StepModel#

class StepModel(tstart, name, rfunc=None, up=None)[source]#

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

Attributes#

nparam

Methods#

`__init__`
`get_nsplit`	Determine in how many time series the contribution can be split.
`get_parameters`	Get parameters and return as array.
`get_settings`	Method to obtain the settings of the stresses.
`get_stress`	Returns the stress(es) of the time series object as a pandas DataFrame.
`set_init_parameters`	Set the initial parameters (back) to their default values.
`simulate`
`to_dict`	Method to export the StepModel object.
`update_stress`	Method to update the settings of the all stresses in the stress model.