StepModel#

class StepModel(tstart, name, rfunc=None, up=True, cutoff=0.999)[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 (class is deprecated)) – Pastas response function used to simulate the effect of the step. Default is rfunc.One, an instant effect.

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

  • cutoff (float, optional) – float between 0 and 1 to determine how long the response is (default is 99.9% of the actual response time). Used to reduce computation times.

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 convoluted with the block response to simulate a step trend.

Attributes#

nparam

Methods#

__init__

dump_stress

Method to dump all stresses in the stresses list.

get_nsplit

Determine in how many timeseries the contribution can be split.

get_stress

Returns the stress or stresses 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 StressModel object.

update_stress

Method to update the settings of the individual TimeSeries.