def _plot_update_evoked(params, bools):
 (picks, evoked) = [params[k] for k in ('picks', 'evoked')]
 times = (evoked.times * 1000.0)
 projs = [proj for (ii, proj) in enumerate(params['projs']) if (ii in np.where(bools)[0])]
 params['proj_bools'] = bools
 new_evoked = evoked.copy()
 new_evoked.info['projs'] = []
 new_evoked.add_proj(projs)
 new_evoked.apply_proj()
 for (ax, t) in zip(params['axes'], params['ch_types_used']):
  this_scaling = params['scalings'][t]
  idx = [picks[i] for i in range(len(picks)) if (params['types'][i] == t)]
  D = (this_scaling * new_evoked.data[idx, :])
  if (params['plot_type'] == 'butterfly'):
   for (line, di) in zip(ax.lines, D):
    line.set_data(times, di)
  else:
   ax.images[0].set_data(D)
 params['fig'].canvas.draw()

def _plot_update_evoked ( params , bools ) : ( picks , evoked ) = [ params [ k ] for k in ( 'picks' , 'evoked' ) ] times = ( evoked . times * 1000.0 ) projs = [ proj for ( ii , proj ) in enumerate ( params [ 'projs' ] ) if ( ii in np . where ( bools ) [ 0 ] ) ] params [ 'proj_bools' ] = bools new_evoked = evoked . copy ( ) new_evoked . info [ 'projs' ] = [ ] new_evoked . add_proj ( projs ) new_evoked . apply_proj ( ) for ( ax , t ) in zip ( params [ 'axes' ] , params [ 'ch_types_used' ] ) : this_scaling = params [ 'scalings' ] [ t ] idx = [ picks [ i ] for i in range ( len ( picks ) ) if ( params [ 'types' ] [ i ] == t ) ] D = ( this_scaling * new_evoked . data [ idx , : ] ) if ( params [ 'plot_type' ] == 'butterfly' ) : for ( line , di ) in zip ( ax . lines , D ) : line . set_data ( times , di ) else : ax . images [ 0 ] . set_data ( D ) params [ 'fig' ] . canvas . draw ( )
update the plot evoked lines .