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

.. toctree::
   :hidden:

   /api/pastas/rfunc/HantushWellModel.get_init_parameters
   /api/pastas/rfunc/HantushWellModel.get_tmax
   /api/pastas/rfunc/HantushWellModel.step
   /api/pastas/rfunc/HantushWellModel.moment
   /api/pastas/rfunc/HantushWellModel.variance_gain
   /api/pastas/rfunc/HantushWellModel.to_dict

.. py:class:: pastas.rfunc.HantushWellModel(cutoff: float = 0.999, quad: bool = False, **kwargs)



   
   An implementation of the Hantush well function for multiple pumping wells.

   :param up: indicates whether a positive stress will cause the head to go up (True,
              default) or down (False).
   :type up: bool, optional
   :param gain_scale_factor: the scale factor is used to set the initial value and the bounds of the gain
                             parameter, computed as 1 / gain_scale_factor.
   :type gain_scale_factor: float, optional
   :param cutoff: proportion after which the step function is cut off.
   :type cutoff: float, optional
   :param quad: Use the method 'numba_quad' to compute the step_response.
   :type quad: bool, optional

   .. rubric:: Notes

   The impulse response function for this class can be viewed on the Documentation
   website or using `latexify` by running the following code in a Jupyter notebook
   environment::

       ps.HantushWellModel.impulse

   where r is the distance from the pumping well to the observation point and must
   be specified. A, a, and b are parameters, which are slightly different from the
   Hantush response function. The gain is defined as:

   :math:`\text{gain} = A K_0 \left( 2r \sqrt(b) \right)`

   The implementation used here is explained in :cite:t:`veling_hantush_2010`.















   ..
       !! processed by numpydoc !!
Methods
-------

.. autoapisummary::

   pastas.rfunc.HantushWellModel.get_init_parameters
   pastas.rfunc.HantushWellModel.get_tmax
   pastas.rfunc.HantushWellModel.step
   pastas.rfunc.HantushWellModel.moment
   pastas.rfunc.HantushWellModel.variance_gain
   pastas.rfunc.HantushWellModel.to_dict