg, 'WARN' ) |
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[723] | 488 | return |
---|
| 489 | else: |
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| 490 | raise RuntimeError(msg) |
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[975] | 491 | from asap import scantable |
---|
| 492 | if not isinstance(self.data, scantable): |
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[723] | 493 | msg = "Not a scantable" |
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| 494 | if rcParams['verbose']: |
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[1612] | 495 | #print msg |
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| 496 | casalog.post( msg, 'WARN' ) |
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[723] | 497 | return |
---|
| 498 | else: |
---|
| 499 | raise TypeError(msg) |
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[113] | 500 | scan = self.data.copy() |
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[259] | 501 | scan._setspectrum(self.fitter.getresidual()) |
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[723] | 502 | print_log() |
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[1092] | 503 | return scan |
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[113] | 504 | |
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[723] | 505 | def plot(self, residual=False, components=None, plotparms=False, filename=None): |
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[113] | 506 | """ |
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| 507 | Plot the last fit. |
---|
| 508 | Parameters: |
---|
| 509 | residual: an optional parameter indicating if the residual |
---|
| 510 | should be plotted (default 'False') |
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[526] | 511 | components: a list of components to plot, e.g [0,1], |
---|
| 512 | -1 plots the total fit. Default is to only |
---|
| 513 | plot the total fit. |
---|
| 514 | plotparms: Inidicates if the parameter values should be present |
---|
| 515 | on the plot |
---|
[113] | 516 | """ |
---|
| 517 | if not self.fitted: |
---|
| 518 | return |
---|
[723] | 519 | if not self._p or self._p.is_dead: |
---|
| 520 | if rcParams['plotter.gui']: |
---|
| 521 | from asap.asaplotgui import asaplotgui as asaplot |
---|
| 522 | else: |
---|
| 523 | from asap.asaplot import asaplot |
---|
| 524 | self._p = asaplot() |
---|
| 525 | self._p.hold() |
---|
[113] | 526 | self._p.clear() |
---|
[515] | 527 | self._p.set_panels() |
---|
[652] | 528 | self._p.palette(0) |
---|
[113] | 529 | tlab = 'Spectrum' |
---|
[723] | 530 | xlab = 'Abcissa' |
---|
[1017] | 531 | ylab = 'Ordinate' |
---|
[1273] | 532 | from matplotlib.numerix import ma,logical_not,logical_and,array |
---|
| 533 | m = self.mask |
---|
[113] | 534 | if self.data: |
---|
[515] | 535 | tlab = self.data._getsourcename(self._fittedrow) |
---|
| 536 | xlab = self.data._getabcissalabel(self._fittedrow) |
---|
[1273] | 537 | m = logical_and(self.mask, |
---|
[1306] | 538 | array(self.data._getmask(self._fittedrow), |
---|
| 539 | copy=False)) |
---|
[1389] | 540 | |
---|
[626] | 541 | ylab = self.data._get_ordinate_label() |
---|
[515] | 542 | |
---|
[1075] | 543 | colours = ["#777777","#dddddd","red","orange","purple","green","magenta", "cyan"] |
---|
[1461] | 544 | nomask=True |
---|
| 545 | for i in range(len(m)): |
---|
| 546 | nomask = nomask and m[i] |
---|
| 547 | label0='Masked Region' |
---|
| 548 | label1='Spectrum' |
---|
| 549 | if ( nomask ): |
---|
| 550 | label0=label1 |
---|
| 551 | else: |
---|
| 552 | y = ma.masked_array( self.y, mask = m ) |
---|
| 553 | self._p.palette(1,colours) |
---|
| 554 | self._p.set_line( label = label1 ) |
---|
| 555 | self._p.plot( self.x, y ) |
---|
[652] | 556 | self._p.palette(0,colours) |
---|
[1461] | 557 | self._p.set_line(label=label0) |
---|
[1273] | 558 | y = ma.masked_array(self.y,mask=logical_not(m)) |
---|
[1088] | 559 | self._p.plot(self.x, y) |
---|
[113] | 560 | if residual: |
---|
[1461] | 561 | self._p.palette(7) |
---|
[515] | 562 | self._p.set_line(label='Residual') |
---|
[1116] | 563 | y = ma.masked_array(self.get_residual(), |
---|
[1273] | 564 | mask=logical_not(m)) |
---|
[1088] | 565 | self._p.plot(self.x, y) |
---|
[652] | 566 | self._p.palette(2) |
---|
[515] | 567 | if components is not None: |
---|
| 568 | cs = components |
---|
| 569 | if isinstance(components,int): cs = [components] |
---|
[526] | 570 | if plotparms: |
---|
[1031] | 571 | self._p.text(0.15,0.15,str(self.get_parameters()['formatted']),size=8) |
---|
[515] | 572 | n = len(self.components) |
---|
[652] | 573 | self._p.palette(3) |
---|
[515] | 574 | for c in cs: |
---|
| 575 | if 0 <= c < n: |
---|
| 576 | lab = self.fitfuncs[c]+str(c) |
---|
| 577 | self._p.set_line(label=lab) |
---|
[1116] | 578 | y = ma.masked_array(self.fitter.evaluate(c), |
---|
[1273] | 579 | mask=logical_not(m)) |
---|
[1088] | 580 | |
---|
| 581 | self._p.plot(self.x, y) |
---|
[515] | 582 | elif c == -1: |
---|
[652] | 583 | self._p.palette(2) |
---|
[515] | 584 | self._p.set_line(label="Total Fit") |
---|
[1116] | 585 | y = ma.masked_array(self.fitter.getfit(), |
---|
[1273] | 586 | mask=logical_not(m)) |
---|
[1088] | 587 | self._p.plot(self.x, y) |
---|
[515] | 588 | else: |
---|
[652] | 589 | self._p.palette(2) |
---|
[515] | 590 | self._p.set_line(label='Fit') |
---|
[1116] | 591 | y = ma.masked_array(self.fitter.getfit(), |
---|
[1273] | 592 | mask=logical_not(m)) |
---|
[1088] | 593 | self._p.plot(self.x, y) |
---|
[723] | 594 | xlim=[min(self.x),max(self.x)] |
---|
| 595 | self._p.axes.set_xlim(xlim) |
---|
[113] | 596 | self._p.set_axes('xlabel',xlab) |
---|
| 597 | self._p.set_axes('ylabel',ylab) |
---|
| 598 | self._p.set_axes('title',tlab) |
---|
| 599 | self._p.release() |
---|
[723] | 600 | if (not rcParams['plotter.gui']): |
---|
| 601 | self._p.save(filename) |
---|
| 602 | print_log() |
---|
[113] | 603 | |
---|
[1061] | 604 | def auto_fit(self, insitu=None, plot=False): |
---|
[113] | 605 | """ |
---|
[515] | 606 | Return a scan where the function is applied to all rows for |
---|
| 607 | all Beams/IFs/Pols. |
---|
[723] | 608 | |
---|
[113] | 609 | """ |
---|
| 610 | from asap import scantable |
---|
[515] | 611 | if not isinstance(self.data, scantable) : |
---|
[723] | 612 | msg = "Data is not a scantable" |
---|
| 613 | if rcParams['verbose']: |
---|
[1612] | 614 | #print msg |
---|
| 615 | casalog.post( msg, 'WARN' ) |
---|
[723] | 616 | return |
---|
| 617 | else: |
---|
| 618 | raise TypeError(msg) |
---|
[259] | 619 | if insitu is None: insitu = rcParams['insitu'] |
---|
| 620 | if not insitu: |
---|
| 621 | scan = self.data.copy() |
---|
| 622 | else: |
---|
| 623 | scan = self.data |
---|
[880] | 624 | rows = xrange(scan.nrow()) |
---|
[1446] | 625 | # Save parameters of baseline fits as a class attribute. |
---|
| 626 | # NOTICE: This does not reflect changes in scantable! |
---|
| 627 | if len(rows) > 0: self.blpars=[] |
---|
[723] | 628 | from asap import asaplog |
---|
[876] | 629 | asaplog.push("Fitting:") |
---|
| 630 | for r in rows: |
---|
[1603] | 631 | out = " Scan[%d] Beam[%d] IF[%d] Pol[%d] Cycle[%d]" % (scan.getscan(r), |
---|
| 632 | scan.getbeam(r), |
---|
| 633 | scan.getif(r), |
---|
| 634 | scan.getpol(r), |
---|
| 635 | scan.getcycle(r)) |
---|
[880] | 636 | asaplog.push(out, False) |
---|
[876] | 637 | self.x = scan._getabcissa(r) |
---|
| 638 | self.y = scan._getspectrum(r) |
---|
[1603] | 639 | self.mask = mask_and(self.mask, scan._getmask(r)) |
---|
[876] | 640 | self.data = None |
---|
| 641 | self.fit() |
---|
[1603] | 642 | x = self.get_parameters() |
---|
[1446] | 643 | fpar = self.get_parameters() |
---|
[1061] | 644 | if plot: |
---|
| 645 | self.plot(residual=True) |
---|
| 646 | x = raw_input("Accept fit ([y]/n): ") |
---|
| 647 | if x.upper() == 'N': |
---|
[1446] | 648 | self.blpars.append(None) |
---|
[1061] | 649 | continue |
---|
[880] | 650 | scan._setspectrum(self.fitter.getresidual(), r) |
---|
[1446] | 651 | self.blpars.append(fpar) |
---|
[1061] | 652 | if plot: |
---|
| 653 | self._p.unmap() |
---|
| 654 | self._p = None |
---|
[876] | 655 | print_log() |
---|
| 656 | return scan |
---|
[794] | 657 | |
---|