pastas.rfunc.Kraijenhoff
========================

.. toctree::
   :hidden:

   /api/pastas/rfunc/Kraijenhoff.get_init_parameters
   /api/pastas/rfunc/Kraijenhoff.get_tmax
   /api/pastas/rfunc/Kraijenhoff.step
   /api/pastas/rfunc/Kraijenhoff.impulse
   /api/pastas/rfunc/Kraijenhoff.to_dict

.. py:class:: pastas.rfunc.Kraijenhoff(cutoff: float = 0.999, n_terms: int = 10, **kwargs)



   
   The response function of :cite:t:`van_de_leur_study_1958`.

   :param up: indicates whether a positive stress will cause the head to go up (True,
              default) or down (False), if None the head can go both ways.
   :type up: bool or None, 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 n_terms: Number of terms.
   :type n_terms: int, optional

   .. rubric:: Notes

   The Kraijenhoff van de Leur function is explained in
   :cite:t:`van_de_leur_study_1958`.

   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.Kraijenhoff.impulse

   The function describes the response of a domain between two drainage channels.
   The function gives the same outcome as equation 133.15 in
   :cite:t:`bruggeman_analytical_1999`. This is the response that is actually
   calculated with this function.

   The response function has three parameters A, a and b:

   - A is the gain (scaled),
   - a is the reservoir coefficient (j in :cite:t:`van_de_leur_study_1958`),
   - b is the location in the domain with the origin in the middle. This means that
     b=0 is in the middle and b=1/2 is at the drainage channel. At b=1/4 the
     response function is most similar to the exponential response function.















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

.. autoapisummary::

   pastas.rfunc.Kraijenhoff.get_init_parameters
   pastas.rfunc.Kraijenhoff.get_tmax
   pastas.rfunc.Kraijenhoff.step
   pastas.rfunc.Kraijenhoff.impulse
   pastas.rfunc.Kraijenhoff.to_dict