[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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[2666] | 5 | from numpy import ndarray |
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[113] | 6 | |
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| 7 | class fitter: |
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| 8 | """ |
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| 9 | The fitting class for ASAP. |
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| 10 | """ |
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| 11 | def __init__(self): |
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| 12 | """ |
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| 13 | Create a fitter object. No state is set. |
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| 14 | """ |
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| 15 | self.fitter = _asap.fitter() |
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| 16 | self.x = None |
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| 17 | self.y = None |
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| 18 | self.mask = None |
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| 19 | self.fitfunc = None |
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[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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[2666] | 28 | self._constraints = [] |
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[113] | 29 | |
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| 30 | def set_data(self, xdat, ydat, mask=None): |
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| 31 | """ |
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[158] | 32 | Set the absissa and ordinate for the fit. Also set the mask |
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[2153] | 33 | indicating valid points. |
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[113] | 34 | This can be used for data vectors retrieved from a scantable. |
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| 35 | For scantable fitting use 'fitter.set_scan(scan, mask)'. |
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| 36 | Parameters: |
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[158] | 37 | xdat: the abcissa values |
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[113] | 38 | ydat: the ordinate values |
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| 39 | mask: an optional mask |
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[723] | 40 | |
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[113] | 41 | """ |
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| 42 | self.fitted = False |
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| 43 | self.x = xdat |
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| 44 | self.y = ydat |
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| 45 | if mask == None: |
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[1295] | 46 | self.mask = _n_bools(len(xdat), True) |
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[113] | 47 | else: |
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| 48 | self.mask = mask |
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| 49 | return |
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| 50 | |
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[1862] | 51 | @asaplog_post_dec |
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[113] | 52 | def set_scan(self, thescan=None, mask=None): |
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| 53 | """ |
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| 54 | Set the 'data' (a scantable) of the fitter. |
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| 55 | Parameters: |
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| 56 | thescan: a scantable |
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[1420] | 57 | mask: a msk retrieved from the scantable |
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[113] | 58 | """ |
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| 59 | if not thescan: |
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[723] | 60 | msg = "Please give a correct scan" |
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[1859] | 61 | raise TypeError(msg) |
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[113] | 62 | self.fitted = False |
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| 63 | self.data = thescan |
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[1075] | 64 | self.mask = None |
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[113] | 65 | if mask is None: |
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[1295] | 66 | self.mask = _n_bools(self.data.nchan(), True) |
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[113] | 67 | else: |
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| 68 | self.mask = mask |
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| 69 | return |
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| 70 | |
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[1862] | 71 | @asaplog_post_dec |
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[113] | 72 | def set_function(self, **kwargs): |
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| 73 | """ |
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| 74 | Set the function to be fit. |
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| 75 | Parameters: |
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[2666] | 76 | poly: use a polynomial of the order given with nonlinear |
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| 77 | least squares fit |
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| 78 | lpoly: use polynomial of the order given with linear least |
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| 79 | squares fit |
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[2047] | 80 | gauss: fit the number of gaussian specified |
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| 81 | lorentz: fit the number of lorentzian specified |
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| 82 | sinusoid: fit the number of sinusoid specified |
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[113] | 83 | Example: |
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[2666] | 84 | fitter.set_function(poly=3) # will fit a 3rd order polynomial |
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| 85 | # via nonlinear method |
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| 86 | fitter.set_function(lpoly=3) # will fit a 3rd order polynomial |
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| 87 | # via linear method |
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[1819] | 88 | fitter.set_function(gauss=2) # will fit two gaussians |
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| 89 | fitter.set_function(lorentz=2) # will fit two lorentzians |
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[2047] | 90 | fitter.set_function(sinusoid=3) # will fit three sinusoids |
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[113] | 91 | """ |
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[723] | 92 | #default poly order 0 |
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[515] | 93 | n=0 |
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[113] | 94 | if kwargs.has_key('poly'): |
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| 95 | self.fitfunc = 'poly' |
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[1938] | 96 | self.fitfuncs = ['poly'] |
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[113] | 97 | n = kwargs.get('poly') |
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[1938] | 98 | self.components = [n+1] |
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[1589] | 99 | self.uselinear = False |
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[1391] | 100 | elif kwargs.has_key('lpoly'): |
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| 101 | self.fitfunc = 'poly' |
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[1938] | 102 | self.fitfuncs = ['lpoly'] |
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[1391] | 103 | n = kwargs.get('lpoly') |
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[1938] | 104 | self.components = [n+1] |
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[1391] | 105 | self.uselinear = True |
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[113] | 106 | elif kwargs.has_key('gauss'): |
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| 107 | n = kwargs.get('gauss') |
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| 108 | self.fitfunc = 'gauss' |
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[515] | 109 | self.fitfuncs = [ 'gauss' for i in range(n) ] |
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| 110 | self.components = [ 3 for i in range(n) ] |
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[1589] | 111 | self.uselinear = False |
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[1819] | 112 | elif kwargs.has_key('lorentz'): |
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| 113 | n = kwargs.get('lorentz') |
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| 114 | self.fitfunc = 'lorentz' |
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| 115 | self.fitfuncs = [ 'lorentz' for i in range(n) ] |
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| 116 | self.components = [ 3 for i in range(n) ] |
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| 117 | self.uselinear = False |
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[2047] | 118 | elif kwargs.has_key('sinusoid'): |
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| 119 | n = kwargs.get('sinusoid') |
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| 120 | self.fitfunc = 'sinusoid' |
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| 121 | self.fitfuncs = [ 'sinusoid' for i in range(n) ] |
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| 122 | self.components = [ 3 for i in range(n) ] |
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| 123 | self.uselinear = False |
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[2666] | 124 | elif kwargs.has_key('expression'): |
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| 125 | self.uselinear = False |
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| 126 | raise RuntimeError("Not yet implemented") |
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[515] | 127 | else: |
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[723] | 128 | msg = "Invalid function type." |
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[1859] | 129 | raise TypeError(msg) |
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[723] | 130 | |
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[113] | 131 | self.fitter.setexpression(self.fitfunc,n) |
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[2666] | 132 | self._constraints = [] |
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[1232] | 133 | self.fitted = False |
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[113] | 134 | return |
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[723] | 135 | |
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[1862] | 136 | @asaplog_post_dec |
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[1075] | 137 | def fit(self, row=0, estimate=False): |
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[113] | 138 | """ |
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| 139 | Execute the actual fitting process. All the state has to be set. |
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| 140 | Parameters: |
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[1075] | 141 | row: specify the row in the scantable |
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| 142 | estimate: auto-compute an initial parameter set (default False) |
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| 143 | This can be used to compute estimates even if fit was |
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| 144 | called before. |
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[113] | 145 | Example: |
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[515] | 146 | s = scantable('myscan.asap') |
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| 147 | s.set_cursor(thepol=1) # select second pol |
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[113] | 148 | f = fitter() |
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| 149 | f.set_scan(s) |
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| 150 | f.set_function(poly=0) |
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[723] | 151 | f.fit(row=0) # fit first row |
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[113] | 152 | """ |
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| 153 | if ((self.x is None or self.y is None) and self.data is None) \ |
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| 154 | or self.fitfunc is None: |
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[723] | 155 | msg = "Fitter not yet initialised. Please set data & fit function" |
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[1859] | 156 | raise RuntimeError(msg) |
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[723] | 157 | |
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[2666] | 158 | if self.data is not None: |
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| 159 | self.x = self.data._getabcissa(row) |
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| 160 | self.y = self.data._getspectrum(row) |
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| 161 | #self.mask = mask_and(self.mask, self.data._getmask(row)) |
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| 162 | if len(self.x) == len(self.mask): |
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| 163 | self.mask = mask_and(self.mask, self.data._getmask(row)) |
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| 164 | else: |
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| 165 | asaplog.push('lengths of data and mask are not the same. ' |
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| 166 | 'preset mask will be ignored') |
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| 167 | asaplog.post('WARN','asapfit.fit') |
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| 168 | self.mask=self.data._getmask(row) |
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| 169 | asaplog.push("Fitting:") |
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| 170 | i = row |
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| 171 | out = "Scan[%d] Beam[%d] IF[%d] Pol[%d] Cycle[%d]" % ( |
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| 172 | self.data.getscan(i), |
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| 173 | self.data.getbeam(i), |
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| 174 | self.data.getif(i), |
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| 175 | self.data.getpol(i), |
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| 176 | self.data.getcycle(i)) |
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| 177 | |
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| 178 | asaplog.push(out, False) |
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| 179 | |
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[515] | 180 | self.fitter.setdata(self.x, self.y, self.mask) |
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[1819] | 181 | if self.fitfunc == 'gauss' or self.fitfunc == 'lorentz': |
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[113] | 182 | ps = self.fitter.getparameters() |
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[1075] | 183 | if len(ps) == 0 or estimate: |
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[113] | 184 | self.fitter.estimate() |
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[1859] | 185 | fxdpar = list(self.fitter.getfixedparameters()) |
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| 186 | if len(fxdpar) and fxdpar.count(0) == 0: |
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| 187 | raise RuntimeError,"No point fitting, if all parameters are fixed." |
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[2666] | 188 | if self._constraints: |
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| 189 | for c in self._constraints: |
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| 190 | self.fitter.addconstraint(c[0]+[c[-1]]) |
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[1859] | 191 | if self.uselinear: |
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| 192 | converged = self.fitter.lfit() |
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| 193 | else: |
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| 194 | converged = self.fitter.fit() |
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| 195 | if not converged: |
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| 196 | raise RuntimeError,"Fit didn't converge." |
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[515] | 197 | self._fittedrow = row |
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[113] | 198 | self.fitted = True |
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| 199 | return |
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| 200 | |
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[1232] | 201 | def store_fit(self, filename=None): |
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[526] | 202 | """ |
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[1232] | 203 | Save the fit parameters. |
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| 204 | Parameters: |
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| 205 | filename: if specified save as an ASCII file, if None (default) |
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| 206 | store it in the scnatable |
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[526] | 207 | """ |
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[515] | 208 | if self.fitted and self.data is not None: |
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| 209 | pars = list(self.fitter.getparameters()) |
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| 210 | fixed = list(self.fitter.getfixedparameters()) |
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[975] | 211 | from asap.asapfit import asapfit |
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| 212 | fit = asapfit() |
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| 213 | fit.setparameters(pars) |
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| 214 | fit.setfixedparameters(fixed) |
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| 215 | fit.setfunctions(self.fitfuncs) |
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| 216 | fit.setcomponents(self.components) |
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| 217 | fit.setframeinfo(self.data._getcoordinfo()) |
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[1232] | 218 | if filename is not None: |
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| 219 | import os |
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| 220 | filename = os.path.expandvars(os.path.expanduser(filename)) |
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| 221 | if os.path.exists(filename): |
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| 222 | raise IOError("File '%s' exists." % filename) |
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| 223 | fit.save(filename) |
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| 224 | else: |
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| 225 | self.data._addfit(fit,self._fittedrow) |
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[515] | 226 | |
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[1862] | 227 | @asaplog_post_dec |
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[1017] | 228 | def set_parameters(self,*args,**kwargs): |
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[526] | 229 | """ |
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| 230 | Set the parameters to be fitted. |
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| 231 | Parameters: |
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| 232 | params: a vector of parameters |
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| 233 | fixed: a vector of which parameters are to be held fixed |
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| 234 | (default is none) |
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[2047] | 235 | component: in case of multiple gaussians/lorentzians/sinusoidals, |
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| 236 | the index of the target component |
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[1017] | 237 | """ |
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| 238 | component = None |
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| 239 | fixed = None |
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| 240 | params = None |
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[1031] | 241 | |
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[1017] | 242 | if len(args) and isinstance(args[0],dict): |
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| 243 | kwargs = args[0] |
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| 244 | if kwargs.has_key("fixed"): fixed = kwargs["fixed"] |
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| 245 | if kwargs.has_key("params"): params = kwargs["params"] |
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| 246 | if len(args) == 2 and isinstance(args[1], int): |
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| 247 | component = args[1] |
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[515] | 248 | if self.fitfunc is None: |
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[723] | 249 | msg = "Please specify a fitting function first." |
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[1859] | 250 | raise RuntimeError(msg) |
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[2666] | 251 | if (self.fitfunc == "gauss" or self.fitfunc == "lorentz" |
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| 252 | or self.fitfunc == "sinusoid") and component is not None: |
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[1017] | 253 | if not self.fitted and sum(self.fitter.getparameters()) == 0: |
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[1295] | 254 | pars = _n_bools(len(self.components)*3, False) |
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[2047] | 255 | fxd = _n_bools(len(pars), False) |
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[515] | 256 | else: |
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[723] | 257 | pars = list(self.fitter.getparameters()) |
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[2047] | 258 | fxd = list(self.fitter.getfixedparameters()) |
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[515] | 259 | i = 3*component |
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| 260 | pars[i:i+3] = params |
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[2047] | 261 | fxd[i:i+3] = fixed |
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[515] | 262 | params = pars |
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[2047] | 263 | fixed = fxd |
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[113] | 264 | self.fitter.setparameters(params) |
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| 265 | if fixed is not None: |
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| 266 | self.fitter.setfixedparameters(fixed) |
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| 267 | return |
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[515] | 268 | |
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[1862] | 269 | @asaplog_post_dec |
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[1217] | 270 | def set_gauss_parameters(self, peak, centre, fwhm, |
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[1409] | 271 | peakfixed=0, centrefixed=0, |
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[1217] | 272 | fwhmfixed=0, |
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[515] | 273 | component=0): |
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[113] | 274 | """ |
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[515] | 275 | Set the Parameters of a 'Gaussian' component, set with set_function. |
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| 276 | Parameters: |
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[1232] | 277 | peak, centre, fwhm: The gaussian parameters |
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[515] | 278 | peakfixed, |
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[1409] | 279 | centrefixed, |
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[1217] | 280 | fwhmfixed: Optional parameters to indicate if |
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[515] | 281 | the paramters should be held fixed during |
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| 282 | the fitting process. The default is to keep |
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| 283 | all parameters flexible. |
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[526] | 284 | component: The number of the component (Default is the |
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| 285 | component 0) |
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[515] | 286 | """ |
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| 287 | if self.fitfunc != "gauss": |
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[723] | 288 | msg = "Function only operates on Gaussian components." |
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[1859] | 289 | raise ValueError(msg) |
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[515] | 290 | if 0 <= component < len(self.components): |
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[1217] | 291 | d = {'params':[peak, centre, fwhm], |
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[1409] | 292 | 'fixed':[peakfixed, centrefixed, fwhmfixed]} |
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[1017] | 293 | self.set_parameters(d, component) |
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[515] | 294 | else: |
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[723] | 295 | msg = "Please select a valid component." |
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[1859] | 296 | raise ValueError(msg) |
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[723] | 297 | |
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[1862] | 298 | @asaplog_post_dec |
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[1819] | 299 | def set_lorentz_parameters(self, peak, centre, fwhm, |
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| 300 | peakfixed=0, centrefixed=0, |
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| 301 | fwhmfixed=0, |
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| 302 | component=0): |
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| 303 | """ |
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| 304 | Set the Parameters of a 'Lorentzian' component, set with set_function. |
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| 305 | Parameters: |
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[1927] | 306 | peak, centre, fwhm: The lorentzian parameters |
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[1819] | 307 | peakfixed, |
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| 308 | centrefixed, |
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| 309 | fwhmfixed: Optional parameters to indicate if |
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| 310 | the paramters should be held fixed during |
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| 311 | the fitting process. The default is to keep |
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| 312 | all parameters flexible. |
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| 313 | component: The number of the component (Default is the |
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| 314 | component 0) |
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| 315 | """ |
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| 316 | if self.fitfunc != "lorentz": |
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| 317 | msg = "Function only operates on Lorentzian components." |
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[1859] | 318 | raise ValueError(msg) |
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[1819] | 319 | if 0 <= component < len(self.components): |
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| 320 | d = {'params':[peak, centre, fwhm], |
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| 321 | 'fixed':[peakfixed, centrefixed, fwhmfixed]} |
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| 322 | self.set_parameters(d, component) |
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| 323 | else: |
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| 324 | msg = "Please select a valid component." |
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[1859] | 325 | raise ValueError(msg) |
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[1819] | 326 | |
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[2047] | 327 | @asaplog_post_dec |
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| 328 | def set_sinusoid_parameters(self, ampl, period, x0, |
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| 329 | amplfixed=0, periodfixed=0, |
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| 330 | x0fixed=0, |
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| 331 | component=0): |
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| 332 | """ |
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| 333 | Set the Parameters of a 'Sinusoidal' component, set with set_function. |
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| 334 | Parameters: |
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| 335 | ampl, period, x0: The sinusoidal parameters |
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| 336 | amplfixed, |
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| 337 | periodfixed, |
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| 338 | x0fixed: Optional parameters to indicate if |
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| 339 | the paramters should be held fixed during |
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| 340 | the fitting process. The default is to keep |
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| 341 | all parameters flexible. |
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| 342 | component: The number of the component (Default is the |
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| 343 | component 0) |
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| 344 | """ |
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| 345 | if self.fitfunc != "sinusoid": |
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| 346 | msg = "Function only operates on Sinusoidal components." |
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| 347 | raise ValueError(msg) |
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| 348 | if 0 <= component < len(self.components): |
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| 349 | d = {'params':[ampl, period, x0], |
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| 350 | 'fixed': [amplfixed, periodfixed, x0fixed]} |
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| 351 | self.set_parameters(d, component) |
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| 352 | else: |
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| 353 | msg = "Please select a valid component." |
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| 354 | raise ValueError(msg) |
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| 355 | |
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[2666] | 356 | |
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| 357 | def add_constraint(self, xpar, y): |
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| 358 | """Add parameter constraints to the fit. This is done by setting up |
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| 359 | linear equations for the related parameters. |
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| 360 | |
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| 361 | For example a two component gaussian fit where the amplitudes are |
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| 362 | constraint by amp1 = 2*amp2 |
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| 363 | needs a constraint |
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| 364 | |
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| 365 | add_constraint([1, 0, 0, -2, 0, 0, 0], 0) |
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| 366 | |
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| 367 | a velocity difference of v2-v1=17 |
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| 368 | |
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| 369 | add_constraint([0.,-1.,0.,0.,1.,0.], 17.) |
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| 370 | |
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| 371 | """ |
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| 372 | self._constraints.append((xpar, y)) |
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| 373 | |
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| 374 | |
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[975] | 375 | def get_area(self, component=None): |
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| 376 | """ |
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[1819] | 377 | Return the area under the fitted gaussian/lorentzian component. |
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[975] | 378 | Parameters: |
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[1819] | 379 | component: the gaussian/lorentzian component selection, |
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[975] | 380 | default (None) is the sum of all components |
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| 381 | Note: |
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[1819] | 382 | This will only work for gaussian/lorentzian fits. |
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[975] | 383 | """ |
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| 384 | if not self.fitted: return |
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[1819] | 385 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz": |
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[975] | 386 | pars = list(self.fitter.getparameters()) |
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| 387 | from math import log,pi,sqrt |
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[1819] | 388 | if self.fitfunc == "gauss": |
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| 389 | fac = sqrt(pi/log(16.0)) |
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| 390 | elif self.fitfunc == "lorentz": |
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| 391 | fac = pi/2.0 |
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[975] | 392 | areas = [] |
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| 393 | for i in range(len(self.components)): |
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| 394 | j = i*3 |
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| 395 | cpars = pars[j:j+3] |
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| 396 | areas.append(fac * cpars[0] * cpars[2]) |
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| 397 | else: |
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| 398 | return None |
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| 399 | if component is not None: |
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| 400 | return areas[component] |
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| 401 | else: |
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| 402 | return sum(areas) |
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| 403 | |
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[1862] | 404 | @asaplog_post_dec |
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[1075] | 405 | def get_errors(self, component=None): |
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[515] | 406 | """ |
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[1075] | 407 | Return the errors in the parameters. |
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| 408 | Parameters: |
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| 409 | component: get the errors for the specified component |
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| 410 | only, default is all components |
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| 411 | """ |
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| 412 | if not self.fitted: |
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| 413 | msg = "Not yet fitted." |
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[1859] | 414 | raise RuntimeError(msg) |
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[1075] | 415 | errs = list(self.fitter.geterrors()) |
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| 416 | cerrs = errs |
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| 417 | if component is not None: |
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[2666] | 418 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz" \ |
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| 419 | or self.fitfunc == "sinusoid": |
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[1075] | 420 | i = 3*component |
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| 421 | if i < len(errs): |
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| 422 | cerrs = errs[i:i+3] |
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| 423 | return cerrs |
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| 424 | |
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[1859] | 425 | |
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[1862] | 426 | @asaplog_post_dec |
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[1075] | 427 | def get_parameters(self, component=None, errors=False): |
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| 428 | """ |
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[113] | 429 | Return the fit paramters. |
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[526] | 430 | Parameters: |
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| 431 | component: get the parameters for the specified component |
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| 432 | only, default is all components |
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[113] | 433 | """ |
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| 434 | if not self.fitted: |
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[723] | 435 | msg = "Not yet fitted." |
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[1859] | 436 | raise RuntimeError(msg) |
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[113] | 437 | pars = list(self.fitter.getparameters()) |
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| 438 | fixed = list(self.fitter.getfixedparameters()) |
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[1075] | 439 | errs = list(self.fitter.geterrors()) |
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[1039] | 440 | area = [] |
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[723] | 441 | if component is not None: |
---|
[2047] | 442 | if self.fitfunc == "poly" or self.fitfunc == "lpoly": |
---|
| 443 | cpars = pars |
---|
| 444 | cfixed = fixed |
---|
| 445 | cerrs = errs |
---|
| 446 | else: |
---|
[515] | 447 | i = 3*component |
---|
| 448 | cpars = pars[i:i+3] |
---|
| 449 | cfixed = fixed[i:i+3] |
---|
[1075] | 450 | cerrs = errs[i:i+3] |
---|
[2047] | 451 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz": |
---|
| 452 | a = self.get_area(component) |
---|
| 453 | area = [a for i in range(3)] |
---|
[515] | 454 | else: |
---|
| 455 | cpars = pars |
---|
| 456 | cfixed = fixed |
---|
[1075] | 457 | cerrs = errs |
---|
[1819] | 458 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz": |
---|
[1039] | 459 | for c in range(len(self.components)): |
---|
[2047] | 460 | a = self.get_area(c) |
---|
| 461 | area += [a for i in range(3)] |
---|
[1088] | 462 | fpars = self._format_pars(cpars, cfixed, errors and cerrs, area) |
---|
[1859] | 463 | asaplog.push(fpars) |
---|
[1075] | 464 | return {'params':cpars, 'fixed':cfixed, 'formatted': fpars, |
---|
| 465 | 'errors':cerrs} |
---|
[723] | 466 | |
---|
[1075] | 467 | def _format_pars(self, pars, fixed, errors, area): |
---|
[113] | 468 | out = '' |
---|
[2047] | 469 | if self.fitfunc == "poly" or self.fitfunc == "lpoly": |
---|
[113] | 470 | c = 0 |
---|
[515] | 471 | for i in range(len(pars)): |
---|
| 472 | fix = "" |
---|
[1232] | 473 | if len(fixed) and fixed[i]: fix = "(fixed)" |
---|
[2047] | 474 | out += " p%d%s= %3.6f" % (c, fix, pars[i]) |
---|
| 475 | if errors : out += " (%1.6f)" % errors[i] |
---|
| 476 | out += "," |
---|
[113] | 477 | c+=1 |
---|
[515] | 478 | out = out[:-1] # remove trailing ',' |
---|
[2047] | 479 | else: |
---|
[113] | 480 | i = 0 |
---|
| 481 | c = 0 |
---|
[2047] | 482 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz": |
---|
| 483 | pnam = ["peak", "centre", "FWHM"] |
---|
| 484 | elif self.fitfunc == "sinusoid": |
---|
| 485 | pnam = ["amplitude", "period", "x0"] |
---|
| 486 | aunit = "" |
---|
| 487 | ounit = "" |
---|
[113] | 488 | if self.data: |
---|
[515] | 489 | aunit = self.data.get_unit() |
---|
| 490 | ounit = self.data.get_fluxunit() |
---|
[113] | 491 | while i < len(pars): |
---|
[2047] | 492 | fix0 = fix1 = fix2 = "" |
---|
| 493 | if i < len(fixed)-2: |
---|
| 494 | if fixed[i]: fix0 = "(fixed)" |
---|
| 495 | if fixed[i+1]: fix1 = "(fixed)" |
---|
| 496 | if fixed[i+2]: fix2 = "(fixed)" |
---|
| 497 | out += " %2d: " % c |
---|
| 498 | out += "%s%s = %3.3f %s, " % (pnam[0], fix0, pars[i], ounit) |
---|
| 499 | out += "%s%s = %3.3f %s, " % (pnam[1], fix1, pars[i+1], aunit) |
---|
| 500 | out += "%s%s = %3.3f %s\n" % (pnam[2], fix2, pars[i+2], aunit) |
---|
[2666] | 501 | if len(area): out += " area = %3.3f %s %s\n" % (area[i], |
---|
| 502 | ounit, |
---|
| 503 | aunit) |
---|
[113] | 504 | c+=1 |
---|
| 505 | i+=3 |
---|
| 506 | return out |
---|
[723] | 507 | |
---|
[1859] | 508 | |
---|
[1862] | 509 | @asaplog_post_dec |
---|
[113] | 510 | def get_estimate(self): |
---|
| 511 | """ |
---|
[515] | 512 | Return the parameter estimates (for non-linear functions). |
---|
[113] | 513 | """ |
---|
| 514 | pars = self.fitter.getestimate() |
---|
[943] | 515 | fixed = self.fitter.getfixedparameters() |
---|
[1927] | 516 | asaplog.push(self._format_pars(pars,fixed,None,None)) |
---|
[113] | 517 | return pars |
---|
| 518 | |
---|
[1862] | 519 | @asaplog_post_dec |
---|
[113] | 520 | def get_residual(self): |
---|
| 521 | """ |
---|
| 522 | Return the residual of the fit. |
---|
| 523 | """ |
---|
| 524 | if not self.fitted: |
---|
[723] | 525 | msg = "Not yet fitted." |
---|
[1859] | 526 | raise RuntimeError(msg) |
---|
[113] | 527 | return self.fitter.getresidual() |
---|
| 528 | |
---|
[1862] | 529 | @asaplog_post_dec |
---|
[113] | 530 | def get_chi2(self): |
---|
| 531 | """ |
---|
| 532 | Return chi^2. |
---|
| 533 | """ |
---|
| 534 | if not self.fitted: |
---|
[723] | 535 | msg = "Not yet fitted." |
---|
[1859] | 536 | raise RuntimeError(msg) |
---|
[113] | 537 | ch2 = self.fitter.getchi2() |
---|
[1859] | 538 | asaplog.push( 'Chi^2 = %3.3f' % (ch2) ) |
---|
[723] | 539 | return ch2 |
---|
[113] | 540 | |
---|
[1862] | 541 | @asaplog_post_dec |
---|
[113] | 542 | def get_fit(self): |
---|
| 543 | """ |
---|
| 544 | Return the fitted ordinate values. |
---|
| 545 | """ |
---|
| 546 | if not self.fitted: |
---|
[723] | 547 | msg = "Not yet fitted." |
---|
[1859] | 548 | raise RuntimeError(msg) |
---|
[113] | 549 | return self.fitter.getfit() |
---|
| 550 | |
---|
[1862] | 551 | @asaplog_post_dec |
---|
[113] | 552 | def commit(self): |
---|
| 553 | """ |
---|
[526] | 554 | Return a new scan where the fits have been commited (subtracted) |
---|
[113] | 555 | """ |
---|
| 556 | if not self.fitted: |
---|
[723] | 557 | msg = "Not yet fitted." |
---|
[1859] | 558 | raise RuntimeError(msg) |
---|
[975] | 559 | from asap import scantable |
---|
| 560 | if not isinstance(self.data, scantable): |
---|
[723] | 561 | msg = "Not a scantable" |
---|
[1859] | 562 | raise TypeError(msg) |
---|
[113] | 563 | scan = self.data.copy() |
---|
[259] | 564 | scan._setspectrum(self.fitter.getresidual()) |
---|
[1092] | 565 | return scan |
---|
[113] | 566 | |
---|
[1862] | 567 | @asaplog_post_dec |
---|
[1689] | 568 | def plot(self, residual=False, components=None, plotparms=False, |
---|
| 569 | filename=None): |
---|
[113] | 570 | """ |
---|
| 571 | Plot the last fit. |
---|
| 572 | Parameters: |
---|
| 573 | residual: an optional parameter indicating if the residual |
---|
| 574 | should be plotted (default 'False') |
---|
[526] | 575 | components: a list of components to plot, e.g [0,1], |
---|
| 576 | -1 plots the total fit. Default is to only |
---|
| 577 | plot the total fit. |
---|
| 578 | plotparms: Inidicates if the parameter values should be present |
---|
| 579 | on the plot |
---|
[113] | 580 | """ |
---|
[2538] | 581 | from matplotlib import rc as rcp |
---|
[113] | 582 | if not self.fitted: |
---|
| 583 | return |
---|
[2541] | 584 | #if not self._p or self._p.is_dead: |
---|
| 585 | if not (self._p and self._p._alive()): |
---|
[2150] | 586 | from asap.asapplotter import new_asaplot |
---|
[2451] | 587 | del self._p |
---|
[2150] | 588 | self._p = new_asaplot(rcParams['plotter.gui']) |
---|
[723] | 589 | self._p.hold() |
---|
[113] | 590 | self._p.clear() |
---|
[2535] | 591 | rcp('lines', linewidth=1) |
---|
[515] | 592 | self._p.set_panels() |
---|
[652] | 593 | self._p.palette(0) |
---|
[113] | 594 | tlab = 'Spectrum' |
---|
[723] | 595 | xlab = 'Abcissa' |
---|
[1017] | 596 | ylab = 'Ordinate' |
---|
[1739] | 597 | from numpy import ma,logical_not,logical_and,array |
---|
[1273] | 598 | m = self.mask |
---|
[113] | 599 | if self.data: |
---|
[515] | 600 | tlab = self.data._getsourcename(self._fittedrow) |
---|
| 601 | xlab = self.data._getabcissalabel(self._fittedrow) |
---|
[2153] | 602 | if self.data._getflagrow(self._fittedrow): |
---|
| 603 | m = [False] |
---|
| 604 | else: |
---|
| 605 | m = logical_and(self.mask, |
---|
| 606 | array(self.data._getmask(self._fittedrow), |
---|
| 607 | copy=False)) |
---|
[1589] | 608 | |
---|
[626] | 609 | ylab = self.data._get_ordinate_label() |
---|
[515] | 610 | |
---|
[2666] | 611 | colours = ["#777777","#dddddd","red","orange","purple","green", |
---|
| 612 | "magenta", "cyan"] |
---|
[1819] | 613 | nomask=True |
---|
| 614 | for i in range(len(m)): |
---|
| 615 | nomask = nomask and m[i] |
---|
[2153] | 616 | if len(m) == 1: |
---|
| 617 | m = m[0] |
---|
| 618 | invm = (not m) |
---|
| 619 | else: |
---|
| 620 | invm = logical_not(m) |
---|
[1819] | 621 | label0='Masked Region' |
---|
| 622 | label1='Spectrum' |
---|
| 623 | if ( nomask ): |
---|
| 624 | label0=label1 |
---|
| 625 | else: |
---|
| 626 | y = ma.masked_array( self.y, mask = m ) |
---|
| 627 | self._p.palette(1,colours) |
---|
| 628 | self._p.set_line( label = label1 ) |
---|
| 629 | self._p.plot( self.x, y ) |
---|
[652] | 630 | self._p.palette(0,colours) |
---|
[1819] | 631 | self._p.set_line(label=label0) |
---|
[2153] | 632 | y = ma.masked_array(self.y,mask=invm) |
---|
[1088] | 633 | self._p.plot(self.x, y) |
---|
[113] | 634 | if residual: |
---|
[1819] | 635 | self._p.palette(7) |
---|
[515] | 636 | self._p.set_line(label='Residual') |
---|
[1116] | 637 | y = ma.masked_array(self.get_residual(), |
---|
[2153] | 638 | mask=invm) |
---|
[1088] | 639 | self._p.plot(self.x, y) |
---|
[652] | 640 | self._p.palette(2) |
---|
[515] | 641 | if components is not None: |
---|
| 642 | cs = components |
---|
| 643 | if isinstance(components,int): cs = [components] |
---|
[526] | 644 | if plotparms: |
---|
[2666] | 645 | self._p.text(0.15,0.15, |
---|
| 646 | str(self.get_parameters()['formatted']),size=8) |
---|
[515] | 647 | n = len(self.components) |
---|
[652] | 648 | self._p.palette(3) |
---|
[515] | 649 | for c in cs: |
---|
| 650 | if 0 <= c < n: |
---|
| 651 | lab = self.fitfuncs[c]+str(c) |
---|
| 652 | self._p.set_line(label=lab) |
---|
[2153] | 653 | y = ma.masked_array(self.fitter.evaluate(c), mask=invm) |
---|
[1088] | 654 | |
---|
| 655 | self._p.plot(self.x, y) |
---|
[515] | 656 | elif c == -1: |
---|
[652] | 657 | self._p.palette(2) |
---|
[515] | 658 | self._p.set_line(label="Total Fit") |
---|
[1116] | 659 | y = ma.masked_array(self.fitter.getfit(), |
---|
[2153] | 660 | mask=invm) |
---|
[1088] | 661 | self._p.plot(self.x, y) |
---|
[515] | 662 | else: |
---|
[652] | 663 | self._p.palette(2) |
---|
[515] | 664 | self._p.set_line(label='Fit') |
---|
[2153] | 665 | y = ma.masked_array(self.fitter.getfit(),mask=invm) |
---|
[1088] | 666 | self._p.plot(self.x, y) |
---|
[723] | 667 | xlim=[min(self.x),max(self.x)] |
---|
| 668 | self._p.axes.set_xlim(xlim) |
---|
[113] | 669 | self._p.set_axes('xlabel',xlab) |
---|
| 670 | self._p.set_axes('ylabel',ylab) |
---|
| 671 | self._p.set_axes('title',tlab) |
---|
| 672 | self._p.release() |
---|
[723] | 673 | if (not rcParams['plotter.gui']): |
---|
| 674 | self._p.save(filename) |
---|
[113] | 675 | |
---|
[1862] | 676 | @asaplog_post_dec |
---|
[1061] | 677 | def auto_fit(self, insitu=None, plot=False): |
---|
[113] | 678 | """ |
---|
[515] | 679 | Return a scan where the function is applied to all rows for |
---|
| 680 | all Beams/IFs/Pols. |
---|
[723] | 681 | |
---|
[113] | 682 | """ |
---|
| 683 | from asap import scantable |
---|
[515] | 684 | if not isinstance(self.data, scantable) : |
---|
[723] | 685 | msg = "Data is not a scantable" |
---|
[1859] | 686 | raise TypeError(msg) |
---|
[259] | 687 | if insitu is None: insitu = rcParams['insitu'] |
---|
| 688 | if not insitu: |
---|
| 689 | scan = self.data.copy() |
---|
| 690 | else: |
---|
| 691 | scan = self.data |
---|
[880] | 692 | rows = xrange(scan.nrow()) |
---|
[1826] | 693 | # Save parameters of baseline fits as a class attribute. |
---|
[1819] | 694 | # NOTICE: This does not reflect changes in scantable! |
---|
| 695 | if len(rows) > 0: self.blpars=[] |
---|
[876] | 696 | asaplog.push("Fitting:") |
---|
| 697 | for r in rows: |
---|
[2666] | 698 | out = " Scan[%d] Beam[%d] IF[%d] Pol[%d] Cycle[%d]" % ( |
---|
| 699 | scan.getscan(r), |
---|
| 700 | scan.getbeam(r), |
---|
| 701 | scan.getif(r), |
---|
| 702 | scan.getpol(r), |
---|
| 703 | scan.getcycle(r) |
---|
| 704 | ) |
---|
[880] | 705 | asaplog.push(out, False) |
---|
[876] | 706 | self.x = scan._getabcissa(r) |
---|
| 707 | self.y = scan._getspectrum(r) |
---|
[2409] | 708 | #self.mask = mask_and(self.mask, scan._getmask(r)) |
---|
[2408] | 709 | if len(self.x) == len(self.mask): |
---|
| 710 | self.mask = mask_and(self.mask, self.data._getmask(row)) |
---|
| 711 | else: |
---|
[2666] | 712 | asaplog.push('lengths of data and mask are not the same. ' |
---|
| 713 | 'preset mask will be ignored') |
---|
[2408] | 714 | asaplog.post('WARN','asapfit.fit') |
---|
[2409] | 715 | self.mask=self.data._getmask(row) |
---|
[876] | 716 | self.data = None |
---|
| 717 | self.fit() |
---|
| 718 | x = self.get_parameters() |
---|
[1819] | 719 | fpar = self.get_parameters() |
---|
[1061] | 720 | if plot: |
---|
| 721 | self.plot(residual=True) |
---|
| 722 | x = raw_input("Accept fit ([y]/n): ") |
---|
| 723 | if x.upper() == 'N': |
---|
[1819] | 724 | self.blpars.append(None) |
---|
[1061] | 725 | continue |
---|
[880] | 726 | scan._setspectrum(self.fitter.getresidual(), r) |
---|
[1819] | 727 | self.blpars.append(fpar) |
---|
[1061] | 728 | if plot: |
---|
[2151] | 729 | self._p.quit() |
---|
| 730 | del self._p |
---|
[1061] | 731 | self._p = None |
---|
[876] | 732 | return scan |
---|