done

lib/python3.11/site-packages/pandas-stubs/_libs/interval.pyi

@@ -0,0 +1,247 @@
+from typing import (
+    Any,
+    Generic,
+    Literal,
+    TypeVar,
+    overload,
+)
+
+import numpy as np
+from pandas import (
+    IntervalIndex,
+    Series,
+    Timedelta,
+    Timestamp,
+)
+from pandas.core.series import (
+    TimedeltaSeries,
+    TimestampSeries,
+)
+
+from pandas._typing import (
+    IntervalClosedType,
+    IntervalT,
+    np_1darray,
+    npt,
+)
+
+VALID_CLOSED: frozenset[str]
+
+_OrderableScalarT = TypeVar("_OrderableScalarT", bound=int | float)
+_OrderableTimesT = TypeVar("_OrderableTimesT", bound=Timestamp | Timedelta)
+_OrderableT = TypeVar("_OrderableT", bound=int | float | Timestamp | Timedelta)
+
+class _LengthDescriptor:
+    @overload
+    def __get__(
+        self, instance: Interval[_OrderableScalarT], owner: Any
+    ) -> _OrderableScalarT: ...
+    @overload
+    def __get__(
+        self, instance: Interval[_OrderableTimesT], owner: Any
+    ) -> Timedelta: ...
+    @overload
+    def __get__(self, instance: IntervalTree, owner: Any) -> np.ndarray: ...
+
+class _MidDescriptor:
+    @overload
+    def __get__(self, instance: Interval[_OrderableScalarT], owner: Any) -> float: ...
+    @overload
+    def __get__(
+        self, instance: Interval[_OrderableTimesT], owner: Any
+    ) -> _OrderableTimesT: ...
+    @overload
+    def __get__(self, instance: IntervalTree, owner: Any) -> np.ndarray: ...
+
+class IntervalMixin:
+    @property
+    def closed_left(self) -> bool: ...
+    @property
+    def closed_right(self) -> bool: ...
+    @property
+    def open_left(self) -> bool: ...
+    @property
+    def open_right(self) -> bool: ...
+    @property
+    def is_empty(self) -> bool: ...
+
+class Interval(IntervalMixin, Generic[_OrderableT]):
+    @property
+    def left(self: Interval[_OrderableT]) -> _OrderableT: ...
+    @property
+    def right(self: Interval[_OrderableT]) -> _OrderableT: ...
+    @property
+    def closed(self) -> IntervalClosedType: ...
+    mid: _MidDescriptor
+    length: _LengthDescriptor
+    def __init__(
+        self,
+        left: _OrderableT,
+        right: _OrderableT,
+        closed: IntervalClosedType = ...,
+    ) -> None: ...
+    def __hash__(self) -> int: ...
+    # for __contains__, it seems that we have to separate out the 4 cases to make
+    # mypy happy
+    @overload
+    def __contains__(self: Interval[Timestamp], key: Timestamp) -> bool: ...
+    @overload
+    def __contains__(self: Interval[Timedelta], key: Timedelta) -> bool: ...
+    @overload
+    def __contains__(self: Interval[int], key: float) -> bool: ...
+    @overload
+    def __contains__(self: Interval[float], key: float) -> bool: ...
+    @overload
+    def __add__(self: Interval[Timestamp], y: Timedelta) -> Interval[Timestamp]: ...
+    @overload
+    def __add__(self: Interval[Timedelta], y: Timedelta) -> Interval[Timedelta]: ...
+    @overload
+    def __add__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __add__(self: Interval[float], y: float) -> Interval[float]: ...
+    @overload
+    def __radd__(
+        self: Interval[_OrderableTimesT], y: Timedelta
+    ) -> Interval[_OrderableTimesT]: ...
+    @overload
+    def __radd__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __radd__(self: Interval[float], y: float) -> Interval[float]: ...
+    @overload
+    def __sub__(
+        self: Interval[_OrderableTimesT], y: Timedelta
+    ) -> Interval[_OrderableTimesT]: ...
+    @overload
+    def __sub__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __sub__(self: Interval[float], y: float) -> Interval[float]: ...
+    @overload
+    def __rsub__(
+        self: Interval[_OrderableTimesT], y: Timedelta
+    ) -> Interval[_OrderableTimesT]: ...
+    @overload
+    def __rsub__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __rsub__(self: Interval[float], y: float) -> Interval[float]: ...
+    @overload
+    def __mul__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __mul__(self: Interval[float], y: float) -> Interval[float]: ...
+    @overload
+    def __mul__(self: Interval[Timedelta], y: float) -> Interval[Timedelta]: ...
+    @overload
+    def __rmul__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __rmul__(self: Interval[float], y: float) -> Interval[float]: ...
+    @overload
+    def __rmul__(self: Interval[Timedelta], y: float) -> Interval[Timedelta]: ...
+    @overload
+    def __truediv__(self: Interval[int], y: float) -> Interval[float]: ...
+    @overload
+    def __truediv__(self: Interval[float], y: float) -> Interval[float]: ...
+    @overload
+    def __truediv__(self: Interval[Timedelta], y: float) -> Interval[Timedelta]: ...
+    @overload
+    def __floordiv__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __floordiv__(self: Interval[float], y: float) -> Interval[float]: ...
+    @overload
+    def __floordiv__(self: Interval[Timedelta], y: float) -> Interval[Timedelta]: ...
+    @overload
+    def overlaps(self: Interval[_OrderableT], other: Interval[_OrderableT]) -> bool: ...
+    @overload
+    def overlaps(self: Interval[int], other: Interval[float]) -> bool: ...
+    @overload
+    def overlaps(self: Interval[float], other: Interval[int]) -> bool: ...
+    @overload
+    def __gt__(self, other: Interval[_OrderableT]) -> bool: ...
+    @overload
+    def __gt__(
+        self: IntervalT, other: IntervalIndex[IntervalT]
+    ) -> np_1darray[np.bool]: ...
+    @overload
+    def __gt__(
+        self,
+        other: Series[int] | Series[float] | TimestampSeries | TimedeltaSeries,
+    ) -> Series[bool]: ...
+    @overload
+    def __lt__(self, other: Interval[_OrderableT]) -> bool: ...
+    @overload
+    def __lt__(
+        self: IntervalT, other: IntervalIndex[IntervalT]
+    ) -> np_1darray[np.bool]: ...
+    @overload
+    def __lt__(
+        self,
+        other: Series[int] | Series[float] | TimestampSeries | TimedeltaSeries,
+    ) -> Series[bool]: ...
+    @overload
+    def __ge__(self, other: Interval[_OrderableT]) -> bool: ...
+    @overload
+    def __ge__(
+        self: IntervalT, other: IntervalIndex[IntervalT]
+    ) -> np_1darray[np.bool]: ...
+    @overload
+    def __ge__(
+        self,
+        other: Series[int] | Series[float] | TimestampSeries | TimedeltaSeries,
+    ) -> Series[bool]: ...
+    @overload
+    def __le__(self, other: Interval[_OrderableT]) -> bool: ...
+    @overload
+    def __le__(
+        self: IntervalT, other: IntervalIndex[IntervalT]
+    ) -> np_1darray[np.bool]: ...
+    @overload
+    def __eq__(self, other: Interval[_OrderableT]) -> bool: ...  # type: ignore[overload-overlap] # pyright: ignore[reportOverlappingOverload]
+    @overload
+    def __eq__(
+        self: IntervalT, other: IntervalIndex[IntervalT]
+    ) -> np_1darray[np.bool]: ...
+    @overload
+    def __eq__(self, other: Series[_OrderableT]) -> Series[bool]: ...  # type: ignore[overload-overlap]
+    @overload
+    def __eq__(self, other: object) -> Literal[False]: ...
+    @overload
+    def __ne__(self, other: Interval[_OrderableT]) -> bool: ...  # type: ignore[overload-overlap] # pyright: ignore[reportOverlappingOverload]
+    @overload
+    def __ne__(
+        self: IntervalT, other: IntervalIndex[IntervalT]
+    ) -> np_1darray[np.bool]: ...
+    @overload
+    def __ne__(self, other: Series[_OrderableT]) -> Series[bool]: ...  # type: ignore[overload-overlap]
+    @overload
+    def __ne__(self, other: object) -> Literal[True]: ...
+
+class IntervalTree(IntervalMixin):
+    def __init__(
+        self,
+        left: np.ndarray,
+        right: np.ndarray,
+        closed: IntervalClosedType = ...,
+        leaf_size: int = ...,
+    ) -> None: ...
+    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
+    @property
+    def is_overlapping(self) -> bool: ...
+    @property
+    def is_monotonic_increasing(self) -> bool: ...
+    def clear_mapping(self) -> None: ...