copy the dtypes module to the namedarray package.

xarray/core/dtypes.py

@@ -1,188 +1 @@
-from __future__ import annotations
-
-import functools
-
-import numpy as np
-
-from xarray.core 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):
-        return True
-
-    def __eq__(self, other):
-        return isinstance(other, type(self))
-
-
-@functools.total_ordering
-class AlwaysLessThan:
-    def __lt__(self, other):
-        return True
-
-    def __eq__(self, other):
-        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):
-    """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
-    if np.issubdtype(dtype, np.floating):
-        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")
-    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):
-        fill_value = np.nan + np.nan * 1j
-    elif np.issubdtype(dtype, np.datetime64):
-        fill_value = np.datetime64("NaT")
-    else:
-        dtype = object
-        fill_value = np.nan
-
-    dtype = np.dtype(dtype)
-    fill_value = dtype.type(fill_value)
-    return dtype, fill_value
-
-
-NAT_TYPES = {np.datetime64("NaT").dtype, np.timedelta64("NaT").dtype}
-
-
-def get_fill_value(dtype):
-    """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, max_for_int=False):
-    """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):
-        if max_for_int:
-            return np.iinfo(dtype).max
-        else:
-            return np.inf
-
-    if issubclass(dtype.type, np.complexfloating):
-        return np.inf + 1j * np.inf
-
-    return INF
-
-
-def get_neg_infinity(dtype, min_for_int=False):
-    """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):
-        if min_for_int:
-            return np.iinfo(dtype).min
-        else:
-            return -np.inf
-
-    if issubclass(dtype.type, np.complexfloating):
-        return -np.inf - 1j * np.inf
-
-    return NINF
-
-
-def is_datetime_like(dtype):
-    """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:
-    """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)
+from xarray.namedarray.dtypes import *  # noqa: F401, F403

xarray/core/utils.py

@@ -72,6 +72,8 @@
 import numpy as np
 import pandas as pd
 
+from xarray.namedarray.utils import ReprObject  # noqa: F401
+
 if TYPE_CHECKING:
     from xarray.core.types import Dims, ErrorOptionsWithWarn, OrderedDims, T_DuckArray
 
@@ -605,31 +607,6 @@ def __repr__(self: Any) -> str:
         return f"{type(self).__name__}(array={self.array!r})"
 
 
-class ReprObject:
-    """Object that prints as the given value, for use with sentinel values."""
-
-    __slots__ = ("_value",)
-
-    def __init__(self, value: str):
-        self._value = value
-
-    def __repr__(self) -> str:
-        return self._value
-
-    def __eq__(self, other) -> bool:
-        if isinstance(other, ReprObject):
-            return self._value == other._value
-        return False
-
-    def __hash__(self) -> int:
-        return hash((type(self), self._value))
-
-    def __dask_tokenize__(self):
-        from dask.base import normalize_token
-
-        return normalize_token((type(self), self._value))
-
-
 @contextlib.contextmanager
 def close_on_error(f):
     """Context manager to ensure that a file opened by xarray is closed if an