docs: def pca_eig(x):
      """Calculate PCA using eigenvalue decomposition.
      
      Parameters
      ----------
      x : ndarray, shape (channels, samples)
          Two-dimensional input data.
  
      Returns
      -------
      w : ndarray, shape (channels, channels)
          Eigenvectors (principal components) (in columns).
      s : ndarray, shape (channels,)
          Eigenvalues.
      """
      s, w = np.linalg.eigh(x.dot(x.T))
      return w, s