| 1 | from asap.asaplot import ASAPlot
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| 2 | from asap import rcParams
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| 3 |
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| 4 | class asapplotter:
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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 | Note:
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| 10 | Currenly it only plots 'spectra' not Tsys or
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| 11 | other variables.
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| 12 | """
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| 13 | def __init__(self):
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| 14 | self._plotter = ASAPlot()
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| 15 |
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| 16 | self._tdict = {'Time':'t','time':'t','t':'t','T':'t'}
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| 17 | self._bdict = {'Beam':'b','beam':'b','b':'b','B':'b'}
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| 18 | self._idict = {'IF':'i','if':'i','i':'i','I':'i'}
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| 19 | self._pdict = {'Pol':'p','pol':'p','p':'p'}
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| 20 | self._sdict = {'scan':'s','Scan':'s','s':'s','S':'s'}
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| 21 | self._cdict = {'t':'scan.nrow()',
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| 22 | 'b':'scan.nbeam()',
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| 23 | 'i':'scan.nif()',
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| 24 | 'p':'scan.npol()',
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| 25 | 's':'len(scans)'}
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| 26 | self._ldict = {'b':'Beam',
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| 27 | 'i':'IF',
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| 28 | 'p':'Pol',
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| 29 | 's':'Scan'}
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| 30 | self._dicts = [self._tdict,self._bdict,
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| 31 | self._idict,self._pdict,
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| 32 | self._sdict]
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| 33 | self._panels = 's'
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| 34 | self._stacking = rcParams['plotter.stacking']
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| 35 | self._rows = None
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| 36 | self._cols = None
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| 37 | self._autoplot = False
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| 38 | self._minmax = None
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| 39 | self._data = None
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| 40 | self._lmap = []
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| 41 | self._title = None
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| 42 | self._ordinate = None
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| 43 | self._abcissa = None
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| 44 |
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| 45 | def _translate(self, name):
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| 46 | for d in self._dicts:
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| 47 | if d.has_key(name):
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| 48 | return d[name]
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| 49 | return None
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| 50 |
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| 51 | def plot(self,*args):
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| 52 | """
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| 53 | Plot a (list of) scantables.
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| 54 | Parameters:
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| 55 | one or more comma separated scantables
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| 56 | Note:
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| 57 | If a (list) of scantables was specified in a previous call
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| 58 | to plot, no argument has to be given to 'replot'
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| 59 | NO checking is done that the abscissas of the scantables
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| 60 | are consistent e.g. all 'channel' or all 'velocity' etc.
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| 61 | """
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| 62 | if self._plotter.is_dead:
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| 63 | self._plotter = ASAPlot()
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| 64 | self._plotter.clear()
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| 65 | self._plotter.hold()
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| 66 | if len(args) > 0:
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| 67 | self._data = tuple(args)
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| 68 | if self._panels == 't':
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| 69 | if self._data[0].nrow() > 25:
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| 70 | print "Scan to be plotted contains more than 25 rows.\nCan't plot that many panels..."
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| 71 | return
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| 72 | self._plot_time(self._data[0], self._stacking)
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| 73 | elif self._panels == 's':
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| 74 | self._plot_scans(self._data, self._stacking)
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| 75 | else:
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| 76 | self._plot_other(self._data, self._stacking)
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| 77 | if self._minmax is not None:
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| 78 | self._plotter.set_limits(xlim=self._minmax)
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| 79 | self._plotter.release()
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| 80 | return
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| 81 |
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| 82 | def _plot_time(self, scan, colmode):
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| 83 | if colmode == 't':
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| 84 | return
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| 85 | n = scan.nrow()
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| 86 | cdict = {'b':'scan.setbeam(j)',
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| 87 | 'i':'scan.setif(j)',
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| 88 | 'p':'scan.setpol(j)'}
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| 89 | if self._stacking is not None:
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| 90 | ncol = eval(self._cdict.get(colmode))
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| 91 | self._plotter.set_panels()
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| 92 | if n > 1:
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| 93 | if self._rows and self._cols:
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| 94 | n = min(n,self._rows*self._cols)
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| 95 | self._plotter.set_panels(rows=self._rows,cols=self._cols,
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| 96 | nplots=n)
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| 97 | else:
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| 98 | self._plotter.set_panels(rows=n,cols=0,nplots=n)
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| 99 | for i in range(n):
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| 100 | if n > 1:
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| 101 | self._plotter.palette(1)
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| 102 | self._plotter.subplot(i)
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| 103 | for j in range(ncol):
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| 104 | eval(cdict.get(colmode))
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| 105 | x = None
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| 106 | y = None
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| 107 | m = None
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| 108 | if not self._title:
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| 109 | tlab = scan._getsourcename(i)
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| 110 | else:
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| 111 | if len(self._title) == n:
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| 112 | tlab = self._title[i]
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| 113 | else:
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| 114 | tlab = scan._getsourcename(i)
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| 115 | x,xlab = scan.get_abcissa(i)
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| 116 | if self._abcissa: xlab = self._abcissa
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| 117 | y = scan._getspectrum(i)
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| 118 | if self._ordinate:
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| 119 | ylab = self._ordinate
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| 120 | else:
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| 121 | ylab = 'Flux ('+scan.get_fluxunit()+')'
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| 122 | m = scan._getmask(i)
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| 123 | if self._lmap and len(self._lmap) > 0:
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| 124 | llab = self._lmap[j]
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| 125 | else:
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| 126 | llab = self._ldict.get(colmode)+' '+str(j)
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| 127 | self._plotter.set_line(label=llab)
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| 128 | self._plotter.plot(x,y,m)
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| 129 | xlim=[min(x),max(x)]
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| 130 | self._plotter.axes.set_xlim(xlim)
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| 131 | self._plotter.set_axes('xlabel',xlab)
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| 132 | self._plotter.set_axes('ylabel',ylab)
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| 133 | self._plotter.set_axes('title',tlab)
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| 134 | return
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| 135 |
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| 136 | def _plot_scans(self, scans, colmode):
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| 137 | if colmode == 's':
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| 138 | return
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| 139 | cdict = {'b':'scan.setbeam(j)',
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| 140 | 'i':'scan.setif(j)',
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| 141 | 'p':'scan.setpol(j)'}
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| 142 | n = len(scans)
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| 143 | if self._stacking is not None:
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| 144 | scan = scans[0]
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| 145 | ncol = eval(self._cdict.get(colmode))
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| 146 | self._plotter.set_panels()
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| 147 | if n > 1:
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| 148 | if self._rows and self._cols:
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| 149 | n = min(n,self._rows*self._cols)
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| 150 | self._plotter.set_panels(rows=self._rows,cols=self._cols,
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| 151 | nplots=n)
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| 152 | else:
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| 153 | self._plotter.set_panels(rows=n,cols=0,nplots=n)
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| 154 | i = 0
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| 155 | for scan in scans:
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| 156 | if n > 1:
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| 157 | self._plotter.subplot(i)
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| 158 | self._plotter.palette(1)
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| 159 | for j in range(ncol):
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| 160 | eval(cdict.get(colmode))
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| 161 | x = None
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| 162 | y = None
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| 163 | m = None
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| 164 | tlab = self._title
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| 165 | if not self._title:
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| 166 | tlab = scan._getsourcename()
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| 167 | x,xlab = scan.get_abcissa()
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| 168 | if self._abcissa: xlab = self._abcissa
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| 169 | y = scan._getspectrum()
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| 170 | if self._ordinate:
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| 171 | ylab = self._ordinate
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| 172 | else:
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| 173 | ylab = 'Flux ('+scan.get_fluxunit()+')'
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| 174 | m = scan._getmask()
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| 175 | if self._lmap and len(self._lmap) > 0:
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| 176 | llab = self._lmap[j]
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| 177 | else:
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| 178 | llab = self._ldict.get(colmode)+' '+str(j)
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| 179 | self._plotter.set_line(label=llab)
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| 180 | self._plotter.plot(x,y,m)
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| 181 | xlim=[min(x),max(x)]
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| 182 | self._plotter.axes.set_xlim(xlim)
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| 183 |
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| 184 | self._plotter.set_axes('xlabel',xlab)
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| 185 | self._plotter.set_axes('ylabel',ylab)
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| 186 | self._plotter.set_axes('title',tlab)
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| 187 | i += 1
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| 188 | return
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| 189 |
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| 190 | def _plot_other(self,scans,colmode):
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| 191 | if colmode == self._panels:
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| 192 | return
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| 193 | cdict = {'b':'scan.setbeam(j)',
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| 194 | 'i':'scan.setif(j)',
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| 195 | 'p':'scan.setpol(j)',
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| 196 | 's':'scans[j]'}
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| 197 | scan = scans[0]
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| 198 | n = eval(self._cdict.get(self._panels))
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| 199 | if self._stacking is not None:
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| 200 | ncol = eval(self._cdict.get(colmode))
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| 201 | self._plotter.set_panels()
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| 202 | if n > 1:
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| 203 | if self._rows and self._cols:
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| 204 | n = min(n,self._rows*self._cols)
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| 205 | self._plotter.set_panels(rows=self._rows,cols=self._cols,
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| 206 | nplots=n)
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| 207 | else:
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| 208 | print n
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| 209 | self._plotter.set_panels(rows=n,cols=0,nplots=n)
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| 210 | for i in range(n):
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| 211 | if n>1:
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| 212 | self._plotter.subplot(i)
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| 213 | self._plotter.palette(1)
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| 214 | k=0
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| 215 | j=i
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| 216 | eval(cdict.get(self._panels))
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| 217 | for j in range(ncol):
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| 218 | if colmode == 's':
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| 219 | scan = eval(cdict.get(colmode))
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| 220 | elif colmode == 't':
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| 221 | k = j
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| 222 | else:
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| 223 | eval(cdict.get(colmode))
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| 224 | x = None
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| 225 | y = None
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| 226 | m = None
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| 227 | x,xlab = scan.get_abcissa(k)
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| 228 | if self._abcissa: xlab = self._abcissa
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| 229 | y = scan._getspectrum(k)
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| 230 | if self._ordinate:
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| 231 | ylab = self._ordinate
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| 232 | else:
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| 233 | ylab = 'Flux ('+scan.get_fluxunit()+')'
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| 234 | m = scan._getmask(k)
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| 235 | if colmode == 's' or colmode == 't':
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| 236 | if not self._title:
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| 237 | tlab = self._ldict.get(self._panels)+' '+str(i)
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| 238 | else:
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| 239 | if len(self.title) == n:
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| 240 | tlab = self._title[i]
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| 241 | else:
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| 242 | tlab = self._ldict.get(self._panels)+' '+str(i)
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| 243 | llab = scan._getsourcename(k)
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| 244 | else:
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| 245 | if self._title and len(self._title) > 0:
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| 246 | tlab = self._title[i]
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| 247 | else:
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| 248 | tlab = self._ldict.get(self._panels)+' '+str(i)
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| 249 | if self._lmap and len(self._lmap) > 0:
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| 250 | llab = self._lmap[j]
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| 251 | else:
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| 252 | llab = self._ldict.get(colmode)+' '+str(j)
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| 253 | self._plotter.set_line(label=llab)
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| 254 | self._plotter.plot(x,y,m)
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| 255 | xlim=[min(x),max(x)]
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| 256 | self._plotter.axes.set_xlim(xlim)
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| 257 |
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| 258 | self._plotter.set_axes('xlabel',xlab)
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| 259 | self._plotter.set_axes('ylabel',ylab)
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| 260 | self._plotter.set_axes('title',tlab)
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| 261 |
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| 262 | return
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| 263 |
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| 264 |
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| 265 | def set_mode(self, stacking=None, panelling=None):
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| 266 | """
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| 267 | Set the plots look and feel, i.e. what you want to see on the plot.
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| 268 | Parameters:
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| 269 | stacking: tell the plotter which variable to plot
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| 270 | as line colour overlays (default 'pol')
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| 271 | panelling: tell the plotter which variable to plot
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| 272 | across multiple panels (default 'scan'
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| 273 | Note:
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| 274 | Valid modes are:
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| 275 | 'beam' 'Beam' 'b': Beams
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| 276 | 'if' 'IF' 'i': IFs
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| 277 | 'pol' 'Pol' 'p': Polarisations
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| 278 | 'scan' 'Scan' 's': Scans
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| 279 | 'time' 'Time' 't': Times
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| 280 | """
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| 281 | if not self.set_panels(panelling):
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| 282 | print "Invalid mode"
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| 283 | return
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| 284 | if not self.set_stacking(stacking):
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| 285 | print "Invalid mode"
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| 286 | return
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| 287 | if self._data: self.plot()
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| 288 | return
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| 289 |
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| 290 | def set_panels(self, what=None):
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| 291 | """
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| 292 | """
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| 293 | if not what:
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| 294 | what = rcParams['plotter.panelling']
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| 295 | md = self._translate(what)
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| 296 | if md:
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| 297 | self._panels = md
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| 298 | self._title = None
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| 299 | return True
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| 300 | return False
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| 301 |
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| 302 | def set_layout(self,rows=None,cols=None):
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| 303 | """
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| 304 | Set the multi-panel layout, i.e. how many rows and columns plots
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| 305 | are visible.
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| 306 | Parameters:
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| 307 | rows: The number of rows of plots
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| 308 | cols: The number of columns of plots
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| 309 | Note:
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| 310 | If no argument is given, the potter reverts to its auto-plot
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| 311 | behaviour.
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| 312 | """
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| 313 | self._rows = rows
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| 314 | self._cols = cols
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| 315 | if self._data: self.plot()
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| 316 | return
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| 317 |
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| 318 | def set_stacking(self, what=None):
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| 319 | if not what:
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| 320 | what = rcParams['plotter.stacking']
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| 321 | md = self._translate(what)
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| 322 | if md:
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| 323 | self._stacking = md
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| 324 | self._lmap = None
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| 325 | return True
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| 326 | return False
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| 327 |
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| 328 | def set_range(self,start=None,end=None):
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| 329 | """
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| 330 | Set the range of interest on the abcissa of the plot
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| 331 | Parameters:
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| 332 | start,end: The start an end point of the 'zoom' window
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| 333 | Note:
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| 334 | These become non-sensical when the unit changes.
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| 335 | use plotter.set_range() without parameters to reset
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| 336 |
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| 337 | """
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| 338 | if start is None and end is None:
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| 339 | self._minmax = None
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| 340 | if self._data: self.plot()
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| 341 | else:
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| 342 | self._minmax = [start,end]
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| 343 | if self._data: self.plot()
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| 344 | return
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| 345 |
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| 346 | def set_legend(self, mp=None):
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| 347 | """
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| 348 | Specify a mapping for the legend instead of using the default
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| 349 | indices:
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| 350 | Parameters:
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| 351 | mp: a list of 'strings'. This should have the same length
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| 352 | as the number of elements on the legend and then maps
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| 353 | to the indeces in order
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| 354 |
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| 355 | Example:
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| 356 | If the data has two IFs/rest frequencies with index 0 and 1
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| 357 | for CO and SiO:
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| 358 | plotter.set_stacking('i')
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| 359 | plotter.set_legend_map(['CO','SiO'])
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| 360 | plotter.plot()
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| 361 | """
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| 362 | self._lmap = mp
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| 363 | if self._data: self.plot()
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| 364 | return
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| 365 |
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| 366 | def set_title(self, title=None):
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| 367 | self._title = title
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| 368 | if self._data: self.plot()
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| 369 | return
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| 370 |
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| 371 | def set_ordinate(self, ordinate=None):
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| 372 | self._ordinate = ordinate
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| 373 | if self._data: self.plot()
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| 374 | return
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| 375 |
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| 376 | def set_abcissa(self, abcissa=None):
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| 377 | self._abcissa = abcissa
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| 378 | if self._data: self.plot()
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| 379 | return
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| 380 |
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| 381 | def save(self, filename=None):
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| 382 | """
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| 383 | Save the plot to a file. The know formats are 'png', 'ps', 'eps'.
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| 384 | Parameters:
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| 385 | filename: The name of the output file. This is optional
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| 386 | and autodetects the image format from the file
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| 387 | suffix. If non filename is specified a file
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| 388 | called 'yyyymmdd_hhmmss.png' is created in the
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| 389 | current directory.
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| 390 | """
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| 391 | self._plotter.save(filename)
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| 392 | return
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| 393 |
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| 394 | if __name__ == '__main__':
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| 395 | plotter = asapplotter()
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