pastas.model.Model.simulate
===========================

.. py:method:: pastas.model.Model.simulate(p: pastas.typing.ArrayLike | None = None, tmin: pandas.Timestamp | str | None = None, tmax: pandas.Timestamp | str | None = None, freq: str | None = None, warmup: float | None = None, return_warmup: bool = False) -> pandas.Series

   
   Method to simulate the time series model.

   :param p: array_like object with the values as floats representing the model
             parameters. See Model.get_parameters() for more info if parameters is None.
   :type p: array_like, optional
   :param tmin: A string or pandas.Timestamp with the start date for the simulation period
                (E.g. '1980-01-01 00:00:00'). Strings are converted to pandas.Timestamp
                internally. If none is provided, the tmin from the oseries is used.
   :type tmin: pandas.Timestamp or str, optional
   :param tmax: A string or pandas.Timestamp with the end date for the simulation period (
                E.g. '2020-01-01 00:00:00'). Strings are converted to pandas.Timestamp
                internally. If none is provided, the tmax from the oseries is used.
   :type tmax: pandas.Timestamp or str, optional
   :param freq: String with the frequency the stressmodels are simulated. Must be one of
                the following: (D, h, m, s, ms, us, ns) or a multiple of that e.g. "7D".
   :type freq: str, optional
   :param warmup: Warmup period (in Days).
   :type warmup: float, optional
   :param return_warmup: Return the simulation including the warmup period or not, default is False.
   :type return_warmup: bool, optional

   :returns: **sim** -- pandas.Series containing the simulated time series
   :rtype: pandas.Series

   .. rubric:: Notes

   This method can be used without any parameters. When the model is solved,
   the optimal parameters values are used and if not, the initial parameter
   values are used. This allows the user to get an idea of how the simulation
   looks with only the initial parameters and no calibration.















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