docs: def aligned_covariance(fit, type='noise'):
      """
      Covariance rescaled so that eigenvectors sum to 1
      and rotated into data coordinates from PCA space
      """
      cov = fit._covariance_matrix(type)
      # Rescale eigenvectors to sum to 1
      cov /= N.linalg.norm(cov)
      return dot(fit.axes,cov)