pastas.rfunc.HantushWellModel.variance_gain
===========================================

.. py:method:: pastas.rfunc.HantushWellModel.variance_gain(A: float, b: float, var_A: float, var_b: float, cov_Ab: float, r: float = 1.0) -> float | pastas.typing.ArrayLike
   :staticmethod:


   
   Calculate variance of the gain from parameters A and b.

   Variance of the gain is calculated based on propagation of uncertainty using
   optimal values, the variances of A and b and the covariance between A and b.

   .. rubric:: Notes

   Estimated variance can be biased for non-linear functions as it uses
   truncated series expansion.

   :param A: optimal value of parameter A, (e.g. ml.parameters.optimal).
   :type A: float
   :param b: optimal value of parameter b, (e.g. ml.parameters.optimal).
   :type b: float
   :param var_A: variance of parameter A, can be obtained from the diagonal of the
                 covariance matrix (e.g. ml.solver.pcov).
   :type var_A: float
   :param var_b: variance of parameter A, can be obtained from the diagonal of the
                 covariance matrix (e.g. ml.solver.pcov).
   :type var_b: float
   :param cov_Ab: covariance between A and b, can be obtained from the covariance matrix (
                  e.g. ml.solver.pcov).
   :type cov_Ab: float
   :param r: distance(s) between observation well and stress(es), default value is 1.0.
   :type r: float or array_like, optional

   :returns: **var_gain** -- variance of the gain calculated based on propagation of uncertainty of
             parameters A and b.
   :rtype: float or array_like

   .. seealso:: :py:obj:`ps.WellModel.variance_gain`















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