pastas.model.Model.noise
========================

.. py:method:: pastas.model.Model.noise(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) -> pandas.Series | None

   
   Method to simulate the noise when a noisemodel is present.

   :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 or int, optional

   :returns: **noise** -- Pandas series of the noise. None if no noise model is present.
   :rtype: pandas.Series or None

   .. rubric:: Notes

   The noise are the time series that result when applying a noise model.

   .. Note::

       The noise is sometimes also referred to as the innovations in the
       literature.

   .. warning:: This method returns None if no noise model is present in the model.















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