[113] | 1 | import _asap |
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[1826] | 2 | from asap.parameters import rcParams |
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[1862] | 3 | from asap.logging import asaplog, asaplog_post_dec |
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[1826] | 4 | from asap.utils import _n_bools, mask_and |
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[113] | 5 | |
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[1826] | 6 | |
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[113] | 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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[515] | 20 | self.fitfuncs = None |
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[113] | 21 | self.fitted = False |
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| 22 | self.data = None |
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[515] | 23 | self.components = 0 |
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| 24 | self._fittedrow = 0 |
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[113] | 25 | self._p = None |
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[515] | 26 | self._selection = None |
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[1391] | 27 | self.uselinear = False |
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[113] | 28 | |
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| 29 | def set_data(self, xdat, ydat, mask=None): |
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| 30 | """ |
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[158] | 31 | Set the absissa and ordinate for the fit. Also set the mask |
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[113] | 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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[158] | 36 | xdat: the abcissa values |
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[113] | 37 | ydat: the ordinate values |
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| 38 | mask: an optional mask |
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[723] | 39 | |
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[113] | 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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[1295] | 45 | self.mask = _n_bools(len(xdat), True) |
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[113] | 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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[1862] | 50 | @asaplog_post_dec |
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[113] | 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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[1420] | 56 | mask: a msk retrieved from the scantable |
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[113] | 57 | """ |
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| 58 | if not thescan: |
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[723] | 59 | msg = "Please give a correct scan" |
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[1859] | 60 | raise TypeError(msg) |
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[113] | 61 | self.fitted = False |
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| 62 | self.data = thescan |
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[1075] | 63 | self.mask = None |
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[113] | 64 | if mask is None: |
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[1295] | 65 | self.mask = _n_bools(self.data.nchan(), True) |
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[113] | 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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[1862] | 70 | @asaplog_post_dec |
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[113] | 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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[1589] | 75 | poly: use a polynomial of the order given with nonlinear least squares fit |
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[1391] | 76 | lpoly: use polynomial of the order given with linear least squares fit |
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[113] | 77 | gauss: fit the number of gaussian specified |
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[1819] | 78 | lorentz: fit the number of lorentzian specified |
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[113] | 79 | Example: |
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[1391] | 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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[1819] | 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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[113] | 84 | """ |
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[723] | 85 | #default poly order 0 |
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[515] | 86 | n=0 |
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[113] | 87 | if kwargs.has_key('poly'): |
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| 88 | self.fitfunc = 'poly' |
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[1938] | 89 | self.fitfuncs = ['poly'] |
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[113] | 90 | n = kwargs.get('poly') |
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[1938] | 91 | self.components = [n+1] |
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[1589] | 92 | self.uselinear = False |
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[1391] | 93 | elif kwargs.has_key('lpoly'): |
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| 94 | self.fitfunc = 'poly' |
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[1938] | 95 | self.fitfuncs = ['lpoly'] |
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[1391] | 96 | n = kwargs.get('lpoly') |
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[1938] | 97 | self.components = [n+1] |
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[1391] | 98 | self.uselinear = True |
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[113] | 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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[515] | 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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[1589] | 104 | self.uselinear = False |
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[1819] | 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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[515] | 111 | else: |
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[723] | 112 | msg = "Invalid function type." |
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[1859] | 113 | raise TypeError(msg) |
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[723] | 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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[1862] | 119 | @asaplog_post_dec |
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[1075] | 120 | def fit(self, row=0, estimate=False): |
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[113] | 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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[1075] | 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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[113] | 128 | Example: |
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[515] | 129 | s = scantable('myscan.asap') |
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| 130 | s.set_cursor(thepol=1) # select second pol |
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[113] | 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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[723] | 134 | f.fit(row=0) # fit first row |
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[113] | 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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[723] | 138 | msg = "Fitter not yet initialised. Please set data & fit function" |
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[1859] | 139 | raise RuntimeError(msg) |
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[723] | 140 | |
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[113] | 141 | else: |
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| 142 | if self.data is not None: |
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[515] | 143 | self.x = self.data._getabcissa(row) |
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| 144 | self.y = self.data._getspectrum(row) |
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[1536] | 145 | self.mask = mask_and(self.mask, self.data._getmask(row)) |
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[723] | 146 | asaplog.push("Fitting:") |
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[943] | 147 | i = row |
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[1536] | 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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[1589] | 151 | self.data.getpol(i), |
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[1536] | 152 | self.data.getcycle(i)) |
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[1075] | 153 | asaplog.push(out,False) |
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[515] | 154 | self.fitter.setdata(self.x, self.y, self.mask) |
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[1819] | 155 | if self.fitfunc == 'gauss' or self.fitfunc == 'lorentz': |
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[113] | 156 | ps = self.fitter.getparameters() |
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[1075] | 157 | if len(ps) == 0 or estimate: |
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[113] | 158 | self.fitter.estimate() |
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[1859] | 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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[515] | 168 | self._fittedrow = row |
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[113] | 169 | self.fitted = True |
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| 170 | return |
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| 171 | |
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[1232] | 172 | def store_fit(self, filename=None): |
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[526] | 173 | """ |
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[1232] | 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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[526] | 178 | """ |
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[515] | 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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[975] | 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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[1232] | 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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[515] | 197 | |
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[1862] | 198 | @asaplog_post_dec |
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[1017] | 199 | def set_parameters(self,*args,**kwargs): |
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[526] | 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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[1927] | 206 | component: in case of multiple gaussians/lorentzians, |
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| 207 | the index of the component |
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[1017] | 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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[1031] | 212 | |
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[1017] | 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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[515] | 219 | if self.fitfunc is None: |
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[723] | 220 | msg = "Please specify a fitting function first." |
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[1859] | 221 | raise RuntimeError(msg) |
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[1819] | 222 | if (self.fitfunc == "gauss" or self.fitfunc == 'lorentz') and component is not None: |
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[1017] | 223 | if not self.fitted and sum(self.fitter.getparameters()) == 0: |
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[1295] | 224 | pars = _n_bools(len(self.components)*3, False) |
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| 225 | fxd = _n_bools(len(pars), False) |
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[515] | 226 | else: |
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[723] | 227 | pars = list(self.fitter.getparameters()) |
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[515] | 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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[723] | 233 | fixed = fxd |
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[113] | 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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[515] | 238 | |
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[1862] | 239 | @asaplog_post_dec |
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[1217] | 240 | def set_gauss_parameters(self, peak, centre, fwhm, |
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[1409] | 241 | peakfixed=0, centrefixed=0, |
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[1217] | 242 | fwhmfixed=0, |
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[515] | 243 | component=0): |
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[113] | 244 | """ |
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[515] | 245 | Set the Parameters of a 'Gaussian' component, set with set_function. |
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| 246 | Parameters: |
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[1232] | 247 | peak, centre, fwhm: The gaussian parameters |
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[515] | 248 | peakfixed, |
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[1409] | 249 | centrefixed, |
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[1217] | 250 | fwhmfixed: Optional parameters to indicate if |
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[515] | 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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[526] | 254 | component: The number of the component (Default is the |
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| 255 | component 0) |
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[515] | 256 | """ |
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| 257 | if self.fitfunc != "gauss": |
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[723] | 258 | msg = "Function only operates on Gaussian components." |
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[1859] | 259 | raise ValueError(msg) |
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[515] | 260 | if 0 <= component < len(self.components): |
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[1217] | 261 | d = {'params':[peak, centre, fwhm], |
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[1409] | 262 | 'fixed':[peakfixed, centrefixed, fwhmfixed]} |
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[1017] | 263 | self.set_parameters(d, component) |
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[515] | 264 | else: |
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[723] | 265 | msg = "Please select a valid component." |
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[1859] | 266 | raise ValueError(msg) |
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[723] | 267 | |
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[1862] | 268 | @asaplog_post_dec |
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[1819] | 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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[1927] | 276 | peak, centre, fwhm: The lorentzian parameters |
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[1819] | 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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[1859] | 288 | raise ValueError(msg) |
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[1819] | 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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[1859] | 295 | raise ValueError(msg) |
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[1819] | 296 | |
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[975] | 297 | def get_area(self, component=None): |
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| 298 | """ |
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[1819] | 299 | Return the area under the fitted gaussian/lorentzian component. |
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[975] | 300 | Parameters: |
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[1819] | 301 | component: the gaussian/lorentzian component selection, |
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[975] | 302 | default (None) is the sum of all components |
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| 303 | Note: |
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[1819] | 304 | This will only work for gaussian/lorentzian fits. |
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[975] | 305 | """ |
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| 306 | if not self.fitted: return |
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[1819] | 307 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz": |
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[975] | 308 | pars = list(self.fitter.getparameters()) |
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| 309 | from math import log,pi,sqrt |
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[1819] | 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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[975] | 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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[1862] | 326 | @asaplog_post_dec |
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[1075] | 327 | def get_errors(self, component=None): |
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[515] | 328 | """ |
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[1075] | 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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[1859] | 336 | raise RuntimeError(msg) |
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[1075] | 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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[1819] | 340 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz": |
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[1075] | 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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[1859] | 346 | |
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[1862] | 347 | @asaplog_post_dec |
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[1075] | 348 | def get_parameters(self, component=None, errors=False): |
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| 349 | """ |
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[113] | 350 | Return the fit paramters. |
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[526] | 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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[113] | 354 | """ |
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| 355 | if not self.fitted: |
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[723] | 356 | msg = "Not yet fitted." |
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[1859] | 357 | raise RuntimeError(msg) |
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[113] | 358 | pars = list(self.fitter.getparameters()) |
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| 359 | fixed = list(self.fitter.getfixedparameters()) |
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[1075] | 360 | errs = list(self.fitter.geterrors()) |
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[1039] | 361 | area = [] |
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[723] | 362 | if component is not None: |
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[1819] | 363 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz": |
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[515] | 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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[1075] | 367 | cerrs = errs[i:i+3] |
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[1039] | 368 | a = self.get_area(component) |
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| 369 | area = [a for i in range(3)] |
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[515] | 370 | else: |
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| 371 | cpars = pars |
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[723] | 372 | cfixed = fixed |
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[1075] | 373 | cerrs = errs |
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[515] | 374 | else: |
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| 375 | cpars = pars |
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| 376 | cfixed = fixed |
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[1075] | 377 | cerrs = errs |
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[1819] | 378 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz": |
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[1039] | 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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[1088] | 382 | fpars = self._format_pars(cpars, cfixed, errors and cerrs, area) |
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[1859] | 383 | asaplog.push(fpars) |
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[1075] | 384 | return {'params':cpars, 'fixed':cfixed, 'formatted': fpars, |
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| 385 | 'errors':cerrs} |
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[723] | 386 | |
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[1075] | 387 | def _format_pars(self, pars, fixed, errors, area): |
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[113] | 388 | out = '' |
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| 389 | if self.fitfunc == 'poly': |
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| 390 | c = 0 |
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[515] | 391 | for i in range(len(pars)): |
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| 392 | fix = "" |
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[1232] | 393 | if len(fixed) and fixed[i]: fix = "(fixed)" |
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[1088] | 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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[113] | 398 | c+=1 |
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[515] | 399 | out = out[:-1] # remove trailing ',' |
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[1819] | 400 | elif self.fitfunc == 'gauss' or self.fitfunc == 'lorentz': |
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[113] | 401 | i = 0 |
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| 402 | c = 0 |
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[515] | 403 | aunit = '' |
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| 404 | ounit = '' |
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[113] | 405 | if self.data: |
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[515] | 406 | aunit = self.data.get_unit() |
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| 407 | ounit = self.data.get_fluxunit() |
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[113] | 408 | while i < len(pars): |
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[1039] | 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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[1017] | 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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[113] | 413 | c+=1 |
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| 414 | i+=3 |
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| 415 | return out |
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[723] | 416 | |
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[1859] | 417 | |
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[1862] | 418 | @asaplog_post_dec |
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[113] | 419 | def get_estimate(self): |
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| 420 | """ |
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[515] | 421 | Return the parameter estimates (for non-linear functions). |
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[113] | 422 | """ |
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| 423 | pars = self.fitter.getestimate() |
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[943] | 424 | fixed = self.fitter.getfixedparameters() |
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[1927] | 425 | asaplog.push(self._format_pars(pars,fixed,None,None)) |
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[113] | 426 | return pars |
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| 427 | |
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[1862] | 428 | @asaplog_post_dec |
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[113] | 429 | def get_residual(self): |
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| 430 | """ |
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| 431 | Return the residual of the fit. |
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| 432 | """ |
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| 433 | if not self.fitted: |
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[723] | 434 | msg = "Not yet fitted." |
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[1859] | 435 | raise RuntimeError(msg) |
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[113] | 436 | return self.fitter.getresidual() |
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| 437 | |
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[1862] | 438 | @asaplog_post_dec |
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[113] | 439 | def get_chi2(self): |
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| 440 | """ |
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| 441 | Return chi^2. |
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| 442 | """ |
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| 443 | if not self.fitted: |
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[723] | 444 | msg = "Not yet fitted." |
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[1859] | 445 | raise RuntimeError(msg) |
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[113] | 446 | ch2 = self.fitter.getchi2() |
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[1859] | 447 | asaplog.push( 'Chi^2 = %3.3f' % (ch2) ) |
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[723] | 448 | return ch2 |
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[113] | 449 | |
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[1862] | 450 | @asaplog_post_dec |
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[113] | 451 | def get_fit(self): |
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| 452 | """ |
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| 453 | Return the fitted ordinate values. |
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| 454 | """ |
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| 455 | if not self.fitted: |
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[723] | 456 | msg = "Not yet fitted." |
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[1859] | 457 | raise RuntimeError(msg) |
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[113] | 458 | return self.fitter.getfit() |
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| 459 | |
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[1862] | 460 | @asaplog_post_dec |
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[113] | 461 | def commit(self): |
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| 462 | """ |
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[526] | 463 | Return a new scan where the fits have been commited (subtracted) |
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[113] | 464 | """ |
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| 465 | if not self.fitted: |
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[723] | 466 | msg = "Not yet fitted." |
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[1859] | 467 | raise RuntimeError(msg) |
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[975] | 468 | from asap import scantable |
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| 469 | if not isinstance(self.data, scantable): |
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[723] | 470 | msg = "Not a scantable" |
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[1859] | 471 | raise TypeError(msg) |
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[113] | 472 | scan = self.data.copy() |
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[259] | 473 | scan._setspectrum(self.fitter.getresidual()) |
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[1092] | 474 | return scan |
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[113] | 475 | |
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[1862] | 476 | @asaplog_post_dec |
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[1689] | 477 | def plot(self, residual=False, components=None, plotparms=False, |
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| 478 | filename=None): |
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[113] | 479 | """ |
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| 480 | Plot the last fit. |
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| 481 | Parameters: |
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| 482 | residual: an optional parameter indicating if the residual |
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| 483 | should be plotted (default 'False') |
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[526] | 484 | components: a list of components to plot, e.g [0,1], |
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| 485 | -1 plots the total fit. Default is to only |
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| 486 | plot the total fit. |
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| 487 | plotparms: Inidicates if the parameter values should be present |
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| 488 | on the plot |
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[113] | 489 | """ |
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| 490 | if not self.fitted: |
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| 491 | return |
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[723] | 492 | if not self._p or self._p.is_dead: |
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| 493 | if rcParams['plotter.gui']: |
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| 494 | from asap.asaplotgui import asaplotgui as asaplot |
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| 495 | else: |
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| 496 | from asap.asaplot import asaplot |
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| 497 | self._p = asaplot() |
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| 498 | self._p.hold() |
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[113] | 499 | self._p.clear() |
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[515] | 500 | self._p.set_panels() |
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[652] | 501 | self._p.palette(0) |
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[113] | 502 | tlab = 'Spectrum' |
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[723] | 503 | xlab = 'Abcissa' |
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[1017] | 504 | ylab = 'Ordinate' |
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[1739] | 505 | from numpy import ma,logical_not,logical_and,array |
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[1273] | 506 | m = self.mask |
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[113] | 507 | if self.data: |
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[515] | 508 | tlab = self.data._getsourcename(self._fittedrow) |
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| 509 | xlab = self.data._getabcissalabel(self._fittedrow) |
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[1273] | 510 | m = logical_and(self.mask, |
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[1306] | 511 | array(self.data._getmask(self._fittedrow), |
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| 512 | copy=False)) |
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[1589] | 513 | |
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[626] | 514 | ylab = self.data._get_ordinate_label() |
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[515] | 515 | |
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[1075] | 516 | colours = ["#777777","#dddddd","red","orange","purple","green","magenta", "cyan"] |
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[1819] | 517 | nomask=True |
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| 518 | for i in range(len(m)): |
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| 519 | nomask = nomask and m[i] |
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| 520 | label0='Masked Region' |
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| 521 | label1='Spectrum' |
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| 522 | if ( nomask ): |
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| 523 | label0=label1 |
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| 524 | else: |
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| 525 | y = ma.masked_array( self.y, mask = m ) |
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| 526 | self._p.palette(1,colours) |
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| 527 | self._p.set_line( label = label1 ) |
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| 528 | self._p.plot( self.x, y ) |
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[652] | 529 | self._p.palette(0,colours) |
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[1819] | 530 | self._p.set_line(label=label0) |
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[1273] | 531 | y = ma.masked_array(self.y,mask=logical_not(m)) |
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[1088] | 532 | self._p.plot(self.x, y) |
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[113] | 533 | if residual: |
---|
[1819] | 534 | self._p.palette(7) |
---|
[515] | 535 | self._p.set_line(label='Residual') |
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[1116] | 536 | y = ma.masked_array(self.get_residual(), |
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[1273] | 537 | mask=logical_not(m)) |
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[1088] | 538 | self._p.plot(self.x, y) |
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[652] | 539 | self._p.palette(2) |
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[515] | 540 | if components is not None: |
---|
| 541 | cs = components |
---|
| 542 | if isinstance(components,int): cs = [components] |
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[526] | 543 | if plotparms: |
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[1031] | 544 | self._p.text(0.15,0.15,str(self.get_parameters()['formatted']),size=8) |
---|
[515] | 545 | n = len(self.components) |
---|
[652] | 546 | self._p.palette(3) |
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[515] | 547 | for c in cs: |
---|
| 548 | if 0 <= c < n: |
---|
| 549 | lab = self.fitfuncs[c]+str(c) |
---|
| 550 | self._p.set_line(label=lab) |
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[1116] | 551 | y = ma.masked_array(self.fitter.evaluate(c), |
---|
[1273] | 552 | mask=logical_not(m)) |
---|
[1088] | 553 | |
---|
| 554 | self._p.plot(self.x, y) |
---|
[515] | 555 | elif c == -1: |
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[652] | 556 | self._p.palette(2) |
---|
[515] | 557 | self._p.set_line(label="Total Fit") |
---|
[1116] | 558 | y = ma.masked_array(self.fitter.getfit(), |
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[1273] | 559 | mask=logical_not(m)) |
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[1088] | 560 | self._p.plot(self.x, y) |
---|
[515] | 561 | else: |
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[652] | 562 | self._p.palette(2) |
---|
[515] | 563 | self._p.set_line(label='Fit') |
---|
[1116] | 564 | y = ma.masked_array(self.fitter.getfit(), |
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[1273] | 565 | mask=logical_not(m)) |
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[1088] | 566 | self._p.plot(self.x, y) |
---|
[723] | 567 | xlim=[min(self.x),max(self.x)] |
---|
| 568 | self._p.axes.set_xlim(xlim) |
---|
[113] | 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() |
---|
[723] | 573 | if (not rcParams['plotter.gui']): |
---|
| 574 | self._p.save(filename) |
---|
[113] | 575 | |
---|
[1862] | 576 | @asaplog_post_dec |
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[1061] | 577 | def auto_fit(self, insitu=None, plot=False): |
---|
[113] | 578 | """ |
---|
[515] | 579 | Return a scan where the function is applied to all rows for |
---|
| 580 | all Beams/IFs/Pols. |
---|
[723] | 581 | |
---|
[113] | 582 | """ |
---|
| 583 | from asap import scantable |
---|
[515] | 584 | if not isinstance(self.data, scantable) : |
---|
[723] | 585 | msg = "Data is not a scantable" |
---|
[1859] | 586 | raise TypeError(msg) |
---|
[259] | 587 | if insitu is None: insitu = rcParams['insitu'] |
---|
| 588 | if not insitu: |
---|
| 589 | scan = self.data.copy() |
---|
| 590 | else: |
---|
| 591 | scan = self.data |
---|
[880] | 592 | rows = xrange(scan.nrow()) |
---|
[1826] | 593 | # Save parameters of baseline fits as a class attribute. |
---|
[1819] | 594 | # NOTICE: This does not reflect changes in scantable! |
---|
| 595 | if len(rows) > 0: self.blpars=[] |
---|
[876] | 596 | asaplog.push("Fitting:") |
---|
| 597 | for r in rows: |
---|
[1536] | 598 | out = " Scan[%d] Beam[%d] IF[%d] Pol[%d] Cycle[%d]" % (scan.getscan(r), |
---|
| 599 | scan.getbeam(r), |
---|
| 600 | scan.getif(r), |
---|
[1589] | 601 | scan.getpol(r), |
---|
[1536] | 602 | scan.getcycle(r)) |
---|
[880] | 603 | asaplog.push(out, False) |
---|
[876] | 604 | self.x = scan._getabcissa(r) |
---|
| 605 | self.y = scan._getspectrum(r) |
---|
[1536] | 606 | self.mask = mask_and(self.mask, scan._getmask(r)) |
---|
[876] | 607 | self.data = None |
---|
| 608 | self.fit() |
---|
| 609 | x = self.get_parameters() |
---|
[1819] | 610 | fpar = self.get_parameters() |
---|
[1061] | 611 | if plot: |
---|
| 612 | self.plot(residual=True) |
---|
| 613 | x = raw_input("Accept fit ([y]/n): ") |
---|
| 614 | if x.upper() == 'N': |
---|
[1819] | 615 | self.blpars.append(None) |
---|
[1061] | 616 | continue |
---|
[880] | 617 | scan._setspectrum(self.fitter.getresidual(), r) |
---|
[1819] | 618 | self.blpars.append(fpar) |
---|
[1061] | 619 | if plot: |
---|
| 620 | self._p.unmap() |
---|
| 621 | self._p = None |
---|
[876] | 622 | return scan |
---|