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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _initialize_precalculated_series(self, asset, trading_calendar, trading_days, data_portal):
 """ Internal method that pre-calculates the benchmark return series for use in the simulation. Parameters asset: Asset to use trading_calendar: TradingCalendar trading_days: pd.DateTimeIndex data_portal: DataPortal Notes ----- If the benchmark asset started trading after the simulation start, or finished trading before the simulation end, exceptions are raised. If the benchmark asset started trading the same day as the simulation start, the first available minute price on that day is used instead of the previous close. We use history to get an adjusted price history for each day's close, as of the look-back date (the last day of the simulation). Prices are fully adjusted for dividends, splits, and mergers. Returns ------- returns : pd.Series indexed by trading day, whose values represent the % change from close to close. daily_returns : pd.Series the partial daily returns for each minute """

        if self.emission_rate == "minute":
            minutes = trading_calendar.minutes_for_sessions_in_range(
                self.sessions[0], self.sessions[-1]
            )
            benchmark_series = data_portal.get_history_window(
                [asset],
                minutes[-1],
                bar_count=len(minutes) + 1,
                frequency="1m",
                field="price",
                data_frequency=self.emission_rate,
                ffill=True
            )[asset]

            return (
                benchmark_series.pct_change()[1:],
                self.downsample_minute_return_series(
                    trading_calendar,
                    benchmark_series,
                ),
            )

        start_date = asset.start_date
        if start_date < trading_days[0]:
            # get the window of close prices for benchmark_asset from the
            # last trading day of the simulation, going up to one day
            # before the simulation start day (so that we can get the %
            # change on day 1)
            benchmark_series = data_portal.get_history_window(
                [asset],
                trading_days[-1],
                bar_count=len(trading_days) + 1,
                frequency="1d",
                field="price",
                data_frequency=self.emission_rate,
                ffill=True
            )[asset]

            returns = benchmark_series.pct_change()[1:]
            return returns, returns
        elif start_date == trading_days[0]:
            # Attempt to handle case where stock data starts on first
            # day, in this case use the open to close return.
            benchmark_series = data_portal.get_history_window(
                [asset],
                trading_days[-1],
                bar_count=len(trading_days),
                frequency="1d",
                field="price",
                data_frequency=self.emission_rate,
                ffill=True
            )[asset]

            # get a minute history window of the first day
            first_open = data_portal.get_spot_value(
                asset,
                'open',
                trading_days[0],
                'daily',
            )
            first_close = data_portal.get_spot_value(
                asset,
                'close',
                trading_days[0],
                'daily',
            )

            first_day_return = (first_close - first_open) / first_open

            returns = benchmark_series.pct_change()[:]
            returns[0] = first_day_return
            return returns, returns
        else:
            raise ValueError(
                'cannot set benchmark to asset that does not exist during'
                ' the simulation period (asset start date=%r)' % start_date
            )