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#
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Methods#
Method to dump all stresses in the stresses list. |
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Determine in how many timeseries the contribution can be split. |
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Returns the stress or stresses of the time series object as a pandas DataFrame. |
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Set the initial parameters (back) to their default values. |
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Method to export the StressModel object. |
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Method to update the settings of the individual TimeSeries. |