Buckets:
| from typing import ( | |
| Any, | |
| Generic, | |
| TypeVar, | |
| overload, | |
| ) | |
| import numpy as np | |
| import numpy.typing as npt | |
| from pandas._typing import ( | |
| IntervalClosedType, | |
| Timedelta, | |
| Timestamp, | |
| ) | |
| VALID_CLOSED: frozenset[str] | |
| _OrderableScalarT = TypeVar("_OrderableScalarT", int, float) | |
| _OrderableTimesT = TypeVar("_OrderableTimesT", Timestamp, Timedelta) | |
| _OrderableT = TypeVar("_OrderableT", int, float, Timestamp, Timedelta) | |
| class _LengthDescriptor: | |
| def __get__( | |
| self, instance: Interval[_OrderableScalarT], owner: Any | |
| ) -> _OrderableScalarT: ... | |
| def __get__( | |
| self, instance: Interval[_OrderableTimesT], owner: Any | |
| ) -> Timedelta: ... | |
| class _MidDescriptor: | |
| def __get__(self, instance: Interval[_OrderableScalarT], owner: Any) -> float: ... | |
| def __get__( | |
| self, instance: Interval[_OrderableTimesT], owner: Any | |
| ) -> _OrderableTimesT: ... | |
| class IntervalMixin: | |
| def closed_left(self) -> bool: ... | |
| def closed_right(self) -> bool: ... | |
| def open_left(self) -> bool: ... | |
| def open_right(self) -> bool: ... | |
| def is_empty(self) -> bool: ... | |
| def _check_closed_matches(self, other: IntervalMixin, name: str = ...) -> None: ... | |
| class Interval(IntervalMixin, Generic[_OrderableT]): | |
| def left(self: Interval[_OrderableT]) -> _OrderableT: ... | |
| def right(self: Interval[_OrderableT]) -> _OrderableT: ... | |
| def closed(self) -> IntervalClosedType: ... | |
| mid: _MidDescriptor | |
| length: _LengthDescriptor | |
| def __init__( | |
| self, | |
| left: _OrderableT, | |
| right: _OrderableT, | |
| closed: IntervalClosedType = ..., | |
| ) -> None: ... | |
| def __hash__(self) -> int: ... | |
| def __contains__( | |
| self: Interval[Timedelta], key: Timedelta | Interval[Timedelta] | |
| ) -> bool: ... | |
| def __contains__( | |
| self: Interval[Timestamp], key: Timestamp | Interval[Timestamp] | |
| ) -> bool: ... | |
| def __contains__( | |
| self: Interval[_OrderableScalarT], | |
| key: _OrderableScalarT | Interval[_OrderableScalarT], | |
| ) -> bool: ... | |
| def __add__( | |
| self: Interval[_OrderableTimesT], y: Timedelta | |
| ) -> Interval[_OrderableTimesT]: ... | |
| def __add__( | |
| self: Interval[int], y: _OrderableScalarT | |
| ) -> Interval[_OrderableScalarT]: ... | |
| def __add__(self: Interval[float], y: float) -> Interval[float]: ... | |
| def __radd__( | |
| self: Interval[_OrderableTimesT], y: Timedelta | |
| ) -> Interval[_OrderableTimesT]: ... | |
| def __radd__( | |
| self: Interval[int], y: _OrderableScalarT | |
| ) -> Interval[_OrderableScalarT]: ... | |
| def __radd__(self: Interval[float], y: float) -> Interval[float]: ... | |
| def __sub__( | |
| self: Interval[_OrderableTimesT], y: Timedelta | |
| ) -> Interval[_OrderableTimesT]: ... | |
| def __sub__( | |
| self: Interval[int], y: _OrderableScalarT | |
| ) -> Interval[_OrderableScalarT]: ... | |
| def __sub__(self: Interval[float], y: float) -> Interval[float]: ... | |
| def __rsub__( | |
| self: Interval[_OrderableTimesT], y: Timedelta | |
| ) -> Interval[_OrderableTimesT]: ... | |
| def __rsub__( | |
| self: Interval[int], y: _OrderableScalarT | |
| ) -> Interval[_OrderableScalarT]: ... | |
| def __rsub__(self: Interval[float], y: float) -> Interval[float]: ... | |
| def __mul__( | |
| self: Interval[int], y: _OrderableScalarT | |
| ) -> Interval[_OrderableScalarT]: ... | |
| def __mul__(self: Interval[float], y: float) -> Interval[float]: ... | |
| def __rmul__( | |
| self: Interval[int], y: _OrderableScalarT | |
| ) -> Interval[_OrderableScalarT]: ... | |
| def __rmul__(self: Interval[float], y: float) -> Interval[float]: ... | |
| def __truediv__( | |
| self: Interval[int], y: _OrderableScalarT | |
| ) -> Interval[_OrderableScalarT]: ... | |
| def __truediv__(self: Interval[float], y: float) -> Interval[float]: ... | |
| def __floordiv__( | |
| self: Interval[int], y: _OrderableScalarT | |
| ) -> Interval[_OrderableScalarT]: ... | |
| def __floordiv__(self: Interval[float], y: float) -> Interval[float]: ... | |
| def overlaps(self: Interval[_OrderableT], other: Interval[_OrderableT]) -> bool: ... | |
| def intervals_to_interval_bounds( | |
| intervals: np.ndarray, validate_closed: bool = ... | |
| ) -> tuple[np.ndarray, np.ndarray, IntervalClosedType]: ... | |
| class IntervalTree(IntervalMixin): | |
| def __init__( | |
| self, | |
| left: np.ndarray, | |
| right: np.ndarray, | |
| closed: IntervalClosedType = ..., | |
| leaf_size: int = ..., | |
| ) -> None: ... | |
| def mid(self) -> np.ndarray: ... | |
| def length(self) -> np.ndarray: ... | |
| def get_indexer(self, target) -> npt.NDArray[np.intp]: ... | |
| def get_indexer_non_unique( | |
| self, target | |
| ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ... | |
| _na_count: int | |
| def is_overlapping(self) -> bool: ... | |
| def is_monotonic_increasing(self) -> bool: ... | |
| def clear_mapping(self) -> None: ... | |
Xet Storage Details
- Size:
- 5.38 kB
- Xet hash:
- fa9afd292d0f483dad1bce4148af4c49edd4976c631e7a8fe82ac21e0866e348
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.