| 1 | from asap.asaplot import ASAPlot |
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| 2 | |
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| 3 | class asapplotter: |
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| 4 | def __init__(self): |
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| 5 | """ |
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| 6 | The ASAP plotter. |
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| 7 | By default the plotter is set up to plot polarisations |
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| 8 | 'colour stacked' and scantables across panels. |
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| 9 | The defaul plotter is called 'plotter'. |
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| 10 | Note: |
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| 11 | Currenly it only plots 'spectra' not Tsys or |
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| 12 | other variables. |
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| 13 | |
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| 14 | """ |
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| 15 | self._plotter = ASAPlot() |
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| 16 | |
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| 17 | self._tdict = {'Time':'t','time':'t','t':'t','T':'t'} |
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| 18 | self._bdict = {'Beam':'b','beam':'b','b':'b','B':'b'} |
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| 19 | self._idict = {'IF':'i','if':'i','i':'i','I':'i'} |
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| 20 | self._pdict = {'Pol':'p','pol':'p','p':'p'} |
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| 21 | self._sdict = {'scan':'s','Scan':'s','s':'s','S':'s'} |
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| 22 | self._cdict = {'t':'scan.nrow()', |
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| 23 | 'b':'scan.nbeam()', |
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| 24 | 'i':'scan.nif()', |
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| 25 | 'p':'scan.npol()', |
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| 26 | 's':'len(scans)'} |
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| 27 | self._ldict = {'b':'Beam', |
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| 28 | 'i':'IF', |
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| 29 | 'p':'Pol', |
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| 30 | 's':'Scan'} |
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| 31 | self._dicts = [self._tdict,self._bdict, |
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| 32 | self._idict,self._pdict, |
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| 33 | self._sdict] |
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| 34 | self._panels = 's' |
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| 35 | self._stacking = 'p' |
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| 36 | self._autoplot = False |
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| 37 | self._minmax = None |
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| 38 | self._data = None |
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| 39 | self._lmap = [] |
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| 40 | |
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| 41 | def _translate(self, name): |
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| 42 | for d in self._dicts: |
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| 43 | if d.has_key(name): |
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| 44 | return d[name] |
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| 45 | return None |
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| 46 | |
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| 47 | def plot(self,*args): |
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| 48 | """ |
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| 49 | Plot a (list of) scantables. |
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| 50 | Parameters: |
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| 51 | one or more comma separated scantables |
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| 52 | Note: |
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| 53 | If a (list) of scantables was specified in a previous call |
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| 54 | to plot, no argument has to be given to 'replot' |
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| 55 | NO checking is done that the abscissas of the scantables |
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| 56 | are consistent e.g. all 'channel' or all 'velocity' etc. |
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| 57 | """ |
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| 58 | if self._plotter.is_dead: |
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| 59 | self._plotter = ASAPlot() |
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| 60 | self._plotter.clear() |
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| 61 | self._plotter.hold() |
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| 62 | if len(args) > 0: |
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| 63 | self._data = tuple(args) |
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| 64 | if self._panels == 't': |
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| 65 | if self._data[0].nrow() > 25: |
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| 66 | print "Scan to be plotted contains more than 25 rows.\nCan't plot that many panels..." |
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| 67 | return |
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| 68 | self._plot_time(self._data[0], self._stacking) |
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| 69 | elif self._panels == 's': |
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| 70 | self._plot_scans(self._data, self._stacking) |
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| 71 | else: |
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| 72 | self._plot_other(self._data, self._stacking) |
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| 73 | if self._minmax is not None: |
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| 74 | self._plotter.set_limits(xlim=self._minmax) |
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| 75 | self._plotter.release() |
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| 76 | return |
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| 77 | |
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| 78 | def _plot_time(self, scan, colmode): |
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| 79 | if colmode == 't': |
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| 80 | return |
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| 81 | n = scan.nrow() |
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| 82 | cdict = {'b':'scan.setbeam(j)', |
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| 83 | 'i':'scan.setif(j)', |
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| 84 | 'p':'scan.setpol(j)'} |
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| 85 | if self._stacking is not None: |
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| 86 | ncol = eval(self._cdict.get(colmode)) |
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| 87 | self._plotter.set_panels() |
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| 88 | if n > 1: |
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| 89 | self._plotter.set_panels(rows=n) |
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| 90 | for i in range(n): |
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| 91 | if n > 1: |
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| 92 | self._plotter.palette(0) |
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| 93 | self._plotter.subplot(i) |
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| 94 | for j in range(ncol): |
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| 95 | eval(cdict.get(colmode)) |
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| 96 | x = None |
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| 97 | y = None |
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| 98 | m = None |
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| 99 | tlab = scan._getsourcename(i) |
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| 100 | x,xlab = scan.get_abcissa(i) |
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| 101 | y = scan.getspectrum(i) |
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| 102 | ylab = 'Flux ('+scan.get_fluxunit()+')' |
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| 103 | m = scan.getmask(i) |
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| 104 | if len(self._lmap) > 0: |
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| 105 | llab = self._lmap[j] |
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| 106 | else: |
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| 107 | llab = self._ldict.get(colmode)+' '+str(j) |
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| 108 | self._plotter.set_line(label=llab) |
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| 109 | self._plotter.plot(x,y,m) |
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| 110 | xlim=[min(x),max(x)] |
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| 111 | self._plotter.axes.set_xlim(xlim) |
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| 112 | self._plotter.set_axes('xlabel',xlab) |
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| 113 | self._plotter.set_axes('ylabel',ylab) |
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| 114 | self._plotter.set_axes('title',tlab) |
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| 115 | return |
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| 116 | |
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| 117 | def _plot_scans(self, scans, colmode): |
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| 118 | if colmode == 's': |
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| 119 | return |
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| 120 | cdict = {'b':'scan.setbeam(j)', |
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| 121 | 'i':'scan.setif(j)', |
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| 122 | 'p':'scan.setpol(j)'} |
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| 123 | n = len(scans) |
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| 124 | if self._stacking is not None: |
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| 125 | scan = scans[0] |
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| 126 | ncol = eval(self._cdict.get(colmode)) |
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| 127 | self._plotter.set_panels() |
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| 128 | if n > 1: |
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| 129 | self._plotter.set_panels(rows=n) |
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| 130 | i = 0 |
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| 131 | for scan in scans: |
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| 132 | if n > 1: |
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| 133 | self._plotter.subplot(i) |
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| 134 | self._plotter.palette(0) |
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| 135 | for j in range(ncol): |
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| 136 | eval(cdict.get(colmode)) |
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| 137 | x = None |
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| 138 | y = None |
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| 139 | m = None |
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| 140 | tlab = scan._getsourcename() |
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| 141 | x,xlab = scan.get_abcissa() |
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| 142 | y = scan.getspectrum() |
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| 143 | ylab = 'Flux ('+scan.get_fluxunit()+')' |
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| 144 | m = scan.getmask() |
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| 145 | if len(self._lmap) > 0: |
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| 146 | llab = self._lmap[j] |
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| 147 | else: |
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| 148 | llab = self._ldict.get(colmode)+' '+str(j) |
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| 149 | self._plotter.set_line(label=llab) |
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| 150 | self._plotter.plot(x,y,m) |
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| 151 | xlim=[min(x),max(x)] |
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| 152 | self._plotter.axes.set_xlim(xlim) |
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| 153 | |
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| 154 | self._plotter.set_axes('xlabel',xlab) |
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| 155 | self._plotter.set_axes('ylabel',ylab) |
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| 156 | self._plotter.set_axes('title',tlab) |
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| 157 | i += 1 |
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| 158 | return |
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| 159 | |
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| 160 | def _plot_other(self,scans,colmode): |
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| 161 | if colmode == self._panels: |
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| 162 | return |
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| 163 | cdict = {'b':'scan.setbeam(j)', |
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| 164 | 'i':'scan.setif(j)', |
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| 165 | 'p':'scan.setpol(j)', |
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| 166 | 's':'scans[j]'} |
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| 167 | scan = scans[0] |
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| 168 | n = eval(self._cdict.get(self._panels)) |
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| 169 | if self._stacking is not None: |
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| 170 | ncol = eval(self._cdict.get(colmode)) |
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| 171 | self._plotter.set_panels() |
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| 172 | if n > 1: |
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| 173 | self._plotter.set_panels(rows=n) |
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| 174 | for i in range(n): |
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| 175 | if n>1: |
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| 176 | self._plotter.subplot(i) |
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| 177 | self._plotter.palette(0) |
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| 178 | k=0 |
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| 179 | j=i |
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| 180 | eval(cdict.get(self._panels)) |
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| 181 | for j in range(ncol): |
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| 182 | if colmode == 's': |
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| 183 | scan = eval(cdict.get(colmode)) |
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| 184 | elif colmode == 't': |
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| 185 | k = j |
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| 186 | else: |
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| 187 | eval(cdict.get(colmode)) |
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| 188 | x = None |
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| 189 | y = None |
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| 190 | m = None |
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| 191 | x,xlab = scan.get_abcissa(k) |
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| 192 | y = scan.getspectrum(k) |
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| 193 | ylab = 'Flux ('+scan.get_fluxunit()+')' |
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| 194 | m = scan.getmask(k) |
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| 195 | if colmode == 's' or colmode == 't': |
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| 196 | tlab = self._ldict.get(self._panels)+' '+str(i) |
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| 197 | llab = scan._getsourcename(k) |
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| 198 | else: |
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| 199 | tlab = scan._getsourcename(k) |
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| 200 | if len(self._lmap) > 0: |
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| 201 | llab = self._lmap[j] |
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| 202 | else: |
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| 203 | llab = self._ldict.get(colmode)+' '+str(j) |
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| 204 | self._plotter.set_line(label=llab) |
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| 205 | self._plotter.plot(x,y,m) |
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| 206 | xlim=[min(x),max(x)] |
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| 207 | self._plotter.axes.set_xlim(xlim) |
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| 208 | |
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| 209 | self._plotter.set_axes('xlabel',xlab) |
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| 210 | self._plotter.set_axes('ylabel',ylab) |
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| 211 | self._plotter.set_axes('title',tlab) |
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| 212 | |
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| 213 | return |
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| 214 | |
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| 215 | |
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| 216 | def set_mode(self, stacking='pol', panelling='scan'): |
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| 217 | """ |
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| 218 | Parameters: |
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| 219 | stacking: tell the plotter which variable to plot |
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| 220 | as line colour overlays (default 'pol') |
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| 221 | panelling: tell the plotter which variable to plot |
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| 222 | across multiple panels (default 'scan' |
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| 223 | Note: |
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| 224 | Valid modes are: |
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| 225 | 'beam' 'Beam' 'b': Beams |
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| 226 | 'if' 'IF' 'i': IFs |
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| 227 | 'pol' 'Pol' 'p': Polarisations |
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| 228 | 'scan' 'Scan' 's': Scans |
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| 229 | 'time' 'Time' 't': Times |
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| 230 | """ |
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| 231 | if not self.set_panels(panelling): |
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| 232 | print "Invalid mode" |
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| 233 | if not self.set_stacking(stacking): |
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| 234 | print "Invalid mode" |
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| 235 | return |
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| 236 | |
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| 237 | def set_panels(self, what='scan'): |
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| 238 | md = self._translate(what) |
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| 239 | if md: |
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| 240 | self._panels = md |
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| 241 | return True |
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| 242 | return False |
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| 243 | |
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| 244 | def set_stacking(self, what='pol'): |
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| 245 | md = self._translate(what) |
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| 246 | if md: |
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| 247 | self._stacking = md |
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| 248 | return True |
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| 249 | return False |
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| 250 | |
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| 251 | def set_range(self,start=None,end=None): |
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| 252 | """ |
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| 253 | Set the range of interest on the abcissa of the plot |
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| 254 | Parameters: |
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| 255 | start,end: The start an end point of the 'zoom' window |
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| 256 | Note: |
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| 257 | These become non-sensical when the unit changes. |
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| 258 | use plotter.set_range() without parameters to reset |
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| 259 | |
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| 260 | """ |
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| 261 | if start is None and end is None: |
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| 262 | self._minmax = None |
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| 263 | if self._data is not None: |
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| 264 | self.plot() |
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| 265 | else: |
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| 266 | self._minmax = [start,end] |
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| 267 | if self._data is not None: |
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| 268 | self.plot() |
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| 269 | return |
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| 270 | |
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| 271 | def set_legend_map(self,mp=[]): |
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| 272 | """ |
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| 273 | Specify a mapping for the legend instead of using the default |
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| 274 | indices: |
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| 275 | Parameters: |
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| 276 | mp: a list of 'strings'. This should have the same length |
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| 277 | as the number of elements on the legend and then maps |
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| 278 | to the indeces in order |
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| 279 | |
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| 280 | Example: |
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| 281 | If the data has to IFs/rest frequencies with index 0 and 1 |
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| 282 | for CO and SiO: |
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| 283 | plotter.set_stacking('i') |
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| 284 | plotter.set_legend_map(['CO','SiO']) |
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| 285 | plotter.plot() |
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| 286 | """ |
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| 287 | self._lmap = mp |
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| 288 | |
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| 289 | if __name__ == '__main__': |
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| 290 | plotter = asapplotter() |
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