Fetch the coastdat2 from the database, adapt it to the local time zone
    and create a time index.

def fetch_raw_data(sql, connection, geometry):
    """
    Fetch the coastdat2 from the database, adapt it to the local time zone
    and create a time index.
    """
    tmp_dc = {}
    weather_df = pd.DataFrame(
        connection.execute(sql).fetchall(), columns=[
            'gid', 'geom_point', 'geom_polygon', 'data_id', 'time_series',
            'dat_id', 'type_id', 'type', 'height', 'year', 'leap_year']).drop(
        'dat_id', 1)

    # Get the timezone of the geometry
    tz = tools.tz_from_geom(connection, geometry)

    for ix in weather_df.index:
        # Convert the point of the weather location to a shapely object
        weather_df.loc[ix, 'geom_point'] = wkt_loads(
            weather_df['geom_point'][ix])

        # Roll the dataset forward according to the timezone, because the
        # dataset is based on utc (Berlin +1, Kiev +2, London +0)
        utc = timezone('utc')
        offset = int(utc.localize(datetime(2002, 1, 1)).astimezone(
            timezone(tz)).strftime("%z")[:-2])

        # Get the year and the length of the data array
        db_year = weather_df.loc[ix, 'year']
        db_len = len(weather_df['time_series'][ix])

        # Set absolute time index for the data sets to avoid errors.
        tmp_dc[ix] = pd.Series(
            np.roll(np.array(weather_df['time_series'][ix]), offset),
            index=pd.date_range(pd.datetime(db_year, 1, 1, 0),
                                periods=db_len, freq='H', tz=tz))
    weather_df['time_series'] = pd.Series(tmp_dc)
    return weather_df