| 1 | from asap import rcParams, print_log, selector |
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| 2 | from asap import NUM |
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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, visible=None): |
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| 14 | self._visible = rcParams['plotter.gui'] |
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| 15 | if visible is not None: |
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| 16 | self._visible = visible |
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| 17 | self._plotter = self._newplotter() |
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| 18 | |
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| 19 | self._panelling = None |
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| 20 | self._stacking = None |
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| 21 | self.set_panelling() |
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| 22 | self.set_stacking() |
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| 23 | self._rows = None |
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| 24 | self._cols = None |
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| 25 | self._autoplot = False |
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| 26 | self._minmaxx = None |
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| 27 | self._minmaxy = None |
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| 28 | self._datamask = None |
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| 29 | self._data = None |
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| 30 | self._lmap = None |
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| 31 | self._title = None |
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| 32 | self._ordinate = None |
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| 33 | self._abcissa = None |
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| 34 | self._abcunit = None |
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| 35 | self._usermask = [] |
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| 36 | self._maskselection = None |
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| 37 | self._selection = selector() |
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| 38 | self._hist = rcParams['plotter.histogram'] |
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| 39 | |
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| 40 | def _translate(self, instr): |
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| 41 | keys = "s b i p t".split() |
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| 42 | if isinstance(instr, str): |
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| 43 | for key in keys: |
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| 44 | if instr.lower().startswith(key): |
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| 45 | return key |
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| 46 | return None |
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| 47 | |
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| 48 | def _newplotter(self): |
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| 49 | if self._visible: |
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| 50 | from asap.asaplotgui import asaplotgui as asaplot |
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| 51 | else: |
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| 52 | from asap.asaplot import asaplot |
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| 53 | return asaplot() |
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| 54 | |
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| 55 | |
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| 56 | def plot(self, scan=None): |
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| 57 | """ |
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| 58 | Plot a scantable. |
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| 59 | Parameters: |
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| 60 | scan: a scantable |
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| 61 | Note: |
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| 62 | If a scantable was specified in a previous call |
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| 63 | to plot, no argument has to be given to 'replot' |
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| 64 | NO checking is done that the abcissas of the scantable |
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| 65 | are consistent e.g. all 'channel' or all 'velocity' etc. |
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| 66 | """ |
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| 67 | if self._plotter.is_dead: |
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| 68 | self._plotter = self._newplotter() |
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| 69 | self._plotter.hold() |
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| 70 | self._plotter.clear() |
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| 71 | from asap import scantable |
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| 72 | if not self._data and not scan: |
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| 73 | msg = "Input is not a scantable" |
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| 74 | if rcParams['verbose']: |
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| 75 | print msg |
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| 76 | return |
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| 77 | raise TypeError(msg) |
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| 78 | if isinstance(scan, scantable): |
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| 79 | if self._data is not None: |
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| 80 | if scan != self._data: |
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| 81 | self._data = scan |
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| 82 | # reset |
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| 83 | self._reset() |
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| 84 | else: |
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| 85 | self._data = scan |
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| 86 | self._reset() |
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| 87 | # ranges become invalid when unit changes |
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| 88 | if self._abcunit and self._abcunit != self._data.get_unit(): |
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| 89 | self._minmaxx = None |
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| 90 | self._minmaxy = None |
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| 91 | self._abcunit = self._data.get_unit() |
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| 92 | self._datamask = None |
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| 93 | self._plot(self._data) |
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| 94 | if self._minmaxy is not None: |
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| 95 | self._plotter.set_limits(ylim=self._minmaxy) |
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| 96 | self._plotter.release() |
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| 97 | print_log() |
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| 98 | return |
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| 99 | |
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| 100 | def set_mode(self, stacking=None, panelling=None): |
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| 101 | """ |
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| 102 | Set the plots look and feel, i.e. what you want to see on the plot. |
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| 103 | Parameters: |
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| 104 | stacking: tell the plotter which variable to plot |
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| 105 | as line color overlays (default 'pol') |
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| 106 | panelling: tell the plotter which variable to plot |
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| 107 | across multiple panels (default 'scan' |
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| 108 | Note: |
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| 109 | Valid modes are: |
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| 110 | 'beam' 'Beam' 'b': Beams |
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| 111 | 'if' 'IF' 'i': IFs |
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| 112 | 'pol' 'Pol' 'p': Polarisations |
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| 113 | 'scan' 'Scan' 's': Scans |
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| 114 | 'time' 'Time' 't': Times |
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| 115 | """ |
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| 116 | msg = "Invalid mode" |
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| 117 | if not self.set_panelling(panelling) or \ |
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| 118 | not self.set_stacking(stacking): |
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| 119 | if rcParams['verbose']: |
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| 120 | print msg |
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| 121 | return |
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| 122 | else: |
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| 123 | raise TypeError(msg) |
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| 124 | if self._data: self.plot(self._data) |
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| 125 | return |
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| 126 | |
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| 127 | def set_panelling(self, what=None): |
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| 128 | mode = what |
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| 129 | if mode is None: |
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| 130 | mode = rcParams['plotter.panelling'] |
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| 131 | md = self._translate(mode) |
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| 132 | if md: |
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| 133 | self._panelling = md |
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| 134 | self._title = None |
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| 135 | return True |
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| 136 | return False |
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| 137 | |
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| 138 | def set_layout(self,rows=None,cols=None): |
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| 139 | """ |
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| 140 | Set the multi-panel layout, i.e. how many rows and columns plots |
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| 141 | are visible. |
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| 142 | Parameters: |
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| 143 | rows: The number of rows of plots |
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| 144 | cols: The number of columns of plots |
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| 145 | Note: |
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| 146 | If no argument is given, the potter reverts to its auto-plot |
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| 147 | behaviour. |
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| 148 | """ |
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| 149 | self._rows = rows |
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| 150 | self._cols = cols |
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| 151 | if self._data: self.plot(self._data) |
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| 152 | return |
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| 153 | |
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| 154 | def set_stacking(self, what=None): |
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| 155 | mode = what |
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| 156 | if mode is None: |
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| 157 | mode = rcParams['plotter.stacking'] |
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| 158 | md = self._translate(mode) |
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| 159 | if md: |
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| 160 | self._stacking = md |
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| 161 | self._lmap = None |
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| 162 | return True |
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| 163 | return False |
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| 164 | |
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| 165 | def set_range(self,xstart=None,xend=None,ystart=None,yend=None): |
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| 166 | """ |
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| 167 | Set the range of interest on the abcissa of the plot |
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| 168 | Parameters: |
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| 169 | [x,y]start,[x,y]end: The start and end points of the 'zoom' window |
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| 170 | Note: |
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| 171 | These become non-sensical when the unit changes. |
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| 172 | use plotter.set_range() without parameters to reset |
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| 173 | |
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| 174 | """ |
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| 175 | if xstart is None and xend is None: |
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| 176 | self._minmaxx = None |
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| 177 | else: |
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| 178 | self._minmaxx = [xstart,xend] |
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| 179 | if ystart is None and yend is None: |
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| 180 | self._minmaxy = None |
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| 181 | else: |
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| 182 | self._minmaxy = [ystart,yend] |
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| 183 | if self._data: self.plot(self._data) |
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| 184 | return |
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| 185 | |
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| 186 | def set_legend(self, mp=None, fontsize = None, mode = 0): |
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| 187 | """ |
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| 188 | Specify a mapping for the legend instead of using the default |
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| 189 | indices: |
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| 190 | Parameters: |
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| 191 | mp: a list of 'strings'. This should have the same length |
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| 192 | as the number of elements on the legend and then maps |
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| 193 | to the indeces in order. It is possible to uses latex |
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| 194 | math expression. These have to be enclosed in r'', |
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| 195 | e.g. r'$x^{2}$' |
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| 196 | fontsize: The font size of the label (default None) |
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| 197 | mode: where to display the legend |
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| 198 | Any other value for loc else disables the legend: |
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| 199 | 0: auto |
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| 200 | 1: upper right |
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| 201 | 2: upper left |
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| 202 | 3: lower left |
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| 203 | 4: lower right |
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| 204 | 5: right |
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| 205 | 6: center left |
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| 206 | 7: center right |
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| 207 | 8: lower center |
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| 208 | 9: upper center |
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| 209 | 10: center |
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| 210 | |
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| 211 | Example: |
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| 212 | If the data has two IFs/rest frequencies with index 0 and 1 |
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| 213 | for CO and SiO: |
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| 214 | plotter.set_stacking('i') |
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| 215 | plotter.set_legend(['CO','SiO']) |
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| 216 | plotter.plot() |
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| 217 | plotter.set_legend([r'$^{12}CO$', r'SiO']) |
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| 218 | """ |
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| 219 | self._lmap = mp |
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| 220 | self._plotter.legend(mode) |
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| 221 | if isinstance(fontsize, int): |
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| 222 | from matplotlib import rc as rcp |
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| 223 | rcp('legend', fontsize=fontsize) |
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| 224 | if self._data: |
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| 225 | self.plot(self._data) |
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| 226 | return |
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| 227 | |
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| 228 | def set_title(self, title=None, fontsize=None): |
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| 229 | """ |
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| 230 | Set the title of the plot. If multiple panels are plotted, |
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| 231 | multiple titles have to be specified. |
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| 232 | Example: |
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| 233 | # two panels are visible on the plotter |
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| 234 | plotter.set_title(["First Panel","Second Panel"]) |
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| 235 | """ |
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| 236 | self._title = title |
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| 237 | if isinstance(fontsize, int): |
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| 238 | from matplotlib import rc as rcp |
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| 239 | rcp('axes', titlesize=fontsize) |
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| 240 | if self._data: self.plot(self._data) |
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| 241 | return |
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| 242 | |
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| 243 | def set_ordinate(self, ordinate=None, fontsize=None): |
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| 244 | """ |
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| 245 | Set the y-axis label of the plot. If multiple panels are plotted, |
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| 246 | multiple labels have to be specified. |
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| 247 | Parameters: |
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| 248 | ordinate: a list of ordinate labels. None (default) let |
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| 249 | data determine the labels |
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| 250 | Example: |
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| 251 | # two panels are visible on the plotter |
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| 252 | plotter.set_ordinate(["First Y-Axis","Second Y-Axis"]) |
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| 253 | """ |
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| 254 | self._ordinate = ordinate |
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| 255 | if isinstance(fontsize, int): |
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| 256 | from matplotlib import rc as rcp |
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| 257 | rcp('axes', labelsize=fontsize) |
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| 258 | rcp('ytick', labelsize=fontsize) |
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| 259 | if self._data: self.plot(self._data) |
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| 260 | return |
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| 261 | |
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| 262 | def set_abcissa(self, abcissa=None, fontsize=None): |
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| 263 | """ |
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| 264 | Set the x-axis label of the plot. If multiple panels are plotted, |
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| 265 | multiple labels have to be specified. |
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| 266 | Parameters: |
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| 267 | abcissa: a list of abcissa labels. None (default) let |
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| 268 | data determine the labels |
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| 269 | Example: |
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| 270 | # two panels are visible on the plotter |
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| 271 | plotter.set_ordinate(["First X-Axis","Second X-Axis"]) |
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| 272 | """ |
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| 273 | self._abcissa = abcissa |
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| 274 | if isinstance(fontsize, int): |
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| 275 | from matplotlib import rc as rcp |
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| 276 | rcp('axes', labelsize=fontsize) |
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| 277 | rcp('xtick', labelsize=fontsize) |
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| 278 | if self._data: self.plot(self._data) |
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| 279 | return |
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| 280 | |
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| 281 | def set_colors(self, colormap): |
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| 282 | """ |
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| 283 | Set the colors to be used. The plotter will cycle through |
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| 284 | these colors when lines are overlaid (stacking mode). |
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| 285 | Parameters: |
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| 286 | colormap: a list of colour names |
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| 287 | Example: |
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| 288 | plotter.set_colors("red green blue") |
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| 289 | # If for example four lines are overlaid e.g I Q U V |
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| 290 | # 'I' will be 'red', 'Q' will be 'green', U will be 'blue' |
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| 291 | # and 'V' will be 'red' again. |
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| 292 | """ |
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| 293 | if isinstance(colormap,str): |
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| 294 | colormap = colormap.split() |
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| 295 | self._plotter.palette(0,colormap=colormap) |
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| 296 | if self._data: self.plot(self._data) |
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| 297 | |
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| 298 | def set_histogram(self, hist=True, linewidth=None): |
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| 299 | """ |
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| 300 | Enable/Disable histogram-like plotting. |
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| 301 | Parameters: |
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| 302 | hist: True (default) or False. The fisrt default |
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| 303 | is taken from the .asaprc setting |
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| 304 | plotter.histogram |
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| 305 | """ |
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| 306 | self._hist = hist |
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| 307 | if isinstance(linewidth, float) or isinstance(linewidth, int): |
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| 308 | from matplotlib import rc as rcp |
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| 309 | rcp('lines', linewidth=linewidth) |
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| 310 | if self._data: self.plot(self._data) |
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| 311 | |
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| 312 | def set_linestyles(self, linestyles=None, linewidth=None): |
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| 313 | """ |
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| 314 | Set the linestyles to be used. The plotter will cycle through |
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| 315 | these linestyles when lines are overlaid (stacking mode) AND |
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| 316 | only one color has been set. |
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| 317 | Parameters: |
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| 318 | linestyles: a list of linestyles to use. |
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| 319 | 'line', 'dashed', 'dotted', 'dashdot', |
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| 320 | 'dashdotdot' and 'dashdashdot' are |
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| 321 | possible |
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| 322 | |
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| 323 | Example: |
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| 324 | plotter.set_colors("black") |
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| 325 | plotter.set_linestyles("line dashed dotted dashdot") |
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| 326 | # If for example four lines are overlaid e.g I Q U V |
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| 327 | # 'I' will be 'solid', 'Q' will be 'dashed', |
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| 328 | # U will be 'dotted' and 'V' will be 'dashdot'. |
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| 329 | """ |
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| 330 | if isinstance(linestyles,str): |
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| 331 | linestyles = linestyles.split() |
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| 332 | self._plotter.palette(color=0,linestyle=0,linestyles=linestyles) |
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| 333 | if isinstance(linewidth, float) or isinstance(linewidth, int): |
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| 334 | from matplotlib import rc as rcp |
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| 335 | rcp('lines', linewidth=linewidth) |
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| 336 | if self._data: self.plot(self._data) |
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| 337 | |
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| 338 | def set_font(self, family=None, style=None, weight=None, size=None): |
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| 339 | """ |
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| 340 | Set font properties. |
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| 341 | Parameters: |
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| 342 | family: one of 'sans-serif', 'serif', 'cursive', 'fantasy', 'monospace' |
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| 343 | style: one of 'normal' (or 'roman'), 'italic' or 'oblique' |
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| 344 | weight: one of 'normal or 'bold' |
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| 345 | size: the 'general' font size, individual elements can be adjusted |
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| 346 | seperately |
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| 347 | """ |
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| 348 | from matplotlib import rc as rcp |
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| 349 | if isinstance(family, str): |
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| 350 | rcp('font', family=family) |
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| 351 | if isinstance(style, str): |
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| 352 | rcp('font', style=style) |
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| 353 | if isinstance(weight, str): |
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| 354 | rcp('font', weight=weight) |
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| 355 | if isinstance(size, float) or isinstance(size, int): |
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| 356 | rcp('font', size=size) |
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| 357 | if self._data: self.plot(self._data) |
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| 358 | |
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| 359 | def plot_lines(self, linecat=None, offset=0.0, peak=5.0, rotate=0.0, |
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| 360 | location=None): |
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| 361 | """ |
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| 362 | """ |
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| 363 | if not self._data: return |
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| 364 | from asap._asap import linecatalog |
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| 365 | if not isinstance(linecat, linecatalog): return |
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| 366 | if not self._data.get_unit().endswith("GHz"): return |
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| 367 | self._plotter.hold() |
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| 368 | for j in range(len(self._plotter.subplots)): |
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| 369 | self._plotter.subplot(j) |
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| 370 | lims = self._plotter.axes.get_xlim() |
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| 371 | for i in range(linecat.nrow()): |
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| 372 | freq = linecat.get_frequency(i)/1000.0 + offset |
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| 373 | if lims[0] < freq < lims[1]: |
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| 374 | if location is None: |
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| 375 | loc = 'bottom' |
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| 376 | if i%2: loc='top' |
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| 377 | else: loc = location |
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| 378 | self._plotter.vline_with_label(freq, peak, linecat.get_name(i), |
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| 379 | location=loc, rotate=rotate) |
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| 380 | self._plotter.release() |
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| 381 | |
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| 382 | def save(self, filename=None, orientation=None, dpi=None): |
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| 383 | """ |
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| 384 | Save the plot to a file. The know formats are 'png', 'ps', 'eps'. |
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| 385 | Parameters: |
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| 386 | filename: The name of the output file. This is optional |
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| 387 | and autodetects the image format from the file |
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| 388 | suffix. If non filename is specified a file |
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| 389 | called 'yyyymmdd_hhmmss.png' is created in the |
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| 390 | current directory. |
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| 391 | orientation: optional parameter for postscript only (not eps). |
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| 392 | 'landscape', 'portrait' or None (default) are valid. |
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| 393 | If None is choosen for 'ps' output, the plot is |
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| 394 | automatically oriented to fill the page. |
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| 395 | dpi: The dpi of the output non-ps plot |
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| 396 | """ |
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| 397 | self._plotter.save(filename,orientation,dpi) |
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| 398 | return |
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| 399 | |
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| 400 | |
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| 401 | def set_mask(self, mask=None, selection=None): |
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| 402 | """ |
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| 403 | Set a plotting mask for a specific polarization. |
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| 404 | This is useful for masking out "noise" Pangle outside a source. |
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| 405 | Parameters: |
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| 406 | mask: a mask from scantable.create_mask |
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| 407 | selection: the spectra to apply the mask to. |
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| 408 | Example: |
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| 409 | select = selector() |
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| 410 | select.setpolstrings("Pangle") |
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| 411 | plotter.set_mask(mymask, select) |
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| 412 | """ |
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| 413 | if not self._data: |
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| 414 | msg = "Can only set mask after a first call to plot()" |
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| 415 | if rcParams['verbose']: |
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| 416 | print msg |
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| 417 | return |
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| 418 | else: |
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| 419 | raise RuntimeError(msg) |
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| 420 | if len(mask): |
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| 421 | if isinstance(mask, list) or isinstance(mask, tuple): |
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| 422 | self._usermask = array(mask) |
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| 423 | else: |
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| 424 | self._usermask = mask |
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| 425 | if mask is None and selection is None: |
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| 426 | self._usermask = [] |
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| 427 | self._maskselection = None |
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| 428 | if isinstance(selection, selector): |
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| 429 | self._maskselection = {'b': selection.get_beams(), |
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| 430 | 's': selection.get_scans(), |
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| 431 | 'i': selection.get_ifs(), |
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| 432 | 'p': selection.get_pols(), |
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| 433 | 't': [] } |
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| 434 | else: |
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| 435 | self._maskselection = None |
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| 436 | self.plot(self._data) |
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| 437 | |
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| 438 | def _slice_indeces(self, data): |
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| 439 | mn = self._minmaxx[0] |
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| 440 | mx = self._minmaxx[1] |
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| 441 | asc = data[0] < data[-1] |
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| 442 | start=0 |
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| 443 | end = len(data)-1 |
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| 444 | inc = 1 |
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| 445 | if not asc: |
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| 446 | start = len(data)-1 |
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| 447 | end = 0 |
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| 448 | inc = -1 |
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| 449 | # find min index |
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| 450 | while start > 0 and data[start] < mn: |
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| 451 | start+= inc |
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| 452 | # find max index |
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| 453 | while end > 0 and data[end] > mx: |
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| 454 | end-=inc |
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| 455 | if end > 0: end +=1 |
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| 456 | if start > end: |
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| 457 | return end,start |
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| 458 | return start,end |
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| 459 | |
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| 460 | def _reset(self): |
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| 461 | self._usermask = [] |
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| 462 | self._usermaskspectra = None |
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| 463 | self.set_selection(None, False) |
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| 464 | |
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| 465 | def _plot(self, scan): |
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| 466 | savesel = scan.get_selection() |
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| 467 | sel = savesel + self._selection |
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| 468 | d0 = {'s': 'SCANNO', 'b': 'BEAMNO', 'i':'IFNO', |
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| 469 | 'p': 'POLNO', 'c': 'CYCLENO', 't' : 'TIME' } |
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| 470 | order = [d0[self._panelling],d0[self._stacking]] |
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| 471 | sel.set_order(order) |
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| 472 | scan.set_selection(sel) |
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| 473 | d = {'b': scan.getbeam, 's': scan.getscan, |
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| 474 | 'i': scan.getif, 'p': scan.getpol, 't': scan._gettime } |
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| 475 | |
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| 476 | polmodes = dict(zip(self._selection.get_pols(), |
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| 477 | self._selection.get_poltypes())) |
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| 478 | # this returns either a tuple of numbers or a length (ncycles) |
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| 479 | # convert this into lengths |
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| 480 | n0,nstack0 = self._get_selected_n(scan) |
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| 481 | n = len(n0) |
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| 482 | if isinstance(n0, int): n = n0 |
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| 483 | nstack = len(nstack0) |
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| 484 | if isinstance(nstack0, int): nstack = nstack0 |
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| 485 | maxpanel, maxstack = 16,8 |
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| 486 | if n > maxpanel or nstack > maxstack: |
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| 487 | from asap import asaplog |
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| 488 | maxn = 0 |
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| 489 | if nstack > maxstack: maxn = maxstack |
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| 490 | if n > maxpanel: maxn = maxpanel |
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| 491 | msg ="Scan to be plotted contains more than %d selections.\n" \ |
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| 492 | "Selecting first %d selections..." % (maxn, maxn) |
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| 493 | asaplog.push(msg) |
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| 494 | print_log() |
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| 495 | n = min(n,maxpanel) |
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| 496 | nstack = min(nstack,maxstack) |
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| 497 | if n > 1: |
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| 498 | ganged = rcParams['plotter.ganged'] |
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| 499 | if self._rows and self._cols: |
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| 500 | n = min(n,self._rows*self._cols) |
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| 501 | self._plotter.set_panels(rows=self._rows,cols=self._cols, |
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| 502 | nplots=n,ganged=ganged) |
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| 503 | else: |
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| 504 | self._plotter.set_panels(rows=n,cols=0,nplots=n,ganged=ganged) |
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| 505 | else: |
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| 506 | self._plotter.set_panels() |
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| 507 | r=0 |
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| 508 | nr = scan.nrow() |
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| 509 | a0,b0 = -1,-1 |
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| 510 | allxlim = [] |
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| 511 | allylim = [] |
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| 512 | newpanel=True |
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| 513 | panelcount,stackcount = 0,0 |
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| 514 | while r < nr: |
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| 515 | a = d[self._panelling](r) |
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| 516 | b = d[self._stacking](r) |
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| 517 | if a > a0 and panelcount < n: |
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| 518 | if n > 1: |
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| 519 | self._plotter.subplot(panelcount) |
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| 520 | self._plotter.palette(0) |
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| 521 | #title |
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| 522 | xlab = self._abcissa and self._abcissa[panelcount] \ |
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| 523 | or scan._getabcissalabel() |
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| 524 | ylab = self._ordinate and self._ordinate[panelcount] \ |
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| 525 | or scan._get_ordinate_label() |
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| 526 | self._plotter.set_axes('xlabel',xlab) |
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| 527 | self._plotter.set_axes('ylabel',ylab) |
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| 528 | lbl = self._get_label(scan, r, self._panelling, self._title) |
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| 529 | if isinstance(lbl, list) or isinstance(lbl, tuple): |
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| 530 | if 0 <= panelcount < len(lbl): |
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| 531 | lbl = lbl[panelcount] |
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| 532 | else: |
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| 533 | # get default label |
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| 534 | lbl = self._get_label(scan, r, self._panelling, None) |
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| 535 | self._plotter.set_axes('title',lbl) |
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| 536 | newpanel = True |
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| 537 | stackcount =0 |
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| 538 | panelcount += 1 |
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| 539 | if (b > b0 or newpanel) and stackcount < nstack: |
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| 540 | y = [] |
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| 541 | if len(polmodes): |
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| 542 | y = scan._getspectrum(r, polmodes[scan.getpol(r)]) |
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| 543 | else: |
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| 544 | y = scan._getspectrum(r) |
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| 545 | m = scan._getmask(r) |
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| 546 | from matplotlib.numerix import logical_not, logical_and |
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| 547 | if self._maskselection and len(self._usermask) == len(m): |
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| 548 | if d[self._stacking](r) in self._maskselection[self._stacking]: |
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| 549 | m = logical_and(m, self._usermask) |
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| 550 | x = scan._getabcissa(r) |
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| 551 | from matplotlib.numerix import ma, array |
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| 552 | y = ma.masked_array(y,mask=logical_not(array(m,copy=False))) |
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| 553 | if self._minmaxx is not None: |
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| 554 | s,e = self._slice_indeces(x) |
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| 555 | x = x[s:e] |
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| 556 | y = y[s:e] |
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| 557 | if len(x) > 1024 and rcParams['plotter.decimate']: |
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| 558 | fac = len(x)/1024 |
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| 559 | x = x[::fac] |
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| 560 | y = y[::fac] |
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| 561 | llbl = self._get_label(scan, r, self._stacking, self._lmap) |
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| 562 | if isinstance(llbl, list) or isinstance(llbl, tuple): |
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| 563 | if 0 <= stackcount < len(llbl): |
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| 564 | # use user label |
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| 565 | llbl = llbl[stackcount] |
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| 566 | else: |
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| 567 | # get default label |
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| 568 | llbl = self._get_label(scan, r, self._stacking, None) |
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| 569 | self._plotter.set_line(label=llbl) |
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| 570 | plotit = self._plotter.plot |
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| 571 | if self._hist: plotit = self._plotter.hist |
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| 572 | if len(x) > 0: |
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| 573 | plotit(x,y) |
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| 574 | xlim= self._minmaxx or [min(x),max(x)] |
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| 575 | allxlim += xlim |
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| 576 | ylim= self._minmaxy or [ma.minimum(y),ma.maximum(y)] |
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| 577 | allylim += ylim |
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| 578 | stackcount += 1 |
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| 579 | # last in colour stack -> autoscale x |
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| 580 | if stackcount == nstack: |
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| 581 | allxlim.sort() |
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| 582 | self._plotter.axes.set_xlim([allxlim[0],allxlim[-1]]) |
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| 583 | # clear |
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| 584 | allxlim =[] |
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| 585 | |
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| 586 | newpanel = False |
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| 587 | a0=a |
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| 588 | b0=b |
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| 589 | # ignore following rows |
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| 590 | if (panelcount == n) and (stackcount == nstack): |
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| 591 | # last panel -> autoscale y if ganged |
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| 592 | if rcParams['plotter.ganged']: |
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| 593 | allylim.sort() |
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| 594 | self._plotter.set_limits(ylim=[allylim[0],allylim[-1]]) |
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| 595 | break |
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| 596 | r+=1 # next row |
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| 597 | #reset the selector to the scantable's original |
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| 598 | scan.set_selection(savesel) |
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| 599 | |
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| 600 | def set_selection(self, selection=None, refresh=True): |
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| 601 | self._selection = isinstance(selection,selector) and selection or selector() |
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| 602 | d0 = {'s': 'SCANNO', 'b': 'BEAMNO', 'i':'IFNO', |
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| 603 | 'p': 'POLNO', 'c': 'CYCLENO', 't' : 'TIME' } |
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| 604 | order = [d0[self._panelling],d0[self._stacking]] |
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| 605 | self._selection.set_order(order) |
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| 606 | if self._data and refresh: self.plot(self._data) |
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| 607 | |
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| 608 | def _get_selected_n(self, scan): |
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| 609 | d1 = {'b': scan.getbeamnos, 's': scan.getscannos, |
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| 610 | 'i': scan.getifnos, 'p': scan.getpolnos, 't': scan.ncycle } |
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| 611 | d2 = { 'b': self._selection.get_beams(), |
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| 612 | 's': self._selection.get_scans(), |
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| 613 | 'i': self._selection.get_ifs(), |
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| 614 | 'p': self._selection.get_pols(), |
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| 615 | 't': self._selection.get_cycles() } |
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| 616 | n = d2[self._panelling] or d1[self._panelling]() |
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| 617 | nstack = d2[self._stacking] or d1[self._stacking]() |
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| 618 | return n,nstack |
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| 619 | |
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| 620 | def _get_label(self, scan, row, mode, userlabel=None): |
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| 621 | pms = dict(zip(self._selection.get_pols(),self._selection.get_poltypes())) |
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| 622 | if len(pms): |
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| 623 | poleval = scan._getpollabel(scan.getpol(row),pms[scan.getpol(row)]) |
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| 624 | else: |
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| 625 | poleval = scan._getpollabel(scan.getpol(row),scan.poltype()) |
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| 626 | d = {'b': "Beam "+str(scan.getbeam(row)), |
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| 627 | 's': scan._getsourcename(row), |
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| 628 | 'i': "IF"+str(scan.getif(row)), |
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| 629 | 'p': poleval, |
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| 630 | 't': scan._gettime(row) } |
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| 631 | return userlabel or d[mode] |
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