pastas.rfunc.Kraijenhoff#

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

The response function of Van de Leur [1958].

Parameters:

  • cutoff (float, optional) – Fraction of the step response after which the response is truncated. Default is 0.999.

  • use_block (bool, optional) – 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.

  • n_terms (int, optional) – Number of terms used in the truncated series expansion.

up#

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

Type:

bool or None, optional

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

Notes

The Kraijenhoff van de Leur function is explained in Van de Leur [1958].

The function describes the response of a domain between two drainage channels. The function gives the same outcome as equation 133.15 in Bruggeman [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 Van de Leur [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.

property nparam: int#

Number of parameters of the response function.

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.

gain(→ float)

Method to return the gain for the response function.

step(→ pastas.typing.ArrayLike)

Method to return the step function.

block_from_impulse(→ pastas.typing.ArrayLike)

Method to return the block function from the impulse response.

moment(→ float)

Compute the raw moment of the response function.

impulse(→ pastas.typing.ArrayLike)

Method to return the impulse response function.

to_dict()

Method to export the response function to a dictionary.