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153 lines
4.6 KiB
Python
153 lines
4.6 KiB
Python
import datetime
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from zoneinfo import ZoneInfo
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import numpy as np
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import pandas as pd
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from exchange_calendars.calendar_helpers import UTC
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def days_at_time(
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dates: pd.DatetimeIndex,
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time: datetime.time | None,
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tz: ZoneInfo,
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day_offset: int,
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) -> pd.DatetimeIndex:
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"""Return UTC DatetimeIndex of given dates at a given time.
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Parameters
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----------
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dates
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Dates or date (timezone naive with no time component).
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time
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The time to apply as an offset to each day in `dates`.
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tz
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The timezone in which to interpret `time`.
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day_offset
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Number of days by which to offset each date in `dates`.
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Returns
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-------
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pd.DatetimeIndex
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DatetimeIndex comprising Timestamp evaluted from `dates` and `time`
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with `dates` offset by `day_offset` and `time` interpreted as having
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timezone `tz`. DatetimeIndex has UTC timezone.
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Examples
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--------
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In the example below, the times switch from 13:45 to 12:45 UTC because
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March 13th is the daylight savings transition for America/New_York. All
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the times are still 8:45 when interpreted in America/New_York.
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>>> import pandas as pd; import datetime; import pprint
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>>> dts = pd.date_range('2016-03-12', '2016-03-14')
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>>> dts_845 = days_at_time(dts, datetime.time(8, 45), 'America/New_York', 0)
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>>> pprint.pprint([str(dt) for dt in dts_845])
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['2016-03-12 13:45:00+00:00',
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'2016-03-13 12:45:00+00:00',
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'2016-03-14 12:45:00+00:00']
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"""
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if time is None:
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return pd.DatetimeIndex([None for _ in dates]).tz_localize(UTC)
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if len(dates) == 0:
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return dates.tz_localize(UTC)
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# Offset days without tz to avoid timezone issues.
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delta = pd.Timedelta(
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days=day_offset,
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hours=time.hour,
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minutes=time.minute,
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seconds=time.second,
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)
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return (dates + delta).tz_localize(tz).tz_convert(UTC)
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def vectorized_sunday_to_monday(dtix):
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"""A vectorized implementation of
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:func:`pandas.tseries.holiday.sunday_to_monday`.
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Parameters
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----------
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dtix : pd.DatetimeIndex
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The index to shift sundays to mondays.
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Returns
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-------
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sundays_as_mondays : pd.DatetimeIndex
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``dtix`` with all sundays moved to the next monday.
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"""
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values = dtix.values.copy()
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values[dtix.weekday == 6] += np.timedelta64(1, "D")
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return pd.DatetimeIndex(values)
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def longest_run(ser: pd.Series) -> pd.Index:
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"""Get the longest run of consecutive True values in a Series.
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Function can be used to find the longest run of values that meet a
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condition.
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Parameters
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----------
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ser
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pd.Series of bool dtype.
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Index should reflect values against which a condition was
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assessed.
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Values should reflect whether corresponding index value
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met the condition.
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Return
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------
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pd.Index
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Slice of `ser` index that corresponds with the longest run of
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consecutive True values.
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Examples
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--------
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>>> arr = np.arange(0, 88)
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>>> ser = pd.Series(arr, index=arr)
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>>> bv = (
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... ((ser >= 10) & (ser < 16))
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... | ((ser >= 30) & (ser <= 40))
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... | ((ser >= 55) & (ser < 61))
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... )
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>>> longest_run(bv)
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Index([30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40], dtype='int64')
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>>> pd.testing.assert_index_equal(longest_run(bv), ser.index[30:41])
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"""
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# group Trues by only adding to sum when value False.
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trues_grouped = (~ser).cumsum()[ser] # and only take True Values
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group_sizes = trues_grouped.value_counts() # count each run
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max_run_size = group_sizes.max()
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max_run_group_id = group_sizes[group_sizes == max_run_size].index[0]
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return trues_grouped[trues_grouped == max_run_group_id].index
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def indexes_union(indexes: list[pd.Index]) -> pd.Index:
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"""Return union of multiple pd.Index objects.
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Parameters
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----------
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indexes
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Index objects to be joined. All indexes must be of same dtype.
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Examples
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--------
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>>> index1 = pd.date_range('2021-05-01 12:20', periods=2, freq='1h')
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>>> index2 = pd.date_range('2021-05-02 17:10', periods=2, freq='22min')
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>>> index3 = pd.date_range('2021-05-03', periods=2, freq='1D')
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>>> indexes_union([index1, index2, index3])
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DatetimeIndex(['2021-05-01 12:20:00', '2021-05-01 13:20:00',
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'2021-05-02 17:10:00', '2021-05-02 17:32:00',
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'2021-05-03 00:00:00', '2021-05-04 00:00:00'],
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dtype='datetime64[us]', freq=None)
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"""
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index = indexes[0]
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for indx in indexes[1:]:
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index = index.union(indx)
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return index
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