| 1 | import _asap |
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| 2 | from asap.parameters import rcParams |
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| 3 | from asap.logging import asaplog, asaplog_post_dec |
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| 4 | from asap.utils import _n_bools, mask_and |
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| 5 | from numpy import ndarray |
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| 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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| 20 | self.fitfuncs = None |
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| 21 | self.fitted = False |
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| 22 | self.data = None |
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| 23 | self.components = 0 |
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| 24 | self._fittedrow = 0 |
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| 25 | self._p = None |
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| 26 | self._selection = None |
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| 27 | self.uselinear = False |
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| 28 | self._constraints = [] |
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| 29 | |
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| 30 | def set_data(self, xdat, ydat, mask=None): |
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| 31 | """ |
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| 32 | Set the absissa and ordinate for the fit. Also set the mask |
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| 33 | indicating valid points. |
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| 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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| 37 | xdat: the abcissa values |
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| 38 | ydat: the ordinate values |
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| 39 | mask: an optional mask |
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| 40 | |
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| 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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| 46 | self.mask = _n_bools(len(xdat), True) |
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| 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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| 51 | @asaplog_post_dec |
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| 52 | def set_scan(self, thescan=None, mask=None): |
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| 53 | """ |
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| 54 | Set the 'data' (a scantable) of the fitter. |
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| 55 | Parameters: |
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| 56 | thescan: a scantable |
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| 57 | mask: a msk retrieved from the scantable |
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| 58 | """ |
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| 59 | if not thescan: |
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| 60 | msg = "Please give a correct scan" |
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| 61 | raise TypeError(msg) |
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| 62 | self.fitted = False |
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| 63 | self.data = thescan |
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| 64 | self.mask = None |
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| 65 | if mask is None: |
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| 66 | self.mask = _n_bools(self.data.nchan(), True) |
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| 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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| 71 | @asaplog_post_dec |
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| 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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| 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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| 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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| 83 | Example: |
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| 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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| 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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| 90 | fitter.set_function(sinusoid=3) # will fit three sinusoids |
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| 91 | """ |
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| 92 | #default poly order 0 |
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| 93 | n=0 |
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| 94 | if kwargs.has_key('poly'): |
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| 95 | self.fitfunc = 'poly' |
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| 96 | self.fitfuncs = ['poly'] |
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| 97 | n = kwargs.get('poly') |
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| 98 | self.components = [n+1] |
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| 99 | self.uselinear = False |
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| 100 | elif kwargs.has_key('lpoly'): |
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| 101 | self.fitfunc = 'poly' |
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| 102 | self.fitfuncs = ['lpoly'] |
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| 103 | n = kwargs.get('lpoly') |
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| 104 | self.components = [n+1] |
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| 105 | self.uselinear = True |
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| 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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| 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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| 111 | self.uselinear = False |
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| 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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| 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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| 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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| 127 | else: |
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| 128 | msg = "Invalid function type." |
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| 129 | raise TypeError(msg) |
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| 130 | |
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| 131 | self.fitter.setexpression(self.fitfunc,n) |
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| 132 | self._constraints = [] |
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| 133 | self.fitted = False |
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| 134 | return |
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| 135 | |
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| 136 | @asaplog_post_dec |
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| 137 | def fit(self, row=0, estimate=False): |
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| 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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| 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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| 145 | Example: |
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| 146 | s = scantable('myscan.asap') |
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| 147 | s.set_cursor(thepol=1) # select second pol |
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| 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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| 151 | f.fit(row=0) # fit first row |
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| 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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| 155 | msg = "Fitter not yet initialised. Please set data & fit function" |
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| 156 | raise RuntimeError(msg) |
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| 157 | |
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| 158 | if self.data is not None: |
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| 159 | if self.data._getflagrow(row): |
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| 160 | raise RuntimeError,"Can not fit flagged row." |
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| 161 | self.x = self.data._getabcissa(row) |
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| 162 | self.y = self.data._getspectrum(row) |
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| 163 | #self.mask = mask_and(self.mask, self.data._getmask(row)) |
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| 164 | if len(self.x) == len(self.mask): |
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| 165 | self.mask = mask_and(self.mask, self.data._getmask(row)) |
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| 166 | else: |
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| 167 | asaplog.push('lengths of data and mask are not the same. ' |
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| 168 | 'preset mask will be ignored') |
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| 169 | asaplog.post('WARN','asapfit.fit') |
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| 170 | self.mask=self.data._getmask(row) |
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| 171 | asaplog.push("Fitting:") |
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| 172 | i = row |
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| 173 | out = "Scan[%d] Beam[%d] IF[%d] Pol[%d] Cycle[%d]" % ( |
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| 174 | self.data.getscan(i), |
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| 175 | self.data.getbeam(i), |
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| 176 | self.data.getif(i), |
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| 177 | self.data.getpol(i), |
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| 178 | self.data.getcycle(i)) |
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| 179 | |
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| 180 | asaplog.push(out, False) |
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| 181 | |
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| 182 | self.fitter.setdata(self.x, self.y, self.mask) |
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| 183 | if self.fitfunc == 'gauss' or self.fitfunc == 'lorentz': |
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| 184 | ps = self.fitter.getparameters() |
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| 185 | if len(ps) == 0 or estimate: |
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| 186 | self.fitter.estimate() |
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| 187 | fxdpar = list(self.fitter.getfixedparameters()) |
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| 188 | if len(fxdpar) and fxdpar.count(0) == 0: |
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| 189 | raise RuntimeError,"No point fitting, if all parameters are fixed." |
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| 190 | if self._constraints: |
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| 191 | for c in self._constraints: |
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| 192 | self.fitter.addconstraint(c[0]+[c[-1]]) |
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| 193 | if self.uselinear: |
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| 194 | converged = self.fitter.lfit() |
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| 195 | else: |
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| 196 | converged = self.fitter.fit() |
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| 197 | if not converged: |
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| 198 | raise RuntimeError,"Fit didn't converge." |
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| 199 | self._fittedrow = row |
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| 200 | self.fitted = True |
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| 201 | return |
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| 202 | |
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| 203 | def store_fit(self, filename=None): |
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| 204 | """ |
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| 205 | Save the fit parameters. |
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| 206 | Parameters: |
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| 207 | filename: if specified save as an ASCII file, if None (default) |
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| 208 | store it in the scnatable |
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| 209 | """ |
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| 210 | if self.fitted and self.data is not None: |
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| 211 | pars = list(self.fitter.getparameters()) |
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| 212 | fixed = list(self.fitter.getfixedparameters()) |
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| 213 | from asap.asapfit import asapfit |
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| 214 | fit = asapfit() |
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| 215 | fit.setparameters(pars) |
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| 216 | fit.setfixedparameters(fixed) |
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| 217 | fit.setfunctions(self.fitfuncs) |
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| 218 | fit.setcomponents(self.components) |
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| 219 | fit.setframeinfo(self.data._getcoordinfo()) |
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| 220 | if filename is not None: |
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| 221 | import os |
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| 222 | filename = os.path.expandvars(os.path.expanduser(filename)) |
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| 223 | if os.path.exists(filename): |
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| 224 | raise IOError("File '%s' exists." % filename) |
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| 225 | fit.save(filename) |
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| 226 | else: |
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| 227 | self.data._addfit(fit,self._fittedrow) |
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| 228 | |
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| 229 | @asaplog_post_dec |
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| 230 | def set_parameters(self,*args,**kwargs): |
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| 231 | """ |
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| 232 | Set the parameters to be fitted. |
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| 233 | Parameters: |
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| 234 | params: a vector of parameters |
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| 235 | fixed: a vector of which parameters are to be held fixed |
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| 236 | (default is none) |
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| 237 | component: in case of multiple gaussians/lorentzians/sinusoidals, |
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| 238 | the index of the target component |
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| 239 | """ |
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| 240 | component = None |
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| 241 | fixed = None |
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| 242 | params = None |
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| 243 | |
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| 244 | if len(args) and isinstance(args[0],dict): |
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| 245 | kwargs = args[0] |
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| 246 | if kwargs.has_key("fixed"): fixed = kwargs["fixed"] |
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| 247 | if kwargs.has_key("params"): params = kwargs["params"] |
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| 248 | if len(args) == 2 and isinstance(args[1], int): |
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| 249 | component = args[1] |
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| 250 | if self.fitfunc is None: |
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| 251 | msg = "Please specify a fitting function first." |
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| 252 | raise RuntimeError(msg) |
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| 253 | if (self.fitfunc == "gauss" or self.fitfunc == "lorentz" |
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| 254 | or self.fitfunc == "sinusoid") and component is not None: |
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| 255 | if not self.fitted and sum(self.fitter.getparameters()) == 0: |
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| 256 | pars = _n_bools(len(self.components)*3, False) |
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| 257 | fxd = _n_bools(len(pars), False) |
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| 258 | else: |
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| 259 | pars = list(self.fitter.getparameters()) |
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| 260 | fxd = list(self.fitter.getfixedparameters()) |
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| 261 | i = 3*component |
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| 262 | pars[i:i+3] = params |
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| 263 | fxd[i:i+3] = fixed |
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| 264 | params = pars |
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| 265 | fixed = fxd |
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| 266 | self.fitter.setparameters(params) |
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| 267 | if fixed is not None: |
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| 268 | self.fitter.setfixedparameters(fixed) |
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| 269 | return |
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| 270 | |
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| 271 | @asaplog_post_dec |
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| 272 | def set_gauss_parameters(self, peak, centre, fwhm, |
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| 273 | peakfixed=0, centrefixed=0, |
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| 274 | fwhmfixed=0, |
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| 275 | component=0): |
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| 276 | """ |
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| 277 | Set the Parameters of a 'Gaussian' component, set with set_function. |
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| 278 | Parameters: |
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| 279 | peak, centre, fwhm: The gaussian parameters |
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| 280 | peakfixed, |
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| 281 | centrefixed, |
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| 282 | fwhmfixed: Optional parameters to indicate if |
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| 283 | the paramters should be held fixed during |
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| 284 | the fitting process. The default is to keep |
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| 285 | all parameters flexible. |
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| 286 | component: The number of the component (Default is the |
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| 287 | component 0) |
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| 288 | """ |
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| 289 | if self.fitfunc != "gauss": |
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| 290 | msg = "Function only operates on Gaussian components." |
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| 291 | raise ValueError(msg) |
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| 292 | if 0 <= component < len(self.components): |
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| 293 | d = {'params':[peak, centre, fwhm], |
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| 294 | 'fixed':[peakfixed, centrefixed, fwhmfixed]} |
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| 295 | self.set_parameters(d, component) |
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| 296 | else: |
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| 297 | msg = "Please select a valid component." |
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| 298 | raise ValueError(msg) |
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| 299 | |
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| 300 | @asaplog_post_dec |
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| 301 | def set_lorentz_parameters(self, peak, centre, fwhm, |
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| 302 | peakfixed=0, centrefixed=0, |
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| 303 | fwhmfixed=0, |
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| 304 | component=0): |
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| 305 | """ |
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| 306 | Set the Parameters of a 'Lorentzian' component, set with set_function. |
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| 307 | Parameters: |
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| 308 | peak, centre, fwhm: The lorentzian parameters |
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| 309 | peakfixed, |
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| 310 | centrefixed, |
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| 311 | fwhmfixed: Optional parameters to indicate if |
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| 312 | the paramters should be held fixed during |
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| 313 | the fitting process. The default is to keep |
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| 314 | all parameters flexible. |
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| 315 | component: The number of the component (Default is the |
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| 316 | component 0) |
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| 317 | """ |
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| 318 | if self.fitfunc != "lorentz": |
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| 319 | msg = "Function only operates on Lorentzian components." |
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| 320 | raise ValueError(msg) |
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| 321 | if 0 <= component < len(self.components): |
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| 322 | d = {'params':[peak, centre, fwhm], |
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| 323 | 'fixed':[peakfixed, centrefixed, fwhmfixed]} |
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| 324 | self.set_parameters(d, component) |
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| 325 | else: |
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| 326 | msg = "Please select a valid component." |
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| 327 | raise ValueError(msg) |
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| 328 | |
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| 329 | @asaplog_post_dec |
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| 330 | def set_sinusoid_parameters(self, ampl, period, x0, |
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| 331 | amplfixed=0, periodfixed=0, |
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| 332 | x0fixed=0, |
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| 333 | component=0): |
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| 334 | """ |
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| 335 | Set the Parameters of a 'Sinusoidal' component, set with set_function. |
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| 336 | Parameters: |
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| 337 | ampl, period, x0: The sinusoidal parameters |
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| 338 | amplfixed, |
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| 339 | periodfixed, |
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| 340 | x0fixed: Optional parameters to indicate if |
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| 341 | the paramters should be held fixed during |
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| 342 | the fitting process. The default is to keep |
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| 343 | all parameters flexible. |
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| 344 | component: The number of the component (Default is the |
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| 345 | component 0) |
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| 346 | """ |
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| 347 | if self.fitfunc != "sinusoid": |
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| 348 | msg = "Function only operates on Sinusoidal components." |
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| 349 | raise ValueError(msg) |
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| 350 | if 0 <= component < len(self.components): |
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| 351 | d = {'params':[ampl, period, x0], |
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| 352 | 'fixed': [amplfixed, periodfixed, x0fixed]} |
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| 353 | self.set_parameters(d, component) |
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| 354 | else: |
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| 355 | msg = "Please select a valid component." |
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| 356 | raise ValueError(msg) |
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| 357 | |
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| 358 | |
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| 359 | def add_constraint(self, xpar, y): |
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| 360 | """Add parameter constraints to the fit. This is done by setting up |
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| 361 | linear equations for the related parameters. |
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| 362 | |
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| 363 | For example a two component gaussian fit where the amplitudes are |
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| 364 | constraint by amp1 = 2*amp2 and paramaters for the two components |
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| 365 | in the order [amp1,peakv1,sigma1,amp2,peakv2,sigma2] |
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| 366 | needs a constraint |
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| 367 | |
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| 368 | add_constraint([1, 0, 0, -2, 0, 0], 0) |
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| 369 | |
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| 370 | as the linear equation is |
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| 371 | |
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| 372 | 1*amp1 + 0*peakv1 + 0*sigma1 -2*amp2 + 0*peakv2 + 0*sigma2 = 0 |
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| 373 | |
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| 374 | and similarly for a velocity difference of v2-v1=17 |
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| 375 | |
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| 376 | add_constraint([0.,-1.,0.,0.,1.,0.], 17.) |
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| 377 | |
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| 378 | """ |
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| 379 | self._constraints.append((xpar, y)) |
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| 380 | |
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| 381 | |
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| 382 | def get_area(self, component=None): |
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| 383 | """ |
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| 384 | Return the area under the fitted gaussian/lorentzian component. |
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| 385 | Parameters: |
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| 386 | component: the gaussian/lorentzian component selection, |
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| 387 | default (None) is the sum of all components |
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| 388 | Note: |
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| 389 | This will only work for gaussian/lorentzian fits. |
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| 390 | """ |
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| 391 | if not self.fitted: return |
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| 392 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz": |
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| 393 | pars = list(self.fitter.getparameters()) |
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| 394 | from math import log,pi,sqrt |
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| 395 | if self.fitfunc == "gauss": |
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| 396 | fac = sqrt(pi/log(16.0)) |
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| 397 | elif self.fitfunc == "lorentz": |
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| 398 | fac = pi/2.0 |
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| 399 | areas = [] |
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| 400 | for i in range(len(self.components)): |
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| 401 | j = i*3 |
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| 402 | cpars = pars[j:j+3] |
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| 403 | areas.append(fac * cpars[0] * cpars[2]) |
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| 404 | else: |
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| 405 | return None |
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| 406 | if component is not None: |
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| 407 | return areas[component] |
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| 408 | else: |
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| 409 | return sum(areas) |
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| 410 | |
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| 411 | @asaplog_post_dec |
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| 412 | def get_errors(self, component=None): |
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| 413 | """ |
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| 414 | Return the errors in the parameters. |
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| 415 | Parameters: |
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| 416 | component: get the errors for the specified component |
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| 417 | only, default is all components |
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| 418 | """ |
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| 419 | if not self.fitted: |
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| 420 | msg = "Not yet fitted." |
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| 421 | raise RuntimeError(msg) |
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| 422 | errs = list(self.fitter.geterrors()) |
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| 423 | cerrs = errs |
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| 424 | if component is not None: |
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| 425 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz" \ |
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| 426 | or self.fitfunc == "sinusoid": |
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| 427 | i = 3*component |
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| 428 | if i < len(errs): |
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| 429 | cerrs = errs[i:i+3] |
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| 430 | return cerrs |
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| 431 | |
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| 432 | |
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| 433 | @asaplog_post_dec |
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| 434 | def get_parameters(self, component=None, errors=False): |
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| 435 | """ |
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| 436 | Return the fit paramters. |
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| 437 | Parameters: |
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| 438 | component: get the parameters for the specified component |
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| 439 | only, default is all components |
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| 440 | """ |
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| 441 | if not self.fitted: |
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| 442 | msg = "Not yet fitted." |
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| 443 | raise RuntimeError(msg) |
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| 444 | pars = list(self.fitter.getparameters()) |
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| 445 | fixed = list(self.fitter.getfixedparameters()) |
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| 446 | errs = list(self.fitter.geterrors()) |
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| 447 | area = [] |
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| 448 | if component is not None: |
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| 449 | if self.fitfunc == "poly" or self.fitfunc == "lpoly": |
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| 450 | cpars = pars |
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| 451 | cfixed = fixed |
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| 452 | cerrs = errs |
|---|
| 453 | else: |
|---|
| 454 | i = 3*component |
|---|
| 455 | cpars = pars[i:i+3] |
|---|
| 456 | cfixed = fixed[i:i+3] |
|---|
| 457 | cerrs = errs[i:i+3] |
|---|
| 458 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz": |
|---|
| 459 | a = self.get_area(component) |
|---|
| 460 | area = [a for i in range(3)] |
|---|
| 461 | else: |
|---|
| 462 | cpars = pars |
|---|
| 463 | cfixed = fixed |
|---|
| 464 | cerrs = errs |
|---|
| 465 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz": |
|---|
| 466 | for c in range(len(self.components)): |
|---|
| 467 | a = self.get_area(c) |
|---|
| 468 | area += [a for i in range(3)] |
|---|
| 469 | fpars = self._format_pars(cpars, cfixed, errors and cerrs, area) |
|---|
| 470 | asaplog.push(fpars) |
|---|
| 471 | return {'params':cpars, 'fixed':cfixed, 'formatted': fpars, |
|---|
| 472 | 'errors':cerrs} |
|---|
| 473 | |
|---|
| 474 | def _format_pars(self, pars, fixed, errors, area): |
|---|
| 475 | out = '' |
|---|
| 476 | if self.fitfunc == "poly" or self.fitfunc == "lpoly": |
|---|
| 477 | c = 0 |
|---|
| 478 | for i in range(len(pars)): |
|---|
| 479 | fix = "" |
|---|
| 480 | if len(fixed) and fixed[i]: fix = "(fixed)" |
|---|
| 481 | out += " p%d%s= %3.6f" % (c, fix, pars[i]) |
|---|
| 482 | if errors : out += " (%1.6f)" % errors[i] |
|---|
| 483 | out += "," |
|---|
| 484 | c+=1 |
|---|
| 485 | out = out[:-1] # remove trailing ',' |
|---|
| 486 | else: |
|---|
| 487 | i = 0 |
|---|
| 488 | c = 0 |
|---|
| 489 | if self.fitfunc == "gauss" or self.fitfunc == "lorentz": |
|---|
| 490 | pnam = ["peak", "centre", "FWHM"] |
|---|
| 491 | elif self.fitfunc == "sinusoid": |
|---|
| 492 | pnam = ["amplitude", "period", "x0"] |
|---|
| 493 | aunit = "" |
|---|
| 494 | ounit = "" |
|---|
| 495 | if self.data: |
|---|
| 496 | aunit = self.data.get_unit() |
|---|
| 497 | ounit = self.data.get_fluxunit() |
|---|
| 498 | while i < len(pars): |
|---|
| 499 | fix0 = fix1 = fix2 = "" |
|---|
| 500 | if i < len(fixed)-2: |
|---|
| 501 | if fixed[i]: fix0 = "(fixed)" |
|---|
| 502 | if fixed[i+1]: fix1 = "(fixed)" |
|---|
| 503 | if fixed[i+2]: fix2 = "(fixed)" |
|---|
| 504 | out += " %2d: " % c |
|---|
| 505 | out += "%s%s = %3.3f %s, " % (pnam[0], fix0, pars[i], ounit) |
|---|
| 506 | out += "%s%s = %3.3f %s, " % (pnam[1], fix1, pars[i+1], aunit) |
|---|
| 507 | out += "%s%s = %3.3f %s\n" % (pnam[2], fix2, pars[i+2], aunit) |
|---|
| 508 | if len(area): out += " area = %3.3f %s %s\n" % (area[i], |
|---|
| 509 | ounit, |
|---|
| 510 | aunit) |
|---|
| 511 | c+=1 |
|---|
| 512 | i+=3 |
|---|
| 513 | return out |
|---|
| 514 | |
|---|
| 515 | |
|---|
| 516 | @asaplog_post_dec |
|---|
| 517 | def get_estimate(self): |
|---|
| 518 | """ |
|---|
| 519 | Return the parameter estimates (for non-linear functions). |
|---|
| 520 | """ |
|---|
| 521 | pars = self.fitter.getestimate() |
|---|
| 522 | fixed = self.fitter.getfixedparameters() |
|---|
| 523 | asaplog.push(self._format_pars(pars,fixed,None,None)) |
|---|
| 524 | return pars |
|---|
| 525 | |
|---|
| 526 | @asaplog_post_dec |
|---|
| 527 | def get_residual(self): |
|---|
| 528 | """ |
|---|
| 529 | Return the residual of the fit. |
|---|
| 530 | """ |
|---|
| 531 | if not self.fitted: |
|---|
| 532 | msg = "Not yet fitted." |
|---|
| 533 | raise RuntimeError(msg) |
|---|
| 534 | return self.fitter.getresidual() |
|---|
| 535 | |
|---|
| 536 | @asaplog_post_dec |
|---|
| 537 | def get_chi2(self): |
|---|
| 538 | """ |
|---|
| 539 | Return chi^2. |
|---|
| 540 | """ |
|---|
| 541 | if not self.fitted: |
|---|
| 542 | msg = "Not yet fitted." |
|---|
| 543 | raise RuntimeError(msg) |
|---|
| 544 | ch2 = self.fitter.getchi2() |
|---|
| 545 | asaplog.push( 'Chi^2 = %3.3f' % (ch2) ) |
|---|
| 546 | return ch2 |
|---|
| 547 | |
|---|
| 548 | @asaplog_post_dec |
|---|
| 549 | def get_fit(self): |
|---|
| 550 | """ |
|---|
| 551 | Return the fitted ordinate values. |
|---|
| 552 | """ |
|---|
| 553 | if not self.fitted: |
|---|
| 554 | msg = "Not yet fitted." |
|---|
| 555 | raise RuntimeError(msg) |
|---|
| 556 | return self.fitter.getfit() |
|---|
| 557 | |
|---|
| 558 | @asaplog_post_dec |
|---|
| 559 | def commit(self): |
|---|
| 560 | """ |
|---|
| 561 | Return a new scan where the fits have been commited (subtracted) |
|---|
| 562 | """ |
|---|
| 563 | if not self.fitted: |
|---|
| 564 | msg = "Not yet fitted." |
|---|
| 565 | raise RuntimeError(msg) |
|---|
| 566 | from asap import scantable |
|---|
| 567 | if not isinstance(self.data, scantable): |
|---|
| 568 | msg = "Not a scantable" |
|---|
| 569 | raise TypeError(msg) |
|---|
| 570 | scan = self.data.copy() |
|---|
| 571 | scan._setspectrum(self.fitter.getresidual()) |
|---|
| 572 | return scan |
|---|
| 573 | |
|---|
| 574 | @asaplog_post_dec |
|---|
| 575 | def plot(self, residual=False, components=None, plotparms=False, |
|---|
| 576 | filename=None): |
|---|
| 577 | """ |
|---|
| 578 | Plot the last fit. |
|---|
| 579 | Parameters: |
|---|
| 580 | residual: an optional parameter indicating if the residual |
|---|
| 581 | should be plotted (default 'False') |
|---|
| 582 | components: a list of components to plot, e.g [0,1], |
|---|
| 583 | -1 plots the total fit. Default is to only |
|---|
| 584 | plot the total fit. |
|---|
| 585 | plotparms: Inidicates if the parameter values should be present |
|---|
| 586 | on the plot |
|---|
| 587 | """ |
|---|
| 588 | from matplotlib import rc as rcp |
|---|
| 589 | if not self.fitted: |
|---|
| 590 | return |
|---|
| 591 | #if not self._p or self._p.is_dead: |
|---|
| 592 | if not (self._p and self._p._alive()): |
|---|
| 593 | from asap.asapplotter import new_asaplot |
|---|
| 594 | del self._p |
|---|
| 595 | self._p = new_asaplot(rcParams['plotter.gui']) |
|---|
| 596 | self._p.hold() |
|---|
| 597 | self._p.clear() |
|---|
| 598 | rcp('lines', linewidth=1) |
|---|
| 599 | self._p.set_panels() |
|---|
| 600 | self._p.palette(0) |
|---|
| 601 | tlab = 'Spectrum' |
|---|
| 602 | xlab = 'Abcissa' |
|---|
| 603 | ylab = 'Ordinate' |
|---|
| 604 | from numpy import ma,logical_not,logical_and,array |
|---|
| 605 | m = self.mask |
|---|
| 606 | if self.data: |
|---|
| 607 | tlab = self.data._getsourcename(self._fittedrow) |
|---|
| 608 | xlab = self.data._getabcissalabel(self._fittedrow) |
|---|
| 609 | if self.data._getflagrow(self._fittedrow): |
|---|
| 610 | m = [False] |
|---|
| 611 | else: |
|---|
| 612 | m = logical_and(self.mask, |
|---|
| 613 | array(self.data._getmask(self._fittedrow), |
|---|
| 614 | copy=False)) |
|---|
| 615 | |
|---|
| 616 | ylab = self.data._get_ordinate_label() |
|---|
| 617 | |
|---|
| 618 | colours = ["#777777","#dddddd","red","orange","purple","green", |
|---|
| 619 | "magenta", "cyan"] |
|---|
| 620 | nomask=True |
|---|
| 621 | for i in range(len(m)): |
|---|
| 622 | nomask = nomask and m[i] |
|---|
| 623 | if len(m) == 1: |
|---|
| 624 | m = m[0] |
|---|
| 625 | invm = (not m) |
|---|
| 626 | else: |
|---|
| 627 | invm = logical_not(m) |
|---|
| 628 | label0='Masked Region' |
|---|
| 629 | label1='Spectrum' |
|---|
| 630 | if ( nomask ): |
|---|
| 631 | label0=label1 |
|---|
| 632 | else: |
|---|
| 633 | y = ma.masked_array( self.y, mask = m ) |
|---|
| 634 | self._p.palette(1,colours) |
|---|
| 635 | self._p.set_line( label = label1 ) |
|---|
| 636 | self._p.plot( self.x, y ) |
|---|
| 637 | self._p.palette(0,colours) |
|---|
| 638 | self._p.set_line(label=label0) |
|---|
| 639 | y = ma.masked_array(self.y,mask=invm) |
|---|
| 640 | self._p.plot(self.x, y) |
|---|
| 641 | if residual: |
|---|
| 642 | self._p.palette(7) |
|---|
| 643 | self._p.set_line(label='Residual') |
|---|
| 644 | y = ma.masked_array(self.get_residual(), |
|---|
| 645 | mask=invm) |
|---|
| 646 | self._p.plot(self.x, y) |
|---|
| 647 | self._p.palette(2) |
|---|
| 648 | if components is not None: |
|---|
| 649 | cs = components |
|---|
| 650 | if isinstance(components,int): cs = [components] |
|---|
| 651 | if plotparms: |
|---|
| 652 | self._p.text(0.15,0.15, |
|---|
| 653 | str(self.get_parameters()['formatted']),size=8) |
|---|
| 654 | n = len(self.components) |
|---|
| 655 | self._p.palette(3) |
|---|
| 656 | for c in cs: |
|---|
| 657 | if 0 <= c < n: |
|---|
| 658 | lab = self.fitfuncs[c]+str(c) |
|---|
| 659 | self._p.set_line(label=lab) |
|---|
| 660 | y = ma.masked_array(self.fitter.evaluate(c), mask=invm) |
|---|
| 661 | |
|---|
| 662 | self._p.plot(self.x, y) |
|---|
| 663 | elif c == -1: |
|---|
| 664 | self._p.palette(2) |
|---|
| 665 | self._p.set_line(label="Total Fit") |
|---|
| 666 | y = ma.masked_array(self.fitter.getfit(), |
|---|
| 667 | mask=invm) |
|---|
| 668 | self._p.plot(self.x, y) |
|---|
| 669 | else: |
|---|
| 670 | self._p.palette(2) |
|---|
| 671 | self._p.set_line(label='Fit') |
|---|
| 672 | y = ma.masked_array(self.fitter.getfit(),mask=invm) |
|---|
| 673 | self._p.plot(self.x, y) |
|---|
| 674 | xlim=[min(self.x),max(self.x)] |
|---|
| 675 | self._p.axes.set_xlim(xlim) |
|---|
| 676 | self._p.set_axes('xlabel',xlab) |
|---|
| 677 | self._p.set_axes('ylabel',ylab) |
|---|
| 678 | self._p.set_axes('title',tlab) |
|---|
| 679 | self._p.release() |
|---|
| 680 | if (not rcParams['plotter.gui']): |
|---|
| 681 | self._p.save(filename) |
|---|
| 682 | |
|---|
| 683 | @asaplog_post_dec |
|---|
| 684 | def auto_fit(self, insitu=None, plot=False): |
|---|
| 685 | """ |
|---|
| 686 | Return a scan where the function is applied to all rows for |
|---|
| 687 | all Beams/IFs/Pols. |
|---|
| 688 | |
|---|
| 689 | """ |
|---|
| 690 | from asap import scantable |
|---|
| 691 | if not isinstance(self.data, scantable) : |
|---|
| 692 | msg = "Data is not a scantable" |
|---|
| 693 | raise TypeError(msg) |
|---|
| 694 | if insitu is None: insitu = rcParams['insitu'] |
|---|
| 695 | if not insitu: |
|---|
| 696 | scan = self.data.copy() |
|---|
| 697 | else: |
|---|
| 698 | scan = self.data |
|---|
| 699 | rows = xrange(scan.nrow()) |
|---|
| 700 | # Save parameters of baseline fits as a class attribute. |
|---|
| 701 | # NOTICE: This does not reflect changes in scantable! |
|---|
| 702 | if len(rows) > 0: self.blpars=[] |
|---|
| 703 | asaplog.push("Fitting:") |
|---|
| 704 | for r in rows: |
|---|
| 705 | out = " Scan[%d] Beam[%d] IF[%d] Pol[%d] Cycle[%d]" % ( |
|---|
| 706 | scan.getscan(r), |
|---|
| 707 | scan.getbeam(r), |
|---|
| 708 | scan.getif(r), |
|---|
| 709 | scan.getpol(r), |
|---|
| 710 | scan.getcycle(r) |
|---|
| 711 | ) |
|---|
| 712 | asaplog.push(out, False) |
|---|
| 713 | self.x = scan._getabcissa(r) |
|---|
| 714 | self.y = scan._getspectrum(r) |
|---|
| 715 | #self.mask = mask_and(self.mask, scan._getmask(r)) |
|---|
| 716 | if len(self.x) == len(self.mask): |
|---|
| 717 | self.mask = mask_and(self.mask, self.data._getmask(row)) |
|---|
| 718 | else: |
|---|
| 719 | asaplog.push('lengths of data and mask are not the same. ' |
|---|
| 720 | 'preset mask will be ignored') |
|---|
| 721 | asaplog.post('WARN','asapfit.fit') |
|---|
| 722 | self.mask=self.data._getmask(row) |
|---|
| 723 | self.data = None |
|---|
| 724 | self.fit() |
|---|
| 725 | x = self.get_parameters() |
|---|
| 726 | fpar = self.get_parameters() |
|---|
| 727 | if plot: |
|---|
| 728 | self.plot(residual=True) |
|---|
| 729 | x = raw_input("Accept fit ([y]/n): ") |
|---|
| 730 | if x.upper() == 'N': |
|---|
| 731 | self.blpars.append(None) |
|---|
| 732 | continue |
|---|
| 733 | scan._setspectrum(self.fitter.getresidual(), r) |
|---|
| 734 | self.blpars.append(fpar) |
|---|
| 735 | if plot: |
|---|
| 736 | self._p.quit() |
|---|
| 737 | del self._p |
|---|
| 738 | self._p = None |
|---|
| 739 | return scan |
|---|
