pastas.rfunc.DoubleExponential

class pastas.rfunc.DoubleExponential(cutoff: float = 0.999, use_block: bool = True, **kwargs)

Double Exponential response function with 4 parameters A, alpha, a1 and a2.

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.

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

property nparam: int

Return number of parameters for the response function.

Returns:

Number of parameters (4 for DoubleExponential: A, alpha, a1, a2).

Return type:

int

Methods

get_init_parameters(→ pandas.DataFrame)	Get initial parameters and bounds for the DoubleExponential response function.
get_tmax(→ float)	Get response time for a certain cutoff for DoubleExponential response function.
gain(→ float)	Return the gain of the DoubleExponential response function.
step(→ pastas.typing.ArrayLike)	Return the step function for DoubleExponential response.
moment(→ float)	Calculate the moment of a certain order for the DoubleExponential response function.
impulse(→ pastas.typing.ArrayLike)	Return the impulse response function for DoubleExponential.