code: # SPDX-FileCopyrightText: : 2020 @JanFrederickUnnewehr, The PyPSA-Eur Authors
  #
  # SPDX-License-Identifier: MIT
  
  """
  
  This rule downloads the load data from `Open Power System Data Time series <https://data.open-power-system-data.org/time_series/>`_. For all countries in the network, the per country load timeseries with suffix ``_load_actual_entsoe_transparency`` are extracted from the dataset. After filling small gaps linearly and large gaps by copying time-slice of a given period, the load data is exported to a ``.csv`` file.
  
  Relevant Settings
  -----------------
  
  .. code:: yaml
  
      snapshots:
  
      load:
          interpolate_limit:
          time_shift_for_large_gaps:
          manual_adjustments:
  
  
  .. seealso::
      Documentation of the configuration file ``config.yaml`` at
      :ref:`load_cf`
  
  Inputs
  ------
  
  
  Outputs
  -------
  
  - ``resource/time_series_60min_singleindex_filtered.csv``:
  
  
  """
  
  import logging
  logger = logging.getLogger(__name__)
  from _helpers import configure_logging
  
  import pandas as pd
  import numpy as np
  import dateutil
  from pandas import Timedelta as Delta
  
  
  def load_timeseries(fn, years, countries, powerstatistics=True):
      """
      Read load data from OPSD time-series package version 2020-10-06.
  
      Parameters
      ----------
      years : None or slice()
          Years for which to read load data (defaults to
          slice("2018","2019"))
      fn : str
          File name or url location (file format .csv)
      countries : listlike
          Countries for which to read load data.
      powerstatistics: bool
          Whether the electricity consumption data of the ENTSOE power
          statistics (if true) or of the ENTSOE transparency map (if false)
          should be parsed.
  
      Returns
      -------
      load : pd.DataFrame
          Load time-series with UTC timestamps x ISO-2 countries
      """
      logger.info(f"Retrieving load data from '{fn}'.")
  
      pattern = 'power_statistics' if powerstatistics else '_transparency'
      pattern = f'_load_actual_entsoe_{pattern}'
      rename = lambda s: s[:-len(pattern)]
      date_parser = lambda x: dateutil.parser.parse(x, ignoretz=True)
      return (pd.read_csv(fn, index_col=0, parse_dates=[0], date_parser=date_parser)
              .filter(like=pattern)
              .rename(columns=rename)
              .dropna(how="all", axis=0)
              .rename(columns={'GB_UKM' : 'GB'})
              .filter(items=countries)
              .loc[years])
  
  
  def consecutive_nans(ds):
      return (ds.isnull().astype(int)
              .groupby(ds.notnull().astype(int).cumsum()[ds.isnull()])
              .transform('sum').fillna(0))
  
  
  def fill_large_gaps(ds, shift):
      """
      Fill up large gaps with load data from the previous week.
  
      This function fills gaps ragning from 3 to 168 hours (one week).
      """
      shift = Delta(shift)
      nhours = shift / np.timedelta64(1, 'h')
      if (consecutive_nans(ds) > nhours).any():
          logger.warning('There exist gaps larger then the time shift used for '
                         'copying time slices.')
      time_shift = pd.Series(ds.values, ds.index + shift)
      return ds.where(ds.notnull(), time_shift.reindex_like(ds))
  
  
  def nan_statistics(df):
      def max_consecutive_nans(ds):
          return (ds.isnull().astype(int)
                    .groupby(ds.notnull().astype(int).cumsum())
                    .sum().max())
      consecutive = df.apply(max_consecutive_nans)
      total = df.isnull().sum()
      max_total_per_month = df.isnull().resample('m').sum().max()
      return pd.concat([total, consecutive, max_total_per_month],
                   keys=['total', 'consecutive', 'max_total_per_month'], axis=1)
  
  
  def copy_timeslice(load, cntry, start, stop, delta):
      start = pd.Timestamp(start)
      stop = pd.Timestamp(stop)
      if start-delta in load.index and stop in load.index and cntry in load:
          load.loc[start:stop, cntry] = load.loc[start-delta:stop-delta, cntry].values
  
  
  def manual_adjustment(load, powerstatistics):
      """
      Adjust gaps manual for load data from OPSD time-series package.
  
      1. For the ENTSOE power statistics load data (if powerstatistics is True)
  
      Kosovo (KV) and Albania (AL) do not exist in the data set. Kosovo gets the
      same load curve as Serbia and Albania the same as Macdedonia, both scaled
      by the corresponding ratio of total energy consumptions reported by
      IEA Data browser [0] for the year 2013.
  
      2. For the ENTSOE transparency load data (if powerstatistics is False)
  
      Albania (AL) and Macedonia (MK) do not exist in the data set. Both get the
      same load curve as Montenegro,  scaled by the corresponding ratio of total energy
      consumptions reported by  IEA Data browser [0] for the year 2016.
  
      [0] https://www.iea.org/data-and-statistics?country=WORLD&fuel=Electricity%20and%20heat&indicator=TotElecCons
  
  
      Parameters
      ----------
      load : pd.DataFrame
          Load time-series with UTC timestamps x ISO-2 countries
      powerstatistics: bool
          Whether argument load comprises the electricity consumption data of
          the ENTSOE power statistics or of the ENTSOE transparency map
  
      Returns
      -------
      load : pd.DataFrame
          Manual adjusted and interpolated load time-series with UTC
          timestamps x ISO-2 countries
      """
  
      if powerstatistics:
          if 'MK' in load.columns:
              if 'AL' not in load.columns or load.AL.isnull().values.all():
                  load['AL'] = load['MK'] * (4.1 / 7.4)
          if 'RS' in load.columns:
              if 'KV' not in load.columns or load.KV.isnull().values.all():
                  load['KV'] = load['RS'] * (4.8 / 27.)
  
          copy_timeslice(load, 'GR', '2015-08-11 21:00', '2015-08-15 20:00', Delta(weeks=1))
          copy_timeslice(load, 'AT', '2018-12-31 22:00', '2019-01-01 22:00', Delta(days=2))
          copy_timeslice(load, 'CH', '2010-01-19 07:00', '2010-01-19 22:00', Delta(days=1))
          copy_timeslice(load, 'CH', '2010-03-28 00:00', '2010-03-28 21:00', Delta(days=1))
          # is a WE, so take WE before
          copy_timeslice(load, 'CH', '2010-10-08 13:00', '2010-10-10 21:00', Delta(weeks=1))
          copy_timeslice(load, 'CH', '2010-11-04 04:00', '2010-11-04 22:00', Delta(days=1))
          copy_timeslice(load, 'NO', '2010-12-09 11:00', '2010-12-09 18:00', Delta(days=1))
          # whole january missing
          copy_timeslice(load, 'GB', '2009-12-31 23:00', '2010-01-31 23:00', Delta(days=-364))
  
      else:
          if 'ME' in load:
              if 'AL' not in load and 'AL' in countries:
                  load['AL'] = load.ME * (5.7/2.9)
              if 'MK' not in load and 'MK' in countries:
                  load['MK'] = load.ME * (6.7/2.9)
          copy_timeslice(load, 'BG', '2018-10-27 21:00', '2018-10-28 22:00', Delta(weeks=1))
  
      return load
  
  
  if __name__ == "__main__":
  
      if 'snakemake' not in globals():
          from _helpers import mock_snakemake
          snakemake = mock_snakemake('build_load_data')
  
      configure_logging(snakemake)
  
      config = snakemake.config
      powerstatistics = config['load']['power_statistics']
      interpolate_limit = config['load']['interpolate_limit']
      countries = config['countries']
      snapshots = pd.date_range(freq='h', **config['snapshots'])
      years = slice(snapshots[0], snapshots[-1])
      time_shift = config['load']['time_shift_for_large_gaps']
  
      load = load_timeseries(snakemake.input[0], years, countries, powerstatistics)
  
      if config['load']['manual_adjustments']:
          load = manual_adjustment(load, powerstatistics)
  
      logger.info(f"Linearly interpolate gaps of size {interpolate_limit} and less.")
      load = load.interpolate(method='linear', limit=interpolate_limit)
  
      logger.info("Filling larger gaps by copying time-slices of period "
                  f"'{time_shift}'.")
      load = load.apply(fill_large_gaps, shift=time_shift)
  
      assert not load.isna().any().any(), (
          'Load data contains nans. Adjust the parameters '
          '`time_shift_for_large_gaps` or modify the `manual_adjustment` function '
          'for implementing the needed load data modifications.')
  
      load.to_csv(snakemake.output[0])
  
  
apis: pandas.Series pandas.date_range numpy.timedelta64 pandas.read_csv pandas.Timedelta pandas.concat pandas.Timestamp