pastas.model.Model.observations
===============================

.. py:method:: pastas.model.Model.observations(tmin: pandas.Timestamp | str | None = None, tmax: pandas.Timestamp | str | None = None, freq: str | None = None, update_observations: bool = False) -> pandas.Series

   
   Method that returns the observations series used for calibration.

   :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 update_observations: If True, force recalculation of the observations, default is False.
   :type update_observations: bool, optional

   :returns: **oseries_calib** -- pandas series of the oseries used for calibration of the model.
   :rtype: pandas.Series

   .. rubric:: Notes

   This method makes sure the simulation is compared to the nearest observation.
   It finds the index closest to sim_index, and then returns a selection of the
   oseries. In the `residuals` method, the simulation is interpolated to the
   observation-timestamps.















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