pastas.rfunc.Hantush#

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

The Hantush well function, using the standard A, a, b parameters.

Parameters:

  • up (bool or None, optional) – 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.

  • gain_scale_factor (float, optional) – the scale factor is used to set the initial value and the bounds of the gain parameter, computed as 1 / gain_scale_factor.

  • cutoff (float, optional) – proportion after which the step function is cut off.

  • quad (bool, optional) – Use the method ‘numba_quad’ to compute the step_response.

Notes

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.Hantush.impulse

The implementation used here is explained in Veling and Maas [2010].

Methods#

get_init_parameters(→ pandas.DataFrame)

Get initial parameters and bounds. It is called by the stressmodel.

get_tmax(→ float)

Method to get the response time for a certain cutoff.

step(→ pastas.typing.ArrayLike)

Method to return the step function.

impulse(→ pastas.typing.ArrayLike)

Method to return the impulse response function.

to_dict()

Method to export the response function to a dictionary.