pastas.model.Model.noise#

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.

Parameters:

  • p (array_like, optional) – array_like object with the values as floats representing the model parameters. See Model.get_parameters() for more info if parameters is None.

  • tmin (pandas.Timestamp or str, optional) – 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.

  • tmax (pandas.Timestamp or str, optional) – 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.

  • freq (str, optional) – 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”.

  • warmup (float or int, optional) – Warmup period (in Days).

Returns:

noise – Pandas series of the noise. None if no noise model is present.

Return type:

pandas.Series or None

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.