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

.. toctree::
   :hidden:

   /api/pastas/timeseries/TimeSeries.update_series
   /api/pastas/timeseries/TimeSeries.to_dict

.. py:class:: pastas.timeseries.TimeSeries(series: pandas.Series, name: str | None = None, settings: str | dict[str, Any] | None = None, metadata: dict | None = None)

   
   Class that deals with all user-provided time series.

   :param series: pandas.Series with pandas.DatetimeIndex.
   :type series: pandas.Series
   :param name: String with the name of the time series, if None is provided, pastas will try
                to derive the name from the series.
   :type name: str, optional
   :param settings: The settings of the stress. This can be a string referring to a predefined
                    settings dictionary (defined in ps.rcParams["timeseries"]), or a dictionary with
                    the settings to apply. For more information refer to Time series settings
                    section below.
   :type settings: str or dict, optional
   :param metadata: Dictionary with metadata of the time series.
   :type metadata: dict, optional

   :returns: **series** -- Returns a pastas.TimeSeries object.
   :rtype: pastas.TimeSeries

   :param Time series settings:
   :param fill_nan:
                    Method for filling NaNs.
                       * `drop`: drop NaNs from time series
                       * `mean`: fill NaNs with mean value of time series
                       * `interpolate`: fill NaNs by interpolating between finite values
                       * `float`: fill NaN with provided value, e.g. 0.0
   :type fill_nan: {"drop", "mean", "interpolate"} or float
   :param fill_before:
                       Method for extending time series into past.
                          * `mean`: extend time series into past with mean value of time series
                          * `bfill`: extend time series into past by back-filling first value
                          * `float`: extend time series into past with provided value, e.g. 0.0
   :type fill_before: {"mean", "bfill"} or float
   :param fill_after:
                      Method for extending time series into future.
                         * `mean`: extend time series into future with mean value of time series
                         * `ffill`: extend time series into future by forward-filling last value
                         * `float`: extend time series into future with provided value, e.g. 0.0
   :type fill_after: {"mean", "ffill"} or float
   :param sample_up: Method for up-sampling time series (increasing frequency, e.g. going from weekly
                     to daily values).
                        * `bfill` or `backfill`: fill up-sampled time steps by back-filling current
                          values
                        * `ffill` or `pad`: fill up-sampled time steps by forward-filling current
                          values
                        * `mean`: fill up-sampled time steps with mean of timeseries
                        * `interpolate`: fill up-sampled time steps by interpolating between current
                          values
                        * `divide`: fill up-sampled steps with current value divided by length of
                          current time steps (i.e. spread value over new time steps).
   :type sample_up: {"mean", "interpolate", "divide"} or float
   :param sample_down: Method for down-sampling time series (decreasing frequency, e.g. going from
                       daily to weekly values).
                          * `mean`: resample time series by taking the mean
                          * `drop`: resample the time series by taking the mean, dropping any
                            NaN-values
                          * `sum`: resample time series by summing values
                          * `max`: resample time series with maximum value
                          * `min`: resample time series with minimum value
   :type sample_down: {"mean", "drop", "sum", "min", "max"}

   .. rubric:: Examples

   To obtain the predefined TimeSeries settings, you can run the following line of
   code:

   >>> ps.rcParams["timeseries"]

   .. seealso::

      :py:obj:`pastas.timeseries.TimeSeries.update_series`
          For the individual options for the different settings.















   ..
       !! processed by numpydoc !!
Methods
-------

.. autoapisummary::

   pastas.timeseries.TimeSeries.update_series
   pastas.timeseries.TimeSeries.to_dict