edisgo.network.timeseries.TimeSeriesRaw
=======================================

.. py:class:: edisgo.network.timeseries.TimeSeriesRaw

   Holds raw time series data, e.g. sector-specific demand and standing times of EV.

   Normalised time series are e.g. sector-specific demand time series or
   technology-specific feed-in time series. Time series needed for
   flexibilities are e.g. heat time series or curtailment time series.

   .. attribute:: q_control

      Dataframe with information on applied reactive power control or in case of
      conventional loads assumed reactive power behavior. Index of the dataframe are
      the component names as in index of
      :attr:`~.network.topology.Topology.generators_df`,
      :attr:`~.network.topology.Topology.loads_df`, and
      :attr:`~.network.topology.Topology.storage_units_df`. Columns are
      "type" with the type of Q-control applied (can be "fixed_cosphi", "cosphi(P)",
      or "Q(V)"),
      "power_factor" with the (maximum) power factor,
      "q_sign" giving the sign of the reactive power (only applicable to
      "fixed_cosphi"),
      "parametrisation" with the parametrisation of the
      respective Q-control (only applicable to "cosphi(P)" and "Q(V)").

      :type: :pandas:`pandas.DataFrame<DataFrame>`

   .. attribute:: fluctuating_generators_active_power_by_technology

      DataFrame with feed-in time series per technology or technology and
      weather cell ID normalized to a nominal capacity of 1.
      Columns can either just contain the technology type as string or
      be a :pandas:`pandas.MultiIndex<MultiIndex>` with the
      first level containing the technology as string and the second level
      the weather cell ID as integer.
      Index is a :pandas:`pandas.DatetimeIndex<DatetimeIndex>`.

      :type: :pandas:`pandas.DataFrame<DataFrame>`

   .. attribute:: dispatchable_generators_active_power_by_technology

      DataFrame with feed-in time series per technology normalized to a nominal
      capacity of 1.
      Columns contain the technology type as string.
      Index is a :pandas:`pandas.DatetimeIndex<DatetimeIndex>`.

      :type: :pandas:`pandas.DataFrame<DataFrame>`

   .. attribute:: conventional_loads_active_power_by_sector

      DataFrame with load time series of each type of conventional load
      normalized to an annual consumption of 1. Index needs to
      be a :pandas:`pandas.DatetimeIndex<DatetimeIndex>`.
      Columns represent load type. In ding0 grids the
      differentiated sectors are 'residential', 'cts', 'industrial', and
      'agricultural'.

      :type: :pandas:`pandas.DataFrame<DataFrame>`

   .. attribute:: charging_points_active_power_by_use_case

      DataFrame with charging demand time series per use case normalized to a nominal
      capacity of 1.
      Columns contain the use case as string.
      Index is a :pandas:`pandas.DatetimeIndex<DatetimeIndex>`.

      :type: :pandas:`pandas.DataFrame<DataFrame>`


   .. py:attribute:: q_control


   .. py:attribute:: fluctuating_generators_active_power_by_technology


   .. py:attribute:: dispatchable_generators_active_power_by_technology


   .. py:attribute:: conventional_loads_active_power_by_sector


   .. py:attribute:: charging_points_active_power_by_use_case


   .. py:method:: reduce_memory(attr_to_reduce=None, to_type='float32')

      Reduces size of dataframes to save memory.

      See :attr:`~.edisgo.EDisGo.reduce_memory` for more information.

      :param attr_to_reduce: List of attributes to reduce size for. Attributes need to be
                             dataframes containing only time series. Per default the following attributes
                             are reduced if they exist: q_control,
                             fluctuating_generators_active_power_by_technology,
                             dispatchable_generators_active_power_by_technology,
                             conventional_loads_active_power_by_sector,
                             charging_points_active_power_by_use_case.
      :type attr_to_reduce: list(str), optional
      :param to_type: Data type to convert time series data to. This is a tradeoff
                      between precision and memory. Default: "float32".
      :type to_type: str, optional



   .. py:method:: to_csv(directory, reduce_memory=False, **kwargs)

      Saves time series to csv.

      Saves all attributes that are set to csv files with the same file name.
      See class definition for possible attributes.

      :param directory: Directory to save time series in.
      :type directory: str
      :param reduce_memory: If True, size of dataframes is reduced using
                            :attr:`~.network.timeseries.TimeSeriesRaw.reduce_memory`. Optional
                            parameters of
                            :attr:`~.network.timeseries.TimeSeriesRaw.reduce_memory`
                            can be passed as kwargs to this function. Default: False.
      :type reduce_memory: bool, optional

      :param kwargs: Kwargs may contain optional arguments of
                     :attr:`~.network.timeseries.TimeSeriesRaw.reduce_memory`.



   .. py:method:: from_csv(directory)

      Restores time series from csv files.

      See :func:`~to_csv` for more information on which time series are
      saved.

      :param directory: Directory time series are saved in.
      :type directory: str