SOVTree.getImportantModes(locarg=None, sov_data=None, eps=0.0001, sort_type='timescale', return_importance=False)[source]

Returns the most importand eigenmodes (those whose importance is above the threshold defined by eps)

  • locarg (None or list of locations) –

  • sov_data (None or tuple of mode matrices) – One of the keyword arguments locarg or sov_data must not be None. If locarg is not None, the importance is evaluated at these locations (see neat.MorphTree._parseLocArg()). If sov_data is not None, it is a tuple of a vector of the reciprocals of the mode timescales and a matrix with the corresponding spatial mode functions.

  • eps (float) – the cutoff threshold in relative importance below which modes are truncated

  • sort_type (string ('timescale' or 'importance')) – specifies in which order the modes are returned. If ‘timescale’, modes are sorted in order of decreasing time-scale, if ‘importance’, modes are sorted in order of decreasing importance.

  • return_importance (bool) – if True, returns the importance metric associated with each mode


  • alphas (np.ndarray of complex (ndim = 1)) – the reciprocals of mode time-scales [kHz]

  • gammas (np.ndarray of complex (ndim = 2)) – the spatial function associated with each mode, evaluated at each locations. Dimension 0 is number of modes and dimension 1 number of locations

  • importance (np.ndarray (shape matches alphas, only if return_importance is True)) – value of importance metric for each mode