preheat_open.time.DateRange

class preheat_open.time.DateRange(ranges=None, resolution=TimeResolution.RAW, start=None, end=None)

Bases: object

Represents a collection of SubDateRange objects.

Parameters:

ranges (List[SubDateRange]) – A list of SubDateRange objects.
resolution (TimeResolution) – The time resolution of the DateRange.

Examples:

>>> from datetime import datetime
>>> from preheat_open.time import SubDateRange, DateRange, TimeResolution
>>> sdr = SubDateRange(datetime(2023, 1, 1), datetime(2023, 1, 2))
>>> dr = DateRange([sdr], resolution=TimeResolution.DAY)
>>> print(dr)
DateRange(2023-01-01--2023-01-02, continuous, resolution=TimeResolution.DAY)

__init__(ranges=None, resolution=TimeResolution.RAW, start=None, end=None)

Initialize a DateRange object.

Parameters:

ranges (List[SubDateRange]) – A list of SubDateRange objects.
resolution (TimeResolution) – The time resolution of the DateRange.
start (Optional[datetime]) – Optional start datetime.
end (Optional[datetime]) – Optional end datetime.

Methods

`__init__`([ranges, resolution, start, end])	Initialize a DateRange object.
`astimezone`([tz])	Convert the DateRange to a new timezone.
`gaps`()	Calculate the gaps between consecutive DateRanges.
`intersection`(other)	Return the intersection of two DateRange objects.
`iter_ranges`(freq)	Iterate over the ranges of the DateRange.
`remove_overlaps`()	Remove overlapping ranges by merging them.
`to_pandas_date_range`()	Convert the DateRange to a pandas DateRange object.
`union`()	Return a DateRange covering the entire period, including gaps.

Attributes

`empty`	Checks if the DateRange is empty.
`lstart`	Returns the start date of the DateRange.
`rend`	Returns the end date of the DateRange.
`segmented`	Checks if the DateRange is segmented.
`tz_aware`	Checks if the DateRange is timezone-aware.

astimezone(tz=None)

Convert the DateRange to a new timezone.

Parameters:: tz (Optional[tzinfo]) – The new timezone.
Return type:: DateRange

property empty: bool

Checks if the DateRange is empty.

Returns:: True if empty, False otherwise.
Return type:: bool

gaps()

Calculate the gaps between consecutive DateRanges.

Returns:: A new DateRange object representing the gaps between ranges.
Return type:: DateRange

intersection(other)

Return the intersection of two DateRange objects.

Parameters:: other (DateRange) – Another DateRange object.
Returns:: A new DateRange object representing the intersections.
Return type:: DateRange

iter_ranges(freq)

Iterate over the ranges of the DateRange.

Returns:: A generator yielding tuples of start and end datetimes.
Return type:: Generator[tuple[datetime, datetime], None, None]

property lstart: datetime | None

Returns the start date of the DateRange.

Returns:: The start date.
Return type:: Optional[datetime]

remove_overlaps(): Remove overlapping ranges by merging them.

property rend: datetime | None

Returns the end date of the DateRange.

Returns:: The end date.
Return type:: Optional[datetime]

property segmented: bool

Checks if the DateRange is segmented.

Returns:: True if segmented, False otherwise.
Return type:: bool

to_pandas_date_range()

Convert the DateRange to a pandas DateRange object.

Returns:: A pandas DateRange object.
Return type:: pandas.core.indexes.datetimes.DatetimeIndex

property tz_aware: bool

Checks if the DateRange is timezone-aware.

Returns:: True if timezone-aware, False otherwise.
Return type:: bool

union()

Return a DateRange covering the entire period, including gaps.

Returns:: A new DateRange covering the full period from the earliest start to the latest end.
Return type:: DateRange