[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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| 89 | n = kwargs.get('poly') |
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[515] | 90 | self.components = [n] |
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[1589] | 91 | self.uselinear = False |
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[1391] | 92 | elif kwargs.has_key('lpoly'): |
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| 93 | self.fitfunc = 'poly' |
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| 94 | n = kwargs.get('lpoly') |
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| 95 | self.components = [n] |
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| 96 | self.uselinear = True |
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[113] | 97 | elif kwargs.has_key('gauss'): |
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| 98 | n = kwargs.get('gauss') |
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| 99 | self.fitfunc = 'gauss' |
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[515] | 100 | self.fitfuncs = [ 'gauss' for i in range(n) ] |
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| 101 | self.components = [ 3 for i in range(n) ] |
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[1589] | 102 | self.uselinear = False |
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[1819] | 103 | elif kwargs.has_key('lorentz'): |
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| 104 | n = kwargs.get('lorentz') |
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| 105 | self.fitfunc = 'lorentz' |
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| 106 | self.fitfuncs = [ 'lorentz' for i in range(n) ] |
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| 107 | self.components = [ 3 for i in range(n) ] |
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| 108 | self.uselinear = False |
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[515] | 109 | else: |
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[723] | 110 | msg = "Invalid function type." |
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[1859] | 111 | raise TypeError(msg) |
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[723] | 112 | |
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[113] | 113 | self.fitter.setexpression(self.fitfunc,n) |
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[1232] | 114 | self.fitted = False |
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[113] | 115 | return |
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[723] | 116 | |
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[1862] | 117 | @asaplog_post_dec |
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[1075] | 118 | def fit(self, row=0, estimate=False): |
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[113] | 119 | """ |
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| 120 | Execute the actual fitting process. All the state has to be set. |
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| 121 | Parameters: |
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[1075] | 122 | row: specify the row in the scantable |
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| 123 | estimate: auto-compute an initial parameter set (default False) |
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| 124 | This can be used to compute estimates even if fit was |
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| 125 | called before. |
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[113] | 126 | Example: |
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[515] | 127 | s = scantable('myscan.asap') |
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| 128 | s.set_cursor(thepol=1) # select second pol |
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[113] | 129 | f = fitter() |
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| 130 | f.set_scan(s) |
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| 131 | f.set_function(poly=0) |
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[723] | 132 | f.fit(row=0) # fit first row |
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[113] | 133 | """ |
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| 134 | if ((self.x is None or self.y is None) and self.data is None) \ |
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| 135 | or self.fitfunc is None: |
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[723] | 136 | msg = "Fitter not yet initialised. Please set data & fit function" |
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[1859] | 137 | raise RuntimeError(msg) |
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[723] | 138 | |
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[113] | 139 | else: |
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| 140 | if self.data is not None: |
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[515] | 141 | self.x = self.data._getabcissa(row) |
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| 142 | self.y = self.data._getspectrum(row) |
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[1536] | 143 | self.mask = mask_and(self.mask, self.data._getmask(row)) |
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[723] | 144 | asaplog.push("Fitting:") |
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[943] | 145 | i = row |
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[1536] | 146 | out = "Scan[%d] Beam[%d] IF[%d] Pol[%d] Cycle[%d]" % (self.data.getscan(i), |
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| 147 | self.data.getbeam(i), |
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| 148 | self.data.getif(i), |
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[1589] | 149 | self.data.getpol(i), |
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[1536] | 150 | self.data.getcycle(i)) |
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[1075] | 151 | asaplog.push(out,False) |
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[515] | 152 | self.fitter.setdata(self.x, self.y, self.mask) |
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[1819] | 153 | if self.fitfunc == 'gauss' or self.fitfunc == 'lorentz': |
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[113] | 154 | ps = self.fitter.getparameters() |
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[1075] | 155 | if len(ps) == 0 or estimate: |
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[113] | 156 | self.fitter.estimate() |
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[1859] | 157 | fxdpar = list(self.fitter.getfixedparameters()) |
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| 158 | if len(fxdpar) and fxdpar.count(0) == 0: |
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| 159 | raise RuntimeError,"No point fitting, if all parameters are fixed." |
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| 160 | if self.uselinear: |
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| 161 | converged = self.fitter.lfit() |
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| 162 | else: |
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| 163 | converged = self.fitter.fit() |
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| 164 | if not converged: |
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| 165 | raise RuntimeError,"Fit didn't converge." |
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[515] | 166 | self._fittedrow = row |
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[113] | 167 | self.fitted = True |
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| 168 | return |
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| 169 | |
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[1232] | 170 | def store_fit(self, filename=None): |
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[526] | 171 | """ |
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[1232] | 172 | Save the fit parameters. |
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| 173 | Parameters: |
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| 174 | filename: if specified save as an ASCII file, if None (default) |
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| 175 | store it in the scnatable |
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[526] | 176 | """ |
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[515] | 177 | if self.fitted and self.data is not None: |
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| 178 | pars = list(self.fitter.getparameters()) |
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| 179 | fixed = list(self.fitter.getfixedparameters()) |
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[975] | 180 | from asap.asapfit import asapfit |
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| 181 | fit = asapfit() |
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| 182 | fit.setparameters(pars) |
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| 183 | fit.setfixedparameters(fixed) |
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| 184 | fit.setfunctions(self.fitfuncs) |
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| 185 | fit.setcomponents(self.components) |
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| 186 | fit.setframeinfo(self.data._getcoordinfo()) |
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[1232] | 187 | if filename is not None: |
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| 188 | import os |
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| 189 | filename = os.path.expandvars(os.path.expanduser(filename)) |
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| 190 | if os.path.exists(filename): |
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| 191 | raise IOError("File '%s' exists." % filename) |
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| 192 | fit.save(filename) |
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| 193 | else: |
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| 194 | self.data._addfit(fit,self._fittedrow) |
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[515] | 195 | |
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[1862] | 196 | @asaplog_post_dec |
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[1017] | 197 | def set_parameters(self,*args,**kwargs): |
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[526] | 198 | """ |
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| 199 | Set the parameters to be fitted. |
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| 200 | Parameters: |
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| 201 | params: a vector of parameters |
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| 202 | fixed: a vector of which parameters are to be held fixed |
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| 203 | (default is none) |
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| 204 | component: in case of multiple gaussians, the index of the |
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| 205 | component |
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[1017] | 206 | """ |
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| 207 | component = None |
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| 208 | fixed = None |
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| 209 | params = None |
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[1031] | 210 | |
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[1017] | 211 | if len(args) and isinstance(args[0],dict): |
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| 212 | kwargs = args[0] |
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| 213 | if kwargs.has_key("fixed"): fixed = kwargs["fixed"] |
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| 214 | if kwargs.has_key("params"): params = kwargs["params"] |
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| 215 | if len(args) == 2 and isinstance(args[1], int): |
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| 216 | component = args[1] |
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[515] | 217 | if self.fitfunc is None: |
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[723] | 218 | msg = "Please specify a fitting function first." |
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[1859] | 219 | raise RuntimeError(msg) |
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[1819] | 220 | if (self.fitfunc == "gauss" or self.fitfunc == 'lorentz') and component is not None: |
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[1017] | 221 | if not self.fitted and sum(self.fitter.getparameters()) == 0: |
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[1295] | 222 | pars = _n_bools(len(self.components)*3, False) |
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| 223 | fxd = _n_bools(len(pars), False) |
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[515] | 224 | else: |
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[723] | 225 | pars = list(self.fitter.getparameters()) |
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[515] | 226 | fxd = list(self.fitter.getfixedparameters()) |
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| 227 | i = 3*component |
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| 228 | pars[i:i+3] = params |
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| 229 | fxd[i:i+3] = fixed |
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| 230 | params = pars |
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[723] | 231 | fixed = fxd |
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[113] | 232 | self.fitter.setparameters(params) |
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| 233 | if fixed is not None: |
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| 234 | self.fitter.setfixedparameters(fixed) |
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| 235 | return |
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[515] | 236 | |
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[1862] | 237 | @asaplog_post_dec |
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[1217] | 238 | def set_gauss_parameters(self, peak, centre, fwhm, |
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[1409] | 239 | peakfixed=0, centrefixed=0, |
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[1217] | 240 | fwhmfixed=0, |
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[515] | 241 | component=0): |
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[113] | 242 | """ |
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[515] | 243 | Set the Parameters of a 'Gaussian' component, set with set_function. |
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| 244 | Parameters: |
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[1232] | 245 | peak, centre, fwhm: The gaussian parameters |
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[515] | 246 | peakfixed, |
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[1409] | 247 | centrefixed, |
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[1217] | 248 | fwhmfixed: Optional parameters to indicate if |
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[515] | 249 | the paramters should be held fixed during |
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| 250 | the fitting process. The default is to keep |
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| 251 | all parameters flexible. |
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[526] | 252 | component: The number of the component (Default is the |
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| 253 | component 0) |
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[515] | 254 | """ |
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| 255 | if self.fitfunc != "gauss": |
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[723] | 256 | msg = "Function only operates on Gaussian components." |
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[1859] | 257 | raise ValueError(msg) |
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[515] | 258 | if 0 <= component < len(self.components): |
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[1217] | 259 | d = {'params':[peak, centre, fwhm], |
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[1409] | 260 | 'fixed':[peakfixed, centrefixed, fwhmfixed]} |
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[1017] | 261 | self.set_parameters(d, component) |
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[515] | 262 | else: |
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[723] | 263 | msg = "Please select a valid component." |
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[1859] | 264 | raise ValueError(msg) |
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[723] | 265 | |
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[1862] | 266 | @asaplog_post_dec |
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[1819] | 267 | def set_lorentz_parameters(self, peak, centre, fwhm, |
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| 268 | peakfixed=0, centrefixed=0, |
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| 269 | fwhmfixed=0, |
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| 270 | component=0): |
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| 271 | """ |
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| 272 | Set the Parameters of a 'Lorentzian' component, set with set_function. |
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| 273 | Parameters: |
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| 274 | peak, centre, fwhm: The gaussian parameters |
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| 275 | peakfixed, |
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| 276 | centrefixed, |
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| 277 | fwhmfixed: Optional parameters to indicate if |
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| 278 | the paramters should be held fixed during |
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| 279 | the fitting process. The default is to keep |
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| 280 | all parameters flexible. |
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| 281 | component: The number of the component (Default is the |
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| 282 | component 0) |
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| 283 | """ |
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| 284 | if self.fitfunc != "lorentz": |
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| 285 | msg = "Function only operates on Lorentzian components." |
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[1859] | 286 | raise ValueError(msg) |
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[1819] | 287 | if 0 <= component < len(self.components): |
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| 288 | d = {'params':[peak, centre, fwhm], |
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| 289 | 'fixed':[peakfixed, centrefixed, fwhmfixed]} |
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| 290 | self.set_parameters(d, component) |
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| 291 | else: |
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| 292 | msg = "Please select a valid component." |
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[1859] | 293 | raise ValueError(msg) |
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[1819] | 294 | |
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[975] | 295 | def get_area(self, component=None): |
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| 296 | """ |
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[1819] | 297 | Return the area under the fitted gaussian/lorentzian component. |
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[975] | 298 | Parameters: |
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[1819] | 299 | component: the gaussian/lorentzian component selection, |
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[975] | 300 | default (None) is the sum of all components |
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| 301 | Note: |
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[1819] | 302 | This will only work for gaussian/lorentzian fits. |
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[975] | 303 | """ |
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| 304 | if not self.fitted: return |
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[1819] | 305 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz": |
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[975] | 306 | pars = list(self.fitter.getparameters()) |
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| 307 | from math import log,pi,sqrt |
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[1819] | 308 | if self.fitfunc == "gauss": |
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| 309 | fac = sqrt(pi/log(16.0)) |
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| 310 | elif self.fitfunc == "lorentz": |
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| 311 | fac = pi/2.0 |
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[975] | 312 | areas = [] |
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| 313 | for i in range(len(self.components)): |
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| 314 | j = i*3 |
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| 315 | cpars = pars[j:j+3] |
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| 316 | areas.append(fac * cpars[0] * cpars[2]) |
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| 317 | else: |
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| 318 | return None |
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| 319 | if component is not None: |
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| 320 | return areas[component] |
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| 321 | else: |
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| 322 | return sum(areas) |
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| 323 | |
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[1862] | 324 | @asaplog_post_dec |
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[1075] | 325 | def get_errors(self, component=None): |
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[515] | 326 | """ |
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[1075] | 327 | Return the errors in the parameters. |
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| 328 | Parameters: |
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| 329 | component: get the errors for the specified component |
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| 330 | only, default is all components |
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| 331 | """ |
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| 332 | if not self.fitted: |
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| 333 | msg = "Not yet fitted." |
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[1859] | 334 | raise RuntimeError(msg) |
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[1075] | 335 | errs = list(self.fitter.geterrors()) |
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| 336 | cerrs = errs |
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| 337 | if component is not None: |
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[1819] | 338 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz": |
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[1075] | 339 | i = 3*component |
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| 340 | if i < len(errs): |
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| 341 | cerrs = errs[i:i+3] |
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| 342 | return cerrs |
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| 343 | |
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[1859] | 344 | |
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[1862] | 345 | @asaplog_post_dec |
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[1075] | 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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[1859] | 355 | raise RuntimeError(msg) |
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[113] | 356 | pars = list(self.fitter.getparameters()) |
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| 357 | fixed = list(self.fitter.getfixedparameters()) |
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[1075] | 358 | errs = list(self.fitter.geterrors()) |
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[1039] | 359 | area = [] |
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[723] | 360 | if component is not None: |
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[1819] | 361 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz": |
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[515] | 362 | i = 3*component |
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| 363 | cpars = pars[i:i+3] |
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| 364 | cfixed = fixed[i:i+3] |
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[1075] | 365 | cerrs = errs[i:i+3] |
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[1039] | 366 | a = self.get_area(component) |
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| 367 | area = [a for i in range(3)] |
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[515] | 368 | else: |
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| 369 | cpars = pars |
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[723] | 370 | cfixed = fixed |
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[1075] | 371 | cerrs = errs |
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[515] | 372 | else: |
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| 373 | cpars = pars |
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| 374 | cfixed = fixed |
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[1075] | 375 | cerrs = errs |
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[1819] | 376 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz": |
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[1039] | 377 | for c in range(len(self.components)): |
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| 378 | a = self.get_area(c) |
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| 379 | area += [a for i in range(3)] |
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[1088] | 380 | fpars = self._format_pars(cpars, cfixed, errors and cerrs, area) |
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[1859] | 381 | asaplog.push(fpars) |
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[1075] | 382 | return {'params':cpars, 'fixed':cfixed, 'formatted': fpars, |
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| 383 | 'errors':cerrs} |
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[723] | 384 | |
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[1075] | 385 | def _format_pars(self, pars, fixed, errors, area): |
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[113] | 386 | out = '' |
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| 387 | if self.fitfunc == 'poly': |
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| 388 | c = 0 |
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[515] | 389 | for i in range(len(pars)): |
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| 390 | fix = "" |
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[1232] | 391 | if len(fixed) and fixed[i]: fix = "(fixed)" |
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[1088] | 392 | if errors : |
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| 393 | out += ' p%d%s= %3.6f (%1.6f),' % (c,fix,pars[i], errors[i]) |
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| 394 | else: |
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| 395 | out += ' p%d%s= %3.6f,' % (c,fix,pars[i]) |
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[113] | 396 | c+=1 |
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[515] | 397 | out = out[:-1] # remove trailing ',' |
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[1819] | 398 | elif self.fitfunc == 'gauss' or self.fitfunc == 'lorentz': |
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[113] | 399 | i = 0 |
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| 400 | c = 0 |
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[515] | 401 | aunit = '' |
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| 402 | ounit = '' |
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[113] | 403 | if self.data: |
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[515] | 404 | aunit = self.data.get_unit() |
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| 405 | ounit = self.data.get_fluxunit() |
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[113] | 406 | while i < len(pars): |
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[1039] | 407 | if len(area): |
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| 408 | 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] | 409 | else: |
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| 410 | 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] | 411 | c+=1 |
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| 412 | i+=3 |
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| 413 | return out |
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[723] | 414 | |
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[1859] | 415 | |
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[1862] | 416 | @asaplog_post_dec |
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[113] | 417 | def get_estimate(self): |
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| 418 | """ |
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[515] | 419 | Return the parameter estimates (for non-linear functions). |
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[113] | 420 | """ |
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| 421 | pars = self.fitter.getestimate() |
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[943] | 422 | fixed = self.fitter.getfixedparameters() |
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[1859] | 423 | asaplog.push(self._format_pars(pars,fixed,None)) |
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[113] | 424 | return pars |
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| 425 | |
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[1862] | 426 | @asaplog_post_dec |
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[113] | 427 | def get_residual(self): |
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| 428 | """ |
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| 429 | Return the residual of the fit. |
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| 430 | """ |
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| 431 | if not self.fitted: |
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[723] | 432 | msg = "Not yet fitted." |
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[1859] | 433 | raise RuntimeError(msg) |
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[113] | 434 | return self.fitter.getresidual() |
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| 435 | |
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[1862] | 436 | @asaplog_post_dec |
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[113] | 437 | def get_chi2(self): |
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| 438 | """ |
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| 439 | Return chi^2. |
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| 440 | """ |
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| 441 | if not self.fitted: |
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[723] | 442 | msg = "Not yet fitted." |
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[1859] | 443 | raise RuntimeError(msg) |
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[113] | 444 | ch2 = self.fitter.getchi2() |
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[1859] | 445 | asaplog.push( 'Chi^2 = %3.3f' % (ch2) ) |
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[723] | 446 | return ch2 |
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[113] | 447 | |
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[1862] | 448 | @asaplog_post_dec |
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[113] | 449 | def get_fit(self): |
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| 450 | """ |
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| 451 | Return the fitted ordinate values. |
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| 452 | """ |
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| 453 | if not self.fitted: |
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[723] | 454 | msg = "Not yet fitted." |
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[1859] | 455 | raise RuntimeError(msg) |
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[113] | 456 | return self.fitter.getfit() |
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| 457 | |
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[1862] | 458 | @asaplog_post_dec |
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[113] | 459 | def commit(self): |
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| 460 | """ |
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[526] | 461 | Return a new scan where the fits have been commited (subtracted) |
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[113] | 462 | """ |
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| 463 | if not self.fitted: |
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[723] | 464 | msg = "Not yet fitted." |
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[1859] | 465 | raise RuntimeError(msg) |
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[975] | 466 | from asap import scantable |
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| 467 | if not isinstance(self.data, scantable): |
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[723] | 468 | msg = "Not a scantable" |
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[1859] | 469 | raise TypeError(msg) |
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[113] | 470 | scan = self.data.copy() |
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[259] | 471 | scan._setspectrum(self.fitter.getresidual()) |
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[1092] | 472 | return scan |
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[113] | 473 | |
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[1862] | 474 | @asaplog_post_dec |
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[1689] | 475 | def plot(self, residual=False, components=None, plotparms=False, |
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| 476 | filename=None): |
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[113] | 477 | """ |
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| 478 | Plot the last fit. |
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| 479 | Parameters: |
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| 480 | residual: an optional parameter indicating if the residual |
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| 481 | should be plotted (default 'False') |
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[526] | 482 | components: a list of components to plot, e.g [0,1], |
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| 483 | -1 plots the total fit. Default is to only |
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| 484 | plot the total fit. |
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| 485 | plotparms: Inidicates if the parameter values should be present |
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| 486 | on the plot |
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[113] | 487 | """ |
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| 488 | if not self.fitted: |
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| 489 | return |
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[723] | 490 | if not self._p or self._p.is_dead: |
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| 491 | if rcParams['plotter.gui']: |
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| 492 | from asap.asaplotgui import asaplotgui as asaplot |
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| 493 | else: |
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| 494 | from asap.asaplot import asaplot |
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| 495 | self._p = asaplot() |
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| 496 | self._p.hold() |
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[113] | 497 | self._p.clear() |
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[515] | 498 | self._p.set_panels() |
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[652] | 499 | self._p.palette(0) |
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[113] | 500 | tlab = 'Spectrum' |
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[723] | 501 | xlab = 'Abcissa' |
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[1017] | 502 | ylab = 'Ordinate' |
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[1739] | 503 | from numpy import ma,logical_not,logical_and,array |
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[1273] | 504 | m = self.mask |
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[113] | 505 | if self.data: |
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[515] | 506 | tlab = self.data._getsourcename(self._fittedrow) |
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| 507 | xlab = self.data._getabcissalabel(self._fittedrow) |
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[1273] | 508 | m = logical_and(self.mask, |
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[1306] | 509 | array(self.data._getmask(self._fittedrow), |
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| 510 | copy=False)) |
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[1589] | 511 | |
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[626] | 512 | ylab = self.data._get_ordinate_label() |
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[515] | 513 | |
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[1075] | 514 | colours = ["#777777","#dddddd","red","orange","purple","green","magenta", "cyan"] |
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[1819] | 515 | nomask=True |
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| 516 | for i in range(len(m)): |
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| 517 | nomask = nomask and m[i] |
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| 518 | label0='Masked Region' |
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| 519 | label1='Spectrum' |
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| 520 | if ( nomask ): |
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| 521 | label0=label1 |
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| 522 | else: |
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| 523 | y = ma.masked_array( self.y, mask = m ) |
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| 524 | self._p.palette(1,colours) |
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| 525 | self._p.set_line( label = label1 ) |
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| 526 | self._p.plot( self.x, y ) |
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[652] | 527 | self._p.palette(0,colours) |
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[1819] | 528 | self._p.set_line(label=label0) |
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[1273] | 529 | y = ma.masked_array(self.y,mask=logical_not(m)) |
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[1088] | 530 | self._p.plot(self.x, y) |
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[113] | 531 | if residual: |
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[1819] | 532 | self._p.palette(7) |
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[515] | 533 | self._p.set_line(label='Residual') |
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[1116] | 534 | y = ma.masked_array(self.get_residual(), |
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[1273] | 535 | mask=logical_not(m)) |
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[1088] | 536 | self._p.plot(self.x, y) |
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[652] | 537 | self._p.palette(2) |
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[515] | 538 | if components is not None: |
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| 539 | cs = components |
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| 540 | if isinstance(components,int): cs = [components] |
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[526] | 541 | if plotparms: |
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[1031] | 542 | self._p.text(0.15,0.15,str(self.get_parameters()['formatted']),size=8) |
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[515] | 543 | n = len(self.components) |
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[652] | 544 | self._p.palette(3) |
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[515] | 545 | for c in cs: |
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| 546 | if 0 <= c < n: |
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| 547 | lab = self.fitfuncs[c]+str(c) |
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| 548 | self._p.set_line(label=lab) |
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[1116] | 549 | y = ma.masked_array(self.fitter.evaluate(c), |
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[1273] | 550 | mask=logical_not(m)) |
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[1088] | 551 | |
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| 552 | self._p.plot(self.x, y) |
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[515] | 553 | elif c == -1: |
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[652] | 554 | self._p.palette(2) |
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[515] | 555 | self._p.set_line(label="Total Fit") |
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[1116] | 556 | y = ma.masked_array(self.fitter.getfit(), |
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[1273] | 557 | mask=logical_not(m)) |
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[1088] | 558 | self._p.plot(self.x, y) |
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[515] | 559 | else: |
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[652] | 560 | self._p.palette(2) |
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[515] | 561 | self._p.set_line(label='Fit') |
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[1116] | 562 | y = ma.masked_array(self.fitter.getfit(), |
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[1273] | 563 | mask=logical_not(m)) |
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[1088] | 564 | self._p.plot(self.x, y) |
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[723] | 565 | xlim=[min(self.x),max(self.x)] |
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| 566 | self._p.axes.set_xlim(xlim) |
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[113] | 567 | self._p.set_axes('xlabel',xlab) |
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| 568 | self._p.set_axes('ylabel',ylab) |
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| 569 | self._p.set_axes('title',tlab) |
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| 570 | self._p.release() |
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[723] | 571 | if (not rcParams['plotter.gui']): |
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| 572 | self._p.save(filename) |
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[113] | 573 | |
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[1862] | 574 | @asaplog_post_dec |
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[1061] | 575 | def auto_fit(self, insitu=None, plot=False): |
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[113] | 576 | """ |
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[515] | 577 | Return a scan where the function is applied to all rows for |
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| 578 | all Beams/IFs/Pols. |
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[723] | 579 | |
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[113] | 580 | """ |
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| 581 | from asap import scantable |
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[515] | 582 | if not isinstance(self.data, scantable) : |
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[723] | 583 | msg = "Data is not a scantable" |
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[1859] | 584 | raise TypeError(msg) |
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[259] | 585 | if insitu is None: insitu = rcParams['insitu'] |
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| 586 | if not insitu: |
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| 587 | scan = self.data.copy() |
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| 588 | else: |
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| 589 | scan = self.data |
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[880] | 590 | rows = xrange(scan.nrow()) |
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[1826] | 591 | # Save parameters of baseline fits as a class attribute. |
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[1819] | 592 | # NOTICE: This does not reflect changes in scantable! |
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| 593 | if len(rows) > 0: self.blpars=[] |
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[876] | 594 | asaplog.push("Fitting:") |
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| 595 | for r in rows: |
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[1536] | 596 | out = " Scan[%d] Beam[%d] IF[%d] Pol[%d] Cycle[%d]" % (scan.getscan(r), |
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| 597 | scan.getbeam(r), |
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| 598 | scan.getif(r), |
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[1589] | 599 | scan.getpol(r), |
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[1536] | 600 | scan.getcycle(r)) |
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[880] | 601 | asaplog.push(out, False) |
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[876] | 602 | self.x = scan._getabcissa(r) |
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| 603 | self.y = scan._getspectrum(r) |
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[1536] | 604 | self.mask = mask_and(self.mask, scan._getmask(r)) |
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[876] | 605 | self.data = None |
---|
| 606 | self.fit() |
---|
| 607 | x = self.get_parameters() |
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[1819] | 608 | fpar = self.get_parameters() |
---|
[1061] | 609 | if plot: |
---|
| 610 | self.plot(residual=True) |
---|
| 611 | x = raw_input("Accept fit ([y]/n): ") |
---|
| 612 | if x.upper() == 'N': |
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[1819] | 613 | self.blpars.append(None) |
---|
[1061] | 614 | continue |
---|
[880] | 615 | scan._setspectrum(self.fitter.getresidual(), r) |
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[1819] | 616 | self.blpars.append(fpar) |
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[1061] | 617 | if plot: |
---|
| 618 | self._p.unmap() |
---|
| 619 | self._p = None |
---|
[876] | 620 | return scan |
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