Пример #1
0
 def _update_mask(self):
     # Min and Max for new mask
     xstart = self.rect['world'][0]
     xend = self.rect['world'][2]
     if xstart <= xend: newlist=[xstart,xend]
     else: newlist = [xend,xstart]
     # Mask or unmask
     invmask = None
     if self.rect['button'] == 1:
         invmask = False
         mflg = 'Mask'
     elif self.rect['button'] == 3:
         invmask = True
         mflg = 'UNmask'
     asaplog.push(mflg+': '+str(newlist))
     asaplog.post()
     newmask = self.scan.create_mask(newlist,invert=invmask)
     # Logic operation to update mask
     if invmask:
         self.mask = mask_and(self.mask,newmask)
     else:
         self.mask = mask_or(self.mask,newmask)
     # Plot masked regions
     #if self.showmask or not self.once: self._plot_mask()
     if self.showmask: self._plot_mask()
Пример #2
0
 def _subplot_stats(self,selection):
     statstr = ['max', 'min', 'median', 'mean', 'sum', 'std'] #'rms']
     panelstr = selection['axes'].title.get_text()
     ssep = "-"*70
     asaplog.push(ssep)
     asaplog.post()
     for line in selection['axes'].lines:
         # Don't include annotations
         if line.get_label().startswith("_"):
             continue
         label = panelstr + ", "+line.get_label()
         x = line.get_xdata()
         newmsk = None
         selmsk = self._create_flag_from_array(x,
                                               selection['worldx'],
                                               selection['invert'])
         ydat = None
         y = line.get_ydata()
         if numpy.ma.isMaskedArray(y):
             ydat = y.data
             basemsk = y.mask
         else:
             ydat = y
             basemsk = False
         if not isinstance(basemsk, bool):
             # should be ndarray
             newmsk = mask_or(selmsk, basemsk)
         elif basemsk:
             # the whole original spectrum is flagged
             newmsk = basemsk
         else:
             # no channel was flagged originally
             newmsk = selmsk
         mdata = numpy.ma.masked_array(ydat, mask=newmsk)
         statval = {}
         for stat in statstr:
             # need to get the stat functions from the ma module!!!
             statval[stat] = getattr(numpy.ma,stat)(mdata)
         self._print_stats(statval, statstr=statstr, label=label,\
                           mask=selection['worldx'],\
                           unmask=selection['invert'])
         asaplog.push(ssep)
         asaplog.post()