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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 lows(self, assets, dt):
 """ The low field's aggregation returns the smallest low seen between the market open and the current dt. If there has been no data on or before the `dt` the low is `nan`. Returns ------- np.array with dtype=float64, in order of assets parameter. """

        market_open, prev_dt, dt_value, entries = self._prelude(dt, 'low')

        lows = []
        session_label = self._trading_calendar.minute_to_session_label(dt)

        for asset in assets:
            if not asset.is_alive_for_session(session_label):
                lows.append(np.NaN)
                continue

            if prev_dt is None:
                val = self._minute_reader.get_value(asset, dt, 'low')
                entries[asset] = (dt_value, val)
                lows.append(val)
                continue
            else:
                try:
                    last_visited_dt, last_min = entries[asset]
                    if last_visited_dt == dt_value:
                        lows.append(last_min)
                        continue
                    elif last_visited_dt == prev_dt:
                        curr_val = self._minute_reader.get_value(
                            asset, dt, 'low')
                        val = np.nanmin([last_min, curr_val])
                        entries[asset] = (dt_value, val)
                        lows.append(val)
                        continue
                    else:
                        after_last = pd.Timestamp(
                            last_visited_dt + self._one_min, tz='UTC')
                        window = self._minute_reader.load_raw_arrays(
                            ['low'],
                            after_last,
                            dt,
                            [asset],
                        )[0].T
                        val = np.nanmin(np.append(window, last_min))
                        entries[asset] = (dt_value, val)
                        lows.append(val)
                        continue
                except KeyError:
                    window = self._minute_reader.load_raw_arrays(
                        ['low'],
                        market_open,
                        dt,
                        [asset],
                    )[0].T
                    val = np.nanmin(window)
                    entries[asset] = (dt_value, val)
                    lows.append(val)
                    continue
        return np.array(lows)