from collections.abc import Iterable
from datetime import date, datetime
from typing import Unpack
from polars import DataFrame, Series
from rics.misc import tname
from ..._docstrings import docs
from ...types import DatetimeIndexSplitterKwargs, MetricsType
from .._log_progress import LogProgressArg
from ..base import DatetimeSplit, split_data
[docs]
@docs
def split_polars(
data: DataFrame,
time_column: str,
*,
log_progress: LogProgressArg[MetricsType] = False,
**kwargs: Unpack[DatetimeIndexSplitterKwargs],
) -> Iterable[DatetimeSplit[DataFrame]]:
"""Split a polars frame.
Args:
data: A ``polars.DataFrame``.
time_column: A column to split on.
log_progress: {log_progress}
**kwargs: {DatetimeIndexSplitterKwargs}
{USER_GUIDE}
Yields:
Tuples ``(data, future_data, bounds)``.
Raises:
TypeError: If `time_column` is not datetime-like.
"""
indexer = _Indexer(time_column)
yield from split_data(
data,
log_progress=log_progress,
as_available=indexer.as_available,
select=indexer.select,
**kwargs,
)
class _Indexer:
def __init__(self, time_column: str) -> None:
self.time_column = time_column
def as_available(self, data: DataFrame) -> Series:
time = self._get_time(data)
if isinstance(time[0], date):
return time
msg = f"Elements of data[{self.time_column!r}] element are {tname(time[0])}, expected datetime-like."
raise TypeError(msg)
def _get_time(self, data: DataFrame) -> Series:
return data[self.time_column]
def select(self, data: DataFrame, left: datetime, right: datetime) -> DataFrame:
"""Select data based on the given bounds."""
time = self._get_time(data)
return data.filter(time.is_between(left, right, closed="left"))
