bdew.py 文件源码

def create_bdew_load_profiles(self, dt_index, slp_types, holidays=None):
        """Calculates the hourly electricity load profile in MWh/h of a region.
        """

        # define file path of slp csv data
        file_path = os.path.join(self.datapath, 'selp_series.csv')

        # Read standard load profile series from csv file
        selp_series = pd.read_csv(file_path)
        tmp_df = selp_series
        # Create an index to merge. The year and month will be ignored only the
        # time index is necessary.
        index = pd.date_range(
            pd.datetime(2007, 1, 1, 0), periods=2016, freq='15Min')
        tmp_df.set_index(index, inplace=True)

        # Create empty DataFrame to take the results.
        new_df = pd.DataFrame(index=dt_index, columns=slp_types).fillna(0)
        new_df = add_weekdays2df(new_df, holidays=holidays,
                                 holiday_is_sunday=True)

        new_df['hour'] = dt_index.hour + 1
        new_df['minute'] = dt_index.minute
        time_df = new_df[['date', 'hour', 'minute', 'weekday']].copy()
        tmp_df[slp_types] = tmp_df[slp_types].astype(float)

        # Inner join the slps on the time_df to the slp's for a whole year
        tmp_df['hour_of_day'] = tmp_df.index.hour + 1
        tmp_df['minute_of_hour'] = tmp_df.index.minute
        left_cols = ['hour_of_day', 'minute_of_hour', 'weekday']
        right_cols = ['hour', 'minute', 'weekday']
        tmp_df = tmp_df.reset_index()
        tmp_df.pop('index')

        for p in self.seasons.keys():
            a = pd.datetime(self.year, self.seasons[p][0],
                            self.seasons[p][1], 0, 0)
            b = pd.datetime(self.year, self.seasons[p][2],
                            self.seasons[p][3], 23, 59)
            new_df.update(pd.DataFrame.merge(
                tmp_df[tmp_df['period'] == p[:-1]], time_df[a:b],
                left_on=left_cols, right_on=right_cols,
                how='inner', left_index=True).sort_index().drop(
                ['hour_of_day'], 1))

        new_df.drop('date', axis=1, inplace=True)
        return new_df.div(new_df.sum(axis=0), axis=1)