docs: def standardised_euclidean(x, y, sigma=_mock_ones):
      """Euclidean distance standardised against a vector of standard
      deviations per coordinate.
  
      ..math::
          D(x, y) = \sqrt{\sum_i \frac{(x_i - y_i)**2}{v_i}}
      """
      result = 0.0
      for i in range(x.shape[0]):
          result += ((x[i] - y[i]) ** 2) / sigma[i]
  
      return np.sqrt(result)