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from __future__ import annotations
import functools
from typing import Any, Literal, TypeGuard
import numpy as np
from xarray.namedarray import utils
# Use as a sentinel value to indicate a dtype appropriate NA value.
NA = utils.ReprObject("<NA>")
@functools.total_ordering
class AlwaysGreaterThan:
def __gt__(self, other: object) -> Literal[True]:
return True
def __eq__(self, other: object) -> bool:
return isinstance(other, type(self))
@functools.total_ordering
class AlwaysLessThan:
def __lt__(self, other: object) -> Literal[True]:
return True
def __eq__(self, other: object) -> bool:
return isinstance(other, type(self))
# Equivalence to np.inf (-np.inf) for object-type
INF = AlwaysGreaterThan()
NINF = AlwaysLessThan()
# Pairs of types that, if both found, should be promoted to object dtype
# instead of following NumPy's own type-promotion rules. These type promotion
# rules match pandas instead. For reference, see the NumPy type hierarchy:
# https://numpy.org/doc/stable/reference/arrays.scalars.html
PROMOTE_TO_OBJECT: tuple[tuple[type[np.generic], type[np.generic]], ...] = (
(np.number, np.character), # numpy promotes to character
(np.bool_, np.character), # numpy promotes to character
(np.bytes_, np.str_), # numpy promotes to unicode
)
def maybe_promote(dtype: np.dtype[np.generic]) -> tuple[np.dtype[np.generic], Any]:
"""Simpler equivalent of pandas.core.common._maybe_promote
Parameters
----------
dtype : np.dtype
Returns
-------
dtype : Promoted dtype that can hold missing values.
fill_value : Valid missing value for the promoted dtype.
"""
# N.B. these casting rules should match pandas
dtype_: np.typing.DTypeLike
fill_value: Any
if np.issubdtype(dtype, np.floating):
dtype_ = dtype
fill_value = np.nan
elif np.issubdtype(dtype, np.timedelta64):
# See https://github.com/numpy/numpy/issues/10685
# np.timedelta64 is a subclass of np.integer
# Check np.timedelta64 before np.integer
fill_value = np.timedelta64("NaT")
dtype_ = dtype
elif np.issubdtype(dtype, np.integer):
dtype_ = np.float32 if dtype.itemsize <= 2 else np.float64
fill_value = np.nan
elif np.issubdtype(dtype, np.complexfloating):
dtype_ = dtype
fill_value = np.nan + np.nan * 1j
elif np.issubdtype(dtype, np.datetime64):
dtype_ = dtype
fill_value = np.datetime64("NaT")
else:
dtype_ = object
fill_value = np.nan
dtype_out = np.dtype(dtype_)
fill_value = dtype_out.type(fill_value)
return dtype_out, fill_value
NAT_TYPES = {np.datetime64("NaT").dtype, np.timedelta64("NaT").dtype}
def get_fill_value(dtype: np.dtype[np.generic]) -> Any:
"""Return an appropriate fill value for this dtype.
Parameters
----------
dtype : np.dtype
Returns
-------
fill_value : Missing value corresponding to this dtype.
"""
_, fill_value = maybe_promote(dtype)
return fill_value
def get_pos_infinity(
dtype: np.dtype[np.generic], max_for_int: bool = False
) -> float | complex | AlwaysGreaterThan:
"""Return an appropriate positive infinity for this dtype.
Parameters
----------
dtype : np.dtype
max_for_int : bool
Return np.iinfo(dtype).max instead of np.inf
Returns
-------
fill_value : positive infinity value corresponding to this dtype.
"""
if issubclass(dtype.type, np.floating):
return np.inf
if issubclass(dtype.type, np.integer):
return np.iinfo(dtype.type).max if max_for_int else np.inf
if issubclass(dtype.type, np.complexfloating):
return np.inf + 1j * np.inf
return INF
def get_neg_infinity(
dtype: np.dtype[np.generic], min_for_int: bool = False
) -> float | complex | AlwaysLessThan:
"""Return an appropriate positive infinity for this dtype.
Parameters
----------
dtype : np.dtype
min_for_int : bool
Return np.iinfo(dtype).min instead of -np.inf
Returns
-------
fill_value : positive infinity value corresponding to this dtype.
"""
if issubclass(dtype.type, np.floating):
return -np.inf
if issubclass(dtype.type, np.integer):
return np.iinfo(dtype.type).min if min_for_int else -np.inf
if issubclass(dtype.type, np.complexfloating):
return -np.inf - 1j * np.inf
return NINF
def is_datetime_like(
dtype: np.dtype[np.generic],
) -> TypeGuard[np.datetime64 | np.timedelta64]:
"""Check if a dtype is a subclass of the numpy datetime types"""
return np.issubdtype(dtype, np.datetime64) or np.issubdtype(dtype, np.timedelta64)
def result_type(
*arrays_and_dtypes: np.typing.ArrayLike | np.typing.DTypeLike,
) -> np.dtype[np.generic]:
"""Like np.result_type, but with type promotion rules matching pandas.
Examples of changed behavior:
number + string -> object (not string)
bytes + unicode -> object (not unicode)
Parameters
----------
*arrays_and_dtypes : list of arrays and dtypes
The dtype is extracted from both numpy and dask arrays.
Returns
-------
numpy.dtype for the result.
"""
types = {np.result_type(t).type for t in arrays_and_dtypes}
for left, right in PROMOTE_TO_OBJECT:
if any(issubclass(t, left) for t in types) and any(
issubclass(t, right) for t in types
):
return np.dtype(object)
return np.result_type(*arrays_and_dtypes)
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