docs: def connectivity(measure_names, b, c=None, nfft=512):
      """Calculate connectivity measures.
  
      Parameters
      ----------
      measure_names : str or list of str
          Name(s) of the connectivity measure(s) to calculate. See
          :class:`Connectivity` for supported measures.
      b : array, shape (n_channels, n_channels * model_order)
          VAR model coefficients. See :ref:`var-model-coefficients` for details
          about the arrangement of coefficients.
      c : array, shape (n_channels, n_channels), optional
          Covariance matrix of the driving noise process. Identity matrix is used
          if set to None (default).
      nfft : int, optional
          Number of frequency bins to calculate. Note that these points cover the
          range between 0 and half the sampling rate.
  
      Returns
      -------
      result : array, shape (n_channels, n_channels, `nfft`)
          An array of shape (m, m, nfft) is returned if measures is a string. If
          measures is a list of strings, a dictionary is returned, where each key
          is the name of the measure, and the corresponding values are arrays of
          shape (m, m, nfft).
  
      Notes
      -----
      When using this function, it is more efficient to get several measures at
      once than calling the function multiple times.
  
      Examples
      --------
      >>> c = connectivity(['DTF', 'PDC'], [[0.3, 0.6], [0.0, 0.9]])
      """
      con = Connectivity(b, c, nfft)
      try:
          return getattr(con, measure_names)()
      except TypeError:
          return dict((m, getattr(con, m)()) for m in measure_names)