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author | Charles Harris <charlesr.harris@gmail.com> | 2020-10-16 09:43:13 -0600 |
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committer | GitHub <noreply@github.com> | 2020-10-16 09:43:13 -0600 |
commit | b9dd2be0108cb312b4c34239a1dc8d24ef3a05a9 (patch) | |
tree | c2acac172a6b6ee298a4ae9165f111a070438480 | |
parent | b4718373f5412ea0d52ecbe2a3f9bbed824953a0 (diff) | |
parent | b81ab444c0e56011e96c8895a19e18906ab4e731 (diff) | |
download | numpy-b9dd2be0108cb312b4c34239a1dc8d24ef3a05a9.tar.gz |
Merge pull request #16759 from person142/dtype-generic
ENH: make dtype generic over scalar type
-rw-r--r-- | numpy/__init__.pyi | 318 | ||||
-rw-r--r-- | numpy/typing/__init__.py | 2 | ||||
-rw-r--r-- | numpy/typing/_dtype_like.py | 30 | ||||
-rw-r--r-- | numpy/typing/tests/data/fail/dtype.py | 9 | ||||
-rw-r--r-- | numpy/typing/tests/data/reveal/dtype.py | 33 |
5 files changed, 370 insertions, 22 deletions
diff --git a/numpy/__init__.pyi b/numpy/__init__.pyi index e41c3cd78..64e4c75ce 100644 --- a/numpy/__init__.pyi +++ b/numpy/__init__.pyi @@ -15,6 +15,8 @@ from numpy.typing import ( _FloatLike, _ComplexLike, _NumberLike, + _SupportsDtype, + _VoidDtypeLike, ) from numpy.typing._callable import ( _BoolOp, @@ -508,16 +510,322 @@ where: Any who: Any _NdArraySubClass = TypeVar("_NdArraySubClass", bound=ndarray) +_DTypeScalar = TypeVar("_DTypeScalar", bound=generic) _ByteOrder = Literal["S", "<", ">", "=", "|", "L", "B", "N", "I"] -class dtype: +class dtype(Generic[_DTypeScalar]): names: Optional[Tuple[str, ...]] - def __init__( - self, - dtype: DtypeLike, + # Overload for subclass of generic + @overload + def __new__( + cls, + dtype: Type[_DTypeScalar], align: bool = ..., copy: bool = ..., - ) -> None: ... + ) -> dtype[_DTypeScalar]: ... + # Overloads for string aliases, Python types, and some assorted + # other special cases. Order is sometimes important because of the + # subtype relationships + # + # bool < int < float < complex + # + # so we have to make sure the overloads for the narrowest type is + # first. + @overload + def __new__( + cls, + dtype: Union[ + Type[bool], + Literal[ + "?", + "=?", + "<?", + ">?", + "bool", + "bool_", + ], + ], + align: bool = ..., + copy: bool = ..., + ) -> dtype[bool_]: ... + @overload + def __new__( + cls, + dtype: Literal[ + "uint8", + "u1", + "=u1", + "<u1", + ">u1", + ], + align: bool = ..., + copy: bool = ..., + ) -> dtype[uint8]: ... + @overload + def __new__( + cls, + dtype: Literal[ + "uint16", + "u2", + "=u2", + "<u2", + ">u2", + ], + align: bool = ..., + copy: bool = ..., + ) -> dtype[uint16]: ... + @overload + def __new__( + cls, + dtype: Literal[ + "uint32", + "u4", + "=u4", + "<u4", + ">u4", + ], + align: bool = ..., + copy: bool = ..., + ) -> dtype[uint32]: ... + @overload + def __new__( + cls, + dtype: Literal[ + "uint64", + "u8", + "=u8", + "<u8", + ">u8", + ], + align: bool = ..., + copy: bool = ..., + ) -> dtype[uint64]: ... + @overload + def __new__( + cls, + dtype: Literal[ + "int8", + "i1", + "=i1", + "<i1", + ">i1", + ], + align: bool = ..., + copy: bool = ..., + ) -> dtype[int8]: ... + @overload + def __new__( + cls, + dtype: Literal[ + "int16", + "i2", + "=i2", + "<i2", + ">i2", + ], + align: bool = ..., + copy: bool = ..., + ) -> dtype[int16]: ... + @overload + def __new__( + cls, + dtype: Literal[ + "int32", + "i4", + "=i4", + "<i4", + ">i4", + ], + align: bool = ..., + copy: bool = ..., + ) -> dtype[int32]: ... + @overload + def __new__( + cls, + dtype: Literal[ + "int64", + "i8", + "=i8", + "<i8", + ">i8", + ], + align: bool = ..., + copy: bool = ..., + ) -> dtype[int64]: ... + # "int"/int resolve to int_, which is system dependent and as of + # now untyped. Long-term we'll do something fancier here. + @overload + def __new__( + cls, + dtype: Union[Type[int], Literal["int"]], + align: bool = ..., + copy: bool = ..., + ) -> dtype: ... + @overload + def __new__( + cls, + dtype: Literal[ + "float16", + "f4", + "=f4", + "<f4", + ">f4", + "e", + "=e", + "<e", + ">e", + "half", + ], + align: bool = ..., + copy: bool = ..., + ) -> dtype[float16]: ... + @overload + def __new__( + cls, + dtype: Literal[ + "float32", + "f4", + "=f4", + "<f4", + ">f4", + "f", + "=f", + "<f", + ">f", + "single", + ], + align: bool = ..., + copy: bool = ..., + ) -> dtype[float32]: ... + @overload + def __new__( + cls, + dtype: Union[ + None, + Type[float], + Literal[ + "float64", + "f8", + "=f8", + "<f8", + ">f8", + "d", + "<d", + ">d", + "float", + "double", + "float_", + ], + ], + align: bool = ..., + copy: bool = ..., + ) -> dtype[float64]: ... + @overload + def __new__( + cls, + dtype: Literal[ + "complex64", + "c8", + "=c8", + "<c8", + ">c8", + "F", + "=F", + "<F", + ">F", + ], + align: bool = ..., + copy: bool = ..., + ) -> dtype[complex64]: ... + @overload + def __new__( + cls, + dtype: Union[ + Type[complex], + Literal[ + "complex128", + "c16", + "=c16", + "<c16", + ">c16", + "D", + "=D", + "<D", + ">D", + ], + ], + align: bool = ..., + copy: bool = ..., + ) -> dtype[complex128]: ... + @overload + def __new__( + cls, + dtype: Union[ + Type[bytes], + Literal[ + "S", + "=S", + "<S", + ">S", + "bytes", + "bytes_", + "bytes0", + ], + ], + align: bool = ..., + copy: bool = ..., + ) -> dtype[bytes_]: ... + @overload + def __new__( + cls, + dtype: Union[ + Type[str], + Literal[ + "U", + "=U", + # <U and >U intentionally not included; they are not + # the same dtype and which one dtype("U") translates + # to is platform-dependent. + "str", + "str_", + "str0", + ], + ], + align: bool = ..., + copy: bool = ..., + ) -> dtype[str_]: ... + # dtype of a dtype is the same dtype + @overload + def __new__( + cls, + dtype: dtype[_DTypeScalar], + align: bool = ..., + copy: bool = ..., + ) -> dtype[_DTypeScalar]: ... + # TODO: handle _SupportsDtype better + @overload + def __new__( + cls, + dtype: _SupportsDtype, + align: bool = ..., + copy: bool = ..., + ) -> dtype[Any]: ... + # Handle strings that can't be expressed as literals; i.e. s1, s2, ... + @overload + def __new__( + cls, + dtype: str, + align: bool = ..., + copy: bool = ..., + ) -> dtype[Any]: ... + # Catchall overload + @overload + def __new__( + cls, + dtype: _VoidDtypeLike, + align: bool = ..., + copy: bool = ..., + ) -> dtype[void]: ... def __eq__(self, other: DtypeLike) -> bool: ... def __ne__(self, other: DtypeLike) -> bool: ... def __gt__(self, other: DtypeLike) -> bool: ... diff --git a/numpy/typing/__init__.py b/numpy/typing/__init__.py index 987aa39aa..dafabd95a 100644 --- a/numpy/typing/__init__.py +++ b/numpy/typing/__init__.py @@ -102,7 +102,7 @@ from ._scalars import ( ) from ._array_like import _SupportsArray, ArrayLike from ._shape import _Shape, _ShapeLike -from ._dtype_like import DtypeLike +from ._dtype_like import _SupportsDtype, _VoidDtypeLike, DtypeLike from numpy._pytesttester import PytestTester test = PytestTester(__name__) diff --git a/numpy/typing/_dtype_like.py b/numpy/typing/_dtype_like.py index 7c1946a3e..5bfd8ffdc 100644 --- a/numpy/typing/_dtype_like.py +++ b/numpy/typing/_dtype_like.py @@ -38,18 +38,9 @@ else: _DtypeDict = Any _SupportsDtype = Any -# Anything that can be coerced into numpy.dtype. -# Reference: https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html -DtypeLike = Union[ - dtype, - # default data type (float64) - None, - # array-scalar types and generic types - type, # TODO: enumerate these when we add type hints for numpy scalars - # anything with a dtype attribute - _SupportsDtype, - # character codes, type strings or comma-separated fields, e.g., 'float64' - str, + +# Would create a dtype[np.void] +_VoidDtypeLike = Union[ # (flexible_dtype, itemsize) Tuple[_DtypeLikeNested, int], # (fixed_dtype, shape) @@ -67,6 +58,21 @@ DtypeLike = Union[ Tuple[_DtypeLikeNested, _DtypeLikeNested], ] +# Anything that can be coerced into numpy.dtype. +# Reference: https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html +DtypeLike = Union[ + dtype, + # default data type (float64) + None, + # array-scalar types and generic types + type, # TODO: enumerate these when we add type hints for numpy scalars + # anything with a dtype attribute + _SupportsDtype, + # character codes, type strings or comma-separated fields, e.g., 'float64' + str, + _VoidDtypeLike, +] + # NOTE: while it is possible to provide the dtype as a dict of # dtype-like objects (e.g. `{'field1': ..., 'field2': ..., ...}`), # this syntax is officially discourged and diff --git a/numpy/typing/tests/data/fail/dtype.py b/numpy/typing/tests/data/fail/dtype.py index 3dc027daf..7d4783d8f 100644 --- a/numpy/typing/tests/data/fail/dtype.py +++ b/numpy/typing/tests/data/fail/dtype.py @@ -1,15 +1,16 @@ import numpy as np - class Test: not_dtype = float -np.dtype(Test()) # E: Argument 1 to "dtype" has incompatible type +np.dtype(Test()) # E: No overload variant of "dtype" matches -np.dtype( - { # E: Argument 1 to "dtype" has incompatible type +np.dtype( # E: No overload variant of "dtype" matches + { "field1": (float, 1), "field2": (int, 3), } ) + +np.dtype[np.float64](np.int64) # E: Argument 1 to "dtype" has incompatible type diff --git a/numpy/typing/tests/data/reveal/dtype.py b/numpy/typing/tests/data/reveal/dtype.py new file mode 100644 index 000000000..e0802299e --- /dev/null +++ b/numpy/typing/tests/data/reveal/dtype.py @@ -0,0 +1,33 @@ +import numpy as np + +reveal_type(np.dtype(np.float64)) # E: numpy.dtype[numpy.float64*] +reveal_type(np.dtype(np.int64)) # E: numpy.dtype[numpy.int64*] + +# String aliases +reveal_type(np.dtype("float64")) # E: numpy.dtype[numpy.float64] +reveal_type(np.dtype("float32")) # E: numpy.dtype[numpy.float32] +reveal_type(np.dtype("int64")) # E: numpy.dtype[numpy.int64] +reveal_type(np.dtype("int32")) # E: numpy.dtype[numpy.int32] +reveal_type(np.dtype("bool")) # E: numpy.dtype[numpy.bool_] +reveal_type(np.dtype("bytes")) # E: numpy.dtype[numpy.bytes_] +reveal_type(np.dtype("str")) # E: numpy.dtype[numpy.str_] + +# Python types +reveal_type(np.dtype(complex)) # E: numpy.dtype[numpy.complex128] +reveal_type(np.dtype(float)) # E: numpy.dtype[numpy.float64] +reveal_type(np.dtype(int)) # E: numpy.dtype +reveal_type(np.dtype(bool)) # E: numpy.dtype[numpy.bool_] +reveal_type(np.dtype(str)) # E: numpy.dtype[numpy.str_] +reveal_type(np.dtype(bytes)) # E: numpy.dtype[numpy.bytes_] + +# Special case for None +reveal_type(np.dtype(None)) # E: numpy.dtype[numpy.float64] + +# Dtypes of dtypes +reveal_type(np.dtype(np.dtype(np.float64))) # E: numpy.dtype[numpy.float64*] + +# Parameterized dtypes +reveal_type(np.dtype("S8")) # E: numpy.dtype + +# Void +reveal_type(np.dtype(("U", 10))) # E: numpy.dtype[numpy.void] |