docs: def max_langevin_fixed_point(x, r, m):
      """
      Largest fixed point of dynamics  :math:argmax_x {h(x)=0}` estimated from polynomial :math:`h(x)`,
      which has been fitted to the deterministic dynamics of Langevin model
  
      .. math::
          \dot(x)(t) = h(x(t)) + R \mathcal(N)(0,1)
  
      as described by
  
          Friedrich et al. (2000): Physics Letters A 271, p. 217-222
          *Extracting model equations from experimental data*
  
      For short time-series this method is highly dependent on the parameters.
  
      :param x: the time series to calculate the feature of
      :type x: numpy.ndarray
      :param m: order of polynom to fit for estimating fixed points of dynamics
      :type m: int
      :param r: number of quantils to use for averaging
      :type r: float
  
      :return: Largest fixed point of deterministic dynamics
      :return type: float
      """
  
      coeff = _estimate_friedrich_coefficients(x, m, r)
  
      try:
          max_fixed_point = np.max(np.real(np.roots(coeff)))
      except (np.linalg.LinAlgError, ValueError):
          return np.nan
  
      return max_fixed_point