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

.. toctree::
   :hidden:

   /api/pastas/rfunc/Kraijenhoff.get_init_parameters
   /api/pastas/rfunc/Kraijenhoff.get_tmax
   /api/pastas/rfunc/Kraijenhoff.gain
   /api/pastas/rfunc/Kraijenhoff.step
   /api/pastas/rfunc/Kraijenhoff.block_from_impulse
   /api/pastas/rfunc/Kraijenhoff.moment
   /api/pastas/rfunc/Kraijenhoff.impulse
   /api/pastas/rfunc/Kraijenhoff.to_dict

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



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

   :param cutoff: Fraction of the step response after which the response is truncated.
                  Default is 0.999.
   :type cutoff: float, optional
   :param use_block: Use the block response (rather than the impulse response) to simulate
                     the effect of a stress. The block response approximates the stress
                     as uniform during a time interval dt. When False, the impulse response
                     is used which means that the the entire stress occurs midway the time
                     interval dt. The impulse response is generally quicker to compute.
   :type use_block: bool, optional
   :param n_terms: Number of terms used in the truncated series expansion.
   :type n_terms: int, optional

   .. attribute:: up

      Whether a positive stress causes the head to go up (`True`), down
      (`False`), or either direction (`None`).

      :type: bool or None, optional

   .. attribute:: gain_scale_factor

      Scale factor used to set the initial value and bounds of the gain
      parameter, computed as `1 / gain_scale_factor`.

      :type: float, optional

   .. rubric:: Notes

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

   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 !!

   .. py:property:: nparam
      :type: int


      
      Number of parameters of the response function.
















      ..
          !! processed by numpydoc !!

Methods
-------

.. autoapisummary::

   pastas.rfunc.Kraijenhoff.get_init_parameters
   pastas.rfunc.Kraijenhoff.get_tmax
   pastas.rfunc.Kraijenhoff.gain
   pastas.rfunc.Kraijenhoff.step
   pastas.rfunc.Kraijenhoff.block_from_impulse
   pastas.rfunc.Kraijenhoff.moment
   pastas.rfunc.Kraijenhoff.impulse
   pastas.rfunc.Kraijenhoff.to_dict