pastas.timeseries.TimeSeries.update_series
==========================================

.. py:method:: pastas.timeseries.TimeSeries.update_series(force_update: bool = False, **kwargs) -> None

   
   Method to update the series with new options.

   :param force_update: argument that is used to force an update, even when no changes are found.
                        Internally used by the __init__ method. Default is False.
   :type force_update: bool, optional
   :param freq: String representing the desired frequency of the time series. 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 sample_up: String with the method to use when the frequency is increased (e.g.,
                     Weekly to daily). Possible values are: "backfill", "bfill", "pad",
                     "ffill", "mean", "interpolate", "divide" or a float value to fill the gaps.
   :type sample_up: str or float, optional
   :param sample_down: String with the method to use when the frequency decreases (e.g., from
                       daily to weekly values). Possible values are: "mean", "drop", "sum",
                       "min", "max".
   :type sample_down: str, optional
   :param fill_nan: Method to use when there ar nan-values in the time series. Possible
                    values are: "mean", "drop", "interpolate" (default) or a float value.
   :type fill_nan: str or float, optional
   :param fill_before: Method used to extend a time series before any measurements are
                       available. possible values are: "mean" or a float value.
   :type fill_before: str or float, optional
   :param fill_after: Method used to extend a time series after any measurements are available.
                      Possible values are: "mean" or a float value.
   :type fill_after: str or float, optional
   :param tmin: A string or pandas.Timestamp with the minimum time of the series
                (E.g. '1980-01-01 00:00:00').
   :type tmin: pandas.Timestamp or str, optional
   :param tmax: A string or pandas.Timestamp with the maximum time of the series
                (E.g. '2020-01-01 00:00:00'). Strings are converted to

                pandas.Timestamp internally.
   :type tmax: pandas.Timestamp or str, optional

   .. rubric:: Notes

   The method will validate if any of the settings is changed to determine if
   the series need to be updated.















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