Hantush#

class Hantush(up=True, gain_scale_factor=1.0, cutoff=0.999, use_numba=False, quad=False, **kwargs)[source]#

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

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].

Attributes#

impulse

Methods#

`__init__`
`block`	Method to return the block function.
`gain`
`get_init_parameters`	Get initial parameters and bounds.
`get_t`	Internal method to determine the times at which to evaluate the step response, from t=0.
`get_tmax`	Method to get the response time for a certain cutoff.
`numba_step`
`numpy_step`
`quad_step`
`step`	Method to return the step function.
`to_dict`	Method to export the response function to a dictionary.