pastas.stressmodels.StepModel
=============================

.. toctree::
   :hidden:

   /api/pastas/stressmodels/StepModel.set_init_parameters
   /api/pastas/stressmodels/StepModel.simulate
   /api/pastas/stressmodels/StepModel.to_dict

.. py: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.

   :param tstart: 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.
   :type tstart: str or Timestamp
   :param rfunc: Pastas response function used to simulate the effect of the step. Default is
                 ps.rfunc.One(), an instant effect.
   :type rfunc: pastas.rfunc instance
   :param name: Name of the stressmodel. Default is "step".
   :type name: str
   :param up: Force a direction of the step. Default is None.
   :type up: bool, optional

   .. rubric:: 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.















   ..
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   .. py:property:: stresses
      :type: tuple


      
      Return the stresses used by the stress model.
















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   .. py:property:: nsplit
      :type: int


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
















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Methods
-------

.. autoapisummary::

   pastas.stressmodels.StepModel.set_init_parameters
   pastas.stressmodels.StepModel.simulate
   pastas.stressmodels.StepModel.to_dict