pastas.stressmodels.StepModel#

class pastas.stressmodels.StepModel(tstart: pandas.Timestamp | str, rfunc: pastas.typing.RFunc | None = None, name: str = 'step', up: bool | None = None, max_cache_size: int | None = None)#

Stressmodel that simulates a step trend.

Parameters:

  • tstart (str or Timestamp) – String with the start date of the step, e.g. ‘2018-01-01’. This value is fixed by default. Use ml.set_parameter(“step_tstart”, vary=True) to vary the start time of the step trend.

  • rfunc (pastas.rfunc instance) – Pastas response function used to simulate the effect of the step. Default is ps.rfunc.One(), an instant effect.

  • name (str) – Name of the stressmodel. Default is “step”.

  • up (bool, optional) – Force a direction of the step. Default is None.

Notes

The step trend is calculated as follows. First, a binary series is created, with zero values before tstart, and ones after the start. This series is convolved with the block response to simulate a step trend.

property stresses: tuple#

Return the stresses used by the stress model.

property nsplit: int#

Determine in how many time series the contribution can be split.

Methods#

set_init_parameters(→ None)

Set the initial parameters (back) to their default values.

simulate(→ pandas.Series)

Simulate the stress model contribution.

to_dict(→ dict)

Export the stress model to a dictionary.