def _estimate_rank_meeg_signals(data, info, scalings, tol='auto', return_singular=False):
 picks_list = _picks_by_type(info)
 _apply_scaling_array(data, picks_list, scalings)
 if (data.shape[1] < data.shape[0]):
  ValueError("You've got fewer samples than channels, your rank estimate might be inaccurate.")
 out = estimate_rank(data, tol=tol, norm=False, return_singular=return_singular)
 rank = (out[0] if isinstance(out, tuple) else out)
 ch_type = ' + '.join(list(zip(*picks_list))[0])
 logger.info(('estimated rank (%s): %d' % (ch_type, rank)))
 _undo_scaling_array(data, picks_list, scalings)
 return out

def _estimate_rank_meeg_signals ( data , info , scalings , tol = 'auto' , return_singular = False ) : picks_list = _picks_by_type ( info ) _apply_scaling_array ( data , picks_list , scalings ) if ( data . shape [ 1 ] < data . shape [ 0 ] ) : ValueError ( "You've got fewer samples than channels, your rank estimate might be inaccurate." ) out = estimate_rank ( data , tol = tol , norm = False , return_singular = return_singular ) rank = ( out [ 0 ] if isinstance ( out , tuple ) else out ) ch_type = ' + ' . join ( list ( zip ( * picks_list ) ) [ 0 ] ) logger . info ( ( 'estimated rank (%s): %d' % ( ch_type , rank ) ) ) _undo_scaling_array ( data , picks_list , scalings ) return out
estimate rank for m/eeg data .