From d594768ade744d9b5baf46d270dbb83259c906f2 Mon Sep 17 00:00:00 2001 From: Hugo Defois Date: Wed, 7 Jul 2021 23:53:05 +0200 Subject: BUG: fix a PyArray_DiscoverDTypeAndShape_Recursive leak --- numpy/core/src/multiarray/array_coercion.c | 1 + 1 file changed, 1 insertion(+) (limited to 'numpy') diff --git a/numpy/core/src/multiarray/array_coercion.c b/numpy/core/src/multiarray/array_coercion.c index 847bdafc3..8e5183424 100644 --- a/numpy/core/src/multiarray/array_coercion.c +++ b/numpy/core/src/multiarray/array_coercion.c @@ -1089,6 +1089,7 @@ PyArray_DiscoverDTypeAndShape_Recursive( } /* The cache takes ownership of the sequence here. */ if (npy_new_coercion_cache(obj, seq, 1, coercion_cache_tail_ptr, curr_dims) < 0) { + Py_DECREF(seq); return -1; } -- cgit v1.2.1 From 93fd427d5061dbec7684208317e08e887a996043 Mon Sep 17 00:00:00 2001 From: Hugo Defois Date: Wed, 7 Jul 2021 23:53:42 +0200 Subject: BUG: fix a PyInit__multiarray_umath leak --- numpy/core/src/multiarray/multiarraymodule.c | 1 + 1 file changed, 1 insertion(+) (limited to 'numpy') diff --git a/numpy/core/src/multiarray/multiarraymodule.c b/numpy/core/src/multiarray/multiarraymodule.c index 5ceed1678..5dccaf94c 100644 --- a/numpy/core/src/multiarray/multiarraymodule.c +++ b/numpy/core/src/multiarray/multiarraymodule.c @@ -4914,5 +4914,6 @@ PyMODINIT_FUNC PyInit__multiarray_umath(void) { PyErr_SetString(PyExc_RuntimeError, "cannot load multiarray module."); } + Py_XDECREF(m); return NULL; } -- cgit v1.2.1 From c2de2d1fed4a7fd69e439d25bfb1b91f9df6bd1e Mon Sep 17 00:00:00 2001 From: Hugo Defois Date: Wed, 7 Jul 2021 23:54:23 +0200 Subject: BUG: fix a PyArray_CastScalarToCtype leak --- numpy/core/src/multiarray/scalarapi.c | 2 ++ 1 file changed, 2 insertions(+) (limited to 'numpy') diff --git a/numpy/core/src/multiarray/scalarapi.c b/numpy/core/src/multiarray/scalarapi.c index e409e9874..28c010f8f 100644 --- a/numpy/core/src/multiarray/scalarapi.c +++ b/numpy/core/src/multiarray/scalarapi.c @@ -235,6 +235,7 @@ PyArray_CastScalarToCtype(PyObject *scalar, void *ctypeptr, descr = PyArray_DescrFromScalar(scalar); castfunc = PyArray_GetCastFunc(descr, outcode->type_num); if (castfunc == NULL) { + Py_XDECREF(descr); return -1; } if (PyTypeNum_ISEXTENDED(descr->type_num) || @@ -254,6 +255,7 @@ PyArray_CastScalarToCtype(PyObject *scalar, void *ctypeptr, NPY_ARRAY_CARRAY, NULL); if (aout == NULL) { Py_DECREF(ain); + Py_DECREF(descr); return -1; } castfunc(PyArray_DATA(ain), PyArray_DATA(aout), 1, ain, aout); -- cgit v1.2.1 From 09326ebc9ee8720a00afbd34068a54414d741e94 Mon Sep 17 00:00:00 2001 From: Sebastian Berg Date: Wed, 29 Sep 2021 14:00:22 -0500 Subject: Fixups based on review comments. --- numpy/core/src/multiarray/array_coercion.c | 2 +- numpy/core/src/multiarray/multiarraymodule.c | 12 ++++++------ numpy/core/src/multiarray/scalarapi.c | 5 ++++- 3 files changed, 11 insertions(+), 8 deletions(-) (limited to 'numpy') diff --git a/numpy/core/src/multiarray/array_coercion.c b/numpy/core/src/multiarray/array_coercion.c index 8e5183424..3c44d312c 100644 --- a/numpy/core/src/multiarray/array_coercion.c +++ b/numpy/core/src/multiarray/array_coercion.c @@ -555,6 +555,7 @@ npy_new_coercion_cache( cache = PyMem_Malloc(sizeof(coercion_cache_obj)); } if (cache == NULL) { + Py_DECREF(arr_or_sequence); PyErr_NoMemory(); return -1; } @@ -1089,7 +1090,6 @@ PyArray_DiscoverDTypeAndShape_Recursive( } /* The cache takes ownership of the sequence here. */ if (npy_new_coercion_cache(obj, seq, 1, coercion_cache_tail_ptr, curr_dims) < 0) { - Py_DECREF(seq); return -1; } diff --git a/numpy/core/src/multiarray/multiarraymodule.c b/numpy/core/src/multiarray/multiarraymodule.c index 5dccaf94c..184f9d05f 100644 --- a/numpy/core/src/multiarray/multiarraymodule.c +++ b/numpy/core/src/multiarray/multiarraymodule.c @@ -4669,14 +4669,14 @@ PyMODINIT_FUNC PyInit__multiarray_umath(void) { PyObject *m, *d, *s; PyObject *c_api; - /* Initialize CPU features */ - if (npy_cpu_init() < 0) { - goto err; - } - /* Create the module and add the functions */ m = PyModule_Create(&moduledef); if (!m) { + return NULL; + } + + /* Initialize CPU features */ + if (npy_cpu_init() < 0) { goto err; } @@ -4914,6 +4914,6 @@ PyMODINIT_FUNC PyInit__multiarray_umath(void) { PyErr_SetString(PyExc_RuntimeError, "cannot load multiarray module."); } - Py_XDECREF(m); + Py_DECREF(m); return NULL; } diff --git a/numpy/core/src/multiarray/scalarapi.c b/numpy/core/src/multiarray/scalarapi.c index 28c010f8f..564352f1f 100644 --- a/numpy/core/src/multiarray/scalarapi.c +++ b/numpy/core/src/multiarray/scalarapi.c @@ -233,9 +233,12 @@ PyArray_CastScalarToCtype(PyObject *scalar, void *ctypeptr, PyArray_VectorUnaryFunc* castfunc; descr = PyArray_DescrFromScalar(scalar); + if (descr == NULL) { + return -1; + } castfunc = PyArray_GetCastFunc(descr, outcode->type_num); if (castfunc == NULL) { - Py_XDECREF(descr); + Py_DECREF(descr); return -1; } if (PyTypeNum_ISEXTENDED(descr->type_num) || -- cgit v1.2.1 From 7dcf62379f41407d8f9583d1c2016e3d8ec48384 Mon Sep 17 00:00:00 2001 From: Hector Martin Date: Thu, 14 Oct 2021 14:58:52 +0900 Subject: MAINT: Fix issue with C compiler args containing spaces Instead of doing a dumb string split, use shlex to make sure args containing spaces are handled properly. --- numpy/distutils/unixccompiler.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) (limited to 'numpy') diff --git a/numpy/distutils/unixccompiler.py b/numpy/distutils/unixccompiler.py index 733a9fc50..4884960fd 100644 --- a/numpy/distutils/unixccompiler.py +++ b/numpy/distutils/unixccompiler.py @@ -5,6 +5,7 @@ unixccompiler - can handle very long argument lists for ar. import os import sys import subprocess +import shlex from distutils.errors import CompileError, DistutilsExecError, LibError from distutils.unixccompiler import UnixCCompiler @@ -30,15 +31,15 @@ def UnixCCompiler__compile(self, obj, src, ext, cc_args, extra_postargs, pp_opts if 'OPT' in os.environ: # XXX who uses this? from sysconfig import get_config_vars - opt = " ".join(os.environ['OPT'].split()) - gcv_opt = " ".join(get_config_vars('OPT')[0].split()) - ccomp_s = " ".join(self.compiler_so) + opt = shlex.join(shlex.split(os.environ['OPT'])) + gcv_opt = shlex.join(shlex.split(get_config_vars('OPT')[0])) + ccomp_s = shlex.join(self.compiler_so) if opt not in ccomp_s: ccomp_s = ccomp_s.replace(gcv_opt, opt) - self.compiler_so = ccomp_s.split() - llink_s = " ".join(self.linker_so) + self.compiler_so = shlex.split(ccomp_s) + llink_s = shlex.join(self.linker_so) if opt not in llink_s: - self.linker_so = llink_s.split() + opt.split() + self.linker_so = self.linker_so + shlex.split(opt) display = '%s: %s' % (os.path.basename(self.compiler_so[0]), src) -- cgit v1.2.1 From 1d90222e3a10621791a58ffb040e60d9bcc67795 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=82=85=E7=AB=8B=E4=B8=9A=EF=BC=88Chris=20Fu=EF=BC=89?= <17433201@qq.com> Date: Fri, 29 Oct 2021 11:41:10 +0800 Subject: BUG: Fix shadowed reference of `dtype` in type stubs Direct use of `dtype` in `ndarray` or `generic` would be shadowed by `ndarray.dtype` or `generic.dtype`, so an alias is used here. See #20223. --- numpy/__init__.pyi | 65 ++++++++++++++++++++++++++++-------------------------- 1 file changed, 34 insertions(+), 31 deletions(-) (limited to 'numpy') diff --git a/numpy/__init__.pyi b/numpy/__init__.pyi index 463d4a713..bc95c5d06 100644 --- a/numpy/__init__.pyi +++ b/numpy/__init__.pyi @@ -1431,6 +1431,9 @@ _ArrayComplex_co = NDArray[Union[bool_, integer[Any], floating[Any], complexfloa _ArrayNumber_co = NDArray[Union[bool_, number[Any]]] _ArrayTD64_co = NDArray[Union[bool_, integer[Any], timedelta64]] +# Introduce an alias for `dtype` to avoid naming conflicts. +_dtype = dtype + class _SupportsItem(Protocol[_T_co]): def item(self, args: Any, /) -> _T_co: ... @@ -1452,13 +1455,13 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @property def real( self: NDArray[_SupportsReal[_ScalarType]], # type: ignore[type-var] - ) -> ndarray[_ShapeType, dtype[_ScalarType]]: ... + ) -> ndarray[_ShapeType, _dtype[_ScalarType]]: ... @real.setter def real(self, value: ArrayLike) -> None: ... @property def imag( self: NDArray[_SupportsImag[_ScalarType]], # type: ignore[type-var] - ) -> ndarray[_ShapeType, dtype[_ScalarType]]: ... + ) -> ndarray[_ShapeType, _dtype[_ScalarType]]: ... @imag.setter def imag(self, value: ArrayLike) -> None: ... def __new__( @@ -1530,7 +1533,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __getitem__(self: NDArray[void], key: str) -> NDArray[Any]: ... @overload - def __getitem__(self: NDArray[void], key: list[str]) -> ndarray[_ShapeType, dtype[void]]: ... + def __getitem__(self: NDArray[void], key: list[str]) -> ndarray[_ShapeType, _dtype[void]]: ... @property def ctypes(self) -> _ctypes[int]: ... @@ -1550,12 +1553,12 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): # Use the same output type as that of the underlying `generic` @overload def item( - self: ndarray[Any, dtype[_SupportsItem[_T]]], # type: ignore[type-var] + self: ndarray[Any, _dtype[_SupportsItem[_T]]], # type: ignore[type-var] *args: SupportsIndex, ) -> _T: ... @overload def item( - self: ndarray[Any, dtype[_SupportsItem[_T]]], # type: ignore[type-var] + self: ndarray[Any, _dtype[_SupportsItem[_T]]], # type: ignore[type-var] args: Tuple[SupportsIndex, ...], /, ) -> _T: ... @@ -1596,7 +1599,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): axis: Optional[SupportsIndex] = ..., kind: _PartitionKind = ..., order: Union[None, str, Sequence[str]] = ..., - ) -> ndarray[Any, dtype[intp]]: ... + ) -> ndarray[Any, _dtype[intp]]: ... def diagonal( self, @@ -1615,7 +1618,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def dot(self, b: ArrayLike, out: _NdArraySubClass) -> _NdArraySubClass: ... # `nonzero()` is deprecated for 0d arrays/generics - def nonzero(self) -> Tuple[ndarray[Any, dtype[intp]], ...]: ... + def nonzero(self) -> Tuple[ndarray[Any, _dtype[intp]], ...]: ... def partition( self, @@ -1647,7 +1650,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): v: ArrayLike, side: _SortSide = ..., sorter: Optional[_ArrayLikeInt_co] = ..., - ) -> ndarray[Any, dtype[intp]]: ... + ) -> ndarray[Any, _dtype[intp]]: ... def setfield( self, @@ -1684,7 +1687,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def take( # type: ignore[misc] - self: ndarray[Any, dtype[_ScalarType]], + self: ndarray[Any, _dtype[_ScalarType]], indices: _IntLike_co, axis: Optional[SupportsIndex] = ..., out: None = ..., @@ -1781,19 +1784,19 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): # Dispatch to the underlying `generic` via protocols def __int__( - self: ndarray[Any, dtype[SupportsInt]], # type: ignore[type-var] + self: ndarray[Any, _dtype[SupportsInt]], # type: ignore[type-var] ) -> int: ... def __float__( - self: ndarray[Any, dtype[SupportsFloat]], # type: ignore[type-var] + self: ndarray[Any, _dtype[SupportsFloat]], # type: ignore[type-var] ) -> float: ... def __complex__( - self: ndarray[Any, dtype[SupportsComplex]], # type: ignore[type-var] + self: ndarray[Any, _dtype[SupportsComplex]], # type: ignore[type-var] ) -> complex: ... def __index__( - self: ndarray[Any, dtype[SupportsIndex]], # type: ignore[type-var] + self: ndarray[Any, _dtype[SupportsIndex]], # type: ignore[type-var] ) -> int: ... def __len__(self) -> int: ... @@ -1925,7 +1928,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __mod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc] @overload - def __mod__(self: _ArrayTD64_co, other: _SupportsArray[dtype[timedelta64]] | _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> NDArray[timedelta64]: ... + def __mod__(self: _ArrayTD64_co, other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> NDArray[timedelta64]: ... @overload def __mod__(self: NDArray[object_], other: Any) -> Any: ... @overload @@ -1940,7 +1943,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rmod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc] @overload - def __rmod__(self: _ArrayTD64_co, other: _SupportsArray[dtype[timedelta64]] | _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> NDArray[timedelta64]: ... + def __rmod__(self: _ArrayTD64_co, other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> NDArray[timedelta64]: ... @overload def __rmod__(self: NDArray[object_], other: Any) -> Any: ... @overload @@ -1955,7 +1958,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __divmod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _2Tuple[NDArray[floating[Any]]]: ... # type: ignore[misc] @overload - def __divmod__(self: _ArrayTD64_co, other: _SupportsArray[dtype[timedelta64]] | _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> Tuple[NDArray[int64], NDArray[timedelta64]]: ... + def __divmod__(self: _ArrayTD64_co, other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> Tuple[NDArray[int64], NDArray[timedelta64]]: ... @overload def __rdivmod__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> _2Tuple[NDArray[int8]]: ... # type: ignore[misc] @@ -1966,7 +1969,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rdivmod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _2Tuple[NDArray[floating[Any]]]: ... # type: ignore[misc] @overload - def __rdivmod__(self: _ArrayTD64_co, other: _SupportsArray[dtype[timedelta64]] | _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> Tuple[NDArray[int64], NDArray[timedelta64]]: ... + def __rdivmod__(self: _ArrayTD64_co, other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> Tuple[NDArray[int64], NDArray[timedelta64]]: ... @overload def __add__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @@ -2099,7 +2102,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __floordiv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc] @overload - def __floordiv__(self: NDArray[timedelta64], other: _SupportsArray[dtype[timedelta64]] | _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> NDArray[int64]: ... + def __floordiv__(self: NDArray[timedelta64], other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> NDArray[int64]: ... @overload def __floordiv__(self: NDArray[timedelta64], other: _ArrayLikeBool_co) -> NoReturn: ... @overload @@ -2118,7 +2121,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rfloordiv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc] @overload - def __rfloordiv__(self: NDArray[timedelta64], other: _SupportsArray[dtype[timedelta64]] | _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> NDArray[int64]: ... + def __rfloordiv__(self: NDArray[timedelta64], other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> NDArray[int64]: ... @overload def __rfloordiv__(self: NDArray[bool_], other: _ArrayLikeTD64_co) -> NoReturn: ... @overload @@ -2165,7 +2168,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __truediv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __truediv__(self: NDArray[timedelta64], other: _SupportsArray[dtype[timedelta64]] | _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> NDArray[float64]: ... + def __truediv__(self: NDArray[timedelta64], other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> NDArray[float64]: ... @overload def __truediv__(self: NDArray[timedelta64], other: _ArrayLikeBool_co) -> NoReturn: ... @overload @@ -2182,7 +2185,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rtruediv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __rtruediv__(self: NDArray[timedelta64], other: _SupportsArray[dtype[timedelta64]] | _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> NDArray[float64]: ... + def __rtruediv__(self: NDArray[timedelta64], other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> NDArray[float64]: ... @overload def __rtruediv__(self: NDArray[bool_], other: _ArrayLikeTD64_co) -> NoReturn: ... @overload @@ -2394,7 +2397,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __imod__(self: NDArray[floating[_NBit1]], other: _ArrayLikeFloat_co) -> NDArray[floating[_NBit1]]: ... @overload - def __imod__(self: NDArray[timedelta64], other: _SupportsArray[dtype[timedelta64]] | _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> NDArray[timedelta64]: ... + def __imod__(self: NDArray[timedelta64], other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> NDArray[timedelta64]: ... @overload def __imod__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... @@ -2459,7 +2462,7 @@ class generic(_ArrayOrScalarCommon): @abstractmethod def __init__(self, *args: Any, **kwargs: Any) -> None: ... @overload - def __array__(self: _ScalarType, dtype: None = ..., /) -> ndarray[Any, dtype[_ScalarType]]: ... + def __array__(self: _ScalarType, dtype: None = ..., /) -> ndarray[Any, _dtype[_ScalarType]]: ... @overload def __array__(self, dtype: _DType, /) -> ndarray[Any, _DType]: ... @property @@ -2474,7 +2477,7 @@ class generic(_ArrayOrScalarCommon): def strides(self) -> Tuple[()]: ... def byteswap(self: _ScalarType, inplace: L[False] = ...) -> _ScalarType: ... @property - def flat(self: _ScalarType) -> flatiter[ndarray[Any, dtype[_ScalarType]]]: ... + def flat(self: _ScalarType) -> flatiter[ndarray[Any, _dtype[_ScalarType]]]: ... @overload def astype( @@ -2547,7 +2550,7 @@ class generic(_ArrayOrScalarCommon): axis: Optional[SupportsIndex] = ..., out: None = ..., mode: _ModeKind = ..., - ) -> ndarray[Any, dtype[_ScalarType]]: ... + ) -> ndarray[Any, _dtype[_ScalarType]]: ... @overload def take( self, @@ -2561,26 +2564,26 @@ class generic(_ArrayOrScalarCommon): self: _ScalarType, repeats: _ArrayLikeInt_co, axis: Optional[SupportsIndex] = ..., - ) -> ndarray[Any, dtype[_ScalarType]]: ... + ) -> ndarray[Any, _dtype[_ScalarType]]: ... def flatten( self: _ScalarType, order: _OrderKACF = ..., - ) -> ndarray[Any, dtype[_ScalarType]]: ... + ) -> ndarray[Any, _dtype[_ScalarType]]: ... def ravel( self: _ScalarType, order: _OrderKACF = ..., - ) -> ndarray[Any, dtype[_ScalarType]]: ... + ) -> ndarray[Any, _dtype[_ScalarType]]: ... @overload def reshape( self: _ScalarType, shape: _ShapeLike, /, *, order: _OrderACF = ... - ) -> ndarray[Any, dtype[_ScalarType]]: ... + ) -> ndarray[Any, _dtype[_ScalarType]]: ... @overload def reshape( self: _ScalarType, *shape: SupportsIndex, order: _OrderACF = ... - ) -> ndarray[Any, dtype[_ScalarType]]: ... + ) -> ndarray[Any, _dtype[_ScalarType]]: ... def squeeze( self: _ScalarType, axis: Union[L[0], Tuple[()]] = ... @@ -2588,7 +2591,7 @@ class generic(_ArrayOrScalarCommon): def transpose(self: _ScalarType, axes: Tuple[()] = ..., /) -> _ScalarType: ... # Keep `dtype` at the bottom to avoid name conflicts with `np.dtype` @property - def dtype(self: _ScalarType) -> dtype[_ScalarType]: ... + def dtype(self: _ScalarType) -> _dtype[_ScalarType]: ... class number(generic, Generic[_NBit1]): # type: ignore @property -- cgit v1.2.1 From 4149a37f99f259d61ebf8dbdc18366a560139d21 Mon Sep 17 00:00:00 2001 From: HowJMay Date: Tue, 24 Aug 2021 11:10:58 +0800 Subject: ENH: Add conversion for 512bit intrin to 256bit intrin 512-bits intrinsics sometimes not exist in some operations. We can construct the 512-bits intrinsics with two 256-bits intrinsics. Therfore, NPYV_IMPL_AVX512_FROM_AVX2_PS_1ARG and NPYV_IMPL_AVX512_FROM_AVX2_PD_1ARG are added. And the error in npyv512_combine_ps256 has been fixed too. --- numpy/core/src/common/simd/avx512/utils.h | 22 +++++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) (limited to 'numpy') diff --git a/numpy/core/src/common/simd/avx512/utils.h b/numpy/core/src/common/simd/avx512/utils.h index 8066283c6..c3079283f 100644 --- a/numpy/core/src/common/simd/avx512/utils.h +++ b/numpy/core/src/common/simd/avx512/utils.h @@ -26,7 +26,7 @@ #define npyv512_combine_ps256(A, B) _mm512_insertf32x8(_mm512_castps256_ps512(A), B, 1) #else #define npyv512_combine_ps256(A, B) \ - _mm512_castsi512_ps(npyv512_combine_si256(_mm512_castps_si512(A), _mm512_castps_si512(B))) + _mm512_castsi512_ps(npyv512_combine_si256(_mm256_castps_si256(A), _mm256_castps_si256(B))) #endif #define NPYV_IMPL_AVX512_FROM_AVX2_1ARG(FN_NAME, INTRIN) \ @@ -39,6 +39,26 @@ return npyv512_combine_si256(l_a, h_a); \ } +#define NPYV_IMPL_AVX512_FROM_AVX2_PS_1ARG(FN_NAME, INTRIN) \ + NPY_FINLINE __m512 FN_NAME(__m512 a) \ + { \ + __m256 l_a = npyv512_lower_ps256(a); \ + __m256 h_a = npyv512_higher_ps256(a); \ + l_a = INTRIN(l_a); \ + h_a = INTRIN(h_a); \ + return npyv512_combine_ps256(l_a, h_a); \ + } + +#define NPYV_IMPL_AVX512_FROM_AVX2_PD_1ARG(FN_NAME, INTRIN) \ + NPY_FINLINE __m512d FN_NAME(__m512d a) \ + { \ + __m256d l_a = npyv512_lower_pd256(a); \ + __m256d h_a = npyv512_higher_pd256(a); \ + l_a = INTRIN(l_a); \ + h_a = INTRIN(h_a); \ + return npyv512_combine_pd256(l_a, h_a); \ + } + #define NPYV_IMPL_AVX512_FROM_AVX2_2ARG(FN_NAME, INTRIN) \ NPY_FINLINE __m512i FN_NAME(__m512i a, __m512i b) \ { \ -- cgit v1.2.1 From 6ccad06d45c682f5fd199cf61a175575f431cbac Mon Sep 17 00:00:00 2001 From: HowJMay Date: Tue, 24 Aug 2021 11:11:41 +0800 Subject: ENH: Implement SIMD for ceil --- numpy/core/src/_simd/_simd.dispatch.c.src | 4 ++-- numpy/core/src/common/simd/avx2/math.h | 4 ++++ numpy/core/src/common/simd/avx512/math.h | 4 ++++ numpy/core/src/common/simd/neon/math.h | 16 ++++++++++++++++ numpy/core/src/common/simd/sse/math.h | 31 +++++++++++++++++++++++++++++++ numpy/core/src/common/simd/vsx/math.h | 4 ++++ numpy/core/tests/test_simd.py | 27 +++++++++++++++++++++++++++ 7 files changed, 88 insertions(+), 2 deletions(-) (limited to 'numpy') diff --git a/numpy/core/src/_simd/_simd.dispatch.c.src b/numpy/core/src/_simd/_simd.dispatch.c.src index 54770959c..5c494ae7a 100644 --- a/numpy/core/src/_simd/_simd.dispatch.c.src +++ b/numpy/core/src/_simd/_simd.dispatch.c.src @@ -381,7 +381,7 @@ SIMD_IMPL_INTRIN_1(sumup_@sfx@, @esfx@, v@sfx@) ***************************/ #if @fp_only@ /**begin repeat1 - * #intrin = sqrt, recip, abs, square# + * #intrin = sqrt, recip, abs, square, ceil# */ SIMD_IMPL_INTRIN_1(@intrin@_@sfx@, v@sfx@, v@sfx@) /**end repeat1**/ @@ -615,7 +615,7 @@ SIMD_INTRIN_DEF(sumup_@sfx@) ***************************/ #if @fp_only@ /**begin repeat1 - * #intrin = sqrt, recip, abs, square# + * #intrin = sqrt, recip, abs, square, ceil# */ SIMD_INTRIN_DEF(@intrin@_@sfx@) /**end repeat1**/ diff --git a/numpy/core/src/common/simd/avx2/math.h b/numpy/core/src/common/simd/avx2/math.h index 9460183df..b1f3915a6 100644 --- a/numpy/core/src/common/simd/avx2/math.h +++ b/numpy/core/src/common/simd/avx2/math.h @@ -105,4 +105,8 @@ NPY_FINLINE npyv_s64 npyv_min_s64(npyv_s64 a, npyv_s64 b) return _mm256_blendv_epi8(a, b, _mm256_cmpgt_epi64(a, b)); } +// ceil +#define npyv_ceil_f32 _mm256_ceil_ps +#define npyv_ceil_f64 _mm256_ceil_pd + #endif // _NPY_SIMD_AVX2_MATH_H diff --git a/numpy/core/src/common/simd/avx512/math.h b/numpy/core/src/common/simd/avx512/math.h index 0949b2b06..c4f8d3410 100644 --- a/numpy/core/src/common/simd/avx512/math.h +++ b/numpy/core/src/common/simd/avx512/math.h @@ -112,4 +112,8 @@ NPY_FINLINE npyv_f64 npyv_minp_f64(npyv_f64 a, npyv_f64 b) #define npyv_min_u64 _mm512_min_epu64 #define npyv_min_s64 _mm512_min_epi64 +// ceil +#define npyv_ceil_f32(A) _mm512_roundscale_ps(A, _MM_FROUND_TO_POS_INF) +#define npyv_ceil_f64(A) _mm512_roundscale_pd(A, _MM_FROUND_TO_POS_INF) + #endif // _NPY_SIMD_AVX512_MATH_H diff --git a/numpy/core/src/common/simd/neon/math.h b/numpy/core/src/common/simd/neon/math.h index 19ea6f22f..8d370e624 100644 --- a/numpy/core/src/common/simd/neon/math.h +++ b/numpy/core/src/common/simd/neon/math.h @@ -153,4 +153,20 @@ NPY_FINLINE npyv_s64 npyv_min_s64(npyv_s64 a, npyv_s64 b) return vbslq_s64(npyv_cmplt_s64(a, b), a, b); } +// ceil +#ifdef NPY_HAVE_ASIMD + #define npyv_ceil_f32 vrndpq_f32 +#else + NPY_FINLINE npyv_f32 npyv_ceil_f32(npyv_f32 a) + { + npyv_f32 conv_trunc = vcvtq_f32_s32(vcvtq_s32_f32(a)); + npyv_f32 conv_trunc_add_one = npyv_add_f32(conv_trunc, vdupq_n_f32(1.0f)); + npyv_u32 mask = vcltq_f32(conv_trunc, a); + return vbslq_f32(mask, conv_trunc, conv_trunc_add_one); + } +#endif +#if NPY_SIMD_F64 + #define npyv_ceil_f64 vrndpq_f64 +#endif // NPY_SIMD_F64 + #endif // _NPY_SIMD_NEON_MATH_H diff --git a/numpy/core/src/common/simd/sse/math.h b/numpy/core/src/common/simd/sse/math.h index 97d35afc5..02eb06a29 100644 --- a/numpy/core/src/common/simd/sse/math.h +++ b/numpy/core/src/common/simd/sse/math.h @@ -143,4 +143,35 @@ NPY_FINLINE npyv_s64 npyv_min_s64(npyv_s64 a, npyv_s64 b) return npyv_select_s64(npyv_cmplt_s64(a, b), a, b); } +// ceil +#ifdef NPY_HAVE_SSE41 + #define npyv_ceil_f32 _mm_ceil_ps + #define npyv_ceil_f64 _mm_ceil_pd +#else + NPY_FINLINE npyv_f32 npyv_ceil_f32(npyv_f32 a) + { + const npyv_f32 szero = _mm_set1_ps(-0.0f); + const npyv_f32 one = _mm_set1_ps(1.0f); + npyv_s32 roundi = _mm_cvttps_epi32(a); + npyv_f32 round = _mm_cvtepi32_ps(roundi); + npyv_f32 ceil = _mm_add_ps(round, _mm_and_ps(_mm_cmplt_ps(round, a), one)); + // respect signed zero, e.g. -0.5 -> -0.0 + npyv_f32 rzero = _mm_or_ps(ceil, _mm_and_ps(a, szero)); + // if overflow return a + return npyv_select_f32(_mm_cmpeq_epi32(roundi, _mm_castps_si128(szero)), a, rzero); + } + NPY_FINLINE npyv_f64 npyv_ceil_f64(npyv_f64 a) + { + const npyv_f64 szero = _mm_set1_pd(-0.0); + const npyv_f64 one = _mm_set1_pd(1.0); + const npyv_f64 two_power_52 = _mm_set1_pd(0x10000000000000); + npyv_f64 sign_two52 = _mm_or_pd(two_power_52, _mm_and_pd(a, szero)); + // round by add magic number 2^52 + npyv_f64 round = _mm_sub_pd(_mm_add_pd(a, sign_two52), sign_two52); + npyv_f64 ceil = _mm_add_pd(round, _mm_and_pd(_mm_cmplt_pd(round, a), one)); + // respect signed zero, e.g. -0.5 -> -0.0 + return _mm_or_pd(ceil, _mm_and_pd(a, szero)); + } +#endif + #endif // _NPY_SIMD_SSE_MATH_H diff --git a/numpy/core/src/common/simd/vsx/math.h b/numpy/core/src/common/simd/vsx/math.h index b2e393c7c..f387dac4d 100644 --- a/numpy/core/src/common/simd/vsx/math.h +++ b/numpy/core/src/common/simd/vsx/math.h @@ -69,4 +69,8 @@ NPY_FINLINE npyv_f64 npyv_square_f64(npyv_f64 a) #define npyv_min_u64 vec_min #define npyv_min_s64 vec_min +// ceil +#define npyv_ceil_f32 vec_ceil +#define npyv_ceil_f64 vec_ceil + #endif // _NPY_SIMD_VSX_MATH_H diff --git a/numpy/core/tests/test_simd.py b/numpy/core/tests/test_simd.py index 0270ad901..379fef8af 100644 --- a/numpy/core/tests/test_simd.py +++ b/numpy/core/tests/test_simd.py @@ -330,6 +330,33 @@ class _SIMD_FP(_Test_Utility): square = self.square(vdata) assert square == data_square + @pytest.mark.parametrize("intrin, func", [("self.ceil", math.ceil)]) + def test_rounding(self, intrin, func): + """ + Test intrinsics: + npyv_ceil_##SFX + """ + intrin = eval(intrin) + pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan() + # special cases + round_cases = ((nan, nan), (pinf, pinf), (ninf, ninf)) + for case, desired in round_cases: + data_round = [desired]*self.nlanes + _round = intrin(self.setall(case)) + assert _round == pytest.approx(data_round, nan_ok=True) + for x in range(0, 2**20, 256**2): + for w in (-1.05, -1.10, -1.15, 1.05, 1.10, 1.15): + data = [x*w+a for a in range(self.nlanes)] + vdata = self.load(data) + data_round = [func(x) for x in data] + _round = intrin(vdata) + assert _round == data_round + # signed zero + for w in (-0.25, -0.30, -0.45): + _round = self._to_unsigned(intrin(self.setall(w))) + data_round = self._to_unsigned(self.setall(-0.0)) + assert _round == data_round + def test_max(self): """ Test intrinsics: -- cgit v1.2.1 From 521b3afc15b7a8c181ebb7c043f3744555c1e9c9 Mon Sep 17 00:00:00 2001 From: Sayed Adel Date: Mon, 1 Nov 2021 18:49:15 +0200 Subject: SIMD: Fix impl of intrinsic `npyv_ceil_f32` on armv7/neon --- numpy/core/src/common/simd/neon/math.h | 41 +++++++++++++++++++++++++--------- 1 file changed, 31 insertions(+), 10 deletions(-) (limited to 'numpy') diff --git a/numpy/core/src/common/simd/neon/math.h b/numpy/core/src/common/simd/neon/math.h index 8d370e624..38c3899e4 100644 --- a/numpy/core/src/common/simd/neon/math.h +++ b/numpy/core/src/common/simd/neon/math.h @@ -88,16 +88,16 @@ NPY_FINLINE npyv_f32 npyv_recip_f32(npyv_f32 a) #define npyv_max_f64 vmaxq_f64 // Maximum, supports IEEE floating-point arithmetic (IEC 60559), // - If one of the two vectors contains NaN, the equivalent element of the other vector is set -// - Only if both corresponded elements are NaN, NaN is set. +// - Only if both corresponded elements are NaN, NaN is set. #ifdef NPY_HAVE_ASIMD #define npyv_maxp_f32 vmaxnmq_f32 #else NPY_FINLINE npyv_f32 npyv_maxp_f32(npyv_f32 a, npyv_f32 b) - { + { npyv_u32 nn_a = vceqq_f32(a, a); npyv_u32 nn_b = vceqq_f32(b, b); return vmaxq_f32(vbslq_f32(nn_a, a, b), vbslq_f32(nn_b, b, a)); - } + } #endif #if NPY_SIMD_F64 #define npyv_maxp_f64 vmaxnmq_f64 @@ -123,16 +123,16 @@ NPY_FINLINE npyv_s64 npyv_max_s64(npyv_s64 a, npyv_s64 b) #define npyv_min_f64 vminq_f64 // Minimum, supports IEEE floating-point arithmetic (IEC 60559), // - If one of the two vectors contains NaN, the equivalent element of the other vector is set -// - Only if both corresponded elements are NaN, NaN is set. +// - Only if both corresponded elements are NaN, NaN is set. #ifdef NPY_HAVE_ASIMD #define npyv_minp_f32 vminnmq_f32 #else NPY_FINLINE npyv_f32 npyv_minp_f32(npyv_f32 a, npyv_f32 b) - { + { npyv_u32 nn_a = vceqq_f32(a, a); npyv_u32 nn_b = vceqq_f32(b, b); return vminq_f32(vbslq_f32(nn_a, a, b), vbslq_f32(nn_b, b, a)); - } + } #endif #if NPY_SIMD_F64 #define npyv_minp_f64 vminnmq_f64 @@ -159,10 +159,31 @@ NPY_FINLINE npyv_s64 npyv_min_s64(npyv_s64 a, npyv_s64 b) #else NPY_FINLINE npyv_f32 npyv_ceil_f32(npyv_f32 a) { - npyv_f32 conv_trunc = vcvtq_f32_s32(vcvtq_s32_f32(a)); - npyv_f32 conv_trunc_add_one = npyv_add_f32(conv_trunc, vdupq_n_f32(1.0f)); - npyv_u32 mask = vcltq_f32(conv_trunc, a); - return vbslq_f32(mask, conv_trunc, conv_trunc_add_one); + const npyv_s32 szero = vreinterpretq_s32_f32(vdupq_n_f32(-0.0f)); + const npyv_u32 one = vreinterpretq_u32_f32(vdupq_n_f32(1.0f)); + const npyv_s32 max_int = vdupq_n_s32(0x7fffffff); + /** + * On armv7, vcvtq.f32 handles special cases as follows: + * NaN return 0 + * +inf or +outrange return 0x80000000(-0.0f) + * -inf or -outrange return 0x7fffffff(nan) + */ + npyv_s32 roundi = vcvtq_s32_f32(a); + npyv_f32 round = vcvtq_f32_s32(roundi); + npyv_f32 ceil = vaddq_f32(round, vreinterpretq_f32_u32( + vandq_u32(vcltq_f32(round, a), one)) + ); + // respect signed zero, e.g. -0.5 -> -0.0 + npyv_f32 rzero = vreinterpretq_f32_s32(vorrq_s32( + vreinterpretq_s32_f32(ceil), + vandq_s32(vreinterpretq_s32_f32(a), szero) + )); + // if nan or overflow return a + npyv_u32 nnan = npyv_notnan_f32(a); + npyv_u32 overflow = vorrq_u32( + vceqq_s32(roundi, szero), vceqq_s32(roundi, max_int) + ); + return vbslq_f32(vbicq_u32(nnan, overflow), rzero, a); } #endif #if NPY_SIMD_F64 -- cgit v1.2.1 From 35e2d8fc23adf982994d00d959ab3a7ec27c84f1 Mon Sep 17 00:00:00 2001 From: Bas van Beek <43369155+BvB93@users.noreply.github.com> Date: Mon, 1 Nov 2021 22:01:05 +0100 Subject: BUG: Fix shadowed reference of `str` in the type stubs --- numpy/__init__.pyi | 26 ++++++++++++-------------- 1 file changed, 12 insertions(+), 14 deletions(-) (limited to 'numpy') diff --git a/numpy/__init__.pyi b/numpy/__init__.pyi index bc95c5d06..de01f92ef 100644 --- a/numpy/__init__.pyi +++ b/numpy/__init__.pyi @@ -682,7 +682,7 @@ _DTypeScalar_co = TypeVar("_DTypeScalar_co", covariant=True, bound=generic) _ByteOrder = L["S", "<", ">", "=", "|", "L", "B", "N", "I"] class dtype(Generic[_DTypeScalar_co]): - names: None | Tuple[str, ...] + names: None | Tuple[builtins.str, ...] # Overload for subclass of generic @overload def __new__( @@ -709,7 +709,7 @@ class dtype(Generic[_DTypeScalar_co]): @overload def __new__(cls, dtype: Type[complex], align: bool = ..., copy: bool = ...) -> dtype[complex_]: ... @overload - def __new__(cls, dtype: Type[str], align: bool = ..., copy: bool = ...) -> dtype[str_]: ... + def __new__(cls, dtype: Type[builtins.str], align: bool = ..., copy: bool = ...) -> dtype[str_]: ... @overload def __new__(cls, dtype: Type[bytes], align: bool = ..., copy: bool = ...) -> dtype[bytes_]: ... @@ -823,7 +823,7 @@ class dtype(Generic[_DTypeScalar_co]): @overload def __new__( cls, - dtype: str, + dtype: builtins.str, align: bool = ..., copy: bool = ..., ) -> dtype[Any]: ... @@ -848,9 +848,9 @@ class dtype(Generic[_DTypeScalar_co]): def __class_getitem__(self, item: Any) -> GenericAlias: ... @overload - def __getitem__(self: dtype[void], key: List[str]) -> dtype[void]: ... + def __getitem__(self: dtype[void], key: List[builtins.str]) -> dtype[void]: ... @overload - def __getitem__(self: dtype[void], key: str | SupportsIndex) -> dtype[Any]: ... + def __getitem__(self: dtype[void], key: builtins.str | SupportsIndex) -> dtype[Any]: ... # NOTE: In the future 1-based multiplications will also yield `flexible` dtypes @overload @@ -884,15 +884,15 @@ class dtype(Generic[_DTypeScalar_co]): @property def base(self) -> dtype[Any]: ... @property - def byteorder(self) -> str: ... + def byteorder(self) -> builtins.str: ... @property - def char(self) -> str: ... + def char(self) -> builtins.str: ... @property - def descr(self) -> List[Tuple[str, str] | Tuple[str, str, _Shape]]: ... + def descr(self) -> List[Tuple[builtins.str, builtins.str] | Tuple[builtins.str, builtins.str, _Shape]]: ... @property def fields( self, - ) -> None | MappingProxyType[str, Tuple[dtype[Any], int] | Tuple[dtype[Any], int, Any]]: ... + ) -> None | MappingProxyType[builtins.str, Tuple[dtype[Any], int] | Tuple[dtype[Any], int, Any]]: ... @property def flags(self) -> int: ... @property @@ -906,11 +906,11 @@ class dtype(Generic[_DTypeScalar_co]): @property def itemsize(self) -> int: ... @property - def kind(self) -> str: ... + def kind(self) -> builtins.str: ... @property - def metadata(self) -> None | MappingProxyType[str, Any]: ... + def metadata(self) -> None | MappingProxyType[builtins.str, Any]: ... @property - def name(self) -> str: ... + def name(self) -> builtins.str: ... @property def num(self) -> int: ... @property @@ -920,8 +920,6 @@ class dtype(Generic[_DTypeScalar_co]): @property def subdtype(self) -> None | Tuple[dtype[Any], _Shape]: ... def newbyteorder(self: _DType, __new_order: _ByteOrder = ...) -> _DType: ... - # Leave str and type for end to avoid having to use `builtins.str` - # everywhere. See https://github.com/python/mypy/issues/3775 @property def str(self) -> builtins.str: ... @property -- cgit v1.2.1 From a888ae827d34a47eb8fab7a01353175ab39bba76 Mon Sep 17 00:00:00 2001 From: Hameer Abbasi Date: Mon, 24 May 2021 15:22:01 +0200 Subject: ENH: Add the __dlpack__ and __dlpack_device__ methods to ndarray. --- numpy/__init__.pyi | 10 ++ numpy/core/src/common/dlpack/dlpack.h | 188 ++++++++++++++++++++++++++++++++++ numpy/core/src/multiarray/methods.c | 163 +++++++++++++++++++++++++++++ 3 files changed, 361 insertions(+) create mode 100644 numpy/core/src/common/dlpack/dlpack.h (limited to 'numpy') diff --git a/numpy/__init__.pyi b/numpy/__init__.pyi index 637f8578a..a162a637c 100644 --- a/numpy/__init__.pyi +++ b/numpy/__init__.pyi @@ -1432,6 +1432,10 @@ _ArrayTD64_co = NDArray[Union[bool_, integer[Any], timedelta64]] # Introduce an alias for `dtype` to avoid naming conflicts. _dtype = dtype +# `builtins.PyCapsule` unfortunately lacks annotations as of the moment; +# use `Any` as a stopgap measure +_PyCapsule = Any + class _SupportsItem(Protocol[_T_co]): def item(self, args: Any, /) -> _T_co: ... @@ -2439,6 +2443,12 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __ior__(self: NDArray[signedinteger[_NBit1]], other: _ArrayLikeInt_co) -> NDArray[signedinteger[_NBit1]]: ... @overload def __ior__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... + @overload + def __ior__(self: NDArray[_ScalarType], other: _RecursiveSequence) -> NDArray[_ScalarType]: ... + @overload + def __dlpack__(self: NDArray[number[Any]], *, stream: None = ...) -> _PyCapsule: ... + @overload + def __dlpack_device__(self) -> Tuple[L[1], L[0]]: ... # Keep `dtype` at the bottom to avoid name conflicts with `np.dtype` @property diff --git a/numpy/core/src/common/dlpack/dlpack.h b/numpy/core/src/common/dlpack/dlpack.h new file mode 100644 index 000000000..84afca248 --- /dev/null +++ b/numpy/core/src/common/dlpack/dlpack.h @@ -0,0 +1,188 @@ +/*! + * Copyright (c) 2017 by Contributors + * \file dlpack.h + * \brief The common header of DLPack. + */ +#ifndef DLPACK_DLPACK_H_ +#define DLPACK_DLPACK_H_ + +#ifdef __cplusplus +#define DLPACK_EXTERN_C extern "C" +#else +#define DLPACK_EXTERN_C +#endif + +/*! \brief The current version of dlpack */ +#define DLPACK_VERSION 050 + +/*! \brief DLPACK_DLL prefix for windows */ +#ifdef _WIN32 +#ifdef DLPACK_EXPORTS +#define DLPACK_DLL __declspec(dllexport) +#else +#define DLPACK_DLL __declspec(dllimport) +#endif +#else +#define DLPACK_DLL +#endif + +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif +/*! + * \brief The device type in DLDevice. + */ +typedef enum { + /*! \brief CPU device */ + kDLCPU = 1, + /*! \brief CUDA GPU device */ + kDLCUDA = 2, + /*! + * \brief Pinned CUDA CPU memory by cudaMallocHost + */ + kDLCUDAHost = 3, + /*! \brief OpenCL devices. */ + kDLOpenCL = 4, + /*! \brief Vulkan buffer for next generation graphics. */ + kDLVulkan = 7, + /*! \brief Metal for Apple GPU. */ + kDLMetal = 8, + /*! \brief Verilog simulator buffer */ + kDLVPI = 9, + /*! \brief ROCm GPUs for AMD GPUs */ + kDLROCM = 10, + /*! + * \brief Reserved extension device type, + * used for quickly test extension device + * The semantics can differ depending on the implementation. + */ + kDLExtDev = 12, +} DLDeviceType; + +/*! + * \brief A Device for Tensor and operator. + */ +typedef struct { + /*! \brief The device type used in the device. */ + DLDeviceType device_type; + /*! \brief The device index */ + int device_id; +} DLDevice; + +/*! + * \brief The type code options DLDataType. + */ +typedef enum { + /*! \brief signed integer */ + kDLInt = 0U, + /*! \brief unsigned integer */ + kDLUInt = 1U, + /*! \brief IEEE floating point */ + kDLFloat = 2U, + /*! + * \brief Opaque handle type, reserved for testing purposes. + * Frameworks need to agree on the handle data type for the exchange to be well-defined. + */ + kDLOpaqueHandle = 3U, + /*! \brief bfloat16 */ + kDLBfloat = 4U, + /*! + * \brief complex number + * (C/C++/Python layout: compact struct per complex number) + */ + kDLComplex = 5U, +} DLDataTypeCode; + +/*! + * \brief The data type the tensor can hold. + * + * Examples + * - float: type_code = 2, bits = 32, lanes=1 + * - float4(vectorized 4 float): type_code = 2, bits = 32, lanes=4 + * - int8: type_code = 0, bits = 8, lanes=1 + * - std::complex: type_code = 5, bits = 64, lanes = 1 + */ +typedef struct { + /*! + * \brief Type code of base types. + * We keep it uint8_t instead of DLDataTypeCode for minimal memory + * footprint, but the value should be one of DLDataTypeCode enum values. + * */ + uint8_t code; + /*! + * \brief Number of bits, common choices are 8, 16, 32. + */ + uint8_t bits; + /*! \brief Number of lanes in the type, used for vector types. */ + uint16_t lanes; +} DLDataType; + +/*! + * \brief Plain C Tensor object, does not manage memory. + */ +typedef struct { + /*! + * \brief The opaque data pointer points to the allocated data. This will be + * CUDA device pointer or cl_mem handle in OpenCL. This pointer is always + * aligned to 256 bytes as in CUDA. + * + * For given DLTensor, the size of memory required to store the contents of + * data is calculated as follows: + * + * \code{.c} + * static inline size_t GetDataSize(const DLTensor* t) { + * size_t size = 1; + * for (tvm_index_t i = 0; i < t->ndim; ++i) { + * size *= t->shape[i]; + * } + * size *= (t->dtype.bits * t->dtype.lanes + 7) / 8; + * return size; + * } + * \endcode + */ + void* data; + /*! \brief The device of the tensor */ + DLDevice device; + /*! \brief Number of dimensions */ + int ndim; + /*! \brief The data type of the pointer*/ + DLDataType dtype; + /*! \brief The shape of the tensor */ + int64_t* shape; + /*! + * \brief strides of the tensor (in number of elements, not bytes) + * can be NULL, indicating tensor is compact and row-majored. + */ + int64_t* strides; + /*! \brief The offset in bytes to the beginning pointer to data */ + uint64_t byte_offset; +} DLTensor; + +/*! + * \brief C Tensor object, manage memory of DLTensor. This data structure is + * intended to facilitate the borrowing of DLTensor by another framework. It is + * not meant to transfer the tensor. When the borrowing framework doesn't need + * the tensor, it should call the deleter to notify the host that the resource + * is no longer needed. + */ +typedef struct DLManagedTensor { + /*! \brief DLTensor which is being memory managed */ + DLTensor dl_tensor; + /*! \brief the context of the original host framework of DLManagedTensor in + * which DLManagedTensor is used in the framework. It can also be NULL. + */ + void * manager_ctx; + /*! \brief Destructor signature void (*)(void*) - this should be called + * to destruct manager_ctx which holds the DLManagedTensor. It can be NULL + * if there is no way for the caller to provide a reasonable destructor. + * The destructors deletes the argument self as well. + */ + void (*deleter)(struct DLManagedTensor * self); +} DLManagedTensor; +#ifdef __cplusplus +} // DLPACK_EXTERN_C +#endif +#endif // DLPACK_DLPACK_H_ diff --git a/numpy/core/src/multiarray/methods.c b/numpy/core/src/multiarray/methods.c index 2d66c77dc..2251d4b69 100644 --- a/numpy/core/src/multiarray/methods.c +++ b/numpy/core/src/multiarray/methods.c @@ -31,6 +31,7 @@ #include "alloc.h" #include +#include "common/dlpack/dlpack.h" /* NpyArg_ParseKeywords @@ -2762,6 +2763,158 @@ array_class_getitem(PyObject *cls, PyObject *args) generic_alias = NULL; #endif return generic_alias; + +#define NPY_DLPACK_CAPSULE_NAME "dltensor" +#define NPY_DLPACK_USED_CAPSULE_NAME "used_dltensor" + +static void array_dlpack_capsule_deleter(PyObject *self) +{ + if (!PyCapsule_IsValid(self, NPY_DLPACK_CAPSULE_NAME)) { + if (!PyCapsule_IsValid(self, NPY_DLPACK_USED_CAPSULE_NAME)) { + PyErr_SetString(PyExc_RuntimeError, "Invalid capsule name."); + } + return; + } + + DLManagedTensor *managed = + (DLManagedTensor *)PyCapsule_GetPointer(self, NPY_DLPACK_CAPSULE_NAME); + managed->deleter(managed); +} + +static void array_dlpack_deleter(DLManagedTensor *self) +{ + PyArrayObject *array = (PyArrayObject *)self->manager_ctx; + // This will also free the strides as it's one allocation. + PyMem_Free(self->dl_tensor.shape); + PyMem_Free(self); + + PyArray_XDECREF(array); +} + +static PyObject * +array_dlpack(PyArrayObject *self, + PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames) +{ + PyObject *stream = Py_None; + NPY_PREPARE_ARGPARSER; + if (npy_parse_arguments("__dlpack__", args, len_args, kwnames, + "$stream", NULL, &stream, NULL, NULL, NULL)) { + return NULL; + } + + if (stream != Py_None) { + PyErr_SetString(PyExc_RuntimeError, "NumPy only supports " + "stream=None."); + return NULL; + } + + npy_intp itemsize = PyArray_ITEMSIZE(self); + int ndim = PyArray_NDIM(self); + npy_intp *strides = PyArray_STRIDES(self); + npy_intp *shape = PyArray_SHAPE(self); + + for (int i = 0; i < ndim; ++i) { + if (strides[i] % itemsize != 0) { + PyErr_SetString(PyExc_RuntimeError, + "DLPack only supports strides which are a multiple of " + "itemsize."); + return NULL; + } + } + + DLDataType managed_dtype; + PyArray_Descr *dtype = PyArray_DESCR(self); + + if (PyDataType_ISBYTESWAPPED(dtype)) { + PyErr_SetString(PyExc_TypeError, "DLPack only supports native " + "byte swapping."); + return NULL; + } + + managed_dtype.bits = 8 * itemsize; + managed_dtype.lanes = 1; + + if (PyDataType_ISSIGNED(dtype)) { + managed_dtype.code = kDLInt; + } else if (PyDataType_ISUNSIGNED(dtype)) { + managed_dtype.code = kDLUInt; + } else if (PyDataType_ISFLOAT(dtype)) { + // We can't be sure that the dtype is + // IEEE or padded. + if (itemsize > 8) { + PyErr_SetString(PyExc_TypeError, "DLPack only supports IEEE " + "floating point types without padding."); + return NULL; + } + managed_dtype.code = kDLFloat; + } else if (PyDataType_ISCOMPLEX(dtype)) { + // We can't be sure that the dtype is + // IEEE or padded. + if (itemsize > 16) { + PyErr_SetString(PyExc_TypeError, "DLPack only supports IEEE " + "complex point types without padding."); + return NULL; + } + managed_dtype.code = kDLComplex; + } else { + PyErr_SetString(PyExc_TypeError, + "DLPack only supports signed/unsigned integers, float " + "and complex dtypes."); + return NULL; + } + + DLManagedTensor *managed = PyMem_Malloc(sizeof(DLManagedTensor)); + if (managed == NULL) { + PyErr_NoMemory(); + return NULL; + } + + managed->dl_tensor.data = PyArray_DATA(self); + managed->dl_tensor.device.device_type = kDLCPU; + managed->dl_tensor.device.device_id = 0; + managed->dl_tensor.dtype = managed_dtype; + + + int64_t *managed_shape_strides = PyMem_Malloc(sizeof(int64_t) * ndim * 2); + if (managed_shape_strides == NULL) { + PyErr_NoMemory(); + PyMem_Free(managed); + return NULL; + } + + int64_t *managed_shape = managed_shape_strides; + int64_t *managed_strides = managed_shape_strides + ndim; + for (int i = 0; i < ndim; ++i) { + managed_shape[i] = shape[i]; + // Strides in DLPack are items; in NumPy are bytes. + managed_strides[i] = strides[i] / itemsize; + } + + managed->dl_tensor.ndim = ndim; + managed->dl_tensor.shape = managed_shape; + managed->dl_tensor.strides = managed_strides; + managed->dl_tensor.byte_offset = 0; + managed->manager_ctx = self; + managed->deleter = array_dlpack_deleter; + + PyObject *capsule = PyCapsule_New(managed, NPY_DLPACK_CAPSULE_NAME, + array_dlpack_capsule_deleter); + if (capsule == NULL) { + PyMem_Free(managed); + PyMem_Free(managed_shape_strides); + return NULL; + } + + // the capsule holds a reference + PyArray_INCREF(self); + return capsule; +} + +static PyObject * +array_dlpack_device(PyArrayObject *NPY_UNUSED(self), PyObject *NPY_UNUSED(args)) +{ + return Py_BuildValue("ii", kDLCPU, 0); +>>>>>>> ENH: Add the __dlpack__ and __dlpack_device__ methods to ndarray. } NPY_NO_EXPORT PyMethodDef array_methods[] = { @@ -2989,5 +3142,15 @@ NPY_NO_EXPORT PyMethodDef array_methods[] = { {"view", (PyCFunction)array_view, METH_FASTCALL | METH_KEYWORDS, NULL}, + + // For data interchange between libraries + {"__dlpack__", + (PyCFunction)array_dlpack, + METH_FASTCALL | METH_KEYWORDS, NULL}, + + {"__dlpack_device__", + (PyCFunction)array_dlpack_device, + METH_NOARGS, NULL}, + {NULL, NULL, 0, NULL} /* sentinel */ }; -- cgit v1.2.1 From 3163d57bd1550591e5f6ff1b063d4423d8d2123e Mon Sep 17 00:00:00 2001 From: Hameer Abbasi Date: Thu, 27 May 2021 12:03:55 +0200 Subject: ENH, TST: Add the from_dlpack method and test DLPack. --- numpy/core/_add_newdocs.py | 5 + numpy/core/multiarray.py | 9 +- numpy/core/numeric.py | 6 +- numpy/core/setup.py | 2 + numpy/core/src/common/npy_dlpack.h | 25 +++++ numpy/core/src/multiarray/methods.c | 25 +++-- numpy/core/src/multiarray/multiarraymodule.c | 153 +++++++++++++++++++++++++++ numpy/core/tests/test_dlpack.py | 76 +++++++++++++ 8 files changed, 287 insertions(+), 14 deletions(-) create mode 100644 numpy/core/src/common/npy_dlpack.h create mode 100644 numpy/core/tests/test_dlpack.py (limited to 'numpy') diff --git a/numpy/core/_add_newdocs.py b/numpy/core/_add_newdocs.py index c8a24db0c..a4c588a3b 100644 --- a/numpy/core/_add_newdocs.py +++ b/numpy/core/_add_newdocs.py @@ -1573,6 +1573,11 @@ add_newdoc('numpy.core.multiarray', 'frombuffer', array_function_like_doc, )) +add_newdoc('numpy.core.multiarray', 'from_dlpack', + """ + Create a NumPy array from a DLPack struct. + """) + add_newdoc('numpy.core', 'fastCopyAndTranspose', """_fastCopyAndTranspose(a)""") diff --git a/numpy/core/multiarray.py b/numpy/core/multiarray.py index 351cd3a1b..9f431f01b 100644 --- a/numpy/core/multiarray.py +++ b/numpy/core/multiarray.py @@ -31,10 +31,10 @@ __all__ = [ 'count_nonzero', 'c_einsum', 'datetime_as_string', 'datetime_data', 'dot', 'dragon4_positional', 'dragon4_scientific', 'dtype', 'empty', 'empty_like', 'error', 'flagsobj', 'flatiter', 'format_longfloat', - 'frombuffer', 'fromfile', 'fromiter', 'fromstring', 'get_handler_name', - 'inner', 'interp', 'interp_complex', 'is_busday', 'lexsort', - 'matmul', 'may_share_memory', 'min_scalar_type', 'ndarray', 'nditer', - 'nested_iters', 'normalize_axis_index', 'packbits', + 'frombuffer', 'from_dlpack', 'fromfile', 'fromiter', 'fromstring', + 'get_handler_name', 'inner', 'interp', 'interp_complex', 'is_busday', + 'lexsort', 'matmul', 'may_share_memory', 'min_scalar_type', 'ndarray', + 'nditer', 'nested_iters', 'normalize_axis_index', 'packbits', 'promote_types', 'putmask', 'ravel_multi_index', 'result_type', 'scalar', 'set_datetimeparse_function', 'set_legacy_print_mode', 'set_numeric_ops', 'set_string_function', 'set_typeDict', 'shares_memory', @@ -55,6 +55,7 @@ asfortranarray.__module__ = 'numpy' datetime_data.__module__ = 'numpy' empty.__module__ = 'numpy' frombuffer.__module__ = 'numpy' +from_dlpack.__module__ = 'numpy' fromfile.__module__ = 'numpy' fromiter.__module__ = 'numpy' frompyfunc.__module__ = 'numpy' diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py index 1654e8364..987470f92 100644 --- a/numpy/core/numeric.py +++ b/numpy/core/numeric.py @@ -13,8 +13,8 @@ from .multiarray import ( WRAP, arange, array, asarray, asanyarray, ascontiguousarray, asfortranarray, broadcast, can_cast, compare_chararrays, concatenate, copyto, dot, dtype, empty, - empty_like, flatiter, frombuffer, fromfile, fromiter, fromstring, - inner, lexsort, matmul, may_share_memory, + empty_like, flatiter, frombuffer, from_dlpack, fromfile, fromiter, + fromstring, inner, lexsort, matmul, may_share_memory, min_scalar_type, ndarray, nditer, nested_iters, promote_types, putmask, result_type, set_numeric_ops, shares_memory, vdot, where, zeros, normalize_axis_index) @@ -41,7 +41,7 @@ __all__ = [ 'newaxis', 'ndarray', 'flatiter', 'nditer', 'nested_iters', 'ufunc', 'arange', 'array', 'asarray', 'asanyarray', 'ascontiguousarray', 'asfortranarray', 'zeros', 'count_nonzero', 'empty', 'broadcast', 'dtype', - 'fromstring', 'fromfile', 'frombuffer', 'where', + 'fromstring', 'fromfile', 'frombuffer', 'from_dlpack', 'where', 'argwhere', 'copyto', 'concatenate', 'fastCopyAndTranspose', 'lexsort', 'set_numeric_ops', 'can_cast', 'promote_types', 'min_scalar_type', 'result_type', 'isfortran', 'empty_like', 'zeros_like', 'ones_like', diff --git a/numpy/core/setup.py b/numpy/core/setup.py index 3e1ed4c9b..5c6fd4dcf 100644 --- a/numpy/core/setup.py +++ b/numpy/core/setup.py @@ -740,6 +740,7 @@ def configuration(parent_package='',top_path=None): ####################################################################### common_deps = [ + join('src', 'common', 'dlpack', 'dlpack.h'), join('src', 'common', 'array_assign.h'), join('src', 'common', 'binop_override.h'), join('src', 'common', 'cblasfuncs.h'), @@ -749,6 +750,7 @@ def configuration(parent_package='',top_path=None): join('src', 'common', 'npy_cblas.h'), join('src', 'common', 'npy_config.h'), join('src', 'common', 'npy_ctypes.h'), + join('src', 'common', 'npy_dlpack.h'), join('src', 'common', 'npy_extint128.h'), join('src', 'common', 'npy_import.h'), join('src', 'common', 'npy_hashtable.h'), diff --git a/numpy/core/src/common/npy_dlpack.h b/numpy/core/src/common/npy_dlpack.h new file mode 100644 index 000000000..407469058 --- /dev/null +++ b/numpy/core/src/common/npy_dlpack.h @@ -0,0 +1,25 @@ +#include "Python.h" +#include "dlpack/dlpack.h" + +#ifndef NPY_DLPACK_H +#define NPY_DLPACK_H + +#define NPY_DLPACK_CAPSULE_NAME "dltensor" +#define NPY_DLPACK_USED_CAPSULE_NAME "used_dltensor" +#define NPY_DLPACK_INTERNAL_CAPSULE_NAME "numpy_dltensor" + +static void array_dlpack_capsule_deleter(PyObject *self) +{ + if (!PyCapsule_IsValid(self, NPY_DLPACK_CAPSULE_NAME) && + !PyCapsule_IsValid(self, NPY_DLPACK_INTERNAL_CAPSULE_NAME)) { + if (!PyCapsule_IsValid(self, NPY_DLPACK_USED_CAPSULE_NAME)) { + PyErr_SetString(PyExc_RuntimeError, "Invalid capsule name."); + } + return; + } + DLManagedTensor *managed = + (DLManagedTensor *)PyCapsule_GetPointer(self, PyCapsule_GetName(self)); + managed->deleter(managed); +} + +#endif \ No newline at end of file diff --git a/numpy/core/src/multiarray/methods.c b/numpy/core/src/multiarray/methods.c index 2251d4b69..1f1a5d019 100644 --- a/numpy/core/src/multiarray/methods.c +++ b/numpy/core/src/multiarray/methods.c @@ -31,7 +31,9 @@ #include "alloc.h" #include + #include "common/dlpack/dlpack.h" +#include "common/npy_dlpack.h" /* NpyArg_ParseKeywords @@ -2582,8 +2584,6 @@ array_round(PyArrayObject *self, PyObject *args, PyObject *kwds) } } - - static PyObject * array_setflags(PyArrayObject *self, PyObject *args, PyObject *kwds) { @@ -2781,16 +2781,17 @@ static void array_dlpack_capsule_deleter(PyObject *self) managed->deleter(managed); } -static void array_dlpack_deleter(DLManagedTensor *self) +static void +array_dlpack_deleter(DLManagedTensor *self) { PyArrayObject *array = (PyArrayObject *)self->manager_ctx; // This will also free the strides as it's one allocation. PyMem_Free(self->dl_tensor.shape); PyMem_Free(self); - - PyArray_XDECREF(array); + Py_XDECREF(array); } + static PyObject * array_dlpack(PyArrayObject *self, PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames) @@ -2906,13 +2907,23 @@ array_dlpack(PyArrayObject *self, } // the capsule holds a reference - PyArray_INCREF(self); + Py_INCREF(self); return capsule; } static PyObject * -array_dlpack_device(PyArrayObject *NPY_UNUSED(self), PyObject *NPY_UNUSED(args)) +array_dlpack_device(PyArrayObject *self, PyObject *NPY_UNUSED(args)) { + PyObject *base = PyArray_BASE(self); + if (PyCapsule_IsValid(base, NPY_DLPACK_INTERNAL_CAPSULE_NAME)) { + DLManagedTensor *managed = PyCapsule_GetPointer(base, + NPY_DLPACK_INTERNAL_CAPSULE_NAME); + if (managed == NULL) { + return NULL; + } + return Py_BuildValue("ii", managed->dl_tensor.device.device_type, + managed->dl_tensor.device.device_id); + } return Py_BuildValue("ii", kDLCPU, 0); >>>>>>> ENH: Add the __dlpack__ and __dlpack_device__ methods to ndarray. } diff --git a/numpy/core/src/multiarray/multiarraymodule.c b/numpy/core/src/multiarray/multiarraymodule.c index dea828ed9..7414cb9a8 100644 --- a/numpy/core/src/multiarray/multiarraymodule.c +++ b/numpy/core/src/multiarray/multiarraymodule.c @@ -70,6 +70,8 @@ NPY_NO_EXPORT int NPY_NUMUSERTYPES = 0; #include "get_attr_string.h" #include "experimental_public_dtype_api.h" /* _get_experimental_dtype_api */ +#include "common/npy_dlpack.h" + /* ***************************************************************************** ** INCLUDE GENERATED CODE ** @@ -4231,6 +4233,155 @@ _reload_guard(PyObject *NPY_UNUSED(self)) { Py_RETURN_NONE; } +static void array_dlpack_deleter(DLManagedTensor *self) +{ + PyArrayObject *array = (PyArrayObject *)self->manager_ctx; + // This will also free the strides as it's one allocation. + PyMem_Free(self->dl_tensor.shape); + PyMem_Free(self); + Py_XDECREF(array); +} + + +NPY_NO_EXPORT PyObject * +from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { + PyObject *capsule = PyObject_CallMethod(obj, "__dlpack__", NULL); + if (capsule == NULL) { + return NULL; + } + + DLManagedTensor *managed = + (DLManagedTensor *)PyCapsule_GetPointer(capsule, + NPY_DLPACK_CAPSULE_NAME); + + if (managed == NULL) { + Py_XDECREF(capsule); + return NULL; + } + + const int ndim = managed->dl_tensor.ndim; + if (ndim >= NPY_MAXDIMS) { + PyErr_SetString(PyExc_RuntimeError, + "maxdims of DLPack tensor is higher than the supported " + "maxdims."); + Py_XDECREF(capsule); + return NULL; + } + + if (managed->dl_tensor.device.device_type != kDLCPU && + managed->dl_tensor.device.device_type != kDLCUDAHost) { + PyErr_SetString(PyExc_RuntimeError, + "Unsupported device in DLTensor."); + Py_XDECREF(capsule); + return NULL; + } + + if (managed->dl_tensor.dtype.lanes != 1) { + PyErr_SetString(PyExc_RuntimeError, + "Unsupported lanes in DLTensor dtype."); + Py_XDECREF(capsule); + return NULL; + } + + int typenum = -1; + const uint8_t bits = managed->dl_tensor.dtype.bits; + const npy_intp itemsize = bits / 8; + switch(managed->dl_tensor.dtype.code) { + case kDLInt: + switch (bits) + { + case 8: typenum = NPY_INT8; break; + case 16: typenum = NPY_INT16; break; + case 32: typenum = NPY_INT32; break; + case 64: typenum = NPY_INT64; break; + } + break; + case kDLUInt: + switch (bits) + { + case 8: typenum = NPY_UINT8; break; + case 16: typenum = NPY_UINT16; break; + case 32: typenum = NPY_UINT32; break; + case 64: typenum = NPY_UINT64; break; + } + break; + case kDLFloat: + switch (bits) + { + case 16: typenum = NPY_FLOAT16; break; + case 32: typenum = NPY_FLOAT32; break; + case 64: typenum = NPY_FLOAT64; break; + } + break; + case kDLComplex: + switch (bits) + { + case 64: typenum = NPY_COMPLEX64; break; + case 128: typenum = NPY_COMPLEX128; break; + } + break; + } + + if (typenum == -1) { + PyErr_SetString(PyExc_RuntimeError, + "Unsupported dtype in DLTensor."); + Py_XDECREF(capsule); + return NULL; + } + + PyArray_Descr *descr = PyArray_DescrFromType(typenum); + if (descr == NULL) { + Py_XDECREF(capsule); + return NULL; + } + + npy_intp shape[NPY_MAXDIMS]; + npy_intp strides[NPY_MAXDIMS]; + + for (int i = 0; i < ndim; ++i) { + shape[i] = managed->dl_tensor.shape[i]; + strides[i] = managed->dl_tensor.strides[i] * itemsize; + } + + char *data = (char *)managed->dl_tensor.data + + managed->dl_tensor.byte_offset; + + PyObject *ret = PyArray_NewFromDescr(&PyArray_Type, descr, ndim, shape, + strides, data, 0, NULL); + if (ret == NULL) { + Py_XDECREF(capsule); + Py_XDECREF(descr); + return NULL; + } + + PyObject *new_capsule = PyCapsule_New(managed, + NPY_DLPACK_INTERNAL_CAPSULE_NAME, array_dlpack_capsule_deleter); + if (new_capsule == NULL) { + Py_XDECREF(descr); + Py_XDECREF(ret); + Py_XDECREF(capsule); + return NULL; + } + + if (PyArray_SetBaseObject((PyArrayObject *)ret, new_capsule) < 0) { + Py_XDECREF(descr); + Py_XDECREF(ret); + Py_XDECREF(new_capsule); + Py_XDECREF(capsule); + return NULL; + } + + if (PyCapsule_SetName(capsule, NPY_DLPACK_USED_CAPSULE_NAME) < 0) { + Py_XDECREF(descr); + Py_XDECREF(ret); + Py_XDECREF(new_capsule); + Py_XDECREF(capsule); + return NULL; + } + + Py_XDECREF(capsule); + return ret; +} static struct PyMethodDef array_module_methods[] = { {"_get_implementing_args", @@ -4445,6 +4596,8 @@ static struct PyMethodDef array_module_methods[] = { {"_reload_guard", (PyCFunction)_reload_guard, METH_NOARGS, "Give a warning on reload and big warning in sub-interpreters."}, + {"from_dlpack", (PyCFunction)from_dlpack, + METH_O, NULL}, {NULL, NULL, 0, NULL} /* sentinel */ }; diff --git a/numpy/core/tests/test_dlpack.py b/numpy/core/tests/test_dlpack.py new file mode 100644 index 000000000..72fbab0de --- /dev/null +++ b/numpy/core/tests/test_dlpack.py @@ -0,0 +1,76 @@ +import sys +import pytest + +import numpy as np +from numpy.testing import assert_array_equal + + +class TestDLPack: + def test_dunder_dlpack_refcount(self): + x = np.arange(5) + y = x.__dlpack__() + assert sys.getrefcount(x) == 3 + del y + assert sys.getrefcount(x) == 2 + + def test_dunder_dlpack_stream(self): + x = np.arange(5) + x.__dlpack__(stream=None) + + dt = np.dtype([('int', np.int32), ('char', np.int8)]) + y = np.zeros((5,), dtype=dt) + z = y['int'] + + with pytest.raises(RuntimeError): + np.from_dlpack(z) + + def test_from_dlpack_refcount(self): + x = np.arange(5) + y = np.from_dlpack(x) + assert sys.getrefcount(x) == 3 + del y + assert sys.getrefcount(x) == 2 + + @pytest.mark.parametrize("dtype", [ + np.int8, np.int16, np.int32, np.int64, + np.uint8, np.uint16, np.uint32, np.uint64, + np.float16, np.float32, np.float64, + np.complex64, np.complex64 + ]) + def test_dtype_passthrough(self, dtype): + x = np.arange(5, dtype=dtype) + y = np.from_dlpack(x) + + assert y.dtype == x.dtype + assert_array_equal(x, y) + + def test_invalid_dtype(self): + x = np.asarray(np.datetime64('2021-05-27')) + + with pytest.raises(TypeError): + np.from_dlpack(x) + + def test_invalid_byte_swapping(self): + dt = np.dtype('=i8').newbyteorder() + x = np.arange(5, dtype=dt) + + with pytest.raises(TypeError): + np.from_dlpack(x) + + def test_non_contiguous(self): + x = np.arange(25).reshape((5, 5)) + + y1 = x[0] + assert_array_equal(y1, np.from_dlpack(y1)) + + y2 = x[:, 0] + assert_array_equal(y2, np.from_dlpack(y2)) + + y3 = x[1, :] + assert_array_equal(y3, np.from_dlpack(y3)) + + y4 = x[1] + assert_array_equal(y4, np.from_dlpack(y4)) + + y5 = np.diagonal(x) + assert_array_equal(y5, np.from_dlpack(y5)) -- cgit v1.2.1 From eb6ee260c94e14481325a0d5ef5db89bbd64349f Mon Sep 17 00:00:00 2001 From: Hameer Abbasi Date: Thu, 27 May 2021 17:26:40 +0200 Subject: MAINT, BUG: Documentation for DLPack protocol and refcounting bug fixes. --- numpy/__init__.pyi | 7 ++++++ numpy/core/_add_newdocs.py | 19 +++++++++++++++- numpy/core/src/common/dlpack/dlpack.h | 13 ++++++++++- numpy/core/src/common/npy_dlpack.h | 5 +++++ numpy/core/src/multiarray/multiarraymodule.c | 33 ++++++++++------------------ numpy/core/tests/test_dlpack.py | 6 +++-- 6 files changed, 58 insertions(+), 25 deletions(-) (limited to 'numpy') diff --git a/numpy/__init__.pyi b/numpy/__init__.pyi index a162a637c..c808f0baf 100644 --- a/numpy/__init__.pyi +++ b/numpy/__init__.pyi @@ -1413,6 +1413,7 @@ _SupportsBuffer = Union[ _T = TypeVar("_T") _T_co = TypeVar("_T_co", covariant=True) +_T_contra = TypeVar("_T_contra", contravariant=True) _2Tuple = Tuple[_T, _T] _CastingKind = L["no", "equiv", "safe", "same_kind", "unsafe"] @@ -4330,3 +4331,9 @@ class chararray(ndarray[_ShapeType, _CharDType]): # NOTE: Deprecated # class MachAr: ... + +class _SupportsDLPack(Protocol[_T_contra]): + def __dlpack__(self, *, stream: Optional[int] = ...) -> _PyCapsule: ... + +def from_dlpack(__obj: _SupportsDLPack[None]) -> NDArray[Any]: ... + diff --git a/numpy/core/_add_newdocs.py b/numpy/core/_add_newdocs.py index a4c588a3b..f1a42dffe 100644 --- a/numpy/core/_add_newdocs.py +++ b/numpy/core/_add_newdocs.py @@ -1575,7 +1575,15 @@ add_newdoc('numpy.core.multiarray', 'frombuffer', add_newdoc('numpy.core.multiarray', 'from_dlpack', """ - Create a NumPy array from a DLPack struct. + from_dlpack(x, /) + + Create a NumPy array from an object implementing the ``__dlpack__`` + protocol. + + See Also + -------- + `Array API documentation + `_ """) add_newdoc('numpy.core', 'fastCopyAndTranspose', @@ -2268,6 +2276,15 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_priority__', add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_struct__', """Array protocol: C-struct side.""")) +add_newdoc('numpy.core.multiarray', 'ndarray', ('__dlpack__', + """a.__dlpack__(*, stream=None) + + DLPack Protocol: Part of the Array API.""")) + +add_newdoc('numpy.core.multiarray', 'ndarray', ('__dlpack_device__', + """a.__dlpack_device__() + + DLPack Protocol: Part of the Array API.""")) add_newdoc('numpy.core.multiarray', 'ndarray', ('base', """ diff --git a/numpy/core/src/common/dlpack/dlpack.h b/numpy/core/src/common/dlpack/dlpack.h index 84afca248..8b19ea2b1 100644 --- a/numpy/core/src/common/dlpack/dlpack.h +++ b/numpy/core/src/common/dlpack/dlpack.h @@ -54,12 +54,20 @@ typedef enum { kDLVPI = 9, /*! \brief ROCm GPUs for AMD GPUs */ kDLROCM = 10, + /*! + * \brief Pinned ROCm CPU memory allocated by hipMallocHost + */ + kDLROCMHost = 11, /*! * \brief Reserved extension device type, * used for quickly test extension device * The semantics can differ depending on the implementation. */ kDLExtDev = 12, + /*! + * \brief CUDA managed/unified memory allocated by cudaMallocManaged + */ + kDLCUDAManaged = 13, } DLDeviceType; /*! @@ -68,7 +76,10 @@ typedef enum { typedef struct { /*! \brief The device type used in the device. */ DLDeviceType device_type; - /*! \brief The device index */ + /*! + * \brief The device index. + * For vanilla CPU memory, pinned memory, or managed memory, this is set to 0. + */ int device_id; } DLDevice; diff --git a/numpy/core/src/common/npy_dlpack.h b/numpy/core/src/common/npy_dlpack.h index 407469058..47559191e 100644 --- a/numpy/core/src/common/npy_dlpack.h +++ b/numpy/core/src/common/npy_dlpack.h @@ -4,8 +4,13 @@ #ifndef NPY_DLPACK_H #define NPY_DLPACK_H +// Part of the Array API specification. #define NPY_DLPACK_CAPSULE_NAME "dltensor" #define NPY_DLPACK_USED_CAPSULE_NAME "used_dltensor" + +// Used internally by NumPy to store a base object +// as it has to release a reference to the original +// capsule. #define NPY_DLPACK_INTERNAL_CAPSULE_NAME "numpy_dltensor" static void array_dlpack_capsule_deleter(PyObject *self) diff --git a/numpy/core/src/multiarray/multiarraymodule.c b/numpy/core/src/multiarray/multiarraymodule.c index 7414cb9a8..e348a36cb 100644 --- a/numpy/core/src/multiarray/multiarraymodule.c +++ b/numpy/core/src/multiarray/multiarraymodule.c @@ -4233,19 +4233,11 @@ _reload_guard(PyObject *NPY_UNUSED(self)) { Py_RETURN_NONE; } -static void array_dlpack_deleter(DLManagedTensor *self) -{ - PyArrayObject *array = (PyArrayObject *)self->manager_ctx; - // This will also free the strides as it's one allocation. - PyMem_Free(self->dl_tensor.shape); - PyMem_Free(self); - Py_XDECREF(array); -} - NPY_NO_EXPORT PyObject * from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { - PyObject *capsule = PyObject_CallMethod(obj, "__dlpack__", NULL); + PyObject *capsule = PyObject_CallMethod((PyObject *)obj->ob_type, + "__dlpack__", "O", obj); if (capsule == NULL) { return NULL; } @@ -4260,7 +4252,7 @@ from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { } const int ndim = managed->dl_tensor.ndim; - if (ndim >= NPY_MAXDIMS) { + if (ndim > NPY_MAXDIMS) { PyErr_SetString(PyExc_RuntimeError, "maxdims of DLPack tensor is higher than the supported " "maxdims."); @@ -4268,8 +4260,11 @@ from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { return NULL; } - if (managed->dl_tensor.device.device_type != kDLCPU && - managed->dl_tensor.device.device_type != kDLCUDAHost) { + DLDeviceType device_type = managed->dl_tensor.device.device_type; + if (device_type != kDLCPU && + device_type != kDLCUDAHost && + device_type != kDLROCMHost && + device_type != kDLCUDAManaged) { PyErr_SetString(PyExc_RuntimeError, "Unsupported device in DLTensor."); Py_XDECREF(capsule); @@ -4340,6 +4335,7 @@ from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { for (int i = 0; i < ndim; ++i) { shape[i] = managed->dl_tensor.shape[i]; + // DLPack has elements as stride units, NumPy has bytes. strides[i] = managed->dl_tensor.strides[i] * itemsize; } @@ -4357,25 +4353,20 @@ from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { PyObject *new_capsule = PyCapsule_New(managed, NPY_DLPACK_INTERNAL_CAPSULE_NAME, array_dlpack_capsule_deleter); if (new_capsule == NULL) { - Py_XDECREF(descr); - Py_XDECREF(ret); Py_XDECREF(capsule); + Py_XDECREF(ret); return NULL; } if (PyArray_SetBaseObject((PyArrayObject *)ret, new_capsule) < 0) { - Py_XDECREF(descr); - Py_XDECREF(ret); - Py_XDECREF(new_capsule); Py_XDECREF(capsule); + Py_XDECREF(ret); return NULL; } if (PyCapsule_SetName(capsule, NPY_DLPACK_USED_CAPSULE_NAME) < 0) { - Py_XDECREF(descr); - Py_XDECREF(ret); - Py_XDECREF(new_capsule); Py_XDECREF(capsule); + Py_XDECREF(ret); return NULL; } diff --git a/numpy/core/tests/test_dlpack.py b/numpy/core/tests/test_dlpack.py index 72fbab0de..9f5cf9192 100644 --- a/numpy/core/tests/test_dlpack.py +++ b/numpy/core/tests/test_dlpack.py @@ -2,10 +2,11 @@ import sys import pytest import numpy as np -from numpy.testing import assert_array_equal +from numpy.testing import assert_array_equal, IS_PYPY class TestDLPack: + @pytest.mark.skipif(IS_PYPY, reason="PyPy can't get refcounts.") def test_dunder_dlpack_refcount(self): x = np.arange(5) y = x.__dlpack__() @@ -24,6 +25,7 @@ class TestDLPack: with pytest.raises(RuntimeError): np.from_dlpack(z) + @pytest.mark.skipif(IS_PYPY, reason="PyPy can't get refcounts.") def test_from_dlpack_refcount(self): x = np.arange(5) y = np.from_dlpack(x) @@ -35,7 +37,7 @@ class TestDLPack: np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64, np.float16, np.float32, np.float64, - np.complex64, np.complex64 + np.complex64, np.complex128 ]) def test_dtype_passthrough(self, dtype): x = np.arange(5, dtype=dtype) -- cgit v1.2.1 From 3e5b274164ee8b44ea881922019d61d4905b790f Mon Sep 17 00:00:00 2001 From: Hameer Abbasi Date: Thu, 27 May 2021 18:52:43 +0200 Subject: MAINT: Add URL to DLPack GitHub. --- numpy/core/src/common/dlpack/dlpack.h | 2 ++ 1 file changed, 2 insertions(+) (limited to 'numpy') diff --git a/numpy/core/src/common/dlpack/dlpack.h b/numpy/core/src/common/dlpack/dlpack.h index 8b19ea2b1..29209aee1 100644 --- a/numpy/core/src/common/dlpack/dlpack.h +++ b/numpy/core/src/common/dlpack/dlpack.h @@ -1,3 +1,5 @@ +// Taken from: +// https://github.com/dmlc/dlpack/blob/9b6176fdecb55e9bf39b16f08b96913ed3f275b4/include/dlpack/dlpack.h /*! * Copyright (c) 2017 by Contributors * \file dlpack.h -- cgit v1.2.1 From e12695d85bc98193014259ef4cf3ce46a750820f Mon Sep 17 00:00:00 2001 From: Hameer Abbasi Date: Thu, 27 May 2021 19:00:52 +0200 Subject: MAINT: Split up capsule deleter. --- numpy/core/src/common/npy_dlpack.h | 23 +++++++++++++++++------ numpy/core/src/multiarray/multiarraymodule.c | 3 ++- 2 files changed, 19 insertions(+), 7 deletions(-) (limited to 'numpy') diff --git a/numpy/core/src/common/npy_dlpack.h b/numpy/core/src/common/npy_dlpack.h index 47559191e..ef2c7c6fd 100644 --- a/numpy/core/src/common/npy_dlpack.h +++ b/numpy/core/src/common/npy_dlpack.h @@ -13,17 +13,28 @@ // capsule. #define NPY_DLPACK_INTERNAL_CAPSULE_NAME "numpy_dltensor" +/* This is exactly as mandated by dlpack */ static void array_dlpack_capsule_deleter(PyObject *self) { - if (!PyCapsule_IsValid(self, NPY_DLPACK_CAPSULE_NAME) && - !PyCapsule_IsValid(self, NPY_DLPACK_INTERNAL_CAPSULE_NAME)) { - if (!PyCapsule_IsValid(self, NPY_DLPACK_USED_CAPSULE_NAME)) { - PyErr_SetString(PyExc_RuntimeError, "Invalid capsule name."); - } + if (PyCapsule_IsValid(self, NPY_DLPACK_USED_CAPSULE_NAME)) { return; } DLManagedTensor *managed = - (DLManagedTensor *)PyCapsule_GetPointer(self, PyCapsule_GetName(self)); + (DLManagedTensor *)PyCapsule_GetPointer(self, NPY_DLPACK_CAPSULE_NAME); + if (managed == NULL) { + return; + } + managed->deleter(managed); +} + +/* used internally */ +static void array_dlpack_internal_capsule_deleter(PyObject *self) +{ + DLManagedTensor *managed = + (DLManagedTensor *)PyCapsule_GetPointer(self, NPY_DLPACK_INTERNAL_CAPSULE_NAME); + if (managed == NULL) { + return; + } managed->deleter(managed); } diff --git a/numpy/core/src/multiarray/multiarraymodule.c b/numpy/core/src/multiarray/multiarraymodule.c index e348a36cb..ad77177cd 100644 --- a/numpy/core/src/multiarray/multiarraymodule.c +++ b/numpy/core/src/multiarray/multiarraymodule.c @@ -4351,7 +4351,8 @@ from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { } PyObject *new_capsule = PyCapsule_New(managed, - NPY_DLPACK_INTERNAL_CAPSULE_NAME, array_dlpack_capsule_deleter); + NPY_DLPACK_INTERNAL_CAPSULE_NAME, + array_dlpack_internal_capsule_deleter); if (new_capsule == NULL) { Py_XDECREF(capsule); Py_XDECREF(ret); -- cgit v1.2.1 From 094e0aac120519216a68244d241a5d3bc9497d52 Mon Sep 17 00:00:00 2001 From: Hameer Abbasi Date: Thu, 27 May 2021 19:20:37 +0200 Subject: MAINT: Move around code so that there are no more unused warnings. --- numpy/core/src/common/npy_dlpack.h | 25 ------------------------- numpy/core/src/multiarray/methods.c | 13 +++++++++++++ numpy/core/src/multiarray/multiarraymodule.c | 11 +++++++++++ 3 files changed, 24 insertions(+), 25 deletions(-) (limited to 'numpy') diff --git a/numpy/core/src/common/npy_dlpack.h b/numpy/core/src/common/npy_dlpack.h index ef2c7c6fd..446d44afa 100644 --- a/numpy/core/src/common/npy_dlpack.h +++ b/numpy/core/src/common/npy_dlpack.h @@ -13,29 +13,4 @@ // capsule. #define NPY_DLPACK_INTERNAL_CAPSULE_NAME "numpy_dltensor" -/* This is exactly as mandated by dlpack */ -static void array_dlpack_capsule_deleter(PyObject *self) -{ - if (PyCapsule_IsValid(self, NPY_DLPACK_USED_CAPSULE_NAME)) { - return; - } - DLManagedTensor *managed = - (DLManagedTensor *)PyCapsule_GetPointer(self, NPY_DLPACK_CAPSULE_NAME); - if (managed == NULL) { - return; - } - managed->deleter(managed); -} - -/* used internally */ -static void array_dlpack_internal_capsule_deleter(PyObject *self) -{ - DLManagedTensor *managed = - (DLManagedTensor *)PyCapsule_GetPointer(self, NPY_DLPACK_INTERNAL_CAPSULE_NAME); - if (managed == NULL) { - return; - } - managed->deleter(managed); -} - #endif \ No newline at end of file diff --git a/numpy/core/src/multiarray/methods.c b/numpy/core/src/multiarray/methods.c index 1f1a5d019..0cc860514 100644 --- a/numpy/core/src/multiarray/methods.c +++ b/numpy/core/src/multiarray/methods.c @@ -2791,6 +2791,19 @@ array_dlpack_deleter(DLManagedTensor *self) Py_XDECREF(array); } +/* This is exactly as mandated by dlpack */ +static void array_dlpack_capsule_deleter(PyObject *self) +{ + if (PyCapsule_IsValid(self, NPY_DLPACK_USED_CAPSULE_NAME)) { + return; + } + DLManagedTensor *managed = + (DLManagedTensor *)PyCapsule_GetPointer(self, NPY_DLPACK_CAPSULE_NAME); + if (managed == NULL) { + return; + } + managed->deleter(managed); +} static PyObject * array_dlpack(PyArrayObject *self, diff --git a/numpy/core/src/multiarray/multiarraymodule.c b/numpy/core/src/multiarray/multiarraymodule.c index ad77177cd..517234c88 100644 --- a/numpy/core/src/multiarray/multiarraymodule.c +++ b/numpy/core/src/multiarray/multiarraymodule.c @@ -4233,6 +4233,17 @@ _reload_guard(PyObject *NPY_UNUSED(self)) { Py_RETURN_NONE; } +/* used internally */ +static void array_dlpack_internal_capsule_deleter(PyObject *self) +{ + DLManagedTensor *managed = + (DLManagedTensor *)PyCapsule_GetPointer(self, NPY_DLPACK_INTERNAL_CAPSULE_NAME); + if (managed == NULL) { + return; + } + managed->deleter(managed); +} + NPY_NO_EXPORT PyObject * from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { -- cgit v1.2.1 From 158c7283b5de5ec7b832407c15fafacc95cb8ee6 Mon Sep 17 00:00:00 2001 From: Hameer Abbasi Date: Fri, 28 May 2021 09:17:08 +0200 Subject: TST: Improve testing coverage for DLPack. --- numpy/core/tests/test_dlpack.py | 11 +++++++++++ 1 file changed, 11 insertions(+) (limited to 'numpy') diff --git a/numpy/core/tests/test_dlpack.py b/numpy/core/tests/test_dlpack.py index 9f5cf9192..4553c943d 100644 --- a/numpy/core/tests/test_dlpack.py +++ b/numpy/core/tests/test_dlpack.py @@ -18,6 +18,10 @@ class TestDLPack: x = np.arange(5) x.__dlpack__(stream=None) + with pytest.raises(RuntimeError): + x.__dlpack__(stream=1) + + def test_strides_not_multiple_of_itemsize(self): dt = np.dtype([('int', np.int32), ('char', np.int8)]) y = np.zeros((5,), dtype=dt) z = y['int'] @@ -76,3 +80,10 @@ class TestDLPack: y5 = np.diagonal(x) assert_array_equal(y5, np.from_dlpack(y5)) + + @pytest.mark.parametrize("ndim", range(33)) + def test_higher_dims(self, ndim): + shape = (1,) * ndim + x = np.zeros(shape, dtype=np.float64, order='C') + + assert shape == np.from_dlpack(x).shape -- cgit v1.2.1 From 9ebee26c6c5cd89623d531608eed25a770d01fff Mon Sep 17 00:00:00 2001 From: Hameer Abbasi Date: Fri, 28 May 2021 18:21:55 +0200 Subject: BUG: Fix handling of C-contiguous and 1-element arrays. --- numpy/core/src/multiarray/methods.c | 19 ++++++++++++------- numpy/core/src/multiarray/multiarraymodule.c | 7 +++++-- numpy/core/tests/test_dlpack.py | 2 +- 3 files changed, 18 insertions(+), 10 deletions(-) (limited to 'numpy') diff --git a/numpy/core/src/multiarray/methods.c b/numpy/core/src/multiarray/methods.c index 0cc860514..42464014c 100644 --- a/numpy/core/src/multiarray/methods.c +++ b/numpy/core/src/multiarray/methods.c @@ -2827,12 +2827,14 @@ array_dlpack(PyArrayObject *self, npy_intp *strides = PyArray_STRIDES(self); npy_intp *shape = PyArray_SHAPE(self); - for (int i = 0; i < ndim; ++i) { - if (strides[i] % itemsize != 0) { - PyErr_SetString(PyExc_RuntimeError, - "DLPack only supports strides which are a multiple of " - "itemsize."); - return NULL; + if (!PyArray_IS_C_CONTIGUOUS(self) && PyArray_SIZE(self) != 1) { + for (int i = 0; i < ndim; ++i) { + if (strides[i] % itemsize != 0) { + PyErr_SetString(PyExc_RuntimeError, + "DLPack only supports strides which are a multiple of " + "itemsize."); + return NULL; + } } } @@ -2906,7 +2908,10 @@ array_dlpack(PyArrayObject *self, managed->dl_tensor.ndim = ndim; managed->dl_tensor.shape = managed_shape; - managed->dl_tensor.strides = managed_strides; + managed->dl_tensor.strides = NULL; + if (PyArray_SIZE(self) != 1 && !PyArray_IS_C_CONTIGUOUS(self)) { + managed->dl_tensor.strides = managed_strides; + } managed->dl_tensor.byte_offset = 0; managed->manager_ctx = self; managed->deleter = array_dlpack_deleter; diff --git a/numpy/core/src/multiarray/multiarraymodule.c b/numpy/core/src/multiarray/multiarraymodule.c index 517234c88..b04debe10 100644 --- a/numpy/core/src/multiarray/multiarraymodule.c +++ b/numpy/core/src/multiarray/multiarraymodule.c @@ -4347,14 +4347,17 @@ from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { for (int i = 0; i < ndim; ++i) { shape[i] = managed->dl_tensor.shape[i]; // DLPack has elements as stride units, NumPy has bytes. - strides[i] = managed->dl_tensor.strides[i] * itemsize; + if (managed->dl_tensor.strides != NULL) + { + strides[i] = managed->dl_tensor.strides[i] * itemsize; + } } char *data = (char *)managed->dl_tensor.data + managed->dl_tensor.byte_offset; PyObject *ret = PyArray_NewFromDescr(&PyArray_Type, descr, ndim, shape, - strides, data, 0, NULL); + managed->dl_tensor.strides != NULL ? strides : NULL, data, 0, NULL); if (ret == NULL) { Py_XDECREF(capsule); Py_XDECREF(descr); diff --git a/numpy/core/tests/test_dlpack.py b/numpy/core/tests/test_dlpack.py index 4553c943d..19f4e9281 100644 --- a/numpy/core/tests/test_dlpack.py +++ b/numpy/core/tests/test_dlpack.py @@ -84,6 +84,6 @@ class TestDLPack: @pytest.mark.parametrize("ndim", range(33)) def test_higher_dims(self, ndim): shape = (1,) * ndim - x = np.zeros(shape, dtype=np.float64, order='C') + x = np.zeros(shape, dtype=np.float64) assert shape == np.from_dlpack(x).shape -- cgit v1.2.1 From e83b8d8044763d36c42d3ab103a5437893fb09d8 Mon Sep 17 00:00:00 2001 From: Hameer Abbasi Date: Sun, 30 May 2021 11:57:46 +0200 Subject: BUG, TST: Device bugfix and test __dl_device__. --- numpy/__init__.pyi | 2 +- numpy/core/src/multiarray/methods.c | 39 ++++++++++++++++++++++++------------- numpy/core/tests/test_dlpack.py | 5 +++++ 3 files changed, 32 insertions(+), 14 deletions(-) (limited to 'numpy') diff --git a/numpy/__init__.pyi b/numpy/__init__.pyi index c808f0baf..63e723a35 100644 --- a/numpy/__init__.pyi +++ b/numpy/__init__.pyi @@ -2449,7 +2449,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __dlpack__(self: NDArray[number[Any]], *, stream: None = ...) -> _PyCapsule: ... @overload - def __dlpack_device__(self) -> Tuple[L[1], L[0]]: ... + def __dlpack_device__(self) -> Tuple[int, L[0]]: ... # Keep `dtype` at the bottom to avoid name conflicts with `np.dtype` @property diff --git a/numpy/core/src/multiarray/methods.c b/numpy/core/src/multiarray/methods.c index 42464014c..29372fe2f 100644 --- a/numpy/core/src/multiarray/methods.c +++ b/numpy/core/src/multiarray/methods.c @@ -2805,6 +2805,23 @@ static void array_dlpack_capsule_deleter(PyObject *self) managed->deleter(managed); } +static DLDevice +array_get_dl_device(PyArrayObject *self) { + DLDevice ret; + ret.device_type = kDLCPU; + ret.device_id = 0; + PyObject *base = PyArray_BASE(self); + if (PyCapsule_IsValid(base, NPY_DLPACK_INTERNAL_CAPSULE_NAME)) { + DLManagedTensor *managed = PyCapsule_GetPointer( + base, NPY_DLPACK_INTERNAL_CAPSULE_NAME); + if (managed == NULL) { + return ret; + } + return managed->dl_tensor.device; + } + return ret; +} + static PyObject * array_dlpack(PyArrayObject *self, PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames) @@ -2886,8 +2903,11 @@ array_dlpack(PyArrayObject *self, } managed->dl_tensor.data = PyArray_DATA(self); - managed->dl_tensor.device.device_type = kDLCPU; - managed->dl_tensor.device.device_id = 0; + managed->dl_tensor.device = array_get_dl_device(self); + if (PyErr_Occurred()) { + PyMem_Free(managed); + return NULL; + } managed->dl_tensor.dtype = managed_dtype; @@ -2932,18 +2952,11 @@ array_dlpack(PyArrayObject *self, static PyObject * array_dlpack_device(PyArrayObject *self, PyObject *NPY_UNUSED(args)) { - PyObject *base = PyArray_BASE(self); - if (PyCapsule_IsValid(base, NPY_DLPACK_INTERNAL_CAPSULE_NAME)) { - DLManagedTensor *managed = PyCapsule_GetPointer(base, - NPY_DLPACK_INTERNAL_CAPSULE_NAME); - if (managed == NULL) { - return NULL; - } - return Py_BuildValue("ii", managed->dl_tensor.device.device_type, - managed->dl_tensor.device.device_id); + DLDevice device = array_get_dl_device(self); + if (PyErr_Occurred()) { + return NULL; } - return Py_BuildValue("ii", kDLCPU, 0); ->>>>>>> ENH: Add the __dlpack__ and __dlpack_device__ methods to ndarray. + return Py_BuildValue("ii", device.device_type, device.device_id); } NPY_NO_EXPORT PyMethodDef array_methods[] = { diff --git a/numpy/core/tests/test_dlpack.py b/numpy/core/tests/test_dlpack.py index 19f4e9281..926668c59 100644 --- a/numpy/core/tests/test_dlpack.py +++ b/numpy/core/tests/test_dlpack.py @@ -87,3 +87,8 @@ class TestDLPack: x = np.zeros(shape, dtype=np.float64) assert shape == np.from_dlpack(x).shape + + def test_dlpack_device(self): + x = np.arange(5) + assert x.__dlpack_device__() == (1, 0) + assert np.from_dlpack(x).__dlpack_device__() == (1, 0) -- cgit v1.2.1 From e167da747b53baed13a5148750c9c82746ffcb30 Mon Sep 17 00:00:00 2001 From: Hameer Abbasi Date: Mon, 31 May 2021 09:46:20 +0200 Subject: MAINT: Robustify dlpack_capsule_deleter and add comments. --- numpy/core/src/multiarray/methods.c | 33 +++++++++++++++++++++++++-------- numpy/core/tests/test_dlpack.py | 9 +++++++++ 2 files changed, 34 insertions(+), 8 deletions(-) (limited to 'numpy') diff --git a/numpy/core/src/multiarray/methods.c b/numpy/core/src/multiarray/methods.c index 29372fe2f..9a16adecd 100644 --- a/numpy/core/src/multiarray/methods.c +++ b/numpy/core/src/multiarray/methods.c @@ -2792,25 +2792,42 @@ array_dlpack_deleter(DLManagedTensor *self) } /* This is exactly as mandated by dlpack */ -static void array_dlpack_capsule_deleter(PyObject *self) -{ - if (PyCapsule_IsValid(self, NPY_DLPACK_USED_CAPSULE_NAME)) { +static void dlpack_capsule_deleter(PyObject *self){ + if (PyCapsule_IsValid(self, "used_dltensor")) { return; } - DLManagedTensor *managed = - (DLManagedTensor *)PyCapsule_GetPointer(self, NPY_DLPACK_CAPSULE_NAME); + + /* an exception may be in-flight, we must save it in case we create another one */ + PyObject *type, *value, *traceback; + PyErr_Fetch(&type, &value, &traceback); + + DLManagedTensor *managed = (DLManagedTensor *)PyCapsule_GetPointer(self, "dltensor"); if (managed == NULL) { - return; + PyErr_WriteUnraisable(self); + goto done; } - managed->deleter(managed); + /* the spec says the deleter can be NULL if there is no way for the caller to provide a reasonable destructor. */ + if (managed->deleter) { + managed->deleter(managed); + /* TODO: is the deleter allowed to set a python exception? */ + assert(!PyErr_Occurred()); + } + +done: + PyErr_Restore(type, value, traceback); } +// This function cannot return NULL, but it can fail, +// So call PyErr_Occurred to check if it failed after +// calling it. static DLDevice array_get_dl_device(PyArrayObject *self) { DLDevice ret; ret.device_type = kDLCPU; ret.device_id = 0; PyObject *base = PyArray_BASE(self); + // The outer if is due to the fact that NumPy arrays are on the CPU + // by default. if (PyCapsule_IsValid(base, NPY_DLPACK_INTERNAL_CAPSULE_NAME)) { DLManagedTensor *managed = PyCapsule_GetPointer( base, NPY_DLPACK_INTERNAL_CAPSULE_NAME); @@ -2937,7 +2954,7 @@ array_dlpack(PyArrayObject *self, managed->deleter = array_dlpack_deleter; PyObject *capsule = PyCapsule_New(managed, NPY_DLPACK_CAPSULE_NAME, - array_dlpack_capsule_deleter); + dlpack_capsule_deleter); if (capsule == NULL) { PyMem_Free(managed); PyMem_Free(managed_shape_strides); diff --git a/numpy/core/tests/test_dlpack.py b/numpy/core/tests/test_dlpack.py index 926668c59..2561991e8 100644 --- a/numpy/core/tests/test_dlpack.py +++ b/numpy/core/tests/test_dlpack.py @@ -92,3 +92,12 @@ class TestDLPack: x = np.arange(5) assert x.__dlpack_device__() == (1, 0) assert np.from_dlpack(x).__dlpack_device__() == (1, 0) + + def dlpack_deleter_exception(self): + x = np.arange(5) + _ = x.__dlpack__() + raise RuntimeError + + def test_dlpack_destructor_exception(self): + with pytest.raises(RuntimeError): + self.dlpack_deleter_exception() -- cgit v1.2.1 From a4cc97abdc4e347b293886f4847d1ddd5a9bfaa7 Mon Sep 17 00:00:00 2001 From: Hameer Abbasi Date: Mon, 31 May 2021 14:32:48 +0200 Subject: BUG: Offset not properly stored/computed. --- numpy/core/src/multiarray/methods.c | 37 +++++++++++++++++++++------- numpy/core/src/multiarray/multiarraymodule.c | 2 +- 2 files changed, 29 insertions(+), 10 deletions(-) (limited to 'numpy') diff --git a/numpy/core/src/multiarray/methods.c b/numpy/core/src/multiarray/methods.c index 9a16adecd..d3162e549 100644 --- a/numpy/core/src/multiarray/methods.c +++ b/numpy/core/src/multiarray/methods.c @@ -2792,7 +2792,7 @@ array_dlpack_deleter(DLManagedTensor *self) } /* This is exactly as mandated by dlpack */ -static void dlpack_capsule_deleter(PyObject *self){ +static void dlpack_capsule_deleter(PyObject *self) { if (PyCapsule_IsValid(self, "used_dltensor")) { return; } @@ -2827,7 +2827,7 @@ array_get_dl_device(PyArrayObject *self) { ret.device_id = 0; PyObject *base = PyArray_BASE(self); // The outer if is due to the fact that NumPy arrays are on the CPU - // by default. + // by default (if not created from DLPack). if (PyCapsule_IsValid(base, NPY_DLPACK_INTERNAL_CAPSULE_NAME)) { DLManagedTensor *managed = PyCapsule_GetPointer( base, NPY_DLPACK_INTERNAL_CAPSULE_NAME); @@ -2839,6 +2839,20 @@ array_get_dl_device(PyArrayObject *self) { return ret; } +static char * +array_get_dl_data(PyArrayObject *self) { + PyObject *base = PyArray_BASE(self); + if (PyCapsule_IsValid(base, NPY_DLPACK_INTERNAL_CAPSULE_NAME)) { + DLManagedTensor *managed = PyCapsule_GetPointer( + base, NPY_DLPACK_INTERNAL_CAPSULE_NAME); + if (managed == NULL) { + return NULL; + } + return managed->dl_tensor.data; + } + return PyArray_DATA(self); +} + static PyObject * array_dlpack(PyArrayObject *self, PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames) @@ -2913,18 +2927,23 @@ array_dlpack(PyArrayObject *self, return NULL; } + DLDevice device = array_get_dl_device(self); + if (PyErr_Occurred()) { + return NULL; + } + char *data = array_get_dl_data(self); + if (data == NULL) { + return NULL; + } + DLManagedTensor *managed = PyMem_Malloc(sizeof(DLManagedTensor)); if (managed == NULL) { PyErr_NoMemory(); return NULL; } - managed->dl_tensor.data = PyArray_DATA(self); - managed->dl_tensor.device = array_get_dl_device(self); - if (PyErr_Occurred()) { - PyMem_Free(managed); - return NULL; - } + managed->dl_tensor.data = data; + managed->dl_tensor.device = device; managed->dl_tensor.dtype = managed_dtype; @@ -2949,7 +2968,7 @@ array_dlpack(PyArrayObject *self, if (PyArray_SIZE(self) != 1 && !PyArray_IS_C_CONTIGUOUS(self)) { managed->dl_tensor.strides = managed_strides; } - managed->dl_tensor.byte_offset = 0; + managed->dl_tensor.byte_offset = (char *)PyArray_DATA(self) - data; managed->manager_ctx = self; managed->deleter = array_dlpack_deleter; diff --git a/numpy/core/src/multiarray/multiarraymodule.c b/numpy/core/src/multiarray/multiarraymodule.c index b04debe10..60e3875fc 100644 --- a/numpy/core/src/multiarray/multiarraymodule.c +++ b/numpy/core/src/multiarray/multiarraymodule.c @@ -4384,7 +4384,7 @@ from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { Py_XDECREF(ret); return NULL; } - + Py_XDECREF(capsule); return ret; } -- cgit v1.2.1 From f96aaa2ac484cab4e9f40b36902544e1174e0513 Mon Sep 17 00:00:00 2001 From: mattip Date: Mon, 1 Nov 2021 09:09:33 +0200 Subject: move dlpack functions to a new file --- numpy/core/code_generators/genapi.py | 1 + numpy/core/setup.py | 1 + numpy/core/src/common/npy_dlpack.h | 14 +- numpy/core/src/multiarray/dlpack.c | 382 +++++++++++++++++++++++++++ numpy/core/src/multiarray/dlpack.h | 18 ++ numpy/core/src/multiarray/methods.c | 238 +---------------- numpy/core/src/multiarray/multiarraymodule.c | 158 +---------- 7 files changed, 419 insertions(+), 393 deletions(-) create mode 100644 numpy/core/src/multiarray/dlpack.c create mode 100644 numpy/core/src/multiarray/dlpack.h (limited to 'numpy') diff --git a/numpy/core/code_generators/genapi.py b/numpy/core/code_generators/genapi.py index c2458c2b5..b401ee6a5 100644 --- a/numpy/core/code_generators/genapi.py +++ b/numpy/core/code_generators/genapi.py @@ -41,6 +41,7 @@ API_FILES = [join('multiarray', 'alloc.c'), join('multiarray', 'datetime_busdaycal.c'), join('multiarray', 'datetime_strings.c'), join('multiarray', 'descriptor.c'), + join('multiarray', 'dlpack.c'), join('multiarray', 'dtypemeta.c'), join('multiarray', 'einsum.c.src'), join('multiarray', 'flagsobject.c'), diff --git a/numpy/core/setup.py b/numpy/core/setup.py index 5c6fd4dcf..2c99060ec 100644 --- a/numpy/core/setup.py +++ b/numpy/core/setup.py @@ -883,6 +883,7 @@ def configuration(parent_package='',top_path=None): join('src', 'multiarray', 'datetime_busday.c'), join('src', 'multiarray', 'datetime_busdaycal.c'), join('src', 'multiarray', 'descriptor.c'), + join('src', 'multiarray', 'dlpack.c'), join('src', 'multiarray', 'dtypemeta.c'), join('src', 'multiarray', 'dragon4.c'), join('src', 'multiarray', 'dtype_transfer.c'), diff --git a/numpy/core/src/common/npy_dlpack.h b/numpy/core/src/common/npy_dlpack.h index 446d44afa..cb926a262 100644 --- a/numpy/core/src/common/npy_dlpack.h +++ b/numpy/core/src/common/npy_dlpack.h @@ -13,4 +13,16 @@ // capsule. #define NPY_DLPACK_INTERNAL_CAPSULE_NAME "numpy_dltensor" -#endif \ No newline at end of file +PyObject * +array_dlpack(PyArrayObject *self, PyObject *const *args, Py_ssize_t len_args, + PyObject *kwnames); + + +PyObject * +array_dlpack_device(PyArrayObject *self, PyObject *NPY_UNUSED(args)); + + +NPY_NO_EXPORT PyObject * +from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj); + +#endif diff --git a/numpy/core/src/multiarray/dlpack.c b/numpy/core/src/multiarray/dlpack.c new file mode 100644 index 000000000..591eddfaf --- /dev/null +++ b/numpy/core/src/multiarray/dlpack.c @@ -0,0 +1,382 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + +#define PY_SSIZE_T_CLEAN +#include + +#include "numpy/arrayobject.h" +#include "common/npy_argparse.h" + +#include "common/dlpack/dlpack.h" +#include "common/npy_dlpack.h" + +static void +array_dlpack_deleter(DLManagedTensor *self) +{ + PyArrayObject *array = (PyArrayObject *)self->manager_ctx; + // This will also free the strides as it's one allocation. + PyMem_Free(self->dl_tensor.shape); + PyMem_Free(self); + Py_XDECREF(array); +} + +/* This is exactly as mandated by dlpack */ +static void dlpack_capsule_deleter(PyObject *self) { + if (PyCapsule_IsValid(self, "used_dltensor")) { + return; + } + + /* an exception may be in-flight, we must save it in case we create another one */ + PyObject *type, *value, *traceback; + PyErr_Fetch(&type, &value, &traceback); + + DLManagedTensor *managed = (DLManagedTensor *)PyCapsule_GetPointer(self, "dltensor"); + if (managed == NULL) { + PyErr_WriteUnraisable(self); + goto done; + } + /* the spec says the deleter can be NULL if there is no way for the caller to provide a reasonable destructor. */ + if (managed->deleter) { + managed->deleter(managed); + /* TODO: is the deleter allowed to set a python exception? */ + assert(!PyErr_Occurred()); + } + +done: + PyErr_Restore(type, value, traceback); +} + +// This function cannot return NULL, but it can fail, +// So call PyErr_Occurred to check if it failed after +// calling it. +static DLDevice +array_get_dl_device(PyArrayObject *self) { + DLDevice ret; + ret.device_type = kDLCPU; + ret.device_id = 0; + PyObject *base = PyArray_BASE(self); + // The outer if is due to the fact that NumPy arrays are on the CPU + // by default (if not created from DLPack). + if (PyCapsule_IsValid(base, NPY_DLPACK_INTERNAL_CAPSULE_NAME)) { + DLManagedTensor *managed = PyCapsule_GetPointer( + base, NPY_DLPACK_INTERNAL_CAPSULE_NAME); + if (managed == NULL) { + return ret; + } + return managed->dl_tensor.device; + } + return ret; +} + +static char * +array_get_dl_data(PyArrayObject *self) { + PyObject *base = PyArray_BASE(self); + if (PyCapsule_IsValid(base, NPY_DLPACK_INTERNAL_CAPSULE_NAME)) { + DLManagedTensor *managed = PyCapsule_GetPointer( + base, NPY_DLPACK_INTERNAL_CAPSULE_NAME); + if (managed == NULL) { + return NULL; + } + return managed->dl_tensor.data; + } + return PyArray_DATA(self); +} + +/* used internally */ +static void array_dlpack_internal_capsule_deleter(PyObject *self) +{ + DLManagedTensor *managed = + (DLManagedTensor *)PyCapsule_GetPointer(self, NPY_DLPACK_INTERNAL_CAPSULE_NAME); + if (managed == NULL) { + return; + } + managed->deleter(managed); +} + +PyObject * +array_dlpack(PyArrayObject *self, + PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames) +{ + PyObject *stream = Py_None; + NPY_PREPARE_ARGPARSER; + if (npy_parse_arguments("__dlpack__", args, len_args, kwnames, + "$stream", NULL, &stream, NULL, NULL, NULL)) { + return NULL; + } + + if (stream != Py_None) { + PyErr_SetString(PyExc_RuntimeError, "NumPy only supports " + "stream=None."); + return NULL; + } + + npy_intp itemsize = PyArray_ITEMSIZE(self); + int ndim = PyArray_NDIM(self); + npy_intp *strides = PyArray_STRIDES(self); + npy_intp *shape = PyArray_SHAPE(self); + + if (!PyArray_IS_C_CONTIGUOUS(self) && PyArray_SIZE(self) != 1) { + for (int i = 0; i < ndim; ++i) { + if (strides[i] % itemsize != 0) { + PyErr_SetString(PyExc_RuntimeError, + "DLPack only supports strides which are a multiple of " + "itemsize."); + return NULL; + } + } + } + + DLDataType managed_dtype; + PyArray_Descr *dtype = PyArray_DESCR(self); + + if (PyDataType_ISBYTESWAPPED(dtype)) { + PyErr_SetString(PyExc_TypeError, "DLPack only supports native " + "byte swapping."); + return NULL; + } + + managed_dtype.bits = 8 * itemsize; + managed_dtype.lanes = 1; + + if (PyDataType_ISSIGNED(dtype)) { + managed_dtype.code = kDLInt; + } else if (PyDataType_ISUNSIGNED(dtype)) { + managed_dtype.code = kDLUInt; + } else if (PyDataType_ISFLOAT(dtype)) { + // We can't be sure that the dtype is + // IEEE or padded. + if (itemsize > 8) { + PyErr_SetString(PyExc_TypeError, "DLPack only supports IEEE " + "floating point types without padding."); + return NULL; + } + managed_dtype.code = kDLFloat; + } else if (PyDataType_ISCOMPLEX(dtype)) { + // We can't be sure that the dtype is + // IEEE or padded. + if (itemsize > 16) { + PyErr_SetString(PyExc_TypeError, "DLPack only supports IEEE " + "complex point types without padding."); + return NULL; + } + managed_dtype.code = kDLComplex; + } else { + PyErr_SetString(PyExc_TypeError, + "DLPack only supports signed/unsigned integers, float " + "and complex dtypes."); + return NULL; + } + + DLDevice device = array_get_dl_device(self); + if (PyErr_Occurred()) { + return NULL; + } + char *data = array_get_dl_data(self); + if (data == NULL) { + return NULL; + } + + DLManagedTensor *managed = PyMem_Malloc(sizeof(DLManagedTensor)); + if (managed == NULL) { + PyErr_NoMemory(); + return NULL; + } + + managed->dl_tensor.data = data; + managed->dl_tensor.device = device; + managed->dl_tensor.dtype = managed_dtype; + + + int64_t *managed_shape_strides = PyMem_Malloc(sizeof(int64_t) * ndim * 2); + if (managed_shape_strides == NULL) { + PyErr_NoMemory(); + PyMem_Free(managed); + return NULL; + } + + int64_t *managed_shape = managed_shape_strides; + int64_t *managed_strides = managed_shape_strides + ndim; + for (int i = 0; i < ndim; ++i) { + managed_shape[i] = shape[i]; + // Strides in DLPack are items; in NumPy are bytes. + managed_strides[i] = strides[i] / itemsize; + } + + managed->dl_tensor.ndim = ndim; + managed->dl_tensor.shape = managed_shape; + managed->dl_tensor.strides = NULL; + if (PyArray_SIZE(self) != 1 && !PyArray_IS_C_CONTIGUOUS(self)) { + managed->dl_tensor.strides = managed_strides; + } + managed->dl_tensor.byte_offset = (char *)PyArray_DATA(self) - data; + managed->manager_ctx = self; + managed->deleter = array_dlpack_deleter; + + PyObject *capsule = PyCapsule_New(managed, NPY_DLPACK_CAPSULE_NAME, + dlpack_capsule_deleter); + if (capsule == NULL) { + PyMem_Free(managed); + PyMem_Free(managed_shape_strides); + return NULL; + } + + // the capsule holds a reference + Py_INCREF(self); + return capsule; +} + +PyObject * +array_dlpack_device(PyArrayObject *self, PyObject *NPY_UNUSED(args)) +{ + DLDevice device = array_get_dl_device(self); + if (PyErr_Occurred()) { + return NULL; + } + return Py_BuildValue("ii", device.device_type, device.device_id); +} + +NPY_NO_EXPORT PyObject * +from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { + PyObject *capsule = PyObject_CallMethod((PyObject *)obj->ob_type, + "__dlpack__", "O", obj); + if (capsule == NULL) { + return NULL; + } + + DLManagedTensor *managed = + (DLManagedTensor *)PyCapsule_GetPointer(capsule, + NPY_DLPACK_CAPSULE_NAME); + + if (managed == NULL) { + Py_XDECREF(capsule); + return NULL; + } + + const int ndim = managed->dl_tensor.ndim; + if (ndim > NPY_MAXDIMS) { + PyErr_SetString(PyExc_RuntimeError, + "maxdims of DLPack tensor is higher than the supported " + "maxdims."); + Py_XDECREF(capsule); + return NULL; + } + + DLDeviceType device_type = managed->dl_tensor.device.device_type; + if (device_type != kDLCPU && + device_type != kDLCUDAHost && + device_type != kDLROCMHost && + device_type != kDLCUDAManaged) { + PyErr_SetString(PyExc_RuntimeError, + "Unsupported device in DLTensor."); + Py_XDECREF(capsule); + return NULL; + } + + if (managed->dl_tensor.dtype.lanes != 1) { + PyErr_SetString(PyExc_RuntimeError, + "Unsupported lanes in DLTensor dtype."); + Py_XDECREF(capsule); + return NULL; + } + + int typenum = -1; + const uint8_t bits = managed->dl_tensor.dtype.bits; + const npy_intp itemsize = bits / 8; + switch(managed->dl_tensor.dtype.code) { + case kDLInt: + switch (bits) + { + case 8: typenum = NPY_INT8; break; + case 16: typenum = NPY_INT16; break; + case 32: typenum = NPY_INT32; break; + case 64: typenum = NPY_INT64; break; + } + break; + case kDLUInt: + switch (bits) + { + case 8: typenum = NPY_UINT8; break; + case 16: typenum = NPY_UINT16; break; + case 32: typenum = NPY_UINT32; break; + case 64: typenum = NPY_UINT64; break; + } + break; + case kDLFloat: + switch (bits) + { + case 16: typenum = NPY_FLOAT16; break; + case 32: typenum = NPY_FLOAT32; break; + case 64: typenum = NPY_FLOAT64; break; + } + break; + case kDLComplex: + switch (bits) + { + case 64: typenum = NPY_COMPLEX64; break; + case 128: typenum = NPY_COMPLEX128; break; + } + break; + } + + if (typenum == -1) { + PyErr_SetString(PyExc_RuntimeError, + "Unsupported dtype in DLTensor."); + Py_XDECREF(capsule); + return NULL; + } + + PyArray_Descr *descr = PyArray_DescrFromType(typenum); + if (descr == NULL) { + Py_XDECREF(capsule); + return NULL; + } + + npy_intp shape[NPY_MAXDIMS]; + npy_intp strides[NPY_MAXDIMS]; + + for (int i = 0; i < ndim; ++i) { + shape[i] = managed->dl_tensor.shape[i]; + // DLPack has elements as stride units, NumPy has bytes. + if (managed->dl_tensor.strides != NULL) + { + strides[i] = managed->dl_tensor.strides[i] * itemsize; + } + } + + char *data = (char *)managed->dl_tensor.data + + managed->dl_tensor.byte_offset; + + PyObject *ret = PyArray_NewFromDescr(&PyArray_Type, descr, ndim, shape, + managed->dl_tensor.strides != NULL ? strides : NULL, data, 0, NULL); + if (ret == NULL) { + Py_XDECREF(capsule); + Py_XDECREF(descr); + return NULL; + } + + PyObject *new_capsule = PyCapsule_New(managed, + NPY_DLPACK_INTERNAL_CAPSULE_NAME, + array_dlpack_internal_capsule_deleter); + if (new_capsule == NULL) { + Py_XDECREF(capsule); + Py_XDECREF(ret); + return NULL; + } + + if (PyArray_SetBaseObject((PyArrayObject *)ret, new_capsule) < 0) { + Py_XDECREF(capsule); + Py_XDECREF(ret); + return NULL; + } + + if (PyCapsule_SetName(capsule, NPY_DLPACK_USED_CAPSULE_NAME) < 0) { + Py_XDECREF(capsule); + Py_XDECREF(ret); + return NULL; + } + + Py_XDECREF(capsule); + return ret; +} + + diff --git a/numpy/core/src/multiarray/dlpack.h b/numpy/core/src/multiarray/dlpack.h new file mode 100644 index 000000000..7d4a289d9 --- /dev/null +++ b/numpy/core/src/multiarray/dlpack.h @@ -0,0 +1,18 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + +#define PY_SSIZE_T_CLEAN +#include +#include "numpy/arrayobject.h" + +static PyObject * +array_dlpack(PyArrayObject *self, PyObject *const *args, Py_ssize_t len_args, + PyObject *kwnames); + + +static PyObject * +array_dlpack_device(PyArrayObject *self, PyObject *NPY_UNUSED(args)); + + +NPY_NO_EXPORT PyObject * +from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj); diff --git a/numpy/core/src/multiarray/methods.c b/numpy/core/src/multiarray/methods.c index d3162e549..f40db492c 100644 --- a/numpy/core/src/multiarray/methods.c +++ b/numpy/core/src/multiarray/methods.c @@ -26,15 +26,13 @@ #include "shape.h" #include "strfuncs.h" #include "array_assign.h" +#include "npy_dlpack.h" #include "methods.h" #include "alloc.h" #include -#include "common/dlpack/dlpack.h" -#include "common/npy_dlpack.h" - /* NpyArg_ParseKeywords * @@ -2584,6 +2582,8 @@ array_round(PyArrayObject *self, PyObject *args, PyObject *kwds) } } + + static PyObject * array_setflags(PyArrayObject *self, PyObject *args, PyObject *kwds) { @@ -2763,236 +2763,6 @@ array_class_getitem(PyObject *cls, PyObject *args) generic_alias = NULL; #endif return generic_alias; - -#define NPY_DLPACK_CAPSULE_NAME "dltensor" -#define NPY_DLPACK_USED_CAPSULE_NAME "used_dltensor" - -static void array_dlpack_capsule_deleter(PyObject *self) -{ - if (!PyCapsule_IsValid(self, NPY_DLPACK_CAPSULE_NAME)) { - if (!PyCapsule_IsValid(self, NPY_DLPACK_USED_CAPSULE_NAME)) { - PyErr_SetString(PyExc_RuntimeError, "Invalid capsule name."); - } - return; - } - - DLManagedTensor *managed = - (DLManagedTensor *)PyCapsule_GetPointer(self, NPY_DLPACK_CAPSULE_NAME); - managed->deleter(managed); -} - -static void -array_dlpack_deleter(DLManagedTensor *self) -{ - PyArrayObject *array = (PyArrayObject *)self->manager_ctx; - // This will also free the strides as it's one allocation. - PyMem_Free(self->dl_tensor.shape); - PyMem_Free(self); - Py_XDECREF(array); -} - -/* This is exactly as mandated by dlpack */ -static void dlpack_capsule_deleter(PyObject *self) { - if (PyCapsule_IsValid(self, "used_dltensor")) { - return; - } - - /* an exception may be in-flight, we must save it in case we create another one */ - PyObject *type, *value, *traceback; - PyErr_Fetch(&type, &value, &traceback); - - DLManagedTensor *managed = (DLManagedTensor *)PyCapsule_GetPointer(self, "dltensor"); - if (managed == NULL) { - PyErr_WriteUnraisable(self); - goto done; - } - /* the spec says the deleter can be NULL if there is no way for the caller to provide a reasonable destructor. */ - if (managed->deleter) { - managed->deleter(managed); - /* TODO: is the deleter allowed to set a python exception? */ - assert(!PyErr_Occurred()); - } - -done: - PyErr_Restore(type, value, traceback); -} - -// This function cannot return NULL, but it can fail, -// So call PyErr_Occurred to check if it failed after -// calling it. -static DLDevice -array_get_dl_device(PyArrayObject *self) { - DLDevice ret; - ret.device_type = kDLCPU; - ret.device_id = 0; - PyObject *base = PyArray_BASE(self); - // The outer if is due to the fact that NumPy arrays are on the CPU - // by default (if not created from DLPack). - if (PyCapsule_IsValid(base, NPY_DLPACK_INTERNAL_CAPSULE_NAME)) { - DLManagedTensor *managed = PyCapsule_GetPointer( - base, NPY_DLPACK_INTERNAL_CAPSULE_NAME); - if (managed == NULL) { - return ret; - } - return managed->dl_tensor.device; - } - return ret; -} - -static char * -array_get_dl_data(PyArrayObject *self) { - PyObject *base = PyArray_BASE(self); - if (PyCapsule_IsValid(base, NPY_DLPACK_INTERNAL_CAPSULE_NAME)) { - DLManagedTensor *managed = PyCapsule_GetPointer( - base, NPY_DLPACK_INTERNAL_CAPSULE_NAME); - if (managed == NULL) { - return NULL; - } - return managed->dl_tensor.data; - } - return PyArray_DATA(self); -} - -static PyObject * -array_dlpack(PyArrayObject *self, - PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames) -{ - PyObject *stream = Py_None; - NPY_PREPARE_ARGPARSER; - if (npy_parse_arguments("__dlpack__", args, len_args, kwnames, - "$stream", NULL, &stream, NULL, NULL, NULL)) { - return NULL; - } - - if (stream != Py_None) { - PyErr_SetString(PyExc_RuntimeError, "NumPy only supports " - "stream=None."); - return NULL; - } - - npy_intp itemsize = PyArray_ITEMSIZE(self); - int ndim = PyArray_NDIM(self); - npy_intp *strides = PyArray_STRIDES(self); - npy_intp *shape = PyArray_SHAPE(self); - - if (!PyArray_IS_C_CONTIGUOUS(self) && PyArray_SIZE(self) != 1) { - for (int i = 0; i < ndim; ++i) { - if (strides[i] % itemsize != 0) { - PyErr_SetString(PyExc_RuntimeError, - "DLPack only supports strides which are a multiple of " - "itemsize."); - return NULL; - } - } - } - - DLDataType managed_dtype; - PyArray_Descr *dtype = PyArray_DESCR(self); - - if (PyDataType_ISBYTESWAPPED(dtype)) { - PyErr_SetString(PyExc_TypeError, "DLPack only supports native " - "byte swapping."); - return NULL; - } - - managed_dtype.bits = 8 * itemsize; - managed_dtype.lanes = 1; - - if (PyDataType_ISSIGNED(dtype)) { - managed_dtype.code = kDLInt; - } else if (PyDataType_ISUNSIGNED(dtype)) { - managed_dtype.code = kDLUInt; - } else if (PyDataType_ISFLOAT(dtype)) { - // We can't be sure that the dtype is - // IEEE or padded. - if (itemsize > 8) { - PyErr_SetString(PyExc_TypeError, "DLPack only supports IEEE " - "floating point types without padding."); - return NULL; - } - managed_dtype.code = kDLFloat; - } else if (PyDataType_ISCOMPLEX(dtype)) { - // We can't be sure that the dtype is - // IEEE or padded. - if (itemsize > 16) { - PyErr_SetString(PyExc_TypeError, "DLPack only supports IEEE " - "complex point types without padding."); - return NULL; - } - managed_dtype.code = kDLComplex; - } else { - PyErr_SetString(PyExc_TypeError, - "DLPack only supports signed/unsigned integers, float " - "and complex dtypes."); - return NULL; - } - - DLDevice device = array_get_dl_device(self); - if (PyErr_Occurred()) { - return NULL; - } - char *data = array_get_dl_data(self); - if (data == NULL) { - return NULL; - } - - DLManagedTensor *managed = PyMem_Malloc(sizeof(DLManagedTensor)); - if (managed == NULL) { - PyErr_NoMemory(); - return NULL; - } - - managed->dl_tensor.data = data; - managed->dl_tensor.device = device; - managed->dl_tensor.dtype = managed_dtype; - - - int64_t *managed_shape_strides = PyMem_Malloc(sizeof(int64_t) * ndim * 2); - if (managed_shape_strides == NULL) { - PyErr_NoMemory(); - PyMem_Free(managed); - return NULL; - } - - int64_t *managed_shape = managed_shape_strides; - int64_t *managed_strides = managed_shape_strides + ndim; - for (int i = 0; i < ndim; ++i) { - managed_shape[i] = shape[i]; - // Strides in DLPack are items; in NumPy are bytes. - managed_strides[i] = strides[i] / itemsize; - } - - managed->dl_tensor.ndim = ndim; - managed->dl_tensor.shape = managed_shape; - managed->dl_tensor.strides = NULL; - if (PyArray_SIZE(self) != 1 && !PyArray_IS_C_CONTIGUOUS(self)) { - managed->dl_tensor.strides = managed_strides; - } - managed->dl_tensor.byte_offset = (char *)PyArray_DATA(self) - data; - managed->manager_ctx = self; - managed->deleter = array_dlpack_deleter; - - PyObject *capsule = PyCapsule_New(managed, NPY_DLPACK_CAPSULE_NAME, - dlpack_capsule_deleter); - if (capsule == NULL) { - PyMem_Free(managed); - PyMem_Free(managed_shape_strides); - return NULL; - } - - // the capsule holds a reference - Py_INCREF(self); - return capsule; -} - -static PyObject * -array_dlpack_device(PyArrayObject *self, PyObject *NPY_UNUSED(args)) -{ - DLDevice device = array_get_dl_device(self); - if (PyErr_Occurred()) { - return NULL; - } - return Py_BuildValue("ii", device.device_type, device.device_id); } NPY_NO_EXPORT PyMethodDef array_methods[] = { @@ -3220,7 +2990,6 @@ NPY_NO_EXPORT PyMethodDef array_methods[] = { {"view", (PyCFunction)array_view, METH_FASTCALL | METH_KEYWORDS, NULL}, - // For data interchange between libraries {"__dlpack__", (PyCFunction)array_dlpack, @@ -3229,6 +2998,5 @@ NPY_NO_EXPORT PyMethodDef array_methods[] = { {"__dlpack_device__", (PyCFunction)array_dlpack_device, METH_NOARGS, NULL}, - {NULL, NULL, 0, NULL} /* sentinel */ }; diff --git a/numpy/core/src/multiarray/multiarraymodule.c b/numpy/core/src/multiarray/multiarraymodule.c index 60e3875fc..ac4c1ae0b 100644 --- a/numpy/core/src/multiarray/multiarraymodule.c +++ b/numpy/core/src/multiarray/multiarraymodule.c @@ -70,7 +70,7 @@ NPY_NO_EXPORT int NPY_NUMUSERTYPES = 0; #include "get_attr_string.h" #include "experimental_public_dtype_api.h" /* _get_experimental_dtype_api */ -#include "common/npy_dlpack.h" +#include "npy_dlpack.h" /* ***************************************************************************** @@ -4233,162 +4233,6 @@ _reload_guard(PyObject *NPY_UNUSED(self)) { Py_RETURN_NONE; } -/* used internally */ -static void array_dlpack_internal_capsule_deleter(PyObject *self) -{ - DLManagedTensor *managed = - (DLManagedTensor *)PyCapsule_GetPointer(self, NPY_DLPACK_INTERNAL_CAPSULE_NAME); - if (managed == NULL) { - return; - } - managed->deleter(managed); -} - - -NPY_NO_EXPORT PyObject * -from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { - PyObject *capsule = PyObject_CallMethod((PyObject *)obj->ob_type, - "__dlpack__", "O", obj); - if (capsule == NULL) { - return NULL; - } - - DLManagedTensor *managed = - (DLManagedTensor *)PyCapsule_GetPointer(capsule, - NPY_DLPACK_CAPSULE_NAME); - - if (managed == NULL) { - Py_XDECREF(capsule); - return NULL; - } - - const int ndim = managed->dl_tensor.ndim; - if (ndim > NPY_MAXDIMS) { - PyErr_SetString(PyExc_RuntimeError, - "maxdims of DLPack tensor is higher than the supported " - "maxdims."); - Py_XDECREF(capsule); - return NULL; - } - - DLDeviceType device_type = managed->dl_tensor.device.device_type; - if (device_type != kDLCPU && - device_type != kDLCUDAHost && - device_type != kDLROCMHost && - device_type != kDLCUDAManaged) { - PyErr_SetString(PyExc_RuntimeError, - "Unsupported device in DLTensor."); - Py_XDECREF(capsule); - return NULL; - } - - if (managed->dl_tensor.dtype.lanes != 1) { - PyErr_SetString(PyExc_RuntimeError, - "Unsupported lanes in DLTensor dtype."); - Py_XDECREF(capsule); - return NULL; - } - - int typenum = -1; - const uint8_t bits = managed->dl_tensor.dtype.bits; - const npy_intp itemsize = bits / 8; - switch(managed->dl_tensor.dtype.code) { - case kDLInt: - switch (bits) - { - case 8: typenum = NPY_INT8; break; - case 16: typenum = NPY_INT16; break; - case 32: typenum = NPY_INT32; break; - case 64: typenum = NPY_INT64; break; - } - break; - case kDLUInt: - switch (bits) - { - case 8: typenum = NPY_UINT8; break; - case 16: typenum = NPY_UINT16; break; - case 32: typenum = NPY_UINT32; break; - case 64: typenum = NPY_UINT64; break; - } - break; - case kDLFloat: - switch (bits) - { - case 16: typenum = NPY_FLOAT16; break; - case 32: typenum = NPY_FLOAT32; break; - case 64: typenum = NPY_FLOAT64; break; - } - break; - case kDLComplex: - switch (bits) - { - case 64: typenum = NPY_COMPLEX64; break; - case 128: typenum = NPY_COMPLEX128; break; - } - break; - } - - if (typenum == -1) { - PyErr_SetString(PyExc_RuntimeError, - "Unsupported dtype in DLTensor."); - Py_XDECREF(capsule); - return NULL; - } - - PyArray_Descr *descr = PyArray_DescrFromType(typenum); - if (descr == NULL) { - Py_XDECREF(capsule); - return NULL; - } - - npy_intp shape[NPY_MAXDIMS]; - npy_intp strides[NPY_MAXDIMS]; - - for (int i = 0; i < ndim; ++i) { - shape[i] = managed->dl_tensor.shape[i]; - // DLPack has elements as stride units, NumPy has bytes. - if (managed->dl_tensor.strides != NULL) - { - strides[i] = managed->dl_tensor.strides[i] * itemsize; - } - } - - char *data = (char *)managed->dl_tensor.data + - managed->dl_tensor.byte_offset; - - PyObject *ret = PyArray_NewFromDescr(&PyArray_Type, descr, ndim, shape, - managed->dl_tensor.strides != NULL ? strides : NULL, data, 0, NULL); - if (ret == NULL) { - Py_XDECREF(capsule); - Py_XDECREF(descr); - return NULL; - } - - PyObject *new_capsule = PyCapsule_New(managed, - NPY_DLPACK_INTERNAL_CAPSULE_NAME, - array_dlpack_internal_capsule_deleter); - if (new_capsule == NULL) { - Py_XDECREF(capsule); - Py_XDECREF(ret); - return NULL; - } - - if (PyArray_SetBaseObject((PyArrayObject *)ret, new_capsule) < 0) { - Py_XDECREF(capsule); - Py_XDECREF(ret); - return NULL; - } - - if (PyCapsule_SetName(capsule, NPY_DLPACK_USED_CAPSULE_NAME) < 0) { - Py_XDECREF(capsule); - Py_XDECREF(ret); - return NULL; - } - - Py_XDECREF(capsule); - return ret; -} - static struct PyMethodDef array_module_methods[] = { {"_get_implementing_args", (PyCFunction)array__get_implementing_args, -- cgit v1.2.1 From 0c992dca1cc23f12af14a8d66101166ef6c92355 Mon Sep 17 00:00:00 2001 From: mattip Date: Mon, 1 Nov 2021 09:15:18 +0200 Subject: BUG: fixes from review --- numpy/core/src/multiarray/dlpack.c | 104 +++++++++++++++++++++++-------------- 1 file changed, 64 insertions(+), 40 deletions(-) (limited to 'numpy') diff --git a/numpy/core/src/multiarray/dlpack.c b/numpy/core/src/multiarray/dlpack.c index 591eddfaf..f1591bb1f 100644 --- a/numpy/core/src/multiarray/dlpack.c +++ b/numpy/core/src/multiarray/dlpack.c @@ -22,7 +22,7 @@ array_dlpack_deleter(DLManagedTensor *self) /* This is exactly as mandated by dlpack */ static void dlpack_capsule_deleter(PyObject *self) { - if (PyCapsule_IsValid(self, "used_dltensor")) { + if (PyCapsule_IsValid(self, NPY_DLPACK_USED_CAPSULE_NAME)) { return; } @@ -30,12 +30,16 @@ static void dlpack_capsule_deleter(PyObject *self) { PyObject *type, *value, *traceback; PyErr_Fetch(&type, &value, &traceback); - DLManagedTensor *managed = (DLManagedTensor *)PyCapsule_GetPointer(self, "dltensor"); + DLManagedTensor *managed = + (DLManagedTensor *)PyCapsule_GetPointer(self, NPY_DLPACK_CAPSULE_NAME); if (managed == NULL) { PyErr_WriteUnraisable(self); goto done; } - /* the spec says the deleter can be NULL if there is no way for the caller to provide a reasonable destructor. */ + /* + * the spec says the deleter can be NULL if there is no way for the caller + * to provide a reasonable destructor. + */ if (managed->deleter) { managed->deleter(managed); /* TODO: is the deleter allowed to set a python exception? */ @@ -46,6 +50,34 @@ done: PyErr_Restore(type, value, traceback); } +/* used internally, almost identical to dlpack_capsule_deleter() */ +static void array_dlpack_internal_capsule_deleter(PyObject *self) +{ + /* an exception may be in-flight, we must save it in case we create another one */ + PyObject *type, *value, *traceback; + PyErr_Fetch(&type, &value, &traceback); + + DLManagedTensor *managed = + (DLManagedTensor *)PyCapsule_GetPointer(self, NPY_DLPACK_INTERNAL_CAPSULE_NAME); + if (managed == NULL) { + PyErr_WriteUnraisable(self); + goto done; + } + /* + * the spec says the deleter can be NULL if there is no way for the caller + * to provide a reasonable destructor. + */ + if (managed->deleter) { + managed->deleter(managed); + /* TODO: is the deleter allowed to set a python exception? */ + assert(!PyErr_Occurred()); + } + +done: + PyErr_Restore(type, value, traceback); +} + + // This function cannot return NULL, but it can fail, // So call PyErr_Occurred to check if it failed after // calling it. @@ -82,17 +114,6 @@ array_get_dl_data(PyArrayObject *self) { return PyArray_DATA(self); } -/* used internally */ -static void array_dlpack_internal_capsule_deleter(PyObject *self) -{ - DLManagedTensor *managed = - (DLManagedTensor *)PyCapsule_GetPointer(self, NPY_DLPACK_INTERNAL_CAPSULE_NAME); - if (managed == NULL) { - return; - } - managed->deleter(managed); -} - PyObject * array_dlpack(PyArrayObject *self, PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames) @@ -140,9 +161,11 @@ array_dlpack(PyArrayObject *self, if (PyDataType_ISSIGNED(dtype)) { managed_dtype.code = kDLInt; - } else if (PyDataType_ISUNSIGNED(dtype)) { + } + else if (PyDataType_ISUNSIGNED(dtype)) { managed_dtype.code = kDLUInt; - } else if (PyDataType_ISFLOAT(dtype)) { + } + else if (PyDataType_ISFLOAT(dtype)) { // We can't be sure that the dtype is // IEEE or padded. if (itemsize > 8) { @@ -151,7 +174,8 @@ array_dlpack(PyArrayObject *self, return NULL; } managed_dtype.code = kDLFloat; - } else if (PyDataType_ISCOMPLEX(dtype)) { + } + else if (PyDataType_ISCOMPLEX(dtype)) { // We can't be sure that the dtype is // IEEE or padded. if (itemsize > 16) { @@ -160,7 +184,8 @@ array_dlpack(PyArrayObject *self, return NULL; } managed_dtype.code = kDLComplex; - } else { + } + else { PyErr_SetString(PyExc_TypeError, "DLPack only supports signed/unsigned integers, float " "and complex dtypes."); @@ -243,12 +268,12 @@ from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { return NULL; } - DLManagedTensor *managed = + DLManagedTensor *managed = (DLManagedTensor *)PyCapsule_GetPointer(capsule, NPY_DLPACK_CAPSULE_NAME); if (managed == NULL) { - Py_XDECREF(capsule); + Py_DECREF(capsule); return NULL; } @@ -257,7 +282,7 @@ from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { PyErr_SetString(PyExc_RuntimeError, "maxdims of DLPack tensor is higher than the supported " "maxdims."); - Py_XDECREF(capsule); + Py_DECREF(capsule); return NULL; } @@ -268,14 +293,14 @@ from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { device_type != kDLCUDAManaged) { PyErr_SetString(PyExc_RuntimeError, "Unsupported device in DLTensor."); - Py_XDECREF(capsule); + Py_DECREF(capsule); return NULL; } if (managed->dl_tensor.dtype.lanes != 1) { PyErr_SetString(PyExc_RuntimeError, "Unsupported lanes in DLTensor dtype."); - Py_XDECREF(capsule); + Py_DECREF(capsule); return NULL; } @@ -321,13 +346,7 @@ from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { if (typenum == -1) { PyErr_SetString(PyExc_RuntimeError, "Unsupported dtype in DLTensor."); - Py_XDECREF(capsule); - return NULL; - } - - PyArray_Descr *descr = PyArray_DescrFromType(typenum); - if (descr == NULL) { - Py_XDECREF(capsule); + Py_DECREF(capsule); return NULL; } @@ -346,11 +365,16 @@ from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { char *data = (char *)managed->dl_tensor.data + managed->dl_tensor.byte_offset; + PyArray_Descr *descr = PyArray_DescrFromType(typenum); + if (descr == NULL) { + Py_DECREF(capsule); + return NULL; + } + PyObject *ret = PyArray_NewFromDescr(&PyArray_Type, descr, ndim, shape, managed->dl_tensor.strides != NULL ? strides : NULL, data, 0, NULL); if (ret == NULL) { - Py_XDECREF(capsule); - Py_XDECREF(descr); + Py_DECREF(capsule); return NULL; } @@ -358,24 +382,24 @@ from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { NPY_DLPACK_INTERNAL_CAPSULE_NAME, array_dlpack_internal_capsule_deleter); if (new_capsule == NULL) { - Py_XDECREF(capsule); - Py_XDECREF(ret); + Py_DECREF(capsule); + Py_DECREF(ret); return NULL; } if (PyArray_SetBaseObject((PyArrayObject *)ret, new_capsule) < 0) { - Py_XDECREF(capsule); - Py_XDECREF(ret); + Py_DECREF(capsule); + Py_DECREF(ret); return NULL; } if (PyCapsule_SetName(capsule, NPY_DLPACK_USED_CAPSULE_NAME) < 0) { - Py_XDECREF(capsule); - Py_XDECREF(ret); + Py_DECREF(capsule); + Py_DECREF(ret); return NULL; } - - Py_XDECREF(capsule); + + Py_DECREF(capsule); return ret; } -- cgit v1.2.1 From ef04f59d1a5cc7320866c48d75059aa52d390003 Mon Sep 17 00:00:00 2001 From: Matti Picus Date: Tue, 2 Nov 2021 09:50:33 +0200 Subject: Updates Co-authored-by: Sebastian Berg Co-authored-by: Bas van Beek <43369155+BvB93@users.noreply.github.com> --- numpy/__init__.pyi | 2 +- numpy/core/src/multiarray/dlpack.c | 6 ++---- 2 files changed, 3 insertions(+), 5 deletions(-) (limited to 'numpy') diff --git a/numpy/__init__.pyi b/numpy/__init__.pyi index 63e723a35..1562ce89e 100644 --- a/numpy/__init__.pyi +++ b/numpy/__init__.pyi @@ -4333,7 +4333,7 @@ class chararray(ndarray[_ShapeType, _CharDType]): # class MachAr: ... class _SupportsDLPack(Protocol[_T_contra]): - def __dlpack__(self, *, stream: Optional[int] = ...) -> _PyCapsule: ... + def __dlpack__(self, *, stream: None | _T_contra = ...) -> _PyCapsule: ... def from_dlpack(__obj: _SupportsDLPack[None]) -> NDArray[Any]: ... diff --git a/numpy/core/src/multiarray/dlpack.c b/numpy/core/src/multiarray/dlpack.c index f1591bb1f..9de304379 100644 --- a/numpy/core/src/multiarray/dlpack.c +++ b/numpy/core/src/multiarray/dlpack.c @@ -211,7 +211,6 @@ array_dlpack(PyArrayObject *self, managed->dl_tensor.device = device; managed->dl_tensor.dtype = managed_dtype; - int64_t *managed_shape_strides = PyMem_Malloc(sizeof(int64_t) * ndim * 2); if (managed_shape_strides == NULL) { PyErr_NoMemory(); @@ -307,7 +306,7 @@ from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { int typenum = -1; const uint8_t bits = managed->dl_tensor.dtype.bits; const npy_intp itemsize = bits / 8; - switch(managed->dl_tensor.dtype.code) { + switch (managed->dl_tensor.dtype.code) { case kDLInt: switch (bits) { @@ -356,8 +355,7 @@ from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { for (int i = 0; i < ndim; ++i) { shape[i] = managed->dl_tensor.shape[i]; // DLPack has elements as stride units, NumPy has bytes. - if (managed->dl_tensor.strides != NULL) - { + if (managed->dl_tensor.strides != NULL) { strides[i] = managed->dl_tensor.strides[i] * itemsize; } } -- cgit v1.2.1 From ba8fcbe2ba0a26cd52dfa9bf40dd2e945e5b298f Mon Sep 17 00:00:00 2001 From: mattip Date: Tue, 2 Nov 2021 11:30:32 +0200 Subject: change from_dlpack to _dlpack, remove unused header --- numpy/__init__.pyi | 2 +- numpy/array_api/__init__.py | 4 ++-- numpy/array_api/_creation_functions.py | 2 +- numpy/core/_add_newdocs.py | 4 ++-- numpy/core/multiarray.py | 17 +++++++++-------- numpy/core/numeric.py | 4 ++-- numpy/core/src/common/npy_dlpack.h | 2 +- numpy/core/src/multiarray/dlpack.c | 2 +- numpy/core/src/multiarray/dlpack.h | 18 ------------------ numpy/core/src/multiarray/multiarraymodule.c | 2 +- numpy/core/tests/test_dlpack.py | 24 ++++++++++++------------ 11 files changed, 32 insertions(+), 49 deletions(-) delete mode 100644 numpy/core/src/multiarray/dlpack.h (limited to 'numpy') diff --git a/numpy/__init__.pyi b/numpy/__init__.pyi index 1562ce89e..b2e9eec77 100644 --- a/numpy/__init__.pyi +++ b/numpy/__init__.pyi @@ -4335,5 +4335,5 @@ class chararray(ndarray[_ShapeType, _CharDType]): class _SupportsDLPack(Protocol[_T_contra]): def __dlpack__(self, *, stream: None | _T_contra = ...) -> _PyCapsule: ... -def from_dlpack(__obj: _SupportsDLPack[None]) -> NDArray[Any]: ... +def _from_dlpack(__obj: _SupportsDLPack[None]) -> NDArray[Any]: ... diff --git a/numpy/array_api/__init__.py b/numpy/array_api/__init__.py index d8b29057e..89f5e9cba 100644 --- a/numpy/array_api/__init__.py +++ b/numpy/array_api/__init__.py @@ -136,7 +136,7 @@ from ._creation_functions import ( empty, empty_like, eye, - from_dlpack, + _from_dlpack, full, full_like, linspace, @@ -155,7 +155,7 @@ __all__ += [ "empty", "empty_like", "eye", - "from_dlpack", + "_from_dlpack", "full", "full_like", "linspace", diff --git a/numpy/array_api/_creation_functions.py b/numpy/array_api/_creation_functions.py index e36807468..c3644ac2c 100644 --- a/numpy/array_api/_creation_functions.py +++ b/numpy/array_api/_creation_functions.py @@ -151,7 +151,7 @@ def eye( return Array._new(np.eye(n_rows, M=n_cols, k=k, dtype=dtype)) -def from_dlpack(x: object, /) -> Array: +def _from_dlpack(x: object, /) -> Array: # Note: dlpack support is not yet implemented on Array raise NotImplementedError("DLPack support is not yet implemented") diff --git a/numpy/core/_add_newdocs.py b/numpy/core/_add_newdocs.py index f1a42dffe..cae5bc281 100644 --- a/numpy/core/_add_newdocs.py +++ b/numpy/core/_add_newdocs.py @@ -1573,9 +1573,9 @@ add_newdoc('numpy.core.multiarray', 'frombuffer', array_function_like_doc, )) -add_newdoc('numpy.core.multiarray', 'from_dlpack', +add_newdoc('numpy.core.multiarray', '_from_dlpack', """ - from_dlpack(x, /) + _from_dlpack(x, /) Create a NumPy array from an object implementing the ``__dlpack__`` protocol. diff --git a/numpy/core/multiarray.py b/numpy/core/multiarray.py index 9f431f01b..f96274263 100644 --- a/numpy/core/multiarray.py +++ b/numpy/core/multiarray.py @@ -14,8 +14,9 @@ from ._multiarray_umath import * # noqa: F403 # do not change them. issue gh-15518 # _get_ndarray_c_version is semi-public, on purpose not added to __all__ from ._multiarray_umath import ( - _fastCopyAndTranspose, _flagdict, _insert, _reconstruct, _vec_string, - _ARRAY_API, _monotonicity, _get_ndarray_c_version, _set_madvise_hugepage, + _fastCopyAndTranspose, _flagdict, _from_dlpack, _insert, _reconstruct, + _vec_string, _ARRAY_API, _monotonicity, _get_ndarray_c_version, + _set_madvise_hugepage, ) __all__ = [ @@ -23,15 +24,15 @@ __all__ = [ 'ITEM_HASOBJECT', 'ITEM_IS_POINTER', 'LIST_PICKLE', 'MAXDIMS', 'MAY_SHARE_BOUNDS', 'MAY_SHARE_EXACT', 'NEEDS_INIT', 'NEEDS_PYAPI', 'RAISE', 'USE_GETITEM', 'USE_SETITEM', 'WRAP', '_fastCopyAndTranspose', - '_flagdict', '_insert', '_reconstruct', '_vec_string', '_monotonicity', - 'add_docstring', 'arange', 'array', 'asarray', 'asanyarray', - 'ascontiguousarray', 'asfortranarray', 'bincount', 'broadcast', - 'busday_count', 'busday_offset', 'busdaycalendar', 'can_cast', + '_flagdict', '_from_dlpack', '_insert', '_reconstruct', '_vec_string', + '_monotonicity', 'add_docstring', 'arange', 'array', 'asarray', + 'asanyarray', 'ascontiguousarray', 'asfortranarray', 'bincount', + 'broadcast', 'busday_count', 'busday_offset', 'busdaycalendar', 'can_cast', 'compare_chararrays', 'concatenate', 'copyto', 'correlate', 'correlate2', 'count_nonzero', 'c_einsum', 'datetime_as_string', 'datetime_data', 'dot', 'dragon4_positional', 'dragon4_scientific', 'dtype', 'empty', 'empty_like', 'error', 'flagsobj', 'flatiter', 'format_longfloat', - 'frombuffer', 'from_dlpack', 'fromfile', 'fromiter', 'fromstring', + 'frombuffer', 'fromfile', 'fromiter', 'fromstring', 'get_handler_name', 'inner', 'interp', 'interp_complex', 'is_busday', 'lexsort', 'matmul', 'may_share_memory', 'min_scalar_type', 'ndarray', 'nditer', 'nested_iters', 'normalize_axis_index', 'packbits', @@ -46,6 +47,7 @@ _reconstruct.__module__ = 'numpy.core.multiarray' scalar.__module__ = 'numpy.core.multiarray' +_from_dlpack.__module__ = 'numpy' arange.__module__ = 'numpy' array.__module__ = 'numpy' asarray.__module__ = 'numpy' @@ -55,7 +57,6 @@ asfortranarray.__module__ = 'numpy' datetime_data.__module__ = 'numpy' empty.__module__ = 'numpy' frombuffer.__module__ = 'numpy' -from_dlpack.__module__ = 'numpy' fromfile.__module__ = 'numpy' fromiter.__module__ = 'numpy' frompyfunc.__module__ = 'numpy' diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py index 987470f92..344d40d93 100644 --- a/numpy/core/numeric.py +++ b/numpy/core/numeric.py @@ -13,7 +13,7 @@ from .multiarray import ( WRAP, arange, array, asarray, asanyarray, ascontiguousarray, asfortranarray, broadcast, can_cast, compare_chararrays, concatenate, copyto, dot, dtype, empty, - empty_like, flatiter, frombuffer, from_dlpack, fromfile, fromiter, + empty_like, flatiter, frombuffer, _from_dlpack, fromfile, fromiter, fromstring, inner, lexsort, matmul, may_share_memory, min_scalar_type, ndarray, nditer, nested_iters, promote_types, putmask, result_type, set_numeric_ops, shares_memory, vdot, where, @@ -41,7 +41,7 @@ __all__ = [ 'newaxis', 'ndarray', 'flatiter', 'nditer', 'nested_iters', 'ufunc', 'arange', 'array', 'asarray', 'asanyarray', 'ascontiguousarray', 'asfortranarray', 'zeros', 'count_nonzero', 'empty', 'broadcast', 'dtype', - 'fromstring', 'fromfile', 'frombuffer', 'from_dlpack', 'where', + 'fromstring', 'fromfile', 'frombuffer', '_from_dlpack', 'where', 'argwhere', 'copyto', 'concatenate', 'fastCopyAndTranspose', 'lexsort', 'set_numeric_ops', 'can_cast', 'promote_types', 'min_scalar_type', 'result_type', 'isfortran', 'empty_like', 'zeros_like', 'ones_like', diff --git a/numpy/core/src/common/npy_dlpack.h b/numpy/core/src/common/npy_dlpack.h index cb926a262..14ca352c0 100644 --- a/numpy/core/src/common/npy_dlpack.h +++ b/numpy/core/src/common/npy_dlpack.h @@ -23,6 +23,6 @@ array_dlpack_device(PyArrayObject *self, PyObject *NPY_UNUSED(args)); NPY_NO_EXPORT PyObject * -from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj); +_from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj); #endif diff --git a/numpy/core/src/multiarray/dlpack.c b/numpy/core/src/multiarray/dlpack.c index 9de304379..21930b0ef 100644 --- a/numpy/core/src/multiarray/dlpack.c +++ b/numpy/core/src/multiarray/dlpack.c @@ -260,7 +260,7 @@ array_dlpack_device(PyArrayObject *self, PyObject *NPY_UNUSED(args)) } NPY_NO_EXPORT PyObject * -from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { +_from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { PyObject *capsule = PyObject_CallMethod((PyObject *)obj->ob_type, "__dlpack__", "O", obj); if (capsule == NULL) { diff --git a/numpy/core/src/multiarray/dlpack.h b/numpy/core/src/multiarray/dlpack.h deleted file mode 100644 index 7d4a289d9..000000000 --- a/numpy/core/src/multiarray/dlpack.h +++ /dev/null @@ -1,18 +0,0 @@ -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE - -#define PY_SSIZE_T_CLEAN -#include -#include "numpy/arrayobject.h" - -static PyObject * -array_dlpack(PyArrayObject *self, PyObject *const *args, Py_ssize_t len_args, - PyObject *kwnames); - - -static PyObject * -array_dlpack_device(PyArrayObject *self, PyObject *NPY_UNUSED(args)); - - -NPY_NO_EXPORT PyObject * -from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj); diff --git a/numpy/core/src/multiarray/multiarraymodule.c b/numpy/core/src/multiarray/multiarraymodule.c index ac4c1ae0b..3cb4e83f1 100644 --- a/numpy/core/src/multiarray/multiarraymodule.c +++ b/numpy/core/src/multiarray/multiarraymodule.c @@ -4446,7 +4446,7 @@ static struct PyMethodDef array_module_methods[] = { {"_reload_guard", (PyCFunction)_reload_guard, METH_NOARGS, "Give a warning on reload and big warning in sub-interpreters."}, - {"from_dlpack", (PyCFunction)from_dlpack, + {"_from_dlpack", (PyCFunction)_from_dlpack, METH_O, NULL}, {NULL, NULL, 0, NULL} /* sentinel */ }; diff --git a/numpy/core/tests/test_dlpack.py b/numpy/core/tests/test_dlpack.py index 2561991e8..06fc042ec 100644 --- a/numpy/core/tests/test_dlpack.py +++ b/numpy/core/tests/test_dlpack.py @@ -27,12 +27,12 @@ class TestDLPack: z = y['int'] with pytest.raises(RuntimeError): - np.from_dlpack(z) + np._from_dlpack(z) @pytest.mark.skipif(IS_PYPY, reason="PyPy can't get refcounts.") def test_from_dlpack_refcount(self): x = np.arange(5) - y = np.from_dlpack(x) + y = np._from_dlpack(x) assert sys.getrefcount(x) == 3 del y assert sys.getrefcount(x) == 2 @@ -45,7 +45,7 @@ class TestDLPack: ]) def test_dtype_passthrough(self, dtype): x = np.arange(5, dtype=dtype) - y = np.from_dlpack(x) + y = np._from_dlpack(x) assert y.dtype == x.dtype assert_array_equal(x, y) @@ -54,44 +54,44 @@ class TestDLPack: x = np.asarray(np.datetime64('2021-05-27')) with pytest.raises(TypeError): - np.from_dlpack(x) + np._from_dlpack(x) def test_invalid_byte_swapping(self): dt = np.dtype('=i8').newbyteorder() x = np.arange(5, dtype=dt) with pytest.raises(TypeError): - np.from_dlpack(x) + np._from_dlpack(x) def test_non_contiguous(self): x = np.arange(25).reshape((5, 5)) y1 = x[0] - assert_array_equal(y1, np.from_dlpack(y1)) + assert_array_equal(y1, np._from_dlpack(y1)) y2 = x[:, 0] - assert_array_equal(y2, np.from_dlpack(y2)) + assert_array_equal(y2, np._from_dlpack(y2)) y3 = x[1, :] - assert_array_equal(y3, np.from_dlpack(y3)) + assert_array_equal(y3, np._from_dlpack(y3)) y4 = x[1] - assert_array_equal(y4, np.from_dlpack(y4)) + assert_array_equal(y4, np._from_dlpack(y4)) y5 = np.diagonal(x) - assert_array_equal(y5, np.from_dlpack(y5)) + assert_array_equal(y5, np._from_dlpack(y5)) @pytest.mark.parametrize("ndim", range(33)) def test_higher_dims(self, ndim): shape = (1,) * ndim x = np.zeros(shape, dtype=np.float64) - assert shape == np.from_dlpack(x).shape + assert shape == np._from_dlpack(x).shape def test_dlpack_device(self): x = np.arange(5) assert x.__dlpack_device__() == (1, 0) - assert np.from_dlpack(x).__dlpack_device__() == (1, 0) + assert np._from_dlpack(x).__dlpack_device__() == (1, 0) def dlpack_deleter_exception(self): x = np.arange(5) -- cgit v1.2.1 From f4a992c12b94df0f5c69d08a72ed2d89816d3b0d Mon Sep 17 00:00:00 2001 From: Bas van Beek <43369155+BvB93@users.noreply.github.com> Date: Tue, 2 Nov 2021 14:23:32 +0100 Subject: ENH: Add annotations for `np.fft` --- numpy/fft/__init__.pyi | 43 ++++++++++-------- numpy/fft/_pocketfft.pyi | 111 +++++++++++++++++++++++++++++++++++++++++++++++ numpy/fft/helper.pyi | 50 +++++++++++++++++++++ 3 files changed, 185 insertions(+), 19 deletions(-) create mode 100644 numpy/fft/_pocketfft.pyi create mode 100644 numpy/fft/helper.pyi (limited to 'numpy') diff --git a/numpy/fft/__init__.pyi b/numpy/fft/__init__.pyi index 648b0bf79..510e576d3 100644 --- a/numpy/fft/__init__.pyi +++ b/numpy/fft/__init__.pyi @@ -2,25 +2,30 @@ from typing import Any, List from numpy._pytesttester import PytestTester +from numpy.fft._pocketfft import ( + fft as fft, + ifft as ifft, + rfft as rfft, + irfft as irfft, + hfft as hfft, + ihfft as ihfft, + rfftn as rfftn, + irfftn as irfftn, + rfft2 as rfft2, + irfft2 as irfft2, + fft2 as fft2, + ifft2 as ifft2, + fftn as fftn, + ifftn as ifftn, +) + +from numpy.fft.helper import ( + fftshift as fftshift, + ifftshift as ifftshift, + fftfreq as fftfreq, + rfftfreq as rfftfreq, +) + __all__: List[str] __path__: List[str] test: PytestTester - -def fft(a, n=..., axis=..., norm=...): ... -def ifft(a, n=..., axis=..., norm=...): ... -def rfft(a, n=..., axis=..., norm=...): ... -def irfft(a, n=..., axis=..., norm=...): ... -def hfft(a, n=..., axis=..., norm=...): ... -def ihfft(a, n=..., axis=..., norm=...): ... -def fftn(a, s=..., axes=..., norm=...): ... -def ifftn(a, s=..., axes=..., norm=...): ... -def rfftn(a, s=..., axes=..., norm=...): ... -def irfftn(a, s=..., axes=..., norm=...): ... -def fft2(a, s=..., axes=..., norm=...): ... -def ifft2(a, s=..., axes=..., norm=...): ... -def rfft2(a, s=..., axes=..., norm=...): ... -def irfft2(a, s=..., axes=..., norm=...): ... -def fftshift(x, axes=...): ... -def ifftshift(x, axes=...): ... -def fftfreq(n, d=...): ... -def rfftfreq(n, d=...): ... diff --git a/numpy/fft/_pocketfft.pyi b/numpy/fft/_pocketfft.pyi new file mode 100644 index 000000000..86cf6a60d --- /dev/null +++ b/numpy/fft/_pocketfft.pyi @@ -0,0 +1,111 @@ +from typing import ( + Literal as L, + List, + Sequence, +) + +from numpy import complex128, float64 +from numpy.typing import ArrayLike, NDArray, _ArrayLikeNumber_co + +_NormKind = L[None, "backward", "ortho", "forward"] + +__all__: List[str] + +def fft( + a: ArrayLike, + n: None | int = ..., + axis: int = ..., + norm: _NormKind = ..., +) -> NDArray[complex128]: ... + +def ifft( + a: ArrayLike, + n: None | int = ..., + axis: int = ..., + norm: _NormKind = ..., +) -> NDArray[complex128]: ... + +def rfft( + a: ArrayLike, + n: None | int = ..., + axis: int = ..., + norm: _NormKind = ..., +) -> NDArray[complex128]: ... + +def irfft( + a: ArrayLike, + n: None | int = ..., + axis: int = ..., + norm: _NormKind = ..., +) -> NDArray[float64]: ... + +# Input array must be compatible with `np.conjugate` +def hfft( + a: _ArrayLikeNumber_co, + n: None | int = ..., + axis: int = ..., + norm: _NormKind = ..., +) -> NDArray[float64]: ... + +def ihfft( + a: ArrayLike, + n: None | int = ..., + axis: int = ..., + norm: _NormKind = ..., +) -> NDArray[complex128]: ... + +def fftn( + a: ArrayLike, + s: None | Sequence[int] = ..., + axes: None | Sequence[int] = ..., + norm: _NormKind = ..., +) -> NDArray[complex128]: ... + +def ifftn( + a: ArrayLike, + s: None | Sequence[int] = ..., + axes: None | Sequence[int] = ..., + norm: _NormKind = ..., +) -> NDArray[complex128]: ... + +def rfftn( + a: ArrayLike, + s: None | Sequence[int] = ..., + axes: None | Sequence[int] = ..., + norm: _NormKind = ..., +) -> NDArray[complex128]: ... + +def irfftn( + a: ArrayLike, + s: None | Sequence[int] = ..., + axes: None | Sequence[int] = ..., + norm: _NormKind = ..., +) -> NDArray[float64]: ... + +def fft2( + a: ArrayLike, + s: None | Sequence[int] = ..., + axes: None | Sequence[int] = ..., + norm: _NormKind = ..., +) -> NDArray[complex128]: ... + +def ifft2( + a: ArrayLike, + s: None | Sequence[int] = ..., + axes: None | Sequence[int] = ..., + norm: _NormKind = ..., +) -> NDArray[complex128]: ... + +def rfft2( + a: ArrayLike, + s: None | Sequence[int] = ..., + axes: None | Sequence[int] = ..., + norm: _NormKind = ..., +) -> NDArray[complex128]: ... + +def irfft2( + a: ArrayLike, + s: None | Sequence[int] = ..., + axes: None | Sequence[int] = ..., + norm: _NormKind = ..., +) -> NDArray[float64]: ... diff --git a/numpy/fft/helper.pyi b/numpy/fft/helper.pyi new file mode 100644 index 000000000..d75826f4e --- /dev/null +++ b/numpy/fft/helper.pyi @@ -0,0 +1,50 @@ +from typing import List, Any, TypeVar, overload + +from numpy import generic, dtype, integer, floating, complexfloating +from numpy.typing import ( + NDArray, + ArrayLike, + _ShapeLike, + _SupportsArray, + _FiniteNestedSequence, + _ArrayLikeFloat_co, + _ArrayLikeComplex_co, +) + +_SCT = TypeVar("_SCT", bound=generic) + +_ArrayLike = _FiniteNestedSequence[_SupportsArray[dtype[_SCT]]] + +__all__: List[str] + +@overload +def fftshift(x: _ArrayLike[_SCT], axes: None | _ShapeLike = ...) -> NDArray[_SCT]: ... +@overload +def fftshift(x: ArrayLike, axes: None | _ShapeLike = ...) -> NDArray[Any]: ... + +@overload +def ifftshift(x: _ArrayLike[_SCT], axes: None | _ShapeLike = ...) -> NDArray[_SCT]: ... +@overload +def ifftshift(x: ArrayLike, axes: None | _ShapeLike = ...) -> NDArray[Any]: ... + +@overload +def fftfreq( + n: int | integer[Any], + d: _ArrayLikeFloat_co, +) -> NDArray[floating[Any]]: ... +@overload +def fftfreq( + n: int | integer[Any], + d: _ArrayLikeComplex_co, +) -> NDArray[complexfloating[Any, Any]]: ... + +@overload +def rfftfreq( + n: int | integer[Any], + d: _ArrayLikeFloat_co, +) -> NDArray[floating[Any]]: ... +@overload +def rfftfreq( + n: int | integer[Any], + d: _ArrayLikeComplex_co, +) -> NDArray[complexfloating[Any, Any]]: ... -- cgit v1.2.1 From fb3a205b0c41b9bfeffb960a2840f6fd63011e23 Mon Sep 17 00:00:00 2001 From: Bas van Beek <43369155+BvB93@users.noreply.github.com> Date: Tue, 2 Nov 2021 14:33:02 +0100 Subject: TST: Add typing tests for `np.fft` --- numpy/typing/tests/data/reveal/fft.pyi | 35 ++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 numpy/typing/tests/data/reveal/fft.pyi (limited to 'numpy') diff --git a/numpy/typing/tests/data/reveal/fft.pyi b/numpy/typing/tests/data/reveal/fft.pyi new file mode 100644 index 000000000..316ddd189 --- /dev/null +++ b/numpy/typing/tests/data/reveal/fft.pyi @@ -0,0 +1,35 @@ +import numpy as np +import numpy.typing as npt + +AR_f8: npt.NDArray[np.float64] +AR_c16: npt.NDArray[np.complex128] +AR_LIKE_f8: list[float] + +reveal_type(np.fft.fftshift(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.fft.fftshift(AR_LIKE_f8, axes=0)) # E: numpy.ndarray[Any, numpy.dtype[Any]] + +reveal_type(np.fft.ifftshift(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.fft.ifftshift(AR_LIKE_f8, axes=0)) # E: numpy.ndarray[Any, numpy.dtype[Any]] + +reveal_type(np.fft.fftfreq(5, AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(np.fft.fftfreq(np.int64(), AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] + +reveal_type(np.fft.fftfreq(5, AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(np.fft.fftfreq(np.int64(), AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] + +reveal_type(np.fft.fft(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] +reveal_type(np.fft.ifft(AR_f8, axis=1)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] +reveal_type(np.fft.rfft(AR_f8, n=None)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] +reveal_type(np.fft.irfft(AR_f8, norm="ortho")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.fft.hfft(AR_f8, n=2)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.fft.ihfft(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] + +reveal_type(np.fft.fftn(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] +reveal_type(np.fft.ifftn(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] +reveal_type(np.fft.rfftn(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] +reveal_type(np.fft.irfftn(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] + +reveal_type(np.fft.rfft2(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] +reveal_type(np.fft.ifft2(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] +reveal_type(np.fft.fft2(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] +reveal_type(np.fft.irfft2(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -- cgit v1.2.1 From ae4af75cb766d3f5d6b791658ef51a9f16249e8b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9rome=20Eertmans?= Date: Tue, 2 Nov 2021 16:14:03 +0100 Subject: ENH: Check that the lengths of the inputs to histogram2d are the same. (#20228) Improves exception message when inputs have different shapes. Closes gh-20050 Co-authored-by: Ross Barnowski --- numpy/lib/tests/test_twodim_base.py | 10 ++++++++++ numpy/lib/twodim_base.py | 3 +++ 2 files changed, 13 insertions(+) (limited to 'numpy') diff --git a/numpy/lib/tests/test_twodim_base.py b/numpy/lib/tests/test_twodim_base.py index cce683bfe..c1c5a1615 100644 --- a/numpy/lib/tests/test_twodim_base.py +++ b/numpy/lib/tests/test_twodim_base.py @@ -18,6 +18,9 @@ import numpy as np from numpy.core.tests.test_overrides import requires_array_function +import pytest + + def get_mat(n): data = arange(n) data = add.outer(data, data) @@ -295,6 +298,13 @@ class TestHistogram2d: r = histogram2d(xy, xy, weights=s_d) assert_(r, ((ShouldDispatch,), (xy, xy), dict(weights=s_d))) + @pytest.mark.parametrize(("x_len", "y_len"), [(10, 11), (20, 19)]) + def test_bad_length(self, x_len, y_len): + x, y = np.ones(x_len), np.ones(y_len) + with pytest.raises(ValueError, + match='x and y must have the same length.'): + histogram2d(x, y) + class TestTri: def test_dtype(self): diff --git a/numpy/lib/twodim_base.py b/numpy/lib/twodim_base.py index 811faff79..3e5ad31ff 100644 --- a/numpy/lib/twodim_base.py +++ b/numpy/lib/twodim_base.py @@ -804,6 +804,9 @@ def histogram2d(x, y, bins=10, range=None, normed=None, weights=None, >>> plt.show() """ from numpy import histogramdd + + if len(x) != len(y): + raise ValueError('x and y must have the same length.') try: N = len(bins) -- cgit v1.2.1 From 9a067abf210ecd9696167f300f2b8454b48af4f5 Mon Sep 17 00:00:00 2001 From: Daniel Ching Date: Tue, 2 Nov 2021 15:19:07 -0500 Subject: DOC: Correct grammar in isfinite docstring Finiteness means both not infinity and also not NaN. --- numpy/core/code_generators/ufunc_docstrings.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'numpy') diff --git a/numpy/core/code_generators/ufunc_docstrings.py b/numpy/core/code_generators/ufunc_docstrings.py index 8d9316f2c..c9be94569 100644 --- a/numpy/core/code_generators/ufunc_docstrings.py +++ b/numpy/core/code_generators/ufunc_docstrings.py @@ -1678,7 +1678,7 @@ add_newdoc('numpy.core.umath', 'invert', add_newdoc('numpy.core.umath', 'isfinite', """ - Test element-wise for finiteness (not infinity or not Not a Number). + Test element-wise for finiteness (not infinity and not Not a Number). The result is returned as a boolean array. -- cgit v1.2.1 From eb5e7c683fd9cc4b5c967e2260ae11f4365494a6 Mon Sep 17 00:00:00 2001 From: warren Date: Wed, 3 Nov 2021 01:20:07 -0400 Subject: BUG: Fix duplication of names in 'numpy.__all__'. Closes gh-10198. --- numpy/__init__.py | 4 ++++ numpy/core/__init__.py | 1 - numpy/tests/test__all__.py | 9 +++++++++ 3 files changed, 13 insertions(+), 1 deletion(-) create mode 100644 numpy/tests/test__all__.py (limited to 'numpy') diff --git a/numpy/__init__.py b/numpy/__init__.py index c34434e75..8f7a42d10 100644 --- a/numpy/__init__.py +++ b/numpy/__init__.py @@ -233,6 +233,10 @@ else: __all__.extend(lib.__all__) __all__.extend(['linalg', 'fft', 'random', 'ctypeslib', 'ma']) + # Remove one of the two occurrences of `issubdtype`, which is exposed as + # both `numpy.core.issubdtype` and `numpy.lib.issubdtype`. + __all__.remove('issubdtype') + # These are exported by np.core, but are replaced by the builtins below # remove them to ensure that we don't end up with `np.long == np.int_`, # which would be a breaking change. diff --git a/numpy/core/__init__.py b/numpy/core/__init__.py index 332f9940e..b89e27f0f 100644 --- a/numpy/core/__init__.py +++ b/numpy/core/__init__.py @@ -106,7 +106,6 @@ from . import _methods __all__ = ['char', 'rec', 'memmap'] __all__ += numeric.__all__ -__all__ += fromnumeric.__all__ __all__ += ['record', 'recarray', 'format_parser'] __all__ += ['chararray'] __all__ += function_base.__all__ diff --git a/numpy/tests/test__all__.py b/numpy/tests/test__all__.py new file mode 100644 index 000000000..e44bda3d5 --- /dev/null +++ b/numpy/tests/test__all__.py @@ -0,0 +1,9 @@ + +import collections +import numpy as np + + +def test_no_duplicates_in_np__all__(): + # Regression test for gh-10198. + dups = {k: v for k, v in collections.Counter(np.__all__).items() if v > 1} + assert len(dups) == 0 -- cgit v1.2.1 From bc087bb2b3104547b097e03e669fb5fa77b16f05 Mon Sep 17 00:00:00 2001 From: Amit Kumar Date: Wed, 3 Nov 2021 11:09:28 +0000 Subject: TST: Add a test for device property in `array_api` namespace (#20271) --- numpy/array_api/tests/test_array_object.py | 11 +++++++++++ 1 file changed, 11 insertions(+) (limited to 'numpy') diff --git a/numpy/array_api/tests/test_array_object.py b/numpy/array_api/tests/test_array_object.py index 7959f92b4..fb42cf621 100644 --- a/numpy/array_api/tests/test_array_object.py +++ b/numpy/array_api/tests/test_array_object.py @@ -285,3 +285,14 @@ def test_python_scalar_construtors(): assert_raises(TypeError, lambda: operator.index(b)) assert_raises(TypeError, lambda: operator.index(f)) + + +def test_device_property(): + a = ones((3, 4)) + assert a.device == 'cpu' + + assert np.array_equal(a.to_device('cpu'), a) + assert_raises(ValueError, lambda: a.to_device('gpu')) + + assert np.array_equal(asarray(a, device='cpu'), a) + assert_raises(ValueError, lambda: asarray(a, device='gpu')) -- cgit v1.2.1 From b7dc11cbc1dc4c41475f001f2ee5ccbfe6d1a847 Mon Sep 17 00:00:00 2001 From: Sayantika Banik Date: Wed, 3 Nov 2021 22:06:49 +0530 Subject: TST,MAINT: Allow module names to be excluded in typing tests (#20287) --- numpy/typing/tests/data/reveal/arithmetic.pyi | 280 +-- .../tests/data/reveal/array_constructors.pyi | 266 +-- numpy/typing/tests/data/reveal/arraypad.pyi | 8 +- numpy/typing/tests/data/reveal/arraysetops.pyi | 98 +- numpy/typing/tests/data/reveal/arrayterator.pyi | 16 +- numpy/typing/tests/data/reveal/bitwise_ops.pyi | 36 +- numpy/typing/tests/data/reveal/char.pyi | 204 +-- numpy/typing/tests/data/reveal/chararray.pyi | 176 +- numpy/typing/tests/data/reveal/comparisons.pyi | 418 ++--- numpy/typing/tests/data/reveal/constants.pyi | 4 +- numpy/typing/tests/data/reveal/ctypeslib.pyi | 18 +- numpy/typing/tests/data/reveal/dtype.pyi | 92 +- numpy/typing/tests/data/reveal/einsumfunc.pyi | 4 +- numpy/typing/tests/data/reveal/fft.pyi | 56 +- numpy/typing/tests/data/reveal/flatiter.pyi | 20 +- numpy/typing/tests/data/reveal/fromnumeric.pyi | 150 +- numpy/typing/tests/data/reveal/getlimits.pyi | 12 +- numpy/typing/tests/data/reveal/histograms.pyi | 22 +- numpy/typing/tests/data/reveal/index_tricks.pyi | 42 +- .../typing/tests/data/reveal/lib_function_base.pyi | 172 +- numpy/typing/tests/data/reveal/lib_polynomial.pyi | 172 +- numpy/typing/tests/data/reveal/linalg.pyi | 122 +- numpy/typing/tests/data/reveal/matrix.pyi | 86 +- numpy/typing/tests/data/reveal/memmap.pyi | 6 +- numpy/typing/tests/data/reveal/mod.pyi | 32 +- numpy/typing/tests/data/reveal/modules.pyi | 2 +- numpy/typing/tests/data/reveal/multiarray.pyi | 72 +- .../tests/data/reveal/ndarray_conversion.pyi | 38 +- numpy/typing/tests/data/reveal/ndarray_misc.pyi | 80 +- .../data/reveal/ndarray_shape_manipulation.pyi | 34 +- numpy/typing/tests/data/reveal/nditer.pyi | 30 +- numpy/typing/tests/data/reveal/npyio.pyi | 64 +- numpy/typing/tests/data/reveal/numeric.pyi | 144 +- numpy/typing/tests/data/reveal/numerictypes.pyi | 2 +- numpy/typing/tests/data/reveal/random.pyi | 1840 ++++++++++---------- numpy/typing/tests/data/reveal/rec.pyi | 34 +- numpy/typing/tests/data/reveal/scalars.pyi | 70 +- numpy/typing/tests/data/reveal/shape_base.pyi | 58 +- numpy/typing/tests/data/reveal/stride_tricks.pyi | 26 +- numpy/typing/tests/data/reveal/testing.pyi | 4 +- numpy/typing/tests/data/reveal/twodim_base.pyi | 66 +- numpy/typing/tests/data/reveal/type_check.pyi | 44 +- numpy/typing/tests/data/reveal/ufunc_config.pyi | 24 +- numpy/typing/tests/data/reveal/ufunclike.pyi | 30 +- numpy/typing/tests/data/reveal/ufuncs.pyi | 14 +- .../tests/data/reveal/warnings_and_errors.pyi | 14 +- numpy/typing/tests/test_typing.py | 15 +- 47 files changed, 2614 insertions(+), 2603 deletions(-) (limited to 'numpy') diff --git a/numpy/typing/tests/data/reveal/arithmetic.pyi b/numpy/typing/tests/data/reveal/arithmetic.pyi index 0d9132e5b..c5b467469 100644 --- a/numpy/typing/tests/data/reveal/arithmetic.pyi +++ b/numpy/typing/tests/data/reveal/arithmetic.pyi @@ -45,104 +45,104 @@ AR_LIKE_O: List[np.object_] # Array subtraction -reveal_type(AR_b - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_b - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_b - AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_b - AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_b - AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_b - AR_LIKE_u) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_b - AR_LIKE_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_b - AR_LIKE_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_b - AR_LIKE_c) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_b - AR_LIKE_m) # E: ndarray[Any, dtype[timedelta64]] reveal_type(AR_b - AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_u - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_LIKE_i - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_LIKE_f - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_c - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_m - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_LIKE_M - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] +reveal_type(AR_LIKE_u - AR_b) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_LIKE_i - AR_b) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_LIKE_f - AR_b) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_c - AR_b) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_LIKE_m - AR_b) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_LIKE_M - AR_b) # E: ndarray[Any, dtype[datetime64]] reveal_type(AR_LIKE_O - AR_b) # E: Any -reveal_type(AR_u - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_u - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_u - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_u - AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_u - AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_u - AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_u - AR_LIKE_b) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_u - AR_LIKE_u) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_u - AR_LIKE_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_u - AR_LIKE_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_u - AR_LIKE_c) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_u - AR_LIKE_m) # E: ndarray[Any, dtype[timedelta64]] reveal_type(AR_u - AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_LIKE_u - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_LIKE_i - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_LIKE_f - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_c - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_m - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_LIKE_M - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] +reveal_type(AR_LIKE_b - AR_u) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_LIKE_u - AR_u) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_LIKE_i - AR_u) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_LIKE_f - AR_u) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_c - AR_u) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_LIKE_m - AR_u) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_LIKE_M - AR_u) # E: ndarray[Any, dtype[datetime64]] reveal_type(AR_LIKE_O - AR_u) # E: Any -reveal_type(AR_i - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_i - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_i - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_i - AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_i - AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_i - AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_i - AR_LIKE_b) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_i - AR_LIKE_u) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_i - AR_LIKE_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_i - AR_LIKE_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_i - AR_LIKE_c) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_i - AR_LIKE_m) # E: ndarray[Any, dtype[timedelta64]] reveal_type(AR_i - AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_LIKE_u - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_LIKE_i - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_LIKE_f - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_c - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_m - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_LIKE_M - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] +reveal_type(AR_LIKE_b - AR_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_LIKE_u - AR_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_LIKE_i - AR_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_LIKE_f - AR_i) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_c - AR_i) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_LIKE_m - AR_i) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_LIKE_M - AR_i) # E: ndarray[Any, dtype[datetime64]] reveal_type(AR_LIKE_O - AR_i) # E: Any -reveal_type(AR_f - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_f - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_f - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_f - AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_f - AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_f - AR_LIKE_b) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_f - AR_LIKE_u) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_f - AR_LIKE_i) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_f - AR_LIKE_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_f - AR_LIKE_c) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] reveal_type(AR_f - AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b - AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_u - AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_i - AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_f - AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_c - AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_b - AR_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_u - AR_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_i - AR_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_f - AR_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_c - AR_f) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] reveal_type(AR_LIKE_O - AR_f) # E: Any -reveal_type(AR_c - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_c - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_c - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_c - AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_c - AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_c - AR_LIKE_b) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_c - AR_LIKE_u) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_c - AR_LIKE_i) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_c - AR_LIKE_f) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_c - AR_LIKE_c) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] reveal_type(AR_c - AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b - AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_u - AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_i - AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_f - AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_c - AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_b - AR_c) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_LIKE_u - AR_c) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_LIKE_i - AR_c) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_LIKE_f - AR_c) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_LIKE_c - AR_c) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] reveal_type(AR_LIKE_O - AR_c) # E: Any -reveal_type(AR_m - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_m - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_m - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_m - AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_m - AR_LIKE_b) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_m - AR_LIKE_u) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_m - AR_LIKE_i) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_m - AR_LIKE_m) # E: ndarray[Any, dtype[timedelta64]] reveal_type(AR_m - AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b - AR_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_LIKE_u - AR_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_LIKE_i - AR_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_LIKE_m - AR_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_LIKE_M - AR_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] +reveal_type(AR_LIKE_b - AR_m) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_LIKE_u - AR_m) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_LIKE_i - AR_m) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_LIKE_m - AR_m) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_LIKE_M - AR_m) # E: ndarray[Any, dtype[datetime64]] reveal_type(AR_LIKE_O - AR_m) # E: Any -reveal_type(AR_M - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(AR_M - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(AR_M - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(AR_M - AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(AR_M - AR_LIKE_M) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_M - AR_LIKE_b) # E: ndarray[Any, dtype[datetime64]] +reveal_type(AR_M - AR_LIKE_u) # E: ndarray[Any, dtype[datetime64]] +reveal_type(AR_M - AR_LIKE_i) # E: ndarray[Any, dtype[datetime64]] +reveal_type(AR_M - AR_LIKE_m) # E: ndarray[Any, dtype[datetime64]] +reveal_type(AR_M - AR_LIKE_M) # E: ndarray[Any, dtype[timedelta64]] reveal_type(AR_M - AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_M - AR_M) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_LIKE_M - AR_M) # E: ndarray[Any, dtype[timedelta64]] reveal_type(AR_LIKE_O - AR_M) # E: Any reveal_type(AR_O - AR_LIKE_b) # E: Any @@ -165,64 +165,64 @@ reveal_type(AR_LIKE_O - AR_O) # E: Any # Array floor division -reveal_type(AR_b // AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[{int8}]] -reveal_type(AR_b // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_b // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_b // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_b // AR_LIKE_b) # E: ndarray[Any, dtype[{int8}]] +reveal_type(AR_b // AR_LIKE_u) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_b // AR_LIKE_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_b // AR_LIKE_f) # E: ndarray[Any, dtype[floating[Any]]] reveal_type(AR_b // AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b // AR_b) # E: numpy.ndarray[Any, numpy.dtype[{int8}]] -reveal_type(AR_LIKE_u // AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_LIKE_i // AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_LIKE_f // AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_b // AR_b) # E: ndarray[Any, dtype[{int8}]] +reveal_type(AR_LIKE_u // AR_b) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_LIKE_i // AR_b) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_LIKE_f // AR_b) # E: ndarray[Any, dtype[floating[Any]]] reveal_type(AR_LIKE_O // AR_b) # E: Any -reveal_type(AR_u // AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_u // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_u // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_u // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_u // AR_LIKE_b) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_u // AR_LIKE_u) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_u // AR_LIKE_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_u // AR_LIKE_f) # E: ndarray[Any, dtype[floating[Any]]] reveal_type(AR_u // AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_LIKE_u // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_LIKE_i // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_LIKE_f // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_m // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_LIKE_b // AR_u) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_LIKE_u // AR_u) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_LIKE_i // AR_u) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_LIKE_f // AR_u) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_m // AR_u) # E: ndarray[Any, dtype[timedelta64]] reveal_type(AR_LIKE_O // AR_u) # E: Any -reveal_type(AR_i // AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_i // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_i // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_i // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_i // AR_LIKE_b) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_i // AR_LIKE_u) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_i // AR_LIKE_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_i // AR_LIKE_f) # E: ndarray[Any, dtype[floating[Any]]] reveal_type(AR_i // AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_LIKE_u // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_LIKE_i // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_LIKE_f // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_m // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_LIKE_b // AR_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_LIKE_u // AR_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_LIKE_i // AR_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_LIKE_f // AR_i) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_m // AR_i) # E: ndarray[Any, dtype[timedelta64]] reveal_type(AR_LIKE_O // AR_i) # E: Any -reveal_type(AR_f // AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_f // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_f // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_f // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_f // AR_LIKE_b) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_f // AR_LIKE_u) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_f // AR_LIKE_i) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_f // AR_LIKE_f) # E: ndarray[Any, dtype[floating[Any]]] reveal_type(AR_f // AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_u // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_i // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_f // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_m // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_LIKE_b // AR_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_u // AR_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_i // AR_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_f // AR_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_m // AR_f) # E: ndarray[Any, dtype[timedelta64]] reveal_type(AR_LIKE_O // AR_f) # E: Any -reveal_type(AR_m // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_m // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_m // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_m // AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] +reveal_type(AR_m // AR_LIKE_u) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_m // AR_LIKE_i) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_m // AR_LIKE_f) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_m // AR_LIKE_m) # E: ndarray[Any, dtype[{int64}]] reveal_type(AR_m // AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_m // AR_m) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] +reveal_type(AR_LIKE_m // AR_m) # E: ndarray[Any, dtype[{int64}]] reveal_type(AR_LIKE_O // AR_m) # E: Any reveal_type(AR_O // AR_LIKE_b) # E: Any @@ -252,7 +252,7 @@ reveal_type(-i8) # E: {int64} reveal_type(-i4) # E: {int32} reveal_type(-u8) # E: {uint64} reveal_type(-u4) # E: {uint32} -reveal_type(-td) # E: numpy.timedelta64 +reveal_type(-td) # E: timedelta64 reveal_type(-AR_f) # E: Any reveal_type(+f16) # E: {float128} @@ -264,7 +264,7 @@ reveal_type(+i8) # E: {int64} reveal_type(+i4) # E: {int32} reveal_type(+u8) # E: {uint64} reveal_type(+u4) # E: {uint32} -reveal_type(+td) # E: numpy.timedelta64 +reveal_type(+td) # E: timedelta64 reveal_type(+AR_f) # E: Any reveal_type(abs(f16)) # E: {float128} @@ -276,32 +276,32 @@ reveal_type(abs(i8)) # E: {int64} reveal_type(abs(i4)) # E: {int32} reveal_type(abs(u8)) # E: {uint64} reveal_type(abs(u4)) # E: {uint32} -reveal_type(abs(td)) # E: numpy.timedelta64 -reveal_type(abs(b_)) # E: numpy.bool_ +reveal_type(abs(td)) # E: timedelta64 +reveal_type(abs(b_)) # E: bool_ reveal_type(abs(AR_f)) # E: Any # Time structures -reveal_type(dt + td) # E: numpy.datetime64 -reveal_type(dt + i) # E: numpy.datetime64 -reveal_type(dt + i4) # E: numpy.datetime64 -reveal_type(dt + i8) # E: numpy.datetime64 -reveal_type(dt - dt) # E: numpy.timedelta64 -reveal_type(dt - i) # E: numpy.datetime64 -reveal_type(dt - i4) # E: numpy.datetime64 -reveal_type(dt - i8) # E: numpy.datetime64 - -reveal_type(td + td) # E: numpy.timedelta64 -reveal_type(td + i) # E: numpy.timedelta64 -reveal_type(td + i4) # E: numpy.timedelta64 -reveal_type(td + i8) # E: numpy.timedelta64 -reveal_type(td - td) # E: numpy.timedelta64 -reveal_type(td - i) # E: numpy.timedelta64 -reveal_type(td - i4) # E: numpy.timedelta64 -reveal_type(td - i8) # E: numpy.timedelta64 -reveal_type(td / f) # E: numpy.timedelta64 -reveal_type(td / f4) # E: numpy.timedelta64 -reveal_type(td / f8) # E: numpy.timedelta64 +reveal_type(dt + td) # E: datetime64 +reveal_type(dt + i) # E: datetime64 +reveal_type(dt + i4) # E: datetime64 +reveal_type(dt + i8) # E: datetime64 +reveal_type(dt - dt) # E: timedelta64 +reveal_type(dt - i) # E: datetime64 +reveal_type(dt - i4) # E: datetime64 +reveal_type(dt - i8) # E: datetime64 + +reveal_type(td + td) # E: timedelta64 +reveal_type(td + i) # E: timedelta64 +reveal_type(td + i4) # E: timedelta64 +reveal_type(td + i8) # E: timedelta64 +reveal_type(td - td) # E: timedelta64 +reveal_type(td - i) # E: timedelta64 +reveal_type(td - i4) # E: timedelta64 +reveal_type(td - i8) # E: timedelta64 +reveal_type(td / f) # E: timedelta64 +reveal_type(td / f4) # E: timedelta64 +reveal_type(td / f8) # E: timedelta64 reveal_type(td / td) # E: {float64} reveal_type(td // td) # E: {int64} @@ -378,7 +378,7 @@ reveal_type(c8 + b_) # E: {complex64} reveal_type(c8 + b) # E: {complex64} reveal_type(c8 + c) # E: {complex128} reveal_type(c8 + f) # E: {complex128} -reveal_type(c8 + i) # E: numpy.complexfloating[{_NBitInt}, {_NBitInt}] +reveal_type(c8 + i) # E: complexfloating[{_NBitInt}, {_NBitInt}] reveal_type(c8 + AR_f) # E: Any reveal_type(f16 + c8) # E: {complex256} @@ -392,7 +392,7 @@ reveal_type(b_ + c8) # E: {complex64} reveal_type(b + c8) # E: {complex64} reveal_type(c + c8) # E: {complex128} reveal_type(f + c8) # E: {complex128} -reveal_type(i + c8) # E: numpy.complexfloating[{_NBitInt}, {_NBitInt}] +reveal_type(i + c8) # E: complexfloating[{_NBitInt}, {_NBitInt}] reveal_type(AR_f + c8) # E: Any # Float @@ -430,7 +430,7 @@ reveal_type(f4 + b_) # E: {float32} reveal_type(f4 + b) # E: {float32} reveal_type(f4 + c) # E: {complex128} reveal_type(f4 + f) # E: {float64} -reveal_type(f4 + i) # E: numpy.floating[{_NBitInt}] +reveal_type(f4 + i) # E: floating[{_NBitInt}] reveal_type(f4 + AR_f) # E: Any reveal_type(f16 + f4) # E: {float128} @@ -442,7 +442,7 @@ reveal_type(b_ + f4) # E: {float32} reveal_type(b + f4) # E: {float32} reveal_type(c + f4) # E: {complex128} reveal_type(f + f4) # E: {float64} -reveal_type(i + f4) # E: numpy.floating[{_NBitInt}] +reveal_type(i + f4) # E: floating[{_NBitInt}] reveal_type(AR_f + f4) # E: Any # Int diff --git a/numpy/typing/tests/data/reveal/array_constructors.pyi b/numpy/typing/tests/data/reveal/array_constructors.pyi index 0aea4ea96..233988e63 100644 --- a/numpy/typing/tests/data/reveal/array_constructors.pyi +++ b/numpy/typing/tests/data/reveal/array_constructors.pyi @@ -16,167 +16,167 @@ C: List[int] def func(i: int, j: int, **kwargs: Any) -> SubClass[np.float64]: ... -reveal_type(np.empty_like(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.empty_like(A)) # E: ndarray[Any, dtype[{float64}]] reveal_type(np.empty_like(B)) # E: SubClass[{float64}] -reveal_type(np.empty_like([1, 1.0])) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.empty_like(A, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.empty_like(A, dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.empty_like([1, 1.0])) # E: ndarray[Any, dtype[Any]] +reveal_type(np.empty_like(A, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.empty_like(A, dtype='c16')) # E: ndarray[Any, dtype[Any]] -reveal_type(np.array(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.array(B)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.array(A)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.array(B)) # E: ndarray[Any, dtype[{float64}]] reveal_type(np.array(B, subok=True)) # E: SubClass[{float64}] -reveal_type(np.array([1, 1.0])) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.array(A, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.array(A, dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.zeros([1, 5, 6])) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.zeros([1, 5, 6], dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.zeros([1, 5, 6], dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.empty([1, 5, 6])) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.empty([1, 5, 6], dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.empty([1, 5, 6], dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.concatenate(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.concatenate([1, 1.0])) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.concatenate(A, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.concatenate(A, dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.concatenate([1, 1.0], out=A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] - -reveal_type(np.asarray(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.asarray(B)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.asarray([1, 1.0])) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.asarray(A, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.asarray(A, dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.asanyarray(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.array([1, 1.0])) # E: ndarray[Any, dtype[Any]] +reveal_type(np.array(A, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.array(A, dtype='c16')) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.zeros([1, 5, 6])) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.zeros([1, 5, 6], dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.zeros([1, 5, 6], dtype='c16')) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.empty([1, 5, 6])) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.empty([1, 5, 6], dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.empty([1, 5, 6], dtype='c16')) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.concatenate(A)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.concatenate([1, 1.0])) # E: ndarray[Any, dtype[Any]] +reveal_type(np.concatenate(A, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.concatenate(A, dtype='c16')) # E: ndarray[Any, dtype[Any]] +reveal_type(np.concatenate([1, 1.0], out=A)) # E: ndarray[Any, dtype[{float64}]] + +reveal_type(np.asarray(A)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.asarray(B)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.asarray([1, 1.0])) # E: ndarray[Any, dtype[Any]] +reveal_type(np.asarray(A, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.asarray(A, dtype='c16')) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.asanyarray(A)) # E: ndarray[Any, dtype[{float64}]] reveal_type(np.asanyarray(B)) # E: SubClass[{float64}] -reveal_type(np.asanyarray([1, 1.0])) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.asanyarray(A, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.asanyarray(A, dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.ascontiguousarray(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.ascontiguousarray(B)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.ascontiguousarray([1, 1.0])) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.ascontiguousarray(A, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.ascontiguousarray(A, dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.asfortranarray(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.asfortranarray(B)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.asfortranarray([1, 1.0])) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.asfortranarray(A, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.asfortranarray(A, dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.fromstring("1 1 1", sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.fromstring(b"1 1 1", sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.fromstring("1 1 1", dtype=np.int64, sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.fromstring(b"1 1 1", dtype=np.int64, sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.fromstring("1 1 1", dtype="c16", sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.fromstring(b"1 1 1", dtype="c16", sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.fromfile("test.txt", sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.fromfile("test.txt", dtype=np.int64, sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.fromfile("test.txt", dtype="c16", sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.asanyarray([1, 1.0])) # E: ndarray[Any, dtype[Any]] +reveal_type(np.asanyarray(A, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.asanyarray(A, dtype='c16')) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.ascontiguousarray(A)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.ascontiguousarray(B)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.ascontiguousarray([1, 1.0])) # E: ndarray[Any, dtype[Any]] +reveal_type(np.ascontiguousarray(A, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.ascontiguousarray(A, dtype='c16')) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.asfortranarray(A)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.asfortranarray(B)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.asfortranarray([1, 1.0])) # E: ndarray[Any, dtype[Any]] +reveal_type(np.asfortranarray(A, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.asfortranarray(A, dtype='c16')) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.fromstring("1 1 1", sep=" ")) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.fromstring(b"1 1 1", sep=" ")) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.fromstring("1 1 1", dtype=np.int64, sep=" ")) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.fromstring(b"1 1 1", dtype=np.int64, sep=" ")) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.fromstring("1 1 1", dtype="c16", sep=" ")) # E: ndarray[Any, dtype[Any]] +reveal_type(np.fromstring(b"1 1 1", dtype="c16", sep=" ")) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.fromfile("test.txt", sep=" ")) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.fromfile("test.txt", dtype=np.int64, sep=" ")) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.fromfile("test.txt", dtype="c16", sep=" ")) # E: ndarray[Any, dtype[Any]] with open("test.txt") as f: - reveal_type(np.fromfile(f, sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] - reveal_type(np.fromfile(b"test.txt", sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] - reveal_type(np.fromfile(Path("test.txt"), sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] - -reveal_type(np.fromiter("12345", np.float64)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.fromiter("12345", float)) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.frombuffer(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.frombuffer(A, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.frombuffer(A, dtype="c16")) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.arange(False, True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.arange(10)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.arange(0, 10, step=2)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.arange(10.0)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.arange(start=0, stop=10.0)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.arange(np.timedelta64(0))) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(np.arange(0, np.timedelta64(10))) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(np.arange(np.datetime64("0"), np.datetime64("10"))) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(np.arange(10, dtype=np.float64)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.arange(0, 10, step=2, dtype=np.int16)) # E: numpy.ndarray[Any, numpy.dtype[{int16}]] -reveal_type(np.arange(10, dtype=int)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.arange(0, 10, dtype="f8")) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.require(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] + reveal_type(np.fromfile(f, sep=" ")) # E: ndarray[Any, dtype[{float64}]] + reveal_type(np.fromfile(b"test.txt", sep=" ")) # E: ndarray[Any, dtype[{float64}]] + reveal_type(np.fromfile(Path("test.txt"), sep=" ")) # E: ndarray[Any, dtype[{float64}]] + +reveal_type(np.fromiter("12345", np.float64)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.fromiter("12345", float)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.frombuffer(A)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.frombuffer(A, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.frombuffer(A, dtype="c16")) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.arange(False, True)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.arange(10)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.arange(0, 10, step=2)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.arange(10.0)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.arange(start=0, stop=10.0)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.arange(np.timedelta64(0))) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(np.arange(0, np.timedelta64(10))) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(np.arange(np.datetime64("0"), np.datetime64("10"))) # E: ndarray[Any, dtype[datetime64]] +reveal_type(np.arange(10, dtype=np.float64)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.arange(0, 10, step=2, dtype=np.int16)) # E: ndarray[Any, dtype[{int16}]] +reveal_type(np.arange(10, dtype=int)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.arange(0, 10, dtype="f8")) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.require(A)) # E: ndarray[Any, dtype[{float64}]] reveal_type(np.require(B)) # E: SubClass[{float64}] reveal_type(np.require(B, requirements=None)) # E: SubClass[{float64}] -reveal_type(np.require(B, dtype=int)) # E: numpy.ndarray[Any, Any] -reveal_type(np.require(B, requirements="E")) # E: numpy.ndarray[Any, Any] -reveal_type(np.require(B, requirements=["ENSUREARRAY"])) # E: numpy.ndarray[Any, Any] -reveal_type(np.require(B, requirements={"F", "E"})) # E: numpy.ndarray[Any, Any] +reveal_type(np.require(B, dtype=int)) # E: ndarray[Any, Any] +reveal_type(np.require(B, requirements="E")) # E: ndarray[Any, Any] +reveal_type(np.require(B, requirements=["ENSUREARRAY"])) # E: ndarray[Any, Any] +reveal_type(np.require(B, requirements={"F", "E"})) # E: ndarray[Any, Any] reveal_type(np.require(B, requirements=["C", "OWNDATA"])) # E: SubClass[{float64}] reveal_type(np.require(B, requirements="W")) # E: SubClass[{float64}] reveal_type(np.require(B, requirements="A")) # E: SubClass[{float64}] -reveal_type(np.require(C)) # E: numpy.ndarray[Any, Any] +reveal_type(np.require(C)) # E: ndarray[Any, Any] -reveal_type(np.linspace(0, 10)) # E: numpy.ndarray[Any, Any] -reveal_type(np.linspace(0, 10, retstep=True)) # E: Tuple[numpy.ndarray[Any, Any], Any] -reveal_type(np.logspace(0, 10)) # E: numpy.ndarray[Any, Any] -reveal_type(np.geomspace(1, 10)) # E: numpy.ndarray[Any, Any] +reveal_type(np.linspace(0, 10)) # E: ndarray[Any, Any] +reveal_type(np.linspace(0, 10, retstep=True)) # E: Tuple[ndarray[Any, Any], Any] +reveal_type(np.logspace(0, 10)) # E: ndarray[Any, Any] +reveal_type(np.geomspace(1, 10)) # E: ndarray[Any, Any] -reveal_type(np.zeros_like(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.zeros_like(C)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.zeros_like(A, dtype=float)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.zeros_like(A)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.zeros_like(C)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.zeros_like(A, dtype=float)) # E: ndarray[Any, dtype[Any]] reveal_type(np.zeros_like(B)) # E: SubClass[{float64}] -reveal_type(np.zeros_like(B, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] +reveal_type(np.zeros_like(B, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] -reveal_type(np.ones_like(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.ones_like(C)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.ones_like(A, dtype=float)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.ones_like(A)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.ones_like(C)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.ones_like(A, dtype=float)) # E: ndarray[Any, dtype[Any]] reveal_type(np.ones_like(B)) # E: SubClass[{float64}] -reveal_type(np.ones_like(B, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] +reveal_type(np.ones_like(B, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] -reveal_type(np.full_like(A, i8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.full_like(C, i8)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.full_like(A, i8, dtype=int)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.full_like(A, i8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.full_like(C, i8)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.full_like(A, i8, dtype=int)) # E: ndarray[Any, dtype[Any]] reveal_type(np.full_like(B, i8)) # E: SubClass[{float64}] -reveal_type(np.full_like(B, i8, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] +reveal_type(np.full_like(B, i8, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] -reveal_type(np.ones(1)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.ones([1, 1, 1])) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.ones(5, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.ones(5, dtype=int)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.ones(1)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.ones([1, 1, 1])) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.ones(5, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.ones(5, dtype=int)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.full(1, i8)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.full([1, 1, 1], i8)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.full(1, i8, dtype=np.float64)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.full(1, i8, dtype=float)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.full(1, i8)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.full([1, 1, 1], i8)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.full(1, i8, dtype=np.float64)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.full(1, i8, dtype=float)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.indices([1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(np.indices([1, 2, 3], sparse=True)) # E: tuple[numpy.ndarray[Any, numpy.dtype[{int_}]]] +reveal_type(np.indices([1, 2, 3])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(np.indices([1, 2, 3], sparse=True)) # E: tuple[ndarray[Any, dtype[{int_}]]] reveal_type(np.fromfunction(func, (3, 5))) # E: SubClass[{float64}] -reveal_type(np.identity(10)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.identity(10, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.identity(10, dtype=int)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.identity(10)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.identity(10, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.identity(10, dtype=int)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.atleast_1d(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.atleast_1d(C)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.atleast_1d(A, A)) # E: list[numpy.ndarray[Any, numpy.dtype[Any]]] -reveal_type(np.atleast_1d(A, C)) # E: list[numpy.ndarray[Any, numpy.dtype[Any]]] -reveal_type(np.atleast_1d(C, C)) # E: list[numpy.ndarray[Any, numpy.dtype[Any]]] +reveal_type(np.atleast_1d(A)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.atleast_1d(C)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.atleast_1d(A, A)) # E: list[ndarray[Any, dtype[Any]]] +reveal_type(np.atleast_1d(A, C)) # E: list[ndarray[Any, dtype[Any]]] +reveal_type(np.atleast_1d(C, C)) # E: list[ndarray[Any, dtype[Any]]] -reveal_type(np.atleast_2d(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.atleast_2d(A)) # E: ndarray[Any, dtype[{float64}]] -reveal_type(np.atleast_3d(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.atleast_3d(A)) # E: ndarray[Any, dtype[{float64}]] -reveal_type(np.vstack([A, A])) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.vstack([A, C])) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.vstack([C, C])) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.vstack([A, A])) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.vstack([A, C])) # E: ndarray[Any, dtype[Any]] +reveal_type(np.vstack([C, C])) # E: ndarray[Any, dtype[Any]] -reveal_type(np.hstack([A, A])) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.hstack([A, A])) # E: ndarray[Any, dtype[{float64}]] -reveal_type(np.stack([A, A])) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.stack([A, C])) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.stack([C, C])) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.stack([A, A], axis=0)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.stack([A, A])) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.stack([A, C])) # E: ndarray[Any, dtype[Any]] +reveal_type(np.stack([C, C])) # E: ndarray[Any, dtype[Any]] +reveal_type(np.stack([A, A], axis=0)) # E: ndarray[Any, dtype[{float64}]] reveal_type(np.stack([A, A], out=B)) # E: SubClass[{float64}] -reveal_type(np.block([[A, A], [A, A]])) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.block(C)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.block([[A, A], [A, A]])) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.block(C)) # E: ndarray[Any, dtype[Any]] diff --git a/numpy/typing/tests/data/reveal/arraypad.pyi b/numpy/typing/tests/data/reveal/arraypad.pyi index 03c03fb4e..995f82b57 100644 --- a/numpy/typing/tests/data/reveal/arraypad.pyi +++ b/numpy/typing/tests/data/reveal/arraypad.pyi @@ -14,8 +14,8 @@ AR_i8: npt.NDArray[np.int64] AR_f8: npt.NDArray[np.float64] AR_LIKE: List[int] -reveal_type(np.pad(AR_i8, (2, 3), "constant")) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.pad(AR_LIKE, (2, 3), "constant")) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.pad(AR_i8, (2, 3), "constant")) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.pad(AR_LIKE, (2, 3), "constant")) # E: ndarray[Any, dtype[Any]] -reveal_type(np.pad(AR_f8, (2, 3), mode_func)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.pad(AR_f8, (2, 3), mode_func, a=1, b=2)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.pad(AR_f8, (2, 3), mode_func)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.pad(AR_f8, (2, 3), mode_func, a=1, b=2)) # E: ndarray[Any, dtype[{float64}]] diff --git a/numpy/typing/tests/data/reveal/arraysetops.pyi b/numpy/typing/tests/data/reveal/arraysetops.pyi index c8aeb03ab..9deff8a8e 100644 --- a/numpy/typing/tests/data/reveal/arraysetops.pyi +++ b/numpy/typing/tests/data/reveal/arraysetops.pyi @@ -9,52 +9,52 @@ AR_O: npt.NDArray[np.object_] AR_LIKE_f8: list[float] -reveal_type(np.ediff1d(AR_b)) # E: numpy.ndarray[Any, numpy.dtype[{int8}]] -reveal_type(np.ediff1d(AR_i8, to_end=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.ediff1d(AR_M)) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(np.ediff1d(AR_O)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] -reveal_type(np.ediff1d(AR_LIKE_f8, to_begin=[1, 1.5])) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.intersect1d(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.intersect1d(AR_M, AR_M, assume_unique=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(np.intersect1d(AR_f8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.intersect1d(AR_f8, AR_f8, return_indices=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] - -reveal_type(np.setxor1d(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.setxor1d(AR_M, AR_M, assume_unique=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(np.setxor1d(AR_f8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.in1d(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.in1d(AR_M, AR_M, assume_unique=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.in1d(AR_f8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.in1d(AR_f8, AR_LIKE_f8, invert=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(np.isin(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isin(AR_M, AR_M, assume_unique=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isin(AR_f8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isin(AR_f8, AR_LIKE_f8, invert=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(np.union1d(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.union1d(AR_M, AR_M)) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(np.union1d(AR_f8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.setdiff1d(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.setdiff1d(AR_M, AR_M, assume_unique=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(np.setdiff1d(AR_f8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.unique(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.unique(AR_LIKE_f8, axis=0)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.unique(AR_f8, return_index=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_LIKE_f8, return_index=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_f8, return_inverse=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_LIKE_f8, return_inverse=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_f8, return_counts=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_LIKE_f8, return_counts=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_f8, return_index=True, return_inverse=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_LIKE_f8, return_index=True, return_inverse=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_f8, return_index=True, return_counts=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_LIKE_f8, return_index=True, return_counts=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_f8, return_inverse=True, return_counts=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_LIKE_f8, return_inverse=True, return_counts=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_f8, return_index=True, return_inverse=True, return_counts=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_LIKE_f8, return_index=True, return_inverse=True, return_counts=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] +reveal_type(np.ediff1d(AR_b)) # E: ndarray[Any, dtype[{int8}]] +reveal_type(np.ediff1d(AR_i8, to_end=[1, 2, 3])) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.ediff1d(AR_M)) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(np.ediff1d(AR_O)) # E: ndarray[Any, dtype[object_]] +reveal_type(np.ediff1d(AR_LIKE_f8, to_begin=[1, 1.5])) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.intersect1d(AR_i8, AR_i8)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.intersect1d(AR_M, AR_M, assume_unique=True)) # E: ndarray[Any, dtype[datetime64]] +reveal_type(np.intersect1d(AR_f8, AR_i8)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.intersect1d(AR_f8, AR_f8, return_indices=True)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]] + +reveal_type(np.setxor1d(AR_i8, AR_i8)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.setxor1d(AR_M, AR_M, assume_unique=True)) # E: ndarray[Any, dtype[datetime64]] +reveal_type(np.setxor1d(AR_f8, AR_i8)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.in1d(AR_i8, AR_i8)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.in1d(AR_M, AR_M, assume_unique=True)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.in1d(AR_f8, AR_i8)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.in1d(AR_f8, AR_LIKE_f8, invert=True)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.isin(AR_i8, AR_i8)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isin(AR_M, AR_M, assume_unique=True)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isin(AR_f8, AR_i8)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isin(AR_f8, AR_LIKE_f8, invert=True)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.union1d(AR_i8, AR_i8)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.union1d(AR_M, AR_M)) # E: ndarray[Any, dtype[datetime64]] +reveal_type(np.union1d(AR_f8, AR_i8)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.setdiff1d(AR_i8, AR_i8)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.setdiff1d(AR_M, AR_M, assume_unique=True)) # E: ndarray[Any, dtype[datetime64]] +reveal_type(np.setdiff1d(AR_f8, AR_i8)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.unique(AR_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.unique(AR_LIKE_f8, axis=0)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.unique(AR_f8, return_index=True)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_LIKE_f8, return_index=True)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_f8, return_inverse=True)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_LIKE_f8, return_inverse=True)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_f8, return_counts=True)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_LIKE_f8, return_counts=True)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_f8, return_index=True, return_inverse=True)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_LIKE_f8, return_index=True, return_inverse=True)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_f8, return_index=True, return_counts=True)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_LIKE_f8, return_index=True, return_counts=True)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_f8, return_inverse=True, return_counts=True)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_LIKE_f8, return_inverse=True, return_counts=True)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_f8, return_index=True, return_inverse=True, return_counts=True)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_LIKE_f8, return_index=True, return_inverse=True, return_counts=True)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]] diff --git a/numpy/typing/tests/data/reveal/arrayterator.pyi b/numpy/typing/tests/data/reveal/arrayterator.pyi index ea4e75612..2dab9d08c 100644 --- a/numpy/typing/tests/data/reveal/arrayterator.pyi +++ b/numpy/typing/tests/data/reveal/arrayterator.pyi @@ -4,7 +4,7 @@ import numpy as np AR_i8: np.ndarray[Any, np.dtype[np.int64]] ar_iter = np.lib.Arrayterator(AR_i8) -reveal_type(ar_iter.var) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] +reveal_type(ar_iter.var) # E: ndarray[Any, dtype[{int64}]] reveal_type(ar_iter.buf_size) # E: Union[None, builtins.int] reveal_type(ar_iter.start) # E: builtins.list[builtins.int] reveal_type(ar_iter.stop) # E: builtins.list[builtins.int] @@ -12,13 +12,13 @@ reveal_type(ar_iter.step) # E: builtins.list[builtins.int] reveal_type(ar_iter.shape) # E: builtins.tuple[builtins.int] reveal_type(ar_iter.flat) # E: typing.Generator[{int64}, None, None] -reveal_type(ar_iter.__array__()) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] +reveal_type(ar_iter.__array__()) # E: ndarray[Any, dtype[{int64}]] for i in ar_iter: - reveal_type(i) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] + reveal_type(i) # E: ndarray[Any, dtype[{int64}]] -reveal_type(ar_iter[0]) # E: numpy.lib.arrayterator.Arrayterator[Any, numpy.dtype[{int64}]] -reveal_type(ar_iter[...]) # E: numpy.lib.arrayterator.Arrayterator[Any, numpy.dtype[{int64}]] -reveal_type(ar_iter[:]) # E: numpy.lib.arrayterator.Arrayterator[Any, numpy.dtype[{int64}]] -reveal_type(ar_iter[0, 0, 0]) # E: numpy.lib.arrayterator.Arrayterator[Any, numpy.dtype[{int64}]] -reveal_type(ar_iter[..., 0, :]) # E: numpy.lib.arrayterator.Arrayterator[Any, numpy.dtype[{int64}]] +reveal_type(ar_iter[0]) # E: lib.arrayterator.Arrayterator[Any, dtype[{int64}]] +reveal_type(ar_iter[...]) # E: lib.arrayterator.Arrayterator[Any, dtype[{int64}]] +reveal_type(ar_iter[:]) # E: lib.arrayterator.Arrayterator[Any, dtype[{int64}]] +reveal_type(ar_iter[0, 0, 0]) # E: lib.arrayterator.Arrayterator[Any, dtype[{int64}]] +reveal_type(ar_iter[..., 0, :]) # E: lib.arrayterator.Arrayterator[Any, dtype[{int64}]] diff --git a/numpy/typing/tests/data/reveal/bitwise_ops.pyi b/numpy/typing/tests/data/reveal/bitwise_ops.pyi index 6b9969568..f293ef65b 100644 --- a/numpy/typing/tests/data/reveal/bitwise_ops.pyi +++ b/numpy/typing/tests/data/reveal/bitwise_ops.pyi @@ -75,17 +75,17 @@ reveal_type(u4 | u4) # E: {uint32} reveal_type(u4 ^ u4) # E: {uint32} reveal_type(u4 & u4) # E: {uint32} -reveal_type(u4 << i4) # E: numpy.signedinteger[Any] -reveal_type(u4 >> i4) # E: numpy.signedinteger[Any] -reveal_type(u4 | i4) # E: numpy.signedinteger[Any] -reveal_type(u4 ^ i4) # E: numpy.signedinteger[Any] -reveal_type(u4 & i4) # E: numpy.signedinteger[Any] - -reveal_type(u4 << i) # E: numpy.signedinteger[Any] -reveal_type(u4 >> i) # E: numpy.signedinteger[Any] -reveal_type(u4 | i) # E: numpy.signedinteger[Any] -reveal_type(u4 ^ i) # E: numpy.signedinteger[Any] -reveal_type(u4 & i) # E: numpy.signedinteger[Any] +reveal_type(u4 << i4) # E: signedinteger[Any] +reveal_type(u4 >> i4) # E: signedinteger[Any] +reveal_type(u4 | i4) # E: signedinteger[Any] +reveal_type(u4 ^ i4) # E: signedinteger[Any] +reveal_type(u4 & i4) # E: signedinteger[Any] + +reveal_type(u4 << i) # E: signedinteger[Any] +reveal_type(u4 >> i) # E: signedinteger[Any] +reveal_type(u4 | i) # E: signedinteger[Any] +reveal_type(u4 ^ i) # E: signedinteger[Any] +reveal_type(u4 & i) # E: signedinteger[Any] reveal_type(u8 << b_) # E: {uint64} reveal_type(u8 >> b_) # E: {uint64} @@ -101,9 +101,9 @@ reveal_type(u8 & b) # E: {uint64} reveal_type(b_ << b_) # E: {int8} reveal_type(b_ >> b_) # E: {int8} -reveal_type(b_ | b_) # E: numpy.bool_ -reveal_type(b_ ^ b_) # E: numpy.bool_ -reveal_type(b_ & b_) # E: numpy.bool_ +reveal_type(b_ | b_) # E: bool_ +reveal_type(b_ ^ b_) # E: bool_ +reveal_type(b_ & b_) # E: bool_ reveal_type(b_ << AR) # E: Any reveal_type(b_ >> AR) # E: Any @@ -113,9 +113,9 @@ reveal_type(b_ & AR) # E: Any reveal_type(b_ << b) # E: {int8} reveal_type(b_ >> b) # E: {int8} -reveal_type(b_ | b) # E: numpy.bool_ -reveal_type(b_ ^ b) # E: numpy.bool_ -reveal_type(b_ & b) # E: numpy.bool_ +reveal_type(b_ | b) # E: bool_ +reveal_type(b_ ^ b) # E: bool_ +reveal_type(b_ & b) # E: bool_ reveal_type(b_ << i) # E: {int_} reveal_type(b_ >> i) # E: {int_} @@ -127,5 +127,5 @@ reveal_type(~i8) # E: {int64} reveal_type(~i4) # E: {int32} reveal_type(~u8) # E: {uint64} reveal_type(~u4) # E: {uint32} -reveal_type(~b_) # E: numpy.bool_ +reveal_type(~b_) # E: bool_ reveal_type(~AR) # E: Any diff --git a/numpy/typing/tests/data/reveal/char.pyi b/numpy/typing/tests/data/reveal/char.pyi index dd2e76a2d..ce8c1b269 100644 --- a/numpy/typing/tests/data/reveal/char.pyi +++ b/numpy/typing/tests/data/reveal/char.pyi @@ -5,143 +5,143 @@ from typing import Sequence AR_U: npt.NDArray[np.str_] AR_S: npt.NDArray[np.bytes_] -reveal_type(np.char.equal(AR_U, AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.equal(AR_S, AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.char.equal(AR_U, AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.equal(AR_S, AR_S)) # E: ndarray[Any, dtype[bool_]] -reveal_type(np.char.not_equal(AR_U, AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.not_equal(AR_S, AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.char.not_equal(AR_U, AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.not_equal(AR_S, AR_S)) # E: ndarray[Any, dtype[bool_]] -reveal_type(np.char.greater_equal(AR_U, AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.greater_equal(AR_S, AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.char.greater_equal(AR_U, AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.greater_equal(AR_S, AR_S)) # E: ndarray[Any, dtype[bool_]] -reveal_type(np.char.less_equal(AR_U, AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.less_equal(AR_S, AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.char.less_equal(AR_U, AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.less_equal(AR_S, AR_S)) # E: ndarray[Any, dtype[bool_]] -reveal_type(np.char.greater(AR_U, AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.greater(AR_S, AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.char.greater(AR_U, AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.greater(AR_S, AR_S)) # E: ndarray[Any, dtype[bool_]] -reveal_type(np.char.less(AR_U, AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.less(AR_S, AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.char.less(AR_U, AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.less(AR_S, AR_S)) # E: ndarray[Any, dtype[bool_]] -reveal_type(np.char.multiply(AR_U, 5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.multiply(AR_S, [5, 4, 3])) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(np.char.multiply(AR_U, 5)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.multiply(AR_S, [5, 4, 3])) # E: ndarray[Any, dtype[bytes_]] -reveal_type(np.char.mod(AR_U, "test")) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.mod(AR_S, "test")) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(np.char.mod(AR_U, "test")) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.mod(AR_S, "test")) # E: ndarray[Any, dtype[bytes_]] -reveal_type(np.char.capitalize(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.capitalize(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(np.char.capitalize(AR_U)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.capitalize(AR_S)) # E: ndarray[Any, dtype[bytes_]] -reveal_type(np.char.center(AR_U, 5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.center(AR_S, [2, 3, 4], b"a")) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(np.char.center(AR_U, 5)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.center(AR_S, [2, 3, 4], b"a")) # E: ndarray[Any, dtype[bytes_]] -reveal_type(np.char.encode(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(np.char.decode(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] +reveal_type(np.char.encode(AR_U)) # E: ndarray[Any, dtype[bytes_]] +reveal_type(np.char.decode(AR_S)) # E: ndarray[Any, dtype[str_]] -reveal_type(np.char.expandtabs(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.expandtabs(AR_S, tabsize=4)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(np.char.expandtabs(AR_U)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.expandtabs(AR_S, tabsize=4)) # E: ndarray[Any, dtype[bytes_]] -reveal_type(np.char.join(AR_U, "_")) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.join(AR_S, [b"_", b""])) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(np.char.join(AR_U, "_")) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.join(AR_S, [b"_", b""])) # E: ndarray[Any, dtype[bytes_]] -reveal_type(np.char.ljust(AR_U, 5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.ljust(AR_S, [4, 3, 1], fillchar=[b"a", b"b", b"c"])) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(np.char.rjust(AR_U, 5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.rjust(AR_S, [4, 3, 1], fillchar=[b"a", b"b", b"c"])) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(np.char.ljust(AR_U, 5)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.ljust(AR_S, [4, 3, 1], fillchar=[b"a", b"b", b"c"])) # E: ndarray[Any, dtype[bytes_]] +reveal_type(np.char.rjust(AR_U, 5)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.rjust(AR_S, [4, 3, 1], fillchar=[b"a", b"b", b"c"])) # E: ndarray[Any, dtype[bytes_]] -reveal_type(np.char.lstrip(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.lstrip(AR_S, chars=b"_")) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(np.char.rstrip(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.rstrip(AR_S, chars=b"_")) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(np.char.strip(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.strip(AR_S, chars=b"_")) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(np.char.lstrip(AR_U)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.lstrip(AR_S, chars=b"_")) # E: ndarray[Any, dtype[bytes_]] +reveal_type(np.char.rstrip(AR_U)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.rstrip(AR_S, chars=b"_")) # E: ndarray[Any, dtype[bytes_]] +reveal_type(np.char.strip(AR_U)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.strip(AR_S, chars=b"_")) # E: ndarray[Any, dtype[bytes_]] -reveal_type(np.char.partition(AR_U, "\n")) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.partition(AR_S, [b"a", b"b", b"c"])) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(np.char.rpartition(AR_U, "\n")) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.rpartition(AR_S, [b"a", b"b", b"c"])) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(np.char.partition(AR_U, "\n")) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.partition(AR_S, [b"a", b"b", b"c"])) # E: ndarray[Any, dtype[bytes_]] +reveal_type(np.char.rpartition(AR_U, "\n")) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.rpartition(AR_S, [b"a", b"b", b"c"])) # E: ndarray[Any, dtype[bytes_]] -reveal_type(np.char.replace(AR_U, "_", "-")) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.replace(AR_S, [b"_", b""], [b"a", b"b"])) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(np.char.replace(AR_U, "_", "-")) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.replace(AR_S, [b"_", b""], [b"a", b"b"])) # E: ndarray[Any, dtype[bytes_]] -reveal_type(np.char.split(AR_U, "_")) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] -reveal_type(np.char.split(AR_S, maxsplit=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] -reveal_type(np.char.rsplit(AR_U, "_")) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] -reveal_type(np.char.rsplit(AR_S, maxsplit=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] +reveal_type(np.char.split(AR_U, "_")) # E: ndarray[Any, dtype[object_]] +reveal_type(np.char.split(AR_S, maxsplit=[1, 2, 3])) # E: ndarray[Any, dtype[object_]] +reveal_type(np.char.rsplit(AR_U, "_")) # E: ndarray[Any, dtype[object_]] +reveal_type(np.char.rsplit(AR_S, maxsplit=[1, 2, 3])) # E: ndarray[Any, dtype[object_]] -reveal_type(np.char.splitlines(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] -reveal_type(np.char.splitlines(AR_S, keepends=[True, True, False])) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] +reveal_type(np.char.splitlines(AR_U)) # E: ndarray[Any, dtype[object_]] +reveal_type(np.char.splitlines(AR_S, keepends=[True, True, False])) # E: ndarray[Any, dtype[object_]] -reveal_type(np.char.swapcase(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.swapcase(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(np.char.swapcase(AR_U)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.swapcase(AR_S)) # E: ndarray[Any, dtype[bytes_]] -reveal_type(np.char.title(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.title(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(np.char.title(AR_U)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.title(AR_S)) # E: ndarray[Any, dtype[bytes_]] -reveal_type(np.char.upper(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.upper(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(np.char.upper(AR_U)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.upper(AR_S)) # E: ndarray[Any, dtype[bytes_]] -reveal_type(np.char.zfill(AR_U, 5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.zfill(AR_S, [2, 3, 4])) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(np.char.zfill(AR_U, 5)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.zfill(AR_S, [2, 3, 4])) # E: ndarray[Any, dtype[bytes_]] -reveal_type(np.char.count(AR_U, "a", start=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(np.char.count(AR_S, [b"a", b"b", b"c"], end=9)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(np.char.count(AR_U, "a", start=[1, 2, 3])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(np.char.count(AR_S, [b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[{int_}]] -reveal_type(np.char.endswith(AR_U, "a", start=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.endswith(AR_S, [b"a", b"b", b"c"], end=9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.startswith(AR_U, "a", start=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.startswith(AR_S, [b"a", b"b", b"c"], end=9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.char.endswith(AR_U, "a", start=[1, 2, 3])) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.endswith(AR_S, [b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.startswith(AR_U, "a", start=[1, 2, 3])) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.startswith(AR_S, [b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[bool_]] -reveal_type(np.char.find(AR_U, "a", start=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(np.char.find(AR_S, [b"a", b"b", b"c"], end=9)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(np.char.rfind(AR_U, "a", start=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(np.char.rfind(AR_S, [b"a", b"b", b"c"], end=9)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(np.char.find(AR_U, "a", start=[1, 2, 3])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(np.char.find(AR_S, [b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(np.char.rfind(AR_U, "a", start=[1, 2, 3])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(np.char.rfind(AR_S, [b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[{int_}]] -reveal_type(np.char.index(AR_U, "a", start=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(np.char.index(AR_S, [b"a", b"b", b"c"], end=9)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(np.char.rindex(AR_U, "a", start=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(np.char.rindex(AR_S, [b"a", b"b", b"c"], end=9)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(np.char.index(AR_U, "a", start=[1, 2, 3])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(np.char.index(AR_S, [b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(np.char.rindex(AR_U, "a", start=[1, 2, 3])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(np.char.rindex(AR_S, [b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[{int_}]] -reveal_type(np.char.isalpha(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.isalpha(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.char.isalpha(AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.isalpha(AR_S)) # E: ndarray[Any, dtype[bool_]] -reveal_type(np.char.isalnum(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.isalnum(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.char.isalnum(AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.isalnum(AR_S)) # E: ndarray[Any, dtype[bool_]] -reveal_type(np.char.isdecimal(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.isdecimal(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.char.isdecimal(AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.isdecimal(AR_S)) # E: ndarray[Any, dtype[bool_]] -reveal_type(np.char.isdigit(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.isdigit(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.char.isdigit(AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.isdigit(AR_S)) # E: ndarray[Any, dtype[bool_]] -reveal_type(np.char.islower(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.islower(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.char.islower(AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.islower(AR_S)) # E: ndarray[Any, dtype[bool_]] -reveal_type(np.char.isnumeric(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.isnumeric(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.char.isnumeric(AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.isnumeric(AR_S)) # E: ndarray[Any, dtype[bool_]] -reveal_type(np.char.isspace(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.isspace(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.char.isspace(AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.isspace(AR_S)) # E: ndarray[Any, dtype[bool_]] -reveal_type(np.char.istitle(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.istitle(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.char.istitle(AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.istitle(AR_S)) # E: ndarray[Any, dtype[bool_]] -reveal_type(np.char.isupper(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.isupper(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.char.isupper(AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.isupper(AR_S)) # E: ndarray[Any, dtype[bool_]] -reveal_type(np.char.str_len(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(np.char.str_len(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(np.char.str_len(AR_U)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(np.char.str_len(AR_S)) # E: ndarray[Any, dtype[{int_}]] -reveal_type(np.char.array(AR_U)) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.array(AR_S, order="K")) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(np.char.array("bob", copy=True)) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.array(b"bob", itemsize=5)) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(np.char.array(1, unicode=False)) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(np.char.array(1, unicode=True)) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] +reveal_type(np.char.array(AR_U)) # E: chararray[Any, dtype[str_]] +reveal_type(np.char.array(AR_S, order="K")) # E: chararray[Any, dtype[bytes_]] +reveal_type(np.char.array("bob", copy=True)) # E: chararray[Any, dtype[str_]] +reveal_type(np.char.array(b"bob", itemsize=5)) # E: chararray[Any, dtype[bytes_]] +reveal_type(np.char.array(1, unicode=False)) # E: chararray[Any, dtype[bytes_]] +reveal_type(np.char.array(1, unicode=True)) # E: chararray[Any, dtype[str_]] -reveal_type(np.char.asarray(AR_U)) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.asarray(AR_S, order="K")) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(np.char.asarray("bob")) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.asarray(b"bob", itemsize=5)) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(np.char.asarray(1, unicode=False)) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(np.char.asarray(1, unicode=True)) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] +reveal_type(np.char.asarray(AR_U)) # E: chararray[Any, dtype[str_]] +reveal_type(np.char.asarray(AR_S, order="K")) # E: chararray[Any, dtype[bytes_]] +reveal_type(np.char.asarray("bob")) # E: chararray[Any, dtype[str_]] +reveal_type(np.char.asarray(b"bob", itemsize=5)) # E: chararray[Any, dtype[bytes_]] +reveal_type(np.char.asarray(1, unicode=False)) # E: chararray[Any, dtype[bytes_]] +reveal_type(np.char.asarray(1, unicode=True)) # E: chararray[Any, dtype[str_]] diff --git a/numpy/typing/tests/data/reveal/chararray.pyi b/numpy/typing/tests/data/reveal/chararray.pyi index c0a39c92b..3da2e1599 100644 --- a/numpy/typing/tests/data/reveal/chararray.pyi +++ b/numpy/typing/tests/data/reveal/chararray.pyi @@ -4,126 +4,126 @@ from typing import Any AR_U: np.chararray[Any, np.dtype[np.str_]] AR_S: np.chararray[Any, np.dtype[np.bytes_]] -reveal_type(AR_U == AR_U) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(AR_S == AR_S) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(AR_U == AR_U) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S == AR_S) # E: ndarray[Any, dtype[bool_]] -reveal_type(AR_U != AR_U) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(AR_S != AR_S) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(AR_U != AR_U) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S != AR_S) # E: ndarray[Any, dtype[bool_]] -reveal_type(AR_U >= AR_U) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(AR_S >= AR_S) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(AR_U >= AR_U) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S >= AR_S) # E: ndarray[Any, dtype[bool_]] -reveal_type(AR_U <= AR_U) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(AR_S <= AR_S) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(AR_U <= AR_U) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S <= AR_S) # E: ndarray[Any, dtype[bool_]] -reveal_type(AR_U > AR_U) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(AR_S > AR_S) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(AR_U > AR_U) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S > AR_S) # E: ndarray[Any, dtype[bool_]] -reveal_type(AR_U < AR_U) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(AR_S < AR_S) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(AR_U < AR_U) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S < AR_S) # E: ndarray[Any, dtype[bool_]] -reveal_type(AR_U * 5) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(AR_S * [5]) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(AR_U * 5) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S * [5]) # E: chararray[Any, dtype[bytes_]] -reveal_type(AR_U % "test") # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(AR_S % b"test") # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(AR_U % "test") # E: chararray[Any, dtype[str_]] +reveal_type(AR_S % b"test") # E: chararray[Any, dtype[bytes_]] -reveal_type(AR_U.capitalize()) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(AR_S.capitalize()) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(AR_U.capitalize()) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.capitalize()) # E: chararray[Any, dtype[bytes_]] -reveal_type(AR_U.center(5)) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(AR_S.center([2, 3, 4], b"a")) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(AR_U.center(5)) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.center([2, 3, 4], b"a")) # E: chararray[Any, dtype[bytes_]] -reveal_type(AR_U.encode()) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(AR_S.decode()) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] +reveal_type(AR_U.encode()) # E: chararray[Any, dtype[bytes_]] +reveal_type(AR_S.decode()) # E: chararray[Any, dtype[str_]] -reveal_type(AR_U.expandtabs()) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(AR_S.expandtabs(tabsize=4)) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(AR_U.expandtabs()) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.expandtabs(tabsize=4)) # E: chararray[Any, dtype[bytes_]] -reveal_type(AR_U.join("_")) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(AR_S.join([b"_", b""])) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(AR_U.join("_")) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.join([b"_", b""])) # E: chararray[Any, dtype[bytes_]] -reveal_type(AR_U.ljust(5)) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(AR_S.ljust([4, 3, 1], fillchar=[b"a", b"b", b"c"])) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(AR_U.rjust(5)) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(AR_S.rjust([4, 3, 1], fillchar=[b"a", b"b", b"c"])) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(AR_U.ljust(5)) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.ljust([4, 3, 1], fillchar=[b"a", b"b", b"c"])) # E: chararray[Any, dtype[bytes_]] +reveal_type(AR_U.rjust(5)) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.rjust([4, 3, 1], fillchar=[b"a", b"b", b"c"])) # E: chararray[Any, dtype[bytes_]] -reveal_type(AR_U.lstrip()) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(AR_S.lstrip(chars=b"_")) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(AR_U.rstrip()) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(AR_S.rstrip(chars=b"_")) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(AR_U.strip()) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(AR_S.strip(chars=b"_")) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(AR_U.lstrip()) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.lstrip(chars=b"_")) # E: chararray[Any, dtype[bytes_]] +reveal_type(AR_U.rstrip()) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.rstrip(chars=b"_")) # E: chararray[Any, dtype[bytes_]] +reveal_type(AR_U.strip()) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.strip(chars=b"_")) # E: chararray[Any, dtype[bytes_]] -reveal_type(AR_U.partition("\n")) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(AR_S.partition([b"a", b"b", b"c"])) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(AR_U.rpartition("\n")) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(AR_S.rpartition([b"a", b"b", b"c"])) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(AR_U.partition("\n")) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.partition([b"a", b"b", b"c"])) # E: chararray[Any, dtype[bytes_]] +reveal_type(AR_U.rpartition("\n")) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.rpartition([b"a", b"b", b"c"])) # E: chararray[Any, dtype[bytes_]] -reveal_type(AR_U.replace("_", "-")) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(AR_S.replace([b"_", b""], [b"a", b"b"])) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(AR_U.replace("_", "-")) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.replace([b"_", b""], [b"a", b"b"])) # E: chararray[Any, dtype[bytes_]] -reveal_type(AR_U.split("_")) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] -reveal_type(AR_S.split(maxsplit=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] -reveal_type(AR_U.rsplit("_")) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] -reveal_type(AR_S.rsplit(maxsplit=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] +reveal_type(AR_U.split("_")) # E: ndarray[Any, dtype[object_]] +reveal_type(AR_S.split(maxsplit=[1, 2, 3])) # E: ndarray[Any, dtype[object_]] +reveal_type(AR_U.rsplit("_")) # E: ndarray[Any, dtype[object_]] +reveal_type(AR_S.rsplit(maxsplit=[1, 2, 3])) # E: ndarray[Any, dtype[object_]] -reveal_type(AR_U.splitlines()) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] -reveal_type(AR_S.splitlines(keepends=[True, True, False])) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] +reveal_type(AR_U.splitlines()) # E: ndarray[Any, dtype[object_]] +reveal_type(AR_S.splitlines(keepends=[True, True, False])) # E: ndarray[Any, dtype[object_]] -reveal_type(AR_U.swapcase()) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(AR_S.swapcase()) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(AR_U.swapcase()) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.swapcase()) # E: chararray[Any, dtype[bytes_]] -reveal_type(AR_U.title()) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(AR_S.title()) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(AR_U.title()) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.title()) # E: chararray[Any, dtype[bytes_]] -reveal_type(AR_U.upper()) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(AR_S.upper()) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(AR_U.upper()) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.upper()) # E: chararray[Any, dtype[bytes_]] -reveal_type(AR_U.zfill(5)) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(AR_S.zfill([2, 3, 4])) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(AR_U.zfill(5)) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.zfill([2, 3, 4])) # E: chararray[Any, dtype[bytes_]] -reveal_type(AR_U.count("a", start=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(AR_S.count([b"a", b"b", b"c"], end=9)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(AR_U.count("a", start=[1, 2, 3])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(AR_S.count([b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[{int_}]] -reveal_type(AR_U.endswith("a", start=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(AR_S.endswith([b"a", b"b", b"c"], end=9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(AR_U.startswith("a", start=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(AR_S.startswith([b"a", b"b", b"c"], end=9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(AR_U.endswith("a", start=[1, 2, 3])) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S.endswith([b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_U.startswith("a", start=[1, 2, 3])) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S.startswith([b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[bool_]] -reveal_type(AR_U.find("a", start=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(AR_S.find([b"a", b"b", b"c"], end=9)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(AR_U.rfind("a", start=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(AR_S.rfind([b"a", b"b", b"c"], end=9)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(AR_U.find("a", start=[1, 2, 3])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(AR_S.find([b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(AR_U.rfind("a", start=[1, 2, 3])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(AR_S.rfind([b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[{int_}]] -reveal_type(AR_U.index("a", start=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(AR_S.index([b"a", b"b", b"c"], end=9)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(AR_U.rindex("a", start=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(AR_S.rindex([b"a", b"b", b"c"], end=9)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(AR_U.index("a", start=[1, 2, 3])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(AR_S.index([b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(AR_U.rindex("a", start=[1, 2, 3])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(AR_S.rindex([b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[{int_}]] -reveal_type(AR_U.isalpha()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(AR_S.isalpha()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(AR_U.isalpha()) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S.isalpha()) # E: ndarray[Any, dtype[bool_]] -reveal_type(AR_U.isalnum()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(AR_S.isalnum()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(AR_U.isalnum()) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S.isalnum()) # E: ndarray[Any, dtype[bool_]] -reveal_type(AR_U.isdecimal()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(AR_S.isdecimal()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(AR_U.isdecimal()) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S.isdecimal()) # E: ndarray[Any, dtype[bool_]] -reveal_type(AR_U.isdigit()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(AR_S.isdigit()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(AR_U.isdigit()) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S.isdigit()) # E: ndarray[Any, dtype[bool_]] -reveal_type(AR_U.islower()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(AR_S.islower()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(AR_U.islower()) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S.islower()) # E: ndarray[Any, dtype[bool_]] -reveal_type(AR_U.isnumeric()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(AR_S.isnumeric()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(AR_U.isnumeric()) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S.isnumeric()) # E: ndarray[Any, dtype[bool_]] -reveal_type(AR_U.isspace()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(AR_S.isspace()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(AR_U.isspace()) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S.isspace()) # E: ndarray[Any, dtype[bool_]] -reveal_type(AR_U.istitle()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(AR_S.istitle()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(AR_U.istitle()) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S.istitle()) # E: ndarray[Any, dtype[bool_]] -reveal_type(AR_U.isupper()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(AR_S.isupper()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(AR_U.isupper()) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S.isupper()) # E: ndarray[Any, dtype[bool_]] diff --git a/numpy/typing/tests/data/reveal/comparisons.pyi b/numpy/typing/tests/data/reveal/comparisons.pyi index 16f21cc39..ecd8ea690 100644 --- a/numpy/typing/tests/data/reveal/comparisons.pyi +++ b/numpy/typing/tests/data/reveal/comparisons.pyi @@ -27,226 +27,226 @@ SEQ = (0, 1, 2, 3, 4) # Time structures -reveal_type(dt > dt) # E: numpy.bool_ +reveal_type(dt > dt) # E: bool_ -reveal_type(td > td) # E: numpy.bool_ -reveal_type(td > i) # E: numpy.bool_ -reveal_type(td > i4) # E: numpy.bool_ -reveal_type(td > i8) # E: numpy.bool_ +reveal_type(td > td) # E: bool_ +reveal_type(td > i) # E: bool_ +reveal_type(td > i4) # E: bool_ +reveal_type(td > i8) # E: bool_ -reveal_type(td > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(td > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(AR > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(AR > td) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(SEQ > td) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(SEQ > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(td > AR) # E: ndarray[Any, dtype[bool_]] +reveal_type(td > SEQ) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR > SEQ) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR > td) # E: ndarray[Any, dtype[bool_]] +reveal_type(SEQ > td) # E: ndarray[Any, dtype[bool_]] +reveal_type(SEQ > AR) # E: ndarray[Any, dtype[bool_]] # boolean -reveal_type(b_ > b) # E: numpy.bool_ -reveal_type(b_ > b_) # E: numpy.bool_ -reveal_type(b_ > i) # E: numpy.bool_ -reveal_type(b_ > i8) # E: numpy.bool_ -reveal_type(b_ > i4) # E: numpy.bool_ -reveal_type(b_ > u8) # E: numpy.bool_ -reveal_type(b_ > u4) # E: numpy.bool_ -reveal_type(b_ > f) # E: numpy.bool_ -reveal_type(b_ > f8) # E: numpy.bool_ -reveal_type(b_ > f4) # E: numpy.bool_ -reveal_type(b_ > c) # E: numpy.bool_ -reveal_type(b_ > c16) # E: numpy.bool_ -reveal_type(b_ > c8) # E: numpy.bool_ -reveal_type(b_ > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(b_ > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(b_ > b) # E: bool_ +reveal_type(b_ > b_) # E: bool_ +reveal_type(b_ > i) # E: bool_ +reveal_type(b_ > i8) # E: bool_ +reveal_type(b_ > i4) # E: bool_ +reveal_type(b_ > u8) # E: bool_ +reveal_type(b_ > u4) # E: bool_ +reveal_type(b_ > f) # E: bool_ +reveal_type(b_ > f8) # E: bool_ +reveal_type(b_ > f4) # E: bool_ +reveal_type(b_ > c) # E: bool_ +reveal_type(b_ > c16) # E: bool_ +reveal_type(b_ > c8) # E: bool_ +reveal_type(b_ > AR) # E: ndarray[Any, dtype[bool_]] +reveal_type(b_ > SEQ) # E: ndarray[Any, dtype[bool_]] # Complex -reveal_type(c16 > c16) # E: numpy.bool_ -reveal_type(c16 > f8) # E: numpy.bool_ -reveal_type(c16 > i8) # E: numpy.bool_ -reveal_type(c16 > c8) # E: numpy.bool_ -reveal_type(c16 > f4) # E: numpy.bool_ -reveal_type(c16 > i4) # E: numpy.bool_ -reveal_type(c16 > b_) # E: numpy.bool_ -reveal_type(c16 > b) # E: numpy.bool_ -reveal_type(c16 > c) # E: numpy.bool_ -reveal_type(c16 > f) # E: numpy.bool_ -reveal_type(c16 > i) # E: numpy.bool_ -reveal_type(c16 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(c16 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(c16 > c16) # E: numpy.bool_ -reveal_type(f8 > c16) # E: numpy.bool_ -reveal_type(i8 > c16) # E: numpy.bool_ -reveal_type(c8 > c16) # E: numpy.bool_ -reveal_type(f4 > c16) # E: numpy.bool_ -reveal_type(i4 > c16) # E: numpy.bool_ -reveal_type(b_ > c16) # E: numpy.bool_ -reveal_type(b > c16) # E: numpy.bool_ -reveal_type(c > c16) # E: numpy.bool_ -reveal_type(f > c16) # E: numpy.bool_ -reveal_type(i > c16) # E: numpy.bool_ -reveal_type(AR > c16) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(SEQ > c16) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(c8 > c16) # E: numpy.bool_ -reveal_type(c8 > f8) # E: numpy.bool_ -reveal_type(c8 > i8) # E: numpy.bool_ -reveal_type(c8 > c8) # E: numpy.bool_ -reveal_type(c8 > f4) # E: numpy.bool_ -reveal_type(c8 > i4) # E: numpy.bool_ -reveal_type(c8 > b_) # E: numpy.bool_ -reveal_type(c8 > b) # E: numpy.bool_ -reveal_type(c8 > c) # E: numpy.bool_ -reveal_type(c8 > f) # E: numpy.bool_ -reveal_type(c8 > i) # E: numpy.bool_ -reveal_type(c8 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(c8 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(c16 > c8) # E: numpy.bool_ -reveal_type(f8 > c8) # E: numpy.bool_ -reveal_type(i8 > c8) # E: numpy.bool_ -reveal_type(c8 > c8) # E: numpy.bool_ -reveal_type(f4 > c8) # E: numpy.bool_ -reveal_type(i4 > c8) # E: numpy.bool_ -reveal_type(b_ > c8) # E: numpy.bool_ -reveal_type(b > c8) # E: numpy.bool_ -reveal_type(c > c8) # E: numpy.bool_ -reveal_type(f > c8) # E: numpy.bool_ -reveal_type(i > c8) # E: numpy.bool_ -reveal_type(AR > c8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(SEQ > c8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(c16 > c16) # E: bool_ +reveal_type(c16 > f8) # E: bool_ +reveal_type(c16 > i8) # E: bool_ +reveal_type(c16 > c8) # E: bool_ +reveal_type(c16 > f4) # E: bool_ +reveal_type(c16 > i4) # E: bool_ +reveal_type(c16 > b_) # E: bool_ +reveal_type(c16 > b) # E: bool_ +reveal_type(c16 > c) # E: bool_ +reveal_type(c16 > f) # E: bool_ +reveal_type(c16 > i) # E: bool_ +reveal_type(c16 > AR) # E: ndarray[Any, dtype[bool_]] +reveal_type(c16 > SEQ) # E: ndarray[Any, dtype[bool_]] + +reveal_type(c16 > c16) # E: bool_ +reveal_type(f8 > c16) # E: bool_ +reveal_type(i8 > c16) # E: bool_ +reveal_type(c8 > c16) # E: bool_ +reveal_type(f4 > c16) # E: bool_ +reveal_type(i4 > c16) # E: bool_ +reveal_type(b_ > c16) # E: bool_ +reveal_type(b > c16) # E: bool_ +reveal_type(c > c16) # E: bool_ +reveal_type(f > c16) # E: bool_ +reveal_type(i > c16) # E: bool_ +reveal_type(AR > c16) # E: ndarray[Any, dtype[bool_]] +reveal_type(SEQ > c16) # E: ndarray[Any, dtype[bool_]] + +reveal_type(c8 > c16) # E: bool_ +reveal_type(c8 > f8) # E: bool_ +reveal_type(c8 > i8) # E: bool_ +reveal_type(c8 > c8) # E: bool_ +reveal_type(c8 > f4) # E: bool_ +reveal_type(c8 > i4) # E: bool_ +reveal_type(c8 > b_) # E: bool_ +reveal_type(c8 > b) # E: bool_ +reveal_type(c8 > c) # E: bool_ +reveal_type(c8 > f) # E: bool_ +reveal_type(c8 > i) # E: bool_ +reveal_type(c8 > AR) # E: ndarray[Any, dtype[bool_]] +reveal_type(c8 > SEQ) # E: ndarray[Any, dtype[bool_]] + +reveal_type(c16 > c8) # E: bool_ +reveal_type(f8 > c8) # E: bool_ +reveal_type(i8 > c8) # E: bool_ +reveal_type(c8 > c8) # E: bool_ +reveal_type(f4 > c8) # E: bool_ +reveal_type(i4 > c8) # E: bool_ +reveal_type(b_ > c8) # E: bool_ +reveal_type(b > c8) # E: bool_ +reveal_type(c > c8) # E: bool_ +reveal_type(f > c8) # E: bool_ +reveal_type(i > c8) # E: bool_ +reveal_type(AR > c8) # E: ndarray[Any, dtype[bool_]] +reveal_type(SEQ > c8) # E: ndarray[Any, dtype[bool_]] # Float -reveal_type(f8 > f8) # E: numpy.bool_ -reveal_type(f8 > i8) # E: numpy.bool_ -reveal_type(f8 > f4) # E: numpy.bool_ -reveal_type(f8 > i4) # E: numpy.bool_ -reveal_type(f8 > b_) # E: numpy.bool_ -reveal_type(f8 > b) # E: numpy.bool_ -reveal_type(f8 > c) # E: numpy.bool_ -reveal_type(f8 > f) # E: numpy.bool_ -reveal_type(f8 > i) # E: numpy.bool_ -reveal_type(f8 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(f8 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(f8 > f8) # E: numpy.bool_ -reveal_type(i8 > f8) # E: numpy.bool_ -reveal_type(f4 > f8) # E: numpy.bool_ -reveal_type(i4 > f8) # E: numpy.bool_ -reveal_type(b_ > f8) # E: numpy.bool_ -reveal_type(b > f8) # E: numpy.bool_ -reveal_type(c > f8) # E: numpy.bool_ -reveal_type(f > f8) # E: numpy.bool_ -reveal_type(i > f8) # E: numpy.bool_ -reveal_type(AR > f8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(SEQ > f8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(f4 > f8) # E: numpy.bool_ -reveal_type(f4 > i8) # E: numpy.bool_ -reveal_type(f4 > f4) # E: numpy.bool_ -reveal_type(f4 > i4) # E: numpy.bool_ -reveal_type(f4 > b_) # E: numpy.bool_ -reveal_type(f4 > b) # E: numpy.bool_ -reveal_type(f4 > c) # E: numpy.bool_ -reveal_type(f4 > f) # E: numpy.bool_ -reveal_type(f4 > i) # E: numpy.bool_ -reveal_type(f4 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(f4 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(f8 > f4) # E: numpy.bool_ -reveal_type(i8 > f4) # E: numpy.bool_ -reveal_type(f4 > f4) # E: numpy.bool_ -reveal_type(i4 > f4) # E: numpy.bool_ -reveal_type(b_ > f4) # E: numpy.bool_ -reveal_type(b > f4) # E: numpy.bool_ -reveal_type(c > f4) # E: numpy.bool_ -reveal_type(f > f4) # E: numpy.bool_ -reveal_type(i > f4) # E: numpy.bool_ -reveal_type(AR > f4) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(SEQ > f4) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(f8 > f8) # E: bool_ +reveal_type(f8 > i8) # E: bool_ +reveal_type(f8 > f4) # E: bool_ +reveal_type(f8 > i4) # E: bool_ +reveal_type(f8 > b_) # E: bool_ +reveal_type(f8 > b) # E: bool_ +reveal_type(f8 > c) # E: bool_ +reveal_type(f8 > f) # E: bool_ +reveal_type(f8 > i) # E: bool_ +reveal_type(f8 > AR) # E: ndarray[Any, dtype[bool_]] +reveal_type(f8 > SEQ) # E: ndarray[Any, dtype[bool_]] + +reveal_type(f8 > f8) # E: bool_ +reveal_type(i8 > f8) # E: bool_ +reveal_type(f4 > f8) # E: bool_ +reveal_type(i4 > f8) # E: bool_ +reveal_type(b_ > f8) # E: bool_ +reveal_type(b > f8) # E: bool_ +reveal_type(c > f8) # E: bool_ +reveal_type(f > f8) # E: bool_ +reveal_type(i > f8) # E: bool_ +reveal_type(AR > f8) # E: ndarray[Any, dtype[bool_]] +reveal_type(SEQ > f8) # E: ndarray[Any, dtype[bool_]] + +reveal_type(f4 > f8) # E: bool_ +reveal_type(f4 > i8) # E: bool_ +reveal_type(f4 > f4) # E: bool_ +reveal_type(f4 > i4) # E: bool_ +reveal_type(f4 > b_) # E: bool_ +reveal_type(f4 > b) # E: bool_ +reveal_type(f4 > c) # E: bool_ +reveal_type(f4 > f) # E: bool_ +reveal_type(f4 > i) # E: bool_ +reveal_type(f4 > AR) # E: ndarray[Any, dtype[bool_]] +reveal_type(f4 > SEQ) # E: ndarray[Any, dtype[bool_]] + +reveal_type(f8 > f4) # E: bool_ +reveal_type(i8 > f4) # E: bool_ +reveal_type(f4 > f4) # E: bool_ +reveal_type(i4 > f4) # E: bool_ +reveal_type(b_ > f4) # E: bool_ +reveal_type(b > f4) # E: bool_ +reveal_type(c > f4) # E: bool_ +reveal_type(f > f4) # E: bool_ +reveal_type(i > f4) # E: bool_ +reveal_type(AR > f4) # E: ndarray[Any, dtype[bool_]] +reveal_type(SEQ > f4) # E: ndarray[Any, dtype[bool_]] # Int -reveal_type(i8 > i8) # E: numpy.bool_ -reveal_type(i8 > u8) # E: numpy.bool_ -reveal_type(i8 > i4) # E: numpy.bool_ -reveal_type(i8 > u4) # E: numpy.bool_ -reveal_type(i8 > b_) # E: numpy.bool_ -reveal_type(i8 > b) # E: numpy.bool_ -reveal_type(i8 > c) # E: numpy.bool_ -reveal_type(i8 > f) # E: numpy.bool_ -reveal_type(i8 > i) # E: numpy.bool_ -reveal_type(i8 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(i8 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(u8 > u8) # E: numpy.bool_ -reveal_type(u8 > i4) # E: numpy.bool_ -reveal_type(u8 > u4) # E: numpy.bool_ -reveal_type(u8 > b_) # E: numpy.bool_ -reveal_type(u8 > b) # E: numpy.bool_ -reveal_type(u8 > c) # E: numpy.bool_ -reveal_type(u8 > f) # E: numpy.bool_ -reveal_type(u8 > i) # E: numpy.bool_ -reveal_type(u8 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(u8 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(i8 > i8) # E: numpy.bool_ -reveal_type(u8 > i8) # E: numpy.bool_ -reveal_type(i4 > i8) # E: numpy.bool_ -reveal_type(u4 > i8) # E: numpy.bool_ -reveal_type(b_ > i8) # E: numpy.bool_ -reveal_type(b > i8) # E: numpy.bool_ -reveal_type(c > i8) # E: numpy.bool_ -reveal_type(f > i8) # E: numpy.bool_ -reveal_type(i > i8) # E: numpy.bool_ -reveal_type(AR > i8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(SEQ > i8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(u8 > u8) # E: numpy.bool_ -reveal_type(i4 > u8) # E: numpy.bool_ -reveal_type(u4 > u8) # E: numpy.bool_ -reveal_type(b_ > u8) # E: numpy.bool_ -reveal_type(b > u8) # E: numpy.bool_ -reveal_type(c > u8) # E: numpy.bool_ -reveal_type(f > u8) # E: numpy.bool_ -reveal_type(i > u8) # E: numpy.bool_ -reveal_type(AR > u8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(SEQ > u8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(i4 > i8) # E: numpy.bool_ -reveal_type(i4 > i4) # E: numpy.bool_ -reveal_type(i4 > i) # E: numpy.bool_ -reveal_type(i4 > b_) # E: numpy.bool_ -reveal_type(i4 > b) # E: numpy.bool_ -reveal_type(i4 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(i4 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(u4 > i8) # E: numpy.bool_ -reveal_type(u4 > i4) # E: numpy.bool_ -reveal_type(u4 > u8) # E: numpy.bool_ -reveal_type(u4 > u4) # E: numpy.bool_ -reveal_type(u4 > i) # E: numpy.bool_ -reveal_type(u4 > b_) # E: numpy.bool_ -reveal_type(u4 > b) # E: numpy.bool_ -reveal_type(u4 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(u4 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(i8 > i4) # E: numpy.bool_ -reveal_type(i4 > i4) # E: numpy.bool_ -reveal_type(i > i4) # E: numpy.bool_ -reveal_type(b_ > i4) # E: numpy.bool_ -reveal_type(b > i4) # E: numpy.bool_ -reveal_type(AR > i4) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(SEQ > i4) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(i8 > u4) # E: numpy.bool_ -reveal_type(i4 > u4) # E: numpy.bool_ -reveal_type(u8 > u4) # E: numpy.bool_ -reveal_type(u4 > u4) # E: numpy.bool_ -reveal_type(b_ > u4) # E: numpy.bool_ -reveal_type(b > u4) # E: numpy.bool_ -reveal_type(i > u4) # E: numpy.bool_ -reveal_type(AR > u4) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(SEQ > u4) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(i8 > i8) # E: bool_ +reveal_type(i8 > u8) # E: bool_ +reveal_type(i8 > i4) # E: bool_ +reveal_type(i8 > u4) # E: bool_ +reveal_type(i8 > b_) # E: bool_ +reveal_type(i8 > b) # E: bool_ +reveal_type(i8 > c) # E: bool_ +reveal_type(i8 > f) # E: bool_ +reveal_type(i8 > i) # E: bool_ +reveal_type(i8 > AR) # E: ndarray[Any, dtype[bool_]] +reveal_type(i8 > SEQ) # E: ndarray[Any, dtype[bool_]] + +reveal_type(u8 > u8) # E: bool_ +reveal_type(u8 > i4) # E: bool_ +reveal_type(u8 > u4) # E: bool_ +reveal_type(u8 > b_) # E: bool_ +reveal_type(u8 > b) # E: bool_ +reveal_type(u8 > c) # E: bool_ +reveal_type(u8 > f) # E: bool_ +reveal_type(u8 > i) # E: bool_ +reveal_type(u8 > AR) # E: ndarray[Any, dtype[bool_]] +reveal_type(u8 > SEQ) # E: ndarray[Any, dtype[bool_]] + +reveal_type(i8 > i8) # E: bool_ +reveal_type(u8 > i8) # E: bool_ +reveal_type(i4 > i8) # E: bool_ +reveal_type(u4 > i8) # E: bool_ +reveal_type(b_ > i8) # E: bool_ +reveal_type(b > i8) # E: bool_ +reveal_type(c > i8) # E: bool_ +reveal_type(f > i8) # E: bool_ +reveal_type(i > i8) # E: bool_ +reveal_type(AR > i8) # E: ndarray[Any, dtype[bool_]] +reveal_type(SEQ > i8) # E: ndarray[Any, dtype[bool_]] + +reveal_type(u8 > u8) # E: bool_ +reveal_type(i4 > u8) # E: bool_ +reveal_type(u4 > u8) # E: bool_ +reveal_type(b_ > u8) # E: bool_ +reveal_type(b > u8) # E: bool_ +reveal_type(c > u8) # E: bool_ +reveal_type(f > u8) # E: bool_ +reveal_type(i > u8) # E: bool_ +reveal_type(AR > u8) # E: ndarray[Any, dtype[bool_]] +reveal_type(SEQ > u8) # E: ndarray[Any, dtype[bool_]] + +reveal_type(i4 > i8) # E: bool_ +reveal_type(i4 > i4) # E: bool_ +reveal_type(i4 > i) # E: bool_ +reveal_type(i4 > b_) # E: bool_ +reveal_type(i4 > b) # E: bool_ +reveal_type(i4 > AR) # E: ndarray[Any, dtype[bool_]] +reveal_type(i4 > SEQ) # E: ndarray[Any, dtype[bool_]] + +reveal_type(u4 > i8) # E: bool_ +reveal_type(u4 > i4) # E: bool_ +reveal_type(u4 > u8) # E: bool_ +reveal_type(u4 > u4) # E: bool_ +reveal_type(u4 > i) # E: bool_ +reveal_type(u4 > b_) # E: bool_ +reveal_type(u4 > b) # E: bool_ +reveal_type(u4 > AR) # E: ndarray[Any, dtype[bool_]] +reveal_type(u4 > SEQ) # E: ndarray[Any, dtype[bool_]] + +reveal_type(i8 > i4) # E: bool_ +reveal_type(i4 > i4) # E: bool_ +reveal_type(i > i4) # E: bool_ +reveal_type(b_ > i4) # E: bool_ +reveal_type(b > i4) # E: bool_ +reveal_type(AR > i4) # E: ndarray[Any, dtype[bool_]] +reveal_type(SEQ > i4) # E: ndarray[Any, dtype[bool_]] + +reveal_type(i8 > u4) # E: bool_ +reveal_type(i4 > u4) # E: bool_ +reveal_type(u8 > u4) # E: bool_ +reveal_type(u4 > u4) # E: bool_ +reveal_type(b_ > u4) # E: bool_ +reveal_type(b > u4) # E: bool_ +reveal_type(i > u4) # E: bool_ +reveal_type(AR > u4) # E: ndarray[Any, dtype[bool_]] +reveal_type(SEQ > u4) # E: ndarray[Any, dtype[bool_]] diff --git a/numpy/typing/tests/data/reveal/constants.pyi b/numpy/typing/tests/data/reveal/constants.pyi index 9a46bfded..37f54ccda 100644 --- a/numpy/typing/tests/data/reveal/constants.pyi +++ b/numpy/typing/tests/data/reveal/constants.pyi @@ -43,8 +43,8 @@ reveal_type(np.WRAP) # E: Literal[1] reveal_type(np.tracemalloc_domain) # E: Literal[389047] reveal_type(np.little_endian) # E: bool -reveal_type(np.True_) # E: numpy.bool_ -reveal_type(np.False_) # E: numpy.bool_ +reveal_type(np.True_) # E: bool_ +reveal_type(np.False_) # E: bool_ reveal_type(np.UFUNC_PYVALS_NAME) # E: Literal['UFUNC_PYVALS'] diff --git a/numpy/typing/tests/data/reveal/ctypeslib.pyi b/numpy/typing/tests/data/reveal/ctypeslib.pyi index 461a447d9..ccbdfe36e 100644 --- a/numpy/typing/tests/data/reveal/ctypeslib.pyi +++ b/numpy/typing/tests/data/reveal/ctypeslib.pyi @@ -24,12 +24,12 @@ pointer: ctypes.pointer[Any] reveal_type(np.ctypeslib.c_intp()) # E: {c_intp} -reveal_type(np.ctypeslib.ndpointer()) # E: Type[numpy.ctypeslib._ndptr[None]] -reveal_type(np.ctypeslib.ndpointer(dtype=np.float64)) # E: Type[numpy.ctypeslib._ndptr[numpy.dtype[{float64}]]] -reveal_type(np.ctypeslib.ndpointer(dtype=float)) # E: Type[numpy.ctypeslib._ndptr[numpy.dtype[Any]]] -reveal_type(np.ctypeslib.ndpointer(shape=(10, 3))) # E: Type[numpy.ctypeslib._ndptr[None]] -reveal_type(np.ctypeslib.ndpointer(np.int64, shape=(10, 3))) # E: Type[numpy.ctypeslib._concrete_ndptr[numpy.dtype[{int64}]]] -reveal_type(np.ctypeslib.ndpointer(int, shape=(1,))) # E: Type[numpy.ctypeslib._concrete_ndptr[numpy.dtype[Any]]] +reveal_type(np.ctypeslib.ndpointer()) # E: Type[ctypeslib._ndptr[None]] +reveal_type(np.ctypeslib.ndpointer(dtype=np.float64)) # E: Type[ctypeslib._ndptr[dtype[{float64}]]] +reveal_type(np.ctypeslib.ndpointer(dtype=float)) # E: Type[ctypeslib._ndptr[dtype[Any]]] +reveal_type(np.ctypeslib.ndpointer(shape=(10, 3))) # E: Type[ctypeslib._ndptr[None]] +reveal_type(np.ctypeslib.ndpointer(np.int64, shape=(10, 3))) # E: Type[ctypeslib._concrete_ndptr[dtype[{int64}]]] +reveal_type(np.ctypeslib.ndpointer(int, shape=(1,))) # E: Type[ctypeslib._concrete_ndptr[dtype[Any]]] reveal_type(np.ctypeslib.as_ctypes_type(np.bool_)) # E: Type[ctypes.c_bool] reveal_type(np.ctypeslib.as_ctypes_type(np.ubyte)) # E: Type[{c_ubyte}] @@ -82,6 +82,6 @@ reveal_type(np.ctypeslib.as_ctypes(AR_double)) # E: ctypes.Array[{c_double}] reveal_type(np.ctypeslib.as_ctypes(AR_longdouble)) # E: ctypes.Array[{c_longdouble}] reveal_type(np.ctypeslib.as_ctypes(AR_void)) # E: ctypes.Array[Any] -reveal_type(np.ctypeslib.as_array(AR_ubyte)) # E: numpy.ndarray[Any, numpy.dtype[{ubyte}]] -reveal_type(np.ctypeslib.as_array(1)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.ctypeslib.as_array(pointer)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.ctypeslib.as_array(AR_ubyte)) # E: ndarray[Any, dtype[{ubyte}]] +reveal_type(np.ctypeslib.as_array(1)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.ctypeslib.as_array(pointer)) # E: ndarray[Any, dtype[Any]] diff --git a/numpy/typing/tests/data/reveal/dtype.pyi b/numpy/typing/tests/data/reveal/dtype.pyi index 364d1dcab..934d7da5e 100644 --- a/numpy/typing/tests/data/reveal/dtype.pyi +++ b/numpy/typing/tests/data/reveal/dtype.pyi @@ -5,72 +5,72 @@ dtype_U: np.dtype[np.str_] dtype_V: np.dtype[np.void] dtype_i8: np.dtype[np.int64] -reveal_type(np.dtype(np.float64)) # E: numpy.dtype[{float64}] -reveal_type(np.dtype(np.int64)) # E: numpy.dtype[{int64}] +reveal_type(np.dtype(np.float64)) # E: dtype[{float64}] +reveal_type(np.dtype(np.int64)) # E: dtype[{int64}] # String aliases -reveal_type(np.dtype("float64")) # E: numpy.dtype[{float64}] -reveal_type(np.dtype("float32")) # E: numpy.dtype[{float32}] -reveal_type(np.dtype("int64")) # E: numpy.dtype[{int64}] -reveal_type(np.dtype("int32")) # E: numpy.dtype[{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_] +reveal_type(np.dtype("float64")) # E: dtype[{float64}] +reveal_type(np.dtype("float32")) # E: dtype[{float32}] +reveal_type(np.dtype("int64")) # E: dtype[{int64}] +reveal_type(np.dtype("int32")) # E: dtype[{int32}] +reveal_type(np.dtype("bool")) # E: dtype[bool_] +reveal_type(np.dtype("bytes")) # E: dtype[bytes_] +reveal_type(np.dtype("str")) # E: dtype[str_] # Python types -reveal_type(np.dtype(complex)) # E: numpy.dtype[{cdouble}] -reveal_type(np.dtype(float)) # E: numpy.dtype[{double}] -reveal_type(np.dtype(int)) # E: numpy.dtype[{int_}] -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_] -reveal_type(np.dtype(object)) # E: numpy.dtype[numpy.object_] +reveal_type(np.dtype(complex)) # E: dtype[{cdouble}] +reveal_type(np.dtype(float)) # E: dtype[{double}] +reveal_type(np.dtype(int)) # E: dtype[{int_}] +reveal_type(np.dtype(bool)) # E: dtype[bool_] +reveal_type(np.dtype(str)) # E: dtype[str_] +reveal_type(np.dtype(bytes)) # E: dtype[bytes_] +reveal_type(np.dtype(object)) # E: dtype[object_] # ctypes -reveal_type(np.dtype(ct.c_double)) # E: numpy.dtype[{double}] -reveal_type(np.dtype(ct.c_longlong)) # E: numpy.dtype[{longlong}] -reveal_type(np.dtype(ct.c_uint32)) # E: numpy.dtype[{uint32}] -reveal_type(np.dtype(ct.c_bool)) # E: numpy.dtype[numpy.bool_] -reveal_type(np.dtype(ct.c_char)) # E: numpy.dtype[numpy.bytes_] -reveal_type(np.dtype(ct.py_object)) # E: numpy.dtype[numpy.object_] +reveal_type(np.dtype(ct.c_double)) # E: dtype[{double}] +reveal_type(np.dtype(ct.c_longlong)) # E: dtype[{longlong}] +reveal_type(np.dtype(ct.c_uint32)) # E: dtype[{uint32}] +reveal_type(np.dtype(ct.c_bool)) # E: dtype[bool_] +reveal_type(np.dtype(ct.c_char)) # E: dtype[bytes_] +reveal_type(np.dtype(ct.py_object)) # E: dtype[object_] # Special case for None -reveal_type(np.dtype(None)) # E: numpy.dtype[{double}] +reveal_type(np.dtype(None)) # E: dtype[{double}] # Dtypes of dtypes -reveal_type(np.dtype(np.dtype(np.float64))) # E: numpy.dtype[{float64}] +reveal_type(np.dtype(np.dtype(np.float64))) # E: dtype[{float64}] # Parameterized dtypes -reveal_type(np.dtype("S8")) # E: numpy.dtype +reveal_type(np.dtype("S8")) # E: dtype # Void -reveal_type(np.dtype(("U", 10))) # E: numpy.dtype[numpy.void] +reveal_type(np.dtype(("U", 10))) # E: dtype[void] # Methods and attributes -reveal_type(dtype_U.base) # E: numpy.dtype[Any] -reveal_type(dtype_U.subdtype) # E: Union[None, Tuple[numpy.dtype[Any], builtins.tuple[builtins.int]]] -reveal_type(dtype_U.newbyteorder()) # E: numpy.dtype[numpy.str_] -reveal_type(dtype_U.type) # E: Type[numpy.str_] +reveal_type(dtype_U.base) # E: dtype[Any] +reveal_type(dtype_U.subdtype) # E: Union[None, Tuple[dtype[Any], builtins.tuple[builtins.int]]] +reveal_type(dtype_U.newbyteorder()) # E: dtype[str_] +reveal_type(dtype_U.type) # E: Type[str_] reveal_type(dtype_U.name) # E: str reveal_type(dtype_U.names) # E: Union[None, builtins.tuple[builtins.str]] -reveal_type(dtype_U * 0) # E: numpy.dtype[numpy.str_] -reveal_type(dtype_U * 1) # E: numpy.dtype[numpy.str_] -reveal_type(dtype_U * 2) # E: numpy.dtype[numpy.str_] +reveal_type(dtype_U * 0) # E: dtype[str_] +reveal_type(dtype_U * 1) # E: dtype[str_] +reveal_type(dtype_U * 2) # E: dtype[str_] -reveal_type(dtype_i8 * 0) # E: numpy.dtype[numpy.void] -reveal_type(dtype_i8 * 1) # E: numpy.dtype[{int64}] -reveal_type(dtype_i8 * 2) # E: numpy.dtype[numpy.void] +reveal_type(dtype_i8 * 0) # E: dtype[void] +reveal_type(dtype_i8 * 1) # E: dtype[{int64}] +reveal_type(dtype_i8 * 2) # E: dtype[void] -reveal_type(0 * dtype_U) # E: numpy.dtype[numpy.str_] -reveal_type(1 * dtype_U) # E: numpy.dtype[numpy.str_] -reveal_type(2 * dtype_U) # E: numpy.dtype[numpy.str_] +reveal_type(0 * dtype_U) # E: dtype[str_] +reveal_type(1 * dtype_U) # E: dtype[str_] +reveal_type(2 * dtype_U) # E: dtype[str_] -reveal_type(0 * dtype_i8) # E: numpy.dtype[Any] -reveal_type(1 * dtype_i8) # E: numpy.dtype[Any] -reveal_type(2 * dtype_i8) # E: numpy.dtype[Any] +reveal_type(0 * dtype_i8) # E: dtype[Any] +reveal_type(1 * dtype_i8) # E: dtype[Any] +reveal_type(2 * dtype_i8) # E: dtype[Any] -reveal_type(dtype_V["f0"]) # E: numpy.dtype[Any] -reveal_type(dtype_V[0]) # E: numpy.dtype[Any] -reveal_type(dtype_V[["f0", "f1"]]) # E: numpy.dtype[numpy.void] -reveal_type(dtype_V[["f0"]]) # E: numpy.dtype[numpy.void] +reveal_type(dtype_V["f0"]) # E: dtype[Any] +reveal_type(dtype_V[0]) # E: dtype[Any] +reveal_type(dtype_V[["f0", "f1"]]) # E: dtype[void] +reveal_type(dtype_V[["f0"]]) # E: dtype[void] diff --git a/numpy/typing/tests/data/reveal/einsumfunc.pyi b/numpy/typing/tests/data/reveal/einsumfunc.pyi index f1a90428d..5b07e6d3c 100644 --- a/numpy/typing/tests/data/reveal/einsumfunc.pyi +++ b/numpy/typing/tests/data/reveal/einsumfunc.pyi @@ -18,8 +18,8 @@ reveal_type(np.einsum("i,i->i", AR_LIKE_c, AR_LIKE_c)) # E: Any reveal_type(np.einsum("i,i->i", AR_LIKE_b, AR_LIKE_i)) # E: Any reveal_type(np.einsum("i,i,i,i->i", AR_LIKE_b, AR_LIKE_u, AR_LIKE_i, AR_LIKE_c)) # E: Any -reveal_type(np.einsum("i,i->i", AR_LIKE_c, AR_LIKE_c, out=OUT_f)) # E: numpy.ndarray[Any, numpy.dtype[{float64}] -reveal_type(np.einsum("i,i->i", AR_LIKE_U, AR_LIKE_U, dtype=bool, casting="unsafe", out=OUT_f)) # E: numpy.ndarray[Any, numpy.dtype[{float64}] +reveal_type(np.einsum("i,i->i", AR_LIKE_c, AR_LIKE_c, out=OUT_f)) # E: ndarray[Any, dtype[{float64}] +reveal_type(np.einsum("i,i->i", AR_LIKE_U, AR_LIKE_U, dtype=bool, casting="unsafe", out=OUT_f)) # E: ndarray[Any, dtype[{float64}] reveal_type(np.einsum("i,i->i", AR_LIKE_f, AR_LIKE_f, dtype="c16")) # E: Any reveal_type(np.einsum("i,i->i", AR_LIKE_U, AR_LIKE_U, dtype=bool, casting="unsafe")) # E: Any diff --git a/numpy/typing/tests/data/reveal/fft.pyi b/numpy/typing/tests/data/reveal/fft.pyi index 316ddd189..0667938e4 100644 --- a/numpy/typing/tests/data/reveal/fft.pyi +++ b/numpy/typing/tests/data/reveal/fft.pyi @@ -5,31 +5,31 @@ AR_f8: npt.NDArray[np.float64] AR_c16: npt.NDArray[np.complex128] AR_LIKE_f8: list[float] -reveal_type(np.fft.fftshift(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.fft.fftshift(AR_LIKE_f8, axes=0)) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.fft.ifftshift(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.fft.ifftshift(AR_LIKE_f8, axes=0)) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.fft.fftfreq(5, AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.fft.fftfreq(np.int64(), AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] - -reveal_type(np.fft.fftfreq(5, AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.fft.fftfreq(np.int64(), AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] - -reveal_type(np.fft.fft(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] -reveal_type(np.fft.ifft(AR_f8, axis=1)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] -reveal_type(np.fft.rfft(AR_f8, n=None)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] -reveal_type(np.fft.irfft(AR_f8, norm="ortho")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.fft.hfft(AR_f8, n=2)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.fft.ihfft(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] - -reveal_type(np.fft.fftn(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] -reveal_type(np.fft.ifftn(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] -reveal_type(np.fft.rfftn(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] -reveal_type(np.fft.irfftn(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] - -reveal_type(np.fft.rfft2(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] -reveal_type(np.fft.ifft2(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] -reveal_type(np.fft.fft2(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] -reveal_type(np.fft.irfft2(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.fft.fftshift(AR_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.fft.fftshift(AR_LIKE_f8, axes=0)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.fft.ifftshift(AR_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.fft.ifftshift(AR_LIKE_f8, axes=0)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.fft.fftfreq(5, AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.fft.fftfreq(np.int64(), AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] + +reveal_type(np.fft.fftfreq(5, AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.fft.fftfreq(np.int64(), AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] + +reveal_type(np.fft.fft(AR_f8)) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(np.fft.ifft(AR_f8, axis=1)) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(np.fft.rfft(AR_f8, n=None)) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(np.fft.irfft(AR_f8, norm="ortho")) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.fft.hfft(AR_f8, n=2)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.fft.ihfft(AR_f8)) # E: ndarray[Any, dtype[{complex128}]] + +reveal_type(np.fft.fftn(AR_f8)) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(np.fft.ifftn(AR_f8)) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(np.fft.rfftn(AR_f8)) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(np.fft.irfftn(AR_f8)) # E: ndarray[Any, dtype[{float64}]] + +reveal_type(np.fft.rfft2(AR_f8)) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(np.fft.ifft2(AR_f8)) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(np.fft.fft2(AR_f8)) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(np.fft.irfft2(AR_f8)) # E: ndarray[Any, dtype[{float64}]] diff --git a/numpy/typing/tests/data/reveal/flatiter.pyi b/numpy/typing/tests/data/reveal/flatiter.pyi index 97776dd9f..ef89acb58 100644 --- a/numpy/typing/tests/data/reveal/flatiter.pyi +++ b/numpy/typing/tests/data/reveal/flatiter.pyi @@ -3,15 +3,15 @@ import numpy as np a: np.flatiter[np.ndarray[Any, np.dtype[np.str_]]] -reveal_type(a.base) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(a.copy()) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] +reveal_type(a.base) # E: ndarray[Any, dtype[str_]] +reveal_type(a.copy()) # E: ndarray[Any, dtype[str_]] reveal_type(a.coords) # E: tuple[builtins.int] reveal_type(a.index) # E: int -reveal_type(iter(a)) # E: Iterator[numpy.str_] -reveal_type(next(a)) # E: numpy.str_ -reveal_type(a[0]) # E: numpy.str_ -reveal_type(a[[0, 1, 2]]) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(a[...]) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(a[:]) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(a.__array__()) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(a.__array__(np.dtype(np.float64))) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(iter(a)) # E: Iterator[str_] +reveal_type(next(a)) # E: str_ +reveal_type(a[0]) # E: str_ +reveal_type(a[[0, 1, 2]]) # E: ndarray[Any, dtype[str_]] +reveal_type(a[...]) # E: ndarray[Any, dtype[str_]] +reveal_type(a[:]) # E: ndarray[Any, dtype[str_]] +reveal_type(a.__array__()) # E: ndarray[Any, dtype[str_]] +reveal_type(a.__array__(np.dtype(np.float64))) # E: ndarray[Any, dtype[{float64}]] diff --git a/numpy/typing/tests/data/reveal/fromnumeric.pyi b/numpy/typing/tests/data/reveal/fromnumeric.pyi index bbcfbb85a..2ee1952cf 100644 --- a/numpy/typing/tests/data/reveal/fromnumeric.pyi +++ b/numpy/typing/tests/data/reveal/fromnumeric.pyi @@ -1,4 +1,4 @@ -"""Tests for :mod:`numpy.core.fromnumeric`.""" +"""Tests for :mod:`core.fromnumeric`.""" import numpy as np @@ -20,37 +20,37 @@ reveal_type(np.take(B, 0)) # E: Any reveal_type(np.take(A, [0])) # E: Any reveal_type(np.take(B, [0])) # E: Any -reveal_type(np.reshape(a, 1)) # E: numpy.ndarray[Any, Any] -reveal_type(np.reshape(b, 1)) # E: numpy.ndarray[Any, Any] -reveal_type(np.reshape(c, 1)) # E: numpy.ndarray[Any, Any] -reveal_type(np.reshape(A, 1)) # E: numpy.ndarray[Any, Any] -reveal_type(np.reshape(B, 1)) # E: numpy.ndarray[Any, Any] +reveal_type(np.reshape(a, 1)) # E: ndarray[Any, Any] +reveal_type(np.reshape(b, 1)) # E: ndarray[Any, Any] +reveal_type(np.reshape(c, 1)) # E: ndarray[Any, Any] +reveal_type(np.reshape(A, 1)) # E: ndarray[Any, Any] +reveal_type(np.reshape(B, 1)) # E: ndarray[Any, Any] reveal_type(np.choose(a, [True, True])) # E: Any reveal_type(np.choose(A, [True, True])) # E: Any -reveal_type(np.repeat(a, 1)) # E: numpy.ndarray[Any, Any] -reveal_type(np.repeat(b, 1)) # E: numpy.ndarray[Any, Any] -reveal_type(np.repeat(c, 1)) # E: numpy.ndarray[Any, Any] -reveal_type(np.repeat(A, 1)) # E: numpy.ndarray[Any, Any] -reveal_type(np.repeat(B, 1)) # E: numpy.ndarray[Any, Any] +reveal_type(np.repeat(a, 1)) # E: ndarray[Any, Any] +reveal_type(np.repeat(b, 1)) # E: ndarray[Any, Any] +reveal_type(np.repeat(c, 1)) # E: ndarray[Any, Any] +reveal_type(np.repeat(A, 1)) # E: ndarray[Any, Any] +reveal_type(np.repeat(B, 1)) # E: ndarray[Any, Any] # TODO: Add tests for np.put() -reveal_type(np.swapaxes(A, 0, 0)) # E: numpy.ndarray[Any, Any] -reveal_type(np.swapaxes(B, 0, 0)) # E: numpy.ndarray[Any, Any] +reveal_type(np.swapaxes(A, 0, 0)) # E: ndarray[Any, Any] +reveal_type(np.swapaxes(B, 0, 0)) # E: ndarray[Any, Any] -reveal_type(np.transpose(a)) # E: numpy.ndarray[Any, Any] -reveal_type(np.transpose(b)) # E: numpy.ndarray[Any, Any] -reveal_type(np.transpose(c)) # E: numpy.ndarray[Any, Any] -reveal_type(np.transpose(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.transpose(B)) # E: numpy.ndarray[Any, Any] +reveal_type(np.transpose(a)) # E: ndarray[Any, Any] +reveal_type(np.transpose(b)) # E: ndarray[Any, Any] +reveal_type(np.transpose(c)) # E: ndarray[Any, Any] +reveal_type(np.transpose(A)) # E: ndarray[Any, Any] +reveal_type(np.transpose(B)) # E: ndarray[Any, Any] -reveal_type(np.partition(a, 0, axis=None)) # E: numpy.ndarray[Any, Any] -reveal_type(np.partition(b, 0, axis=None)) # E: numpy.ndarray[Any, Any] -reveal_type(np.partition(c, 0, axis=None)) # E: numpy.ndarray[Any, Any] -reveal_type(np.partition(A, 0)) # E: numpy.ndarray[Any, Any] -reveal_type(np.partition(B, 0)) # E: numpy.ndarray[Any, Any] +reveal_type(np.partition(a, 0, axis=None)) # E: ndarray[Any, Any] +reveal_type(np.partition(b, 0, axis=None)) # E: ndarray[Any, Any] +reveal_type(np.partition(c, 0, axis=None)) # E: ndarray[Any, Any] +reveal_type(np.partition(A, 0)) # E: ndarray[Any, Any] +reveal_type(np.partition(B, 0)) # E: ndarray[Any, Any] reveal_type(np.argpartition(a, 0)) # E: Any reveal_type(np.argpartition(b, 0)) # E: Any @@ -58,11 +58,11 @@ reveal_type(np.argpartition(c, 0)) # E: Any reveal_type(np.argpartition(A, 0)) # E: Any reveal_type(np.argpartition(B, 0)) # E: Any -reveal_type(np.sort(A, 0)) # E: numpy.ndarray[Any, Any] -reveal_type(np.sort(B, 0)) # E: numpy.ndarray[Any, Any] +reveal_type(np.sort(A, 0)) # E: ndarray[Any, Any] +reveal_type(np.sort(B, 0)) # E: ndarray[Any, Any] -reveal_type(np.argsort(A, 0)) # E: numpy.ndarray[Any, Any] -reveal_type(np.argsort(B, 0)) # E: numpy.ndarray[Any, Any] +reveal_type(np.argsort(A, 0)) # E: ndarray[Any, Any] +reveal_type(np.argsort(B, 0)) # E: ndarray[Any, Any] reveal_type(np.argmax(A)) # E: {intp} reveal_type(np.argmax(B)) # E: {intp} @@ -76,38 +76,38 @@ reveal_type(np.argmin(B, axis=0)) # E: Any reveal_type(np.searchsorted(A[0], 0)) # E: {intp} reveal_type(np.searchsorted(B[0], 0)) # E: {intp} -reveal_type(np.searchsorted(A[0], [0])) # E: numpy.ndarray[Any, Any] -reveal_type(np.searchsorted(B[0], [0])) # E: numpy.ndarray[Any, Any] +reveal_type(np.searchsorted(A[0], [0])) # E: ndarray[Any, Any] +reveal_type(np.searchsorted(B[0], [0])) # E: ndarray[Any, Any] -reveal_type(np.resize(a, (5, 5))) # E: numpy.ndarray[Any, Any] -reveal_type(np.resize(b, (5, 5))) # E: numpy.ndarray[Any, Any] -reveal_type(np.resize(c, (5, 5))) # E: numpy.ndarray[Any, Any] -reveal_type(np.resize(A, (5, 5))) # E: numpy.ndarray[Any, Any] -reveal_type(np.resize(B, (5, 5))) # E: numpy.ndarray[Any, Any] +reveal_type(np.resize(a, (5, 5))) # E: ndarray[Any, Any] +reveal_type(np.resize(b, (5, 5))) # E: ndarray[Any, Any] +reveal_type(np.resize(c, (5, 5))) # E: ndarray[Any, Any] +reveal_type(np.resize(A, (5, 5))) # E: ndarray[Any, Any] +reveal_type(np.resize(B, (5, 5))) # E: ndarray[Any, Any] -reveal_type(np.squeeze(a)) # E: numpy.bool_ +reveal_type(np.squeeze(a)) # E: bool_ reveal_type(np.squeeze(b)) # E: {float32} -reveal_type(np.squeeze(c)) # E: numpy.ndarray[Any, Any] -reveal_type(np.squeeze(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.squeeze(B)) # E: numpy.ndarray[Any, Any] +reveal_type(np.squeeze(c)) # E: ndarray[Any, Any] +reveal_type(np.squeeze(A)) # E: ndarray[Any, Any] +reveal_type(np.squeeze(B)) # E: ndarray[Any, Any] -reveal_type(np.diagonal(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.diagonal(B)) # E: numpy.ndarray[Any, Any] +reveal_type(np.diagonal(A)) # E: ndarray[Any, Any] +reveal_type(np.diagonal(B)) # E: ndarray[Any, Any] reveal_type(np.trace(A)) # E: Any reveal_type(np.trace(B)) # E: Any -reveal_type(np.ravel(a)) # E: numpy.ndarray[Any, Any] -reveal_type(np.ravel(b)) # E: numpy.ndarray[Any, Any] -reveal_type(np.ravel(c)) # E: numpy.ndarray[Any, Any] -reveal_type(np.ravel(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.ravel(B)) # E: numpy.ndarray[Any, Any] +reveal_type(np.ravel(a)) # E: ndarray[Any, Any] +reveal_type(np.ravel(b)) # E: ndarray[Any, Any] +reveal_type(np.ravel(c)) # E: ndarray[Any, Any] +reveal_type(np.ravel(A)) # E: ndarray[Any, Any] +reveal_type(np.ravel(B)) # E: ndarray[Any, Any] -reveal_type(np.nonzero(a)) # E: tuple[numpy.ndarray[Any, Any]] -reveal_type(np.nonzero(b)) # E: tuple[numpy.ndarray[Any, Any]] -reveal_type(np.nonzero(c)) # E: tuple[numpy.ndarray[Any, Any]] -reveal_type(np.nonzero(A)) # E: tuple[numpy.ndarray[Any, Any]] -reveal_type(np.nonzero(B)) # E: tuple[numpy.ndarray[Any, Any]] +reveal_type(np.nonzero(a)) # E: tuple[ndarray[Any, Any]] +reveal_type(np.nonzero(b)) # E: tuple[ndarray[Any, Any]] +reveal_type(np.nonzero(c)) # E: tuple[ndarray[Any, Any]] +reveal_type(np.nonzero(A)) # E: tuple[ndarray[Any, Any]] +reveal_type(np.nonzero(B)) # E: tuple[ndarray[Any, Any]] reveal_type(np.shape(a)) # E: tuple[builtins.int] reveal_type(np.shape(b)) # E: tuple[builtins.int] @@ -115,11 +115,11 @@ reveal_type(np.shape(c)) # E: tuple[builtins.int] reveal_type(np.shape(A)) # E: tuple[builtins.int] reveal_type(np.shape(B)) # E: tuple[builtins.int] -reveal_type(np.compress([True], a)) # E: numpy.ndarray[Any, Any] -reveal_type(np.compress([True], b)) # E: numpy.ndarray[Any, Any] -reveal_type(np.compress([True], c)) # E: numpy.ndarray[Any, Any] -reveal_type(np.compress([True], A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.compress([True], B)) # E: numpy.ndarray[Any, Any] +reveal_type(np.compress([True], a)) # E: ndarray[Any, Any] +reveal_type(np.compress([True], b)) # E: ndarray[Any, Any] +reveal_type(np.compress([True], c)) # E: ndarray[Any, Any] +reveal_type(np.compress([True], A)) # E: ndarray[Any, Any] +reveal_type(np.compress([True], B)) # E: ndarray[Any, Any] reveal_type(np.clip(a, 0, 1.0)) # E: Any reveal_type(np.clip(b, -1, 1)) # E: Any @@ -135,31 +135,31 @@ reveal_type(np.sum(B)) # E: Any reveal_type(np.sum(A, axis=0)) # E: Any reveal_type(np.sum(B, axis=0)) # E: Any -reveal_type(np.all(a)) # E: numpy.bool_ -reveal_type(np.all(b)) # E: numpy.bool_ -reveal_type(np.all(c)) # E: numpy.bool_ -reveal_type(np.all(A)) # E: numpy.bool_ -reveal_type(np.all(B)) # E: numpy.bool_ +reveal_type(np.all(a)) # E: bool_ +reveal_type(np.all(b)) # E: bool_ +reveal_type(np.all(c)) # E: bool_ +reveal_type(np.all(A)) # E: bool_ +reveal_type(np.all(B)) # E: bool_ reveal_type(np.all(A, axis=0)) # E: Any reveal_type(np.all(B, axis=0)) # E: Any reveal_type(np.all(A, keepdims=True)) # E: Any reveal_type(np.all(B, keepdims=True)) # E: Any -reveal_type(np.any(a)) # E: numpy.bool_ -reveal_type(np.any(b)) # E: numpy.bool_ -reveal_type(np.any(c)) # E: numpy.bool_ -reveal_type(np.any(A)) # E: numpy.bool_ -reveal_type(np.any(B)) # E: numpy.bool_ +reveal_type(np.any(a)) # E: bool_ +reveal_type(np.any(b)) # E: bool_ +reveal_type(np.any(c)) # E: bool_ +reveal_type(np.any(A)) # E: bool_ +reveal_type(np.any(B)) # E: bool_ reveal_type(np.any(A, axis=0)) # E: Any reveal_type(np.any(B, axis=0)) # E: Any reveal_type(np.any(A, keepdims=True)) # E: Any reveal_type(np.any(B, keepdims=True)) # E: Any -reveal_type(np.cumsum(a)) # E: numpy.ndarray[Any, Any] -reveal_type(np.cumsum(b)) # E: numpy.ndarray[Any, Any] -reveal_type(np.cumsum(c)) # E: numpy.ndarray[Any, Any] -reveal_type(np.cumsum(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.cumsum(B)) # E: numpy.ndarray[Any, Any] +reveal_type(np.cumsum(a)) # E: ndarray[Any, Any] +reveal_type(np.cumsum(b)) # E: ndarray[Any, Any] +reveal_type(np.cumsum(c)) # E: ndarray[Any, Any] +reveal_type(np.cumsum(A)) # E: ndarray[Any, Any] +reveal_type(np.cumsum(B)) # E: ndarray[Any, Any] reveal_type(np.ptp(a)) # E: Any reveal_type(np.ptp(b)) # E: Any @@ -203,11 +203,11 @@ reveal_type(np.prod(B, keepdims=True)) # E: Any reveal_type(np.prod(b, out=d)) # E: Any reveal_type(np.prod(B, out=d)) # E: Any -reveal_type(np.cumprod(a)) # E: numpy.ndarray[Any, Any] -reveal_type(np.cumprod(b)) # E: numpy.ndarray[Any, Any] -reveal_type(np.cumprod(c)) # E: numpy.ndarray[Any, Any] -reveal_type(np.cumprod(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.cumprod(B)) # E: numpy.ndarray[Any, Any] +reveal_type(np.cumprod(a)) # E: ndarray[Any, Any] +reveal_type(np.cumprod(b)) # E: ndarray[Any, Any] +reveal_type(np.cumprod(c)) # E: ndarray[Any, Any] +reveal_type(np.cumprod(A)) # E: ndarray[Any, Any] +reveal_type(np.cumprod(B)) # E: ndarray[Any, Any] reveal_type(np.ndim(a)) # E: int reveal_type(np.ndim(b)) # E: int diff --git a/numpy/typing/tests/data/reveal/getlimits.pyi b/numpy/typing/tests/data/reveal/getlimits.pyi index 90bcb06c8..1614b577e 100644 --- a/numpy/typing/tests/data/reveal/getlimits.pyi +++ b/numpy/typing/tests/data/reveal/getlimits.pyi @@ -10,12 +10,12 @@ u4: np.uint32 finfo_f8: np.finfo[np.float64] iinfo_i8: np.iinfo[np.int64] -reveal_type(np.finfo(f)) # E: numpy.finfo[{double}] -reveal_type(np.finfo(f8)) # E: numpy.finfo[{float64}] -reveal_type(np.finfo(c8)) # E: numpy.finfo[{float32}] -reveal_type(np.finfo('f2')) # E: numpy.finfo[numpy.floating[Any]] +reveal_type(np.finfo(f)) # E: finfo[{double}] +reveal_type(np.finfo(f8)) # E: finfo[{float64}] +reveal_type(np.finfo(c8)) # E: finfo[{float32}] +reveal_type(np.finfo('f2')) # E: finfo[floating[Any]] -reveal_type(finfo_f8.dtype) # E: numpy.dtype[{float64}] +reveal_type(finfo_f8.dtype) # E: dtype[{float64}] reveal_type(finfo_f8.bits) # E: int reveal_type(finfo_f8.eps) # E: {float64} reveal_type(finfo_f8.epsneg) # E: {float64} @@ -39,7 +39,7 @@ reveal_type(np.iinfo(i8)) # E: iinfo[{int64}] reveal_type(np.iinfo(u4)) # E: iinfo[{uint32}] reveal_type(np.iinfo('i2')) # E: iinfo[Any] -reveal_type(iinfo_i8.dtype) # E: numpy.dtype[{int64}] +reveal_type(iinfo_i8.dtype) # E: dtype[{int64}] reveal_type(iinfo_i8.kind) # E: str reveal_type(iinfo_i8.bits) # E: int reveal_type(iinfo_i8.key) # E: str diff --git a/numpy/typing/tests/data/reveal/histograms.pyi b/numpy/typing/tests/data/reveal/histograms.pyi index 55fa9518f..d96e44f09 100644 --- a/numpy/typing/tests/data/reveal/histograms.pyi +++ b/numpy/typing/tests/data/reveal/histograms.pyi @@ -4,16 +4,16 @@ import numpy.typing as npt AR_i8: npt.NDArray[np.int64] AR_f8: npt.NDArray[np.float64] -reveal_type(np.histogram_bin_edges(AR_i8, bins="auto")) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.histogram_bin_edges(AR_i8, bins="rice", range=(0, 3))) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.histogram_bin_edges(AR_i8, bins="scott", weights=AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.histogram_bin_edges(AR_i8, bins="auto")) # E: ndarray[Any, dtype[Any]] +reveal_type(np.histogram_bin_edges(AR_i8, bins="rice", range=(0, 3))) # E: ndarray[Any, dtype[Any]] +reveal_type(np.histogram_bin_edges(AR_i8, bins="scott", weights=AR_f8)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.histogram(AR_i8, bins="auto")) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[Any]]] -reveal_type(np.histogram(AR_i8, bins="rice", range=(0, 3))) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[Any]]] -reveal_type(np.histogram(AR_i8, bins="scott", weights=AR_f8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[Any]]] -reveal_type(np.histogram(AR_f8, bins=1, density=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[Any]]] +reveal_type(np.histogram(AR_i8, bins="auto")) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]] +reveal_type(np.histogram(AR_i8, bins="rice", range=(0, 3))) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]] +reveal_type(np.histogram(AR_i8, bins="scott", weights=AR_f8)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]] +reveal_type(np.histogram(AR_f8, bins=1, density=True)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]] -reveal_type(np.histogramdd(AR_i8, bins=[1])) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], builtins.list[numpy.ndarray[Any, numpy.dtype[Any]]]] -reveal_type(np.histogramdd(AR_i8, range=[(0, 3)])) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], builtins.list[numpy.ndarray[Any, numpy.dtype[Any]]]] -reveal_type(np.histogramdd(AR_i8, weights=AR_f8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], builtins.list[numpy.ndarray[Any, numpy.dtype[Any]]]] -reveal_type(np.histogramdd(AR_f8, density=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], builtins.list[numpy.ndarray[Any, numpy.dtype[Any]]]] +reveal_type(np.histogramdd(AR_i8, bins=[1])) # E: Tuple[ndarray[Any, dtype[Any]], builtins.list[ndarray[Any, dtype[Any]]]] +reveal_type(np.histogramdd(AR_i8, range=[(0, 3)])) # E: Tuple[ndarray[Any, dtype[Any]], builtins.list[ndarray[Any, dtype[Any]]]] +reveal_type(np.histogramdd(AR_i8, weights=AR_f8)) # E: Tuple[ndarray[Any, dtype[Any]], builtins.list[ndarray[Any, dtype[Any]]]] +reveal_type(np.histogramdd(AR_f8, density=True)) # E: Tuple[ndarray[Any, dtype[Any]], builtins.list[ndarray[Any, dtype[Any]]]] diff --git a/numpy/typing/tests/data/reveal/index_tricks.pyi b/numpy/typing/tests/data/reveal/index_tricks.pyi index 863d60220..cee4d8c3e 100644 --- a/numpy/typing/tests/data/reveal/index_tricks.pyi +++ b/numpy/typing/tests/data/reveal/index_tricks.pyi @@ -8,41 +8,41 @@ AR_LIKE_U: List[str] AR_i8: np.ndarray[Any, np.dtype[np.int64]] -reveal_type(np.ndenumerate(AR_i8)) # E: numpy.ndenumerate[{int64}] -reveal_type(np.ndenumerate(AR_LIKE_f)) # E: numpy.ndenumerate[{double}] -reveal_type(np.ndenumerate(AR_LIKE_U)) # E: numpy.ndenumerate[numpy.str_] +reveal_type(np.ndenumerate(AR_i8)) # E: ndenumerate[{int64}] +reveal_type(np.ndenumerate(AR_LIKE_f)) # E: ndenumerate[{double}] +reveal_type(np.ndenumerate(AR_LIKE_U)) # E: ndenumerate[str_] -reveal_type(np.ndenumerate(AR_i8).iter) # E: numpy.flatiter[numpy.ndarray[Any, numpy.dtype[{int64}]]] -reveal_type(np.ndenumerate(AR_LIKE_f).iter) # E: numpy.flatiter[numpy.ndarray[Any, numpy.dtype[{double}]]] -reveal_type(np.ndenumerate(AR_LIKE_U).iter) # E: numpy.flatiter[numpy.ndarray[Any, numpy.dtype[numpy.str_]]] +reveal_type(np.ndenumerate(AR_i8).iter) # E: flatiter[ndarray[Any, dtype[{int64}]]] +reveal_type(np.ndenumerate(AR_LIKE_f).iter) # E: flatiter[ndarray[Any, dtype[{double}]]] +reveal_type(np.ndenumerate(AR_LIKE_U).iter) # E: flatiter[ndarray[Any, dtype[str_]]] reveal_type(next(np.ndenumerate(AR_i8))) # E: Tuple[builtins.tuple[builtins.int], {int64}] reveal_type(next(np.ndenumerate(AR_LIKE_f))) # E: Tuple[builtins.tuple[builtins.int], {double}] -reveal_type(next(np.ndenumerate(AR_LIKE_U))) # E: Tuple[builtins.tuple[builtins.int], numpy.str_] +reveal_type(next(np.ndenumerate(AR_LIKE_U))) # E: Tuple[builtins.tuple[builtins.int], str_] reveal_type(iter(np.ndenumerate(AR_i8))) # E: Iterator[Tuple[builtins.tuple[builtins.int], {int64}]] reveal_type(iter(np.ndenumerate(AR_LIKE_f))) # E: Iterator[Tuple[builtins.tuple[builtins.int], {double}]] -reveal_type(iter(np.ndenumerate(AR_LIKE_U))) # E: Iterator[Tuple[builtins.tuple[builtins.int], numpy.str_]] +reveal_type(iter(np.ndenumerate(AR_LIKE_U))) # E: Iterator[Tuple[builtins.tuple[builtins.int], str_]] reveal_type(iter(np.ndindex(1, 2, 3))) # E: Iterator[builtins.tuple[builtins.int]] reveal_type(next(np.ndindex(1, 2, 3))) # E: builtins.tuple[builtins.int] -reveal_type(np.unravel_index([22, 41, 37], (7, 6))) # E: tuple[numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unravel_index([31, 41, 13], (7, 6), order="F")) # E: tuple[numpy.ndarray[Any, numpy.dtype[{intp}]]] +reveal_type(np.unravel_index([22, 41, 37], (7, 6))) # E: tuple[ndarray[Any, dtype[{intp}]]] +reveal_type(np.unravel_index([31, 41, 13], (7, 6), order="F")) # E: tuple[ndarray[Any, dtype[{intp}]]] reveal_type(np.unravel_index(1621, (6, 7, 8, 9))) # E: tuple[{intp}] -reveal_type(np.ravel_multi_index([[1]], (7, 6))) # E: numpy.ndarray[Any, numpy.dtype[{intp}]] +reveal_type(np.ravel_multi_index([[1]], (7, 6))) # E: ndarray[Any, dtype[{intp}]] reveal_type(np.ravel_multi_index(AR_LIKE_i, (7, 6))) # E: {intp} reveal_type(np.ravel_multi_index(AR_LIKE_i, (7, 6), order="F")) # E: {intp} reveal_type(np.ravel_multi_index(AR_LIKE_i, (4, 6), mode="clip")) # E: {intp} reveal_type(np.ravel_multi_index(AR_LIKE_i, (4, 4), mode=("clip", "wrap"))) # E: {intp} reveal_type(np.ravel_multi_index((3, 1, 4, 1), (6, 7, 8, 9))) # E: {intp} -reveal_type(np.mgrid[1:1:2]) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.mgrid[1:1:2, None:10]) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.mgrid[1:1:2]) # E: ndarray[Any, dtype[Any]] +reveal_type(np.mgrid[1:1:2, None:10]) # E: ndarray[Any, dtype[Any]] -reveal_type(np.ogrid[1:1:2]) # E: list[numpy.ndarray[Any, numpy.dtype[Any]]] -reveal_type(np.ogrid[1:1:2, None:10]) # E: list[numpy.ndarray[Any, numpy.dtype[Any]]] +reveal_type(np.ogrid[1:1:2]) # E: list[ndarray[Any, dtype[Any]]] +reveal_type(np.ogrid[1:1:2, None:10]) # E: list[ndarray[Any, dtype[Any]]] reveal_type(np.index_exp[0:1]) # E: Tuple[builtins.slice] reveal_type(np.index_exp[0:1, None:3]) # E: Tuple[builtins.slice, builtins.slice] @@ -52,13 +52,13 @@ reveal_type(np.s_[0:1]) # E: builtins.slice reveal_type(np.s_[0:1, None:3]) # E: Tuple[builtins.slice, builtins.slice] reveal_type(np.s_[0, 0:1, ..., [0, 1, 3]]) # E: Tuple[Literal[0]?, builtins.slice, builtins.ellipsis, builtins.list[builtins.int]] -reveal_type(np.ix_(AR_LIKE_b)) # E: tuple[numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(np.ix_(AR_LIKE_i, AR_LIKE_f)) # E: tuple[numpy.ndarray[Any, numpy.dtype[{double}]]] -reveal_type(np.ix_(AR_i8)) # E: tuple[numpy.ndarray[Any, numpy.dtype[{int64}]]] +reveal_type(np.ix_(AR_LIKE_b)) # E: tuple[ndarray[Any, dtype[bool_]]] +reveal_type(np.ix_(AR_LIKE_i, AR_LIKE_f)) # E: tuple[ndarray[Any, dtype[{double}]]] +reveal_type(np.ix_(AR_i8)) # E: tuple[ndarray[Any, dtype[{int64}]]] reveal_type(np.fill_diagonal(AR_i8, 5)) # E: None -reveal_type(np.diag_indices(4)) # E: tuple[numpy.ndarray[Any, numpy.dtype[{int_}]]] -reveal_type(np.diag_indices(2, 3)) # E: tuple[numpy.ndarray[Any, numpy.dtype[{int_}]]] +reveal_type(np.diag_indices(4)) # E: tuple[ndarray[Any, dtype[{int_}]]] +reveal_type(np.diag_indices(2, 3)) # E: tuple[ndarray[Any, dtype[{int_}]]] -reveal_type(np.diag_indices_from(AR_i8)) # E: tuple[numpy.ndarray[Any, numpy.dtype[{int_}]]] +reveal_type(np.diag_indices_from(AR_i8)) # E: tuple[ndarray[Any, dtype[{int_}]]] diff --git a/numpy/typing/tests/data/reveal/lib_function_base.pyi b/numpy/typing/tests/data/reveal/lib_function_base.pyi index bced08894..854b955b4 100644 --- a/numpy/typing/tests/data/reveal/lib_function_base.pyi +++ b/numpy/typing/tests/data/reveal/lib_function_base.pyi @@ -26,9 +26,9 @@ reveal_type(vectorized_func.signature) # E: Union[None, builtins.str] reveal_type(vectorized_func.otypes) # E: Union[None, builtins.str] reveal_type(vectorized_func.excluded) # E: set[Union[builtins.int, builtins.str]] reveal_type(vectorized_func.__doc__) # E: Union[None, builtins.str] -reveal_type(vectorized_func([1])) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.vectorize(int)) # E: numpy.vectorize -reveal_type(np.vectorize( # E: numpy.vectorize +reveal_type(vectorized_func([1])) # E: ndarray[Any, dtype[Any]] +reveal_type(np.vectorize(int)) # E: vectorize +reveal_type(np.vectorize( # E: vectorize int, otypes="i", doc="doc", excluded=(), cache=True, signature=None )) @@ -36,63 +36,63 @@ reveal_type(np.add_newdoc("__main__", "blabla", doc="test doc")) # E: None reveal_type(np.add_newdoc("__main__", "blabla", doc=("meth", "test doc"))) # E: None reveal_type(np.add_newdoc("__main__", "blabla", doc=[("meth", "test doc")])) # E: None -reveal_type(np.rot90(AR_f8, k=2)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.rot90(AR_LIKE_f8, axes=(0, 1))) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.rot90(AR_f8, k=2)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.rot90(AR_LIKE_f8, axes=(0, 1))) # E: ndarray[Any, dtype[Any]] reveal_type(np.flip(f8)) # E: {float64} reveal_type(np.flip(1.0)) # E: Any -reveal_type(np.flip(AR_f8, axis=(0, 1))) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.flip(AR_LIKE_f8, axis=0)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.flip(AR_f8, axis=(0, 1))) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.flip(AR_LIKE_f8, axis=0)) # E: ndarray[Any, dtype[Any]] reveal_type(np.iterable(1)) # E: bool reveal_type(np.iterable([1])) # E: bool -reveal_type(np.average(AR_f8)) # E: numpy.floating[Any] -reveal_type(np.average(AR_f8, weights=AR_c16)) # E: numpy.complexfloating[Any, Any] +reveal_type(np.average(AR_f8)) # E: floating[Any] +reveal_type(np.average(AR_f8, weights=AR_c16)) # E: complexfloating[Any, Any] reveal_type(np.average(AR_O)) # E: Any -reveal_type(np.average(AR_f8, returned=True)) # E: Tuple[numpy.floating[Any], numpy.floating[Any]] -reveal_type(np.average(AR_f8, weights=AR_c16, returned=True)) # E: Tuple[numpy.complexfloating[Any, Any], numpy.complexfloating[Any, Any]] +reveal_type(np.average(AR_f8, returned=True)) # E: Tuple[floating[Any], floating[Any]] +reveal_type(np.average(AR_f8, weights=AR_c16, returned=True)) # E: Tuple[complexfloating[Any, Any], complexfloating[Any, Any]] reveal_type(np.average(AR_O, returned=True)) # E: Tuple[Any, Any] reveal_type(np.average(AR_f8, axis=0)) # E: Any reveal_type(np.average(AR_f8, axis=0, returned=True)) # E: Tuple[Any, Any] -reveal_type(np.asarray_chkfinite(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.asarray_chkfinite(AR_LIKE_f8)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.asarray_chkfinite(AR_f8, dtype=np.float64)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.asarray_chkfinite(AR_f8, dtype=float)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.asarray_chkfinite(AR_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.asarray_chkfinite(AR_LIKE_f8)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.asarray_chkfinite(AR_f8, dtype=np.float64)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.asarray_chkfinite(AR_f8, dtype=float)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.piecewise(AR_f8, AR_b, [func])) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.piecewise(AR_LIKE_f8, AR_b, [func])) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.piecewise(AR_f8, AR_b, [func])) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.piecewise(AR_LIKE_f8, AR_b, [func])) # E: ndarray[Any, dtype[Any]] -reveal_type(np.select([AR_f8], [AR_f8])) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.select([AR_f8], [AR_f8])) # E: ndarray[Any, dtype[Any]] -reveal_type(np.copy(AR_LIKE_f8)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.copy(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.copy(CHAR_AR_U)) # E: numpy.ndarray[Any, Any] -reveal_type(np.copy(CHAR_AR_U, "K", subok=True)) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.copy(CHAR_AR_U, subok=True)) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] +reveal_type(np.copy(AR_LIKE_f8)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.copy(AR_U)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.copy(CHAR_AR_U)) # E: ndarray[Any, Any] +reveal_type(np.copy(CHAR_AR_U, "K", subok=True)) # E: chararray[Any, dtype[str_]] +reveal_type(np.copy(CHAR_AR_U, subok=True)) # E: chararray[Any, dtype[str_]] reveal_type(np.gradient(AR_f8, axis=None)) # E: Any reveal_type(np.gradient(AR_LIKE_f8, edge_order=2)) # E: Any reveal_type(np.diff("bob", n=0)) # E: str -reveal_type(np.diff(AR_f8, axis=0)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.diff(AR_LIKE_f8, prepend=1.5)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.diff(AR_f8, axis=0)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.diff(AR_LIKE_f8, prepend=1.5)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.angle(AR_f8)) # E: numpy.floating[Any] -reveal_type(np.angle(AR_c16, deg=True)) # E: numpy.complexfloating[Any, Any] +reveal_type(np.angle(AR_f8)) # E: floating[Any] +reveal_type(np.angle(AR_c16, deg=True)) # E: complexfloating[Any, Any] reveal_type(np.angle(AR_O)) # E: Any -reveal_type(np.unwrap(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.unwrap(AR_O)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] +reveal_type(np.unwrap(AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.unwrap(AR_O)) # E: ndarray[Any, dtype[object_]] -reveal_type(np.sort_complex(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(np.sort_complex(AR_f8)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] -reveal_type(np.trim_zeros(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.trim_zeros(AR_f8)) # E: ndarray[Any, dtype[{float64}]] reveal_type(np.trim_zeros(AR_LIKE_f8)) # E: list[builtins.float] -reveal_type(np.extract(AR_i8, AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.extract(AR_i8, AR_LIKE_f8)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.extract(AR_i8, AR_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.extract(AR_i8, AR_LIKE_f8)) # E: ndarray[Any, dtype[Any]] reveal_type(np.place(AR_f8, mask=AR_i8, vals=5.0)) # E: None @@ -100,81 +100,81 @@ reveal_type(np.disp(1, linefeed=True)) # E: None with open("test", "w") as f: reveal_type(np.disp("message", device=f)) # E: None -reveal_type(np.cov(AR_f8, bias=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.cov(AR_f8, AR_c16, ddof=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.cov(AR_f8, aweights=AR_f8, dtype=np.float32)) # E: numpy.ndarray[Any, numpy.dtype[{float32}]] -reveal_type(np.cov(AR_f8, fweights=AR_f8, dtype=float)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.cov(AR_f8, bias=True)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.cov(AR_f8, AR_c16, ddof=1)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.cov(AR_f8, aweights=AR_f8, dtype=np.float32)) # E: ndarray[Any, dtype[{float32}]] +reveal_type(np.cov(AR_f8, fweights=AR_f8, dtype=float)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.corrcoef(AR_f8, rowvar=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.corrcoef(AR_f8, AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.corrcoef(AR_f8, dtype=np.float32)) # E: numpy.ndarray[Any, numpy.dtype[{float32}]] -reveal_type(np.corrcoef(AR_f8, dtype=float)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.corrcoef(AR_f8, rowvar=True)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.corrcoef(AR_f8, AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.corrcoef(AR_f8, dtype=np.float32)) # E: ndarray[Any, dtype[{float32}]] +reveal_type(np.corrcoef(AR_f8, dtype=float)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.blackman(5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.bartlett(6)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.hanning(4.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.hamming(0)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.i0(AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.kaiser(4, 5.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(np.blackman(5)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.bartlett(6)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.hanning(4.5)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.hamming(0)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.i0(AR_i8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.kaiser(4, 5.9)) # E: ndarray[Any, dtype[floating[Any]]] -reveal_type(np.sinc(1.0)) # E: numpy.floating[Any] -reveal_type(np.sinc(1j)) # E: numpy.complexfloating[Any, Any] -reveal_type(np.sinc(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.sinc(AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(np.sinc(1.0)) # E: floating[Any] +reveal_type(np.sinc(1j)) # E: complexfloating[Any, Any] +reveal_type(np.sinc(AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.sinc(AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] reveal_type(np.msort(CHAR_AR_U)) # E: Any -reveal_type(np.msort(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.msort(AR_LIKE_f8)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.msort(AR_U)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.msort(AR_LIKE_f8)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.median(AR_f8, keepdims=False)) # E: numpy.floating[Any] -reveal_type(np.median(AR_c16, overwrite_input=True)) # E: numpy.complexfloating[Any, Any] -reveal_type(np.median(AR_m)) # E: numpy.timedelta64 +reveal_type(np.median(AR_f8, keepdims=False)) # E: floating[Any] +reveal_type(np.median(AR_c16, overwrite_input=True)) # E: complexfloating[Any, Any] +reveal_type(np.median(AR_m)) # E: timedelta64 reveal_type(np.median(AR_O)) # E: Any reveal_type(np.median(AR_f8, keepdims=True)) # E: Any reveal_type(np.median(AR_c16, axis=0)) # E: Any -reveal_type(np.median(AR_LIKE_f8, out=AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] +reveal_type(np.median(AR_LIKE_f8, out=AR_c16)) # E: ndarray[Any, dtype[{complex128}]] reveal_type(np.add_newdoc_ufunc(np.add, "docstring")) # E: None -reveal_type(np.percentile(AR_f8, 50)) # E: numpy.floating[Any] -reveal_type(np.percentile(AR_c16, 50)) # E: numpy.complexfloating[Any, Any] -reveal_type(np.percentile(AR_m, 50)) # E: numpy.timedelta64 -reveal_type(np.percentile(AR_M, 50, overwrite_input=True)) # E: numpy.datetime64 +reveal_type(np.percentile(AR_f8, 50)) # E: floating[Any] +reveal_type(np.percentile(AR_c16, 50)) # E: complexfloating[Any, Any] +reveal_type(np.percentile(AR_m, 50)) # E: timedelta64 +reveal_type(np.percentile(AR_M, 50, overwrite_input=True)) # E: datetime64 reveal_type(np.percentile(AR_O, 50)) # E: Any -reveal_type(np.percentile(AR_f8, [50])) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.percentile(AR_c16, [50])) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.percentile(AR_m, [50])) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(np.percentile(AR_M, [50], interpolation="nearest")) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(np.percentile(AR_O, [50])) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] +reveal_type(np.percentile(AR_f8, [50])) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.percentile(AR_c16, [50])) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.percentile(AR_m, [50])) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(np.percentile(AR_M, [50], interpolation="nearest")) # E: ndarray[Any, dtype[datetime64]] +reveal_type(np.percentile(AR_O, [50])) # E: ndarray[Any, dtype[object_]] reveal_type(np.percentile(AR_f8, [50], keepdims=True)) # E: Any reveal_type(np.percentile(AR_f8, [50], axis=[1])) # E: Any -reveal_type(np.percentile(AR_f8, [50], out=AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] +reveal_type(np.percentile(AR_f8, [50], out=AR_c16)) # E: ndarray[Any, dtype[{complex128}]] -reveal_type(np.quantile(AR_f8, 0.5)) # E: numpy.floating[Any] -reveal_type(np.quantile(AR_c16, 0.5)) # E: numpy.complexfloating[Any, Any] -reveal_type(np.quantile(AR_m, 0.5)) # E: numpy.timedelta64 -reveal_type(np.quantile(AR_M, 0.5, overwrite_input=True)) # E: numpy.datetime64 +reveal_type(np.quantile(AR_f8, 0.5)) # E: floating[Any] +reveal_type(np.quantile(AR_c16, 0.5)) # E: complexfloating[Any, Any] +reveal_type(np.quantile(AR_m, 0.5)) # E: timedelta64 +reveal_type(np.quantile(AR_M, 0.5, overwrite_input=True)) # E: datetime64 reveal_type(np.quantile(AR_O, 0.5)) # E: Any -reveal_type(np.quantile(AR_f8, [0.5])) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.quantile(AR_c16, [0.5])) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.quantile(AR_m, [0.5])) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(np.quantile(AR_M, [0.5], interpolation="nearest")) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(np.quantile(AR_O, [0.5])) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] +reveal_type(np.quantile(AR_f8, [0.5])) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.quantile(AR_c16, [0.5])) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.quantile(AR_m, [0.5])) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(np.quantile(AR_M, [0.5], interpolation="nearest")) # E: ndarray[Any, dtype[datetime64]] +reveal_type(np.quantile(AR_O, [0.5])) # E: ndarray[Any, dtype[object_]] reveal_type(np.quantile(AR_f8, [0.5], keepdims=True)) # E: Any reveal_type(np.quantile(AR_f8, [0.5], axis=[1])) # E: Any -reveal_type(np.quantile(AR_f8, [0.5], out=AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] +reveal_type(np.quantile(AR_f8, [0.5], out=AR_c16)) # E: ndarray[Any, dtype[{complex128}]] -reveal_type(np.meshgrid(AR_f8, AR_i8, copy=False)) # E: list[numpy.ndarray[Any, numpy.dtype[Any]]] -reveal_type(np.meshgrid(AR_f8, AR_i8, AR_c16, indexing="ij")) # E: list[numpy.ndarray[Any, numpy.dtype[Any]]] +reveal_type(np.meshgrid(AR_f8, AR_i8, copy=False)) # E: list[ndarray[Any, dtype[Any]]] +reveal_type(np.meshgrid(AR_f8, AR_i8, AR_c16, indexing="ij")) # E: list[ndarray[Any, dtype[Any]]] -reveal_type(np.delete(AR_f8, np.s_[:5])) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.delete(AR_LIKE_f8, [0, 4, 9], axis=0)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.delete(AR_f8, np.s_[:5])) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.delete(AR_LIKE_f8, [0, 4, 9], axis=0)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.insert(AR_f8, np.s_[:5], 5)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.insert(AR_LIKE_f8, [0, 4, 9], [0.5, 9.2, 7], axis=0)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.insert(AR_f8, np.s_[:5], 5)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.insert(AR_LIKE_f8, [0, 4, 9], [0.5, 9.2, 7], axis=0)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.append(AR_f8, 5)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.append(AR_LIKE_f8, 1j, axis=0)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.append(AR_f8, 5)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.append(AR_LIKE_f8, 1j, axis=0)) # E: ndarray[Any, dtype[Any]] reveal_type(np.digitize(4.5, [1])) # E: {intp} -reveal_type(np.digitize(AR_f8, [1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[{intp}]] +reveal_type(np.digitize(AR_f8, [1, 2, 3])) # E: ndarray[Any, dtype[{intp}]] diff --git a/numpy/typing/tests/data/reveal/lib_polynomial.pyi b/numpy/typing/tests/data/reveal/lib_polynomial.pyi index 5a4a3c424..de8950724 100644 --- a/numpy/typing/tests/data/reveal/lib_polynomial.pyi +++ b/numpy/typing/tests/data/reveal/lib_polynomial.pyi @@ -13,99 +13,99 @@ poly_obj: np.poly1d reveal_type(poly_obj.variable) # E: str reveal_type(poly_obj.order) # E: int reveal_type(poly_obj.o) # E: int -reveal_type(poly_obj.roots) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(poly_obj.r) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(poly_obj.coeffs) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(poly_obj.c) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(poly_obj.coef) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(poly_obj.coefficients) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(poly_obj.roots) # E: ndarray[Any, dtype[Any]] +reveal_type(poly_obj.r) # E: ndarray[Any, dtype[Any]] +reveal_type(poly_obj.coeffs) # E: ndarray[Any, dtype[Any]] +reveal_type(poly_obj.c) # E: ndarray[Any, dtype[Any]] +reveal_type(poly_obj.coef) # E: ndarray[Any, dtype[Any]] +reveal_type(poly_obj.coefficients) # E: ndarray[Any, dtype[Any]] reveal_type(poly_obj.__hash__) # E: None reveal_type(poly_obj(1)) # E: Any -reveal_type(poly_obj([1])) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(poly_obj(poly_obj)) # E: numpy.poly1d +reveal_type(poly_obj([1])) # E: ndarray[Any, dtype[Any]] +reveal_type(poly_obj(poly_obj)) # E: poly1d reveal_type(len(poly_obj)) # E: int -reveal_type(-poly_obj) # E: numpy.poly1d -reveal_type(+poly_obj) # E: numpy.poly1d - -reveal_type(poly_obj * 5) # E: numpy.poly1d -reveal_type(5 * poly_obj) # E: numpy.poly1d -reveal_type(poly_obj + 5) # E: numpy.poly1d -reveal_type(5 + poly_obj) # E: numpy.poly1d -reveal_type(poly_obj - 5) # E: numpy.poly1d -reveal_type(5 - poly_obj) # E: numpy.poly1d -reveal_type(poly_obj**1) # E: numpy.poly1d -reveal_type(poly_obj**1.0) # E: numpy.poly1d -reveal_type(poly_obj / 5) # E: numpy.poly1d -reveal_type(5 / poly_obj) # E: numpy.poly1d +reveal_type(-poly_obj) # E: poly1d +reveal_type(+poly_obj) # E: poly1d + +reveal_type(poly_obj * 5) # E: poly1d +reveal_type(5 * poly_obj) # E: poly1d +reveal_type(poly_obj + 5) # E: poly1d +reveal_type(5 + poly_obj) # E: poly1d +reveal_type(poly_obj - 5) # E: poly1d +reveal_type(5 - poly_obj) # E: poly1d +reveal_type(poly_obj**1) # E: poly1d +reveal_type(poly_obj**1.0) # E: poly1d +reveal_type(poly_obj / 5) # E: poly1d +reveal_type(5 / poly_obj) # E: poly1d reveal_type(poly_obj[0]) # E: Any poly_obj[0] = 5 reveal_type(iter(poly_obj)) # E: Iterator[Any] -reveal_type(poly_obj.deriv()) # E: numpy.poly1d -reveal_type(poly_obj.integ()) # E: numpy.poly1d - -reveal_type(np.poly(poly_obj)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.poly(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.poly(AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] - -reveal_type(np.polyint(poly_obj)) # E: numpy.poly1d -reveal_type(np.polyint(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.polyint(AR_f8, k=AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.polyint(AR_O, m=2)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] - -reveal_type(np.polyder(poly_obj)) # E: numpy.poly1d -reveal_type(np.polyder(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.polyder(AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.polyder(AR_O, m=2)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] - -reveal_type(np.polyfit(AR_f8, AR_f8, 2)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.polyfit(AR_f8, AR_i8, 1, full=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]], numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{float64}]]] -reveal_type(np.polyfit(AR_u4, AR_f8, 1.0, cov="unscaled")) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{float64}]]] -reveal_type(np.polyfit(AR_c16, AR_f8, 2)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] -reveal_type(np.polyfit(AR_f8, AR_c16, 1, full=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{complex128}]], numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]], numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{float64}]]] -reveal_type(np.polyfit(AR_u4, AR_c16, 1.0, cov=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{complex128}]], numpy.ndarray[Any, numpy.dtype[{complex128}]]] - -reveal_type(np.polyval(AR_b, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.polyval(AR_u4, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(np.polyval(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.polyval(AR_f8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.polyval(AR_i8, AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.polyval(AR_O, AR_O)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] - -reveal_type(np.polyadd(poly_obj, AR_i8)) # E: numpy.poly1d -reveal_type(np.polyadd(AR_f8, poly_obj)) # E: numpy.poly1d -reveal_type(np.polyadd(AR_b, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.polyadd(AR_u4, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(np.polyadd(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.polyadd(AR_f8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.polyadd(AR_i8, AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.polyadd(AR_O, AR_O)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] - -reveal_type(np.polysub(poly_obj, AR_i8)) # E: numpy.poly1d -reveal_type(np.polysub(AR_f8, poly_obj)) # E: numpy.poly1d +reveal_type(poly_obj.deriv()) # E: poly1d +reveal_type(poly_obj.integ()) # E: poly1d + +reveal_type(np.poly(poly_obj)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.poly(AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.poly(AR_c16)) # E: ndarray[Any, dtype[floating[Any]]] + +reveal_type(np.polyint(poly_obj)) # E: poly1d +reveal_type(np.polyint(AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.polyint(AR_f8, k=AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.polyint(AR_O, m=2)) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.polyder(poly_obj)) # E: poly1d +reveal_type(np.polyder(AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.polyder(AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.polyder(AR_O, m=2)) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.polyfit(AR_f8, AR_f8, 2)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.polyfit(AR_f8, AR_i8, 1, full=True)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[signedinteger[typing._32Bit]]], ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]]] +reveal_type(np.polyfit(AR_u4, AR_f8, 1.0, cov="unscaled")) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]]] +reveal_type(np.polyfit(AR_c16, AR_f8, 2)) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(np.polyfit(AR_f8, AR_c16, 1, full=True)) # E: Tuple[ndarray[Any, dtype[{complex128}]], ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[signedinteger[typing._32Bit]]], ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]]] +reveal_type(np.polyfit(AR_u4, AR_c16, 1.0, cov=True)) # E: Tuple[ndarray[Any, dtype[{complex128}]], ndarray[Any, dtype[{complex128}]]] + +reveal_type(np.polyval(AR_b, AR_b)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.polyval(AR_u4, AR_b)) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(np.polyval(AR_i8, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.polyval(AR_f8, AR_i8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.polyval(AR_i8, AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.polyval(AR_O, AR_O)) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.polyadd(poly_obj, AR_i8)) # E: poly1d +reveal_type(np.polyadd(AR_f8, poly_obj)) # E: poly1d +reveal_type(np.polyadd(AR_b, AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.polyadd(AR_u4, AR_b)) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(np.polyadd(AR_i8, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.polyadd(AR_f8, AR_i8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.polyadd(AR_i8, AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.polyadd(AR_O, AR_O)) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.polysub(poly_obj, AR_i8)) # E: poly1d +reveal_type(np.polysub(AR_f8, poly_obj)) # E: poly1d reveal_type(np.polysub(AR_b, AR_b)) # E: -reveal_type(np.polysub(AR_u4, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(np.polysub(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.polysub(AR_f8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.polysub(AR_i8, AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.polysub(AR_O, AR_O)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] - -reveal_type(np.polymul(poly_obj, AR_i8)) # E: numpy.poly1d -reveal_type(np.polymul(AR_f8, poly_obj)) # E: numpy.poly1d -reveal_type(np.polymul(AR_b, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.polymul(AR_u4, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(np.polymul(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.polymul(AR_f8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.polymul(AR_i8, AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.polymul(AR_O, AR_O)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] - -reveal_type(np.polydiv(poly_obj, AR_i8)) # E: numpy.poly1d -reveal_type(np.polydiv(AR_f8, poly_obj)) # E: numpy.poly1d -reveal_type(np.polydiv(AR_b, AR_b)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.polydiv(AR_u4, AR_b)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.polydiv(AR_i8, AR_i8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.polydiv(AR_f8, AR_i8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.polydiv(AR_i8, AR_c16)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(np.polydiv(AR_O, AR_O)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[Any]]] +reveal_type(np.polysub(AR_u4, AR_b)) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(np.polysub(AR_i8, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.polysub(AR_f8, AR_i8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.polysub(AR_i8, AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.polysub(AR_O, AR_O)) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.polymul(poly_obj, AR_i8)) # E: poly1d +reveal_type(np.polymul(AR_f8, poly_obj)) # E: poly1d +reveal_type(np.polymul(AR_b, AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.polymul(AR_u4, AR_b)) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(np.polymul(AR_i8, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.polymul(AR_f8, AR_i8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.polymul(AR_i8, AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.polymul(AR_O, AR_O)) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.polydiv(poly_obj, AR_i8)) # E: poly1d +reveal_type(np.polydiv(AR_f8, poly_obj)) # E: poly1d +reveal_type(np.polydiv(AR_b, AR_b)) # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]] +reveal_type(np.polydiv(AR_u4, AR_b)) # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]] +reveal_type(np.polydiv(AR_i8, AR_i8)) # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]] +reveal_type(np.polydiv(AR_f8, AR_i8)) # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]] +reveal_type(np.polydiv(AR_i8, AR_c16)) # E: Tuple[ndarray[Any, dtype[complexfloating[Any, Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]] +reveal_type(np.polydiv(AR_O, AR_O)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]] diff --git a/numpy/typing/tests/data/reveal/linalg.pyi b/numpy/typing/tests/data/reveal/linalg.pyi index fecdc0d37..19e13aed6 100644 --- a/numpy/typing/tests/data/reveal/linalg.pyi +++ b/numpy/typing/tests/data/reveal/linalg.pyi @@ -8,57 +8,57 @@ AR_O: npt.NDArray[np.object_] AR_m: npt.NDArray[np.timedelta64] AR_S: npt.NDArray[np.str_] -reveal_type(np.linalg.tensorsolve(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.linalg.tensorsolve(AR_i8, AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.linalg.tensorsolve(AR_c16, AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] - -reveal_type(np.linalg.solve(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.linalg.solve(AR_i8, AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.linalg.solve(AR_c16, AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] - -reveal_type(np.linalg.tensorinv(AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.linalg.tensorinv(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.linalg.tensorinv(AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] - -reveal_type(np.linalg.inv(AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.linalg.inv(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.linalg.inv(AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] - -reveal_type(np.linalg.matrix_power(AR_i8, -1)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.linalg.matrix_power(AR_f8, 0)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.linalg.matrix_power(AR_c16, 1)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.linalg.matrix_power(AR_O, 2)) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.linalg.cholesky(AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.linalg.cholesky(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.linalg.cholesky(AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] - -reveal_type(np.linalg.qr(AR_i8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{float64}]]] -reveal_type(np.linalg.qr(AR_f8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.linalg.qr(AR_c16)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] - -reveal_type(np.linalg.eigvals(AR_i8)) # E: Union[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{complex128}]]] -reveal_type(np.linalg.eigvals(AR_f8)) # E: Union[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(np.linalg.eigvals(AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] - -reveal_type(np.linalg.eigvalsh(AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.linalg.eigvalsh(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.linalg.eigvalsh(AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] - -reveal_type(np.linalg.eig(AR_i8)) # E: Union[Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{float64}]]], Tuple[numpy.ndarray[Any, numpy.dtype[{complex128}]], numpy.ndarray[Any, numpy.dtype[{complex128}]]]] -reveal_type(np.linalg.eig(AR_f8)) # E: Union[Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]], Tuple[numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]]] -reveal_type(np.linalg.eig(AR_c16)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] - -reveal_type(np.linalg.eigh(AR_i8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{float64}]]] -reveal_type(np.linalg.eigh(AR_f8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.linalg.eigh(AR_c16)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] - -reveal_type(np.linalg.svd(AR_i8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{float64}]]] -reveal_type(np.linalg.svd(AR_f8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.linalg.svd(AR_c16)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(np.linalg.svd(AR_i8, compute_uv=False)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.linalg.svd(AR_f8, compute_uv=False)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.linalg.svd(AR_c16, compute_uv=False)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(np.linalg.tensorsolve(AR_i8, AR_i8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.linalg.tensorsolve(AR_i8, AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.linalg.tensorsolve(AR_c16, AR_f8)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] + +reveal_type(np.linalg.solve(AR_i8, AR_i8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.linalg.solve(AR_i8, AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.linalg.solve(AR_c16, AR_f8)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] + +reveal_type(np.linalg.tensorinv(AR_i8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.linalg.tensorinv(AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.linalg.tensorinv(AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] + +reveal_type(np.linalg.inv(AR_i8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.linalg.inv(AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.linalg.inv(AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] + +reveal_type(np.linalg.matrix_power(AR_i8, -1)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.linalg.matrix_power(AR_f8, 0)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.linalg.matrix_power(AR_c16, 1)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.linalg.matrix_power(AR_O, 2)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.linalg.cholesky(AR_i8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.linalg.cholesky(AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.linalg.cholesky(AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] + +reveal_type(np.linalg.qr(AR_i8)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]]] +reveal_type(np.linalg.qr(AR_f8)) # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]] +reveal_type(np.linalg.qr(AR_c16)) # E: Tuple[ndarray[Any, dtype[complexfloating[Any, Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]] + +reveal_type(np.linalg.eigvals(AR_i8)) # E: Union[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{complex128}]]] +reveal_type(np.linalg.eigvals(AR_f8)) # E: Union[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]] +reveal_type(np.linalg.eigvals(AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] + +reveal_type(np.linalg.eigvalsh(AR_i8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.linalg.eigvalsh(AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.linalg.eigvalsh(AR_c16)) # E: ndarray[Any, dtype[floating[Any]]] + +reveal_type(np.linalg.eig(AR_i8)) # E: Union[Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]]], Tuple[ndarray[Any, dtype[{complex128}]], ndarray[Any, dtype[{complex128}]]]] +reveal_type(np.linalg.eig(AR_f8)) # E: Union[Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]], Tuple[ndarray[Any, dtype[complexfloating[Any, Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]]] +reveal_type(np.linalg.eig(AR_c16)) # E: Tuple[ndarray[Any, dtype[complexfloating[Any, Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]] + +reveal_type(np.linalg.eigh(AR_i8)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]]] +reveal_type(np.linalg.eigh(AR_f8)) # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]] +reveal_type(np.linalg.eigh(AR_c16)) # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]] + +reveal_type(np.linalg.svd(AR_i8)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]]] +reveal_type(np.linalg.svd(AR_f8)) # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]] +reveal_type(np.linalg.svd(AR_c16)) # E: Tuple[ndarray[Any, dtype[complexfloating[Any, Any]]], ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]] +reveal_type(np.linalg.svd(AR_i8, compute_uv=False)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.linalg.svd(AR_f8, compute_uv=False)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.linalg.svd(AR_c16, compute_uv=False)) # E: ndarray[Any, dtype[floating[Any]]] reveal_type(np.linalg.cond(AR_i8)) # E: Any reveal_type(np.linalg.cond(AR_f8)) # E: Any @@ -68,9 +68,9 @@ reveal_type(np.linalg.matrix_rank(AR_i8)) # E: Any reveal_type(np.linalg.matrix_rank(AR_f8)) # E: Any reveal_type(np.linalg.matrix_rank(AR_c16)) # E: Any -reveal_type(np.linalg.pinv(AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.linalg.pinv(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.linalg.pinv(AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(np.linalg.pinv(AR_i8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.linalg.pinv(AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.linalg.pinv(AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] reveal_type(np.linalg.slogdet(AR_i8)) # E: Tuple[Any, Any] reveal_type(np.linalg.slogdet(AR_f8)) # E: Tuple[Any, Any] @@ -80,14 +80,14 @@ reveal_type(np.linalg.det(AR_i8)) # E: Any reveal_type(np.linalg.det(AR_f8)) # E: Any reveal_type(np.linalg.det(AR_c16)) # E: Any -reveal_type(np.linalg.lstsq(AR_i8, AR_i8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{float64}]], {int32}, numpy.ndarray[Any, numpy.dtype[{float64}]]] -reveal_type(np.linalg.lstsq(AR_i8, AR_f8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], {int32}, numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.linalg.lstsq(AR_f8, AR_c16)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], {int32}, numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] +reveal_type(np.linalg.lstsq(AR_i8, AR_i8)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]], {int32}, ndarray[Any, dtype[{float64}]]] +reveal_type(np.linalg.lstsq(AR_i8, AR_f8)) # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]], {int32}, ndarray[Any, dtype[floating[Any]]]] +reveal_type(np.linalg.lstsq(AR_f8, AR_c16)) # E: Tuple[ndarray[Any, dtype[complexfloating[Any, Any]]], ndarray[Any, dtype[floating[Any]]], {int32}, ndarray[Any, dtype[floating[Any]]]] -reveal_type(np.linalg.norm(AR_i8)) # E: numpy.floating[Any] -reveal_type(np.linalg.norm(AR_f8)) # E: numpy.floating[Any] -reveal_type(np.linalg.norm(AR_c16)) # E: numpy.floating[Any] -reveal_type(np.linalg.norm(AR_S)) # E: numpy.floating[Any] +reveal_type(np.linalg.norm(AR_i8)) # E: floating[Any] +reveal_type(np.linalg.norm(AR_f8)) # E: floating[Any] +reveal_type(np.linalg.norm(AR_c16)) # E: floating[Any] +reveal_type(np.linalg.norm(AR_S)) # E: floating[Any] reveal_type(np.linalg.norm(AR_f8, axis=0)) # E: Any reveal_type(np.linalg.multi_dot([AR_i8, AR_i8])) # E: Any diff --git a/numpy/typing/tests/data/reveal/matrix.pyi b/numpy/typing/tests/data/reveal/matrix.pyi index def33f458..21c39067e 100644 --- a/numpy/typing/tests/data/reveal/matrix.pyi +++ b/numpy/typing/tests/data/reveal/matrix.pyi @@ -5,11 +5,11 @@ import numpy.typing as npt mat: np.matrix[Any, np.dtype[np.int64]] ar_f8: npt.NDArray[np.float64] -reveal_type(mat * 5) # E: numpy.matrix[Any, Any] -reveal_type(5 * mat) # E: numpy.matrix[Any, Any] +reveal_type(mat * 5) # E: matrix[Any, Any] +reveal_type(5 * mat) # E: matrix[Any, Any] mat *= 5 -reveal_type(mat**5) # E: numpy.matrix[Any, Any] +reveal_type(mat**5) # E: matrix[Any, Any] mat **= 5 reveal_type(mat.sum()) # E: Any @@ -17,53 +17,53 @@ reveal_type(mat.mean()) # E: Any reveal_type(mat.std()) # E: Any reveal_type(mat.var()) # E: Any reveal_type(mat.prod()) # E: Any -reveal_type(mat.any()) # E: numpy.bool_ -reveal_type(mat.all()) # E: numpy.bool_ +reveal_type(mat.any()) # E: bool_ +reveal_type(mat.all()) # E: bool_ reveal_type(mat.max()) # E: {int64} reveal_type(mat.min()) # E: {int64} reveal_type(mat.argmax()) # E: {intp} reveal_type(mat.argmin()) # E: {intp} reveal_type(mat.ptp()) # E: {int64} -reveal_type(mat.sum(axis=0)) # E: numpy.matrix[Any, Any] -reveal_type(mat.mean(axis=0)) # E: numpy.matrix[Any, Any] -reveal_type(mat.std(axis=0)) # E: numpy.matrix[Any, Any] -reveal_type(mat.var(axis=0)) # E: numpy.matrix[Any, Any] -reveal_type(mat.prod(axis=0)) # E: numpy.matrix[Any, Any] -reveal_type(mat.any(axis=0)) # E: numpy.matrix[Any, numpy.dtype[numpy.bool_]] -reveal_type(mat.all(axis=0)) # E: numpy.matrix[Any, numpy.dtype[numpy.bool_]] -reveal_type(mat.max(axis=0)) # E: numpy.matrix[Any, numpy.dtype[{int64}]] -reveal_type(mat.min(axis=0)) # E: numpy.matrix[Any, numpy.dtype[{int64}]] -reveal_type(mat.argmax(axis=0)) # E: numpy.matrix[Any, numpy.dtype[{intp}]] -reveal_type(mat.argmin(axis=0)) # E: numpy.matrix[Any, numpy.dtype[{intp}]] -reveal_type(mat.ptp(axis=0)) # E: numpy.matrix[Any, numpy.dtype[{int64}]] +reveal_type(mat.sum(axis=0)) # E: matrix[Any, Any] +reveal_type(mat.mean(axis=0)) # E: matrix[Any, Any] +reveal_type(mat.std(axis=0)) # E: matrix[Any, Any] +reveal_type(mat.var(axis=0)) # E: matrix[Any, Any] +reveal_type(mat.prod(axis=0)) # E: matrix[Any, Any] +reveal_type(mat.any(axis=0)) # E: matrix[Any, dtype[bool_]] +reveal_type(mat.all(axis=0)) # E: matrix[Any, dtype[bool_]] +reveal_type(mat.max(axis=0)) # E: matrix[Any, dtype[{int64}]] +reveal_type(mat.min(axis=0)) # E: matrix[Any, dtype[{int64}]] +reveal_type(mat.argmax(axis=0)) # E: matrix[Any, dtype[{intp}]] +reveal_type(mat.argmin(axis=0)) # E: matrix[Any, dtype[{intp}]] +reveal_type(mat.ptp(axis=0)) # E: matrix[Any, dtype[{int64}]] -reveal_type(mat.sum(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(mat.mean(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(mat.std(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(mat.var(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(mat.prod(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(mat.any(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(mat.all(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(mat.max(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(mat.min(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(mat.argmax(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(mat.argmin(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(mat.ptp(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(mat.sum(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(mat.mean(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(mat.std(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(mat.var(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(mat.prod(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(mat.any(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(mat.all(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(mat.max(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(mat.min(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(mat.argmax(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(mat.argmin(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(mat.ptp(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] -reveal_type(mat.T) # E: numpy.matrix[Any, numpy.dtype[{int64}]] -reveal_type(mat.I) # E: numpy.matrix[Any, Any] -reveal_type(mat.A) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(mat.A1) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(mat.H) # E: numpy.matrix[Any, numpy.dtype[{int64}]] -reveal_type(mat.getT()) # E: numpy.matrix[Any, numpy.dtype[{int64}]] -reveal_type(mat.getI()) # E: numpy.matrix[Any, Any] -reveal_type(mat.getA()) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(mat.getA1()) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(mat.getH()) # E: numpy.matrix[Any, numpy.dtype[{int64}]] +reveal_type(mat.T) # E: matrix[Any, dtype[{int64}]] +reveal_type(mat.I) # E: matrix[Any, Any] +reveal_type(mat.A) # E: ndarray[Any, dtype[{int64}]] +reveal_type(mat.A1) # E: ndarray[Any, dtype[{int64}]] +reveal_type(mat.H) # E: matrix[Any, dtype[{int64}]] +reveal_type(mat.getT()) # E: matrix[Any, dtype[{int64}]] +reveal_type(mat.getI()) # E: matrix[Any, Any] +reveal_type(mat.getA()) # E: ndarray[Any, dtype[{int64}]] +reveal_type(mat.getA1()) # E: ndarray[Any, dtype[{int64}]] +reveal_type(mat.getH()) # E: matrix[Any, dtype[{int64}]] -reveal_type(np.bmat(ar_f8)) # E: numpy.matrix[Any, Any] -reveal_type(np.bmat([[0, 1, 2]])) # E: numpy.matrix[Any, Any] -reveal_type(np.bmat("mat")) # E: numpy.matrix[Any, Any] +reveal_type(np.bmat(ar_f8)) # E: matrix[Any, Any] +reveal_type(np.bmat([[0, 1, 2]])) # E: matrix[Any, Any] +reveal_type(np.bmat("mat")) # E: matrix[Any, Any] -reveal_type(np.asmatrix(ar_f8, dtype=np.int64)) # E: numpy.matrix[Any, Any] +reveal_type(np.asmatrix(ar_f8, dtype=np.int64)) # E: matrix[Any, Any] diff --git a/numpy/typing/tests/data/reveal/memmap.pyi b/numpy/typing/tests/data/reveal/memmap.pyi index c1d8edc67..86de8eb08 100644 --- a/numpy/typing/tests/data/reveal/memmap.pyi +++ b/numpy/typing/tests/data/reveal/memmap.pyi @@ -10,7 +10,7 @@ reveal_type(memmap_obj.offset) # E: int reveal_type(memmap_obj.mode) # E: str reveal_type(memmap_obj.flush()) # E: None -reveal_type(np.memmap("file.txt", offset=5)) # E: numpy.memmap[Any, numpy.dtype[{uint8}]] -reveal_type(np.memmap(b"file.txt", dtype=np.float64, shape=(10, 3))) # E: numpy.memmap[Any, numpy.dtype[{float64}]] +reveal_type(np.memmap("file.txt", offset=5)) # E: memmap[Any, dtype[{uint8}]] +reveal_type(np.memmap(b"file.txt", dtype=np.float64, shape=(10, 3))) # E: memmap[Any, dtype[{float64}]] with open("file.txt", "rb") as f: - reveal_type(np.memmap(f, dtype=float, order="K")) # E: numpy.memmap[Any, numpy.dtype[Any]] + reveal_type(np.memmap(f, dtype=float, order="K")) # E: memmap[Any, dtype[Any]] diff --git a/numpy/typing/tests/data/reveal/mod.pyi b/numpy/typing/tests/data/reveal/mod.pyi index bf45b8c58..b2790b7f3 100644 --- a/numpy/typing/tests/data/reveal/mod.pyi +++ b/numpy/typing/tests/data/reveal/mod.pyi @@ -21,13 +21,13 @@ AR_m: np.ndarray[Any, np.dtype[np.timedelta64]] # Time structures -reveal_type(td % td) # E: numpy.timedelta64 +reveal_type(td % td) # E: timedelta64 reveal_type(AR_m % td) # E: Any reveal_type(td % AR_m) # E: Any -reveal_type(divmod(td, td)) # E: Tuple[{int64}, numpy.timedelta64] -reveal_type(divmod(AR_m, td)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]], numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(divmod(td, AR_m)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]], numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] +reveal_type(divmod(td, td)) # E: Tuple[{int64}, timedelta64] +reveal_type(divmod(AR_m, td)) # E: Tuple[ndarray[Any, dtype[signedinteger[typing._64Bit]]], ndarray[Any, dtype[timedelta64]]] +reveal_type(divmod(td, AR_m)) # E: Tuple[ndarray[Any, dtype[signedinteger[typing._64Bit]]], ndarray[Any, dtype[timedelta64]]] # Bool @@ -38,7 +38,7 @@ reveal_type(b_ % b_) # E: {int8} reveal_type(b_ % i8) # E: {int64} reveal_type(b_ % u8) # E: {uint64} reveal_type(b_ % f8) # E: {float64} -reveal_type(b_ % AR_b) # E: numpy.ndarray[Any, numpy.dtype[{int8}]] +reveal_type(b_ % AR_b) # E: ndarray[Any, dtype[{int8}]] reveal_type(divmod(b_, b)) # E: Tuple[{int8}, {int8}] reveal_type(divmod(b_, i)) # E: Tuple[{int_}, {int_}] @@ -47,7 +47,7 @@ reveal_type(divmod(b_, b_)) # E: Tuple[{int8}, {int8}] reveal_type(divmod(b_, i8)) # E: Tuple[{int64}, {int64}] reveal_type(divmod(b_, u8)) # E: Tuple[{uint64}, {uint64}] reveal_type(divmod(b_, f8)) # E: Tuple[{float64}, {float64}] -reveal_type(divmod(b_, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[{int8}]], numpy.ndarray[Any, numpy.dtype[{int8}]]] +reveal_type(divmod(b_, AR_b)) # E: ndarray[Any, dtype[{int8}]], ndarray[Any, dtype[{int8}]]] reveal_type(b % b_) # E: {int8} reveal_type(i % b_) # E: {int_} @@ -56,7 +56,7 @@ reveal_type(b_ % b_) # E: {int8} reveal_type(i8 % b_) # E: {int64} reveal_type(u8 % b_) # E: {uint64} reveal_type(f8 % b_) # E: {float64} -reveal_type(AR_b % b_) # E: numpy.ndarray[Any, numpy.dtype[{int8}]] +reveal_type(AR_b % b_) # E: ndarray[Any, dtype[{int8}]] reveal_type(divmod(b, b_)) # E: Tuple[{int8}, {int8}] reveal_type(divmod(i, b_)) # E: Tuple[{int_}, {int_}] @@ -65,7 +65,7 @@ reveal_type(divmod(b_, b_)) # E: Tuple[{int8}, {int8}] reveal_type(divmod(i8, b_)) # E: Tuple[{int64}, {int64}] reveal_type(divmod(u8, b_)) # E: Tuple[{uint64}, {uint64}] reveal_type(divmod(f8, b_)) # E: Tuple[{float64}, {float64}] -reveal_type(divmod(AR_b, b_)) # E: numpy.ndarray[Any, numpy.dtype[{int8}]], numpy.ndarray[Any, numpy.dtype[{int8}]]] +reveal_type(divmod(AR_b, b_)) # E: ndarray[Any, dtype[{int8}]], ndarray[Any, dtype[{int8}]]] # int @@ -78,7 +78,7 @@ reveal_type(i4 % i8) # E: {int64} reveal_type(i4 % f8) # E: {float64} reveal_type(i4 % i4) # E: {int32} reveal_type(i4 % f4) # E: {float32} -reveal_type(i8 % AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(i8 % AR_b) # E: ndarray[Any, dtype[signedinteger[Any]]] reveal_type(divmod(i8, b)) # E: Tuple[{int64}, {int64}] reveal_type(divmod(i8, i)) # E: Tuple[{int64}, {int64}] @@ -89,7 +89,7 @@ reveal_type(divmod(i8, i4)) # E: Tuple[{int64}, {int64}] reveal_type(divmod(i8, f4)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(i4, i4)) # E: Tuple[{int32}, {int32}] reveal_type(divmod(i4, f4)) # E: Tuple[{float32}, {float32}] -reveal_type(divmod(i8, AR_b)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] +reveal_type(divmod(i8, AR_b)) # E: Tuple[ndarray[Any, dtype[signedinteger[Any]]], ndarray[Any, dtype[signedinteger[Any]]]] reveal_type(b % i8) # E: {int64} reveal_type(i % i8) # E: {int64} @@ -100,7 +100,7 @@ reveal_type(i8 % i4) # E: {int64} reveal_type(f8 % i4) # E: {float64} reveal_type(i4 % i4) # E: {int32} reveal_type(f4 % i4) # E: {float32} -reveal_type(AR_b % i8) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_b % i8) # E: ndarray[Any, dtype[signedinteger[Any]]] reveal_type(divmod(b, i8)) # E: Tuple[{int64}, {int64}] reveal_type(divmod(i, i8)) # E: Tuple[{int64}, {int64}] @@ -111,7 +111,7 @@ reveal_type(divmod(i4, i8)) # E: Tuple[{int64}, {int64}] reveal_type(divmod(f4, i8)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(i4, i4)) # E: Tuple[{int32}, {int32}] reveal_type(divmod(f4, i4)) # E: Tuple[{float32}, {float32}] -reveal_type(divmod(AR_b, i8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] +reveal_type(divmod(AR_b, i8)) # E: Tuple[ndarray[Any, dtype[signedinteger[Any]]], ndarray[Any, dtype[signedinteger[Any]]]] # float @@ -120,7 +120,7 @@ reveal_type(f8 % i) # E: {float64} reveal_type(f8 % f) # E: {float64} reveal_type(i8 % f4) # E: {float64} reveal_type(f4 % f4) # E: {float32} -reveal_type(f8 % AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(f8 % AR_b) # E: ndarray[Any, dtype[floating[Any]]] reveal_type(divmod(f8, b)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(f8, i)) # E: Tuple[{float64}, {float64}] @@ -128,7 +128,7 @@ reveal_type(divmod(f8, f)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(f8, f8)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(f8, f4)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(f4, f4)) # E: Tuple[{float32}, {float32}] -reveal_type(divmod(f8, AR_b)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] +reveal_type(divmod(f8, AR_b)) # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]] reveal_type(b % f8) # E: {float64} reveal_type(i % f8) # E: {float64} @@ -136,7 +136,7 @@ reveal_type(f % f8) # E: {float64} reveal_type(f8 % f8) # E: {float64} reveal_type(f8 % f8) # E: {float64} reveal_type(f4 % f4) # E: {float32} -reveal_type(AR_b % f8) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_b % f8) # E: ndarray[Any, dtype[floating[Any]]] reveal_type(divmod(b, f8)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(i, f8)) # E: Tuple[{float64}, {float64}] @@ -144,4 +144,4 @@ reveal_type(divmod(f, f8)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(f8, f8)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(f4, f8)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(f4, f4)) # E: Tuple[{float32}, {float32}] -reveal_type(divmod(AR_b, f8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] +reveal_type(divmod(AR_b, f8)) # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]] diff --git a/numpy/typing/tests/data/reveal/modules.pyi b/numpy/typing/tests/data/reveal/modules.pyi index 7e695433e..ba830eb0d 100644 --- a/numpy/typing/tests/data/reveal/modules.pyi +++ b/numpy/typing/tests/data/reveal/modules.pyi @@ -32,7 +32,7 @@ reveal_type(np.polynomial.polynomial) # E: ModuleType reveal_type(np.__path__) # E: list[builtins.str] reveal_type(np.__version__) # E: str reveal_type(np.__git_version__) # E: str -reveal_type(np.test) # E: numpy._pytesttester.PytestTester +reveal_type(np.test) # E: _pytesttester.PytestTester reveal_type(np.test.module_name) # E: str reveal_type(np.__all__) # E: list[builtins.str] diff --git a/numpy/typing/tests/data/reveal/multiarray.pyi b/numpy/typing/tests/data/reveal/multiarray.pyi index ee818c08a..0e91a7afd 100644 --- a/numpy/typing/tests/data/reveal/multiarray.pyi +++ b/numpy/typing/tests/data/reveal/multiarray.pyi @@ -32,7 +32,7 @@ def func(a: int) -> bool: ... reveal_type(next(b_f8)) # E: tuple[Any] reveal_type(b_f8.reset()) # E: None reveal_type(b_f8.index) # E: int -reveal_type(b_f8.iters) # E: tuple[numpy.flatiter[Any]] +reveal_type(b_f8.iters) # E: tuple[flatiter[Any]] reveal_type(b_f8.nd) # E: int reveal_type(b_f8.ndim) # E: int reveal_type(b_f8.numiter) # E: int @@ -42,7 +42,7 @@ reveal_type(b_f8.size) # E: int reveal_type(next(b_i8_f8_f8)) # E: tuple[Any] reveal_type(b_i8_f8_f8.reset()) # E: None reveal_type(b_i8_f8_f8.index) # E: int -reveal_type(b_i8_f8_f8.iters) # E: tuple[numpy.flatiter[Any]] +reveal_type(b_i8_f8_f8.iters) # E: tuple[flatiter[Any]] reveal_type(b_i8_f8_f8.nd) # E: int reveal_type(b_i8_f8_f8.ndim) # E: int reveal_type(b_i8_f8_f8.numiter) # E: int @@ -51,8 +51,8 @@ reveal_type(b_i8_f8_f8.size) # E: int reveal_type(np.inner(AR_f8, AR_i8)) # E: Any -reveal_type(np.where([True, True, False])) # E: tuple[numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.where([True, True, False], 1, 0)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.where([True, True, False])) # E: tuple[ndarray[Any, dtype[{intp}]]] +reveal_type(np.where([True, True, False], 1, 0)) # E: ndarray[Any, dtype[Any]] reveal_type(np.lexsort([0, 1, 2])) # E: Any @@ -60,32 +60,32 @@ reveal_type(np.can_cast(np.dtype("i8"), int)) # E: bool reveal_type(np.can_cast(AR_f8, "f8")) # E: bool reveal_type(np.can_cast(AR_f8, np.complex128, casting="unsafe")) # E: bool -reveal_type(np.min_scalar_type([1])) # E: numpy.dtype[Any] -reveal_type(np.min_scalar_type(AR_f8)) # E: numpy.dtype[Any] +reveal_type(np.min_scalar_type([1])) # E: dtype[Any] +reveal_type(np.min_scalar_type(AR_f8)) # E: dtype[Any] -reveal_type(np.result_type(int, [1])) # E: numpy.dtype[Any] -reveal_type(np.result_type(AR_f8, AR_u1)) # E: numpy.dtype[Any] -reveal_type(np.result_type(AR_f8, np.complex128)) # E: numpy.dtype[Any] +reveal_type(np.result_type(int, [1])) # E: dtype[Any] +reveal_type(np.result_type(AR_f8, AR_u1)) # E: dtype[Any] +reveal_type(np.result_type(AR_f8, np.complex128)) # E: dtype[Any] reveal_type(np.dot(AR_LIKE_f, AR_i8)) # E: Any reveal_type(np.dot(AR_u1, 1)) # E: Any reveal_type(np.dot(1.5j, 1)) # E: Any -reveal_type(np.dot(AR_u1, 1, out=AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.dot(AR_u1, 1, out=AR_f8)) # E: ndarray[Any, dtype[{float64}]] -reveal_type(np.vdot(AR_LIKE_f, AR_i8)) # E: numpy.floating[Any] -reveal_type(np.vdot(AR_u1, 1)) # E: numpy.signedinteger[Any] -reveal_type(np.vdot(1.5j, 1)) # E: numpy.complexfloating[Any, Any] +reveal_type(np.vdot(AR_LIKE_f, AR_i8)) # E: floating[Any] +reveal_type(np.vdot(AR_u1, 1)) # E: signedinteger[Any] +reveal_type(np.vdot(1.5j, 1)) # E: complexfloating[Any, Any] -reveal_type(np.bincount(AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{intp}]] +reveal_type(np.bincount(AR_i8)) # E: ndarray[Any, dtype[{intp}]] reveal_type(np.copyto(AR_f8, [1., 1.5, 1.6])) # E: None reveal_type(np.putmask(AR_f8, [True, True, False], 1.5)) # E: None -reveal_type(np.packbits(AR_i8)) # numpy.ndarray[Any, numpy.dtype[{uint8}]] -reveal_type(np.packbits(AR_u1)) # numpy.ndarray[Any, numpy.dtype[{uint8}]] +reveal_type(np.packbits(AR_i8)) # ndarray[Any, dtype[{uint8}]] +reveal_type(np.packbits(AR_u1)) # ndarray[Any, dtype[{uint8}]] -reveal_type(np.unpackbits(AR_u1)) # numpy.ndarray[Any, numpy.dtype[{uint8}]] +reveal_type(np.unpackbits(AR_u1)) # ndarray[Any, dtype[{uint8}]] reveal_type(np.shares_memory(1, 2)) # E: bool reveal_type(np.shares_memory(AR_f8, AR_f8, max_work=1)) # E: bool @@ -97,36 +97,36 @@ reveal_type(np.geterrobj()) # E: list[Any] reveal_type(np.seterrobj([8192, 521, None])) # E: None -reveal_type(np.promote_types(np.int32, np.int64)) # E: numpy.dtype[Any] -reveal_type(np.promote_types("f4", float)) # E: numpy.dtype[Any] +reveal_type(np.promote_types(np.int32, np.int64)) # E: dtype[Any] +reveal_type(np.promote_types("f4", float)) # E: dtype[Any] -reveal_type(np.frompyfunc(func, 1, 1, identity=None)) # numpy.ufunc +reveal_type(np.frompyfunc(func, 1, 1, identity=None)) # ufunc reveal_type(np.datetime_data("m8[D]")) # E: Tuple[builtins.str, builtins.int] reveal_type(np.datetime_data(np.datetime64)) # E: Tuple[builtins.str, builtins.int] reveal_type(np.datetime_data(np.dtype(np.timedelta64))) # E: Tuple[builtins.str, builtins.int] reveal_type(np.busday_count("2011-01", "2011-02")) # E: {int_} -reveal_type(np.busday_count(["2011-01"], "2011-02")) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(np.busday_count(["2011-01"], "2011-02")) # E: ndarray[Any, dtype[{int_}]] -reveal_type(np.busday_offset(M, m)) # E: numpy.datetime64 -reveal_type(np.busday_offset(M, 5)) # E: numpy.datetime64 -reveal_type(np.busday_offset(AR_M, m)) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(np.busday_offset("2011-01", "2011-02", roll="forward")) # E: numpy.datetime64 -reveal_type(np.busday_offset(["2011-01"], "2011-02", roll="forward")) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] +reveal_type(np.busday_offset(M, m)) # E: datetime64 +reveal_type(np.busday_offset(M, 5)) # E: datetime64 +reveal_type(np.busday_offset(AR_M, m)) # E: ndarray[Any, dtype[datetime64]] +reveal_type(np.busday_offset("2011-01", "2011-02", roll="forward")) # E: datetime64 +reveal_type(np.busday_offset(["2011-01"], "2011-02", roll="forward")) # E: ndarray[Any, dtype[datetime64]] -reveal_type(np.is_busday("2012")) # E: numpy.bool_ -reveal_type(np.is_busday(["2012"])) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.is_busday("2012")) # E: bool_ +reveal_type(np.is_busday(["2012"])) # E: ndarray[Any, dtype[bool_]] -reveal_type(np.datetime_as_string(M)) # E: numpy.str_ -reveal_type(np.datetime_as_string(AR_M)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] +reveal_type(np.datetime_as_string(M)) # E: str_ +reveal_type(np.datetime_as_string(AR_M)) # E: ndarray[Any, dtype[str_]] -reveal_type(np.compare_chararrays("a", "b", "!=", rstrip=False)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.compare_chararrays(b"a", b"a", "==", True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.compare_chararrays("a", "b", "!=", rstrip=False)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.compare_chararrays(b"a", b"a", "==", True)) # E: ndarray[Any, dtype[bool_]] reveal_type(np.add_docstring(func, "test")) # E: None -reveal_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], flags=["c_index"])) # E: tuple[numpy.nditer] -reveal_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], op_flags=[["readonly", "readonly"]])) # E: tuple[numpy.nditer] -reveal_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], op_dtypes=np.int_)) # E: tuple[numpy.nditer] -reveal_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], order="C", casting="no")) # E: tuple[numpy.nditer] +reveal_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], flags=["c_index"])) # E: tuple[nditer] +reveal_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], op_flags=[["readonly", "readonly"]])) # E: tuple[nditer] +reveal_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], op_dtypes=np.int_)) # E: tuple[nditer] +reveal_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], order="C", casting="no")) # E: tuple[nditer] diff --git a/numpy/typing/tests/data/reveal/ndarray_conversion.pyi b/numpy/typing/tests/data/reveal/ndarray_conversion.pyi index 03f2faf43..6885d4fd6 100644 --- a/numpy/typing/tests/data/reveal/ndarray_conversion.pyi +++ b/numpy/typing/tests/data/reveal/ndarray_conversion.pyi @@ -20,32 +20,32 @@ reveal_type(nd.tolist()) # E: Any # dumps is pretty simple # astype -reveal_type(nd.astype("float")) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(nd.astype(float)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(nd.astype(np.float64)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(nd.astype(np.float64, "K")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(nd.astype(np.float64, "K", "unsafe")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(nd.astype(np.float64, "K", "unsafe", True)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(nd.astype(np.float64, "K", "unsafe", True, True)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(nd.astype("float")) # E: ndarray[Any, dtype[Any]] +reveal_type(nd.astype(float)) # E: ndarray[Any, dtype[Any]] +reveal_type(nd.astype(np.float64)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(nd.astype(np.float64, "K")) # E: ndarray[Any, dtype[{float64}]] +reveal_type(nd.astype(np.float64, "K", "unsafe")) # E: ndarray[Any, dtype[{float64}]] +reveal_type(nd.astype(np.float64, "K", "unsafe", True)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(nd.astype(np.float64, "K", "unsafe", True, True)) # E: ndarray[Any, dtype[{float64}]] # byteswap -reveal_type(nd.byteswap()) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(nd.byteswap(True)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(nd.byteswap()) # E: ndarray[Any, dtype[{int_}]] +reveal_type(nd.byteswap(True)) # E: ndarray[Any, dtype[{int_}]] # copy -reveal_type(nd.copy()) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(nd.copy("C")) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(nd.copy()) # E: ndarray[Any, dtype[{int_}]] +reveal_type(nd.copy("C")) # E: ndarray[Any, dtype[{int_}]] -reveal_type(nd.view()) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(nd.view(np.float64)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(nd.view(float)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(nd.view(np.float64, np.matrix)) # E: numpy.matrix[Any, Any] +reveal_type(nd.view()) # E: ndarray[Any, dtype[{int_}]] +reveal_type(nd.view(np.float64)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(nd.view(float)) # E: ndarray[Any, dtype[Any]] +reveal_type(nd.view(np.float64, np.matrix)) # E: matrix[Any, Any] # getfield -reveal_type(nd.getfield("float")) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(nd.getfield(float)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(nd.getfield(np.float64)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(nd.getfield(np.float64, 8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(nd.getfield("float")) # E: ndarray[Any, dtype[Any]] +reveal_type(nd.getfield(float)) # E: ndarray[Any, dtype[Any]] +reveal_type(nd.getfield(np.float64)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(nd.getfield(np.float64, 8)) # E: ndarray[Any, dtype[{float64}]] # setflags does not return a value # fill does not return a value diff --git a/numpy/typing/tests/data/reveal/ndarray_misc.pyi b/numpy/typing/tests/data/reveal/ndarray_misc.pyi index 2d900c53d..cd1c3136f 100644 --- a/numpy/typing/tests/data/reveal/ndarray_misc.pyi +++ b/numpy/typing/tests/data/reveal/ndarray_misc.pyi @@ -33,14 +33,14 @@ reveal_type(ctypes_obj.data_as(ct.c_void_p)) # E: ctypes.c_void_p reveal_type(ctypes_obj.shape_as(ct.c_longlong)) # E: ctypes.Array[ctypes.c_longlong] reveal_type(ctypes_obj.strides_as(ct.c_ubyte)) # E: ctypes.Array[ctypes.c_ubyte] -reveal_type(f8.all()) # E: numpy.bool_ -reveal_type(AR_f8.all()) # E: numpy.bool_ +reveal_type(f8.all()) # E: bool_ +reveal_type(AR_f8.all()) # E: bool_ reveal_type(AR_f8.all(axis=0)) # E: Any reveal_type(AR_f8.all(keepdims=True)) # E: Any reveal_type(AR_f8.all(out=B)) # E: SubClass -reveal_type(f8.any()) # E: numpy.bool_ -reveal_type(AR_f8.any()) # E: numpy.bool_ +reveal_type(f8.any()) # E: bool_ +reveal_type(AR_f8.any()) # E: bool_ reveal_type(AR_f8.any(axis=0)) # E: Any reveal_type(AR_f8.any(keepdims=True)) # E: Any reveal_type(AR_f8.any(out=B)) # E: SubClass @@ -55,11 +55,11 @@ reveal_type(AR_f8.argmin()) # E: {intp} reveal_type(AR_f8.argmin(axis=0)) # E: Any reveal_type(AR_f8.argmin(out=B)) # E: SubClass -reveal_type(f8.argsort()) # E: numpy.ndarray[Any, Any] -reveal_type(AR_f8.argsort()) # E: numpy.ndarray[Any, Any] +reveal_type(f8.argsort()) # E: ndarray[Any, Any] +reveal_type(AR_f8.argsort()) # E: ndarray[Any, Any] -reveal_type(f8.astype(np.int64).choose([()])) # E: numpy.ndarray[Any, Any] -reveal_type(AR_f8.choose([0])) # E: numpy.ndarray[Any, Any] +reveal_type(f8.astype(np.int64).choose([()])) # E: ndarray[Any, Any] +reveal_type(AR_f8.choose([0])) # E: ndarray[Any, Any] reveal_type(AR_f8.choose([0], out=B)) # E: SubClass reveal_type(f8.clip(1)) # E: Any @@ -68,24 +68,24 @@ reveal_type(AR_f8.clip(None, 1)) # E: Any reveal_type(AR_f8.clip(1, out=B)) # E: SubClass reveal_type(AR_f8.clip(None, 1, out=B)) # E: SubClass -reveal_type(f8.compress([0])) # E: numpy.ndarray[Any, Any] -reveal_type(AR_f8.compress([0])) # E: numpy.ndarray[Any, Any] +reveal_type(f8.compress([0])) # E: ndarray[Any, Any] +reveal_type(AR_f8.compress([0])) # E: ndarray[Any, Any] reveal_type(AR_f8.compress([0], out=B)) # E: SubClass reveal_type(f8.conj()) # E: {float64} -reveal_type(AR_f8.conj()) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(AR_f8.conj()) # E: ndarray[Any, dtype[{float64}]] reveal_type(B.conj()) # E: SubClass reveal_type(f8.conjugate()) # E: {float64} -reveal_type(AR_f8.conjugate()) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(AR_f8.conjugate()) # E: ndarray[Any, dtype[{float64}]] reveal_type(B.conjugate()) # E: SubClass -reveal_type(f8.cumprod()) # E: numpy.ndarray[Any, Any] -reveal_type(AR_f8.cumprod()) # E: numpy.ndarray[Any, Any] +reveal_type(f8.cumprod()) # E: ndarray[Any, Any] +reveal_type(AR_f8.cumprod()) # E: ndarray[Any, Any] reveal_type(AR_f8.cumprod(out=B)) # E: SubClass -reveal_type(f8.cumsum()) # E: numpy.ndarray[Any, Any] -reveal_type(AR_f8.cumsum()) # E: numpy.ndarray[Any, Any] +reveal_type(f8.cumsum()) # E: ndarray[Any, Any] +reveal_type(AR_f8.cumsum()) # E: ndarray[Any, Any] reveal_type(AR_f8.cumsum(out=B)) # E: SubClass reveal_type(f8.max()) # E: Any @@ -107,7 +107,7 @@ reveal_type(AR_f8.min(keepdims=True)) # E: Any reveal_type(AR_f8.min(out=B)) # E: SubClass reveal_type(f8.newbyteorder()) # E: {float64} -reveal_type(AR_f8.newbyteorder()) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(AR_f8.newbyteorder()) # E: ndarray[Any, dtype[{float64}]] reveal_type(B.newbyteorder('|')) # E: SubClass reveal_type(f8.prod()) # E: Any @@ -123,12 +123,12 @@ reveal_type(AR_f8.ptp(keepdims=True)) # E: Any reveal_type(AR_f8.ptp(out=B)) # E: SubClass reveal_type(f8.round()) # E: {float64} -reveal_type(AR_f8.round()) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(AR_f8.round()) # E: ndarray[Any, dtype[{float64}]] reveal_type(AR_f8.round(out=B)) # E: SubClass -reveal_type(f8.repeat(1)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(AR_f8.repeat(1)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(B.repeat(1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] +reveal_type(f8.repeat(1)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(AR_f8.repeat(1)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(B.repeat(1)) # E: ndarray[Any, dtype[object_]] reveal_type(f8.std()) # E: Any reveal_type(AR_f8.std()) # E: Any @@ -144,7 +144,7 @@ reveal_type(AR_f8.sum(out=B)) # E: SubClass reveal_type(f8.take(0)) # E: {float64} reveal_type(AR_f8.take(0)) # E: {float64} -reveal_type(AR_f8.take([0])) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(AR_f8.take([0])) # E: ndarray[Any, dtype[{float64}]] reveal_type(AR_f8.take(0, out=B)) # E: SubClass reveal_type(AR_f8.take([0], out=B)) # E: SubClass @@ -154,18 +154,18 @@ reveal_type(AR_f8.var(axis=0)) # E: Any reveal_type(AR_f8.var(keepdims=True)) # E: Any reveal_type(AR_f8.var(out=B)) # E: SubClass -reveal_type(AR_f8.argpartition([0])) # E: numpy.ndarray[Any, numpy.dtype[{intp}]] +reveal_type(AR_f8.argpartition([0])) # E: ndarray[Any, dtype[{intp}]] -reveal_type(AR_f8.diagonal()) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(AR_f8.diagonal()) # E: ndarray[Any, dtype[{float64}]] -reveal_type(AR_f8.dot(1)) # E: numpy.ndarray[Any, Any] +reveal_type(AR_f8.dot(1)) # E: ndarray[Any, Any] reveal_type(AR_f8.dot([1])) # E: Any reveal_type(AR_f8.dot(1, out=B)) # E: SubClass -reveal_type(AR_f8.nonzero()) # E: tuple[numpy.ndarray[Any, numpy.dtype[{intp}]]] +reveal_type(AR_f8.nonzero()) # E: tuple[ndarray[Any, dtype[{intp}]]] reveal_type(AR_f8.searchsorted(1)) # E: {intp} -reveal_type(AR_f8.searchsorted([1])) # E: numpy.ndarray[Any, numpy.dtype[{intp}]] +reveal_type(AR_f8.searchsorted([1])) # E: ndarray[Any, dtype[{intp}]] reveal_type(AR_f8.trace()) # E: Any reveal_type(AR_f8.trace(out=B)) # E: SubClass @@ -173,14 +173,14 @@ reveal_type(AR_f8.trace(out=B)) # E: SubClass reveal_type(AR_f8.item()) # E: float reveal_type(AR_U.item()) # E: str -reveal_type(AR_f8.ravel()) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(AR_U.ravel()) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] +reveal_type(AR_f8.ravel()) # E: ndarray[Any, dtype[{float64}]] +reveal_type(AR_U.ravel()) # E: ndarray[Any, dtype[str_]] -reveal_type(AR_f8.flatten()) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(AR_U.flatten()) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] +reveal_type(AR_f8.flatten()) # E: ndarray[Any, dtype[{float64}]] +reveal_type(AR_U.flatten()) # E: ndarray[Any, dtype[str_]] -reveal_type(AR_f8.reshape(1)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(AR_U.reshape(1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] +reveal_type(AR_f8.reshape(1)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(AR_U.reshape(1)) # E: ndarray[Any, dtype[str_]] reveal_type(int(AR_f8)) # E: int reveal_type(int(AR_U)) # E: int @@ -192,18 +192,18 @@ reveal_type(complex(AR_f8)) # E: complex reveal_type(operator.index(AR_i8)) # E: int -reveal_type(AR_f8.__array_prepare__(B)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] -reveal_type(AR_f8.__array_wrap__(B)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] +reveal_type(AR_f8.__array_prepare__(B)) # E: ndarray[Any, dtype[object_]] +reveal_type(AR_f8.__array_wrap__(B)) # E: ndarray[Any, dtype[object_]] reveal_type(AR_V[0]) # E: Any reveal_type(AR_V[0, 0]) # E: Any reveal_type(AR_V[AR_i8]) # E: Any reveal_type(AR_V[AR_i8, AR_i8]) # E: Any -reveal_type(AR_V[AR_i8, None]) # E: numpy.ndarray[Any, numpy.dtype[numpy.void]] -reveal_type(AR_V[0, ...]) # E: numpy.ndarray[Any, numpy.dtype[numpy.void]] -reveal_type(AR_V[:]) # E: numpy.ndarray[Any, numpy.dtype[numpy.void]] -reveal_type(AR_V["a"]) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(AR_V[["a", "b"]]) # E: numpy.ndarray[Any, numpy.dtype[numpy.void]] +reveal_type(AR_V[AR_i8, None]) # E: ndarray[Any, dtype[void]] +reveal_type(AR_V[0, ...]) # E: ndarray[Any, dtype[void]] +reveal_type(AR_V[:]) # E: ndarray[Any, dtype[void]] +reveal_type(AR_V["a"]) # E: ndarray[Any, dtype[Any]] +reveal_type(AR_V[["a", "b"]]) # E: ndarray[Any, dtype[void]] reveal_type(AR_f8.dump("test_file")) # E: None reveal_type(AR_f8.dump(b"test_file")) # E: None diff --git a/numpy/typing/tests/data/reveal/ndarray_shape_manipulation.pyi b/numpy/typing/tests/data/reveal/ndarray_shape_manipulation.pyi index a44e1cfa1..c000bf45c 100644 --- a/numpy/typing/tests/data/reveal/ndarray_shape_manipulation.pyi +++ b/numpy/typing/tests/data/reveal/ndarray_shape_manipulation.pyi @@ -3,33 +3,33 @@ import numpy as np nd = np.array([[1, 2], [3, 4]]) # reshape -reveal_type(nd.reshape()) # E: numpy.ndarray -reveal_type(nd.reshape(4)) # E: numpy.ndarray -reveal_type(nd.reshape(2, 2)) # E: numpy.ndarray -reveal_type(nd.reshape((2, 2))) # E: numpy.ndarray +reveal_type(nd.reshape()) # E: ndarray +reveal_type(nd.reshape(4)) # E: ndarray +reveal_type(nd.reshape(2, 2)) # E: ndarray +reveal_type(nd.reshape((2, 2))) # E: ndarray -reveal_type(nd.reshape((2, 2), order="C")) # E: numpy.ndarray -reveal_type(nd.reshape(4, order="C")) # E: numpy.ndarray +reveal_type(nd.reshape((2, 2), order="C")) # E: ndarray +reveal_type(nd.reshape(4, order="C")) # E: ndarray # resize does not return a value # transpose -reveal_type(nd.transpose()) # E: numpy.ndarray -reveal_type(nd.transpose(1, 0)) # E: numpy.ndarray -reveal_type(nd.transpose((1, 0))) # E: numpy.ndarray +reveal_type(nd.transpose()) # E: ndarray +reveal_type(nd.transpose(1, 0)) # E: ndarray +reveal_type(nd.transpose((1, 0))) # E: ndarray # swapaxes -reveal_type(nd.swapaxes(0, 1)) # E: numpy.ndarray +reveal_type(nd.swapaxes(0, 1)) # E: ndarray # flatten -reveal_type(nd.flatten()) # E: numpy.ndarray -reveal_type(nd.flatten("C")) # E: numpy.ndarray +reveal_type(nd.flatten()) # E: ndarray +reveal_type(nd.flatten("C")) # E: ndarray # ravel -reveal_type(nd.ravel()) # E: numpy.ndarray -reveal_type(nd.ravel("C")) # E: numpy.ndarray +reveal_type(nd.ravel()) # E: ndarray +reveal_type(nd.ravel("C")) # E: ndarray # squeeze -reveal_type(nd.squeeze()) # E: numpy.ndarray -reveal_type(nd.squeeze(0)) # E: numpy.ndarray -reveal_type(nd.squeeze((0, 2))) # E: numpy.ndarray +reveal_type(nd.squeeze()) # E: ndarray +reveal_type(nd.squeeze(0)) # E: ndarray +reveal_type(nd.squeeze((0, 2))) # E: ndarray diff --git a/numpy/typing/tests/data/reveal/nditer.pyi b/numpy/typing/tests/data/reveal/nditer.pyi index 473e922a2..65861da54 100644 --- a/numpy/typing/tests/data/reveal/nditer.pyi +++ b/numpy/typing/tests/data/reveal/nditer.pyi @@ -2,12 +2,12 @@ import numpy as np nditer_obj: np.nditer -reveal_type(np.nditer([0, 1], flags=["c_index"])) # E: numpy.nditer -reveal_type(np.nditer([0, 1], op_flags=[["readonly", "readonly"]])) # E: numpy.nditer -reveal_type(np.nditer([0, 1], op_dtypes=np.int_)) # E: numpy.nditer -reveal_type(np.nditer([0, 1], order="C", casting="no")) # E: numpy.nditer +reveal_type(np.nditer([0, 1], flags=["c_index"])) # E: nditer +reveal_type(np.nditer([0, 1], op_flags=[["readonly", "readonly"]])) # E: nditer +reveal_type(np.nditer([0, 1], op_dtypes=np.int_)) # E: nditer +reveal_type(np.nditer([0, 1], order="C", casting="no")) # E: nditer -reveal_type(nditer_obj.dtypes) # E: tuple[numpy.dtype[Any]] +reveal_type(nditer_obj.dtypes) # E: tuple[dtype[Any]] reveal_type(nditer_obj.finished) # E: bool reveal_type(nditer_obj.has_delayed_bufalloc) # E: bool reveal_type(nditer_obj.has_index) # E: bool @@ -17,16 +17,16 @@ reveal_type(nditer_obj.iterationneedsapi) # E: bool reveal_type(nditer_obj.iterindex) # E: int reveal_type(nditer_obj.iterrange) # E: tuple[builtins.int] reveal_type(nditer_obj.itersize) # E: int -reveal_type(nditer_obj.itviews) # E: tuple[numpy.ndarray[Any, numpy.dtype[Any]]] +reveal_type(nditer_obj.itviews) # E: tuple[ndarray[Any, dtype[Any]]] reveal_type(nditer_obj.multi_index) # E: tuple[builtins.int] reveal_type(nditer_obj.ndim) # E: int reveal_type(nditer_obj.nop) # E: int -reveal_type(nditer_obj.operands) # E: tuple[numpy.ndarray[Any, numpy.dtype[Any]]] +reveal_type(nditer_obj.operands) # E: tuple[ndarray[Any, dtype[Any]]] reveal_type(nditer_obj.shape) # E: tuple[builtins.int] -reveal_type(nditer_obj.value) # E: tuple[numpy.ndarray[Any, numpy.dtype[Any]]] +reveal_type(nditer_obj.value) # E: tuple[ndarray[Any, dtype[Any]]] reveal_type(nditer_obj.close()) # E: None -reveal_type(nditer_obj.copy()) # E: numpy.nditer +reveal_type(nditer_obj.copy()) # E: nditer reveal_type(nditer_obj.debug_print()) # E: None reveal_type(nditer_obj.enable_external_loop()) # E: None reveal_type(nditer_obj.iternext()) # E: bool @@ -35,12 +35,12 @@ reveal_type(nditer_obj.remove_multi_index()) # E: None reveal_type(nditer_obj.reset()) # E: None reveal_type(len(nditer_obj)) # E: int -reveal_type(iter(nditer_obj)) # E: Iterator[builtins.tuple[numpy.ndarray[Any, numpy.dtype[Any]]]] -reveal_type(next(nditer_obj)) # E: tuple[numpy.ndarray[Any, numpy.dtype[Any]]] -reveal_type(nditer_obj.__copy__()) # E: numpy.nditer +reveal_type(iter(nditer_obj)) # E: Iterator[builtins.tuple[ndarray[Any, dtype[Any]]]] +reveal_type(next(nditer_obj)) # E: tuple[ndarray[Any, dtype[Any]]] +reveal_type(nditer_obj.__copy__()) # E: nditer with nditer_obj as f: - reveal_type(f) # E: numpy.nditer -reveal_type(nditer_obj[0]) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(nditer_obj[:]) # E: tuple[numpy.ndarray[Any, numpy.dtype[Any]]] + reveal_type(f) # E: nditer +reveal_type(nditer_obj[0]) # E: ndarray[Any, dtype[Any]] +reveal_type(nditer_obj[:]) # E: tuple[ndarray[Any, dtype[Any]]] nditer_obj[0] = 0 nditer_obj[:] = [0, 1] diff --git a/numpy/typing/tests/data/reveal/npyio.pyi b/numpy/typing/tests/data/reveal/npyio.pyi index bee97a8e1..f54fbf610 100644 --- a/numpy/typing/tests/data/reveal/npyio.pyi +++ b/numpy/typing/tests/data/reveal/npyio.pyi @@ -34,11 +34,11 @@ reveal_type(npz_file.fid) # E: Union[None, typing.IO[builtins.str]] reveal_type(npz_file.files) # E: list[builtins.str] reveal_type(npz_file.allow_pickle) # E: bool reveal_type(npz_file.pickle_kwargs) # E: Union[None, typing.Mapping[builtins.str, Any]] -reveal_type(npz_file.f) # E: numpy.lib.npyio.BagObj[numpy.lib.npyio.NpzFile] -reveal_type(npz_file["test"]) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(npz_file.f) # E: lib.npyio.BagObj[lib.npyio.NpzFile] +reveal_type(npz_file["test"]) # E: ndarray[Any, dtype[Any]] reveal_type(len(npz_file)) # E: int with npz_file as f: - reveal_type(f) # E: numpy.lib.npyio.NpzFile + reveal_type(f) # E: lib.npyio.NpzFile reveal_type(np.load(bytes_file)) # E: Any reveal_type(np.load(pathlib_path, allow_pickle=True)) # E: Any @@ -60,32 +60,32 @@ reveal_type(np.savez_compressed(pathlib_path, ar1=AR_i8, ar2=AR_i8)) # E: None reveal_type(np.savez_compressed(str_path, AR_LIKE_f8, ar1=AR_i8)) # E: None reveal_type(np.savez_compressed(bytes_writer, AR_LIKE_f8, ar1=AR_i8)) # E: None -reveal_type(np.loadtxt(bytes_file)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.loadtxt(pathlib_path, dtype=np.str_)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.loadtxt(str_path, dtype=str, skiprows=2)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.loadtxt(str_file, comments="test")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.loadtxt(str_path, delimiter="\n")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.loadtxt(str_path, ndmin=2)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.loadtxt(["1", "2", "3"])) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] - -reveal_type(np.fromregex(bytes_file, "test", np.float64)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.fromregex(str_file, b"test", dtype=float)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.fromregex(str_path, re.compile("test"), dtype=np.str_, encoding="utf8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.fromregex(pathlib_path, "test", np.float64)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.fromregex(bytes_reader, "test", np.float64)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] - -reveal_type(np.genfromtxt(bytes_file)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.genfromtxt(pathlib_path, dtype=np.str_)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.genfromtxt(str_path, dtype=str, skiprows=2)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.genfromtxt(str_file, comments="test")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.genfromtxt(str_path, delimiter="\n")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.genfromtxt(str_path, ndmin=2)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.genfromtxt(["1", "2", "3"], ndmin=2)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] - -reveal_type(np.recfromtxt(bytes_file)) # E: numpy.recarray[Any, numpy.dtype[numpy.record]] -reveal_type(np.recfromtxt(pathlib_path, usemask=True)) # E: numpy.ma.mrecords.MaskedRecords[Any, numpy.dtype[numpy.void]] -reveal_type(np.recfromtxt(["1", "2", "3"])) # E: numpy.recarray[Any, numpy.dtype[numpy.record]] - -reveal_type(np.recfromcsv(bytes_file)) # E: numpy.recarray[Any, numpy.dtype[numpy.record]] -reveal_type(np.recfromcsv(pathlib_path, usemask=True)) # E: numpy.ma.mrecords.MaskedRecords[Any, numpy.dtype[numpy.void]] -reveal_type(np.recfromcsv(["1", "2", "3"])) # E: numpy.recarray[Any, numpy.dtype[numpy.record]] +reveal_type(np.loadtxt(bytes_file)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.loadtxt(pathlib_path, dtype=np.str_)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.loadtxt(str_path, dtype=str, skiprows=2)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.loadtxt(str_file, comments="test")) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.loadtxt(str_path, delimiter="\n")) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.loadtxt(str_path, ndmin=2)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.loadtxt(["1", "2", "3"])) # E: ndarray[Any, dtype[{float64}]] + +reveal_type(np.fromregex(bytes_file, "test", np.float64)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.fromregex(str_file, b"test", dtype=float)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.fromregex(str_path, re.compile("test"), dtype=np.str_, encoding="utf8")) # E: ndarray[Any, dtype[str_]] +reveal_type(np.fromregex(pathlib_path, "test", np.float64)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.fromregex(bytes_reader, "test", np.float64)) # E: ndarray[Any, dtype[{float64}]] + +reveal_type(np.genfromtxt(bytes_file)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.genfromtxt(pathlib_path, dtype=np.str_)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.genfromtxt(str_path, dtype=str, skiprows=2)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.genfromtxt(str_file, comments="test")) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.genfromtxt(str_path, delimiter="\n")) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.genfromtxt(str_path, ndmin=2)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.genfromtxt(["1", "2", "3"], ndmin=2)) # E: ndarray[Any, dtype[{float64}]] + +reveal_type(np.recfromtxt(bytes_file)) # E: recarray[Any, dtype[record]] +reveal_type(np.recfromtxt(pathlib_path, usemask=True)) # E: ma.mrecords.MaskedRecords[Any, dtype[void]] +reveal_type(np.recfromtxt(["1", "2", "3"])) # E: recarray[Any, dtype[record]] + +reveal_type(np.recfromcsv(bytes_file)) # E: recarray[Any, dtype[record]] +reveal_type(np.recfromcsv(pathlib_path, usemask=True)) # E: ma.mrecords.MaskedRecords[Any, dtype[void]] +reveal_type(np.recfromcsv(["1", "2", "3"])) # E: recarray[Any, dtype[record]] diff --git a/numpy/typing/tests/data/reveal/numeric.pyi b/numpy/typing/tests/data/reveal/numeric.pyi index 9b3b1419d..bf5653937 100644 --- a/numpy/typing/tests/data/reveal/numeric.pyi +++ b/numpy/typing/tests/data/reveal/numeric.pyi @@ -1,5 +1,5 @@ """ -Tests for :mod:`numpy.core.numeric`. +Tests for :mod:`core.numeric`. Does not include tests which fall under ``array_constructors``. @@ -34,83 +34,83 @@ reveal_type(np.count_nonzero(AR_i8, axis=0)) # E: Any reveal_type(np.isfortran(i8)) # E: bool reveal_type(np.isfortran(AR_i8)) # E: bool -reveal_type(np.argwhere(i8)) # E: numpy.ndarray[Any, numpy.dtype[{intp}]] -reveal_type(np.argwhere(AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{intp}]] - -reveal_type(np.flatnonzero(i8)) # E: numpy.ndarray[Any, numpy.dtype[{intp}]] -reveal_type(np.flatnonzero(AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{intp}]] - -reveal_type(np.correlate(B, AR_i8, mode="valid")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.correlate(AR_i8, AR_i8, mode="same")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.correlate(AR_b, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.correlate(AR_b, AR_u8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(np.correlate(AR_i8, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.correlate(AR_i8, AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.correlate(AR_i8, AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.correlate(AR_i8, AR_m)) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(np.correlate(AR_O, AR_O)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] - -reveal_type(np.convolve(B, AR_i8, mode="valid")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.convolve(AR_i8, AR_i8, mode="same")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.convolve(AR_b, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.convolve(AR_b, AR_u8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(np.convolve(AR_i8, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.convolve(AR_i8, AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.convolve(AR_i8, AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.convolve(AR_i8, AR_m)) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(np.convolve(AR_O, AR_O)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] - -reveal_type(np.outer(i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.outer(B, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.outer(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(np.argwhere(i8)) # E: ndarray[Any, dtype[{intp}]] +reveal_type(np.argwhere(AR_i8)) # E: ndarray[Any, dtype[{intp}]] + +reveal_type(np.flatnonzero(i8)) # E: ndarray[Any, dtype[{intp}]] +reveal_type(np.flatnonzero(AR_i8)) # E: ndarray[Any, dtype[{intp}]] + +reveal_type(np.correlate(B, AR_i8, mode="valid")) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.correlate(AR_i8, AR_i8, mode="same")) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.correlate(AR_b, AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.correlate(AR_b, AR_u8)) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(np.correlate(AR_i8, AR_b)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.correlate(AR_i8, AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.correlate(AR_i8, AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.correlate(AR_i8, AR_m)) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(np.correlate(AR_O, AR_O)) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.convolve(B, AR_i8, mode="valid")) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.convolve(AR_i8, AR_i8, mode="same")) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.convolve(AR_b, AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.convolve(AR_b, AR_u8)) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(np.convolve(AR_i8, AR_b)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.convolve(AR_i8, AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.convolve(AR_i8, AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.convolve(AR_i8, AR_m)) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(np.convolve(AR_O, AR_O)) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.outer(i8, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.outer(B, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.outer(AR_i8, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] reveal_type(np.outer(AR_i8, AR_i8, out=C)) # E: SubClass -reveal_type(np.outer(AR_b, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.outer(AR_b, AR_u8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(np.outer(AR_i8, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.convolve(AR_i8, AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.outer(AR_i8, AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.outer(AR_i8, AR_m)) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(np.outer(AR_O, AR_O)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] - -reveal_type(np.tensordot(B, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.tensordot(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.tensordot(AR_i8, AR_i8, axes=0)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.tensordot(AR_i8, AR_i8, axes=(0, 1))) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.tensordot(AR_b, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.tensordot(AR_b, AR_u8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(np.tensordot(AR_i8, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.tensordot(AR_i8, AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.tensordot(AR_i8, AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.tensordot(AR_i8, AR_m)) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(np.tensordot(AR_O, AR_O)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] +reveal_type(np.outer(AR_b, AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.outer(AR_b, AR_u8)) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(np.outer(AR_i8, AR_b)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.convolve(AR_i8, AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.outer(AR_i8, AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.outer(AR_i8, AR_m)) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(np.outer(AR_O, AR_O)) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.tensordot(B, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.tensordot(AR_i8, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.tensordot(AR_i8, AR_i8, axes=0)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.tensordot(AR_i8, AR_i8, axes=(0, 1))) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.tensordot(AR_b, AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.tensordot(AR_b, AR_u8)) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(np.tensordot(AR_i8, AR_b)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.tensordot(AR_i8, AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.tensordot(AR_i8, AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.tensordot(AR_i8, AR_m)) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(np.tensordot(AR_O, AR_O)) # E: ndarray[Any, dtype[object_]] reveal_type(np.isscalar(i8)) # E: bool reveal_type(np.isscalar(AR_i8)) # E: bool reveal_type(np.isscalar(B)) # E: bool -reveal_type(np.roll(AR_i8, 1)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.roll(AR_i8, (1, 2))) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.roll(B, 1)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.roll(AR_i8, 1)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.roll(AR_i8, (1, 2))) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.roll(B, 1)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.rollaxis(AR_i8, 0, 1)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] +reveal_type(np.rollaxis(AR_i8, 0, 1)) # E: ndarray[Any, dtype[{int64}]] -reveal_type(np.moveaxis(AR_i8, 0, 1)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.moveaxis(AR_i8, (0, 1), (1, 2))) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] +reveal_type(np.moveaxis(AR_i8, 0, 1)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.moveaxis(AR_i8, (0, 1), (1, 2))) # E: ndarray[Any, dtype[{int64}]] -reveal_type(np.cross(B, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.cross(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.cross(AR_b, AR_u8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(np.cross(AR_i8, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.cross(AR_i8, AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.cross(AR_i8, AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.cross(AR_O, AR_O)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] +reveal_type(np.cross(B, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.cross(AR_i8, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.cross(AR_b, AR_u8)) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(np.cross(AR_i8, AR_b)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.cross(AR_i8, AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.cross(AR_i8, AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.cross(AR_O, AR_O)) # E: ndarray[Any, dtype[object_]] -reveal_type(np.indices([0, 1, 2])) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(np.indices([0, 1, 2], sparse=True)) # E: tuple[numpy.ndarray[Any, numpy.dtype[{int_}]]] -reveal_type(np.indices([0, 1, 2], dtype=np.float64)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.indices([0, 1, 2], sparse=True, dtype=np.float64)) # E: tuple[numpy.ndarray[Any, numpy.dtype[{float64}]]] -reveal_type(np.indices([0, 1, 2], dtype=float)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.indices([0, 1, 2], sparse=True, dtype=float)) # E: tuple[numpy.ndarray[Any, numpy.dtype[Any]]] +reveal_type(np.indices([0, 1, 2])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(np.indices([0, 1, 2], sparse=True)) # E: tuple[ndarray[Any, dtype[{int_}]]] +reveal_type(np.indices([0, 1, 2], dtype=np.float64)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.indices([0, 1, 2], sparse=True, dtype=np.float64)) # E: tuple[ndarray[Any, dtype[{float64}]]] +reveal_type(np.indices([0, 1, 2], dtype=float)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.indices([0, 1, 2], sparse=True, dtype=float)) # E: tuple[ndarray[Any, dtype[Any]]] reveal_type(np.binary_repr(1)) # E: str @@ -120,10 +120,10 @@ reveal_type(np.allclose(i8, AR_i8)) # E: bool reveal_type(np.allclose(B, AR_i8)) # E: bool reveal_type(np.allclose(AR_i8, AR_i8)) # E: bool -reveal_type(np.isclose(i8, i8)) # E: numpy.bool_ -reveal_type(np.isclose(i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isclose(B, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isclose(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.isclose(i8, i8)) # E: bool_ +reveal_type(np.isclose(i8, AR_i8)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isclose(B, AR_i8)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isclose(AR_i8, AR_i8)) # E: ndarray[Any, dtype[bool_]] reveal_type(np.array_equal(i8, AR_i8)) # E: bool reveal_type(np.array_equal(B, AR_i8)) # E: bool diff --git a/numpy/typing/tests/data/reveal/numerictypes.pyi b/numpy/typing/tests/data/reveal/numerictypes.pyi index c50a3a3d6..cc2335264 100644 --- a/numpy/typing/tests/data/reveal/numerictypes.pyi +++ b/numpy/typing/tests/data/reveal/numerictypes.pyi @@ -21,7 +21,7 @@ reveal_type(np.issubclass_(1, 1)) # E: Literal[False] reveal_type(np.sctype2char("S8")) # E: str reveal_type(np.sctype2char(list)) # E: str -reveal_type(np.find_common_type([np.int64], [np.int64])) # E: numpy.dtype[Any] +reveal_type(np.find_common_type([np.int64], [np.int64])) # E: dtype[Any] reveal_type(np.cast[int]) # E: _CastFunc reveal_type(np.cast["i8"]) # E: _CastFunc diff --git a/numpy/typing/tests/data/reveal/random.pyi b/numpy/typing/tests/data/reveal/random.pyi index 6fc35aced..4e06aa7d5 100644 --- a/numpy/typing/tests/data/reveal/random.pyi +++ b/numpy/typing/tests/data/reveal/random.pyi @@ -12,23 +12,23 @@ sfc64 = np.random.SFC64() philox = np.random.Philox() seedless_seq = np.random.bit_generator.SeedlessSeedSequence() -reveal_type(def_rng) # E: numpy.random._generator.Generator -reveal_type(mt19937) # E: numpy.random._mt19937.MT19937 -reveal_type(pcg64) # E: numpy.random._pcg64.PCG64 -reveal_type(sfc64) # E: numpy.random._sfc64.SFC64 -reveal_type(philox) # E: numpy.random._philox.Philox -reveal_type(seed_seq) # E: numpy.random.bit_generator.SeedSequence -reveal_type(seedless_seq) # E: numpy.random.bit_generator.SeedlessSeedSequence +reveal_type(def_rng) # E: random._generator.Generator +reveal_type(mt19937) # E: random._mt19937.MT19937 +reveal_type(pcg64) # E: random._pcg64.PCG64 +reveal_type(sfc64) # E: random._sfc64.SFC64 +reveal_type(philox) # E: random._philox.Philox +reveal_type(seed_seq) # E: random.bit_generator.SeedSequence +reveal_type(seedless_seq) # E: random.bit_generator.SeedlessSeedSequence mt19937_jumped = mt19937.jumped() mt19937_jumped3 = mt19937.jumped(3) mt19937_raw = mt19937.random_raw() mt19937_raw_arr = mt19937.random_raw(5) -reveal_type(mt19937_jumped) # E: numpy.random._mt19937.MT19937 -reveal_type(mt19937_jumped3) # E: numpy.random._mt19937.MT19937 +reveal_type(mt19937_jumped) # E: random._mt19937.MT19937 +reveal_type(mt19937_jumped3) # E: random._mt19937.MT19937 reveal_type(mt19937_raw) # E: int -reveal_type(mt19937_raw_arr) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] +reveal_type(mt19937_raw_arr) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] reveal_type(mt19937.lock) # E: threading.Lock pcg64_jumped = pcg64.jumped() @@ -37,11 +37,11 @@ pcg64_adv = pcg64.advance(3) pcg64_raw = pcg64.random_raw() pcg64_raw_arr = pcg64.random_raw(5) -reveal_type(pcg64_jumped) # E: numpy.random._pcg64.PCG64 -reveal_type(pcg64_jumped3) # E: numpy.random._pcg64.PCG64 -reveal_type(pcg64_adv) # E: numpy.random._pcg64.PCG64 +reveal_type(pcg64_jumped) # E: random._pcg64.PCG64 +reveal_type(pcg64_jumped3) # E: random._pcg64.PCG64 +reveal_type(pcg64_adv) # E: random._pcg64.PCG64 reveal_type(pcg64_raw) # E: int -reveal_type(pcg64_raw_arr) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] +reveal_type(pcg64_raw_arr) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] reveal_type(pcg64.lock) # E: threading.Lock philox_jumped = philox.jumped() @@ -50,25 +50,25 @@ philox_adv = philox.advance(3) philox_raw = philox.random_raw() philox_raw_arr = philox.random_raw(5) -reveal_type(philox_jumped) # E: numpy.random._philox.Philox -reveal_type(philox_jumped3) # E: numpy.random._philox.Philox -reveal_type(philox_adv) # E: numpy.random._philox.Philox +reveal_type(philox_jumped) # E: random._philox.Philox +reveal_type(philox_jumped3) # E: random._philox.Philox +reveal_type(philox_adv) # E: random._philox.Philox reveal_type(philox_raw) # E: int -reveal_type(philox_raw_arr) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] +reveal_type(philox_raw_arr) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] reveal_type(philox.lock) # E: threading.Lock sfc64_raw = sfc64.random_raw() sfc64_raw_arr = sfc64.random_raw(5) reveal_type(sfc64_raw) # E: int -reveal_type(sfc64_raw_arr) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] +reveal_type(sfc64_raw_arr) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] reveal_type(sfc64.lock) # E: threading.Lock -reveal_type(seed_seq.pool) # numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] +reveal_type(seed_seq.pool) # ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] reveal_type(seed_seq.entropy) # E:Union[None, int, Sequence[int]] -reveal_type(seed_seq.spawn(1)) # E: list[numpy.random.bit_generator.SeedSequence] -reveal_type(seed_seq.generate_state(8, "uint32")) # E: numpy.ndarray[Any, numpy.dtype[Union[numpy.unsignedinteger[numpy.typing._32Bit], numpy.unsignedinteger[numpy.typing._64Bit]]]] -reveal_type(seed_seq.generate_state(8, "uint64")) # E: numpy.ndarray[Any, numpy.dtype[Union[numpy.unsignedinteger[numpy.typing._32Bit], numpy.unsignedinteger[numpy.typing._64Bit]]]] +reveal_type(seed_seq.spawn(1)) # E: list[random.bit_generator.SeedSequence] +reveal_type(seed_seq.generate_state(8, "uint32")) # E: ndarray[Any, dtype[Union[unsignedinteger[typing._32Bit], unsignedinteger[typing._64Bit]]]] +reveal_type(seed_seq.generate_state(8, "uint64")) # E: ndarray[Any, dtype[Union[unsignedinteger[typing._32Bit], unsignedinteger[typing._64Bit]]]] def_gen: np.random.Generator = np.random.default_rng() @@ -96,17 +96,17 @@ reveal_type(def_gen.standard_normal(dtype="float32")) # E: float reveal_type(def_gen.standard_normal(dtype="double")) # E: float reveal_type(def_gen.standard_normal(dtype=np.float64)) # E: float reveal_type(def_gen.standard_normal(size=None)) # E: float -reveal_type(def_gen.standard_normal(size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_normal(size=1, dtype=np.float32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.standard_normal(size=1, dtype="f4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.standard_normal(size=1, dtype="float32", out=S_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.standard_normal(dtype=np.float32, out=S_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.standard_normal(size=1, dtype=np.float64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_normal(size=1, dtype="float64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_normal(size=1, dtype="f8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_normal(out=D_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_normal(size=1, dtype="float64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_normal(size=1, dtype="float64", out=D_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] +reveal_type(def_gen.standard_normal(size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_normal(size=1, dtype=np.float32)) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.standard_normal(size=1, dtype="f4")) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.standard_normal(size=1, dtype="float32", out=S_out)) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.standard_normal(dtype=np.float32, out=S_out)) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.standard_normal(size=1, dtype=np.float64)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_normal(size=1, dtype="float64")) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_normal(size=1, dtype="f8")) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_normal(out=D_out)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_normal(size=1, dtype="float64")) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_normal(size=1, dtype="float64", out=D_out)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] reveal_type(def_gen.random()) # E: float reveal_type(def_gen.random(dtype=np.float32)) # E: float @@ -114,21 +114,21 @@ reveal_type(def_gen.random(dtype="float32")) # E: float reveal_type(def_gen.random(dtype="double")) # E: float reveal_type(def_gen.random(dtype=np.float64)) # E: float reveal_type(def_gen.random(size=None)) # E: float -reveal_type(def_gen.random(size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.random(size=1, dtype=np.float32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.random(size=1, dtype="f4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.random(size=1, dtype="float32", out=S_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.random(dtype=np.float32, out=S_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.random(size=1, dtype=np.float64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.random(size=1, dtype="float64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.random(size=1, dtype="f8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.random(out=D_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.random(size=1, dtype="float64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.random(size=1, dtype="float64", out=D_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] +reveal_type(def_gen.random(size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.random(size=1, dtype=np.float32)) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.random(size=1, dtype="f4")) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.random(size=1, dtype="float32", out=S_out)) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.random(dtype=np.float32, out=S_out)) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.random(size=1, dtype=np.float64)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.random(size=1, dtype="float64")) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.random(size=1, dtype="f8")) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.random(out=D_out)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.random(size=1, dtype="float64")) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.random(size=1, dtype="float64", out=D_out)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] reveal_type(def_gen.standard_cauchy()) # E: float reveal_type(def_gen.standard_cauchy(size=None)) # E: float -reveal_type(def_gen.standard_cauchy(size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(def_gen.standard_cauchy(size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(def_gen.standard_exponential()) # E: float reveal_type(def_gen.standard_exponential(method="inv")) # E: float @@ -138,367 +138,367 @@ reveal_type(def_gen.standard_exponential(dtype="double")) # E: float reveal_type(def_gen.standard_exponential(dtype=np.float64)) # E: float reveal_type(def_gen.standard_exponential(size=None)) # E: float reveal_type(def_gen.standard_exponential(size=None, method="inv")) # E: float -reveal_type(def_gen.standard_exponential(size=1, method="inv")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_exponential(size=1, dtype=np.float32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.standard_exponential(size=1, dtype="f4", method="inv")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.standard_exponential(size=1, dtype="float32", out=S_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.standard_exponential(dtype=np.float32, out=S_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.standard_exponential(size=1, dtype=np.float64, method="inv")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_exponential(size=1, dtype="float64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_exponential(size=1, dtype="f8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_exponential(out=D_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_exponential(size=1, dtype="float64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_exponential(size=1, dtype="float64", out=D_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] +reveal_type(def_gen.standard_exponential(size=1, method="inv")) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_exponential(size=1, dtype=np.float32)) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.standard_exponential(size=1, dtype="f4", method="inv")) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.standard_exponential(size=1, dtype="float32", out=S_out)) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.standard_exponential(dtype=np.float32, out=S_out)) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.standard_exponential(size=1, dtype=np.float64, method="inv")) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_exponential(size=1, dtype="float64")) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_exponential(size=1, dtype="f8")) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_exponential(out=D_out)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_exponential(size=1, dtype="float64")) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_exponential(size=1, dtype="float64", out=D_out)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] reveal_type(def_gen.zipf(1.5)) # E: int reveal_type(def_gen.zipf(1.5, size=None)) # E: int -reveal_type(def_gen.zipf(1.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.zipf(D_arr_1p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.zipf(D_arr_1p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.zipf(D_arr_like_1p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.zipf(D_arr_like_1p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] +reveal_type(def_gen.zipf(1.5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.zipf(D_arr_1p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.zipf(D_arr_1p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.zipf(D_arr_like_1p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.zipf(D_arr_like_1p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] reveal_type(def_gen.weibull(0.5)) # E: float reveal_type(def_gen.weibull(0.5, size=None)) # E: float -reveal_type(def_gen.weibull(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.weibull(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.weibull(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.weibull(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.weibull(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(def_gen.weibull(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.weibull(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.weibull(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.weibull(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.weibull(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(def_gen.standard_t(0.5)) # E: float reveal_type(def_gen.standard_t(0.5, size=None)) # E: float -reveal_type(def_gen.standard_t(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.standard_t(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.standard_t(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.standard_t(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.standard_t(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(def_gen.standard_t(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.standard_t(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.standard_t(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.standard_t(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.standard_t(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(def_gen.poisson(0.5)) # E: int reveal_type(def_gen.poisson(0.5, size=None)) # E: int -reveal_type(def_gen.poisson(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.poisson(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.poisson(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.poisson(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.poisson(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] +reveal_type(def_gen.poisson(0.5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.poisson(D_arr_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.poisson(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.poisson(D_arr_like_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.poisson(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] reveal_type(def_gen.power(0.5)) # E: float reveal_type(def_gen.power(0.5, size=None)) # E: float -reveal_type(def_gen.power(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.power(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.power(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.power(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.power(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(def_gen.power(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.power(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.power(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.power(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.power(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(def_gen.pareto(0.5)) # E: float reveal_type(def_gen.pareto(0.5, size=None)) # E: float -reveal_type(def_gen.pareto(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.pareto(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.pareto(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.pareto(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.pareto(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(def_gen.pareto(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.pareto(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.pareto(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.pareto(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.pareto(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(def_gen.chisquare(0.5)) # E: float reveal_type(def_gen.chisquare(0.5, size=None)) # E: float -reveal_type(def_gen.chisquare(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.chisquare(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.chisquare(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.chisquare(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.chisquare(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(def_gen.chisquare(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.chisquare(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.chisquare(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.chisquare(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.chisquare(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(def_gen.exponential(0.5)) # E: float reveal_type(def_gen.exponential(0.5, size=None)) # E: float -reveal_type(def_gen.exponential(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.exponential(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.exponential(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.exponential(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.exponential(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(def_gen.exponential(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.exponential(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.exponential(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.exponential(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.exponential(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(def_gen.geometric(0.5)) # E: int reveal_type(def_gen.geometric(0.5, size=None)) # E: int -reveal_type(def_gen.geometric(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.geometric(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.geometric(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.geometric(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.geometric(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] +reveal_type(def_gen.geometric(0.5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.geometric(D_arr_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.geometric(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.geometric(D_arr_like_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.geometric(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] reveal_type(def_gen.logseries(0.5)) # E: int reveal_type(def_gen.logseries(0.5, size=None)) # E: int -reveal_type(def_gen.logseries(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.logseries(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.logseries(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.logseries(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.logseries(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] +reveal_type(def_gen.logseries(0.5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.logseries(D_arr_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.logseries(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.logseries(D_arr_like_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.logseries(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] reveal_type(def_gen.rayleigh(0.5)) # E: float reveal_type(def_gen.rayleigh(0.5, size=None)) # E: float -reveal_type(def_gen.rayleigh(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.rayleigh(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.rayleigh(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.rayleigh(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.rayleigh(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(def_gen.rayleigh(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.rayleigh(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.rayleigh(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.rayleigh(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.rayleigh(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(def_gen.standard_gamma(0.5)) # E: float reveal_type(def_gen.standard_gamma(0.5, size=None)) # E: float reveal_type(def_gen.standard_gamma(0.5, dtype="float32")) # E: float reveal_type(def_gen.standard_gamma(0.5, size=None, dtype="float32")) # E: float -reveal_type(def_gen.standard_gamma(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_gamma(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_gamma(D_arr_0p5, dtype="f4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.standard_gamma(0.5, size=1, dtype="float32", out=S_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.standard_gamma(D_arr_0p5, dtype=np.float32, out=S_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.standard_gamma(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_gamma(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_gamma(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_gamma(0.5, out=D_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_gamma(D_arr_like_0p5, out=D_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_gamma(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_gamma(D_arr_like_0p5, size=1, out=D_out, dtype=np.float64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] +reveal_type(def_gen.standard_gamma(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_gamma(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_gamma(D_arr_0p5, dtype="f4")) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.standard_gamma(0.5, size=1, dtype="float32", out=S_out)) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.standard_gamma(D_arr_0p5, dtype=np.float32, out=S_out)) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.standard_gamma(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_gamma(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_gamma(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_gamma(0.5, out=D_out)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_gamma(D_arr_like_0p5, out=D_out)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_gamma(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_gamma(D_arr_like_0p5, size=1, out=D_out, dtype=np.float64)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] reveal_type(def_gen.vonmises(0.5, 0.5)) # E: float reveal_type(def_gen.vonmises(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.vonmises(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.vonmises(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.vonmises(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.vonmises(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.vonmises(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.vonmises(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.vonmises(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.vonmises(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.vonmises(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.vonmises(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.vonmises(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(def_gen.vonmises(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.vonmises(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.vonmises(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.vonmises(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.vonmises(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.vonmises(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.vonmises(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.vonmises(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.vonmises(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.vonmises(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.vonmises(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(def_gen.wald(0.5, 0.5)) # E: float reveal_type(def_gen.wald(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.wald(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.wald(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.wald(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.wald(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.wald(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.wald(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.wald(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.wald(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.wald(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.wald(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.wald(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(def_gen.wald(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.wald(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.wald(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.wald(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.wald(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.wald(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.wald(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.wald(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.wald(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.wald(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.wald(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(def_gen.uniform(0.5, 0.5)) # E: float reveal_type(def_gen.uniform(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.uniform(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.uniform(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.uniform(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.uniform(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.uniform(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.uniform(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.uniform(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.uniform(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.uniform(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.uniform(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.uniform(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(def_gen.uniform(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.uniform(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.uniform(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.uniform(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.uniform(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.uniform(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.uniform(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.uniform(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.uniform(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.uniform(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.uniform(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(def_gen.beta(0.5, 0.5)) # E: float reveal_type(def_gen.beta(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.beta(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.beta(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.beta(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.beta(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.beta(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.beta(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.beta(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.beta(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.beta(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.beta(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.beta(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(def_gen.beta(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.beta(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.beta(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.beta(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.beta(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.beta(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.beta(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.beta(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.beta(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.beta(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.beta(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(def_gen.f(0.5, 0.5)) # E: float reveal_type(def_gen.f(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.f(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.f(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.f(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.f(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.f(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.f(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.f(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.f(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.f(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.f(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.f(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(def_gen.f(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.f(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.f(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.f(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.f(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.f(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.f(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.f(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.f(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.f(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.f(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(def_gen.gamma(0.5, 0.5)) # E: float reveal_type(def_gen.gamma(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.gamma(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gamma(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gamma(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gamma(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gamma(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gamma(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gamma(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gamma(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gamma(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gamma(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gamma(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(def_gen.gamma(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gamma(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gamma(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gamma(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gamma(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gamma(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gamma(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gamma(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gamma(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gamma(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gamma(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(def_gen.gumbel(0.5, 0.5)) # E: float reveal_type(def_gen.gumbel(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.gumbel(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gumbel(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gumbel(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gumbel(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gumbel(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gumbel(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gumbel(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gumbel(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gumbel(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gumbel(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gumbel(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(def_gen.gumbel(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gumbel(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gumbel(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gumbel(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gumbel(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gumbel(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gumbel(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gumbel(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gumbel(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gumbel(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gumbel(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(def_gen.laplace(0.5, 0.5)) # E: float reveal_type(def_gen.laplace(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.laplace(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.laplace(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.laplace(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.laplace(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.laplace(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.laplace(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.laplace(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.laplace(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.laplace(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.laplace(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.laplace(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(def_gen.laplace(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.laplace(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.laplace(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.laplace(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.laplace(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.laplace(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.laplace(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.laplace(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.laplace(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.laplace(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.laplace(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(def_gen.logistic(0.5, 0.5)) # E: float reveal_type(def_gen.logistic(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.logistic(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.logistic(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.logistic(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.logistic(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.logistic(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.logistic(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.logistic(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.logistic(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.logistic(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.logistic(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.logistic(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(def_gen.logistic(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.logistic(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.logistic(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.logistic(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.logistic(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.logistic(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.logistic(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.logistic(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.logistic(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.logistic(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.logistic(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(def_gen.lognormal(0.5, 0.5)) # E: float reveal_type(def_gen.lognormal(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.lognormal(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.lognormal(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.lognormal(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.lognormal(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.lognormal(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.lognormal(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.lognormal(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.lognormal(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.lognormal(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.lognormal(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.lognormal(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(def_gen.lognormal(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.lognormal(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.lognormal(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.lognormal(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.lognormal(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.lognormal(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.lognormal(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.lognormal(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.lognormal(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.lognormal(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.lognormal(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(def_gen.noncentral_chisquare(0.5, 0.5)) # E: float reveal_type(def_gen.noncentral_chisquare(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.noncentral_chisquare(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_chisquare(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_chisquare(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_chisquare(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_chisquare(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_chisquare(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_chisquare(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_chisquare(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_chisquare(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(def_gen.noncentral_chisquare(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_chisquare(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_chisquare(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_chisquare(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_chisquare(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_chisquare(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_chisquare(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_chisquare(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_chisquare(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(def_gen.normal(0.5, 0.5)) # E: float reveal_type(def_gen.normal(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.normal(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.normal(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.normal(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.normal(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.normal(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.normal(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.normal(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.normal(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.normal(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.normal(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.normal(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(def_gen.normal(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.normal(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.normal(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.normal(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.normal(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.normal(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.normal(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.normal(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.normal(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.normal(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.normal(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(def_gen.triangular(0.1, 0.5, 0.9)) # E: float reveal_type(def_gen.triangular(0.1, 0.5, 0.9, size=None)) # E: float -reveal_type(def_gen.triangular(0.1, 0.5, 0.9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.triangular(D_arr_0p1, 0.5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.triangular(0.1, D_arr_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.triangular(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.triangular(0.1, D_arr_0p5, 0.9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.triangular(D_arr_like_0p1, 0.5, D_arr_0p9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.triangular(0.5, D_arr_like_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.triangular(D_arr_0p1, D_arr_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.triangular(D_arr_like_0p1, D_arr_like_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.triangular(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.triangular(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(def_gen.triangular(0.1, 0.5, 0.9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.triangular(D_arr_0p1, 0.5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.triangular(0.1, D_arr_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.triangular(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.triangular(0.1, D_arr_0p5, 0.9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.triangular(D_arr_like_0p1, 0.5, D_arr_0p9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.triangular(0.5, D_arr_like_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.triangular(D_arr_0p1, D_arr_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.triangular(D_arr_like_0p1, D_arr_like_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.triangular(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.triangular(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(def_gen.noncentral_f(0.1, 0.5, 0.9)) # E: float reveal_type(def_gen.noncentral_f(0.1, 0.5, 0.9, size=None)) # E: float -reveal_type(def_gen.noncentral_f(0.1, 0.5, 0.9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_f(D_arr_0p1, 0.5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_f(0.1, D_arr_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_f(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_f(0.1, D_arr_0p5, 0.9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_f(D_arr_like_0p1, 0.5, D_arr_0p9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_f(0.5, D_arr_like_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_f(D_arr_0p1, D_arr_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_f(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(def_gen.noncentral_f(0.1, 0.5, 0.9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_f(D_arr_0p1, 0.5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_f(0.1, D_arr_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_f(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_f(0.1, D_arr_0p5, 0.9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_f(D_arr_like_0p1, 0.5, D_arr_0p9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_f(0.5, D_arr_like_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_f(D_arr_0p1, D_arr_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_f(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(def_gen.binomial(10, 0.5)) # E: int reveal_type(def_gen.binomial(10, 0.5, size=None)) # E: int -reveal_type(def_gen.binomial(10, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.binomial(I_arr_10, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.binomial(10, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.binomial(I_arr_10, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.binomial(10, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.binomial(I_arr_like_10, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.binomial(10, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.binomial(I_arr_10, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.binomial(I_arr_like_10, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.binomial(I_arr_10, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.binomial(I_arr_like_10, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] +reveal_type(def_gen.binomial(10, 0.5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.binomial(I_arr_10, 0.5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.binomial(10, D_arr_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.binomial(I_arr_10, 0.5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.binomial(10, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.binomial(I_arr_like_10, 0.5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.binomial(10, D_arr_like_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.binomial(I_arr_10, D_arr_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.binomial(I_arr_like_10, D_arr_like_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.binomial(I_arr_10, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.binomial(I_arr_like_10, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] reveal_type(def_gen.negative_binomial(10, 0.5)) # E: int reveal_type(def_gen.negative_binomial(10, 0.5, size=None)) # E: int -reveal_type(def_gen.negative_binomial(10, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.negative_binomial(I_arr_10, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.negative_binomial(10, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.negative_binomial(I_arr_10, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.negative_binomial(10, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.negative_binomial(I_arr_like_10, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.negative_binomial(10, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.negative_binomial(I_arr_10, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.negative_binomial(I_arr_like_10, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.negative_binomial(I_arr_10, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.negative_binomial(I_arr_like_10, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] +reveal_type(def_gen.negative_binomial(10, 0.5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.negative_binomial(I_arr_10, 0.5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.negative_binomial(10, D_arr_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.negative_binomial(I_arr_10, 0.5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.negative_binomial(10, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.negative_binomial(I_arr_like_10, 0.5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.negative_binomial(10, D_arr_like_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.negative_binomial(I_arr_10, D_arr_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.negative_binomial(I_arr_like_10, D_arr_like_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.negative_binomial(I_arr_10, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.negative_binomial(I_arr_like_10, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] reveal_type(def_gen.hypergeometric(20, 20, 10)) # E: int reveal_type(def_gen.hypergeometric(20, 20, 10, size=None)) # E: int -reveal_type(def_gen.hypergeometric(20, 20, 10, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.hypergeometric(I_arr_20, 20, 10)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.hypergeometric(20, I_arr_20, 10)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.hypergeometric(I_arr_20, 20, I_arr_like_10, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.hypergeometric(20, I_arr_20, 10, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.hypergeometric(I_arr_like_20, 20, I_arr_10)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.hypergeometric(20, I_arr_like_20, 10)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.hypergeometric(I_arr_20, I_arr_20, 10)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.hypergeometric(I_arr_like_20, I_arr_like_20, 10)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.hypergeometric(I_arr_20, I_arr_20, I_arr_10, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.hypergeometric(I_arr_like_20, I_arr_like_20, I_arr_like_10, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] +reveal_type(def_gen.hypergeometric(20, 20, 10, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.hypergeometric(I_arr_20, 20, 10)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.hypergeometric(20, I_arr_20, 10)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.hypergeometric(I_arr_20, 20, I_arr_like_10, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.hypergeometric(20, I_arr_20, 10, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.hypergeometric(I_arr_like_20, 20, I_arr_10)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.hypergeometric(20, I_arr_like_20, 10)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.hypergeometric(I_arr_20, I_arr_20, 10)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.hypergeometric(I_arr_like_20, I_arr_like_20, 10)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.hypergeometric(I_arr_20, I_arr_20, I_arr_10, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.hypergeometric(I_arr_like_20, I_arr_like_20, I_arr_like_10, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] I_int64_100: np.ndarray[Any, np.dtype[np.int64]] = np.array([100], dtype=np.int64) reveal_type(def_gen.integers(0, 100)) # E: int reveal_type(def_gen.integers(100)) # E: int -reveal_type(def_gen.integers([100])) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(0, [100])) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] +reveal_type(def_gen.integers([100])) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(0, [100])) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] I_bool_low: np.ndarray[Any, np.dtype[np.bool_]] = np.array([0], dtype=np.bool_) I_bool_low_like: List[int] = [0] @@ -509,25 +509,25 @@ reveal_type(def_gen.integers(2, dtype=bool)) # E: builtins.bool reveal_type(def_gen.integers(0, 2, dtype=bool)) # E: builtins.bool reveal_type(def_gen.integers(1, dtype=bool, endpoint=True)) # E: builtins.bool reveal_type(def_gen.integers(0, 1, dtype=bool, endpoint=True)) # E: builtins.bool -reveal_type(def_gen.integers(I_bool_low_like, 1, dtype=bool, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(I_bool_high_open, dtype=bool)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(I_bool_low, I_bool_high_open, dtype=bool)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(0, I_bool_high_open, dtype=bool)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(I_bool_high_closed, dtype=bool, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(I_bool_low, I_bool_high_closed, dtype=bool, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(0, I_bool_high_closed, dtype=bool, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] +reveal_type(def_gen.integers(I_bool_low_like, 1, dtype=bool, endpoint=True)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(I_bool_high_open, dtype=bool)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(I_bool_low, I_bool_high_open, dtype=bool)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(0, I_bool_high_open, dtype=bool)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(I_bool_high_closed, dtype=bool, endpoint=True)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(I_bool_low, I_bool_high_closed, dtype=bool, endpoint=True)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(0, I_bool_high_closed, dtype=bool, endpoint=True)) # E: ndarray[Any, dtype[bool_] reveal_type(def_gen.integers(2, dtype=np.bool_)) # E: builtins.bool reveal_type(def_gen.integers(0, 2, dtype=np.bool_)) # E: builtins.bool reveal_type(def_gen.integers(1, dtype=np.bool_, endpoint=True)) # E: builtins.bool reveal_type(def_gen.integers(0, 1, dtype=np.bool_, endpoint=True)) # E: builtins.bool -reveal_type(def_gen.integers(I_bool_low_like, 1, dtype=np.bool_, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(I_bool_high_open, dtype=np.bool_)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(I_bool_low, I_bool_high_open, dtype=np.bool_)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(0, I_bool_high_open, dtype=np.bool_)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(I_bool_high_closed, dtype=np.bool_, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(I_bool_low, I_bool_high_closed, dtype=np.bool_, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(0, I_bool_high_closed, dtype=np.bool_, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] +reveal_type(def_gen.integers(I_bool_low_like, 1, dtype=np.bool_, endpoint=True)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(I_bool_high_open, dtype=np.bool_)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(I_bool_low, I_bool_high_open, dtype=np.bool_)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(0, I_bool_high_open, dtype=np.bool_)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(I_bool_high_closed, dtype=np.bool_, endpoint=True)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(I_bool_low, I_bool_high_closed, dtype=np.bool_, endpoint=True)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(0, I_bool_high_closed, dtype=np.bool_, endpoint=True)) # E: ndarray[Any, dtype[bool_] I_u1_low: np.ndarray[Any, np.dtype[np.uint8]] = np.array([0], dtype=np.uint8) I_u1_low_like: List[int] = [0] @@ -538,37 +538,37 @@ reveal_type(def_gen.integers(256, dtype="u1")) # E: int reveal_type(def_gen.integers(0, 256, dtype="u1")) # E: int reveal_type(def_gen.integers(255, dtype="u1", endpoint=True)) # E: int reveal_type(def_gen.integers(0, 255, dtype="u1", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u1_low_like, 255, dtype="u1", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_high_open, dtype="u1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_low, I_u1_high_open, dtype="u1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(0, I_u1_high_open, dtype="u1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_high_closed, dtype="u1", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_low, I_u1_high_closed, dtype="u1", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(0, I_u1_high_closed, dtype="u1", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_low_like, 255, dtype="u1", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_high_open, dtype="u1")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_low, I_u1_high_open, dtype="u1")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(0, I_u1_high_open, dtype="u1")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_high_closed, dtype="u1", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_low, I_u1_high_closed, dtype="u1", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(0, I_u1_high_closed, dtype="u1", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] reveal_type(def_gen.integers(256, dtype="uint8")) # E: int reveal_type(def_gen.integers(0, 256, dtype="uint8")) # E: int reveal_type(def_gen.integers(255, dtype="uint8", endpoint=True)) # E: int reveal_type(def_gen.integers(0, 255, dtype="uint8", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u1_low_like, 255, dtype="uint8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_high_open, dtype="uint8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_low, I_u1_high_open, dtype="uint8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(0, I_u1_high_open, dtype="uint8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_high_closed, dtype="uint8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_low, I_u1_high_closed, dtype="uint8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(0, I_u1_high_closed, dtype="uint8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_low_like, 255, dtype="uint8", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_high_open, dtype="uint8")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_low, I_u1_high_open, dtype="uint8")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(0, I_u1_high_open, dtype="uint8")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_high_closed, dtype="uint8", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_low, I_u1_high_closed, dtype="uint8", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(0, I_u1_high_closed, dtype="uint8", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] reveal_type(def_gen.integers(256, dtype=np.uint8)) # E: int reveal_type(def_gen.integers(0, 256, dtype=np.uint8)) # E: int reveal_type(def_gen.integers(255, dtype=np.uint8, endpoint=True)) # E: int reveal_type(def_gen.integers(0, 255, dtype=np.uint8, endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u1_low_like, 255, dtype=np.uint8, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_high_open, dtype=np.uint8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_low, I_u1_high_open, dtype=np.uint8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(0, I_u1_high_open, dtype=np.uint8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_high_closed, dtype=np.uint8, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_low, I_u1_high_closed, dtype=np.uint8, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(0, I_u1_high_closed, dtype=np.uint8, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_low_like, 255, dtype=np.uint8, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_high_open, dtype=np.uint8)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_low, I_u1_high_open, dtype=np.uint8)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(0, I_u1_high_open, dtype=np.uint8)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_high_closed, dtype=np.uint8, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_low, I_u1_high_closed, dtype=np.uint8, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(0, I_u1_high_closed, dtype=np.uint8, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] I_u2_low: np.ndarray[Any, np.dtype[np.uint16]] = np.array([0], dtype=np.uint16) I_u2_low_like: List[int] = [0] @@ -579,37 +579,37 @@ reveal_type(def_gen.integers(65536, dtype="u2")) # E: int reveal_type(def_gen.integers(0, 65536, dtype="u2")) # E: int reveal_type(def_gen.integers(65535, dtype="u2", endpoint=True)) # E: int reveal_type(def_gen.integers(0, 65535, dtype="u2", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u2_low_like, 65535, dtype="u2", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_high_open, dtype="u2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_low, I_u2_high_open, dtype="u2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(0, I_u2_high_open, dtype="u2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_high_closed, dtype="u2", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_low, I_u2_high_closed, dtype="u2", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(0, I_u2_high_closed, dtype="u2", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_low_like, 65535, dtype="u2", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_high_open, dtype="u2")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_low, I_u2_high_open, dtype="u2")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(0, I_u2_high_open, dtype="u2")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_high_closed, dtype="u2", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_low, I_u2_high_closed, dtype="u2", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(0, I_u2_high_closed, dtype="u2", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] reveal_type(def_gen.integers(65536, dtype="uint16")) # E: int reveal_type(def_gen.integers(0, 65536, dtype="uint16")) # E: int reveal_type(def_gen.integers(65535, dtype="uint16", endpoint=True)) # E: int reveal_type(def_gen.integers(0, 65535, dtype="uint16", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u2_low_like, 65535, dtype="uint16", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_high_open, dtype="uint16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_low, I_u2_high_open, dtype="uint16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(0, I_u2_high_open, dtype="uint16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_high_closed, dtype="uint16", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_low, I_u2_high_closed, dtype="uint16", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(0, I_u2_high_closed, dtype="uint16", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_low_like, 65535, dtype="uint16", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_high_open, dtype="uint16")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_low, I_u2_high_open, dtype="uint16")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(0, I_u2_high_open, dtype="uint16")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_high_closed, dtype="uint16", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_low, I_u2_high_closed, dtype="uint16", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(0, I_u2_high_closed, dtype="uint16", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] reveal_type(def_gen.integers(65536, dtype=np.uint16)) # E: int reveal_type(def_gen.integers(0, 65536, dtype=np.uint16)) # E: int reveal_type(def_gen.integers(65535, dtype=np.uint16, endpoint=True)) # E: int reveal_type(def_gen.integers(0, 65535, dtype=np.uint16, endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u2_low_like, 65535, dtype=np.uint16, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_high_open, dtype=np.uint16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_low, I_u2_high_open, dtype=np.uint16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(0, I_u2_high_open, dtype=np.uint16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_high_closed, dtype=np.uint16, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_low, I_u2_high_closed, dtype=np.uint16, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(0, I_u2_high_closed, dtype=np.uint16, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_low_like, 65535, dtype=np.uint16, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_high_open, dtype=np.uint16)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_low, I_u2_high_open, dtype=np.uint16)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(0, I_u2_high_open, dtype=np.uint16)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_high_closed, dtype=np.uint16, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_low, I_u2_high_closed, dtype=np.uint16, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(0, I_u2_high_closed, dtype=np.uint16, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] I_u4_low: np.ndarray[Any, np.dtype[np.uint32]] = np.array([0], dtype=np.uint32) I_u4_low_like: List[int] = [0] @@ -620,62 +620,62 @@ reveal_type(def_gen.integers(4294967296, dtype=np.int_)) # E: int reveal_type(def_gen.integers(0, 4294967296, dtype=np.int_)) # E: int reveal_type(def_gen.integers(4294967295, dtype=np.int_, endpoint=True)) # E: int reveal_type(def_gen.integers(0, 4294967295, dtype=np.int_, endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u4_low_like, 4294967295, dtype=np.int_, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(def_gen.integers(I_u4_high_open, dtype=np.int_)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.int_)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(def_gen.integers(0, I_u4_high_open, dtype=np.int_)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(def_gen.integers(I_u4_high_closed, dtype=np.int_, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.int_, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(def_gen.integers(0, I_u4_high_closed, dtype=np.int_, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(def_gen.integers(I_u4_low_like, 4294967295, dtype=np.int_, endpoint=True)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(def_gen.integers(I_u4_high_open, dtype=np.int_)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.int_)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(def_gen.integers(0, I_u4_high_open, dtype=np.int_)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(def_gen.integers(I_u4_high_closed, dtype=np.int_, endpoint=True)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.int_, endpoint=True)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(def_gen.integers(0, I_u4_high_closed, dtype=np.int_, endpoint=True)) # E: ndarray[Any, dtype[{int_}]] reveal_type(def_gen.integers(4294967296, dtype="u4")) # E: int reveal_type(def_gen.integers(0, 4294967296, dtype="u4")) # E: int reveal_type(def_gen.integers(4294967295, dtype="u4", endpoint=True)) # E: int reveal_type(def_gen.integers(0, 4294967295, dtype="u4", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u4_low_like, 4294967295, dtype="u4", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_high_open, dtype="u4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype="u4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(0, I_u4_high_open, dtype="u4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_high_closed, dtype="u4", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype="u4", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(0, I_u4_high_closed, dtype="u4", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_low_like, 4294967295, dtype="u4", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_high_open, dtype="u4")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype="u4")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(0, I_u4_high_open, dtype="u4")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_high_closed, dtype="u4", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype="u4", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(0, I_u4_high_closed, dtype="u4", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] reveal_type(def_gen.integers(4294967296, dtype="uint32")) # E: int reveal_type(def_gen.integers(0, 4294967296, dtype="uint32")) # E: int reveal_type(def_gen.integers(4294967295, dtype="uint32", endpoint=True)) # E: int reveal_type(def_gen.integers(0, 4294967295, dtype="uint32", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u4_low_like, 4294967295, dtype="uint32", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_high_open, dtype="uint32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype="uint32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(0, I_u4_high_open, dtype="uint32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_high_closed, dtype="uint32", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype="uint32", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(0, I_u4_high_closed, dtype="uint32", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_low_like, 4294967295, dtype="uint32", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_high_open, dtype="uint32")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype="uint32")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(0, I_u4_high_open, dtype="uint32")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_high_closed, dtype="uint32", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype="uint32", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(0, I_u4_high_closed, dtype="uint32", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] reveal_type(def_gen.integers(4294967296, dtype=np.uint32)) # E: int reveal_type(def_gen.integers(0, 4294967296, dtype=np.uint32)) # E: int reveal_type(def_gen.integers(4294967295, dtype=np.uint32, endpoint=True)) # E: int reveal_type(def_gen.integers(0, 4294967295, dtype=np.uint32, endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u4_low_like, 4294967295, dtype=np.uint32, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_high_open, dtype=np.uint32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.uint32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(0, I_u4_high_open, dtype=np.uint32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_high_closed, dtype=np.uint32, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.uint32, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(0, I_u4_high_closed, dtype=np.uint32, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_low_like, 4294967295, dtype=np.uint32, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_high_open, dtype=np.uint32)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.uint32)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(0, I_u4_high_open, dtype=np.uint32)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_high_closed, dtype=np.uint32, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.uint32, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(0, I_u4_high_closed, dtype=np.uint32, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] reveal_type(def_gen.integers(4294967296, dtype=np.uint)) # E: int reveal_type(def_gen.integers(0, 4294967296, dtype=np.uint)) # E: int reveal_type(def_gen.integers(4294967295, dtype=np.uint, endpoint=True)) # E: int reveal_type(def_gen.integers(0, 4294967295, dtype=np.uint, endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u4_low_like, 4294967295, dtype=np.uint, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[{uint}]] -reveal_type(def_gen.integers(I_u4_high_open, dtype=np.uint)) # E: numpy.ndarray[Any, numpy.dtype[{uint}]] -reveal_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.uint)) # E: numpy.ndarray[Any, numpy.dtype[{uint}]] -reveal_type(def_gen.integers(0, I_u4_high_open, dtype=np.uint)) # E: numpy.ndarray[Any, numpy.dtype[{uint}]] -reveal_type(def_gen.integers(I_u4_high_closed, dtype=np.uint, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[{uint}]] -reveal_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.uint, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[{uint}]] -reveal_type(def_gen.integers(0, I_u4_high_closed, dtype=np.uint, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[{uint}]] +reveal_type(def_gen.integers(I_u4_low_like, 4294967295, dtype=np.uint, endpoint=True)) # E: ndarray[Any, dtype[{uint}]] +reveal_type(def_gen.integers(I_u4_high_open, dtype=np.uint)) # E: ndarray[Any, dtype[{uint}]] +reveal_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.uint)) # E: ndarray[Any, dtype[{uint}]] +reveal_type(def_gen.integers(0, I_u4_high_open, dtype=np.uint)) # E: ndarray[Any, dtype[{uint}]] +reveal_type(def_gen.integers(I_u4_high_closed, dtype=np.uint, endpoint=True)) # E: ndarray[Any, dtype[{uint}]] +reveal_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.uint, endpoint=True)) # E: ndarray[Any, dtype[{uint}]] +reveal_type(def_gen.integers(0, I_u4_high_closed, dtype=np.uint, endpoint=True)) # E: ndarray[Any, dtype[{uint}]] I_u8_low: np.ndarray[Any, np.dtype[np.uint64]] = np.array([0], dtype=np.uint64) I_u8_low_like: List[int] = [0] @@ -686,37 +686,37 @@ reveal_type(def_gen.integers(18446744073709551616, dtype="u8")) # E: int reveal_type(def_gen.integers(0, 18446744073709551616, dtype="u8")) # E: int reveal_type(def_gen.integers(18446744073709551615, dtype="u8", endpoint=True)) # E: int reveal_type(def_gen.integers(0, 18446744073709551615, dtype="u8", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u8_low_like, 18446744073709551615, dtype="u8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_high_open, dtype="u8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_low, I_u8_high_open, dtype="u8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(0, I_u8_high_open, dtype="u8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_high_closed, dtype="u8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_low, I_u8_high_closed, dtype="u8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(0, I_u8_high_closed, dtype="u8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_low_like, 18446744073709551615, dtype="u8", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_high_open, dtype="u8")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_low, I_u8_high_open, dtype="u8")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(0, I_u8_high_open, dtype="u8")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_high_closed, dtype="u8", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_low, I_u8_high_closed, dtype="u8", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(0, I_u8_high_closed, dtype="u8", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] reveal_type(def_gen.integers(18446744073709551616, dtype="uint64")) # E: int reveal_type(def_gen.integers(0, 18446744073709551616, dtype="uint64")) # E: int reveal_type(def_gen.integers(18446744073709551615, dtype="uint64", endpoint=True)) # E: int reveal_type(def_gen.integers(0, 18446744073709551615, dtype="uint64", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u8_low_like, 18446744073709551615, dtype="uint64", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_high_open, dtype="uint64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_low, I_u8_high_open, dtype="uint64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(0, I_u8_high_open, dtype="uint64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_high_closed, dtype="uint64", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_low, I_u8_high_closed, dtype="uint64", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(0, I_u8_high_closed, dtype="uint64", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_low_like, 18446744073709551615, dtype="uint64", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_high_open, dtype="uint64")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_low, I_u8_high_open, dtype="uint64")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(0, I_u8_high_open, dtype="uint64")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_high_closed, dtype="uint64", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_low, I_u8_high_closed, dtype="uint64", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(0, I_u8_high_closed, dtype="uint64", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] reveal_type(def_gen.integers(18446744073709551616, dtype=np.uint64)) # E: int reveal_type(def_gen.integers(0, 18446744073709551616, dtype=np.uint64)) # E: int reveal_type(def_gen.integers(18446744073709551615, dtype=np.uint64, endpoint=True)) # E: int reveal_type(def_gen.integers(0, 18446744073709551615, dtype=np.uint64, endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u8_low_like, 18446744073709551615, dtype=np.uint64, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_high_open, dtype=np.uint64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_low, I_u8_high_open, dtype=np.uint64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(0, I_u8_high_open, dtype=np.uint64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_high_closed, dtype=np.uint64, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_low, I_u8_high_closed, dtype=np.uint64, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(0, I_u8_high_closed, dtype=np.uint64, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_low_like, 18446744073709551615, dtype=np.uint64, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_high_open, dtype=np.uint64)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_low, I_u8_high_open, dtype=np.uint64)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(0, I_u8_high_open, dtype=np.uint64)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_high_closed, dtype=np.uint64, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_low, I_u8_high_closed, dtype=np.uint64, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(0, I_u8_high_closed, dtype=np.uint64, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] I_i1_low: np.ndarray[Any, np.dtype[np.int8]] = np.array([-128], dtype=np.int8) I_i1_low_like: List[int] = [-128] @@ -727,37 +727,37 @@ reveal_type(def_gen.integers(128, dtype="i1")) # E: int reveal_type(def_gen.integers(-128, 128, dtype="i1")) # E: int reveal_type(def_gen.integers(127, dtype="i1", endpoint=True)) # E: int reveal_type(def_gen.integers(-128, 127, dtype="i1", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i1_low_like, 127, dtype="i1", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_high_open, dtype="i1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_low, I_i1_high_open, dtype="i1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(-128, I_i1_high_open, dtype="i1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_high_closed, dtype="i1", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_low, I_i1_high_closed, dtype="i1", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(-128, I_i1_high_closed, dtype="i1", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_low_like, 127, dtype="i1", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_high_open, dtype="i1")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_low, I_i1_high_open, dtype="i1")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(-128, I_i1_high_open, dtype="i1")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_high_closed, dtype="i1", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_low, I_i1_high_closed, dtype="i1", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(-128, I_i1_high_closed, dtype="i1", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] reveal_type(def_gen.integers(128, dtype="int8")) # E: int reveal_type(def_gen.integers(-128, 128, dtype="int8")) # E: int reveal_type(def_gen.integers(127, dtype="int8", endpoint=True)) # E: int reveal_type(def_gen.integers(-128, 127, dtype="int8", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i1_low_like, 127, dtype="int8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_high_open, dtype="int8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_low, I_i1_high_open, dtype="int8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(-128, I_i1_high_open, dtype="int8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_high_closed, dtype="int8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_low, I_i1_high_closed, dtype="int8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(-128, I_i1_high_closed, dtype="int8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_low_like, 127, dtype="int8", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_high_open, dtype="int8")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_low, I_i1_high_open, dtype="int8")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(-128, I_i1_high_open, dtype="int8")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_high_closed, dtype="int8", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_low, I_i1_high_closed, dtype="int8", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(-128, I_i1_high_closed, dtype="int8", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] reveal_type(def_gen.integers(128, dtype=np.int8)) # E: int reveal_type(def_gen.integers(-128, 128, dtype=np.int8)) # E: int reveal_type(def_gen.integers(127, dtype=np.int8, endpoint=True)) # E: int reveal_type(def_gen.integers(-128, 127, dtype=np.int8, endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i1_low_like, 127, dtype=np.int8, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_high_open, dtype=np.int8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_low, I_i1_high_open, dtype=np.int8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(-128, I_i1_high_open, dtype=np.int8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_high_closed, dtype=np.int8, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_low, I_i1_high_closed, dtype=np.int8, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(-128, I_i1_high_closed, dtype=np.int8, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_low_like, 127, dtype=np.int8, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_high_open, dtype=np.int8)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_low, I_i1_high_open, dtype=np.int8)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(-128, I_i1_high_open, dtype=np.int8)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_high_closed, dtype=np.int8, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_low, I_i1_high_closed, dtype=np.int8, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(-128, I_i1_high_closed, dtype=np.int8, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] I_i2_low: np.ndarray[Any, np.dtype[np.int16]] = np.array([-32768], dtype=np.int16) I_i2_low_like: List[int] = [-32768] @@ -768,37 +768,37 @@ reveal_type(def_gen.integers(32768, dtype="i2")) # E: int reveal_type(def_gen.integers(-32768, 32768, dtype="i2")) # E: int reveal_type(def_gen.integers(32767, dtype="i2", endpoint=True)) # E: int reveal_type(def_gen.integers(-32768, 32767, dtype="i2", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i2_low_like, 32767, dtype="i2", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_high_open, dtype="i2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_low, I_i2_high_open, dtype="i2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(-32768, I_i2_high_open, dtype="i2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_high_closed, dtype="i2", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_low, I_i2_high_closed, dtype="i2", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(-32768, I_i2_high_closed, dtype="i2", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_low_like, 32767, dtype="i2", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_high_open, dtype="i2")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_low, I_i2_high_open, dtype="i2")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(-32768, I_i2_high_open, dtype="i2")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_high_closed, dtype="i2", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_low, I_i2_high_closed, dtype="i2", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(-32768, I_i2_high_closed, dtype="i2", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] reveal_type(def_gen.integers(32768, dtype="int16")) # E: int reveal_type(def_gen.integers(-32768, 32768, dtype="int16")) # E: int reveal_type(def_gen.integers(32767, dtype="int16", endpoint=True)) # E: int reveal_type(def_gen.integers(-32768, 32767, dtype="int16", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i2_low_like, 32767, dtype="int16", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_high_open, dtype="int16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_low, I_i2_high_open, dtype="int16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(-32768, I_i2_high_open, dtype="int16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_high_closed, dtype="int16", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_low, I_i2_high_closed, dtype="int16", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(-32768, I_i2_high_closed, dtype="int16", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_low_like, 32767, dtype="int16", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_high_open, dtype="int16")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_low, I_i2_high_open, dtype="int16")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(-32768, I_i2_high_open, dtype="int16")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_high_closed, dtype="int16", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_low, I_i2_high_closed, dtype="int16", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(-32768, I_i2_high_closed, dtype="int16", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] reveal_type(def_gen.integers(32768, dtype=np.int16)) # E: int reveal_type(def_gen.integers(-32768, 32768, dtype=np.int16)) # E: int reveal_type(def_gen.integers(32767, dtype=np.int16, endpoint=True)) # E: int reveal_type(def_gen.integers(-32768, 32767, dtype=np.int16, endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i2_low_like, 32767, dtype=np.int16, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_high_open, dtype=np.int16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_low, I_i2_high_open, dtype=np.int16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(-32768, I_i2_high_open, dtype=np.int16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_high_closed, dtype=np.int16, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_low, I_i2_high_closed, dtype=np.int16, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(-32768, I_i2_high_closed, dtype=np.int16, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_low_like, 32767, dtype=np.int16, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_high_open, dtype=np.int16)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_low, I_i2_high_open, dtype=np.int16)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(-32768, I_i2_high_open, dtype=np.int16)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_high_closed, dtype=np.int16, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_low, I_i2_high_closed, dtype=np.int16, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(-32768, I_i2_high_closed, dtype=np.int16, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] I_i4_low: np.ndarray[Any, np.dtype[np.int32]] = np.array([-2147483648], dtype=np.int32) I_i4_low_like: List[int] = [-2147483648] @@ -809,37 +809,37 @@ reveal_type(def_gen.integers(2147483648, dtype="i4")) # E: int reveal_type(def_gen.integers(-2147483648, 2147483648, dtype="i4")) # E: int reveal_type(def_gen.integers(2147483647, dtype="i4", endpoint=True)) # E: int reveal_type(def_gen.integers(-2147483648, 2147483647, dtype="i4", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i4_low_like, 2147483647, dtype="i4", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_high_open, dtype="i4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_low, I_i4_high_open, dtype="i4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(-2147483648, I_i4_high_open, dtype="i4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_high_closed, dtype="i4", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_low, I_i4_high_closed, dtype="i4", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(-2147483648, I_i4_high_closed, dtype="i4", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_low_like, 2147483647, dtype="i4", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_high_open, dtype="i4")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_low, I_i4_high_open, dtype="i4")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(-2147483648, I_i4_high_open, dtype="i4")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_high_closed, dtype="i4", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_low, I_i4_high_closed, dtype="i4", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(-2147483648, I_i4_high_closed, dtype="i4", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] reveal_type(def_gen.integers(2147483648, dtype="int32")) # E: int reveal_type(def_gen.integers(-2147483648, 2147483648, dtype="int32")) # E: int reveal_type(def_gen.integers(2147483647, dtype="int32", endpoint=True)) # E: int reveal_type(def_gen.integers(-2147483648, 2147483647, dtype="int32", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i4_low_like, 2147483647, dtype="int32", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_high_open, dtype="int32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_low, I_i4_high_open, dtype="int32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(-2147483648, I_i4_high_open, dtype="int32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_high_closed, dtype="int32", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_low, I_i4_high_closed, dtype="int32", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(-2147483648, I_i4_high_closed, dtype="int32", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_low_like, 2147483647, dtype="int32", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_high_open, dtype="int32")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_low, I_i4_high_open, dtype="int32")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(-2147483648, I_i4_high_open, dtype="int32")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_high_closed, dtype="int32", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_low, I_i4_high_closed, dtype="int32", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(-2147483648, I_i4_high_closed, dtype="int32", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] reveal_type(def_gen.integers(2147483648, dtype=np.int32)) # E: int reveal_type(def_gen.integers(-2147483648, 2147483648, dtype=np.int32)) # E: int reveal_type(def_gen.integers(2147483647, dtype=np.int32, endpoint=True)) # E: int reveal_type(def_gen.integers(-2147483648, 2147483647, dtype=np.int32, endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i4_low_like, 2147483647, dtype=np.int32, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_high_open, dtype=np.int32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_low, I_i4_high_open, dtype=np.int32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(-2147483648, I_i4_high_open, dtype=np.int32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_high_closed, dtype=np.int32, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_low, I_i4_high_closed, dtype=np.int32, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(-2147483648, I_i4_high_closed, dtype=np.int32, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_low_like, 2147483647, dtype=np.int32, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_high_open, dtype=np.int32)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_low, I_i4_high_open, dtype=np.int32)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(-2147483648, I_i4_high_open, dtype=np.int32)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_high_closed, dtype=np.int32, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_low, I_i4_high_closed, dtype=np.int32, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(-2147483648, I_i4_high_closed, dtype=np.int32, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] I_i8_low: np.ndarray[Any, np.dtype[np.int64]] = np.array([-9223372036854775808], dtype=np.int64) I_i8_low_like: List[int] = [-9223372036854775808] @@ -850,37 +850,37 @@ reveal_type(def_gen.integers(9223372036854775808, dtype="i8")) # E: int reveal_type(def_gen.integers(-9223372036854775808, 9223372036854775808, dtype="i8")) # E: int reveal_type(def_gen.integers(9223372036854775807, dtype="i8", endpoint=True)) # E: int reveal_type(def_gen.integers(-9223372036854775808, 9223372036854775807, dtype="i8", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i8_low_like, 9223372036854775807, dtype="i8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_high_open, dtype="i8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_low, I_i8_high_open, dtype="i8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_open, dtype="i8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_high_closed, dtype="i8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_low, I_i8_high_closed, dtype="i8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype="i8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_low_like, 9223372036854775807, dtype="i8", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_high_open, dtype="i8")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_low, I_i8_high_open, dtype="i8")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_open, dtype="i8")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_high_closed, dtype="i8", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_low, I_i8_high_closed, dtype="i8", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype="i8", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] reveal_type(def_gen.integers(9223372036854775808, dtype="int64")) # E: int reveal_type(def_gen.integers(-9223372036854775808, 9223372036854775808, dtype="int64")) # E: int reveal_type(def_gen.integers(9223372036854775807, dtype="int64", endpoint=True)) # E: int reveal_type(def_gen.integers(-9223372036854775808, 9223372036854775807, dtype="int64", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i8_low_like, 9223372036854775807, dtype="int64", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_high_open, dtype="int64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_low, I_i8_high_open, dtype="int64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_open, dtype="int64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_high_closed, dtype="int64", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_low, I_i8_high_closed, dtype="int64", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype="int64", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_low_like, 9223372036854775807, dtype="int64", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_high_open, dtype="int64")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_low, I_i8_high_open, dtype="int64")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_open, dtype="int64")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_high_closed, dtype="int64", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_low, I_i8_high_closed, dtype="int64", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype="int64", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] reveal_type(def_gen.integers(9223372036854775808, dtype=np.int64)) # E: int reveal_type(def_gen.integers(-9223372036854775808, 9223372036854775808, dtype=np.int64)) # E: int reveal_type(def_gen.integers(9223372036854775807, dtype=np.int64, endpoint=True)) # E: int reveal_type(def_gen.integers(-9223372036854775808, 9223372036854775807, dtype=np.int64, endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i8_low_like, 9223372036854775807, dtype=np.int64, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_high_open, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_low, I_i8_high_open, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_open, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_high_closed, dtype=np.int64, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_low, I_i8_high_closed, dtype=np.int64, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype=np.int64, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_low_like, 9223372036854775807, dtype=np.int64, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_high_open, dtype=np.int64)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_low, I_i8_high_open, dtype=np.int64)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_open, dtype=np.int64)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_high_closed, dtype=np.int64, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_low, I_i8_high_closed, dtype=np.int64, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype=np.int64, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] reveal_type(def_gen.bit_generator) # E: BitGenerator @@ -888,50 +888,50 @@ reveal_type(def_gen.bit_generator) # E: BitGenerator reveal_type(def_gen.bytes(2)) # E: bytes reveal_type(def_gen.choice(5)) # E: int -reveal_type(def_gen.choice(5, 3)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.choice(5, 3, replace=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.choice(5, 3, p=[1 / 5] * 5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.choice(5, 3, p=[1 / 5] * 5, replace=False)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] +reveal_type(def_gen.choice(5, 3)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.choice(5, 3, replace=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.choice(5, 3, p=[1 / 5] * 5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.choice(5, 3, p=[1 / 5] * 5, replace=False)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] reveal_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"])) # E: Any -reveal_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3)) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, p=[1 / 4] * 4)) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=True)) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=False, p=np.array([1 / 8, 1 / 8, 1 / 2, 1 / 4]))) # E: numpy.ndarray[Any, Any] - -reveal_type(def_gen.dirichlet([0.5, 0.5])) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.dirichlet(np.array([0.5, 0.5]))) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.dirichlet(np.array([0.5, 0.5]), size=3)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.multinomial(20, [1 / 6.0] * 6)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.multinomial(20, np.array([0.5, 0.5]))) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.multinomial(20, [1 / 6.0] * 6, size=2)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.multinomial([[10], [20]], [1 / 6.0] * 6, size=(2, 2))) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.multinomial(np.array([[10], [20]]), np.array([0.5, 0.5]), size=(2, 2))) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] - -reveal_type(def_gen.multivariate_hypergeometric([3, 5, 7], 2)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, size=4)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, size=(4, 7))) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.multivariate_hypergeometric([3, 5, 7], 2, method="count")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, method="marginals")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] - -reveal_type(def_gen.multivariate_normal([0.0], [[1.0]])) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.multivariate_normal([0.0], np.array([[1.0]]))) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.multivariate_normal(np.array([0.0]), [[1.0]])) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.multivariate_normal([0.0], np.array([[1.0]]))) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.permutation(10)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.permutation([1, 2, 3, 4])) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.permutation(np.array([1, 2, 3, 4]))) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.permutation(D_2D, axis=1)) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.permuted(D_2D)) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.permuted(D_2D_like)) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.permuted(D_2D, axis=1)) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.permuted(D_2D, out=D_2D)) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.permuted(D_2D_like, out=D_2D)) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.permuted(D_2D_like, out=D_2D)) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.permuted(D_2D, axis=1, out=D_2D)) # E: numpy.ndarray[Any, Any] +reveal_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3)) # E: ndarray[Any, Any] +reveal_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, p=[1 / 4] * 4)) # E: ndarray[Any, Any] +reveal_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=True)) # E: ndarray[Any, Any] +reveal_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=False, p=np.array([1 / 8, 1 / 8, 1 / 2, 1 / 4]))) # E: ndarray[Any, Any] + +reveal_type(def_gen.dirichlet([0.5, 0.5])) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.dirichlet(np.array([0.5, 0.5]))) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.dirichlet(np.array([0.5, 0.5]), size=3)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.multinomial(20, [1 / 6.0] * 6)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.multinomial(20, np.array([0.5, 0.5]))) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.multinomial(20, [1 / 6.0] * 6, size=2)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.multinomial([[10], [20]], [1 / 6.0] * 6, size=(2, 2))) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.multinomial(np.array([[10], [20]]), np.array([0.5, 0.5]), size=(2, 2))) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] + +reveal_type(def_gen.multivariate_hypergeometric([3, 5, 7], 2)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, size=4)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, size=(4, 7))) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.multivariate_hypergeometric([3, 5, 7], 2, method="count")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, method="marginals")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] + +reveal_type(def_gen.multivariate_normal([0.0], [[1.0]])) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.multivariate_normal([0.0], np.array([[1.0]]))) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.multivariate_normal(np.array([0.0]), [[1.0]])) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.multivariate_normal([0.0], np.array([[1.0]]))) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.permutation(10)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.permutation([1, 2, 3, 4])) # E: ndarray[Any, Any] +reveal_type(def_gen.permutation(np.array([1, 2, 3, 4]))) # E: ndarray[Any, Any] +reveal_type(def_gen.permutation(D_2D, axis=1)) # E: ndarray[Any, Any] +reveal_type(def_gen.permuted(D_2D)) # E: ndarray[Any, Any] +reveal_type(def_gen.permuted(D_2D_like)) # E: ndarray[Any, Any] +reveal_type(def_gen.permuted(D_2D, axis=1)) # E: ndarray[Any, Any] +reveal_type(def_gen.permuted(D_2D, out=D_2D)) # E: ndarray[Any, Any] +reveal_type(def_gen.permuted(D_2D_like, out=D_2D)) # E: ndarray[Any, Any] +reveal_type(def_gen.permuted(D_2D_like, out=D_2D)) # E: ndarray[Any, Any] +reveal_type(def_gen.permuted(D_2D, axis=1, out=D_2D)) # E: ndarray[Any, Any] reveal_type(def_gen.shuffle(np.arange(10))) # E: None reveal_type(def_gen.shuffle([1, 2, 3, 4, 5])) # E: None @@ -949,559 +949,559 @@ random_st: np.random.RandomState = np.random.RandomState() reveal_type(random_st.standard_normal()) # E: float reveal_type(random_st.standard_normal(size=None)) # E: float -reveal_type(random_st.standard_normal(size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] +reveal_type(random_st.standard_normal(size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] reveal_type(random_st.random()) # E: float reveal_type(random_st.random(size=None)) # E: float -reveal_type(random_st.random(size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] +reveal_type(random_st.random(size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] reveal_type(random_st.standard_cauchy()) # E: float reveal_type(random_st.standard_cauchy(size=None)) # E: float -reveal_type(random_st.standard_cauchy(size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.standard_cauchy(size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.standard_exponential()) # E: float reveal_type(random_st.standard_exponential(size=None)) # E: float -reveal_type(random_st.standard_exponential(size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] +reveal_type(random_st.standard_exponential(size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] reveal_type(random_st.zipf(1.5)) # E: int reveal_type(random_st.zipf(1.5, size=None)) # E: int -reveal_type(random_st.zipf(1.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.zipf(D_arr_1p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.zipf(D_arr_1p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.zipf(D_arr_like_1p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.zipf(D_arr_like_1p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(random_st.zipf(1.5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.zipf(D_arr_1p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.zipf(D_arr_1p5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.zipf(D_arr_like_1p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.zipf(D_arr_like_1p5, size=1)) # E: ndarray[Any, dtype[{int_}]] reveal_type(random_st.weibull(0.5)) # E: float reveal_type(random_st.weibull(0.5, size=None)) # E: float -reveal_type(random_st.weibull(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.weibull(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.weibull(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.weibull(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.weibull(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.weibull(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.weibull(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.weibull(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.weibull(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.weibull(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.standard_t(0.5)) # E: float reveal_type(random_st.standard_t(0.5, size=None)) # E: float -reveal_type(random_st.standard_t(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.standard_t(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.standard_t(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.standard_t(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.standard_t(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.standard_t(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.standard_t(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.standard_t(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.standard_t(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.standard_t(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.poisson(0.5)) # E: int reveal_type(random_st.poisson(0.5, size=None)) # E: int -reveal_type(random_st.poisson(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.poisson(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.poisson(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.poisson(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.poisson(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(random_st.poisson(0.5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.poisson(D_arr_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.poisson(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.poisson(D_arr_like_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.poisson(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] reveal_type(random_st.power(0.5)) # E: float reveal_type(random_st.power(0.5, size=None)) # E: float -reveal_type(random_st.power(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.power(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.power(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.power(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.power(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.power(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.power(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.power(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.power(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.power(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.pareto(0.5)) # E: float reveal_type(random_st.pareto(0.5, size=None)) # E: float -reveal_type(random_st.pareto(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.pareto(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.pareto(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.pareto(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.pareto(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.pareto(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.pareto(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.pareto(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.pareto(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.pareto(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.chisquare(0.5)) # E: float reveal_type(random_st.chisquare(0.5, size=None)) # E: float -reveal_type(random_st.chisquare(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.chisquare(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.chisquare(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.chisquare(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.chisquare(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.chisquare(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.chisquare(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.chisquare(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.chisquare(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.chisquare(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.exponential(0.5)) # E: float reveal_type(random_st.exponential(0.5, size=None)) # E: float -reveal_type(random_st.exponential(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.exponential(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.exponential(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.exponential(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.exponential(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.exponential(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.exponential(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.exponential(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.exponential(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.exponential(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.geometric(0.5)) # E: int reveal_type(random_st.geometric(0.5, size=None)) # E: int -reveal_type(random_st.geometric(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.geometric(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.geometric(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.geometric(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.geometric(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(random_st.geometric(0.5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.geometric(D_arr_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.geometric(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.geometric(D_arr_like_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.geometric(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] reveal_type(random_st.logseries(0.5)) # E: int reveal_type(random_st.logseries(0.5, size=None)) # E: int -reveal_type(random_st.logseries(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.logseries(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.logseries(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.logseries(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.logseries(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(random_st.logseries(0.5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.logseries(D_arr_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.logseries(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.logseries(D_arr_like_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.logseries(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] reveal_type(random_st.rayleigh(0.5)) # E: float reveal_type(random_st.rayleigh(0.5, size=None)) # E: float -reveal_type(random_st.rayleigh(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.rayleigh(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.rayleigh(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.rayleigh(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.rayleigh(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.rayleigh(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.rayleigh(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.rayleigh(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.rayleigh(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.rayleigh(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.standard_gamma(0.5)) # E: float reveal_type(random_st.standard_gamma(0.5, size=None)) # E: float -reveal_type(random_st.standard_gamma(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(random_st.standard_gamma(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(random_st.standard_gamma(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(random_st.standard_gamma(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(random_st.standard_gamma(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(random_st.standard_gamma(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] +reveal_type(random_st.standard_gamma(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(random_st.standard_gamma(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(random_st.standard_gamma(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(random_st.standard_gamma(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(random_st.standard_gamma(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(random_st.standard_gamma(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] reveal_type(random_st.vonmises(0.5, 0.5)) # E: float reveal_type(random_st.vonmises(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.vonmises(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.vonmises(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.vonmises(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.vonmises(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.vonmises(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.vonmises(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.vonmises(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.vonmises(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.vonmises(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.vonmises(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.vonmises(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.vonmises(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.vonmises(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.vonmises(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.vonmises(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.vonmises(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.vonmises(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.vonmises(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.vonmises(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.vonmises(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.vonmises(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.vonmises(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.wald(0.5, 0.5)) # E: float reveal_type(random_st.wald(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.wald(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.wald(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.wald(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.wald(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.wald(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.wald(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.wald(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.wald(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.wald(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.wald(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.wald(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.wald(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.wald(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.wald(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.wald(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.wald(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.wald(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.wald(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.wald(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.wald(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.wald(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.wald(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.uniform(0.5, 0.5)) # E: float reveal_type(random_st.uniform(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.uniform(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.uniform(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.uniform(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.uniform(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.uniform(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.uniform(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.uniform(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.uniform(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.uniform(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.uniform(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.uniform(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.uniform(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.uniform(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.uniform(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.uniform(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.uniform(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.uniform(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.uniform(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.uniform(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.uniform(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.uniform(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.uniform(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.beta(0.5, 0.5)) # E: float reveal_type(random_st.beta(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.beta(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.beta(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.beta(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.beta(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.beta(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.beta(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.beta(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.beta(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.beta(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.beta(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.beta(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.beta(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.beta(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.beta(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.beta(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.beta(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.beta(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.beta(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.beta(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.beta(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.beta(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.beta(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.f(0.5, 0.5)) # E: float reveal_type(random_st.f(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.f(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.f(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.f(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.f(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.f(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.f(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.f(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.f(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.f(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.f(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.f(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.f(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.f(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.f(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.f(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.f(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.f(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.f(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.f(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.f(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.f(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.f(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.gamma(0.5, 0.5)) # E: float reveal_type(random_st.gamma(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.gamma(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gamma(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gamma(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gamma(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gamma(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gamma(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gamma(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gamma(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gamma(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gamma(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gamma(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.gamma(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gamma(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gamma(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gamma(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gamma(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gamma(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gamma(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gamma(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gamma(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gamma(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gamma(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.gumbel(0.5, 0.5)) # E: float reveal_type(random_st.gumbel(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.gumbel(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gumbel(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gumbel(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gumbel(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gumbel(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gumbel(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gumbel(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gumbel(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gumbel(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gumbel(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gumbel(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.gumbel(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gumbel(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gumbel(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gumbel(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gumbel(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gumbel(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gumbel(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gumbel(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gumbel(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gumbel(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gumbel(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.laplace(0.5, 0.5)) # E: float reveal_type(random_st.laplace(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.laplace(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.laplace(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.laplace(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.laplace(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.laplace(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.laplace(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.laplace(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.laplace(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.laplace(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.laplace(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.laplace(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.laplace(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.laplace(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.laplace(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.laplace(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.laplace(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.laplace(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.laplace(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.laplace(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.laplace(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.laplace(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.laplace(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.logistic(0.5, 0.5)) # E: float reveal_type(random_st.logistic(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.logistic(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.logistic(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.logistic(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.logistic(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.logistic(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.logistic(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.logistic(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.logistic(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.logistic(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.logistic(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.logistic(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.logistic(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.logistic(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.logistic(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.logistic(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.logistic(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.logistic(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.logistic(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.logistic(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.logistic(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.logistic(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.logistic(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.lognormal(0.5, 0.5)) # E: float reveal_type(random_st.lognormal(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.lognormal(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.lognormal(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.lognormal(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.lognormal(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.lognormal(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.lognormal(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.lognormal(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.lognormal(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.lognormal(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.lognormal(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.lognormal(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.lognormal(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.lognormal(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.lognormal(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.lognormal(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.lognormal(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.lognormal(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.lognormal(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.lognormal(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.lognormal(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.lognormal(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.lognormal(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.noncentral_chisquare(0.5, 0.5)) # E: float reveal_type(random_st.noncentral_chisquare(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.noncentral_chisquare(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_chisquare(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_chisquare(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_chisquare(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_chisquare(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_chisquare(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_chisquare(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_chisquare(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_chisquare(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.noncentral_chisquare(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_chisquare(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_chisquare(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_chisquare(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_chisquare(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_chisquare(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_chisquare(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_chisquare(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_chisquare(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.normal(0.5, 0.5)) # E: float reveal_type(random_st.normal(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.normal(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.normal(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.normal(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.normal(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.normal(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.normal(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.normal(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.normal(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.normal(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.normal(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.normal(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.normal(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.normal(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.normal(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.normal(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.normal(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.normal(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.normal(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.normal(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.normal(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.normal(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.normal(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.triangular(0.1, 0.5, 0.9)) # E: float reveal_type(random_st.triangular(0.1, 0.5, 0.9, size=None)) # E: float -reveal_type(random_st.triangular(0.1, 0.5, 0.9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.triangular(D_arr_0p1, 0.5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.triangular(0.1, D_arr_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.triangular(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.triangular(0.1, D_arr_0p5, 0.9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.triangular(D_arr_like_0p1, 0.5, D_arr_0p9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.triangular(0.5, D_arr_like_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.triangular(D_arr_0p1, D_arr_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.triangular(D_arr_like_0p1, D_arr_like_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.triangular(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.triangular(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.triangular(0.1, 0.5, 0.9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.triangular(D_arr_0p1, 0.5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.triangular(0.1, D_arr_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.triangular(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.triangular(0.1, D_arr_0p5, 0.9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.triangular(D_arr_like_0p1, 0.5, D_arr_0p9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.triangular(0.5, D_arr_like_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.triangular(D_arr_0p1, D_arr_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.triangular(D_arr_like_0p1, D_arr_like_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.triangular(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.triangular(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.noncentral_f(0.1, 0.5, 0.9)) # E: float reveal_type(random_st.noncentral_f(0.1, 0.5, 0.9, size=None)) # E: float -reveal_type(random_st.noncentral_f(0.1, 0.5, 0.9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_f(D_arr_0p1, 0.5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_f(0.1, D_arr_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_f(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_f(0.1, D_arr_0p5, 0.9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_f(D_arr_like_0p1, 0.5, D_arr_0p9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_f(0.5, D_arr_like_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_f(D_arr_0p1, D_arr_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_f(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.noncentral_f(0.1, 0.5, 0.9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_f(D_arr_0p1, 0.5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_f(0.1, D_arr_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_f(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_f(0.1, D_arr_0p5, 0.9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_f(D_arr_like_0p1, 0.5, D_arr_0p9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_f(0.5, D_arr_like_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_f(D_arr_0p1, D_arr_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_f(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.binomial(10, 0.5)) # E: int reveal_type(random_st.binomial(10, 0.5, size=None)) # E: int -reveal_type(random_st.binomial(10, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.binomial(I_arr_10, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.binomial(10, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.binomial(I_arr_10, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.binomial(10, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.binomial(I_arr_like_10, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.binomial(10, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.binomial(I_arr_10, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.binomial(I_arr_like_10, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.binomial(I_arr_10, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.binomial(I_arr_like_10, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(random_st.binomial(10, 0.5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.binomial(I_arr_10, 0.5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.binomial(10, D_arr_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.binomial(I_arr_10, 0.5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.binomial(10, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.binomial(I_arr_like_10, 0.5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.binomial(10, D_arr_like_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.binomial(I_arr_10, D_arr_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.binomial(I_arr_like_10, D_arr_like_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.binomial(I_arr_10, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.binomial(I_arr_like_10, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] reveal_type(random_st.negative_binomial(10, 0.5)) # E: int reveal_type(random_st.negative_binomial(10, 0.5, size=None)) # E: int -reveal_type(random_st.negative_binomial(10, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.negative_binomial(I_arr_10, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.negative_binomial(10, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.negative_binomial(I_arr_10, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.negative_binomial(10, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.negative_binomial(I_arr_like_10, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.negative_binomial(10, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.negative_binomial(I_arr_10, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.negative_binomial(I_arr_like_10, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.negative_binomial(I_arr_10, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.negative_binomial(I_arr_like_10, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(random_st.negative_binomial(10, 0.5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.negative_binomial(I_arr_10, 0.5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.negative_binomial(10, D_arr_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.negative_binomial(I_arr_10, 0.5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.negative_binomial(10, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.negative_binomial(I_arr_like_10, 0.5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.negative_binomial(10, D_arr_like_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.negative_binomial(I_arr_10, D_arr_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.negative_binomial(I_arr_like_10, D_arr_like_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.negative_binomial(I_arr_10, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.negative_binomial(I_arr_like_10, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] reveal_type(random_st.hypergeometric(20, 20, 10)) # E: int reveal_type(random_st.hypergeometric(20, 20, 10, size=None)) # E: int -reveal_type(random_st.hypergeometric(20, 20, 10, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.hypergeometric(I_arr_20, 20, 10)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.hypergeometric(20, I_arr_20, 10)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.hypergeometric(I_arr_20, 20, I_arr_like_10, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.hypergeometric(20, I_arr_20, 10, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.hypergeometric(I_arr_like_20, 20, I_arr_10)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.hypergeometric(20, I_arr_like_20, 10)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.hypergeometric(I_arr_20, I_arr_20, 10)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.hypergeometric(I_arr_like_20, I_arr_like_20, 10)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.hypergeometric(I_arr_20, I_arr_20, I_arr_10, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.hypergeometric(I_arr_like_20, I_arr_like_20, I_arr_like_10, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(random_st.hypergeometric(20, 20, 10, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.hypergeometric(I_arr_20, 20, 10)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.hypergeometric(20, I_arr_20, 10)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.hypergeometric(I_arr_20, 20, I_arr_like_10, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.hypergeometric(20, I_arr_20, 10, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.hypergeometric(I_arr_like_20, 20, I_arr_10)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.hypergeometric(20, I_arr_like_20, 10)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.hypergeometric(I_arr_20, I_arr_20, 10)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.hypergeometric(I_arr_like_20, I_arr_like_20, 10)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.hypergeometric(I_arr_20, I_arr_20, I_arr_10, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.hypergeometric(I_arr_like_20, I_arr_like_20, I_arr_like_10, size=1)) # E: ndarray[Any, dtype[{int_}]] reveal_type(random_st.randint(0, 100)) # E: int reveal_type(random_st.randint(100)) # E: int -reveal_type(random_st.randint([100])) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.randint(0, [100])) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(random_st.randint([100])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.randint(0, [100])) # E: ndarray[Any, dtype[{int_}]] reveal_type(random_st.randint(2, dtype=bool)) # E: builtins.bool reveal_type(random_st.randint(0, 2, dtype=bool)) # E: builtins.bool -reveal_type(random_st.randint(I_bool_high_open, dtype=bool)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(random_st.randint(I_bool_low, I_bool_high_open, dtype=bool)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(random_st.randint(0, I_bool_high_open, dtype=bool)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] +reveal_type(random_st.randint(I_bool_high_open, dtype=bool)) # E: ndarray[Any, dtype[bool_] +reveal_type(random_st.randint(I_bool_low, I_bool_high_open, dtype=bool)) # E: ndarray[Any, dtype[bool_] +reveal_type(random_st.randint(0, I_bool_high_open, dtype=bool)) # E: ndarray[Any, dtype[bool_] reveal_type(random_st.randint(2, dtype=np.bool_)) # E: builtins.bool reveal_type(random_st.randint(0, 2, dtype=np.bool_)) # E: builtins.bool -reveal_type(random_st.randint(I_bool_high_open, dtype=np.bool_)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(random_st.randint(I_bool_low, I_bool_high_open, dtype=np.bool_)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(random_st.randint(0, I_bool_high_open, dtype=np.bool_)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] +reveal_type(random_st.randint(I_bool_high_open, dtype=np.bool_)) # E: ndarray[Any, dtype[bool_] +reveal_type(random_st.randint(I_bool_low, I_bool_high_open, dtype=np.bool_)) # E: ndarray[Any, dtype[bool_] +reveal_type(random_st.randint(0, I_bool_high_open, dtype=np.bool_)) # E: ndarray[Any, dtype[bool_] reveal_type(random_st.randint(256, dtype="u1")) # E: int reveal_type(random_st.randint(0, 256, dtype="u1")) # E: int -reveal_type(random_st.randint(I_u1_high_open, dtype="u1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(I_u1_low, I_u1_high_open, dtype="u1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(0, I_u1_high_open, dtype="u1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] +reveal_type(random_st.randint(I_u1_high_open, dtype="u1")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(random_st.randint(I_u1_low, I_u1_high_open, dtype="u1")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(random_st.randint(0, I_u1_high_open, dtype="u1")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] reveal_type(random_st.randint(256, dtype="uint8")) # E: int reveal_type(random_st.randint(0, 256, dtype="uint8")) # E: int -reveal_type(random_st.randint(I_u1_high_open, dtype="uint8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(I_u1_low, I_u1_high_open, dtype="uint8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(0, I_u1_high_open, dtype="uint8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] +reveal_type(random_st.randint(I_u1_high_open, dtype="uint8")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(random_st.randint(I_u1_low, I_u1_high_open, dtype="uint8")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(random_st.randint(0, I_u1_high_open, dtype="uint8")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] reveal_type(random_st.randint(256, dtype=np.uint8)) # E: int reveal_type(random_st.randint(0, 256, dtype=np.uint8)) # E: int -reveal_type(random_st.randint(I_u1_high_open, dtype=np.uint8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(I_u1_low, I_u1_high_open, dtype=np.uint8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(0, I_u1_high_open, dtype=np.uint8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] +reveal_type(random_st.randint(I_u1_high_open, dtype=np.uint8)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(random_st.randint(I_u1_low, I_u1_high_open, dtype=np.uint8)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(random_st.randint(0, I_u1_high_open, dtype=np.uint8)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] reveal_type(random_st.randint(65536, dtype="u2")) # E: int reveal_type(random_st.randint(0, 65536, dtype="u2")) # E: int -reveal_type(random_st.randint(I_u2_high_open, dtype="u2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(I_u2_low, I_u2_high_open, dtype="u2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(0, I_u2_high_open, dtype="u2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] +reveal_type(random_st.randint(I_u2_high_open, dtype="u2")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(random_st.randint(I_u2_low, I_u2_high_open, dtype="u2")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(random_st.randint(0, I_u2_high_open, dtype="u2")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] reveal_type(random_st.randint(65536, dtype="uint16")) # E: int reveal_type(random_st.randint(0, 65536, dtype="uint16")) # E: int -reveal_type(random_st.randint(I_u2_high_open, dtype="uint16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(I_u2_low, I_u2_high_open, dtype="uint16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(0, I_u2_high_open, dtype="uint16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] +reveal_type(random_st.randint(I_u2_high_open, dtype="uint16")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(random_st.randint(I_u2_low, I_u2_high_open, dtype="uint16")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(random_st.randint(0, I_u2_high_open, dtype="uint16")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] reveal_type(random_st.randint(65536, dtype=np.uint16)) # E: int reveal_type(random_st.randint(0, 65536, dtype=np.uint16)) # E: int -reveal_type(random_st.randint(I_u2_high_open, dtype=np.uint16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(I_u2_low, I_u2_high_open, dtype=np.uint16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(0, I_u2_high_open, dtype=np.uint16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] +reveal_type(random_st.randint(I_u2_high_open, dtype=np.uint16)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(random_st.randint(I_u2_low, I_u2_high_open, dtype=np.uint16)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(random_st.randint(0, I_u2_high_open, dtype=np.uint16)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] reveal_type(random_st.randint(4294967296, dtype="u4")) # E: int reveal_type(random_st.randint(0, 4294967296, dtype="u4")) # E: int -reveal_type(random_st.randint(I_u4_high_open, dtype="u4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(I_u4_low, I_u4_high_open, dtype="u4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(0, I_u4_high_open, dtype="u4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] +reveal_type(random_st.randint(I_u4_high_open, dtype="u4")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(random_st.randint(I_u4_low, I_u4_high_open, dtype="u4")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(random_st.randint(0, I_u4_high_open, dtype="u4")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] reveal_type(random_st.randint(4294967296, dtype="uint32")) # E: int reveal_type(random_st.randint(0, 4294967296, dtype="uint32")) # E: int -reveal_type(random_st.randint(I_u4_high_open, dtype="uint32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(I_u4_low, I_u4_high_open, dtype="uint32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(0, I_u4_high_open, dtype="uint32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] +reveal_type(random_st.randint(I_u4_high_open, dtype="uint32")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(random_st.randint(I_u4_low, I_u4_high_open, dtype="uint32")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(random_st.randint(0, I_u4_high_open, dtype="uint32")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] reveal_type(random_st.randint(4294967296, dtype=np.uint32)) # E: int reveal_type(random_st.randint(0, 4294967296, dtype=np.uint32)) # E: int -reveal_type(random_st.randint(I_u4_high_open, dtype=np.uint32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(I_u4_low, I_u4_high_open, dtype=np.uint32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(0, I_u4_high_open, dtype=np.uint32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] +reveal_type(random_st.randint(I_u4_high_open, dtype=np.uint32)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(random_st.randint(I_u4_low, I_u4_high_open, dtype=np.uint32)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(random_st.randint(0, I_u4_high_open, dtype=np.uint32)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] reveal_type(random_st.randint(4294967296, dtype=np.uint)) # E: int reveal_type(random_st.randint(0, 4294967296, dtype=np.uint)) # E: int -reveal_type(random_st.randint(I_u4_high_open, dtype=np.uint)) # E: numpy.ndarray[Any, numpy.dtype[{uint}]] -reveal_type(random_st.randint(I_u4_low, I_u4_high_open, dtype=np.uint)) # E: numpy.ndarray[Any, numpy.dtype[{uint}]] -reveal_type(random_st.randint(0, I_u4_high_open, dtype=np.uint)) # E: numpy.ndarray[Any, numpy.dtype[{uint}]] +reveal_type(random_st.randint(I_u4_high_open, dtype=np.uint)) # E: ndarray[Any, dtype[{uint}]] +reveal_type(random_st.randint(I_u4_low, I_u4_high_open, dtype=np.uint)) # E: ndarray[Any, dtype[{uint}]] +reveal_type(random_st.randint(0, I_u4_high_open, dtype=np.uint)) # E: ndarray[Any, dtype[{uint}]] reveal_type(random_st.randint(18446744073709551616, dtype="u8")) # E: int reveal_type(random_st.randint(0, 18446744073709551616, dtype="u8")) # E: int -reveal_type(random_st.randint(I_u8_high_open, dtype="u8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(I_u8_low, I_u8_high_open, dtype="u8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(0, I_u8_high_open, dtype="u8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] +reveal_type(random_st.randint(I_u8_high_open, dtype="u8")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(random_st.randint(I_u8_low, I_u8_high_open, dtype="u8")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(random_st.randint(0, I_u8_high_open, dtype="u8")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] reveal_type(random_st.randint(18446744073709551616, dtype="uint64")) # E: int reveal_type(random_st.randint(0, 18446744073709551616, dtype="uint64")) # E: int -reveal_type(random_st.randint(I_u8_high_open, dtype="uint64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(I_u8_low, I_u8_high_open, dtype="uint64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(0, I_u8_high_open, dtype="uint64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] +reveal_type(random_st.randint(I_u8_high_open, dtype="uint64")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(random_st.randint(I_u8_low, I_u8_high_open, dtype="uint64")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(random_st.randint(0, I_u8_high_open, dtype="uint64")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] reveal_type(random_st.randint(18446744073709551616, dtype=np.uint64)) # E: int reveal_type(random_st.randint(0, 18446744073709551616, dtype=np.uint64)) # E: int -reveal_type(random_st.randint(I_u8_high_open, dtype=np.uint64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(I_u8_low, I_u8_high_open, dtype=np.uint64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(0, I_u8_high_open, dtype=np.uint64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] +reveal_type(random_st.randint(I_u8_high_open, dtype=np.uint64)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(random_st.randint(I_u8_low, I_u8_high_open, dtype=np.uint64)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(random_st.randint(0, I_u8_high_open, dtype=np.uint64)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] reveal_type(random_st.randint(128, dtype="i1")) # E: int reveal_type(random_st.randint(-128, 128, dtype="i1")) # E: int -reveal_type(random_st.randint(I_i1_high_open, dtype="i1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(I_i1_low, I_i1_high_open, dtype="i1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(-128, I_i1_high_open, dtype="i1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] +reveal_type(random_st.randint(I_i1_high_open, dtype="i1")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(random_st.randint(I_i1_low, I_i1_high_open, dtype="i1")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(random_st.randint(-128, I_i1_high_open, dtype="i1")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] reveal_type(random_st.randint(128, dtype="int8")) # E: int reveal_type(random_st.randint(-128, 128, dtype="int8")) # E: int -reveal_type(random_st.randint(I_i1_high_open, dtype="int8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(I_i1_low, I_i1_high_open, dtype="int8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(-128, I_i1_high_open, dtype="int8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] +reveal_type(random_st.randint(I_i1_high_open, dtype="int8")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(random_st.randint(I_i1_low, I_i1_high_open, dtype="int8")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(random_st.randint(-128, I_i1_high_open, dtype="int8")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] reveal_type(random_st.randint(128, dtype=np.int8)) # E: int reveal_type(random_st.randint(-128, 128, dtype=np.int8)) # E: int -reveal_type(random_st.randint(I_i1_high_open, dtype=np.int8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(I_i1_low, I_i1_high_open, dtype=np.int8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(-128, I_i1_high_open, dtype=np.int8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] +reveal_type(random_st.randint(I_i1_high_open, dtype=np.int8)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(random_st.randint(I_i1_low, I_i1_high_open, dtype=np.int8)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(random_st.randint(-128, I_i1_high_open, dtype=np.int8)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] reveal_type(random_st.randint(32768, dtype="i2")) # E: int reveal_type(random_st.randint(-32768, 32768, dtype="i2")) # E: int -reveal_type(random_st.randint(I_i2_high_open, dtype="i2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(I_i2_low, I_i2_high_open, dtype="i2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(-32768, I_i2_high_open, dtype="i2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] +reveal_type(random_st.randint(I_i2_high_open, dtype="i2")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(random_st.randint(I_i2_low, I_i2_high_open, dtype="i2")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(random_st.randint(-32768, I_i2_high_open, dtype="i2")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] reveal_type(random_st.randint(32768, dtype="int16")) # E: int reveal_type(random_st.randint(-32768, 32768, dtype="int16")) # E: int -reveal_type(random_st.randint(I_i2_high_open, dtype="int16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(I_i2_low, I_i2_high_open, dtype="int16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(-32768, I_i2_high_open, dtype="int16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] +reveal_type(random_st.randint(I_i2_high_open, dtype="int16")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(random_st.randint(I_i2_low, I_i2_high_open, dtype="int16")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(random_st.randint(-32768, I_i2_high_open, dtype="int16")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] reveal_type(random_st.randint(32768, dtype=np.int16)) # E: int reveal_type(random_st.randint(-32768, 32768, dtype=np.int16)) # E: int -reveal_type(random_st.randint(I_i2_high_open, dtype=np.int16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(I_i2_low, I_i2_high_open, dtype=np.int16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(-32768, I_i2_high_open, dtype=np.int16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] +reveal_type(random_st.randint(I_i2_high_open, dtype=np.int16)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(random_st.randint(I_i2_low, I_i2_high_open, dtype=np.int16)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(random_st.randint(-32768, I_i2_high_open, dtype=np.int16)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] reveal_type(random_st.randint(2147483648, dtype="i4")) # E: int reveal_type(random_st.randint(-2147483648, 2147483648, dtype="i4")) # E: int -reveal_type(random_st.randint(I_i4_high_open, dtype="i4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(I_i4_low, I_i4_high_open, dtype="i4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(-2147483648, I_i4_high_open, dtype="i4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] +reveal_type(random_st.randint(I_i4_high_open, dtype="i4")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(random_st.randint(I_i4_low, I_i4_high_open, dtype="i4")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(random_st.randint(-2147483648, I_i4_high_open, dtype="i4")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] reveal_type(random_st.randint(2147483648, dtype="int32")) # E: int reveal_type(random_st.randint(-2147483648, 2147483648, dtype="int32")) # E: int -reveal_type(random_st.randint(I_i4_high_open, dtype="int32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(I_i4_low, I_i4_high_open, dtype="int32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(-2147483648, I_i4_high_open, dtype="int32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] +reveal_type(random_st.randint(I_i4_high_open, dtype="int32")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(random_st.randint(I_i4_low, I_i4_high_open, dtype="int32")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(random_st.randint(-2147483648, I_i4_high_open, dtype="int32")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] reveal_type(random_st.randint(2147483648, dtype=np.int32)) # E: int reveal_type(random_st.randint(-2147483648, 2147483648, dtype=np.int32)) # E: int -reveal_type(random_st.randint(I_i4_high_open, dtype=np.int32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(I_i4_low, I_i4_high_open, dtype=np.int32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(-2147483648, I_i4_high_open, dtype=np.int32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] +reveal_type(random_st.randint(I_i4_high_open, dtype=np.int32)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(random_st.randint(I_i4_low, I_i4_high_open, dtype=np.int32)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(random_st.randint(-2147483648, I_i4_high_open, dtype=np.int32)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] reveal_type(random_st.randint(2147483648, dtype=np.int_)) # E: int reveal_type(random_st.randint(-2147483648, 2147483648, dtype=np.int_)) # E: int -reveal_type(random_st.randint(I_i4_high_open, dtype=np.int_)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.randint(I_i4_low, I_i4_high_open, dtype=np.int_)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.randint(-2147483648, I_i4_high_open, dtype=np.int_)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(random_st.randint(I_i4_high_open, dtype=np.int_)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.randint(I_i4_low, I_i4_high_open, dtype=np.int_)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.randint(-2147483648, I_i4_high_open, dtype=np.int_)) # E: ndarray[Any, dtype[{int_}]] reveal_type(random_st.randint(9223372036854775808, dtype="i8")) # E: int reveal_type(random_st.randint(-9223372036854775808, 9223372036854775808, dtype="i8")) # E: int -reveal_type(random_st.randint(I_i8_high_open, dtype="i8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(I_i8_low, I_i8_high_open, dtype="i8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(-9223372036854775808, I_i8_high_open, dtype="i8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] +reveal_type(random_st.randint(I_i8_high_open, dtype="i8")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(random_st.randint(I_i8_low, I_i8_high_open, dtype="i8")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(random_st.randint(-9223372036854775808, I_i8_high_open, dtype="i8")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] reveal_type(random_st.randint(9223372036854775808, dtype="int64")) # E: int reveal_type(random_st.randint(-9223372036854775808, 9223372036854775808, dtype="int64")) # E: int -reveal_type(random_st.randint(I_i8_high_open, dtype="int64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(I_i8_low, I_i8_high_open, dtype="int64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(-9223372036854775808, I_i8_high_open, dtype="int64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] +reveal_type(random_st.randint(I_i8_high_open, dtype="int64")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(random_st.randint(I_i8_low, I_i8_high_open, dtype="int64")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(random_st.randint(-9223372036854775808, I_i8_high_open, dtype="int64")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] reveal_type(random_st.randint(9223372036854775808, dtype=np.int64)) # E: int reveal_type(random_st.randint(-9223372036854775808, 9223372036854775808, dtype=np.int64)) # E: int -reveal_type(random_st.randint(I_i8_high_open, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(I_i8_low, I_i8_high_open, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(-9223372036854775808, I_i8_high_open, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] +reveal_type(random_st.randint(I_i8_high_open, dtype=np.int64)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(random_st.randint(I_i8_low, I_i8_high_open, dtype=np.int64)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(random_st.randint(-9223372036854775808, I_i8_high_open, dtype=np.int64)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] reveal_type(random_st._bit_generator) # E: BitGenerator reveal_type(random_st.bytes(2)) # E: bytes reveal_type(random_st.choice(5)) # E: int -reveal_type(random_st.choice(5, 3)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.choice(5, 3, replace=True)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.choice(5, 3, p=[1 / 5] * 5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.choice(5, 3, p=[1 / 5] * 5, replace=False)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(random_st.choice(5, 3)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.choice(5, 3, replace=True)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.choice(5, 3, p=[1 / 5] * 5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.choice(5, 3, p=[1 / 5] * 5, replace=False)) # E: ndarray[Any, dtype[{int_}]] reveal_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"])) # E: Any -reveal_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3)) # E: numpy.ndarray[Any, Any] -reveal_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, p=[1 / 4] * 4)) # E: numpy.ndarray[Any, Any] -reveal_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=True)) # E: numpy.ndarray[Any, Any] -reveal_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=False, p=np.array([1 / 8, 1 / 8, 1 / 2, 1 / 4]))) # E: numpy.ndarray[Any, Any] +reveal_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3)) # E: ndarray[Any, Any] +reveal_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, p=[1 / 4] * 4)) # E: ndarray[Any, Any] +reveal_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=True)) # E: ndarray[Any, Any] +reveal_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=False, p=np.array([1 / 8, 1 / 8, 1 / 2, 1 / 4]))) # E: ndarray[Any, Any] -reveal_type(random_st.dirichlet([0.5, 0.5])) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.dirichlet(np.array([0.5, 0.5]))) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.dirichlet(np.array([0.5, 0.5]), size=3)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.dirichlet([0.5, 0.5])) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.dirichlet(np.array([0.5, 0.5]))) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.dirichlet(np.array([0.5, 0.5]), size=3)) # E: ndarray[Any, dtype[floating[typing._64Bit]] -reveal_type(random_st.multinomial(20, [1 / 6.0] * 6)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.multinomial(20, np.array([0.5, 0.5]))) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.multinomial(20, [1 / 6.0] * 6, size=2)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(random_st.multinomial(20, [1 / 6.0] * 6)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.multinomial(20, np.array([0.5, 0.5]))) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.multinomial(20, [1 / 6.0] * 6, size=2)) # E: ndarray[Any, dtype[{int_}]] -reveal_type(random_st.multivariate_normal([0.0], [[1.0]])) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.multivariate_normal([0.0], np.array([[1.0]]))) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.multivariate_normal(np.array([0.0]), [[1.0]])) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.multivariate_normal([0.0], np.array([[1.0]]))) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.multivariate_normal([0.0], [[1.0]])) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.multivariate_normal([0.0], np.array([[1.0]]))) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.multivariate_normal(np.array([0.0]), [[1.0]])) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.multivariate_normal([0.0], np.array([[1.0]]))) # E: ndarray[Any, dtype[floating[typing._64Bit]] -reveal_type(random_st.permutation(10)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.permutation([1, 2, 3, 4])) # E: numpy.ndarray[Any, Any] -reveal_type(random_st.permutation(np.array([1, 2, 3, 4]))) # E: numpy.ndarray[Any, Any] -reveal_type(random_st.permutation(D_2D)) # E: numpy.ndarray[Any, Any] +reveal_type(random_st.permutation(10)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.permutation([1, 2, 3, 4])) # E: ndarray[Any, Any] +reveal_type(random_st.permutation(np.array([1, 2, 3, 4]))) # E: ndarray[Any, Any] +reveal_type(random_st.permutation(D_2D)) # E: ndarray[Any, Any] reveal_type(random_st.shuffle(np.arange(10))) # E: None reveal_type(random_st.shuffle([1, 2, 3, 4, 5])) # E: None @@ -1521,19 +1521,19 @@ reveal_type(random_st.seed([0, 1])) # E: None random_st_get_state = random_st.get_state() reveal_type(random_st_state) # E: builtins.dict[builtins.str, Any] random_st_get_state_legacy = random_st.get_state(legacy=True) -reveal_type(random_st_get_state_legacy) # E: Union[builtins.dict[builtins.str, Any], Tuple[builtins.str, numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]], builtins.int, builtins.int, builtins.float]] +reveal_type(random_st_get_state_legacy) # E: Union[builtins.dict[builtins.str, Any], Tuple[builtins.str, ndarray[Any, dtype[unsignedinteger[typing._32Bit]]], builtins.int, builtins.int, builtins.float]] reveal_type(random_st.set_state(random_st_get_state)) # E: None reveal_type(random_st.rand()) # E: float -reveal_type(random_st.rand(1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.rand(1, 2)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.rand(1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.rand(1, 2)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.randn()) # E: float -reveal_type(random_st.randn(1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.randn(1, 2)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.randn(1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.randn(1, 2)) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.random_sample()) # E: float -reveal_type(random_st.random_sample(1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.random_sample(size=(1, 2))) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +reveal_type(random_st.random_sample(1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.random_sample(size=(1, 2))) # E: ndarray[Any, dtype[floating[typing._64Bit]] reveal_type(random_st.tomaxint()) # E: int -reveal_type(random_st.tomaxint(1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.tomaxint((1,))) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(random_st.tomaxint(1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.tomaxint((1,))) # E: ndarray[Any, dtype[{int_}]] diff --git a/numpy/typing/tests/data/reveal/rec.pyi b/numpy/typing/tests/data/reveal/rec.pyi index 2fa8cc7b9..bf51c82a3 100644 --- a/numpy/typing/tests/data/reveal/rec.pyi +++ b/numpy/typing/tests/data/reveal/rec.pyi @@ -12,13 +12,13 @@ format_parser: np.format_parser record: np.record file_obj: io.BufferedIOBase -reveal_type(np.format_parser( # E: numpy.format_parser +reveal_type(np.format_parser( # E: format_parser formats=[np.float64, np.int64, np.bool_], names=["f8", "i8", "?"], titles=None, aligned=True, )) -reveal_type(format_parser.dtype) # E: numpy.dtype[numpy.void] +reveal_type(format_parser.dtype) # E: dtype[void] reveal_type(record.field_a) # E: Any reveal_type(record.field_b) # E: Any @@ -34,72 +34,72 @@ reveal_type(REC_AR_V.field("field_a", AR_i8)) # E: None reveal_type(REC_AR_V["field_a"]) # E: Any reveal_type(REC_AR_V.field_a) # E: Any -reveal_type(np.recarray( # numpy.recarray[Any, numpy.dtype[numpy.record]] +reveal_type(np.recarray( # recarray[Any, dtype[record]] shape=(10, 5), formats=[np.float64, np.int64, np.bool_], order="K", byteorder="|", )) -reveal_type(np.recarray( # numpy.recarray[Any, numpy.dtype[Any]] +reveal_type(np.recarray( # recarray[Any, dtype[Any]] shape=(10, 5), dtype=[("f8", np.float64), ("i8", np.int64)], strides=(5, 5), )) -reveal_type(np.rec.fromarrays( # numpy.recarray[Any, numpy.dtype[numpy.record]] +reveal_type(np.rec.fromarrays( # recarray[Any, dtype[record]] AR_LIST, )) -reveal_type(np.rec.fromarrays( # numpy.recarray[Any, numpy.dtype[Any]] +reveal_type(np.rec.fromarrays( # recarray[Any, dtype[Any]] AR_LIST, dtype=np.int64, )) -reveal_type(np.rec.fromarrays( # numpy.recarray[Any, numpy.dtype[Any]] +reveal_type(np.rec.fromarrays( # recarray[Any, dtype[Any]] AR_LIST, formats=[np.int64, np.float64], names=["i8", "f8"] )) -reveal_type(np.rec.fromrecords( # numpy.recarray[Any, numpy.dtype[numpy.record]] +reveal_type(np.rec.fromrecords( # recarray[Any, dtype[record]] (1, 1.5), )) -reveal_type(np.rec.fromrecords( # numpy.recarray[Any, numpy.dtype[numpy.record]] +reveal_type(np.rec.fromrecords( # recarray[Any, dtype[record]] [(1, 1.5)], dtype=[("i8", np.int64), ("f8", np.float64)], )) -reveal_type(np.rec.fromrecords( # numpy.recarray[Any, numpy.dtype[numpy.record]] +reveal_type(np.rec.fromrecords( # recarray[Any, dtype[record]] REC_AR_V, formats=[np.int64, np.float64], names=["i8", "f8"] )) -reveal_type(np.rec.fromstring( # numpy.recarray[Any, numpy.dtype[numpy.record]] +reveal_type(np.rec.fromstring( # recarray[Any, dtype[record]] b"(1, 1.5)", dtype=[("i8", np.int64), ("f8", np.float64)], )) -reveal_type(np.rec.fromstring( # numpy.recarray[Any, numpy.dtype[numpy.record]] +reveal_type(np.rec.fromstring( # recarray[Any, dtype[record]] REC_AR_V, formats=[np.int64, np.float64], names=["i8", "f8"] )) -reveal_type(np.rec.fromfile( # numpy.recarray[Any, numpy.dtype[Any]] +reveal_type(np.rec.fromfile( # recarray[Any, dtype[Any]] "test_file.txt", dtype=[("i8", np.int64), ("f8", np.float64)], )) -reveal_type(np.rec.fromfile( # numpy.recarray[Any, numpy.dtype[numpy.record]] +reveal_type(np.rec.fromfile( # recarray[Any, dtype[record]] file_obj, formats=[np.int64, np.float64], names=["i8", "f8"] )) -reveal_type(np.rec.array( # numpy.recarray[Any, numpy.dtype[{int64}]] +reveal_type(np.rec.array( # recarray[Any, dtype[{int64}]] AR_i8, )) -reveal_type(np.rec.array( # numpy.recarray[Any, numpy.dtype[Any]] +reveal_type(np.rec.array( # recarray[Any, dtype[Any]] [(1, 1.5)], dtype=[("i8", np.int64), ("f8", np.float64)], )) -reveal_type(np.rec.array( # numpy.recarray[Any, numpy.dtype[numpy.record]] +reveal_type(np.rec.array( # recarray[Any, dtype[record]] [(1, 1.5)], formats=[np.int64, np.float64], names=["i8", "f8"] diff --git a/numpy/typing/tests/data/reveal/scalars.pyi b/numpy/typing/tests/data/reveal/scalars.pyi index a95f8f6f2..383e40ef0 100644 --- a/numpy/typing/tests/data/reveal/scalars.pyi +++ b/numpy/typing/tests/data/reveal/scalars.pyi @@ -29,27 +29,27 @@ reveal_type(c8.squeeze()) # E: {complex64} reveal_type(c8.byteswap()) # E: {complex64} reveal_type(c8.transpose()) # E: {complex64} -reveal_type(c8.dtype) # E: numpy.dtype[{complex64}] +reveal_type(c8.dtype) # E: dtype[{complex64}] reveal_type(c8.real) # E: {float32} reveal_type(c16.imag) # E: {float64} -reveal_type(np.unicode_('foo')) # E: numpy.str_ -reveal_type(np.str0('foo')) # E: numpy.str_ +reveal_type(np.unicode_('foo')) # E: str_ +reveal_type(np.str0('foo')) # E: str_ reveal_type(V[0]) # E: Any reveal_type(V["field1"]) # E: Any -reveal_type(V[["field1", "field2"]]) # E: numpy.void +reveal_type(V[["field1", "field2"]]) # E: void V[0] = 5 # Aliases -reveal_type(np.unicode_()) # E: numpy.str_ -reveal_type(np.str0()) # E: numpy.str_ -reveal_type(np.bool8()) # E: numpy.bool_ -reveal_type(np.bytes0()) # E: numpy.bytes_ -reveal_type(np.string_()) # E: numpy.bytes_ -reveal_type(np.object0()) # E: numpy.object_ -reveal_type(np.void0(0)) # E: numpy.void +reveal_type(np.unicode_()) # E: str_ +reveal_type(np.str0()) # E: str_ +reveal_type(np.bool8()) # E: bool_ +reveal_type(np.bytes0()) # E: bytes_ +reveal_type(np.string_()) # E: bytes_ +reveal_type(np.object0()) # E: object_ +reveal_type(np.void0(0)) # E: void reveal_type(np.byte()) # E: {byte} reveal_type(np.short()) # E: {short} @@ -99,29 +99,29 @@ reveal_type(c16.tolist()) # E: complex reveal_type(U.tolist()) # E: str reveal_type(S.tolist()) # E: bytes -reveal_type(b.ravel()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(i8.ravel()) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(u8.ravel()) # E: numpy.ndarray[Any, numpy.dtype[{uint64}]] -reveal_type(f8.ravel()) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(c16.ravel()) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] -reveal_type(U.ravel()) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(S.ravel()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] - -reveal_type(b.flatten()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(i8.flatten()) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(u8.flatten()) # E: numpy.ndarray[Any, numpy.dtype[{uint64}]] -reveal_type(f8.flatten()) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(c16.flatten()) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] -reveal_type(U.flatten()) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(S.flatten()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] - -reveal_type(b.reshape(1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(i8.reshape(1)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(u8.reshape(1)) # E: numpy.ndarray[Any, numpy.dtype[{uint64}]] -reveal_type(f8.reshape(1)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(c16.reshape(1)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] -reveal_type(U.reshape(1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(S.reshape(1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] +reveal_type(b.ravel()) # E: ndarray[Any, dtype[bool_]] +reveal_type(i8.ravel()) # E: ndarray[Any, dtype[{int64}]] +reveal_type(u8.ravel()) # E: ndarray[Any, dtype[{uint64}]] +reveal_type(f8.ravel()) # E: ndarray[Any, dtype[{float64}]] +reveal_type(c16.ravel()) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(U.ravel()) # E: ndarray[Any, dtype[str_]] +reveal_type(S.ravel()) # E: ndarray[Any, dtype[bytes_]] + +reveal_type(b.flatten()) # E: ndarray[Any, dtype[bool_]] +reveal_type(i8.flatten()) # E: ndarray[Any, dtype[{int64}]] +reveal_type(u8.flatten()) # E: ndarray[Any, dtype[{uint64}]] +reveal_type(f8.flatten()) # E: ndarray[Any, dtype[{float64}]] +reveal_type(c16.flatten()) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(U.flatten()) # E: ndarray[Any, dtype[str_]] +reveal_type(S.flatten()) # E: ndarray[Any, dtype[bytes_]] + +reveal_type(b.reshape(1)) # E: ndarray[Any, dtype[bool_]] +reveal_type(i8.reshape(1)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(u8.reshape(1)) # E: ndarray[Any, dtype[{uint64}]] +reveal_type(f8.reshape(1)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(c16.reshape(1)) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(U.reshape(1)) # E: ndarray[Any, dtype[str_]] +reveal_type(S.reshape(1)) # E: ndarray[Any, dtype[bytes_]] reveal_type(i8.astype(float)) # E: Any reveal_type(i8.astype(np.float64)) # E: {float64} @@ -149,7 +149,7 @@ reveal_type(i8.numerator) # E: {int64} reveal_type(i8.denominator) # E: Literal[1] reveal_type(u8.numerator) # E: {uint64} reveal_type(u8.denominator) # E: Literal[1] -reveal_type(m.numerator) # E: numpy.timedelta64 +reveal_type(m.numerator) # E: timedelta64 reveal_type(m.denominator) # E: Literal[1] reveal_type(round(i8)) # E: int diff --git a/numpy/typing/tests/data/reveal/shape_base.pyi b/numpy/typing/tests/data/reveal/shape_base.pyi index 57633defb..f13678c3a 100644 --- a/numpy/typing/tests/data/reveal/shape_base.pyi +++ b/numpy/typing/tests/data/reveal/shape_base.pyi @@ -11,47 +11,47 @@ AR_f8: NDArray[np.float64] AR_LIKE_f8: List[float] -reveal_type(np.take_along_axis(AR_f8, AR_i8, axis=1)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.take_along_axis(f8, AR_i8, axis=None)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.take_along_axis(AR_f8, AR_i8, axis=1)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.take_along_axis(f8, AR_i8, axis=None)) # E: ndarray[Any, dtype[{float64}]] reveal_type(np.put_along_axis(AR_f8, AR_i8, "1.0", axis=1)) # E: None -reveal_type(np.expand_dims(AR_i8, 2)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.expand_dims(AR_LIKE_f8, 2)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.expand_dims(AR_i8, 2)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.expand_dims(AR_LIKE_f8, 2)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.column_stack([AR_i8])) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.column_stack([AR_LIKE_f8])) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.column_stack([AR_i8])) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.column_stack([AR_LIKE_f8])) # E: ndarray[Any, dtype[Any]] -reveal_type(np.dstack([AR_i8])) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.dstack([AR_LIKE_f8])) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.dstack([AR_i8])) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.dstack([AR_LIKE_f8])) # E: ndarray[Any, dtype[Any]] -reveal_type(np.row_stack([AR_i8])) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.row_stack([AR_LIKE_f8])) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.row_stack([AR_i8])) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.row_stack([AR_LIKE_f8])) # E: ndarray[Any, dtype[Any]] -reveal_type(np.array_split(AR_i8, [3, 5, 6, 10])) # E: list[numpy.ndarray[Any, numpy.dtype[{int64}]]] -reveal_type(np.array_split(AR_LIKE_f8, [3, 5, 6, 10])) # E: list[numpy.ndarray[Any, numpy.dtype[Any]]] +reveal_type(np.array_split(AR_i8, [3, 5, 6, 10])) # E: list[ndarray[Any, dtype[{int64}]]] +reveal_type(np.array_split(AR_LIKE_f8, [3, 5, 6, 10])) # E: list[ndarray[Any, dtype[Any]]] -reveal_type(np.split(AR_i8, [3, 5, 6, 10])) # E: list[numpy.ndarray[Any, numpy.dtype[{int64}]]] -reveal_type(np.split(AR_LIKE_f8, [3, 5, 6, 10])) # E: list[numpy.ndarray[Any, numpy.dtype[Any]]] +reveal_type(np.split(AR_i8, [3, 5, 6, 10])) # E: list[ndarray[Any, dtype[{int64}]]] +reveal_type(np.split(AR_LIKE_f8, [3, 5, 6, 10])) # E: list[ndarray[Any, dtype[Any]]] -reveal_type(np.hsplit(AR_i8, [3, 5, 6, 10])) # E: list[numpy.ndarray[Any, numpy.dtype[{int64}]]] -reveal_type(np.hsplit(AR_LIKE_f8, [3, 5, 6, 10])) # E: list[numpy.ndarray[Any, numpy.dtype[Any]]] +reveal_type(np.hsplit(AR_i8, [3, 5, 6, 10])) # E: list[ndarray[Any, dtype[{int64}]]] +reveal_type(np.hsplit(AR_LIKE_f8, [3, 5, 6, 10])) # E: list[ndarray[Any, dtype[Any]]] -reveal_type(np.vsplit(AR_i8, [3, 5, 6, 10])) # E: list[numpy.ndarray[Any, numpy.dtype[{int64}]]] -reveal_type(np.vsplit(AR_LIKE_f8, [3, 5, 6, 10])) # E: list[numpy.ndarray[Any, numpy.dtype[Any]]] +reveal_type(np.vsplit(AR_i8, [3, 5, 6, 10])) # E: list[ndarray[Any, dtype[{int64}]]] +reveal_type(np.vsplit(AR_LIKE_f8, [3, 5, 6, 10])) # E: list[ndarray[Any, dtype[Any]]] -reveal_type(np.dsplit(AR_i8, [3, 5, 6, 10])) # E: list[numpy.ndarray[Any, numpy.dtype[{int64}]]] -reveal_type(np.dsplit(AR_LIKE_f8, [3, 5, 6, 10])) # E: list[numpy.ndarray[Any, numpy.dtype[Any]]] +reveal_type(np.dsplit(AR_i8, [3, 5, 6, 10])) # E: list[ndarray[Any, dtype[{int64}]]] +reveal_type(np.dsplit(AR_LIKE_f8, [3, 5, 6, 10])) # E: list[ndarray[Any, dtype[Any]]] -reveal_type(np.lib.shape_base.get_array_prepare(AR_i8)) # E: numpy.lib.shape_base._ArrayPrepare -reveal_type(np.lib.shape_base.get_array_prepare(AR_i8, 1)) # E: Union[None, numpy.lib.shape_base._ArrayPrepare] +reveal_type(np.lib.shape_base.get_array_prepare(AR_i8)) # E: lib.shape_base._ArrayPrepare +reveal_type(np.lib.shape_base.get_array_prepare(AR_i8, 1)) # E: Union[None, lib.shape_base._ArrayPrepare] -reveal_type(np.get_array_wrap(AR_i8)) # E: numpy.lib.shape_base._ArrayWrap -reveal_type(np.get_array_wrap(AR_i8, 1)) # E: Union[None, numpy.lib.shape_base._ArrayWrap] +reveal_type(np.get_array_wrap(AR_i8)) # E: lib.shape_base._ArrayWrap +reveal_type(np.get_array_wrap(AR_i8, 1)) # E: Union[None, lib.shape_base._ArrayWrap] -reveal_type(np.kron(AR_b, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.kron(AR_b, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.kron(AR_f8, AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(np.kron(AR_b, AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.kron(AR_b, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.kron(AR_f8, AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] -reveal_type(np.tile(AR_i8, 5)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.tile(AR_LIKE_f8, [2, 2])) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.tile(AR_i8, 5)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.tile(AR_LIKE_f8, [2, 2])) # E: ndarray[Any, dtype[Any]] diff --git a/numpy/typing/tests/data/reveal/stride_tricks.pyi b/numpy/typing/tests/data/reveal/stride_tricks.pyi index 152d9cea6..0d6dcd388 100644 --- a/numpy/typing/tests/data/reveal/stride_tricks.pyi +++ b/numpy/typing/tests/data/reveal/stride_tricks.pyi @@ -6,23 +6,23 @@ AR_f8: npt.NDArray[np.float64] AR_LIKE_f: List[float] interface_dict: Dict[str, Any] -reveal_type(np.lib.stride_tricks.DummyArray(interface_dict)) # E: numpy.lib.stride_tricks.DummyArray +reveal_type(np.lib.stride_tricks.DummyArray(interface_dict)) # E: lib.stride_tricks.DummyArray -reveal_type(np.lib.stride_tricks.as_strided(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.lib.stride_tricks.as_strided(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.lib.stride_tricks.as_strided(AR_f8, strides=(1, 5))) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.lib.stride_tricks.as_strided(AR_f8, shape=[9, 20])) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.lib.stride_tricks.as_strided(AR_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.lib.stride_tricks.as_strided(AR_LIKE_f)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.lib.stride_tricks.as_strided(AR_f8, strides=(1, 5))) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.lib.stride_tricks.as_strided(AR_f8, shape=[9, 20])) # E: ndarray[Any, dtype[{float64}]] -reveal_type(np.lib.stride_tricks.sliding_window_view(AR_f8, 5)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.lib.stride_tricks.sliding_window_view(AR_LIKE_f, (1, 5))) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.lib.stride_tricks.sliding_window_view(AR_f8, [9], axis=1)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.lib.stride_tricks.sliding_window_view(AR_f8, 5)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.lib.stride_tricks.sliding_window_view(AR_LIKE_f, (1, 5))) # E: ndarray[Any, dtype[Any]] +reveal_type(np.lib.stride_tricks.sliding_window_view(AR_f8, [9], axis=1)) # E: ndarray[Any, dtype[{float64}]] -reveal_type(np.broadcast_to(AR_f8, 5)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.broadcast_to(AR_LIKE_f, (1, 5))) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.broadcast_to(AR_f8, [4, 6], subok=True)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.broadcast_to(AR_f8, 5)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.broadcast_to(AR_LIKE_f, (1, 5))) # E: ndarray[Any, dtype[Any]] +reveal_type(np.broadcast_to(AR_f8, [4, 6], subok=True)) # E: ndarray[Any, dtype[{float64}]] reveal_type(np.broadcast_shapes((1, 2), [3, 1], (3, 2))) # E: tuple[builtins.int] reveal_type(np.broadcast_shapes((6, 7), (5, 6, 1), 7, (5, 1, 7))) # E: tuple[builtins.int] -reveal_type(np.broadcast_arrays(AR_f8, AR_f8)) # E: list[numpy.ndarray[Any, numpy.dtype[Any]]] -reveal_type(np.broadcast_arrays(AR_f8, AR_LIKE_f)) # E: list[numpy.ndarray[Any, numpy.dtype[Any]]] +reveal_type(np.broadcast_arrays(AR_f8, AR_f8)) # E: list[ndarray[Any, dtype[Any]]] +reveal_type(np.broadcast_arrays(AR_f8, AR_LIKE_f)) # E: list[ndarray[Any, dtype[Any]]] diff --git a/numpy/typing/tests/data/reveal/testing.pyi b/numpy/typing/tests/data/reveal/testing.pyi index 2b040ff60..9813dc723 100644 --- a/numpy/typing/tests/data/reveal/testing.pyi +++ b/numpy/typing/tests/data/reveal/testing.pyi @@ -147,8 +147,8 @@ reveal_type(np.testing.assert_allclose(AR_i8, AR_f8, verbose=False)) # E: None reveal_type(np.testing.assert_array_almost_equal_nulp(AR_i8, AR_f8, nulp=2)) # E: None -reveal_type(np.testing.assert_array_max_ulp(AR_i8, AR_f8, maxulp=2)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.testing.assert_array_max_ulp(AR_i8, AR_f8, dtype=np.float32)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.testing.assert_array_max_ulp(AR_i8, AR_f8, maxulp=2)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.testing.assert_array_max_ulp(AR_i8, AR_f8, dtype=np.float32)) # E: ndarray[Any, dtype[Any]] reveal_type(np.testing.assert_warns(RuntimeWarning)) # E: _GeneratorContextManager[None] reveal_type(np.testing.assert_warns(RuntimeWarning, func3, 5)) # E: bool diff --git a/numpy/typing/tests/data/reveal/twodim_base.pyi b/numpy/typing/tests/data/reveal/twodim_base.pyi index b95fbc71e..0318c3cf1 100644 --- a/numpy/typing/tests/data/reveal/twodim_base.pyi +++ b/numpy/typing/tests/data/reveal/twodim_base.pyi @@ -23,50 +23,50 @@ AR_O: npt.NDArray[np.object_] AR_LIKE_b: List[bool] -reveal_type(np.fliplr(AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.fliplr(AR_LIKE_b)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.fliplr(AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.fliplr(AR_LIKE_b)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.flipud(AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.flipud(AR_LIKE_b)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.flipud(AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.flipud(AR_LIKE_b)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.eye(10)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.eye(10, M=20, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.eye(10, k=2, dtype=int)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.eye(10)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.eye(10, M=20, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.eye(10, k=2, dtype=int)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.diag(AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.diag(AR_LIKE_b, k=0)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.diag(AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.diag(AR_LIKE_b, k=0)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.diagflat(AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.diagflat(AR_LIKE_b, k=0)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.diagflat(AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.diagflat(AR_LIKE_b, k=0)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.tri(10)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.tri(10, M=20, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.tri(10, k=2, dtype=int)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.tri(10)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.tri(10, M=20, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.tri(10, k=2, dtype=int)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.tril(AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.tril(AR_LIKE_b, k=0)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.tril(AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.tril(AR_LIKE_b, k=0)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.triu(AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.triu(AR_LIKE_b, k=0)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.triu(AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.triu(AR_LIKE_b, k=0)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.vander(AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.vander(AR_u)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.vander(AR_i, N=2)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.vander(AR_f, increasing=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.vander(AR_c)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.vander(AR_O)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] +reveal_type(np.vander(AR_b)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.vander(AR_u)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.vander(AR_i, N=2)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.vander(AR_f, increasing=True)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.vander(AR_c)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.vander(AR_O)) # E: ndarray[Any, dtype[object_]] -reveal_type(np.histogram2d(AR_i, AR_b)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.histogram2d(AR_f, AR_f)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.histogram2d(AR_f, AR_c, weights=AR_LIKE_b)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(np.histogram2d(AR_i, AR_b)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]] +reveal_type(np.histogram2d(AR_f, AR_f)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]] +reveal_type(np.histogram2d(AR_f, AR_c, weights=AR_LIKE_b)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[complexfloating[Any, Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]] -reveal_type(np.mask_indices(10, func1)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.mask_indices(8, func2, "0")) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] +reveal_type(np.mask_indices(10, func1)) # E: Tuple[ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.mask_indices(8, func2, "0")) # E: Tuple[ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]] -reveal_type(np.tril_indices(10)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{int_}]], numpy.ndarray[Any, numpy.dtype[{int_}]]] +reveal_type(np.tril_indices(10)) # E: Tuple[ndarray[Any, dtype[{int_}]], ndarray[Any, dtype[{int_}]]] -reveal_type(np.tril_indices_from(AR_b)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{int_}]], numpy.ndarray[Any, numpy.dtype[{int_}]]] +reveal_type(np.tril_indices_from(AR_b)) # E: Tuple[ndarray[Any, dtype[{int_}]], ndarray[Any, dtype[{int_}]]] -reveal_type(np.triu_indices(10)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{int_}]], numpy.ndarray[Any, numpy.dtype[{int_}]]] +reveal_type(np.triu_indices(10)) # E: Tuple[ndarray[Any, dtype[{int_}]], ndarray[Any, dtype[{int_}]]] -reveal_type(np.triu_indices_from(AR_b)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{int_}]], numpy.ndarray[Any, numpy.dtype[{int_}]]] +reveal_type(np.triu_indices_from(AR_b)) # E: Tuple[ndarray[Any, dtype[{int_}]], ndarray[Any, dtype[{int_}]]] diff --git a/numpy/typing/tests/data/reveal/type_check.pyi b/numpy/typing/tests/data/reveal/type_check.pyi index 416dd42a8..13d41d844 100644 --- a/numpy/typing/tests/data/reveal/type_check.pyi +++ b/numpy/typing/tests/data/reveal/type_check.pyi @@ -24,41 +24,41 @@ class ImagObj: reveal_type(np.mintypecode(["f8"], typeset="qfQF")) -reveal_type(np.asfarray(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.asfarray(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.asfarray(AR_f8, dtype="c16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.asfarray(AR_f8, dtype="i8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(np.asfarray(AR_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.asfarray(AR_LIKE_f)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.asfarray(AR_f8, dtype="c16")) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.asfarray(AR_f8, dtype="i8")) # E: ndarray[Any, dtype[floating[Any]]] reveal_type(np.real(RealObj())) # E: slice -reveal_type(np.real(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.real(AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.real(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.real(AR_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.real(AR_c16)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.real(AR_LIKE_f)) # E: ndarray[Any, dtype[Any]] reveal_type(np.imag(ImagObj())) # E: slice -reveal_type(np.imag(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.imag(AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.imag(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.imag(AR_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.imag(AR_c16)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.imag(AR_LIKE_f)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.iscomplex(f8)) # E: numpy.bool_ -reveal_type(np.iscomplex(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.iscomplex(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.iscomplex(f8)) # E: bool_ +reveal_type(np.iscomplex(AR_f8)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.iscomplex(AR_LIKE_f)) # E: ndarray[Any, dtype[bool_]] -reveal_type(np.isreal(f8)) # E: numpy.bool_ -reveal_type(np.isreal(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isreal(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.isreal(f8)) # E: bool_ +reveal_type(np.isreal(AR_f8)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isreal(AR_LIKE_f)) # E: ndarray[Any, dtype[bool_]] reveal_type(np.iscomplexobj(f8)) # E: bool reveal_type(np.isrealobj(f8)) # E: bool reveal_type(np.nan_to_num(f8)) # E: {float64} reveal_type(np.nan_to_num(f, copy=True)) # E: Any -reveal_type(np.nan_to_num(AR_f8, nan=1.5)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.nan_to_num(AR_LIKE_f, posinf=9999)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.nan_to_num(AR_f8, nan=1.5)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.nan_to_num(AR_LIKE_f, posinf=9999)) # E: ndarray[Any, dtype[Any]] -reveal_type(np.real_if_close(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.real_if_close(AR_c16)) # E: Union[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{complex128}]]] -reveal_type(np.real_if_close(AR_c8)) # E: Union[numpy.ndarray[Any, numpy.dtype[{float32}]], numpy.ndarray[Any, numpy.dtype[{complex64}]]] -reveal_type(np.real_if_close(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.real_if_close(AR_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.real_if_close(AR_c16)) # E: Union[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{complex128}]]] +reveal_type(np.real_if_close(AR_c8)) # E: Union[ndarray[Any, dtype[{float32}]], ndarray[Any, dtype[{complex64}]]] +reveal_type(np.real_if_close(AR_LIKE_f)) # E: ndarray[Any, dtype[Any]] reveal_type(np.typename("h")) # E: Literal['short'] reveal_type(np.typename("B")) # E: Literal['unsigned char'] diff --git a/numpy/typing/tests/data/reveal/ufunc_config.pyi b/numpy/typing/tests/data/reveal/ufunc_config.pyi index 6848a3cb5..2c6fadf92 100644 --- a/numpy/typing/tests/data/reveal/ufunc_config.pyi +++ b/numpy/typing/tests/data/reveal/ufunc_config.pyi @@ -1,4 +1,4 @@ -"""Typing tests for `numpy.core._ufunc_config`.""" +"""Typing tests for `core._ufunc_config`.""" import numpy as np @@ -7,19 +7,19 @@ def func(a: str, b: int) -> None: ... class Write: def write(self, value: str) -> None: ... -reveal_type(np.seterr(all=None)) # E: TypedDict('numpy.core._ufunc_config._ErrDict' -reveal_type(np.seterr(divide="ignore")) # E: TypedDict('numpy.core._ufunc_config._ErrDict' -reveal_type(np.seterr(over="warn")) # E: TypedDict('numpy.core._ufunc_config._ErrDict' -reveal_type(np.seterr(under="call")) # E: TypedDict('numpy.core._ufunc_config._ErrDict' -reveal_type(np.seterr(invalid="raise")) # E: TypedDict('numpy.core._ufunc_config._ErrDict' -reveal_type(np.geterr()) # E: TypedDict('numpy.core._ufunc_config._ErrDict' +reveal_type(np.seterr(all=None)) # E: TypedDict('core._ufunc_config._ErrDict' +reveal_type(np.seterr(divide="ignore")) # E: TypedDict('core._ufunc_config._ErrDict' +reveal_type(np.seterr(over="warn")) # E: TypedDict('core._ufunc_config._ErrDict' +reveal_type(np.seterr(under="call")) # E: TypedDict('core._ufunc_config._ErrDict' +reveal_type(np.seterr(invalid="raise")) # E: TypedDict('core._ufunc_config._ErrDict' +reveal_type(np.geterr()) # E: TypedDict('core._ufunc_config._ErrDict' reveal_type(np.setbufsize(4096)) # E: int reveal_type(np.getbufsize()) # E: int -reveal_type(np.seterrcall(func)) # E: Union[None, def (builtins.str, builtins.int) -> Any, numpy._SupportsWrite[builtins.str]] -reveal_type(np.seterrcall(Write())) # E: Union[None, def (builtins.str, builtins.int) -> Any, numpy._SupportsWrite[builtins.str]] -reveal_type(np.geterrcall()) # E: Union[None, def (builtins.str, builtins.int) -> Any, numpy._SupportsWrite[builtins.str]] +reveal_type(np.seterrcall(func)) # E: Union[None, def (builtins.str, builtins.int) -> Any, _SupportsWrite[builtins.str]] +reveal_type(np.seterrcall(Write())) # E: Union[None, def (builtins.str, builtins.int) -> Any, _SupportsWrite[builtins.str]] +reveal_type(np.geterrcall()) # E: Union[None, def (builtins.str, builtins.int) -> Any, _SupportsWrite[builtins.str]] -reveal_type(np.errstate(call=func, all="call")) # E: numpy.errstate[def (a: builtins.str, b: builtins.int)] -reveal_type(np.errstate(call=Write(), divide="log", over="log")) # E: numpy.errstate[ufunc_config.Write] +reveal_type(np.errstate(call=func, all="call")) # E: errstate[def (a: builtins.str, b: builtins.int)] +reveal_type(np.errstate(call=Write(), divide="log", over="log")) # E: errstate[ufunc_config.Write] diff --git a/numpy/typing/tests/data/reveal/ufunclike.pyi b/numpy/typing/tests/data/reveal/ufunclike.pyi index 8b3aea7ce..2d67c923f 100644 --- a/numpy/typing/tests/data/reveal/ufunclike.pyi +++ b/numpy/typing/tests/data/reveal/ufunclike.pyi @@ -9,21 +9,21 @@ AR_LIKE_O: List[np.object_] AR_U: np.ndarray[Any, np.dtype[np.str_]] -reveal_type(np.fix(AR_LIKE_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.fix(AR_LIKE_u)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.fix(AR_LIKE_i)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.fix(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(np.fix(AR_LIKE_b)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.fix(AR_LIKE_u)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.fix(AR_LIKE_i)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.fix(AR_LIKE_f)) # E: ndarray[Any, dtype[floating[Any]]] reveal_type(np.fix(AR_LIKE_O)) # E: Any -reveal_type(np.fix(AR_LIKE_f, out=AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] +reveal_type(np.fix(AR_LIKE_f, out=AR_U)) # E: ndarray[Any, dtype[str_]] -reveal_type(np.isposinf(AR_LIKE_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isposinf(AR_LIKE_u)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isposinf(AR_LIKE_i)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isposinf(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isposinf(AR_LIKE_f, out=AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] +reveal_type(np.isposinf(AR_LIKE_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isposinf(AR_LIKE_u)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isposinf(AR_LIKE_i)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isposinf(AR_LIKE_f)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isposinf(AR_LIKE_f, out=AR_U)) # E: ndarray[Any, dtype[str_]] -reveal_type(np.isneginf(AR_LIKE_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isneginf(AR_LIKE_u)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isneginf(AR_LIKE_i)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isneginf(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isneginf(AR_LIKE_f, out=AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] +reveal_type(np.isneginf(AR_LIKE_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isneginf(AR_LIKE_u)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isneginf(AR_LIKE_i)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isneginf(AR_LIKE_f)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isneginf(AR_LIKE_f, out=AR_U)) # E: ndarray[Any, dtype[str_]] diff --git a/numpy/typing/tests/data/reveal/ufuncs.pyi b/numpy/typing/tests/data/reveal/ufuncs.pyi index ade45577c..3bf83c820 100644 --- a/numpy/typing/tests/data/reveal/ufuncs.pyi +++ b/numpy/typing/tests/data/reveal/ufuncs.pyi @@ -17,7 +17,7 @@ reveal_type(np.absolute.nout) # E: Literal[1] reveal_type(np.absolute.nargs) # E: Literal[2] reveal_type(np.absolute.signature) # E: None reveal_type(np.absolute(f8)) # E: Any -reveal_type(np.absolute(AR_f8)) # E: numpy.ndarray +reveal_type(np.absolute(AR_f8)) # E: ndarray reveal_type(np.absolute.at(AR_f8, AR_i8)) # E: None reveal_type(np.add.__name__) # E: Literal['add'] @@ -28,13 +28,13 @@ reveal_type(np.add.nout) # E: Literal[1] reveal_type(np.add.nargs) # E: Literal[3] reveal_type(np.add.signature) # E: None reveal_type(np.add(f8, f8)) # E: Any -reveal_type(np.add(AR_f8, f8)) # E: numpy.ndarray +reveal_type(np.add(AR_f8, f8)) # E: ndarray reveal_type(np.add.at(AR_f8, AR_i8, f8)) # E: None reveal_type(np.add.reduce(AR_f8, axis=0)) # E: Any -reveal_type(np.add.accumulate(AR_f8)) # E: numpy.ndarray -reveal_type(np.add.reduceat(AR_f8, AR_i8)) # E: numpy.ndarray +reveal_type(np.add.accumulate(AR_f8)) # E: ndarray +reveal_type(np.add.reduceat(AR_f8, AR_i8)) # E: ndarray reveal_type(np.add.outer(f8, f8)) # E: Any -reveal_type(np.add.outer(AR_f8, f8)) # E: numpy.ndarray +reveal_type(np.add.outer(AR_f8, f8)) # E: ndarray reveal_type(np.frexp.__name__) # E: Literal['frexp'] reveal_type(np.frexp.ntypes) # E: Literal[4] @@ -44,7 +44,7 @@ reveal_type(np.frexp.nout) # E: Literal[2] reveal_type(np.frexp.nargs) # E: Literal[3] reveal_type(np.frexp.signature) # E: None reveal_type(np.frexp(f8)) # E: Tuple[Any, Any] -reveal_type(np.frexp(AR_f8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[Any]]] +reveal_type(np.frexp(AR_f8)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]] reveal_type(np.divmod.__name__) # E: Literal['divmod'] reveal_type(np.divmod.ntypes) # E: Literal[15] @@ -54,7 +54,7 @@ reveal_type(np.divmod.nout) # E: Literal[2] reveal_type(np.divmod.nargs) # E: Literal[4] reveal_type(np.divmod.signature) # E: None reveal_type(np.divmod(f8, f8)) # E: Tuple[Any, Any] -reveal_type(np.divmod(AR_f8, f8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[Any]]] +reveal_type(np.divmod(AR_f8, f8)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]] reveal_type(np.matmul.__name__) # E: Literal['matmul'] reveal_type(np.matmul.ntypes) # E: Literal[19] diff --git a/numpy/typing/tests/data/reveal/warnings_and_errors.pyi b/numpy/typing/tests/data/reveal/warnings_and_errors.pyi index 3f20a0135..d5c50448a 100644 --- a/numpy/typing/tests/data/reveal/warnings_and_errors.pyi +++ b/numpy/typing/tests/data/reveal/warnings_and_errors.pyi @@ -2,10 +2,10 @@ from typing import Type import numpy as np -reveal_type(np.ModuleDeprecationWarning()) # E: numpy.ModuleDeprecationWarning -reveal_type(np.VisibleDeprecationWarning()) # E: numpy.VisibleDeprecationWarning -reveal_type(np.ComplexWarning()) # E: numpy.ComplexWarning -reveal_type(np.RankWarning()) # E: numpy.RankWarning -reveal_type(np.TooHardError()) # E: numpy.TooHardError -reveal_type(np.AxisError("test")) # E: numpy.AxisError -reveal_type(np.AxisError(5, 1)) # E: numpy.AxisError +reveal_type(np.ModuleDeprecationWarning()) # E: ModuleDeprecationWarning +reveal_type(np.VisibleDeprecationWarning()) # E: VisibleDeprecationWarning +reveal_type(np.ComplexWarning()) # E: ComplexWarning +reveal_type(np.RankWarning()) # E: RankWarning +reveal_type(np.TooHardError()) # E: TooHardError +reveal_type(np.AxisError("test")) # E: AxisError +reveal_type(np.AxisError(5, 1)) # E: AxisError diff --git a/numpy/typing/tests/test_typing.py b/numpy/typing/tests/test_typing.py index 2dcfd6082..fe58a8f4c 100644 --- a/numpy/typing/tests/test_typing.py +++ b/numpy/typing/tests/test_typing.py @@ -58,6 +58,11 @@ def _strip_filename(msg: str) -> str: return tail.split(":", 1)[-1] +def strip_func(match: re.Match[str]) -> str: + """`re.sub` helper function for stripping module names.""" + return match.groups()[1] + + @pytest.mark.slow @pytest.mark.skipif(NO_MYPY, reason="Mypy is not installed") @pytest.fixture(scope="module", autouse=True) @@ -373,9 +378,15 @@ def _test_reveal( lineno: int, ) -> None: """Error-reporting helper function for `test_reveal`.""" - if reveal not in expected_reveal: + strip_pattern = re.compile(r"(\w+\.)+(\w+)") + stripped_reveal = strip_pattern.sub(strip_func, reveal) + stripped_expected_reveal = strip_pattern.sub(strip_func, expected_reveal) + if stripped_reveal not in stripped_expected_reveal: raise AssertionError( - _REVEAL_MSG.format(lineno, expression, expected_reveal, reveal) + _REVEAL_MSG.format(lineno, + expression, + stripped_expected_reveal, + stripped_reveal) ) -- cgit v1.2.1 From 167539e4c2fdc6b77d6c73c9eebfa77e1507423e Mon Sep 17 00:00:00 2001 From: Matthias Bussonnier Date: Wed, 3 Nov 2021 09:43:53 -0700 Subject: DOC: Do not leave space between directive name and double colon. From my regular expression foo, those are the only 9 case whereas there are about ~2000 usage that do not have spaces. While this is ok with docutils/sphinx, it does not seem to be documented, and that means that other parsers will see that as comments, leading to for example improper syntax highlighting. This make it also a tiny bit harder to develop alternative rst parsers. --- numpy/lib/polynomial.py | 2 +- numpy/polynomial/chebyshev.py | 4 ++-- numpy/polynomial/polynomial.py | 4 ++-- numpy/polynomial/polyutils.py | 4 ++-- 4 files changed, 7 insertions(+), 7 deletions(-) (limited to 'numpy') diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py index 1cbb3cd88..f824c4c5e 100644 --- a/numpy/lib/polynomial.py +++ b/numpy/lib/polynomial.py @@ -550,7 +550,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False): ----- The solution minimizes the squared error - .. math :: + .. math:: E = \\sum_{j=0}^k |p(x_j) - y_j|^2 in the equations:: diff --git a/numpy/polynomial/chebyshev.py b/numpy/polynomial/chebyshev.py index 2b3268aeb..89ce815d5 100644 --- a/numpy/polynomial/chebyshev.py +++ b/numpy/polynomial/chebyshev.py @@ -88,13 +88,13 @@ Notes The implementations of multiplication, division, integration, and differentiation use the algebraic identities [1]_: -.. math :: +.. math:: T_n(x) = \\frac{z^n + z^{-n}}{2} \\\\ z\\frac{dx}{dz} = \\frac{z - z^{-1}}{2}. where -.. math :: x = \\frac{z + z^{-1}}{2}. +.. math:: x = \\frac{z + z^{-1}}{2}. These identities allow a Chebyshev series to be expressed as a finite, symmetric Laurent series. In this module, this sort of Laurent series diff --git a/numpy/polynomial/polynomial.py b/numpy/polynomial/polynomial.py index 2fead88ab..3c2663b6c 100644 --- a/numpy/polynomial/polynomial.py +++ b/numpy/polynomial/polynomial.py @@ -1304,12 +1304,12 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None): The solution is the coefficients of the polynomial `p` that minimizes the sum of the weighted squared errors - .. math :: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2, + .. math:: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2, where the :math:`w_j` are the weights. This problem is solved by setting up the (typically) over-determined matrix equation: - .. math :: V(x) * c = w * y, + .. math:: V(x) * c = w * y, where `V` is the weighted pseudo Vandermonde matrix of `x`, `c` are the coefficients to be solved for, `w` are the weights, and `y` are the diff --git a/numpy/polynomial/polyutils.py b/numpy/polynomial/polyutils.py index 3b0f0a9e5..a2bc75a4d 100644 --- a/numpy/polynomial/polyutils.py +++ b/numpy/polynomial/polyutils.py @@ -330,12 +330,12 @@ def mapdomain(x, old, new): ----- Effectively, this implements: - .. math :: + .. math:: x\\_out = new[0] + m(x - old[0]) where - .. math :: + .. math:: m = \\frac{new[1]-new[0]}{old[1]-old[0]} Examples -- cgit v1.2.1 From c38b7f328b87d626be544c5ffb542f2b6a97adee Mon Sep 17 00:00:00 2001 From: mattip Date: Thu, 4 Nov 2021 00:11:04 +0200 Subject: make a.__dlpack__() fail if a is readonly --- numpy/core/src/multiarray/dlpack.c | 6 ++++++ numpy/core/tests/test_dlpack.py | 8 +++++++- 2 files changed, 13 insertions(+), 1 deletion(-) (limited to 'numpy') diff --git a/numpy/core/src/multiarray/dlpack.c b/numpy/core/src/multiarray/dlpack.c index 21930b0ef..f061a6bf9 100644 --- a/numpy/core/src/multiarray/dlpack.c +++ b/numpy/core/src/multiarray/dlpack.c @@ -131,6 +131,12 @@ array_dlpack(PyArrayObject *self, return NULL; } + if ( !(PyArray_FLAGS(self) & NPY_ARRAY_WRITEABLE)) { + PyErr_SetString(PyExc_TypeError, "NumPy currently only supports " + "dlpack for writeable arrays"); + return NULL; + } + npy_intp itemsize = PyArray_ITEMSIZE(self); int ndim = PyArray_NDIM(self); npy_intp *strides = PyArray_STRIDES(self); diff --git a/numpy/core/tests/test_dlpack.py b/numpy/core/tests/test_dlpack.py index 06fc042ec..f848b2008 100644 --- a/numpy/core/tests/test_dlpack.py +++ b/numpy/core/tests/test_dlpack.py @@ -78,7 +78,7 @@ class TestDLPack: y4 = x[1] assert_array_equal(y4, np._from_dlpack(y4)) - y5 = np.diagonal(x) + y5 = np.diagonal(x).copy() assert_array_equal(y5, np._from_dlpack(y5)) @pytest.mark.parametrize("ndim", range(33)) @@ -101,3 +101,9 @@ class TestDLPack: def test_dlpack_destructor_exception(self): with pytest.raises(RuntimeError): self.dlpack_deleter_exception() + + def test_readonly(self): + x = np.arange(5) + x.flags.writeable = False + with pytest.raises(TypeError): + x.__dlpack__() -- cgit v1.2.1 From f7a392d43286c39013d98b6bdeb93def645fa8ab Mon Sep 17 00:00:00 2001 From: Matthew Barber Date: Tue, 31 Aug 2021 19:05:21 +0100 Subject: Test array_api entry point exists and points to numpy.array_api --- numpy/tests/test_public_api.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) (limited to 'numpy') diff --git a/numpy/tests/test_public_api.py b/numpy/tests/test_public_api.py index 1e7d389d9..81de66670 100644 --- a/numpy/tests/test_public_api.py +++ b/numpy/tests/test_public_api.py @@ -458,3 +458,19 @@ def test_api_importable(): raise AssertionError("Modules that are not really public but looked " "public and can not be imported: " "{}".format(module_names)) + + +def test_array_api_entry_point(): + """ + Entry point for Array API implementation can be found with importlib and + returns the numpy.array_api namespace. + """ + from numpy import array_api + + eps = importlib.metadata.entry_points() + assert "array_api" in eps.keys() + xp_eps = eps["array_api"] + assert "numpy" in (ep.name for ep in xp_eps) + ep = next(ep for ep in xp_eps if ep.name == "numpy") + xp = importlib.import_module(ep.value) + assert xp is array_api -- cgit v1.2.1 From 0ae911c9246a1366a60ed45ac0c28fd828498211 Mon Sep 17 00:00:00 2001 From: Matthew Barber Date: Wed, 1 Sep 2021 09:47:19 +0100 Subject: Make test compatible with Python >=3.10 --- numpy/tests/test_public_api.py | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) (limited to 'numpy') diff --git a/numpy/tests/test_public_api.py b/numpy/tests/test_public_api.py index 81de66670..d8ebf32ea 100644 --- a/numpy/tests/test_public_api.py +++ b/numpy/tests/test_public_api.py @@ -465,12 +465,18 @@ def test_array_api_entry_point(): Entry point for Array API implementation can be found with importlib and returns the numpy.array_api namespace. """ - from numpy import array_api - eps = importlib.metadata.entry_points() - assert "array_api" in eps.keys() - xp_eps = eps["array_api"] + try: + xp_eps = eps.select(group="array_api") + except AttributeError: + # The select interface for entry_points was introduced in py3.10, + # deprecating its dict interface. We fallback to dict keys for finding + # Array API entry points so that running this test in <=3.9 will + # still work - see https://github.com/numpy/numpy/pull/19800. + assert "array_api" in eps.keys() + xp_eps = eps["array_api"] + assert len(xp_eps) > 0 assert "numpy" in (ep.name for ep in xp_eps) ep = next(ep for ep in xp_eps if ep.name == "numpy") xp = importlib.import_module(ep.value) - assert xp is array_api + assert xp is numpy.array_api -- cgit v1.2.1 From 2d601825d4bcb447911d063519c78bb5d5101dec Mon Sep 17 00:00:00 2001 From: Matthew Barber Date: Wed, 1 Sep 2021 09:59:34 +0100 Subject: Clearer test logic --- numpy/tests/test_public_api.py | 19 +++++++++++++------ 1 file changed, 13 insertions(+), 6 deletions(-) (limited to 'numpy') diff --git a/numpy/tests/test_public_api.py b/numpy/tests/test_public_api.py index d8ebf32ea..26402fa2a 100644 --- a/numpy/tests/test_public_api.py +++ b/numpy/tests/test_public_api.py @@ -473,10 +473,17 @@ def test_array_api_entry_point(): # deprecating its dict interface. We fallback to dict keys for finding # Array API entry points so that running this test in <=3.9 will # still work - see https://github.com/numpy/numpy/pull/19800. - assert "array_api" in eps.keys() - xp_eps = eps["array_api"] - assert len(xp_eps) > 0 - assert "numpy" in (ep.name for ep in xp_eps) - ep = next(ep for ep in xp_eps if ep.name == "numpy") + xp_eps = eps.get("array_api", []) + assert len(xp_eps) > 0, "No entry points for 'array_api' found" + + try: + ep = next(ep for ep in xp_eps if ep.name == "numpy") + except StopIteration: + raise AssertionError("'numpy' not in array_api entry points") from None + xp = importlib.import_module(ep.value) - assert xp is numpy.array_api + msg = ( + f"numpy entry point value '{ep.value}' " + "does not point to our Array API implementation" + ) + assert xp is numpy.array_api, msg -- cgit v1.2.1 From d887db45be0ede22afcfb4fb02aa4570ec6befe0 Mon Sep 17 00:00:00 2001 From: Matthew Barber Date: Wed, 1 Sep 2021 12:13:02 +0100 Subject: Use ep.load() instead of import_module() to get xp Co-authored-by: Zac Hatfield-Dodds --- numpy/tests/test_public_api.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'numpy') diff --git a/numpy/tests/test_public_api.py b/numpy/tests/test_public_api.py index 26402fa2a..1eca25afb 100644 --- a/numpy/tests/test_public_api.py +++ b/numpy/tests/test_public_api.py @@ -481,7 +481,7 @@ def test_array_api_entry_point(): except StopIteration: raise AssertionError("'numpy' not in array_api entry points") from None - xp = importlib.import_module(ep.value) + xp = ep.load() msg = ( f"numpy entry point value '{ep.value}' " "does not point to our Array API implementation" -- cgit v1.2.1 From 5702b51bd22b2a96a87bebb3518a57a2ad6904ca Mon Sep 17 00:00:00 2001 From: Matthew Date: Tue, 2 Nov 2021 17:08:21 +0000 Subject: xfail `test_array_api_entry_point` on python-dbg --- numpy/tests/test_public_api.py | 9 +++++++++ 1 file changed, 9 insertions(+) (limited to 'numpy') diff --git a/numpy/tests/test_public_api.py b/numpy/tests/test_public_api.py index 1eca25afb..fa29c75b5 100644 --- a/numpy/tests/test_public_api.py +++ b/numpy/tests/test_public_api.py @@ -1,4 +1,5 @@ import sys +import sysconfig import subprocess import pkgutil import types @@ -460,6 +461,14 @@ def test_api_importable(): "{}".format(module_names)) +@pytest.mark.xfail( + sysconfig.get_config_var("Py_DEBUG") is not None, + reason=( + "NumPy possibly built with `USE_DEBUG=True ./tools/travis-test.sh`, " + "which does not expose the `array_api` entry point. " + "See https://github.com/numpy/numpy/pull/19800" + ), +) def test_array_api_entry_point(): """ Entry point for Array API implementation can be found with importlib and -- cgit v1.2.1 From 3cb20a03f5337fe5f038f1593508458849612ba2 Mon Sep 17 00:00:00 2001 From: Sebastian Berg Date: Thu, 4 Nov 2021 14:03:30 -0500 Subject: BUG: Fix requirement that user DTypes had to be heaptypes Requiring heap-types currently does not make sense, because the way to create a new DType is clunky if you were to make a heaptype. This was an initial thought, which panned out inconvenient now, so changing. This has no effect, except for experimental user DTypes when `NUMPY_EXPERIMENTAL_DTYPE_API=1` is used. --- numpy/core/src/multiarray/descriptor.c | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) (limited to 'numpy') diff --git a/numpy/core/src/multiarray/descriptor.c b/numpy/core/src/multiarray/descriptor.c index 6a09f92ac..fd2577bc6 100644 --- a/numpy/core/src/multiarray/descriptor.c +++ b/numpy/core/src/multiarray/descriptor.c @@ -2305,8 +2305,9 @@ arraydescr_new(PyTypeObject *subtype, { if (subtype != &PyArrayDescr_Type) { if (Py_TYPE(subtype) == &PyArrayDTypeMeta_Type && - !(PyType_GetFlags(Py_TYPE(subtype)) & Py_TPFLAGS_HEAPTYPE) && - (NPY_DT_SLOTS((PyArray_DTypeMeta *)subtype)) != NULL) { + (NPY_DT_SLOTS((PyArray_DTypeMeta *)subtype)) != NULL && + !NPY_DT_is_legacy((PyArray_DTypeMeta *)subtype) && + subtype->tp_new != PyArrayDescr_Type.tp_new) { /* * Appears to be a properly initialized user DType. Allocate * it and initialize the main part as best we can. @@ -2333,7 +2334,9 @@ arraydescr_new(PyTypeObject *subtype, } /* The DTypeMeta class should prevent this from happening. */ PyErr_Format(PyExc_SystemError, - "'%S' must not inherit np.dtype.__new__().", subtype); + "'%S' must not inherit np.dtype.__new__(). User DTypes should " + "currently call `PyArrayDescr_Type.tp_new` from their new.", + subtype); return NULL; } -- cgit v1.2.1 From cd7a02a4db7e760b881f3feeb832ffd84fa8645a Mon Sep 17 00:00:00 2001 From: abel Date: Thu, 2 Sep 2021 16:34:42 +0200 Subject: MAINT, ENH [#10736] Add interpolation methods to quantile - Added the missing linear interpolation methods. - Updated the existing unit tests. - Added pytest.mark.xfail for boolean arrays See - https://github.com/numpy/numpy/pull/19857#issuecomment-919258693 - https://github.com/numpy/numpy/issues/19154 --- numpy/lib/function_base.py | 593 +++++++++++++++++++++++++++------- numpy/lib/nanfunctions.py | 212 ++++++++++-- numpy/lib/tests/test_function_base.py | 177 +++++++--- 3 files changed, 788 insertions(+), 194 deletions(-) (limited to 'numpy') diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 20e32a78d..67a34f6e1 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -52,6 +52,89 @@ __all__ = [ ] +_QuantileInterpolation = dict( + # --- HYNDMAN and FAN methods + # Discrete methods + inverted_cdf=dict( + get_virtual_index=lambda n, quantiles: _inverted_cdf(n, quantiles), + fix_gamma=lambda gamma, _: gamma, # should never be called + ), + averaged_inverted_cdf=dict( + get_virtual_index=lambda n, quantiles: (n * quantiles) - 1, + fix_gamma=lambda gamma, _: _get_gamma_mask( + shape=gamma.shape, + default_value=1., + conditioned_value=0.5, + where=gamma == 0), + ), + closest_observation=dict( + get_virtual_index=lambda n, quantiles: _closest_observation(n, + quantiles), + fix_gamma=lambda gamma, _: gamma, # should never be called + ), + # Continuous methods + interpolated_inverted_cdf=dict( + get_virtual_index=lambda n, quantiles: + _virtual_index_formula(n, quantiles, 0, 1), + fix_gamma=lambda gamma, _: gamma, + ), + hazen=dict( + get_virtual_index=lambda n, quantiles: + _virtual_index_formula(n, quantiles, 0.5, 0.5), + fix_gamma=lambda gamma, _: gamma, + ), + weibull=dict( + get_virtual_index=lambda n, quantiles: + _virtual_index_formula(n, quantiles, 0, 0), + fix_gamma=lambda gamma, _: gamma, + ), + # Default value + linear=dict( + get_virtual_index=lambda n, quantiles: + _virtual_index_formula(n, quantiles, 1, 1), + fix_gamma=lambda gamma, _: gamma, + ), + median_unbiased=dict( + get_virtual_index=lambda n, quantiles: + _virtual_index_formula(n, quantiles, 1 / 3.0, 1 / 3.0), + fix_gamma=lambda gamma, _: gamma, + ), + normal_unbiased=dict( + get_virtual_index=lambda n, quantiles: + _virtual_index_formula(n, quantiles, 3 / 8.0, 3 / 8.0), + fix_gamma=lambda gamma, _: gamma, + ), + # --- OTHER METHODS fixme add deprecated ? + lower=dict( + get_virtual_index=lambda n, quantiles: np.floor( + (n - 1) * quantiles).astype(np.intp), + fix_gamma=lambda gamma, _: gamma, + # should never be called, index dtype is int + ), + higher=dict( + get_virtual_index=lambda n, quantiles: np.ceil( + (n - 1) * quantiles).astype(np.intp), + fix_gamma=lambda gamma, _: gamma, + # should never be called, index dtype is int + ), + midpoint=dict( + get_virtual_index=lambda n, quantiles: 0.5 * ( + np.floor((n - 1) * quantiles) + + np.ceil((n - 1) * quantiles)), + fix_gamma=lambda gamma, index: _get_gamma_mask( + shape=gamma.shape, + default_value=0.5, + conditioned_value=0., + where=index % 1 == 0), + ), + nearest=dict( + get_virtual_index=lambda n, quantiles: np.around( + (n - 1) * quantiles).astype(np.intp), + fix_gamma=lambda gamma, _: gamma, + # should never be called, index dtype is int + )) + + def _rot90_dispatcher(m, k=None, axes=None): return (m,) @@ -3760,8 +3843,13 @@ def _percentile_dispatcher(a, q, axis=None, out=None, overwrite_input=None, @array_function_dispatch(_percentile_dispatcher) -def percentile(a, q, axis=None, out=None, - overwrite_input=False, interpolation='linear', keepdims=False): +def percentile(a, + q, + axis=None, + out=None, + overwrite_input=False, + interpolation="linear", + keepdims=False): """ Compute the q-th percentile of the data along the specified axis. @@ -3790,20 +3878,75 @@ def percentile(a, q, axis=None, out=None, calculations, to save memory. In this case, the contents of the input `a` after this function completes is undefined. - interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} + interpolation : str + Possible values: 'linear' (default), + 'inverted_cdf', 'averaged_inverted_cdf', + 'closest_observation', 'interpolated_inverted_cdf', + 'hazen', 'weibull', + 'median_unbiased', 'normal_unbiased', + 'lower', 'higher', + 'midpoint', 'nearest'. This optional parameter specifies the interpolation method to - use when the desired percentile lies between two data points - ``i < j``: + use when the desired quantile lies between two data points ``i < j``. + g is the fractional part of the index surrounded by ``i``. + alpha and beta are correction constants modifying i and j: + i + g = (q - alpha) / ( n - alpha - beta + 1 ) + * inverted_cdf: + method 1 of H&F. + This method give discontinuous results: + if g > 0 ; then take j + if g = 0 ; then take i + * averaged_inverted_cdf: + method 2 of H&F. + This method give discontinuous results: + if g > 0 ; then take j + if g = 0 ; then average between bounds + * closest_observation: + method 3 of H&F. + This method give discontinuous results: + if g > 0 ; then take j + if g = 0 and index is odd ; then take j + if g = 0 and index is even ; then take i + * interpolated_inverted_cdf: + method 4 of H&F. + This method give continuous results using: + alpha = 0 + beta = 1 + * hazen: + method 5 of H&F. + This method give continuous results using: + alpha = 1/2 + beta = 1/2 + * weibull: + method 6 of H&F. + This method give continuous results using: + alpha = 0 + beta = 0 + * inclusive: + Default method, aliased with "linear". + method 7 of H&F. + This method give continuous results using: + alpha = 1 + beta = 1 + * median_unbiased: + method 8 of H&F. + This method is probably the best method if the sample distribution + function is unknown (see reference). + This method give continuous results using: + alpha = 1/3 + beta = 1/3 + * normal_unbiased: + method 9 of H&F. + This method is probably the best method if the sample distribution + function is known to be normal. + This method give continuous results using: + alpha = 3/8 + beta = 3/8 + * lower: ``i``. + * higher: ``j``. + * nearest: ``i`` or ``j``, whichever is nearest. + * midpoint: ``(i + j) / 2``. - * 'linear': ``i + (j - i) * fraction``, where ``fraction`` - is the fractional part of the index surrounded by ``i`` - and ``j``. - * 'lower': ``i``. - * 'higher': ``j``. - * 'nearest': ``i`` or ``j``, whichever is nearest. - * 'midpoint': ``(i + j) / 2``. - - .. versionadded:: 1.9.0 keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the @@ -3897,6 +4040,11 @@ def percentile(a, q, axis=None, out=None, ax.legend() plt.show() + References + ---------- + [Hyndman&Fan] Hyndman, R. J., & Fan, Y. (1996). + Sample quantiles in statistical packages. + The American Statistician, 50(4), 361-365. """ q = np.true_divide(q, 100) q = asanyarray(q) # undo any decay that the ufunc performed (see gh-13105) @@ -3912,8 +4060,13 @@ def _quantile_dispatcher(a, q, axis=None, out=None, overwrite_input=None, @array_function_dispatch(_quantile_dispatcher) -def quantile(a, q, axis=None, out=None, - overwrite_input=False, interpolation='linear', keepdims=False): +def quantile(a, + q, + axis=None, + out=None, + overwrite_input=False, + interpolation="linear", + keepdims=False): """ Compute the q-th quantile of the data along the specified axis. @@ -3938,18 +4091,75 @@ def quantile(a, q, axis=None, out=None, If True, then allow the input array `a` to be modified by intermediate calculations, to save memory. In this case, the contents of the input `a` after this function completes is undefined. - interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} + interpolation : str + Possible values: 'linear' (default), + 'inverted_cdf', 'averaged_inverted_cdf', + 'closest_observation', 'interpolated_inverted_cdf', + 'hazen', 'weibull', + 'median_unbiased', 'normal_unbiased', + 'lower', 'higher', + 'midpoint', 'nearest'. This optional parameter specifies the interpolation method to - use when the desired quantile lies between two data points - ``i < j``: - - * linear: ``i + (j - i) * fraction``, where ``fraction`` - is the fractional part of the index surrounded by ``i`` - and ``j``. - * lower: ``i``. - * higher: ``j``. - * nearest: ``i`` or ``j``, whichever is nearest. - * midpoint: ``(i + j) / 2``. + use when the desired quantile lies between two data points ``i < j``. + g is the fractional part of the index surrounded by ``i``. + alpha and beta are correction constants modifying i and j: + i + g = (q - alpha) / ( n - alpha - beta + 1 ) + * inverted_cdf: + method 1 of H&F. + This method give discontinuous results: + if g > 0 ; then take j + if g = 0 ; then take i + * averaged_inverted_cdf: + method 2 of H&F. + This method give discontinuous results: + if g > 0 ; then take j + if g = 0 ; then average between bounds + * closest_observation: + method 3 of H&F. + This method give discontinuous results: + if g > 0 ; then take j + if g = 0 and index is odd ; then take j + if g = 0 and index is even ; then take i + * interpolated_inverted_cdf: + method 4 of H&F. + This method give continuous results using: + alpha = 0 + beta = 1 + * hazen: + method 5 of H&F. + This method give continuous results using: + alpha = 1/2 + beta = 1/2 + * weibull: + method 6 of H&F. + This method give continuous results using: + alpha = 0 + beta = 0 + * linear: + Default method. + method 7 of H&F. + This method give continuous results using: + alpha = 1 + beta = 1 + * median_unbiased: + method 8 of H&F. + This method is probably the best method if the sample distribution + function is unknown (see reference). + This method give continuous results using: + alpha = 1/3 + beta = 1/3 + * normal_unbiased: + method 9 of H&F. + This method is probably the best method if the sample distribution + function is known to be normal. + This method give continuous results using: + alpha = 3/8 + beta = 3/8 + * lower: ``i``. + * higher: ``j``. + * nearest: ``i`` or ``j``, whichever is nearest. + * midpoint: ``(i + j) / 2``. + keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the @@ -4010,6 +4220,12 @@ def quantile(a, q, axis=None, out=None, >>> np.quantile(b, 0.5, axis=1, overwrite_input=True) array([7., 2.]) >>> assert not np.all(a == b) + + References + ---------- + [Hyndman&Fan] Hyndman, R. J., & Fan, Y. (1996). + Sample quantiles in statistical packages. + The American Statistician, 50(4), 361-365. """ q = np.asanyarray(q) if not _quantile_is_valid(q): @@ -4018,10 +4234,19 @@ def quantile(a, q, axis=None, out=None, a, q, axis, out, overwrite_input, interpolation, keepdims) -def _quantile_unchecked(a, q, axis=None, out=None, overwrite_input=False, - interpolation='linear', keepdims=False): +def _quantile_unchecked(a, + q, + axis=None, + out=None, + overwrite_input=False, + interpolation="linear", + keepdims=False): """Assumes that q is in [0, 1], and is an ndarray""" - r, k = _ureduce(a, func=_quantile_ureduce_func, q=q, axis=axis, out=out, + r, k = _ureduce(a, + func=_quantiles_ureduce_func, + q=q, + axis=axis, + out=out, overwrite_input=overwrite_input, interpolation=interpolation) if keepdims: @@ -4042,122 +4267,242 @@ def _quantile_is_valid(q): return True -def _lerp(a, b, t, out=None): - """ Linearly interpolate from a to b by a factor of t """ - diff_b_a = subtract(b, a) - # asanyarray is a stop-gap until gh-13105 - lerp_interpolation = asanyarray(add(a, diff_b_a*t, out=out)) - subtract(b, diff_b_a * (1 - t), out=lerp_interpolation, where=t>=0.5) - if lerp_interpolation.ndim == 0 and out is None: - lerp_interpolation = lerp_interpolation[()] # unpack 0d arrays - return lerp_interpolation +def _virtual_index_formula(n: np.array, + quantiles: np.array, + alpha: float, + beta: float) -> np.array: + """ + Compute the floating point indexes of an array for the linear + interpolation of quantiles. + Reference: + Hyndman&Fan paper "Sample Quantiles in Statistical Packages", + DOI: 10.1080/00031305.1996.10473566 + """ + return n * quantiles + ( + alpha + quantiles * (1 - alpha - beta) + ) - 1 -def _quantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, - interpolation='linear', keepdims=False): - a = asarray(a) +def _get_gamma(virtual_indexes: np.array, + previous_indexes: np.array, + interpolation: _QuantileInterpolation) -> np.array: + """ + Compute the gamma (a.k.a 'm' or weight) for the linear interpolation + of quantiles. + When gamma == 0 the left bound will be picked, + When gamma == 1 the right bound will be. + """ + gamma = np.asanyarray(virtual_indexes - previous_indexes) + gamma = interpolation["fix_gamma"](gamma, virtual_indexes) + return np.asanyarray(gamma) - # ufuncs cause 0d array results to decay to scalars (see gh-13105), which - # makes them problematic for __setitem__ and attribute access. As a - # workaround, we call this on the result of every ufunc on a possibly-0d - # array. - not_scalar = np.asanyarray - # prepare a for partitioning - if overwrite_input: - if axis is None: - ap = a.ravel() - else: - ap = a - else: - if axis is None: - ap = a.flatten() - else: - ap = a.copy() +def _linear_interpolation_formula( + left: np.array, right: np.array, gamma: np.array, out: np.array = None +) -> np.array: + """ + Compute the linear interpolation weighted by gamma on each point of + two same shape array. + """ + # Equivalent to gamma * right + (1 - gamma) * left + # see gh-14685 + diff_right_left = subtract(right, left) + result = asanyarray(add(left, diff_right_left * gamma, out=out)) + result = subtract(right, + diff_right_left * (1 - gamma), + out=result, + where=gamma >= 0.5) + return result - if axis is None: - axis = 0 +def _get_gamma_mask(shape, default_value, conditioned_value, where): + out = np.full(shape, default_value) + out[where] = conditioned_value + return out + + +def _discret_interpolation_to_boundaries(index, gamma_condition_fun): + previous = np.floor(index) + next = previous + 1 + gamma = index - previous + return _get_gamma_mask(shape=index.shape, + default_value=next, + conditioned_value=previous, + where=gamma_condition_fun(gamma, index) + ).astype(np.intp) + + +def _closest_observation(n, quantiles): + gamma_fun = lambda gamma, index: (gamma == 0) & (np.floor(index) % 2 == 0) + return _discret_interpolation_to_boundaries((n * quantiles) - 1 - 0.5, + gamma_fun) + + +def _inverted_cdf(n, quantiles): + gamma_fun = lambda gamma, _: (gamma == 0) + return _discret_interpolation_to_boundaries((n * quantiles) - 1, + gamma_fun) + + +def _quantiles_ureduce_func( + a: np.array, + q: np.array, + axis: int = None, + out=None, + overwrite_input: bool = False, + interpolation="linear", +) -> np.array: if q.ndim > 2: # The code below works fine for nd, but it might not have useful # semantics. For now, keep the supported dimensions the same as it was # before. raise ValueError("q must be a scalar or 1d") - - Nx = ap.shape[axis] - indices = not_scalar(q * (Nx - 1)) - # round fractional indices according to interpolation method - if interpolation == 'lower': - indices = floor(indices).astype(intp) - elif interpolation == 'higher': - indices = ceil(indices).astype(intp) - elif interpolation == 'midpoint': - indices = 0.5 * (floor(indices) + ceil(indices)) - elif interpolation == 'nearest': - indices = around(indices).astype(intp) - elif interpolation == 'linear': - pass # keep index as fraction and interpolate + if overwrite_input: + if axis is None: + axis = 0 + arr = a.ravel() + else: + arr = a else: - raise ValueError( - "interpolation can only be 'linear', 'lower' 'higher', " - "'midpoint', or 'nearest'") + if axis is None: + axis = 0 + arr = a.flatten() + else: + arr = a.copy() + result = _quantile(arr, + quantiles=q, + axis=axis, + interpolation=interpolation, + out=out) + if result.ndim == 0 and out is None: + result = result[()] # unpack 0d arrays + elif result.size == 1 and out is None and q.ndim == 0: + result = result[0] + return result - # The dimensions of `q` are prepended to the output shape, so we need the - # axis being sampled from `ap` to be first. - ap = np.moveaxis(ap, axis, 0) - del axis - if np.issubdtype(indices.dtype, np.integer): - # take the points along axis +def _get_indexes(arr, virtual_indexes, valid_values_count): + """ + Get the valid indexes of arr neighbouring virtual_indexes. + Note + This is a companion function to linear interpolation of + Quantiles - if np.issubdtype(a.dtype, np.inexact): + Returns + ------- + (previous_indexes, next_indexes): Tuple + A Tuple of virtual_indexes neighbouring indexes + """ + previous_indexes = np.asanyarray(np.floor(virtual_indexes)) + next_indexes = np.asanyarray(previous_indexes + 1) + indexes_above_bounds = virtual_indexes >= valid_values_count - 1 + # When indexes is above max index, take the max value of the array + if indexes_above_bounds.any(): + previous_indexes[indexes_above_bounds] = -1 + next_indexes[indexes_above_bounds] = -1 + # When indexes is below min index, take the min value of the array + indexes_below_bounds = virtual_indexes < 0 + if indexes_below_bounds.any(): + previous_indexes[indexes_below_bounds] = 0 + next_indexes[indexes_below_bounds] = 0 + if np.issubdtype(arr.dtype, np.inexact): + # After the sort, slices having NaNs will have for last element a NaN + virtual_indexes_nans = np.isnan(virtual_indexes) + if virtual_indexes_nans.any(): + previous_indexes[virtual_indexes_nans] = -1 + next_indexes[virtual_indexes_nans] = -1 + previous_indexes = previous_indexes.astype(np.intp) + next_indexes = next_indexes.astype(np.intp) + return previous_indexes, next_indexes + + +def _quantile( + arr: np.array, + quantiles: np.array, + axis: int = -1, + interpolation="linear", + out=None, +): + """ + Private function that doesn't support extended axis or keepdims. + These methods are extended to this function using _ureduce + See nanpercentile for parameter usage + It computes the quantiles of the array for the given axis. + A linear interpolation is performed based on the `interpolation`. + + By default, the interpolation is "linear" where + alpha == beta == 1 which performs the 7th method of Hyndman&Fan. + With "median_unbiased" we get alpha == beta == 1/3 + thus the 8th method of Hyndman&Fan. + """ + # --- Setup + arr = np.asanyarray(arr) + values_count = arr.shape[axis] + # The dimensions of `q` are prepended to the output shape, so we need the + # axis being sampled from `arr` to be last. + DATA_AXIS = 0 + arr = np.moveaxis(arr, axis, destination=DATA_AXIS) + # --- Computation of indexes + # Index where to find the value in the sorted array. + # Virtual because it is a floating point value, not an valid index. + # The nearest neighbours are used for interpolation + try: + interpolation = _QuantileInterpolation[interpolation] + except KeyError: + raise ValueError( + f"{interpolation!r} is not a valid interpolation. Use one of: " + f"{_QuantileInterpolation.keys()}") + virtual_indexes = interpolation["get_virtual_index"](values_count, + quantiles) + virtual_indexes = np.asanyarray(virtual_indexes) + if np.issubdtype(virtual_indexes.dtype, np.integer): + # No interpolation needed, take the points along axis + if np.issubdtype(arr.dtype, np.inexact): # may contain nan, which would sort to the end - ap.partition(concatenate((indices.ravel(), [-1])), axis=0) - n = np.isnan(ap[-1]) + arr.partition(concatenate((virtual_indexes.ravel(), [-1])), axis=0) + slices_having_nans = np.isnan(arr[-1]) else: # cannot contain nan - ap.partition(indices.ravel(), axis=0) - n = np.array(False, dtype=bool) - - r = take(ap, indices, axis=0, out=out) - + arr.partition(virtual_indexes.ravel(), axis=0) + slices_having_nans = np.array(False, dtype=bool) + result = np.asanyarray(take(arr, virtual_indexes, axis=0, out=out)) else: - # weight the points above and below the indices - - indices_below = not_scalar(floor(indices)).astype(intp) - indices_above = not_scalar(indices_below + 1) - indices_above[indices_above > Nx - 1] = Nx - 1 - - if np.issubdtype(a.dtype, np.inexact): - # may contain nan, which would sort to the end - ap.partition(concatenate(( - indices_below.ravel(), indices_above.ravel(), [-1] - )), axis=0) - n = np.isnan(ap[-1]) + previous_indexes, next_indexes = _get_indexes(arr, + virtual_indexes, + values_count) + # --- Sorting + arr.partition( + np.unique(np.concatenate(([0, -1], + previous_indexes.ravel(), + next_indexes.ravel(), + ))), + axis=DATA_AXIS) + if np.issubdtype(arr.dtype, np.inexact): + slices_having_nans = np.isnan( + take(arr, indices=-1, axis=DATA_AXIS) + ) else: - # cannot contain nan - ap.partition(concatenate(( - indices_below.ravel(), indices_above.ravel() - )), axis=0) - n = np.array(False, dtype=bool) - - weights_shape = indices.shape + (1,) * (ap.ndim - 1) - weights_above = not_scalar(indices - indices_below).reshape(weights_shape) - - x_below = take(ap, indices_below, axis=0) - x_above = take(ap, indices_above, axis=0) - - r = _lerp(x_below, x_above, weights_above, out=out) - - # if any slice contained a nan, then all results on that slice are also nan - if np.any(n): - if r.ndim == 0 and out is None: + slices_having_nans = None + # --- Get values from indexes + previous = np.take(arr, previous_indexes, axis=DATA_AXIS) + next = np.take(arr, next_indexes, axis=DATA_AXIS) + # --- Linear interpolation + gamma = _get_gamma(virtual_indexes, + previous_indexes, + interpolation) + result_shape = virtual_indexes.shape + (1,) * (arr.ndim - 1) + gamma = gamma.reshape(result_shape) + result = _linear_interpolation_formula(previous, + next, + gamma, + out=out) + if np.any(slices_having_nans): + if result.ndim == 0 and out is None: # can't write to a scalar - r = a.dtype.type(np.nan) + result = np.array(np.nan, dtype=arr.dtype) else: - r[..., n] = a.dtype.type(np.nan) - - return r + result[..., slices_having_nans] = np.nan + return result def _trapz_dispatcher(y, x=None, dx=None, axis=None): diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py index 08d9b42bb..e90c19b4a 100644 --- a/numpy/lib/nanfunctions.py +++ b/numpy/lib/nanfunctions.py @@ -23,6 +23,7 @@ Functions import functools import warnings import numpy as np +from numpy.lib.function_base import _QuantileInterpolation from numpy.lib import function_base from numpy.core import overrides @@ -1229,8 +1230,15 @@ def _nanpercentile_dispatcher(a, q, axis=None, out=None, overwrite_input=None, @array_function_dispatch(_nanpercentile_dispatcher) -def nanpercentile(a, q, axis=None, out=None, overwrite_input=False, - interpolation='linear', keepdims=np._NoValue): +def nanpercentile( + a, + q, + axis=None, + out=None, + overwrite_input=False, + interpolation="linear", + keepdims=np._NoValue, +): """ Compute the qth percentile of the data along the specified axis, while ignoring nan values. @@ -1259,18 +1267,74 @@ def nanpercentile(a, q, axis=None, out=None, overwrite_input=False, If True, then allow the input array `a` to be modified by intermediate calculations, to save memory. In this case, the contents of the input `a` after this function completes is undefined. - interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} + interpolation : str + Possible values: 'linear' (default), + 'inverted_cdf', 'averaged_inverted_cdf', + 'closest_observation', 'interpolated_inverted_cdf', + 'hazen', 'weibull', + 'median_unbiased', 'normal_unbiased', + 'lower', 'higher', + 'midpoint', 'nearest'. This optional parameter specifies the interpolation method to - use when the desired percentile lies between two data points - ``i < j``: - - * 'linear': ``i + (j - i) * fraction``, where ``fraction`` - is the fractional part of the index surrounded by ``i`` - and ``j``. - * 'lower': ``i``. - * 'higher': ``j``. - * 'nearest': ``i`` or ``j``, whichever is nearest. - * 'midpoint': ``(i + j) / 2``. + use when the desired quantile lies between two data points ``i < j``. + g is the fractional part of the index surrounded by ``i``. + alpha and beta are correction constants modifying i and j: + i + g = (q - alpha) / ( n - alpha - beta + 1 ) + * inverted_cdf: + method 1 of H&F. + This method give discontinuous results: + if g > 0 ; then take j + if g = 0 ; then take i + * averaged_inverted_cdf: + method 2 of H&F. + This method give discontinuous results: + if g > 0 ; then take j + if g = 0 ; then average between bounds + * closest_observation: + method 3 of H&F. + This method give discontinuous results: + if g > 0 ; then take j + if g = 0 and index is odd ; then take j + if g = 0 and index is even ; then take i + * interpolated_inverted_cdf: + method 4 of H&F. + This method give continuous results using: + alpha = 0 + beta = 1 + * hazen: + method 5 of H&F. + This method give continuous results using: + alpha = 1/2 + beta = 1/2 + * weibull: + method 6 of H&F. + This method give continuous results using: + alpha = 0 + beta = 0 + * linear: + Default method. + method 7 of H&F. + This method give continuous results using: + alpha = 1 + beta = 1 + * median_unbiased: + method 8 of H&F. + This method is probably the best method if the sample distribution + function is unknown (see reference). + This method give continuous results using: + alpha = 1/3 + beta = 1/3 + * normal_unbiased: + method 9 of H&F. + This method is probably the best method if the sample distribution + function is known to be normal. + This method give continuous results using: + alpha = 3/8 + beta = 3/8 + * lower: ``i``. + * higher: ``j``. + * nearest: ``i`` or ``j``, whichever is nearest. + * midpoint: ``(i + j) / 2``. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the @@ -1342,7 +1406,9 @@ def nanpercentile(a, q, axis=None, out=None, overwrite_input=False, """ a = np.asanyarray(a) - q = np.true_divide(q, 100.0) # handles the asarray for us too + q = np.true_divide(q, 100.0) + # undo any decay that the ufunc performed (see gh-13105) + q = np.asanyarray(q) if not function_base._quantile_is_valid(q): raise ValueError("Percentiles must be in the range [0, 100]") return _nanquantile_unchecked( @@ -1355,8 +1421,15 @@ def _nanquantile_dispatcher(a, q, axis=None, out=None, overwrite_input=None, @array_function_dispatch(_nanquantile_dispatcher) -def nanquantile(a, q, axis=None, out=None, overwrite_input=False, - interpolation='linear', keepdims=np._NoValue): +def nanquantile( + a, + q, + axis=None, + out=None, + overwrite_input=False, + interpolation="linear", + keepdims=np._NoValue, +): """ Compute the qth quantile of the data along the specified axis, while ignoring nan values. @@ -1384,19 +1457,74 @@ def nanquantile(a, q, axis=None, out=None, overwrite_input=False, If True, then allow the input array `a` to be modified by intermediate calculations, to save memory. In this case, the contents of the input `a` after this function completes is undefined. - interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} + interpolation : str + Possible values: 'linear' (default), + 'inverted_cdf', 'averaged_inverted_cdf', + 'closest_observation', 'interpolated_inverted_cdf', + 'hazen', 'weibull', + 'median_unbiased', 'normal_unbiased', + 'lower', 'higher', + 'midpoint', 'nearest'. This optional parameter specifies the interpolation method to - use when the desired quantile lies between two data points - ``i < j``: - - * linear: ``i + (j - i) * fraction``, where ``fraction`` - is the fractional part of the index surrounded by ``i`` - and ``j``. + use when the desired quantile lies between two data points ``i < j``. + g is the fractional part of the index surrounded by ``i``. + alpha and beta are correction constants modifying i and j: + i + g = (q - alpha) / ( n - alpha - beta + 1 ) + * inverted_cdf: + method 1 of H&F. + This method give discontinuous results: + if g > 0 ; then take j + if g = 0 ; then take i + * averaged_inverted_cdf: + method 2 of H&F. + This method give discontinuous results: + if g > 0 ; then take j + if g = 0 ; then average between bounds + * closest_observation: + method 3 of H&F. + This method give discontinuous results: + if g > 0 ; then take j + if g = 0 and index is odd ; then take j + if g = 0 and index is even ; then take i + * interpolated_inverted_cdf: + method 4 of H&F. + This method give continuous results using: + alpha = 0 + beta = 1 + * hazen: + method 5 of H&F. + This method give continuous results using: + alpha = 1/2 + beta = 1/2 + * weibull: + method 6 of H&F. + This method give continuous results using: + alpha = 0 + beta = 0 + * linear: + Default method. + method 7 of H&F. + This method give continuous results using: + alpha = 1 + beta = 1 + * median_unbiased: + method 8 of H&F. + This method is probably the best method if the sample distribution + function is unknown (see reference). + This method give continuous results using: + alpha = 1/3 + beta = 1/3 + * normal_unbiased: + method 9 of H&F. + This method is probably the best method if the sample distribution + function is known to be normal. + This method give continuous results using: + alpha = 3/8 + beta = 3/8 * lower: ``i``. * higher: ``j``. * nearest: ``i`` or ``j``, whichever is nearest. * midpoint: ``(i + j) / 2``. - keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the @@ -1462,26 +1590,39 @@ def nanquantile(a, q, axis=None, out=None, overwrite_input=False, a, q, axis, out, overwrite_input, interpolation, keepdims) -def _nanquantile_unchecked(a, q, axis=None, out=None, overwrite_input=False, - interpolation='linear', keepdims=np._NoValue): +def _nanquantile_unchecked( + a, + q, + axis=None, + out=None, + overwrite_input=False, + interpolation="linear", + keepdims=np._NoValue, +): """Assumes that q is in [0, 1], and is an ndarray""" # apply_along_axis in _nanpercentile doesn't handle empty arrays well, # so deal them upfront if a.size == 0: return np.nanmean(a, axis, out=out, keepdims=keepdims) - - r, k = function_base._ureduce( - a, func=_nanquantile_ureduce_func, q=q, axis=axis, out=out, - overwrite_input=overwrite_input, interpolation=interpolation - ) + r, k = function_base._ureduce(a, + func=_nanquantile_ureduce_func, + q=q, + axis=axis, + out=out, + overwrite_input=overwrite_input, + interpolation=interpolation) if keepdims and keepdims is not np._NoValue: return r.reshape(q.shape + k) else: return r -def _nanquantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, - interpolation='linear'): +def _nanquantile_ureduce_func(a, + q, + axis=None, + out=None, + overwrite_input=False, + interpolation= "linear"): """ Private function that doesn't support extended axis or keepdims. These methods are extended to this function using _ureduce @@ -1504,7 +1645,10 @@ def _nanquantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, return result -def _nanquantile_1d(arr1d, q, overwrite_input=False, interpolation='linear'): +def _nanquantile_1d(arr1d, + q, + overwrite_input=False, + interpolation= "linear"): """ Private function for rank 1 arrays. Compute quantile ignoring NaNs. See nanpercentile for parameter usage diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index c7dfe5673..4f0db7bdb 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -2903,36 +2903,134 @@ class TestPercentile: [1, 1, 1]]) assert_array_equal(np.percentile(x, 50, axis=0), [1, 1, 1]) - def test_linear(self): - + @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) + def test_linear_nan_1D(self, dtype): + # METHOD 1 of H&F + arr = np.asarray([15.0, np.NAN, 35.0, 40.0, 50.0], dtype=dtype) + res = np.percentile( + arr, + 40.0, + interpolation="linear") + np.testing.assert_equal(res, np.NAN) + np.testing.assert_equal(res.dtype, arr.dtype) + + TYPE_CODES = np.typecodes["AllInteger"] + np.typecodes["AllFloat"] + "O" + + @pytest.mark.parametrize("dtype", TYPE_CODES) + def test_linear_inverted_cdf(self, dtype): + # METHOD 1 of H&F + arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) + res = np.percentile( + arr, + 40.0, + interpolation="inverted_cdf") + np.testing.assert_almost_equal(res, 20, 15) + + @pytest.mark.parametrize("dtype", TYPE_CODES) + def test_linear_averaged_inverted_cdf(self, dtype): + # METHOD 2 of H&F + arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) + res = np.percentile( + arr, + 40.0, + interpolation="averaged_inverted_cdf") + np.testing.assert_almost_equal(res, 27.5, 15) + + @pytest.mark.parametrize("dtype", TYPE_CODES) + def test_linear_closest_observation(self, dtype): + # METHOD 3 of H&F + arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) + res = np.percentile( + arr, + 40.0, + interpolation="closest_observation") + np.testing.assert_almost_equal(res, 20, 15) + + @pytest.mark.parametrize("dtype", TYPE_CODES) + def test_linear_interpolated_inverted_cdf(self, dtype): + # METHOD 4 of H&F + arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) + res = np.percentile( + arr, + 40.0, + interpolation="interpolated_inverted_cdf") + np.testing.assert_almost_equal(res, 20, 15) + + @pytest.mark.parametrize("dtype", TYPE_CODES) + def test_linear_hazen(self, dtype): + # METHOD 5 of H&F + arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) + res = np.percentile( + arr, + 40.0, + interpolation="hazen") + np.testing.assert_almost_equal(res, 27.5, 15) + + @pytest.mark.parametrize("dtype", TYPE_CODES) + def test_linear_weibull(self, dtype): + # METHOD 6 of H&F + arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) + res = np.percentile( + arr, + 40.0, + interpolation="weibull") + np.testing.assert_almost_equal(res, 26, 15) + + @pytest.mark.parametrize("dtype", TYPE_CODES) + def test_linear_linear(self, dtype): + # METHOD 7 of H&F # Test defaults assert_equal(np.percentile(range(10), 50), 4.5) - - # explicitly specify interpolation_method 'linear' (the default) - assert_equal(np.percentile(range(10), 50, - interpolation='linear'), 4.5) - - def test_lower_higher(self): - - # interpolation_method 'lower'/'higher' - assert_equal(np.percentile(range(10), 50, + # explicit interpolation_method (the default) + res = np.percentile([15.0, 20.0, 35.0, 40.0, 50.0], + 40, + interpolation="linear") + np.testing.assert_almost_equal(res, 29, 15) + + @pytest.mark.parametrize("dtype", TYPE_CODES) + def test_linear_median_unbiased(self, dtype): + # METHOD 8 of H&F + arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) + res = np.percentile( + arr, + 40.0, + interpolation="median_unbiased") + np.testing.assert_almost_equal(res, 27, 14) + + @pytest.mark.parametrize("dtype", TYPE_CODES) + def test_linear_normal_unbiased(self, dtype): + # METHOD 9 of H&F + arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) + res = np.percentile( + arr, + 40.0, + interpolation="normal_unbiased") + np.testing.assert_almost_equal(res, 27.125, 15) + + @pytest.mark.parametrize("dtype", TYPE_CODES) + def test_lower_higher(self, dtype): + assert_equal(np.percentile(np.arange(10, dtype=dtype), 50, interpolation='lower'), 4) - assert_equal(np.percentile(range(10), 50, + assert_equal(np.percentile(np.arange(10, dtype=dtype), 50, interpolation='higher'), 5) - def test_midpoint(self): - assert_equal(np.percentile(range(10), 51, + @pytest.mark.parametrize("dtype", TYPE_CODES) + def test_midpoint(self, dtype): + assert_equal(np.percentile(np.arange(10, dtype=dtype), 51, interpolation='midpoint'), 4.5) - assert_equal(np.percentile(range(11), 51, + assert_equal(np.percentile(np.arange(9, dtype=dtype) + 1, 50, + interpolation='midpoint'), 5) + assert_equal(np.percentile(np.arange(11, dtype=dtype), 51, interpolation='midpoint'), 5.5) - assert_equal(np.percentile(range(11), 50, + assert_equal(np.percentile(np.arange(11, dtype=dtype), 50, interpolation='midpoint'), 5) - def test_nearest(self): - assert_equal(np.percentile(range(10), 51, + @pytest.mark.parametrize("dtype", TYPE_CODES) + def test_nearest(self, dtype): + assert_equal(np.percentile(np.arange(10, dtype=dtype), 51, interpolation='nearest'), 5) - assert_equal(np.percentile(range(10), 49, - interpolation='nearest'), 4) + assert_equal(np.percentile(np.arange(10, dtype=dtype), 49, + interpolation='nearest'), 4) def test_sequence(self): x = np.arange(8) * 0.5 @@ -3038,18 +3136,18 @@ class TestPercentile: y = np.zeros((3,)) p = (1, 2, 3) np.percentile(x, p, out=y) - assert_equal(y, np.percentile(x, p)) + assert_equal(np.percentile(x, p), y) x = np.array([[1, 2, 3], [4, 5, 6]]) y = np.zeros((3, 3)) np.percentile(x, p, axis=0, out=y) - assert_equal(y, np.percentile(x, p, axis=0)) + assert_equal(np.percentile(x, p, axis=0), y) y = np.zeros((3, 2)) np.percentile(x, p, axis=1, out=y) - assert_equal(y, np.percentile(x, p, axis=1)) + assert_equal(np.percentile(x, p, axis=1), y) x = np.arange(12).reshape(3, 4) # q.dim > 1, float @@ -3293,6 +3391,7 @@ class TestPercentile: with pytest.raises(ValueError, match="Percentiles must be in"): np.percentile([1, 2, 3, 4.0], q) + class TestQuantile: # most of this is already tested by TestPercentile @@ -3302,6 +3401,7 @@ class TestQuantile: assert_equal(np.quantile(x, 1), 3.5) assert_equal(np.quantile(x, 0.5), 1.75) + @pytest.mark.xfail(reason="See gh-19154") def test_correct_quantile_value(self): a = np.array([True]) tf_quant = np.quantile(True, False) @@ -3310,12 +3410,11 @@ class TestQuantile: a = np.array([False, True, True]) quant_res = np.quantile(a, a) assert_array_equal(quant_res, a) - assert_equal(a.dtype, quant_res.dtype) + assert_equal(quant_res.dtype, a.dtype) def test_fraction(self): # fractional input, integral quantile x = [Fraction(i, 2) for i in range(8)] - q = np.quantile(x, 0) assert_equal(q, 0) assert_equal(type(q), Fraction) @@ -3352,6 +3451,12 @@ class TestQuantile: np.quantile(np.arange(100.), p, interpolation="midpoint") assert_array_equal(p, p0) + @pytest.mark.parametrize("dtype", np.typecodes["AllInteger"]) + def test_quantile_preserve_int_type(self, dtype): + res = np.quantile(np.array([1, 2], dtype=dtype), [0.5], + interpolation="nearest") + assert res.dtype == dtype + def test_quantile_monotonic(self): # GH 14685 # test that the return value of quantile is monotonic if p0 is ordered @@ -3380,9 +3485,9 @@ class TestLerp: min_value=-1e300, max_value=1e300), b = st.floats(allow_nan=False, allow_infinity=False, min_value=-1e300, max_value=1e300)) - def test_lerp_monotonic(self, t0, t1, a, b): - l0 = np.lib.function_base._lerp(a, b, t0) - l1 = np.lib.function_base._lerp(a, b, t1) + def test_linear_interpolation_formula_monotonic(self, t0, t1, a, b): + l0 = nfb._linear_interpolation_formula(a, b, t0) + l1 = nfb._linear_interpolation_formula(a, b, t1) if t0 == t1 or a == b: assert l0 == l1 # uninteresting elif (t0 < t1) == (a < b): @@ -3396,11 +3501,11 @@ class TestLerp: min_value=-1e300, max_value=1e300), b=st.floats(allow_nan=False, allow_infinity=False, min_value=-1e300, max_value=1e300)) - def test_lerp_bounded(self, t, a, b): + def test_linear_interpolation_formula_bounded(self, t, a, b): if a <= b: - assert a <= np.lib.function_base._lerp(a, b, t) <= b + assert a <= nfb._linear_interpolation_formula(a, b, t) <= b else: - assert b <= np.lib.function_base._lerp(a, b, t) <= a + assert b <= nfb._linear_interpolation_formula(a, b, t) <= a @hypothesis.given(t=st.floats(allow_nan=False, allow_infinity=False, min_value=0, max_value=1), @@ -3408,17 +3513,17 @@ class TestLerp: min_value=-1e300, max_value=1e300), b=st.floats(allow_nan=False, allow_infinity=False, min_value=-1e300, max_value=1e300)) - def test_lerp_symmetric(self, t, a, b): + def test_linear_interpolation_formula_symmetric(self, t, a, b): # double subtraction is needed to remove the extra precision of t < 0.5 - left = np.lib.function_base._lerp(a, b, 1 - (1 - t)) - right = np.lib.function_base._lerp(b, a, 1 - t) + left = nfb._linear_interpolation_formula(a, b, 1 - (1 - t)) + right = nfb._linear_interpolation_formula(b, a, 1 - t) assert left == right - def test_lerp_0d_inputs(self): + def test_linear_interpolation_formula_0d_inputs(self): a = np.array(2) b = np.array(5) t = np.array(0.2) - assert np.lib.function_base._lerp(a, b, t) == 2.6 + assert nfb._linear_interpolation_formula(a, b, t) == 2.6 class TestMedian: -- cgit v1.2.1 From 303c12cfe7ad1b8b6ed5417c126857b29355b1fb Mon Sep 17 00:00:00 2001 From: Charles Harris Date: Fri, 8 Oct 2021 17:09:51 -0600 Subject: DOC: fix docstrings. Hopefully fix the docstrings of percentile, nanpercentile, quantile, and nanquantile so that CircleCI passes. --- numpy/lib/function_base.py | 424 +++++++++++++++++++++++++++------------------ numpy/lib/nanfunctions.py | 424 ++++++++++++++++++++++++++++----------------- 2 files changed, 523 insertions(+), 325 deletions(-) (limited to 'numpy') diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 67a34f6e1..dbaba87f9 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -3877,75 +3877,31 @@ def percentile(a, If True, then allow the input array `a` to be modified by intermediate calculations, to save memory. In this case, the contents of the input `a` after this function completes is undefined. - - interpolation : str - Possible values: 'linear' (default), - 'inverted_cdf', 'averaged_inverted_cdf', - 'closest_observation', 'interpolated_inverted_cdf', - 'hazen', 'weibull', - 'median_unbiased', 'normal_unbiased', - 'lower', 'higher', - 'midpoint', 'nearest'. - This optional parameter specifies the interpolation method to - use when the desired quantile lies between two data points ``i < j``. - g is the fractional part of the index surrounded by ``i``. - alpha and beta are correction constants modifying i and j: - i + g = (q - alpha) / ( n - alpha - beta + 1 ) - * inverted_cdf: - method 1 of H&F. - This method give discontinuous results: - if g > 0 ; then take j - if g = 0 ; then take i - * averaged_inverted_cdf: - method 2 of H&F. - This method give discontinuous results: - if g > 0 ; then take j - if g = 0 ; then average between bounds - * closest_observation: - method 3 of H&F. - This method give discontinuous results: - if g > 0 ; then take j - if g = 0 and index is odd ; then take j - if g = 0 and index is even ; then take i - * interpolated_inverted_cdf: - method 4 of H&F. - This method give continuous results using: - alpha = 0 - beta = 1 - * hazen: - method 5 of H&F. - This method give continuous results using: - alpha = 1/2 - beta = 1/2 - * weibull: - method 6 of H&F. - This method give continuous results using: - alpha = 0 - beta = 0 - * inclusive: - Default method, aliased with "linear". - method 7 of H&F. - This method give continuous results using: - alpha = 1 - beta = 1 - * median_unbiased: - method 8 of H&F. - This method is probably the best method if the sample distribution - function is unknown (see reference). - This method give continuous results using: - alpha = 1/3 - beta = 1/3 - * normal_unbiased: - method 9 of H&F. - This method is probably the best method if the sample distribution - function is known to be normal. - This method give continuous results using: - alpha = 3/8 - beta = 3/8 - * lower: ``i``. - * higher: ``j``. - * nearest: ``i`` or ``j``, whichever is nearest. - * midpoint: ``(i + j) / 2``. + interpolation : str, optional + This parameter specifies the interpolation method to + use when the desired quantile lies between two data points + There are many different methods, some unique to NumPy. See the + notes for explanation. Options + + * (NPY 1): 'lower' + * (NPY 2): 'higher', + * (NPY 3): 'midpoint' + * (NPY 4): 'nearest' + * (NPY 5): 'linear', aliased with 'inclusive' (default) + + New options: + + * (H&F 1): 'inverted_cdf' + * (H&F 2): 'averaged_inverted_cdf' + * (H&F 3): 'closest_observation' + * (H&F 4): 'interpolated_inverted_cdf' + * (H&F 5): 'hazen' + * (H&F 6): 'weibull' + * (H&F 7): 'inclusive', aliased with 'linear' (default) + * (H&F 8): 'median_unbiased' + * (H&F 9): 'normal_unbiased' + + .. versionadded;: 1.22.0 keepdims : bool, optional If this is set to True, the axes which are reduced are left in @@ -3971,18 +3927,104 @@ def percentile(a, mean median : equivalent to ``percentile(..., 50)`` nanpercentile - quantile : equivalent to percentile, except with q in the range [0, 1]. + quantile : equivalent to percentile, except q in the range [0, 1]. Notes ----- - Given a vector ``V`` of length ``N``, the q-th percentile of - ``V`` is the value ``q/100`` of the way from the minimum to the - maximum in a sorted copy of ``V``. The values and distances of - the two nearest neighbors as well as the `interpolation` parameter - will determine the percentile if the normalized ranking does not - match the location of ``q`` exactly. This function is the same as - the median if ``q=50``, the same as the minimum if ``q=0`` and the - same as the maximum if ``q=100``. + Given a vector ``V`` of length ``N``, the q-th percentile of ``V`` is + the value ``q/100`` of the way from the minimum to the maximum in a + sorted copy of ``V``. The values and distances of the two nearest + neighbors as well as the `interpolation` parameter will determine the + percentile if the normalized ranking does not match the location of + ``q`` exactly. This function is the same as the median if ``q=50``, the + same as the minimum if ``q=0`` and the same as the maximum if + ``q=100``. + + This optional `interpolation` parameter specifies the interpolation + method to use when the desired quantile lies between two data points + ``i < j``. If ``g`` is the fractional part of the index surrounded by + ``i`` and alpha and beta are correction constants modifying i and j. + + .. math:: + i + g = (q - alpha) / ( n - alpha - beta + 1 ) + + The different interpolation methods then work as follows + + inverted_cdf: + method 1 of H&F [1]_. + This method gives discontinuous results: + * if g > 0 ; then take j + * if g = 0 ; then take i + + averaged_inverted_cdf: + method 2 of H&F [1]_. + This method give discontinuous results: + * if g > 0 ; then take j + * if g = 0 ; then average between bounds + + closest_observation: + method 3 of H&F [1]_. + This method give discontinuous results: + * if g > 0 ; then take j + * if g = 0 and index is odd ; then take j + * if g = 0 and index is even ; then take i + + interpolated_inverted_cdf: + method 4 of H&F [1]_. + This method give continuous results using: + * alpha = 0 + * beta = 1 + + hazen: + method 5 of H&F [1]_. + This method give continuous results using: + * alpha = 1/2 + * beta = 1/2 + + weibull: + method 6 of H&F [1]_. + This method give continuous results using: + * alpha = 0 + * beta = 0 + + inclusive: + Default method, aliased with "linear". + method 7 of H&F [1]_. + This method give continuous results using: + * alpha = 1 + * beta = 1 + + median_unbiased: + method 8 of H&F [1]_. + This method is probably the best method if the sample + distribution function is unknown (see reference). + This method give continuous results using: + * alpha = 1/3 + * beta = 1/3 + + normal_unbiased: + method 9 of H&F [1]_. + This method is probably the best method if the sample + distribution function is known to be normal. + This method give continuous results using: + * alpha = 3/8 + * beta = 3/8 + + lower: + NumPy method kept for backwards compatibility. + Takes ``i`` as the interpolation point. + + higher: + NumPy method kept for backwards compatibility. + Takes ``j`` as the interpolation point. + + nearest: + NumPy method kept for backwards compatibility. + Takes ``i`` or ``j``, whichever is nearest. + + midpoint: + NumPy method kept for backwards compatibility. + Uses ``(i + j) / 2``. Examples -------- @@ -4042,9 +4084,10 @@ def percentile(a, References ---------- - [Hyndman&Fan] Hyndman, R. J., & Fan, Y. (1996). - Sample quantiles in statistical packages. - The American Statistician, 50(4), 361-365. + .. [1] R. J. Hyndman and Y. Fan, + "Sample quantiles in statistical packages," + The American Statistician, 50(4), pp. 361-365, 1996 + """ q = np.true_divide(q, 100) q = asanyarray(q) # undo any decay that the ufunc performed (see gh-13105) @@ -4080,85 +4123,42 @@ def quantile(a, Quantile or sequence of quantiles to compute, which must be between 0 and 1 inclusive. axis : {int, tuple of int, None}, optional - Axis or axes along which the quantiles are computed. The - default is to compute the quantile(s) along a flattened - version of the array. + Axis or axes along which the quantiles are computed. The default is + to compute the quantile(s) along a flattened version of the array. out : ndarray, optional - Alternative output array in which to place the result. It must - have the same shape and buffer length as the expected output, - but the type (of the output) will be cast if necessary. + Alternative output array in which to place the result. It must have + the same shape and buffer length as the expected output, but the + type (of the output) will be cast if necessary. overwrite_input : bool, optional - If True, then allow the input array `a` to be modified by intermediate - calculations, to save memory. In this case, the contents of the input - `a` after this function completes is undefined. - interpolation : str - Possible values: 'linear' (default), - 'inverted_cdf', 'averaged_inverted_cdf', - 'closest_observation', 'interpolated_inverted_cdf', - 'hazen', 'weibull', - 'median_unbiased', 'normal_unbiased', - 'lower', 'higher', - 'midpoint', 'nearest'. - This optional parameter specifies the interpolation method to - use when the desired quantile lies between two data points ``i < j``. - g is the fractional part of the index surrounded by ``i``. - alpha and beta are correction constants modifying i and j: - i + g = (q - alpha) / ( n - alpha - beta + 1 ) - * inverted_cdf: - method 1 of H&F. - This method give discontinuous results: - if g > 0 ; then take j - if g = 0 ; then take i - * averaged_inverted_cdf: - method 2 of H&F. - This method give discontinuous results: - if g > 0 ; then take j - if g = 0 ; then average between bounds - * closest_observation: - method 3 of H&F. - This method give discontinuous results: - if g > 0 ; then take j - if g = 0 and index is odd ; then take j - if g = 0 and index is even ; then take i - * interpolated_inverted_cdf: - method 4 of H&F. - This method give continuous results using: - alpha = 0 - beta = 1 - * hazen: - method 5 of H&F. - This method give continuous results using: - alpha = 1/2 - beta = 1/2 - * weibull: - method 6 of H&F. - This method give continuous results using: - alpha = 0 - beta = 0 - * linear: - Default method. - method 7 of H&F. - This method give continuous results using: - alpha = 1 - beta = 1 - * median_unbiased: - method 8 of H&F. - This method is probably the best method if the sample distribution - function is unknown (see reference). - This method give continuous results using: - alpha = 1/3 - beta = 1/3 - * normal_unbiased: - method 9 of H&F. - This method is probably the best method if the sample distribution - function is known to be normal. - This method give continuous results using: - alpha = 3/8 - beta = 3/8 - * lower: ``i``. - * higher: ``j``. - * nearest: ``i`` or ``j``, whichever is nearest. - * midpoint: ``(i + j) / 2``. + If True, then allow the input array `a` to be modified by + intermediate calculations, to save memory. In this case, the + contents of the input `a` after this function completes is + undefined. + interpolation : str, optional + This parameter specifies the interpolation method to use when the + desired quantile lies between two data points There are many + different methods, some unique to NumPy. See the notes for + explanation. Options: + + * (NPY 1): 'lower' + * (NPY 2): 'higher', + * (NPY 3): 'midpoint' + * (NPY 4): 'nearest' + * (NPY 5): 'linear', aliased with 'inclusive' (default) + + New options: + + * (H&F 1): 'inverted_cdf' + * (H&F 2): 'averaged_inverted_cdf' + * (H&F 3): 'closest_observation' + * (H&F 4): 'interpolated_inverted_cdf' + * (H&F 5): 'hazen' + * (H&F 6): 'weibull' + * (H&F 7): 'inclusive', aliased with 'linear' (default) + * (H&F 8): 'median_unbiased' + * (H&F 9): 'normal_unbiased' + + .. versionadded:: 1.22.0 keepdims : bool, optional If this is set to True, the axes which are reduced are left in @@ -4186,14 +4186,99 @@ def quantile(a, Notes ----- - Given a vector ``V`` of length ``N``, the q-th quantile of - ``V`` is the value ``q`` of the way from the minimum to the - maximum in a sorted copy of ``V``. The values and distances of - the two nearest neighbors as well as the `interpolation` parameter - will determine the quantile if the normalized ranking does not - match the location of ``q`` exactly. This function is the same as - the median if ``q=0.5``, the same as the minimum if ``q=0.0`` and the - same as the maximum if ``q=1.0``. + Given a vector ``V`` of length ``N``, the q-th quantile of ``V`` is the + value ``q`` of the way from the minimum to the maximum in a sorted copy of + ``V``. The values and distances of the two nearest neighbors as well as the + `interpolation` parameter will determine the quantile if the normalized + ranking does not match the location of ``q`` exactly. This function is the + same as the median if ``q=0.5``, the same as the minimum if ``q=0.0`` and + the same as the maximum if ``q=1.0``. + + This optional `interpolation` parameter specifies the interpolation method + to use when the desired quantile lies between two data points ``i < j``. If + ``g`` is the fractional part of the index surrounded by ``i`` and alpha + and beta are correction constants modifying i and j. + + .. math:: + i + g = (q - alpha) / ( n - alpha - beta + 1 ) + + The different interpolation methods then work as follows + + inverted_cdf: + method 1 of H&F [1]_. + This method gives discontinuous results: + * if g > 0 ; then take j + * if g = 0 ; then take i + + averaged_inverted_cdf: + method 2 of H&F [1]_. + This method give discontinuous results: + * if g > 0 ; then take j + * if g = 0 ; then average between bounds + + closest_observation: + method 3 of H&F [1]_. + This method give discontinuous results: + * if g > 0 ; then take j + * if g = 0 and index is odd ; then take j + * if g = 0 and index is even ; then take i + + interpolated_inverted_cdf: + method 4 of H&F [1]_. + This method give continuous results using: + * alpha = 0 + * beta = 1 + + hazen: + method 5 of H&F [1]_. + This method give continuous results using: + * alpha = 1/2 + * beta = 1/2 + + weibull: + method 6 of H&F [1]_. + This method give continuous results using: + * alpha = 0 + * beta = 0 + + inclusive: + Default method, aliased with "linear". + method 7 of H&F [1]_. + This method give continuous results using: + * alpha = 1 + * beta = 1 + + median_unbiased: + method 8 of H&F [1]_. + This method is probably the best method if the sample + distribution function is unknown (see reference). + This method give continuous results using: + * alpha = 1/3 + * beta = 1/3 + + normal_unbiased: + method 9 of H&F [1]_. + This method is probably the best method if the sample + distribution function is known to be normal. + This method give continuous results using: + * alpha = 3/8 + * beta = 3/8 + + lower: + NumPy method kept for backwards compatibility. + Takes ``i`` as the interpolation point. + + higher: + NumPy method kept for backwards compatibility. + Takes ``j`` as the interpolation point. + + nearest: + NumPy method kept for backwards compatibility. + Takes ``i`` or ``j``, whichever is nearest. + + midpoint: + NumPy method kept for backwards compatibility. + Uses ``(i + j) / 2``. Examples -------- @@ -4223,9 +4308,10 @@ def quantile(a, References ---------- - [Hyndman&Fan] Hyndman, R. J., & Fan, Y. (1996). - Sample quantiles in statistical packages. - The American Statistician, 50(4), 361-365. + .. [1] R. J. Hyndman and Y. Fan, + "Sample quantiles in statistical packages," + The American Statistician, 50(4), pp. 361-365, 1996 + """ q = np.asanyarray(q) if not _quantile_is_valid(q): diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py index e90c19b4a..710242d59 100644 --- a/numpy/lib/nanfunctions.py +++ b/numpy/lib/nanfunctions.py @@ -1253,88 +1253,47 @@ def nanpercentile( Input array or object that can be converted to an array, containing nan values to be ignored. q : array_like of float - Percentile or sequence of percentiles to compute, which must be between - 0 and 100 inclusive. + Percentile or sequence of percentiles to compute, which must be + between 0 and 100 inclusive. axis : {int, tuple of int, None}, optional - Axis or axes along which the percentiles are computed. The - default is to compute the percentile(s) along a flattened - version of the array. + Axis or axes along which the percentiles are computed. The default + is to compute the percentile(s) along a flattened version of the + array. out : ndarray, optional - Alternative output array in which to place the result. It must - have the same shape and buffer length as the expected output, - but the type (of the output) will be cast if necessary. + Alternative output array in which to place the result. It must have + the same shape and buffer length as the expected output, but the + type (of the output) will be cast if necessary. overwrite_input : bool, optional - If True, then allow the input array `a` to be modified by intermediate - calculations, to save memory. In this case, the contents of the input - `a` after this function completes is undefined. - interpolation : str - Possible values: 'linear' (default), - 'inverted_cdf', 'averaged_inverted_cdf', - 'closest_observation', 'interpolated_inverted_cdf', - 'hazen', 'weibull', - 'median_unbiased', 'normal_unbiased', - 'lower', 'higher', - 'midpoint', 'nearest'. - This optional parameter specifies the interpolation method to - use when the desired quantile lies between two data points ``i < j``. - g is the fractional part of the index surrounded by ``i``. - alpha and beta are correction constants modifying i and j: - i + g = (q - alpha) / ( n - alpha - beta + 1 ) - * inverted_cdf: - method 1 of H&F. - This method give discontinuous results: - if g > 0 ; then take j - if g = 0 ; then take i - * averaged_inverted_cdf: - method 2 of H&F. - This method give discontinuous results: - if g > 0 ; then take j - if g = 0 ; then average between bounds - * closest_observation: - method 3 of H&F. - This method give discontinuous results: - if g > 0 ; then take j - if g = 0 and index is odd ; then take j - if g = 0 and index is even ; then take i - * interpolated_inverted_cdf: - method 4 of H&F. - This method give continuous results using: - alpha = 0 - beta = 1 - * hazen: - method 5 of H&F. - This method give continuous results using: - alpha = 1/2 - beta = 1/2 - * weibull: - method 6 of H&F. - This method give continuous results using: - alpha = 0 - beta = 0 - * linear: - Default method. - method 7 of H&F. - This method give continuous results using: - alpha = 1 - beta = 1 - * median_unbiased: - method 8 of H&F. - This method is probably the best method if the sample distribution - function is unknown (see reference). - This method give continuous results using: - alpha = 1/3 - beta = 1/3 - * normal_unbiased: - method 9 of H&F. - This method is probably the best method if the sample distribution - function is known to be normal. - This method give continuous results using: - alpha = 3/8 - beta = 3/8 - * lower: ``i``. - * higher: ``j``. - * nearest: ``i`` or ``j``, whichever is nearest. - * midpoint: ``(i + j) / 2``. + If True, then allow the input array `a` to be modified by + intermediate calculations, to save memory. In this case, the + contents of the input `a` after this function completes is + undefined. + interpolation : str, optional + This parameter specifies the interpolation method to use when the + desired quantile lies between two data points There are many + different methods, some unique to NumPy. See the notes for + explanation. Options: + + * (NPY 1): 'lower' + * (NPY 2): 'higher', + * (NPY 3): 'midpoint' + * (NPY 4): 'nearest' + * (NPY 5): 'linear', aliased with 'inclusive' (default) + + New options: + + * (H&F 1): 'inverted_cdf' + * (H&F 2): 'averaged_inverted_cdf' + * (H&F 3): 'closest_observation' + * (H&F 4): 'interpolated_inverted_cdf' + * (H&F 5): 'hazen' + * (H&F 6): 'weibull' + * (H&F 7): 'inclusive', aliased with 'linear' (default) + * (H&F 8): 'median_unbiased' + * (H&F 9): 'normal_unbiased' + + .. versionadded:: 1.22.0 + keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the @@ -1363,18 +1322,104 @@ def nanpercentile( nanmean nanmedian : equivalent to ``nanpercentile(..., 50)`` percentile, median, mean - nanquantile : equivalent to nanpercentile, but with q in the range [0, 1]. + nanquantile : equivalent to nanpercentile, except q in range [0, 1]. Notes ----- - Given a vector ``V`` of length ``N``, the ``q``-th percentile of - ``V`` is the value ``q/100`` of the way from the minimum to the - maximum in a sorted copy of ``V``. The values and distances of - the two nearest neighbors as well as the `interpolation` parameter - will determine the percentile if the normalized ranking does not - match the location of ``q`` exactly. This function is the same as - the median if ``q=50``, the same as the minimum if ``q=0`` and the - same as the maximum if ``q=100``. + Given a vector ``V`` of length ``N``, the ``q``-th percentile of ``V`` + is the value ``q/100`` of the way from the minimum to the maximum in a + sorted copy of ``V``. The values and distances of the two nearest + neighbors as well as the `interpolation` parameter will determine the + percentile if the normalized ranking does not match the location of + ``q`` exactly. This function is the same as the median if ``q=50``, the + same as the minimum if ``q=0`` and the same as the maximum if + ``q=100``. + + This optional `interpolation` parameter specifies the interpolation + method to use when the desired quantile lies between two data points + ``i < j``. If ``g`` is the fractional part of the index surrounded by + ``i`` and alpha and beta are correction constants modifying i and j. + + .. math:: + i + g = (q - alpha) / ( n - alpha - beta + 1 ) + + The different interpolation methods then work as follows + + inverted_cdf: + method 1 of H&F [1]_. + This method gives discontinuous results: + * if g > 0 ; then take j + * if g = 0 ; then take i + + averaged_inverted_cdf: + method 2 of H&F [1]_. + This method give discontinuous results: + * if g > 0 ; then take j + * if g = 0 ; then average between bounds + + closest_observation: + method 3 of H&F [1]_. + This method give discontinuous results: + * if g > 0 ; then take j + * if g = 0 and index is odd ; then take j + * if g = 0 and index is even ; then take i + + interpolated_inverted_cdf: + method 4 of H&F [1]_. + This method give continuous results using: + * alpha = 0 + * beta = 1 + + hazen: + method 5 of H&F [1]_. + This method give continuous results using: + * alpha = 1/2 + * beta = 1/2 + + weibull: + method 6 of H&F [1]_. + This method give continuous results using: + * alpha = 0 + * beta = 0 + + inclusive: + Default method, aliased with "linear". + method 7 of H&F [1]_. + This method give continuous results using: + * alpha = 1 + * beta = 1 + + median_unbiased: + method 8 of H&F [1]_. + This method is probably the best method if the sample + distribution function is unknown (see reference). + This method give continuous results using: + * alpha = 1/3 + * beta = 1/3 + + normal_unbiased: + method 9 of H&F [1]_. + This method is probably the best method if the sample + distribution function is known to be normal. + This method give continuous results using: + * alpha = 3/8 + * beta = 3/8 + + lower: + NumPy method kept for backwards compatibility. + Takes ``i`` as the interpolation point. + + higher: + NumPy method kept for backwards compatibility. + Takes ``j`` as the interpolation point. + + nearest: + NumPy method kept for backwards compatibility. + Takes ``i`` or ``j``, whichever is nearest. + + midpoint: + NumPy method kept for backwards compatibility. + Uses ``(i + j) / 2``. Examples -------- @@ -1404,6 +1449,12 @@ def nanpercentile( array([7., 2.]) >>> assert not np.all(a==b) + References + ---------- + .. [1] R. J. Hyndman and Y. Fan, + "Sample quantiles in statistical packages," + The American Statistician, 50(4), pp. 361-365, 1996 + """ a = np.asanyarray(a) q = np.true_divide(q, 100.0) @@ -1457,74 +1508,32 @@ def nanquantile( If True, then allow the input array `a` to be modified by intermediate calculations, to save memory. In this case, the contents of the input `a` after this function completes is undefined. - interpolation : str - Possible values: 'linear' (default), - 'inverted_cdf', 'averaged_inverted_cdf', - 'closest_observation', 'interpolated_inverted_cdf', - 'hazen', 'weibull', - 'median_unbiased', 'normal_unbiased', - 'lower', 'higher', - 'midpoint', 'nearest'. - This optional parameter specifies the interpolation method to - use when the desired quantile lies between two data points ``i < j``. - g is the fractional part of the index surrounded by ``i``. - alpha and beta are correction constants modifying i and j: - i + g = (q - alpha) / ( n - alpha - beta + 1 ) - * inverted_cdf: - method 1 of H&F. - This method give discontinuous results: - if g > 0 ; then take j - if g = 0 ; then take i - * averaged_inverted_cdf: - method 2 of H&F. - This method give discontinuous results: - if g > 0 ; then take j - if g = 0 ; then average between bounds - * closest_observation: - method 3 of H&F. - This method give discontinuous results: - if g > 0 ; then take j - if g = 0 and index is odd ; then take j - if g = 0 and index is even ; then take i - * interpolated_inverted_cdf: - method 4 of H&F. - This method give continuous results using: - alpha = 0 - beta = 1 - * hazen: - method 5 of H&F. - This method give continuous results using: - alpha = 1/2 - beta = 1/2 - * weibull: - method 6 of H&F. - This method give continuous results using: - alpha = 0 - beta = 0 - * linear: - Default method. - method 7 of H&F. - This method give continuous results using: - alpha = 1 - beta = 1 - * median_unbiased: - method 8 of H&F. - This method is probably the best method if the sample distribution - function is unknown (see reference). - This method give continuous results using: - alpha = 1/3 - beta = 1/3 - * normal_unbiased: - method 9 of H&F. - This method is probably the best method if the sample distribution - function is known to be normal. - This method give continuous results using: - alpha = 3/8 - beta = 3/8 - * lower: ``i``. - * higher: ``j``. - * nearest: ``i`` or ``j``, whichever is nearest. - * midpoint: ``(i + j) / 2``. + interpolation : str, optional + This parameter specifies the interpolation method to + use when the desired quantile lies between two data points + There are many different methods, some unique to NumPy. See the + notes for explanation. Options: + + * (NPY 1): 'lower' + * (NPY 2): 'higher', + * (NPY 3): 'midpoint' + * (NPY 4): 'nearest' + * (NPY 5): 'linear', aliased with 'inclusive' (default) + + New options: + + * (H&F 1): 'inverted_cdf' + * (H&F 2): 'averaged_inverted_cdf' + * (H&F 3): 'closest_observation' + * (H&F 4): 'interpolated_inverted_cdf' + * (H&F 5): 'hazen' + * (H&F 6): 'weibull' + * (H&F 7): 'inclusive', aliased with 'linear' (default) + * (H&F 8): 'median_unbiased' + * (H&F 9): 'normal_unbiased' + + .. versionadded;: 1.22.0 + keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the @@ -1555,6 +1564,102 @@ def nanquantile( nanmedian : equivalent to ``nanquantile(..., 0.5)`` nanpercentile : same as nanquantile, but with q in the range [0, 100]. + Notes + ----- + Given a vector ``V`` of length ``N``, the q-th quantile of ``V`` is the + value ``q`` of the way from the minimum to the maximum in a sorted copy of + ``V``. The values and distances of the two nearest neighbors as well as the + `interpolation` parameter will determine the quantile if the normalized + ranking does not match the location of ``q`` exactly. This function is the + same as the median if ``q=0.5``, the same as the minimum if ``q=0.0`` and + the same as the maximum if ``q=1.0``. + + This optional `interpolation` parameter specifies the interpolation method + to use when the desired quantile lies between two data points ``i < j``. If + ``g`` is the fractional part of the index surrounded by ``i`` and alpha + and beta are correction constants modifying i and j. + + .. math:: + i + g = (q - alpha) / ( n - alpha - beta + 1 ) + + The different interpolation methods then work as follows + + inverted_cdf: + method 1 of H&F [1]_. + This method gives discontinuous results: + * if g > 0 ; then take j + * if g = 0 ; then take i + + averaged_inverted_cdf: + method 2 of H&F [1]_. + This method give discontinuous results: + * if g > 0 ; then take j + * if g = 0 ; then average between bounds + + closest_observation: + method 3 of H&F [1]_. + This method give discontinuous results: + * if g > 0 ; then take j + * if g = 0 and index is odd ; then take j + * if g = 0 and index is even ; then take i + + interpolated_inverted_cdf: + method 4 of H&F [1]_. + This method give continuous results using: + * alpha = 0 + * beta = 1 + + hazen: + method 5 of H&F [1]_. + This method give continuous results using: + * alpha = 1/2 + * beta = 1/2 + + weibull: + method 6 of H&F [1]_. + This method give continuous results using: + * alpha = 0 + * beta = 0 + + inclusive: + Default method, aliased with "linear". + method 7 of H&F [1]_. + This method give continuous results using: + * alpha = 1 + * beta = 1 + + median_unbiased: + method 8 of H&F [1]_. + This method is probably the best method if the sample + distribution function is unknown (see reference). + This method give continuous results using: + * alpha = 1/3 + * beta = 1/3 + + normal_unbiased: + method 9 of H&F [1]_. + This method is probably the best method if the sample + distribution function is known to be normal. + This method give continuous results using: + * alpha = 3/8 + * beta = 3/8 + + lower: + NumPy method kept for backwards compatibility. + Takes ``i`` as the interpolation point. + + higher: + NumPy method kept for backwards compatibility. + Takes ``j`` as the interpolation point. + + nearest: + NumPy method kept for backwards compatibility. + Takes ``i`` or ``j``, whichever is nearest. + + midpoint: + NumPy method kept for backwards compatibility. + Uses ``(i + j) / 2``. + Examples -------- >>> a = np.array([[10., 7., 4.], [3., 2., 1.]]) @@ -1581,6 +1686,13 @@ def nanquantile( >>> np.nanquantile(b, 0.5, axis=1, overwrite_input=True) array([7., 2.]) >>> assert not np.all(a==b) + + References + ---------- + .. [1] R. J. Hyndman and Y. Fan, + "Sample quantiles in statistical packages," + The American Statistician, 50(4), pp. 361-365, 1996 + """ a = np.asanyarray(a) q = np.asanyarray(q) -- cgit v1.2.1 From ab19ed256bf9b20340c92cebcfd6158241122c88 Mon Sep 17 00:00:00 2001 From: abel Date: Tue, 19 Oct 2021 12:10:16 +0200 Subject: Fix _lerp - some changes were unrelated to the PR and have been reverted, including, renaming and moving the logic around. - Also renamed _quantile_ureduce_func to its original name --- numpy/lib/function_base.py | 42 ++++++++++++++++------------------- numpy/lib/tests/test_function_base.py | 14 ++++++------ 2 files changed, 26 insertions(+), 30 deletions(-) (limited to 'numpy') diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index dbaba87f9..353490fc2 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -4329,7 +4329,7 @@ def _quantile_unchecked(a, keepdims=False): """Assumes that q is in [0, 1], and is an ndarray""" r, k = _ureduce(a, - func=_quantiles_ureduce_func, + func=_quantile_ureduce_func, q=q, axis=axis, out=out, @@ -4383,22 +4383,22 @@ def _get_gamma(virtual_indexes: np.array, return np.asanyarray(gamma) -def _linear_interpolation_formula( - left: np.array, right: np.array, gamma: np.array, out: np.array = None -) -> np.array: +def _lerp(a, b, t, out=None): """ Compute the linear interpolation weighted by gamma on each point of two same shape array. """ - # Equivalent to gamma * right + (1 - gamma) * left - # see gh-14685 - diff_right_left = subtract(right, left) - result = asanyarray(add(left, diff_right_left * gamma, out=out)) - result = subtract(right, - diff_right_left * (1 - gamma), - out=result, - where=gamma >= 0.5) - return result + # Equivalent to gamma * right + (1 - gamma) * left, see gh-14685 + diff_b_a = subtract(b, a) + # asanyarray is a stop-gap until gh-13105 + lerp_interpolation = asanyarray(add(a, diff_b_a * t, out=out)) + lerp_interpolation = subtract(b, + diff_b_a * (1 - t), + out=lerp_interpolation, + where=t >= 0.5) + if lerp_interpolation.ndim == 0 and out is None: + lerp_interpolation = lerp_interpolation[()] # unpack 0d arrays + return lerp_interpolation def _get_gamma_mask(shape, default_value, conditioned_value, where): @@ -4430,7 +4430,7 @@ def _inverted_cdf(n, quantiles): gamma_fun) -def _quantiles_ureduce_func( +def _quantile_ureduce_func( a: np.array, q: np.array, axis: int = None, @@ -4460,10 +4460,6 @@ def _quantiles_ureduce_func( axis=axis, interpolation=interpolation, out=out) - if result.ndim == 0 and out is None: - result = result[()] # unpack 0d arrays - elif result.size == 1 and out is None and q.ndim == 0: - result = result[0] return result @@ -4551,7 +4547,7 @@ def _quantile( # cannot contain nan arr.partition(virtual_indexes.ravel(), axis=0) slices_having_nans = np.array(False, dtype=bool) - result = np.asanyarray(take(arr, virtual_indexes, axis=0, out=out)) + result = take(arr, virtual_indexes, axis=0, out=out) else: previous_indexes, next_indexes = _get_indexes(arr, virtual_indexes, @@ -4578,10 +4574,10 @@ def _quantile( interpolation) result_shape = virtual_indexes.shape + (1,) * (arr.ndim - 1) gamma = gamma.reshape(result_shape) - result = _linear_interpolation_formula(previous, - next, - gamma, - out=out) + result = _lerp(previous, + next, + gamma, + out=out) if np.any(slices_having_nans): if result.ndim == 0 and out is None: # can't write to a scalar diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index 4f0db7bdb..d59f3a85d 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -3486,8 +3486,8 @@ class TestLerp: b = st.floats(allow_nan=False, allow_infinity=False, min_value=-1e300, max_value=1e300)) def test_linear_interpolation_formula_monotonic(self, t0, t1, a, b): - l0 = nfb._linear_interpolation_formula(a, b, t0) - l1 = nfb._linear_interpolation_formula(a, b, t1) + l0 = nfb._lerp(a, b, t0) + l1 = nfb._lerp(a, b, t1) if t0 == t1 or a == b: assert l0 == l1 # uninteresting elif (t0 < t1) == (a < b): @@ -3503,9 +3503,9 @@ class TestLerp: min_value=-1e300, max_value=1e300)) def test_linear_interpolation_formula_bounded(self, t, a, b): if a <= b: - assert a <= nfb._linear_interpolation_formula(a, b, t) <= b + assert a <= nfb._lerp(a, b, t) <= b else: - assert b <= nfb._linear_interpolation_formula(a, b, t) <= a + assert b <= nfb._lerp(a, b, t) <= a @hypothesis.given(t=st.floats(allow_nan=False, allow_infinity=False, min_value=0, max_value=1), @@ -3515,15 +3515,15 @@ class TestLerp: min_value=-1e300, max_value=1e300)) def test_linear_interpolation_formula_symmetric(self, t, a, b): # double subtraction is needed to remove the extra precision of t < 0.5 - left = nfb._linear_interpolation_formula(a, b, 1 - (1 - t)) - right = nfb._linear_interpolation_formula(b, a, 1 - t) + left = nfb._lerp(a, b, 1 - (1 - t)) + right = nfb._lerp(b, a, 1 - t) assert left == right def test_linear_interpolation_formula_0d_inputs(self): a = np.array(2) b = np.array(5) t = np.array(0.2) - assert nfb._linear_interpolation_formula(a, b, t) == 2.6 + assert nfb._lerp(a, b, t) == 2.6 class TestMedian: -- cgit v1.2.1 From d5e275b2bf65c1848203f6bff7606623793daeed Mon Sep 17 00:00:00 2001 From: abel Date: Tue, 19 Oct 2021 15:22:05 +0200 Subject: DOC: Improve quantile documentation Also removed unused imports --- numpy/lib/function_base.py | 87 +++++++++++++++++++++++++++++++++++----------- 1 file changed, 67 insertions(+), 20 deletions(-) (limited to 'numpy') diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 353490fc2..a5ccc1189 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -9,8 +9,7 @@ import numpy.core.numeric as _nx from numpy.core import transpose from numpy.core.numeric import ( ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty, - ndarray, around, floor, ceil, take, dot, where, intp, - integer, isscalar, absolute + ndarray, take, dot, where, intp, integer, isscalar, absolute ) from numpy.core.umath import ( pi, add, arctan2, frompyfunc, cos, less_equal, sqrt, sin, @@ -51,7 +50,22 @@ __all__ = [ 'quantile' ] - +# _QuantileInterpolation is a dictionary listing all the supported +# interpolation methods to compute quantile/percentile. +# +# Below virtual_index refer to the index of the element where the percentile +# would be found in the sorted sample. +# When the sample contains exactly the percentile wanted, the virtual_index is +# an integer to the index of this element. +# When the percentile wanted is in between two elements, the virtual_index +# is made of a integer part (a.k.a 'i' or 'left') and a fractional part +# (a.k.a 'g' or 'gamma') +# +# Each _QuantileInterpolation have two properties +# get_virtual_index : Callable +# The function used to compute the virtual_index. +# fix_gamma : Callable +# A function used for discret methods to force the index to a specific value. _QuantileInterpolation = dict( # --- HYNDMAN and FAN methods # Discrete methods @@ -75,33 +89,33 @@ _QuantileInterpolation = dict( # Continuous methods interpolated_inverted_cdf=dict( get_virtual_index=lambda n, quantiles: - _virtual_index_formula(n, quantiles, 0, 1), + _compute_virtual_index(n, quantiles, 0, 1), fix_gamma=lambda gamma, _: gamma, ), hazen=dict( get_virtual_index=lambda n, quantiles: - _virtual_index_formula(n, quantiles, 0.5, 0.5), + _compute_virtual_index(n, quantiles, 0.5, 0.5), fix_gamma=lambda gamma, _: gamma, ), weibull=dict( get_virtual_index=lambda n, quantiles: - _virtual_index_formula(n, quantiles, 0, 0), + _compute_virtual_index(n, quantiles, 0, 0), fix_gamma=lambda gamma, _: gamma, ), # Default value linear=dict( get_virtual_index=lambda n, quantiles: - _virtual_index_formula(n, quantiles, 1, 1), + _compute_virtual_index(n, quantiles, 1, 1), fix_gamma=lambda gamma, _: gamma, ), median_unbiased=dict( get_virtual_index=lambda n, quantiles: - _virtual_index_formula(n, quantiles, 1 / 3.0, 1 / 3.0), + _compute_virtual_index(n, quantiles, 1 / 3.0, 1 / 3.0), fix_gamma=lambda gamma, _: gamma, ), normal_unbiased=dict( get_virtual_index=lambda n, quantiles: - _virtual_index_formula(n, quantiles, 3 / 8.0, 3 / 8.0), + _compute_virtual_index(n, quantiles, 3 / 8.0, 3 / 8.0), fix_gamma=lambda gamma, _: gamma, ), # --- OTHER METHODS fixme add deprecated ? @@ -3945,6 +3959,12 @@ def percentile(a, ``i < j``. If ``g`` is the fractional part of the index surrounded by ``i`` and alpha and beta are correction constants modifying i and j. + Below, 'q' is the quantile value, 'n' is the samle size and + alpha and beta are constants. + The following formula gives an interpolation "i + g" of where the quantile + would be in the sorted sample. + With 'i' being the floor and 'g' the fractional part of the result. + .. math:: i + g = (q - alpha) / ( n - alpha - beta + 1 ) @@ -4353,13 +4373,22 @@ def _quantile_is_valid(q): return True -def _virtual_index_formula(n: np.array, - quantiles: np.array, - alpha: float, - beta: float) -> np.array: +def _compute_virtual_index(n, quantiles, alpha: float, beta: float): """ Compute the floating point indexes of an array for the linear interpolation of quantiles. + n : array_like + The sample sizes. + quantiles : array_like + The quantiles values. + alpha : float + A constant used to correct the index computed. + beta : float + A constant used to correct the index computed. + + alpha and beta values depend on the chosen method + (see quantile documentation) + Reference: Hyndman&Fan paper "Sample Quantiles in Statistical Packages", DOI: 10.1080/00031305.1996.10473566 @@ -4369,14 +4398,24 @@ def _virtual_index_formula(n: np.array, ) - 1 -def _get_gamma(virtual_indexes: np.array, - previous_indexes: np.array, - interpolation: _QuantileInterpolation) -> np.array: +def _get_gamma(virtual_indexes, + previous_indexes, + interpolation: _QuantileInterpolation): """ - Compute the gamma (a.k.a 'm' or weight) for the linear interpolation + Compute gamma (a.k.a 'm' or 'weight') for the linear interpolation of quantiles. - When gamma == 0 the left bound will be picked, - When gamma == 1 the right bound will be. + + virtual_indexes : array_like + The indexes where the percentile is supposed to be found in the sorted + sample. + previous_indexes : array_like + The floor values of virtual_indexes. + interpolation : _QuantileInterpolation + The interpolation method chosen, which may have a specific rule + modifying gamma. + + gamma is usually the fractional part of virtual_indexes but can be modified + by the interpolation method. """ gamma = np.asanyarray(virtual_indexes - previous_indexes) gamma = interpolation["fix_gamma"](gamma, virtual_indexes) @@ -4387,8 +4426,16 @@ def _lerp(a, b, t, out=None): """ Compute the linear interpolation weighted by gamma on each point of two same shape array. + + a : array_like + Left bound. + b : array_like + Right bound. + t : array_like + The interpolation weight. + out : array_like + Output array. """ - # Equivalent to gamma * right + (1 - gamma) * left, see gh-14685 diff_b_a = subtract(b, a) # asanyarray is a stop-gap until gh-13105 lerp_interpolation = asanyarray(add(a, diff_b_a * t, out=out)) -- cgit v1.2.1 From 2a5422da7cb6759d75477738cc192fee3ca2a19c Mon Sep 17 00:00:00 2001 From: abel Date: Tue, 19 Oct 2021 17:55:06 +0200 Subject: Fix issue with nan scalar Also added unit test for it. --- numpy/lib/function_base.py | 4 ++-- numpy/lib/tests/test_function_base.py | 6 ++++++ 2 files changed, 8 insertions(+), 2 deletions(-) (limited to 'numpy') diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index a5ccc1189..a16781f53 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -3959,7 +3959,7 @@ def percentile(a, ``i < j``. If ``g`` is the fractional part of the index surrounded by ``i`` and alpha and beta are correction constants modifying i and j. - Below, 'q' is the quantile value, 'n' is the samle size and + Below, 'q' is the quantile value, 'n' is the sample size and alpha and beta are constants. The following formula gives an interpolation "i + g" of where the quantile would be in the sorted sample. @@ -4628,7 +4628,7 @@ def _quantile( if np.any(slices_having_nans): if result.ndim == 0 and out is None: # can't write to a scalar - result = np.array(np.nan, dtype=arr.dtype) + result = arr.dtype.type(np.nan) else: result[..., slices_having_nans] = np.nan return result diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index d59f3a85d..f9854c568 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -3475,6 +3475,12 @@ class TestQuantile: quantile = np.quantile(arr, p0) assert_equal(np.sort(quantile), quantile) + def test_quantile_scalar_nan(self): + a = np.array([[10., 7., 4.], [3., 2., 1.]]) + a[0][1] = np.nan + actual = np.quantile(a, 0.5) + assert np.isscalar(actual) + assert_equal(np.quantile(a, 0.5), np.nan) class TestLerp: @hypothesis.given(t0=st.floats(allow_nan=False, allow_infinity=False, -- cgit v1.2.1 From 8413b5abf27221fb2bea070871c7cd8f8da5519c Mon Sep 17 00:00:00 2001 From: abel Date: Thu, 21 Oct 2021 09:59:53 +0200 Subject: MAINT: Clean following PR comments --- numpy/lib/function_base.py | 9 +++------ numpy/lib/nanfunctions.py | 15 ++++----------- 2 files changed, 7 insertions(+), 17 deletions(-) (limited to 'numpy') diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index a16781f53..c33626df4 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -3915,7 +3915,7 @@ def percentile(a, * (H&F 8): 'median_unbiased' * (H&F 9): 'normal_unbiased' - .. versionadded;: 1.22.0 + .. versionchanged:: 1.22.0 keepdims : bool, optional If this is set to True, the axes which are reduced are left in @@ -4439,10 +4439,7 @@ def _lerp(a, b, t, out=None): diff_b_a = subtract(b, a) # asanyarray is a stop-gap until gh-13105 lerp_interpolation = asanyarray(add(a, diff_b_a * t, out=out)) - lerp_interpolation = subtract(b, - diff_b_a * (1 - t), - out=lerp_interpolation, - where=t >= 0.5) + subtract(b, diff_b_a * (1 - t), out=lerp_interpolation, where=t >= 0.5) if lerp_interpolation.ndim == 0 and out is None: lerp_interpolation = lerp_interpolation[()] # unpack 0d arrays return lerp_interpolation @@ -4580,7 +4577,7 @@ def _quantile( except KeyError: raise ValueError( f"{interpolation!r} is not a valid interpolation. Use one of: " - f"{_QuantileInterpolation.keys()}") + f"{_QuantileInterpolation.keys()}") from None virtual_indexes = interpolation["get_virtual_index"](values_count, quantiles) virtual_indexes = np.asanyarray(virtual_indexes) diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py index 710242d59..c3333a83a 100644 --- a/numpy/lib/nanfunctions.py +++ b/numpy/lib/nanfunctions.py @@ -1532,7 +1532,7 @@ def nanquantile( * (H&F 8): 'median_unbiased' * (H&F 9): 'normal_unbiased' - .. versionadded;: 1.22.0 + .. versionchanged:: 1.22.0 keepdims : bool, optional If this is set to True, the axes which are reduced are left in @@ -1729,12 +1729,8 @@ def _nanquantile_unchecked( return r -def _nanquantile_ureduce_func(a, - q, - axis=None, - out=None, - overwrite_input=False, - interpolation= "linear"): +def _nanquantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, + interpolation="linear"): """ Private function that doesn't support extended axis or keepdims. These methods are extended to this function using _ureduce @@ -1757,10 +1753,7 @@ def _nanquantile_ureduce_func(a, return result -def _nanquantile_1d(arr1d, - q, - overwrite_input=False, - interpolation= "linear"): +def _nanquantile_1d(arr1d, q, overwrite_input=False, interpolation="linear"): """ Private function for rank 1 arrays. Compute quantile ignoring NaNs. See nanpercentile for parameter usage -- cgit v1.2.1 From 98cf811e27138c41e365222cb70f06d70c0db4ee Mon Sep 17 00:00:00 2001 From: abel Date: Thu, 21 Oct 2021 12:31:06 +0200 Subject: TST: Add extrapolation tests --- numpy/lib/tests/test_function_base.py | 9 +++++++++ 1 file changed, 9 insertions(+) (limited to 'numpy') diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index f9854c568..53254c3e5 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -3032,6 +3032,15 @@ class TestPercentile: assert_equal(np.percentile(np.arange(10, dtype=dtype), 49, interpolation='nearest'), 4) + def test_linear_interpolation_extrapolation(self): + arr = np.random.rand(5) + + actual = np.percentile(arr, 100) + np.testing.assert_equal(actual, arr.max()) + + actual = np.percentile(arr, 0) + np.testing.assert_equal(actual, arr.min()) + def test_sequence(self): x = np.arange(8) * 0.5 assert_equal(np.percentile(x, [0, 100, 50]), [0, 3.5, 1.75]) -- cgit v1.2.1 From 2faf8edd635c8c700a3f5215cc747e268541b6bc Mon Sep 17 00:00:00 2001 From: abel Date: Thu, 21 Oct 2021 16:13:29 +0200 Subject: TST: Add parametrize for interpolation methods --- numpy/lib/tests/test_function_base.py | 134 +++++++++++----------------------- 1 file changed, 43 insertions(+), 91 deletions(-) (limited to 'numpy') diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index 53254c3e5..22228405a 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -2914,98 +2914,50 @@ class TestPercentile: np.testing.assert_equal(res, np.NAN) np.testing.assert_equal(res.dtype, arr.dtype) - TYPE_CODES = np.typecodes["AllInteger"] + np.typecodes["AllFloat"] + "O" - - @pytest.mark.parametrize("dtype", TYPE_CODES) - def test_linear_inverted_cdf(self, dtype): - # METHOD 1 of H&F - arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) - res = np.percentile( - arr, - 40.0, - interpolation="inverted_cdf") - np.testing.assert_almost_equal(res, 20, 15) - - @pytest.mark.parametrize("dtype", TYPE_CODES) - def test_linear_averaged_inverted_cdf(self, dtype): - # METHOD 2 of H&F - arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) - res = np.percentile( - arr, - 40.0, - interpolation="averaged_inverted_cdf") - np.testing.assert_almost_equal(res, 27.5, 15) - - @pytest.mark.parametrize("dtype", TYPE_CODES) - def test_linear_closest_observation(self, dtype): - # METHOD 3 of H&F - arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) - res = np.percentile( - arr, - 40.0, - interpolation="closest_observation") - np.testing.assert_almost_equal(res, 20, 15) - - @pytest.mark.parametrize("dtype", TYPE_CODES) - def test_linear_interpolated_inverted_cdf(self, dtype): - # METHOD 4 of H&F - arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) - res = np.percentile( - arr, - 40.0, - interpolation="interpolated_inverted_cdf") - np.testing.assert_almost_equal(res, 20, 15) - - @pytest.mark.parametrize("dtype", TYPE_CODES) - def test_linear_hazen(self, dtype): - # METHOD 5 of H&F - arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) - res = np.percentile( - arr, - 40.0, - interpolation="hazen") - np.testing.assert_almost_equal(res, 27.5, 15) - - @pytest.mark.parametrize("dtype", TYPE_CODES) - def test_linear_weibull(self, dtype): - # METHOD 6 of H&F - arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) - res = np.percentile( - arr, - 40.0, - interpolation="weibull") - np.testing.assert_almost_equal(res, 26, 15) - - @pytest.mark.parametrize("dtype", TYPE_CODES) - def test_linear_linear(self, dtype): - # METHOD 7 of H&F - # Test defaults - assert_equal(np.percentile(range(10), 50), 4.5) - # explicit interpolation_method (the default) - res = np.percentile([15.0, 20.0, 35.0, 40.0, 50.0], - 40, - interpolation="linear") - np.testing.assert_almost_equal(res, 29, 15) - - @pytest.mark.parametrize("dtype", TYPE_CODES) - def test_linear_median_unbiased(self, dtype): - # METHOD 8 of H&F - arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) - res = np.percentile( - arr, - 40.0, - interpolation="median_unbiased") - np.testing.assert_almost_equal(res, 27, 14) + H_F_TYPE_CODES = [(int_type, np.float64) + for int_type in np.typecodes["AllInteger"] + ] + [(np.float16, np.float64), + (np.float32, np.float64), + (np.float64, np.float64), + (np.float128, np.float128), + (np.complex64, np.complex128), + (np.complex128, np.complex128), + (np.complex256, np.complex256), + (np.dtype("O"), np.float64)] + + @pytest.mark.parametrize(["input_dtype", "expected_dtype"], H_F_TYPE_CODES) + @pytest.mark.parametrize(["interpolation", "expected"], + [("inverted_cdf", 20), + ("averaged_inverted_cdf", 27.5), + ("closest_observation", 20), + ("interpolated_inverted_cdf", 20), + ("hazen", 27.5), + ("weibull", 26), + ("linear", 29), + ("median_unbiased", 27), + ("normal_unbiased", 27.125), + ]) + def test_linear_interpolation(self, + interpolation, + expected, + input_dtype, + expected_dtype): + arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=input_dtype) + actual = np.percentile(arr, 40.0, interpolation=interpolation) + + np.testing.assert_almost_equal(actual, expected, 14) + + if interpolation in ["inverted_cdf", "closest_observation"]: + if input_dtype == "O": + np.testing.assert_equal(np.asarray(actual).dtype, np.float64) + else: + np.testing.assert_equal(np.asarray(actual).dtype, + np.dtype(input_dtype)) + else: + np.testing.assert_equal(np.asarray(actual).dtype, + np.dtype(expected_dtype)) - @pytest.mark.parametrize("dtype", TYPE_CODES) - def test_linear_normal_unbiased(self, dtype): - # METHOD 9 of H&F - arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) - res = np.percentile( - arr, - 40.0, - interpolation="normal_unbiased") - np.testing.assert_almost_equal(res, 27.125, 15) + TYPE_CODES = np.typecodes["AllInteger"] + np.typecodes["AllFloat"] + "O" @pytest.mark.parametrize("dtype", TYPE_CODES) def test_lower_higher(self, dtype): -- cgit v1.2.1 From f48e40636879d0f33c0cda8f76db06335043344b Mon Sep 17 00:00:00 2001 From: abel Date: Thu, 21 Oct 2021 16:26:10 +0200 Subject: DOC: Update _InterpolationKind --- numpy/lib/function_base.pyi | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) (limited to 'numpy') diff --git a/numpy/lib/function_base.pyi b/numpy/lib/function_base.pyi index 9a53b24f2..82c625fed 100644 --- a/numpy/lib/function_base.pyi +++ b/numpy/lib/function_base.pyi @@ -501,11 +501,19 @@ def median( ) -> _ArrayType: ... _InterpolationKind = L[ + "inverted_cdf", + "averaged_inverted_cdf", + "closest_observation", + "interpolated_inverted_cdf", + "hazen", + "weibull", + "linear", + "median_unbiased", + "normal_unbiased", "lower", "higher", "midpoint", "nearest", - "linear", ] @overload -- cgit v1.2.1 From a8218af306e7639f2136c8e915a5b9ea1f34356a Mon Sep 17 00:00:00 2001 From: abel Date: Fri, 22 Oct 2021 13:35:40 +0200 Subject: TST: Make use of clongdouble and longdouble On some platforms float128 and complex256 do not exist. Using (c)longdouble aliases should work on all platforms. --- numpy/lib/tests/test_function_base.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'numpy') diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index 22228405a..d5fa012f1 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -2919,10 +2919,10 @@ class TestPercentile: ] + [(np.float16, np.float64), (np.float32, np.float64), (np.float64, np.float64), - (np.float128, np.float128), + (np.longdouble, np.longdouble), (np.complex64, np.complex128), (np.complex128, np.complex128), - (np.complex256, np.complex256), + (np.clongdouble, np.clongdouble), (np.dtype("O"), np.float64)] @pytest.mark.parametrize(["input_dtype", "expected_dtype"], H_F_TYPE_CODES) -- cgit v1.2.1 From 1574011425611a0e43715f81c494004c26b95e92 Mon Sep 17 00:00:00 2001 From: Sebastian Berg Date: Mon, 1 Nov 2021 18:00:20 -0500 Subject: MAINT: Remove unnecessary import of _QuantileInterpolation --- numpy/lib/nanfunctions.py | 1 - 1 file changed, 1 deletion(-) (limited to 'numpy') diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py index c3333a83a..3189d2369 100644 --- a/numpy/lib/nanfunctions.py +++ b/numpy/lib/nanfunctions.py @@ -23,7 +23,6 @@ Functions import functools import warnings import numpy as np -from numpy.lib.function_base import _QuantileInterpolation from numpy.lib import function_base from numpy.core import overrides -- cgit v1.2.1 From f7911c67176c1d370be27726e87195699e4b581e Mon Sep 17 00:00:00 2001 From: Sebastian Berg Date: Thu, 4 Nov 2021 13:26:37 -0500 Subject: DOC: Refer to the quantile/percentile notes for nan versions --- numpy/lib/function_base.py | 2 +- numpy/lib/nanfunctions.py | 203 +-------------------------------------------- 2 files changed, 4 insertions(+), 201 deletions(-) (limited to 'numpy') diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index c33626df4..07d8a4269 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -3893,7 +3893,7 @@ def percentile(a, `a` after this function completes is undefined. interpolation : str, optional This parameter specifies the interpolation method to - use when the desired quantile lies between two data points + use when the desired percentile lies between two data points There are many different methods, some unique to NumPy. See the notes for explanation. Options diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py index 3189d2369..9fab77f45 100644 --- a/numpy/lib/nanfunctions.py +++ b/numpy/lib/nanfunctions.py @@ -1269,7 +1269,7 @@ def nanpercentile( undefined. interpolation : str, optional This parameter specifies the interpolation method to use when the - desired quantile lies between two data points There are many + desired percentile lies between two data points There are many different methods, some unique to NumPy. See the notes for explanation. Options: @@ -1325,100 +1325,7 @@ def nanpercentile( Notes ----- - Given a vector ``V`` of length ``N``, the ``q``-th percentile of ``V`` - is the value ``q/100`` of the way from the minimum to the maximum in a - sorted copy of ``V``. The values and distances of the two nearest - neighbors as well as the `interpolation` parameter will determine the - percentile if the normalized ranking does not match the location of - ``q`` exactly. This function is the same as the median if ``q=50``, the - same as the minimum if ``q=0`` and the same as the maximum if - ``q=100``. - - This optional `interpolation` parameter specifies the interpolation - method to use when the desired quantile lies between two data points - ``i < j``. If ``g`` is the fractional part of the index surrounded by - ``i`` and alpha and beta are correction constants modifying i and j. - - .. math:: - i + g = (q - alpha) / ( n - alpha - beta + 1 ) - - The different interpolation methods then work as follows - - inverted_cdf: - method 1 of H&F [1]_. - This method gives discontinuous results: - * if g > 0 ; then take j - * if g = 0 ; then take i - - averaged_inverted_cdf: - method 2 of H&F [1]_. - This method give discontinuous results: - * if g > 0 ; then take j - * if g = 0 ; then average between bounds - - closest_observation: - method 3 of H&F [1]_. - This method give discontinuous results: - * if g > 0 ; then take j - * if g = 0 and index is odd ; then take j - * if g = 0 and index is even ; then take i - - interpolated_inverted_cdf: - method 4 of H&F [1]_. - This method give continuous results using: - * alpha = 0 - * beta = 1 - - hazen: - method 5 of H&F [1]_. - This method give continuous results using: - * alpha = 1/2 - * beta = 1/2 - - weibull: - method 6 of H&F [1]_. - This method give continuous results using: - * alpha = 0 - * beta = 0 - - inclusive: - Default method, aliased with "linear". - method 7 of H&F [1]_. - This method give continuous results using: - * alpha = 1 - * beta = 1 - - median_unbiased: - method 8 of H&F [1]_. - This method is probably the best method if the sample - distribution function is unknown (see reference). - This method give continuous results using: - * alpha = 1/3 - * beta = 1/3 - - normal_unbiased: - method 9 of H&F [1]_. - This method is probably the best method if the sample - distribution function is known to be normal. - This method give continuous results using: - * alpha = 3/8 - * beta = 3/8 - - lower: - NumPy method kept for backwards compatibility. - Takes ``i`` as the interpolation point. - - higher: - NumPy method kept for backwards compatibility. - Takes ``j`` as the interpolation point. - - nearest: - NumPy method kept for backwards compatibility. - Takes ``i`` or ``j``, whichever is nearest. - - midpoint: - NumPy method kept for backwards compatibility. - Uses ``(i + j) / 2``. + For more information please see `numpy.percentile` Examples -------- @@ -1448,12 +1355,6 @@ def nanpercentile( array([7., 2.]) >>> assert not np.all(a==b) - References - ---------- - .. [1] R. J. Hyndman and Y. Fan, - "Sample quantiles in statistical packages," - The American Statistician, 50(4), pp. 361-365, 1996 - """ a = np.asanyarray(a) q = np.true_divide(q, 100.0) @@ -1565,99 +1466,7 @@ def nanquantile( Notes ----- - Given a vector ``V`` of length ``N``, the q-th quantile of ``V`` is the - value ``q`` of the way from the minimum to the maximum in a sorted copy of - ``V``. The values and distances of the two nearest neighbors as well as the - `interpolation` parameter will determine the quantile if the normalized - ranking does not match the location of ``q`` exactly. This function is the - same as the median if ``q=0.5``, the same as the minimum if ``q=0.0`` and - the same as the maximum if ``q=1.0``. - - This optional `interpolation` parameter specifies the interpolation method - to use when the desired quantile lies between two data points ``i < j``. If - ``g`` is the fractional part of the index surrounded by ``i`` and alpha - and beta are correction constants modifying i and j. - - .. math:: - i + g = (q - alpha) / ( n - alpha - beta + 1 ) - - The different interpolation methods then work as follows - - inverted_cdf: - method 1 of H&F [1]_. - This method gives discontinuous results: - * if g > 0 ; then take j - * if g = 0 ; then take i - - averaged_inverted_cdf: - method 2 of H&F [1]_. - This method give discontinuous results: - * if g > 0 ; then take j - * if g = 0 ; then average between bounds - - closest_observation: - method 3 of H&F [1]_. - This method give discontinuous results: - * if g > 0 ; then take j - * if g = 0 and index is odd ; then take j - * if g = 0 and index is even ; then take i - - interpolated_inverted_cdf: - method 4 of H&F [1]_. - This method give continuous results using: - * alpha = 0 - * beta = 1 - - hazen: - method 5 of H&F [1]_. - This method give continuous results using: - * alpha = 1/2 - * beta = 1/2 - - weibull: - method 6 of H&F [1]_. - This method give continuous results using: - * alpha = 0 - * beta = 0 - - inclusive: - Default method, aliased with "linear". - method 7 of H&F [1]_. - This method give continuous results using: - * alpha = 1 - * beta = 1 - - median_unbiased: - method 8 of H&F [1]_. - This method is probably the best method if the sample - distribution function is unknown (see reference). - This method give continuous results using: - * alpha = 1/3 - * beta = 1/3 - - normal_unbiased: - method 9 of H&F [1]_. - This method is probably the best method if the sample - distribution function is known to be normal. - This method give continuous results using: - * alpha = 3/8 - * beta = 3/8 - - lower: - NumPy method kept for backwards compatibility. - Takes ``i`` as the interpolation point. - - higher: - NumPy method kept for backwards compatibility. - Takes ``j`` as the interpolation point. - - nearest: - NumPy method kept for backwards compatibility. - Takes ``i`` or ``j``, whichever is nearest. - - midpoint: - NumPy method kept for backwards compatibility. - Uses ``(i + j) / 2``. + For more information please see `numpy.quantile` Examples -------- @@ -1686,12 +1495,6 @@ def nanquantile( array([7., 2.]) >>> assert not np.all(a==b) - References - ---------- - .. [1] R. J. Hyndman and Y. Fan, - "Sample quantiles in statistical packages," - The American Statistician, 50(4), pp. 361-365, 1996 - """ a = np.asanyarray(a) q = np.asanyarray(q) -- cgit v1.2.1 From 6f6802b685b254a0c244ec24244b0a61f4a3c91c Mon Sep 17 00:00:00 2001 From: Sebastian Berg Date: Thu, 4 Nov 2021 15:07:42 -0500 Subject: ENH: Check for `__repr__` and `__str__` implementation This adds a check whether `__repr__` or `__str__` appear to be inherited. If they are, it currently leads to cryptic errors adding an unnecessary difficulty to creating a custom user DType. --- numpy/core/src/multiarray/experimental_public_dtype_api.c | 8 ++++++++ 1 file changed, 8 insertions(+) (limited to 'numpy') diff --git a/numpy/core/src/multiarray/experimental_public_dtype_api.c b/numpy/core/src/multiarray/experimental_public_dtype_api.c index ef5030471..4b9c7199b 100644 --- a/numpy/core/src/multiarray/experimental_public_dtype_api.c +++ b/numpy/core/src/multiarray/experimental_public_dtype_api.c @@ -131,6 +131,14 @@ PyArrayInitDTypeMeta_FromSpec( return -1; } + if (((PyTypeObject *)DType)->tp_repr == PyArrayDescr_Type.tp_repr + || ((PyTypeObject *)DType)->tp_str == PyArrayDescr_Type.tp_str) { + PyErr_SetString(PyExc_TypeError, + "A custom DType must implement `__repr__` and `__str__` since " + "the default inherited version (currently) fails."); + return -1; + } + if (spec->typeobj == NULL || !PyType_Check(spec->typeobj)) { PyErr_SetString(PyExc_TypeError, "Not giving a type object is currently not supported, but " -- cgit v1.2.1 From f9c2573898899ee3809dfd4ebba113f6de2d528b Mon Sep 17 00:00:00 2001 From: Antony Lee Date: Fri, 5 Nov 2021 00:05:04 +0100 Subject: PERF: Speedup np.quantile. Avoiding the unnecessary calls to moveaxis() speed up `np.quantile(x, .5)` (`x = np.random.rand(1000)`) by ~10% (although there's a lot of variability) for me. --- numpy/lib/function_base.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) (limited to 'numpy') diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 07d8a4269..4d57acdb2 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -4567,7 +4567,8 @@ def _quantile( # The dimensions of `q` are prepended to the output shape, so we need the # axis being sampled from `arr` to be last. DATA_AXIS = 0 - arr = np.moveaxis(arr, axis, destination=DATA_AXIS) + if axis != DATA_AXIS: # But moveaxis is slow, so only call it if axis!=0. + arr = np.moveaxis(arr, axis, destination=DATA_AXIS) # --- Computation of indexes # Index where to find the value in the sorted array. # Virtual because it is a floating point value, not an valid index. -- cgit v1.2.1 From b9c0a231665a30836964e8c94ba2f83a08255be2 Mon Sep 17 00:00:00 2001 From: Bas van Beek Date: Fri, 5 Nov 2021 11:11:12 +0100 Subject: MAINT: Add `IS_PYSTON` to `np.testing.__all__` --- numpy/testing/__init__.py | 3 +-- numpy/testing/_private/utils.py | 2 +- 2 files changed, 2 insertions(+), 3 deletions(-) (limited to 'numpy') diff --git a/numpy/testing/__init__.py b/numpy/testing/__init__.py index a008f5828..6e06c5b49 100644 --- a/numpy/testing/__init__.py +++ b/numpy/testing/__init__.py @@ -8,8 +8,7 @@ away. from unittest import TestCase from ._private.utils import * -from ._private.utils import (_assert_valid_refcount, _gen_alignment_data, - IS_PYSTON) +from ._private.utils import (_assert_valid_refcount, _gen_alignment_data) from ._private import extbuild, decorators as dec from ._private.nosetester import ( run_module_suite, NoseTester as Tester diff --git a/numpy/testing/_private/utils.py b/numpy/testing/_private/utils.py index 3d52f74b2..4c6b64bc9 100644 --- a/numpy/testing/_private/utils.py +++ b/numpy/testing/_private/utils.py @@ -35,7 +35,7 @@ __all__ = [ 'assert_allclose', 'IgnoreException', 'clear_and_catch_warnings', 'SkipTest', 'KnownFailureException', 'temppath', 'tempdir', 'IS_PYPY', 'HAS_REFCOUNT', 'suppress_warnings', 'assert_array_compare', - 'assert_no_gc_cycles', 'break_cycles', 'HAS_LAPACK64' + 'assert_no_gc_cycles', 'break_cycles', 'HAS_LAPACK64', 'IS_PYSTON', ] -- cgit v1.2.1 From 3930aea7cb333d043f955b35d8d5c0645dfe45ae Mon Sep 17 00:00:00 2001 From: Bas van Beek Date: Fri, 5 Nov 2021 11:11:28 +0100 Subject: MAINT: Add missing annotations for `IS_PYSTON` --- numpy/testing/_private/utils.pyi | 1 + 1 file changed, 1 insertion(+) (limited to 'numpy') diff --git a/numpy/testing/_private/utils.pyi b/numpy/testing/_private/utils.pyi index 26ce52e40..4ba5d82ee 100644 --- a/numpy/testing/_private/utils.pyi +++ b/numpy/testing/_private/utils.pyi @@ -133,6 +133,7 @@ class suppress_warnings: verbose: int IS_PYPY: Final[bool] +IS_PYSTON: Final[bool] HAS_REFCOUNT: Final[bool] HAS_LAPACK64: Final[bool] -- cgit v1.2.1 From 57610d2e01fbc0915b79ef803247a308ad3a9117 Mon Sep 17 00:00:00 2001 From: Bas van Beek Date: Fri, 5 Nov 2021 11:25:49 +0100 Subject: MAINT: Add annotations for a missing `percentile` interpolation kind: `"inclusive"` --- numpy/lib/function_base.pyi | 1 + 1 file changed, 1 insertion(+) (limited to 'numpy') diff --git a/numpy/lib/function_base.pyi b/numpy/lib/function_base.pyi index 82c625fed..4bbd873a3 100644 --- a/numpy/lib/function_base.pyi +++ b/numpy/lib/function_base.pyi @@ -514,6 +514,7 @@ _InterpolationKind = L[ "higher", "midpoint", "nearest", + "inclusive", ] @overload -- cgit v1.2.1 From 035ecde53bf595c2b94849a248c6cf21221e2c6a Mon Sep 17 00:00:00 2001 From: Sayed Adel Date: Wed, 3 Nov 2021 22:08:09 +0200 Subject: SIMD: replace raw SIMD of ceil with universal intrinsics --- numpy/core/code_generators/generate_umath.py | 2 +- numpy/core/src/umath/loops.c.src | 8 ++++---- numpy/core/src/umath/loops.h.src | 6 +++--- numpy/core/src/umath/loops_unary_fp.dispatch.c.src | 21 +++++++++++++-------- numpy/core/src/umath/simd.inc.src | 22 +++++----------------- 5 files changed, 26 insertions(+), 33 deletions(-) (limited to 'numpy') diff --git a/numpy/core/code_generators/generate_umath.py b/numpy/core/code_generators/generate_umath.py index 3a27a34cd..292d9e0d3 100644 --- a/numpy/core/code_generators/generate_umath.py +++ b/numpy/core/code_generators/generate_umath.py @@ -827,7 +827,7 @@ defdict = { docstrings.get('numpy.core.umath.ceil'), None, TD('e', f='ceil', astype={'e': 'f'}), - TD(inexactvec, simd=[('fma', 'fd'), ('avx512f', 'fd')]), + TD(inexactvec, dispatch=[('loops_unary_fp', 'fd')]), TD('fdg', f='ceil'), TD(O, f='npy_ObjectCeil'), ), diff --git a/numpy/core/src/umath/loops.c.src b/numpy/core/src/umath/loops.c.src index 7c0710819..aaa694f34 100644 --- a/numpy/core/src/umath/loops.c.src +++ b/numpy/core/src/umath/loops.c.src @@ -1532,8 +1532,8 @@ TIMEDELTA_mm_qm_divmod(char **args, npy_intp const *dimensions, npy_intp const * */ /**begin repeat - * #func = rint, ceil, floor, trunc# - * #scalarf = npy_rint, npy_ceil, npy_floor, npy_trunc# + * #func = rint, floor, trunc# + * #scalarf = npy_rint, npy_floor, npy_trunc# */ /**begin repeat1 @@ -1568,8 +1568,8 @@ NPY_NO_EXPORT NPY_GCC_OPT_3 void */ /**begin repeat2 - * #func = rint, ceil, floor, trunc# - * #scalarf = npy_rint, npy_ceil, npy_floor, npy_trunc# + * #func = rint, floor, trunc# + * #scalarf = npy_rint, npy_floor, npy_trunc# */ NPY_NO_EXPORT NPY_GCC_OPT_3 void diff --git a/numpy/core/src/umath/loops.h.src b/numpy/core/src/umath/loops.h.src index 0938cd050..081ca9957 100644 --- a/numpy/core/src/umath/loops.h.src +++ b/numpy/core/src/umath/loops.h.src @@ -187,7 +187,7 @@ NPY_NO_EXPORT void * #TYPE = FLOAT, DOUBLE# */ /**begin repeat1 - * #kind = sqrt, absolute, square, reciprocal# + * #kind = ceil, sqrt, absolute, square, reciprocal# */ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@, (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))) @@ -228,7 +228,7 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@func@, /**end repeat**/ /**begin repeat - * #func = sin, cos# + * #func = sin, cos# */ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_@func@, @@ -275,7 +275,7 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@, ( /**end repeat**/ /**begin repeat - * #func = rint, ceil, floor, trunc# + * #func = rint, floor, trunc# */ /**begin repeat1 diff --git a/numpy/core/src/umath/loops_unary_fp.dispatch.c.src b/numpy/core/src/umath/loops_unary_fp.dispatch.c.src index 2d5917282..789733fb6 100644 --- a/numpy/core/src/umath/loops_unary_fp.dispatch.c.src +++ b/numpy/core/src/umath/loops_unary_fp.dispatch.c.src @@ -1,6 +1,8 @@ /*@targets ** $maxopt baseline - ** sse2 vsx2 neon + ** sse2 sse41 + ** vsx2 + ** neon asimd **/ /** * Force use SSE only on x86, even if AVX2 or AVX512F are enabled @@ -65,6 +67,9 @@ NPY_FINLINE double c_square_f64(double a) #define c_sqrt_f64 npy_sqrt #endif +#define c_ceil_f32 npy_ceilf +#define c_ceil_f64 npy_ceil + /******************************************************************************** ** Defining the SIMD kernels ********************************************************************************/ @@ -134,10 +139,10 @@ NPY_FINLINE double c_square_f64(double a) */ #if @VCHK@ /**begin repeat1 - * #kind = sqrt, absolute, square, reciprocal# - * #intr = sqrt, abs, square, recip# - * #repl_0w1 = 0, 0, 0, 1# - * #RECIP_WORKAROUND = 0, 0, 0, WORKAROUND_CLANG_RECIPROCAL_BUG# + * #kind = ceil, sqrt, absolute, square, reciprocal# + * #intr = ceil, sqrt, abs, square, recip# + * #repl_0w1 = 0, 0, 0, 0, 1# + * #RECIP_WORKAROUND = 0, 0, 0, 0, WORKAROUND_CLANG_RECIPROCAL_BUG# */ /**begin repeat2 * #STYPE = CONTIG, NCONTIG, CONTIG, NCONTIG# @@ -245,9 +250,9 @@ static void simd_@TYPE@_@kind@_@STYPE@_@DTYPE@ * #VCHK = NPY_SIMD, NPY_SIMD_F64# */ /**begin repeat1 - * #kind = sqrt, absolute, square, reciprocal# - * #intr = sqrt, abs, square, recip# - * #clear = 0, 1, 0, 0# + * #kind = ceil, sqrt, absolute, square, reciprocal# + * #intr = ceil, sqrt, abs, square, recip# + * #clear = 0, 0, 1, 0, 0# */ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_@kind@) (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)) diff --git a/numpy/core/src/umath/simd.inc.src b/numpy/core/src/umath/simd.inc.src index d47be9a30..0e2c1ab8b 100644 --- a/numpy/core/src/umath/simd.inc.src +++ b/numpy/core/src/umath/simd.inc.src @@ -169,7 +169,7 @@ run_@func@_avx512_skx_@TYPE@(char **args, npy_intp const *dimensions, npy_intp c */ /**begin repeat2 - * #func = rint, floor, ceil, trunc# + * #func = rint, floor, trunc# */ #if defined @CHK@ && defined NPY_HAVE_SSE2_INTRINSICS @@ -849,12 +849,6 @@ fma_floor_@vsub@(@vtype@ x) return _mm256_round_@vsub@(x, _MM_FROUND_TO_NEG_INF); } -NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA @vtype@ -fma_ceil_@vsub@(@vtype@ x) -{ - return _mm256_round_@vsub@(x, _MM_FROUND_TO_POS_INF); -} - NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA @vtype@ fma_trunc_@vsub@(@vtype@ x) { @@ -987,12 +981,6 @@ avx512_floor_@vsub@(@vtype@ x) return _mm512_roundscale_@vsub@(x, 0x09); } -NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ -avx512_ceil_@vsub@(@vtype@ x) -{ - return _mm512_roundscale_@vsub@(x, 0x0A); -} - NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ avx512_trunc_@vsub@(@vtype@ x) { @@ -1327,8 +1315,8 @@ AVX512F_@func@_@TYPE@(char **args, npy_intp const *dimensions, npy_intp const *s */ /**begin repeat1 - * #func = rint, ceil, floor, trunc# - * #vectorf = rint, ceil, floor, trunc# + * #func = rint, floor, trunc# + * #vectorf = rint, floor, trunc# */ #if defined @CHK@ @@ -1398,8 +1386,8 @@ static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_@ISA@ void */ /**begin repeat1 - * #func = rint, ceil, floor, trunc# - * #vectorf = rint, ceil, floor, trunc# + * #func = rint, floor, trunc# + * #vectorf = rint, floor, trunc# */ #if defined @CHK@ -- cgit v1.2.1 From abca5ca34595fda048e16760cb6355a67220f5db Mon Sep 17 00:00:00 2001 From: Sebastian Berg Date: Sat, 6 Nov 2021 16:30:36 -0500 Subject: MAINT: Remove Python <3.8 support from C Remove version switches for support of Python versions before 3.8. The biggest deletion is that we can use Pythons `PyVectorcall_Call` to support the old `*args, **kwargs` style call convention in ufuncs. --- numpy/core/include/numpy/ufuncobject.h | 5 +-- numpy/core/src/multiarray/compiled_base.c | 2 +- numpy/core/src/multiarray/methods.c | 14 ------- numpy/core/src/umath/ufunc_object.c | 70 +------------------------------ 4 files changed, 3 insertions(+), 88 deletions(-) (limited to 'numpy') diff --git a/numpy/core/include/numpy/ufuncobject.h b/numpy/core/include/numpy/ufuncobject.h index 3f184bd45..1d7050bbe 100644 --- a/numpy/core/include/numpy/ufuncobject.h +++ b/numpy/core/include/numpy/ufuncobject.h @@ -173,11 +173,8 @@ typedef struct _tagPyUFuncObject { * but this was never implemented. (This is also why the above * selector is called the "legacy" selector.) */ - #if PY_VERSION_HEX >= 0x03080000 vectorcallfunc vectorcall; - #else - void *reserved2; - #endif + /* Was previously the `PyUFunc_MaskedInnerLoopSelectionFunc` */ void *_always_null_previously_masked_innerloop_selector; diff --git a/numpy/core/src/multiarray/compiled_base.c b/numpy/core/src/multiarray/compiled_base.c index 9910fffe6..5853e068b 100644 --- a/numpy/core/src/multiarray/compiled_base.c +++ b/numpy/core/src/multiarray/compiled_base.c @@ -1393,7 +1393,7 @@ arr_add_docstring(PyObject *NPY_UNUSED(dummy), PyObject *args) { PyObject *obj; PyObject *str; - #if PY_VERSION_HEX >= 0x030700A2 && (!defined(PYPY_VERSION_NUM) || PYPY_VERSION_NUM > 0x07030300) + #if !defined(PYPY_VERSION_NUM) || PYPY_VERSION_NUM > 0x07030300 const char *docstr; #else char *docstr; diff --git a/numpy/core/src/multiarray/methods.c b/numpy/core/src/multiarray/methods.c index 2d66c77dc..8e2cd09eb 100644 --- a/numpy/core/src/multiarray/methods.c +++ b/numpy/core/src/multiarray/methods.c @@ -1821,22 +1821,8 @@ array_reduce_ex_picklebuffer(PyArrayObject *self, int protocol) descr = PyArray_DESCR(self); - /* if the python version is below 3.8, the pickle module does not provide - * built-in support for protocol 5. We try importing the pickle5 - * backport instead */ -#if PY_VERSION_HEX >= 0x03080000 /* we expect protocol 5 to be available in Python 3.8 */ pickle_module = PyImport_ImportModule("pickle"); -#else - pickle_module = PyImport_ImportModule("pickle5"); - if (pickle_module == NULL) { - /* for protocol 5, raise a clear ImportError if pickle5 is not found - */ - PyErr_SetString(PyExc_ImportError, "Using pickle protocol 5 " - "requires the pickle5 module for Python >=3.6 and <3.8"); - return NULL; - } -#endif if (pickle_module == NULL){ return NULL; } diff --git a/numpy/core/src/umath/ufunc_object.c b/numpy/core/src/umath/ufunc_object.c index 237af81b2..186f18a62 100644 --- a/numpy/core/src/umath/ufunc_object.c +++ b/numpy/core/src/umath/ufunc_object.c @@ -4925,65 +4925,6 @@ fail: } -/* - * TODO: The implementation below can be replaced with PyVectorcall_Call - * when available (should be Python 3.8+). - */ -static PyObject * -ufunc_generic_call( - PyUFuncObject *ufunc, PyObject *args, PyObject *kwds) -{ - Py_ssize_t len_args = PyTuple_GET_SIZE(args); - /* - * Wrapper for tp_call to tp_fastcall, to support both on older versions - * of Python. (and generally simplifying support of both versions in the - * same codebase. - */ - if (kwds == NULL) { - return ufunc_generic_fastcall(ufunc, - PySequence_Fast_ITEMS(args), len_args, NULL, NPY_FALSE); - } - - PyObject *new_args[NPY_MAXARGS]; - Py_ssize_t len_kwds = PyDict_Size(kwds); - - if (NPY_UNLIKELY(len_args + len_kwds > NPY_MAXARGS)) { - /* - * We do not have enough scratch-space, so we have to abort; - * In practice this error should not be seen by users. - */ - PyErr_Format(PyExc_ValueError, - "%s() takes from %d to %d positional arguments but " - "%zd were given", - ufunc_get_name_cstr(ufunc) , ufunc->nin, ufunc->nargs, len_args); - return NULL; - } - - /* Copy args into the scratch space */ - for (Py_ssize_t i = 0; i < len_args; i++) { - new_args[i] = PyTuple_GET_ITEM(args, i); - } - - PyObject *kwnames = PyTuple_New(len_kwds); - - PyObject *key, *value; - Py_ssize_t pos = 0; - Py_ssize_t i = 0; - while (PyDict_Next(kwds, &pos, &key, &value)) { - Py_INCREF(key); - PyTuple_SET_ITEM(kwnames, i, key); - new_args[i + len_args] = value; - i++; - } - - PyObject *res = ufunc_generic_fastcall(ufunc, - new_args, len_args, kwnames, NPY_FALSE); - Py_DECREF(kwnames); - return res; -} - - -#if PY_VERSION_HEX >= 0x03080000 /* * Implement vectorcallfunc which should be defined with Python 3.8+. * In principle this could be backported, but the speed gain seems moderate @@ -5001,7 +4942,6 @@ ufunc_generic_vectorcall(PyObject *ufunc, return ufunc_generic_fastcall((PyUFuncObject *)ufunc, args, PyVectorcall_NARGS(len_args), kwnames, NPY_FALSE); } -#endif /* PY_VERSION_HEX >= 0x03080000 */ NPY_NO_EXPORT PyObject * @@ -5178,11 +5118,7 @@ PyUFunc_FromFuncAndDataAndSignatureAndIdentity(PyUFuncGenericFunction *func, voi ufunc->core_dim_flags = NULL; ufunc->userloops = NULL; ufunc->ptr = NULL; -#if PY_VERSION_HEX >= 0x03080000 ufunc->vectorcall = &ufunc_generic_vectorcall; -#else - ufunc->reserved2 = NULL; -#endif ufunc->reserved1 = 0; ufunc->iter_flags = 0; @@ -6437,19 +6373,15 @@ NPY_NO_EXPORT PyTypeObject PyUFunc_Type = { .tp_basicsize = sizeof(PyUFuncObject), .tp_dealloc = (destructor)ufunc_dealloc, .tp_repr = (reprfunc)ufunc_repr, - .tp_call = (ternaryfunc)ufunc_generic_call, + .tp_call = &PyVectorcall_Call, .tp_str = (reprfunc)ufunc_repr, .tp_flags = Py_TPFLAGS_DEFAULT | -#if PY_VERSION_HEX >= 0x03080000 _Py_TPFLAGS_HAVE_VECTORCALL | -#endif Py_TPFLAGS_HAVE_GC, .tp_traverse = (traverseproc)ufunc_traverse, .tp_methods = ufunc_methods, .tp_getset = ufunc_getset, -#if PY_VERSION_HEX >= 0x03080000 .tp_vectorcall_offset = offsetof(PyUFuncObject, vectorcall), -#endif }; /* End of code for ufunc objects */ -- cgit v1.2.1 From 035d853e32d6e60a40a6a845699723238a01431b Mon Sep 17 00:00:00 2001 From: abel Date: Mon, 8 Nov 2021 17:35:19 +0100 Subject: DOC: Remove non-existent alias --- numpy/lib/function_base.py | 14 ++++++-------- numpy/lib/function_base.pyi | 1 - numpy/lib/nanfunctions.py | 8 ++++---- 3 files changed, 10 insertions(+), 13 deletions(-) (limited to 'numpy') diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 4d57acdb2..86125168a 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -3901,7 +3901,7 @@ def percentile(a, * (NPY 2): 'higher', * (NPY 3): 'midpoint' * (NPY 4): 'nearest' - * (NPY 5): 'linear', aliased with 'inclusive' (default) + * (NPY 5): 'linear' New options: @@ -3911,7 +3911,7 @@ def percentile(a, * (H&F 4): 'interpolated_inverted_cdf' * (H&F 5): 'hazen' * (H&F 6): 'weibull' - * (H&F 7): 'inclusive', aliased with 'linear' (default) + * (H&F 7): 'linear' (default) * (H&F 8): 'median_unbiased' * (H&F 9): 'normal_unbiased' @@ -4007,8 +4007,7 @@ def percentile(a, * alpha = 0 * beta = 0 - inclusive: - Default method, aliased with "linear". + linear: method 7 of H&F [1]_. This method give continuous results using: * alpha = 1 @@ -4164,7 +4163,7 @@ def quantile(a, * (NPY 2): 'higher', * (NPY 3): 'midpoint' * (NPY 4): 'nearest' - * (NPY 5): 'linear', aliased with 'inclusive' (default) + * (NPY 5): 'linear' New options: @@ -4174,7 +4173,7 @@ def quantile(a, * (H&F 4): 'interpolated_inverted_cdf' * (H&F 5): 'hazen' * (H&F 6): 'weibull' - * (H&F 7): 'inclusive', aliased with 'linear' (default) + * (H&F 7): 'linear' (default) * (H&F 8): 'median_unbiased' * (H&F 9): 'normal_unbiased' @@ -4261,8 +4260,7 @@ def quantile(a, * alpha = 0 * beta = 0 - inclusive: - Default method, aliased with "linear". + linear: method 7 of H&F [1]_. This method give continuous results using: * alpha = 1 diff --git a/numpy/lib/function_base.pyi b/numpy/lib/function_base.pyi index 4bbd873a3..82c625fed 100644 --- a/numpy/lib/function_base.pyi +++ b/numpy/lib/function_base.pyi @@ -514,7 +514,6 @@ _InterpolationKind = L[ "higher", "midpoint", "nearest", - "inclusive", ] @overload diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py index 9fab77f45..7e953be03 100644 --- a/numpy/lib/nanfunctions.py +++ b/numpy/lib/nanfunctions.py @@ -1277,7 +1277,7 @@ def nanpercentile( * (NPY 2): 'higher', * (NPY 3): 'midpoint' * (NPY 4): 'nearest' - * (NPY 5): 'linear', aliased with 'inclusive' (default) + * (NPY 5): 'linear' (default) New options: @@ -1287,7 +1287,7 @@ def nanpercentile( * (H&F 4): 'interpolated_inverted_cdf' * (H&F 5): 'hazen' * (H&F 6): 'weibull' - * (H&F 7): 'inclusive', aliased with 'linear' (default) + * (H&F 7): 'linear' (default) * (H&F 8): 'median_unbiased' * (H&F 9): 'normal_unbiased' @@ -1418,7 +1418,7 @@ def nanquantile( * (NPY 2): 'higher', * (NPY 3): 'midpoint' * (NPY 4): 'nearest' - * (NPY 5): 'linear', aliased with 'inclusive' (default) + * (NPY 5): 'linear' (default) New options: @@ -1428,7 +1428,7 @@ def nanquantile( * (H&F 4): 'interpolated_inverted_cdf' * (H&F 5): 'hazen' * (H&F 6): 'weibull' - * (H&F 7): 'inclusive', aliased with 'linear' (default) + * (H&F 7): 'linear' (default) * (H&F 8): 'median_unbiased' * (H&F 9): 'normal_unbiased' -- cgit v1.2.1 From f6076ee90985e7839d7ec6ed83ea1f85c0ece44d Mon Sep 17 00:00:00 2001 From: mattip Date: Tue, 9 Nov 2021 11:16:33 +0200 Subject: add release note, error out if offset is used --- numpy/core/src/multiarray/dlpack.c | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) (limited to 'numpy') diff --git a/numpy/core/src/multiarray/dlpack.c b/numpy/core/src/multiarray/dlpack.c index f061a6bf9..b0eaa7786 100644 --- a/numpy/core/src/multiarray/dlpack.c +++ b/numpy/core/src/multiarray/dlpack.c @@ -206,6 +206,12 @@ array_dlpack(PyArrayObject *self, if (data == NULL) { return NULL; } + if ((char *)PyArray_DATA(self) - data != 0) { + PyErr_SetString(PyExc_TypeError, + "Offsets not clearly supported by this " + "version of DLPack."); + return NULL; + } DLManagedTensor *managed = PyMem_Malloc(sizeof(DLManagedTensor)); if (managed == NULL) { @@ -238,7 +244,7 @@ array_dlpack(PyArrayObject *self, if (PyArray_SIZE(self) != 1 && !PyArray_IS_C_CONTIGUOUS(self)) { managed->dl_tensor.strides = managed_strides; } - managed->dl_tensor.byte_offset = (char *)PyArray_DATA(self) - data; + managed->dl_tensor.byte_offset = 0; managed->manager_ctx = self; managed->deleter = array_dlpack_deleter; -- cgit v1.2.1 From 6cd68755c6fd0686dd57a9daec43d8aa09d15c3e Mon Sep 17 00:00:00 2001 From: abel Date: Tue, 9 Nov 2021 10:35:12 +0100 Subject: MTH: Update quantile default lerp method For method 7 of H&F, using `(n - 1) * quantiles` instead of the usual method gives a more accurate result. --- numpy/lib/function_base.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) (limited to 'numpy') diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 86125168a..3c9983edf 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -67,7 +67,7 @@ __all__ = [ # fix_gamma : Callable # A function used for discret methods to force the index to a specific value. _QuantileInterpolation = dict( - # --- HYNDMAN and FAN methods + # --- HYNDMAN AND FAN METHODS # Discrete methods inverted_cdf=dict( get_virtual_index=lambda n, quantiles: _inverted_cdf(n, quantiles), @@ -102,10 +102,12 @@ _QuantileInterpolation = dict( _compute_virtual_index(n, quantiles, 0, 0), fix_gamma=lambda gamma, _: gamma, ), - # Default value + # Default method. + # To avoid some rounding issues, `(n-1) * quantiles` is preferred to + # `_compute_virtual_index(n, quantiles, 1, 1)`. + # They are mathematically equivalent. linear=dict( - get_virtual_index=lambda n, quantiles: - _compute_virtual_index(n, quantiles, 1, 1), + get_virtual_index=lambda n, quantiles: (n - 1) * quantiles, fix_gamma=lambda gamma, _: gamma, ), median_unbiased=dict( @@ -118,7 +120,7 @@ _QuantileInterpolation = dict( _compute_virtual_index(n, quantiles, 3 / 8.0, 3 / 8.0), fix_gamma=lambda gamma, _: gamma, ), - # --- OTHER METHODS fixme add deprecated ? + # --- OTHER METHODS lower=dict( get_virtual_index=lambda n, quantiles: np.floor( (n - 1) * quantiles).astype(np.intp), -- cgit v1.2.1 From 5b94a03b93d171232e0a64dc2160e4f2139e9b1a Mon Sep 17 00:00:00 2001 From: Sebastian Berg Date: Tue, 9 Nov 2021 12:14:13 -0600 Subject: MAINT: Simplify `byte_offset` handling in dlpack.h and add comment If `byte_offset = 0` is forced anyway, there is no point in trying to preserve a previous `data` information from the capsule. (And probably it should have used a base array also, and not just a base DLPack capsule, anyway.) --- numpy/core/src/multiarray/dlpack.c | 40 +++++++++++++++----------------------- 1 file changed, 16 insertions(+), 24 deletions(-) (limited to 'numpy') diff --git a/numpy/core/src/multiarray/dlpack.c b/numpy/core/src/multiarray/dlpack.c index b0eaa7786..291e60a22 100644 --- a/numpy/core/src/multiarray/dlpack.c +++ b/numpy/core/src/multiarray/dlpack.c @@ -3,6 +3,7 @@ #define PY_SSIZE_T_CLEAN #include +#include #include "numpy/arrayobject.h" #include "common/npy_argparse.h" @@ -100,19 +101,6 @@ array_get_dl_device(PyArrayObject *self) { return ret; } -static char * -array_get_dl_data(PyArrayObject *self) { - PyObject *base = PyArray_BASE(self); - if (PyCapsule_IsValid(base, NPY_DLPACK_INTERNAL_CAPSULE_NAME)) { - DLManagedTensor *managed = PyCapsule_GetPointer( - base, NPY_DLPACK_INTERNAL_CAPSULE_NAME); - if (managed == NULL) { - return NULL; - } - return managed->dl_tensor.data; - } - return PyArray_DATA(self); -} PyObject * array_dlpack(PyArrayObject *self, @@ -202,16 +190,6 @@ array_dlpack(PyArrayObject *self, if (PyErr_Occurred()) { return NULL; } - char *data = array_get_dl_data(self); - if (data == NULL) { - return NULL; - } - if ((char *)PyArray_DATA(self) - data != 0) { - PyErr_SetString(PyExc_TypeError, - "Offsets not clearly supported by this " - "version of DLPack."); - return NULL; - } DLManagedTensor *managed = PyMem_Malloc(sizeof(DLManagedTensor)); if (managed == NULL) { @@ -219,7 +197,21 @@ array_dlpack(PyArrayObject *self, return NULL; } - managed->dl_tensor.data = data; + /* + * Note: the `dlpack.h` header suggests/standardizes that `data` must be + * 256-byte aligned. We ignore this intentionally, because `__dlpack__` + * standardizes that `byte_offset` must be 0 (for now) to not break pytorch: + * https://github.com/data-apis/array-api/issues/293#issuecomment-964111413 + * + * We further assume that exporting fully unaligned data is OK even without + * `byte_offset` since the standard does not reject it. + * Presumably, pytorch will support importing `byte_offset != 0` and NumPy + * can choose to use it starting about 2023. At that point, it may be + * that NumPy MUST use `byte_offset` to adhere to the standard (as + * specified in the header)! + */ + managed->dl_tensor.data = PyArray_DATA(self); + managed->dl_tensor.byte_offset = 0; managed->dl_tensor.device = device; managed->dl_tensor.dtype = managed_dtype; -- cgit v1.2.1 From 7e03a56194a9def4296ef502bb380d8cc4a2d061 Mon Sep 17 00:00:00 2001 From: Christoph Reiter Date: Tue, 9 Nov 2021 19:18:52 +0100 Subject: DEP: remove code for supporting GCC <4 in Mingw32CCompiler The last GCC 3.x release before 4.0 was 3.4 which was released 2004, so 17 years ago. Removes all code only used with those old toolchains. --- numpy/distutils/mingw32ccompiler.py | 81 ++++++------------------------------- 1 file changed, 12 insertions(+), 69 deletions(-) (limited to 'numpy') diff --git a/numpy/distutils/mingw32ccompiler.py b/numpy/distutils/mingw32ccompiler.py index 82d296434..fbe3655c9 100644 --- a/numpy/distutils/mingw32ccompiler.py +++ b/numpy/distutils/mingw32ccompiler.py @@ -24,7 +24,6 @@ from numpy.distutils import log # 3. Force windows to use g77 import distutils.cygwinccompiler -from distutils.version import StrictVersion from distutils.unixccompiler import UnixCCompiler from distutils.msvccompiler import get_build_version as get_build_msvc_version from distutils.errors import UnknownFileError @@ -62,35 +61,6 @@ class Mingw32CCompiler(distutils.cygwinccompiler.CygwinCCompiler): distutils.cygwinccompiler.CygwinCCompiler.__init__ (self, verbose, dry_run, force) - # we need to support 3.2 which doesn't match the standard - # get_versions methods regex - if self.gcc_version is None: - try: - out_string = subprocess.check_output(['gcc', '-dumpversion']) - except (OSError, CalledProcessError): - out_string = "" # ignore failures to match old behavior - result = re.search(r'(\d+\.\d+)', out_string) - if result: - self.gcc_version = StrictVersion(result.group(1)) - - # A real mingw32 doesn't need to specify a different entry point, - # but cygwin 2.91.57 in no-cygwin-mode needs it. - if self.gcc_version <= "2.91.57": - entry_point = '--entry _DllMain@12' - else: - entry_point = '' - - if self.linker_dll == 'dllwrap': - # Commented out '--driver-name g++' part that fixes weird - # g++.exe: g++: No such file or directory - # error (mingw 1.0 in Enthon24 tree, gcc-3.4.5). - # If the --driver-name part is required for some environment - # then make the inclusion of this part specific to that - # environment. - self.linker = 'dllwrap' # --driver-name g++' - elif self.linker_dll == 'gcc': - self.linker = 'g++' - # **changes: eric jones 4/11/01 # 1. Check for import library on Windows. Build if it doesn't exist. @@ -113,42 +83,18 @@ class Mingw32CCompiler(distutils.cygwinccompiler.CygwinCCompiler): # kind of bad consequences, like using Py_ModuleInit4 instead of # Py_ModuleInit4_64, etc... So we add it here if get_build_architecture() == 'AMD64': - if self.gcc_version < "4.0": - self.set_executables( - compiler='gcc -g -DDEBUG -DMS_WIN64 -mno-cygwin -O0 -Wall', - compiler_so='gcc -g -DDEBUG -DMS_WIN64 -mno-cygwin -O0' - ' -Wall -Wstrict-prototypes', - linker_exe='gcc -g -mno-cygwin', - linker_so='gcc -g -mno-cygwin -shared') - else: - # gcc-4 series releases do not support -mno-cygwin option - self.set_executables( - compiler='gcc -g -DDEBUG -DMS_WIN64 -O0 -Wall', - compiler_so='gcc -g -DDEBUG -DMS_WIN64 -O0 -Wall -Wstrict-prototypes', - linker_exe='gcc -g', - linker_so='gcc -g -shared') + self.set_executables( + compiler='gcc -g -DDEBUG -DMS_WIN64 -O0 -Wall', + compiler_so='gcc -g -DDEBUG -DMS_WIN64 -O0 -Wall ' + '-Wstrict-prototypes', + linker_exe='gcc -g', + linker_so='gcc -g -shared') else: - if self.gcc_version <= "3.0.0": - self.set_executables( - compiler='gcc -mno-cygwin -O2 -w', - compiler_so='gcc -mno-cygwin -mdll -O2 -w' - ' -Wstrict-prototypes', - linker_exe='g++ -mno-cygwin', - linker_so='%s -mno-cygwin -mdll -static %s' % - (self.linker, entry_point)) - elif self.gcc_version < "4.0": - self.set_executables( - compiler='gcc -mno-cygwin -O2 -Wall', - compiler_so='gcc -mno-cygwin -O2 -Wall' - ' -Wstrict-prototypes', - linker_exe='g++ -mno-cygwin', - linker_so='g++ -mno-cygwin -shared') - else: - # gcc-4 series releases do not support -mno-cygwin option - self.set_executables(compiler='gcc -O2 -Wall', - compiler_so='gcc -O2 -Wall -Wstrict-prototypes', - linker_exe='g++ ', - linker_so='g++ -shared') + self.set_executables( + compiler='gcc -O2 -Wall', + compiler_so='gcc -O2 -Wall -Wstrict-prototypes', + linker_exe='g++ ', + linker_so='g++ -shared') # added for python2.3 support # we can't pass it through set_executables because pre 2.2 would fail self.compiler_cxx = ['g++'] @@ -198,10 +144,7 @@ class Mingw32CCompiler(distutils.cygwinccompiler.CygwinCCompiler): extra_postargs, build_temp, target_lang) - if self.gcc_version < "3.0.0": - func = distutils.cygwinccompiler.CygwinCCompiler.link - else: - func = UnixCCompiler.link + func = UnixCCompiler.link func(*args[:func.__code__.co_argcount]) return -- cgit v1.2.1 From 53e3df3c99a26791cc07e2ea1570e87643fdf7e0 Mon Sep 17 00:00:00 2001 From: abel Date: Tue, 9 Nov 2021 17:35:03 +0100 Subject: TST: Add test for max ulp in default quantile calculation --- numpy/lib/tests/test_function_base.py | 8 ++++++++ 1 file changed, 8 insertions(+) (limited to 'numpy') diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index d5fa012f1..1c274afae 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -3356,6 +3356,14 @@ class TestPercentile: class TestQuantile: # most of this is already tested by TestPercentile + def test_max_ulp(self): + x = [0.0, 0.2, 0.4] + a = np.quantile(x, 0.45) + # The default linear method would result in 0 + 0.2 * (0.45/2) = 0.18. + # 0.18 is not exactly representable and the formula leads to a 1 ULP + # different result. Ensure it is this exact within 1 ULP, see gh-20331. + np.testing.assert_array_max_ulp(a, 0.18, maxulp=1) + def test_basic(self): x = np.arange(8) * 0.5 assert_equal(np.quantile(x, 0), 0.) -- cgit v1.2.1 From 6312c18e99dad89231b65693a05c92c1f06d3671 Mon Sep 17 00:00:00 2001 From: mattip Date: Wed, 10 Nov 2021 00:16:56 +0200 Subject: ENH: add a 'version' field to PyDataMem_Handler --- numpy/core/_add_newdocs.py | 10 +++++++ numpy/core/include/numpy/ndarraytypes.h | 7 ++++- numpy/core/multiarray.py | 16 ++++++------ numpy/core/src/multiarray/alloc.c | 39 +++++++++++++++++++++++++++- numpy/core/src/multiarray/alloc.h | 2 ++ numpy/core/src/multiarray/multiarraymodule.c | 3 +++ numpy/core/tests/test_mem_policy.py | 6 +++++ 7 files changed, 73 insertions(+), 10 deletions(-) (limited to 'numpy') diff --git a/numpy/core/_add_newdocs.py b/numpy/core/_add_newdocs.py index cae5bc281..078c58976 100644 --- a/numpy/core/_add_newdocs.py +++ b/numpy/core/_add_newdocs.py @@ -4759,6 +4759,16 @@ add_newdoc('numpy.core.multiarray', 'get_handler_name', memory, in which case you can traverse ``a.base`` for a memory handler. """) +add_newdoc('numpy.core.multiarray', 'get_handler_version', + """ + get_handler_version(a: ndarray) -> int,None + + Return the version of the memory handler used by `a`. If not provided, + return the version of the memory handler that will be used to allocate data + for the next `ndarray` in this context. May return None if `a` does not own + its memory, in which case you can traverse ``a.base`` for a memory handler. + """) + add_newdoc('numpy.core.multiarray', '_set_madvise_hugepage', """ _set_madvise_hugepage(enabled: bool) -> bool diff --git a/numpy/core/include/numpy/ndarraytypes.h b/numpy/core/include/numpy/ndarraytypes.h index 80177e2bb..2607fb732 100644 --- a/numpy/core/include/numpy/ndarraytypes.h +++ b/numpy/core/include/numpy/ndarraytypes.h @@ -674,10 +674,15 @@ typedef struct { void* (*calloc) (void *ctx, size_t nelem, size_t elsize); void* (*realloc) (void *ctx, void *ptr, size_t new_size); void (*free) (void *ctx, void *ptr, size_t size); + /* + * This is the end of the version=1 struct. Only add new fields after + * this line + */ } PyDataMemAllocator; typedef struct { - char name[128]; /* multiple of 64 to keep the struct aligned */ + char name[127]; /* multiple of 64 to keep the struct aligned */ + uint8_t version; /* currently 1 */ PyDataMemAllocator allocator; } PyDataMem_Handler; diff --git a/numpy/core/multiarray.py b/numpy/core/multiarray.py index f96274263..f88d75978 100644 --- a/numpy/core/multiarray.py +++ b/numpy/core/multiarray.py @@ -33,14 +33,14 @@ __all__ = [ 'dot', 'dragon4_positional', 'dragon4_scientific', 'dtype', 'empty', 'empty_like', 'error', 'flagsobj', 'flatiter', 'format_longfloat', 'frombuffer', 'fromfile', 'fromiter', 'fromstring', - 'get_handler_name', 'inner', 'interp', 'interp_complex', 'is_busday', - 'lexsort', 'matmul', 'may_share_memory', 'min_scalar_type', 'ndarray', - 'nditer', 'nested_iters', 'normalize_axis_index', 'packbits', - 'promote_types', 'putmask', 'ravel_multi_index', 'result_type', 'scalar', - 'set_datetimeparse_function', 'set_legacy_print_mode', 'set_numeric_ops', - 'set_string_function', 'set_typeDict', 'shares_memory', - 'tracemalloc_domain', 'typeinfo', 'unpackbits', 'unravel_index', 'vdot', - 'where', 'zeros'] + 'get_handler_name', 'get_handler_version', 'inner', 'interp', + 'interp_complex', 'is_busday', 'lexsort', 'matmul', 'may_share_memory', + 'min_scalar_type', 'ndarray', 'nditer', 'nested_iters', + 'normalize_axis_index', 'packbits', 'promote_types', 'putmask', + 'ravel_multi_index', 'result_type', 'scalar', 'set_datetimeparse_function', + 'set_legacy_print_mode', 'set_numeric_ops', 'set_string_function', + 'set_typeDict', 'shares_memory', 'tracemalloc_domain', 'typeinfo', + 'unpackbits', 'unravel_index', 'vdot', 'where', 'zeros'] # For backward compatibility, make sure pickle imports these functions from here _reconstruct.__module__ = 'numpy.core.multiarray' diff --git a/numpy/core/src/multiarray/alloc.c b/numpy/core/src/multiarray/alloc.c index e4756264d..d1173410d 100644 --- a/numpy/core/src/multiarray/alloc.c +++ b/numpy/core/src/multiarray/alloc.c @@ -370,6 +370,7 @@ default_free(void *NPY_UNUSED(ctx), void *ptr, size_t size) /* Memory handler global default */ PyDataMem_Handler default_handler = { "default_allocator", + 1, { NULL, /* ctx */ default_malloc, /* malloc */ @@ -395,7 +396,6 @@ PyDataMem_UserNEW(size_t size, PyObject *mem_handler) if (handler == NULL) { return NULL; } - assert(size != 0); result = handler->allocator.malloc(handler->allocator.ctx, size); if (_PyDataMem_eventhook != NULL) { @@ -639,3 +639,40 @@ get_handler_name(PyObject *NPY_UNUSED(self), PyObject *args) Py_DECREF(mem_handler); return name; } + +NPY_NO_EXPORT PyObject * +get_handler_version(PyObject *NPY_UNUSED(self), PyObject *args) +{ + PyObject *arr=NULL; + if (!PyArg_ParseTuple(args, "|O:get_handler_version", &arr)) { + return NULL; + } + if (arr != NULL && !PyArray_Check(arr)) { + PyErr_SetString(PyExc_ValueError, "if supplied, argument must be an ndarray"); + return NULL; + } + PyObject *mem_handler; + PyDataMem_Handler *handler; + PyObject *version; + if (arr != NULL) { + mem_handler = PyArray_HANDLER((PyArrayObject *) arr); + if (mem_handler == NULL) { + Py_RETURN_NONE; + } + Py_INCREF(mem_handler); + } + else { + mem_handler = PyDataMem_GetHandler(); + if (mem_handler == NULL) { + return NULL; + } + } + handler = (PyDataMem_Handler *) PyCapsule_GetPointer(mem_handler, "mem_handler"); + if (handler == NULL) { + Py_DECREF(mem_handler); + return NULL; + } + version = PyLong_FromLong(handler->version); + Py_DECREF(mem_handler); + return version; +} diff --git a/numpy/core/src/multiarray/alloc.h b/numpy/core/src/multiarray/alloc.h index 4f7df1f84..f1ccf0bcd 100644 --- a/numpy/core/src/multiarray/alloc.h +++ b/numpy/core/src/multiarray/alloc.h @@ -47,5 +47,7 @@ extern PyDataMem_Handler default_handler; NPY_NO_EXPORT PyObject * get_handler_name(PyObject *NPY_UNUSED(self), PyObject *obj); +NPY_NO_EXPORT PyObject * +get_handler_version(PyObject *NPY_UNUSED(self), PyObject *obj); #endif /* NUMPY_CORE_SRC_MULTIARRAY_ALLOC_H_ */ diff --git a/numpy/core/src/multiarray/multiarraymodule.c b/numpy/core/src/multiarray/multiarraymodule.c index 84179d5f0..a854bcb3b 100644 --- a/numpy/core/src/multiarray/multiarraymodule.c +++ b/numpy/core/src/multiarray/multiarraymodule.c @@ -4437,6 +4437,9 @@ static struct PyMethodDef array_module_methods[] = { {"get_handler_name", (PyCFunction) get_handler_name, METH_VARARGS, NULL}, + {"get_handler_version", + (PyCFunction) get_handler_version, + METH_VARARGS, NULL}, {"_add_newdoc_ufunc", (PyCFunction)add_newdoc_ufunc, METH_VARARGS, NULL}, {"_get_sfloat_dtype", diff --git a/numpy/core/tests/test_mem_policy.py b/numpy/core/tests/test_mem_policy.py index 7fec8897f..abf340062 100644 --- a/numpy/core/tests/test_mem_policy.py +++ b/numpy/core/tests/test_mem_policy.py @@ -179,6 +179,7 @@ def get_module(tmp_path): }; static PyDataMem_Handler secret_data_handler = { "secret_data_allocator", + 1, { &secret_data_handler_ctx, /* ctx */ shift_alloc, /* malloc */ @@ -212,17 +213,22 @@ def get_module(tmp_path): def test_set_policy(get_module): get_handler_name = np.core.multiarray.get_handler_name + get_handler_version = np.core.multiarray.get_handler_version orig_policy_name = get_handler_name() a = np.arange(10).reshape((2, 5)) # a doesn't own its own data assert get_handler_name(a) is None + assert get_handler_version(a) is None assert get_handler_name(a.base) == orig_policy_name + assert get_handler_version(a.base) == 1 orig_policy = get_module.set_secret_data_policy() b = np.arange(10).reshape((2, 5)) # b doesn't own its own data assert get_handler_name(b) is None + assert get_handler_version(b) is None assert get_handler_name(b.base) == 'secret_data_allocator' + assert get_handler_version(b.base) == 1 if orig_policy_name == 'default_allocator': get_module.set_old_policy(None) # tests PyDataMem_SetHandler(NULL) -- cgit v1.2.1 From 8871c7285fc097fd1bf713aa184cba7e2804f625 Mon Sep 17 00:00:00 2001 From: Bas van Beek Date: Wed, 10 Nov 2021 15:36:00 +0100 Subject: MAINT: Do not forward `__(deep)copy__` calls of `_GenericAlias` to the wrapped type Adapt to the python 3.9.8 changes made in bpo-45167. --- numpy/typing/_generic_alias.py | 2 ++ numpy/typing/tests/test_generic_alias.py | 16 ++++++++++++++++ 2 files changed, 18 insertions(+) (limited to 'numpy') diff --git a/numpy/typing/_generic_alias.py b/numpy/typing/_generic_alias.py index 932f12dd0..1eb2c8c05 100644 --- a/numpy/typing/_generic_alias.py +++ b/numpy/typing/_generic_alias.py @@ -185,6 +185,8 @@ class _GenericAlias: "__mro_entries__", "__reduce__", "__reduce_ex__", + "__copy__", + "__deepcopy__", }) def __getattribute__(self, name: str) -> Any: diff --git a/numpy/typing/tests/test_generic_alias.py b/numpy/typing/tests/test_generic_alias.py index 3021d9859..39343420b 100644 --- a/numpy/typing/tests/test_generic_alias.py +++ b/numpy/typing/tests/test_generic_alias.py @@ -1,6 +1,7 @@ from __future__ import annotations import sys +import copy import types import pickle import weakref @@ -80,6 +81,21 @@ class TestGenericAlias: value_ref = func(NDArray_ref) assert value == value_ref + @pytest.mark.parametrize("name,func", [ + ("__copy__", lambda n: n == copy.copy(n)), + ("__deepcopy__", lambda n: n == copy.deepcopy(n)), + ]) + def test_copy(self, name: str, func: FuncType) -> None: + value = func(NDArray) + + # xref bpo-45167 + GE_398 = ( + sys.version_info[:2] == (3, 9) and sys.version_info >= (3, 9, 8) + ) + if GE_398 or sys.version_info >= (3, 10, 1): + value_ref = func(NDArray_ref) + assert value == value_ref + def test_weakref(self) -> None: """Test ``__weakref__``.""" value = weakref.ref(NDArray)() -- cgit v1.2.1 From cf71558b6c3648256bcc307a488866ecbfa1467e Mon Sep 17 00:00:00 2001 From: Warren Weckesser Date: Thu, 11 Nov 2021 04:55:10 -0500 Subject: DOC: random: Fix a mistake in the zipf example. There was a mistake in the code that generated the plot in the zipf docstring. The Riemann zeta function is `scipy.special.zeta`, not `scipy.special.zetac`. I also tweaked the sample parameters and the plot code so the plot is a bit more informative. --- numpy/random/_generator.pyx | 27 +++++++++++++++++---------- 1 file changed, 17 insertions(+), 10 deletions(-) (limited to 'numpy') diff --git a/numpy/random/_generator.pyx b/numpy/random/_generator.pyx index 5347ea125..c103f42aa 100644 --- a/numpy/random/_generator.pyx +++ b/numpy/random/_generator.pyx @@ -3153,22 +3153,29 @@ cdef class Generator: -------- Draw samples from the distribution: - >>> a = 2. # parameter - >>> s = np.random.default_rng().zipf(a, 1000) + >>> a = 4.0 + >>> n = 20000 + >>> s = np.random.default_rng().zipf(a, size=n) Display the histogram of the samples, along with - the probability density function: + the expected histogram based on the probability + density function: >>> import matplotlib.pyplot as plt - >>> from scipy import special # doctest: +SKIP + >>> from scipy.special import zeta # doctest: +SKIP + + `bincount` provides a fast histogram for small integers. - Truncate s values at 50 so plot is interesting: + >>> count = np.bincount(s) + >>> x = np.arange(1, s.max() + 1) - >>> count, bins, ignored = plt.hist(s[s<50], - ... 50, density=True) - >>> x = np.arange(1., 50.) - >>> y = x**(-a) / special.zetac(a) # doctest: +SKIP - >>> plt.plot(x, y/max(y), linewidth=2, color='r') # doctest: +SKIP + >>> plt.bar(x, count[1:], alpha=0.5, label='sample count') + >>> plt.plot(x, n*(x**-a)/zeta(a), 'k.-', alpha=0.5, + ... label='expected count') # doctest: +SKIP + >>> plt.semilogy() + >>> plt.grid(alpha=0.4) + >>> plt.legend() + >>> plt.title(f'Zipf sample, a={a}, size={n}') >>> plt.show() """ -- cgit v1.2.1 From f0cce63fb7adcbd6664074fec4536d1ab761051e Mon Sep 17 00:00:00 2001 From: warren Date: Thu, 11 Nov 2021 13:28:42 -0500 Subject: DOC: random: Update the zipf example for the legacy RandomState method. --- numpy/random/mtrand.pyx | 26 +++++++++++++++++--------- 1 file changed, 17 insertions(+), 9 deletions(-) (limited to 'numpy') diff --git a/numpy/random/mtrand.pyx b/numpy/random/mtrand.pyx index 81a526ab4..fa7e95412 100644 --- a/numpy/random/mtrand.pyx +++ b/numpy/random/mtrand.pyx @@ -3660,21 +3660,29 @@ cdef class RandomState: -------- Draw samples from the distribution: - >>> a = 2. # parameter - >>> s = np.random.zipf(a, 1000) + >>> a = 4.0 + >>> n = 20000 + >>> s = np.random.zipf(a, n) Display the histogram of the samples, along with - the probability density function: + the expected histogram based on the probability + density function: >>> import matplotlib.pyplot as plt - >>> from scipy import special # doctest: +SKIP + >>> from scipy.special import zeta # doctest: +SKIP + + `bincount` provides a fast histogram for small integers. - Truncate s values at 50 so plot is interesting: + >>> count = np.bincount(s) + >>> x = np.arange(1, s.max() + 1) - >>> count, bins, ignored = plt.hist(s[s<50], 50, density=True) - >>> x = np.arange(1., 50.) - >>> y = x**(-a) / special.zetac(a) # doctest: +SKIP - >>> plt.plot(x, y/max(y), linewidth=2, color='r') # doctest: +SKIP + >>> plt.bar(x, count[1:], alpha=0.5, label='sample count') + >>> plt.plot(x, n*(x**-a)/zeta(a), 'k.-', alpha=0.5, + ... label='expected count') # doctest: +SKIP + >>> plt.semilogy() + >>> plt.grid(alpha=0.4) + >>> plt.legend() + >>> plt.title(f'Zipf sample, a={a}, size={n}') >>> plt.show() """ -- cgit v1.2.1 From fd78f1fd44f46fc3d23f01e601268af2467dfa8b Mon Sep 17 00:00:00 2001 From: warren Date: Thu, 11 Nov 2021 13:41:20 -0500 Subject: DOC: random: Copy-edit zipf docstring: zipf is a discrete distribution. --- numpy/random/_generator.pyx | 13 +++++++------ numpy/random/mtrand.pyx | 13 +++++++------ 2 files changed, 14 insertions(+), 12 deletions(-) (limited to 'numpy') diff --git a/numpy/random/_generator.pyx b/numpy/random/_generator.pyx index c103f42aa..1e65be3f1 100644 --- a/numpy/random/_generator.pyx +++ b/numpy/random/_generator.pyx @@ -3107,7 +3107,7 @@ cdef class Generator: `a` > 1. The Zipf distribution (also known as the zeta distribution) is a - continuous probability distribution that satisfies Zipf's law: the + discrete probability distribution that satisfies Zipf's law: the frequency of an item is inversely proportional to its rank in a frequency table. @@ -3135,9 +3135,10 @@ cdef class Generator: ----- The probability density for the Zipf distribution is - .. math:: p(x) = \\frac{x^{-a}}{\\zeta(a)}, + .. math:: p(k) = \\frac{k^{-a}}{\\zeta(a)}, - where :math:`\\zeta` is the Riemann Zeta function. + for integers ``k`` >= 1, where :math:`\\zeta` is the Riemann Zeta + function. It is named for the American linguist George Kingsley Zipf, who noted that the frequency of any word in a sample of a language is inversely @@ -3167,10 +3168,10 @@ cdef class Generator: `bincount` provides a fast histogram for small integers. >>> count = np.bincount(s) - >>> x = np.arange(1, s.max() + 1) + >>> k = np.arange(1, s.max() + 1) - >>> plt.bar(x, count[1:], alpha=0.5, label='sample count') - >>> plt.plot(x, n*(x**-a)/zeta(a), 'k.-', alpha=0.5, + >>> plt.bar(k, count[1:], alpha=0.5, label='sample count') + >>> plt.plot(k, n*(k**-a)/zeta(a), 'k.-', alpha=0.5, ... label='expected count') # doctest: +SKIP >>> plt.semilogy() >>> plt.grid(alpha=0.4) diff --git a/numpy/random/mtrand.pyx b/numpy/random/mtrand.pyx index fa7e95412..280b0faac 100644 --- a/numpy/random/mtrand.pyx +++ b/numpy/random/mtrand.pyx @@ -3609,7 +3609,7 @@ cdef class RandomState: `a` > 1. The Zipf distribution (also known as the zeta distribution) is a - continuous probability distribution that satisfies Zipf's law: the + discrete probability distribution that satisfies Zipf's law: the frequency of an item is inversely proportional to its rank in a frequency table. @@ -3642,9 +3642,10 @@ cdef class RandomState: ----- The probability density for the Zipf distribution is - .. math:: p(x) = \\frac{x^{-a}}{\\zeta(a)}, + .. math:: p(k) = \\frac{k^{-a}}{\\zeta(a)}, - where :math:`\\zeta` is the Riemann Zeta function. + for integers ``k`` >= 1, where :math:`\\zeta` is the Riemann Zeta + function. It is named for the American linguist George Kingsley Zipf, who noted that the frequency of any word in a sample of a language is inversely @@ -3674,10 +3675,10 @@ cdef class RandomState: `bincount` provides a fast histogram for small integers. >>> count = np.bincount(s) - >>> x = np.arange(1, s.max() + 1) + >>> k = np.arange(1, s.max() + 1) - >>> plt.bar(x, count[1:], alpha=0.5, label='sample count') - >>> plt.plot(x, n*(x**-a)/zeta(a), 'k.-', alpha=0.5, + >>> plt.bar(k, count[1:], alpha=0.5, label='sample count') + >>> plt.plot(k, n*(k**-a)/zeta(a), 'k.-', alpha=0.5, ... label='expected count') # doctest: +SKIP >>> plt.semilogy() >>> plt.grid(alpha=0.4) -- cgit v1.2.1 From abb136cad711a0c23657926f8e6f3f50d9c37572 Mon Sep 17 00:00:00 2001 From: warren Date: Thu, 11 Nov 2021 17:44:57 -0300 Subject: DOC: Fix math formatting for zipf docstring --- numpy/random/_generator.pyx | 2 +- numpy/random/mtrand.pyx | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) (limited to 'numpy') diff --git a/numpy/random/_generator.pyx b/numpy/random/_generator.pyx index 1e65be3f1..7087b6e1d 100644 --- a/numpy/random/_generator.pyx +++ b/numpy/random/_generator.pyx @@ -3137,7 +3137,7 @@ cdef class Generator: .. math:: p(k) = \\frac{k^{-a}}{\\zeta(a)}, - for integers ``k`` >= 1, where :math:`\\zeta` is the Riemann Zeta + for integers :math:`k \geq 1`, where :math:`\\zeta` is the Riemann Zeta function. It is named for the American linguist George Kingsley Zipf, who noted diff --git a/numpy/random/mtrand.pyx b/numpy/random/mtrand.pyx index 280b0faac..3e13503d0 100644 --- a/numpy/random/mtrand.pyx +++ b/numpy/random/mtrand.pyx @@ -3644,7 +3644,7 @@ cdef class RandomState: .. math:: p(k) = \\frac{k^{-a}}{\\zeta(a)}, - for integers ``k`` >= 1, where :math:`\\zeta` is the Riemann Zeta + for integers :math:`k \geq 1`, where :math:`\\zeta` is the Riemann Zeta function. It is named for the American linguist George Kingsley Zipf, who noted -- cgit v1.2.1 From ff2e2a1e7eea29d925063b13922e096d14331222 Mon Sep 17 00:00:00 2001 From: Aaron Meurer Date: Fri, 12 Nov 2021 08:07:23 -0700 Subject: MAINT: A few updates to the array_api (#20066) * Allow casting in the array API asarray() * Restrict multidimensional indexing in the array API namespace The spec has recently been updated to only require multiaxis (i.e., tuple) indices in the case where every axis is indexed, meaning there are either as many indices as axes or the index has an ellipsis. * Fix type promotion for numpy.array_api.where where does value-based promotion for 0-dimensional arrays, so we use the same trick as in the Array operators to avoid this. * Print empty array_api arrays using empty() Printing behavior isn't required by the spec. This is just to make things easier to understand, especially with the array API test suite. * Fix an incorrect slice bounds guard in the array API * Disallow multiple different dtypes in the input to np.array_api.meshgrid * Remove DLPack support from numpy.array_api.asarray() from_dlpack() should be used to create arrays using DLPack. * Remove __len__ from the array API array object * Add astype() to numpy.array_api * Update the unique_* functions in numpy.array_api unique() in the array API was replaced with three separate functions, unique_all(), unique_inverse(), and unique_values(), in order to avoid polymorphic return types. Additionally, it should be noted that these functions to not currently conform to the spec with respect to NaN behavior. The spec requires multiple NaNs to be returned, but np.unique() returns a single NaN. Since this is currently an open issue in NumPy to possibly revert, I have not yet worked around this. See https://github.com/numpy/numpy/issues/20326. * Add the stream argument to the array API to_device method This does nothing in NumPy, and is just present so that the signature is valid according to the spec. * Use the NamedTuple classes for the type signatures * Add unique_counts to the array API namespace * Remove some unused imports * Update the array_api indexing restrictions The "multiaxis indexing must index every axis explicitly or use an ellipsis" was supposed to include any type of index, not just tuple indices. * Use a simpler type annotation for the array API to_device method * Fix a test failure in the array_api submodule The array_api cannot use the NumPy testing functions because array_api arrays do not mix with NumPy arrays, and also NumPy testing functions may use APIs that aren't supported in the array API. * Add dlpack support to the array_api submodule --- numpy/array_api/__init__.py | 10 +-- numpy/array_api/_array_object.py | 48 ++++++++----- numpy/array_api/_creation_functions.py | 19 +++-- numpy/array_api/_data_type_functions.py | 7 ++ numpy/array_api/_searching_functions.py | 1 + numpy/array_api/_set_functions.py | 89 ++++++++++++++++++++---- numpy/array_api/tests/test_array_object.py | 21 +++--- numpy/array_api/tests/test_creation_functions.py | 10 +++ 8 files changed, 156 insertions(+), 49 deletions(-) (limited to 'numpy') diff --git a/numpy/array_api/__init__.py b/numpy/array_api/__init__.py index 89f5e9cba..36e3f3ed5 100644 --- a/numpy/array_api/__init__.py +++ b/numpy/array_api/__init__.py @@ -136,7 +136,7 @@ from ._creation_functions import ( empty, empty_like, eye, - _from_dlpack, + from_dlpack, full, full_like, linspace, @@ -155,7 +155,7 @@ __all__ += [ "empty", "empty_like", "eye", - "_from_dlpack", + "from_dlpack", "full", "full_like", "linspace", @@ -169,6 +169,7 @@ __all__ += [ ] from ._data_type_functions import ( + astype, broadcast_arrays, broadcast_to, can_cast, @@ -178,6 +179,7 @@ from ._data_type_functions import ( ) __all__ += [ + "astype", "broadcast_arrays", "broadcast_to", "can_cast", @@ -358,9 +360,9 @@ from ._searching_functions import argmax, argmin, nonzero, where __all__ += ["argmax", "argmin", "nonzero", "where"] -from ._set_functions import unique +from ._set_functions import unique_all, unique_counts, unique_inverse, unique_values -__all__ += ["unique"] +__all__ += ["unique_all", "unique_counts", "unique_inverse", "unique_values"] from ._sorting_functions import argsort, sort diff --git a/numpy/array_api/_array_object.py b/numpy/array_api/_array_object.py index ef66c5efd..dc74bb8c5 100644 --- a/numpy/array_api/_array_object.py +++ b/numpy/array_api/_array_object.py @@ -32,7 +32,7 @@ from ._dtypes import ( from typing import TYPE_CHECKING, Optional, Tuple, Union, Any if TYPE_CHECKING: - from ._typing import PyCapsule, Device, Dtype + from ._typing import Any, PyCapsule, Device, Dtype import numpy as np @@ -99,9 +99,13 @@ class Array: """ Performs the operation __repr__. """ - prefix = "Array(" suffix = f", dtype={self.dtype.name})" - mid = np.array2string(self._array, separator=', ', prefix=prefix, suffix=suffix) + if 0 in self.shape: + prefix = "empty(" + mid = str(self.shape) + else: + prefix = "Array(" + mid = np.array2string(self._array, separator=', ', prefix=prefix, suffix=suffix) return prefix + mid + suffix # These are various helper functions to make the array behavior match the @@ -244,6 +248,10 @@ class Array: The following cases are allowed by NumPy, but not specified by the array API specification: + - Indices to not include an implicit ellipsis at the end. That is, + every axis of an array must be explicitly indexed or an ellipsis + included. + - The start and stop of a slice may not be out of bounds. In particular, for a slice ``i:j:k`` on an axis of size ``n``, only the following are allowed: @@ -270,6 +278,10 @@ class Array: return key if shape == (): return key + if len(shape) > 1: + raise IndexError( + "Multidimensional arrays must include an index for every axis or use an ellipsis" + ) size = shape[0] # Ensure invalid slice entries are passed through. if key.start is not None: @@ -277,7 +289,7 @@ class Array: operator.index(key.start) except TypeError: return key - if not (-size <= key.start <= max(0, size - 1)): + if not (-size <= key.start <= size): raise IndexError( "Slices with out-of-bounds start are not allowed in the array API namespace" ) @@ -322,6 +334,10 @@ class Array: zip(key[:ellipsis_i:-1], shape[:ellipsis_i:-1]) ): Array._validate_index(idx, (size,)) + if n_ellipsis == 0 and len(key) < len(shape): + raise IndexError( + "Multidimensional arrays must include an index for every axis or use an ellipsis" + ) return key elif isinstance(key, bool): return key @@ -339,7 +355,12 @@ class Array: "newaxis indices are not allowed in the array API namespace" ) try: - return operator.index(key) + key = operator.index(key) + if shape is not None and len(shape) > 1: + raise IndexError( + "Multidimensional arrays must include an index for every axis or use an ellipsis" + ) + return key except TypeError: # Note: This also omits boolean arrays that are not already in # Array() form, like a list of booleans. @@ -403,16 +424,14 @@ class Array: """ Performs the operation __dlpack__. """ - res = self._array.__dlpack__(stream=stream) - return self.__class__._new(res) + return self._array.__dlpack__(stream=stream) def __dlpack_device__(self: Array, /) -> Tuple[IntEnum, int]: """ Performs the operation __dlpack_device__. """ # Note: device support is required for this - res = self._array.__dlpack_device__() - return self.__class__._new(res) + return self._array.__dlpack_device__() def __eq__(self: Array, other: Union[int, float, bool, Array], /) -> Array: """ @@ -527,13 +546,6 @@ class Array: res = self._array.__le__(other._array) return self.__class__._new(res) - # Note: __len__ may end up being removed from the array API spec. - def __len__(self, /) -> int: - """ - Performs the operation __len__. - """ - return self._array.__len__() - def __lshift__(self: Array, other: Union[int, Array], /) -> Array: """ Performs the operation __lshift__. @@ -995,7 +1007,9 @@ class Array: res = self._array.__rxor__(other._array) return self.__class__._new(res) - def to_device(self: Array, device: Device, /) -> Array: + def to_device(self: Array, device: Device, /, stream: None = None) -> Array: + if stream is not None: + raise ValueError("The stream argument to to_device() is not supported") if device == 'cpu': return self raise ValueError(f"Unsupported device {device!r}") diff --git a/numpy/array_api/_creation_functions.py b/numpy/array_api/_creation_functions.py index c3644ac2c..23beec444 100644 --- a/numpy/array_api/_creation_functions.py +++ b/numpy/array_api/_creation_functions.py @@ -9,7 +9,6 @@ if TYPE_CHECKING: Device, Dtype, NestedSequence, - SupportsDLPack, SupportsBufferProtocol, ) from collections.abc import Sequence @@ -36,7 +35,6 @@ def asarray( int, float, NestedSequence[bool | int | float], - SupportsDLPack, SupportsBufferProtocol, ], /, @@ -60,7 +58,9 @@ def asarray( if copy is False: # Note: copy=False is not yet implemented in np.asarray raise NotImplementedError("copy=False is not yet implemented") - if isinstance(obj, Array) and (dtype is None or obj.dtype == dtype): + if isinstance(obj, Array): + if dtype is not None and obj.dtype != dtype: + copy = True if copy is True: return Array._new(np.array(obj._array, copy=True, dtype=dtype)) return obj @@ -151,9 +151,10 @@ def eye( return Array._new(np.eye(n_rows, M=n_cols, k=k, dtype=dtype)) -def _from_dlpack(x: object, /) -> Array: - # Note: dlpack support is not yet implemented on Array - raise NotImplementedError("DLPack support is not yet implemented") +def from_dlpack(x: object, /) -> Array: + from ._array_object import Array + + return Array._new(np._from_dlpack(x)) def full( @@ -240,6 +241,12 @@ def meshgrid(*arrays: Array, indexing: str = "xy") -> List[Array]: """ from ._array_object import Array + # Note: unlike np.meshgrid, only inputs with all the same dtype are + # allowed + + if len({a.dtype for a in arrays}) > 1: + raise ValueError("meshgrid inputs must all have the same dtype") + return [ Array._new(array) for array in np.meshgrid(*[a._array for a in arrays], indexing=indexing) diff --git a/numpy/array_api/_data_type_functions.py b/numpy/array_api/_data_type_functions.py index 7ccbe9469..e4d6db61b 100644 --- a/numpy/array_api/_data_type_functions.py +++ b/numpy/array_api/_data_type_functions.py @@ -13,6 +13,13 @@ if TYPE_CHECKING: import numpy as np +# Note: astype is a function, not an array method as in NumPy. +def astype(x: Array, dtype: Dtype, /, *, copy: bool = True) -> Array: + if not copy and dtype == x.dtype: + return x + return Array._new(x._array.astype(dtype=dtype, copy=copy)) + + def broadcast_arrays(*arrays: Array) -> List[Array]: """ Array API compatible wrapper for :py:func:`np.broadcast_arrays `. diff --git a/numpy/array_api/_searching_functions.py b/numpy/array_api/_searching_functions.py index 3dcef61c3..40f5a4d2e 100644 --- a/numpy/array_api/_searching_functions.py +++ b/numpy/array_api/_searching_functions.py @@ -43,4 +43,5 @@ def where(condition: Array, x1: Array, x2: Array, /) -> Array: """ # Call result type here just to raise on disallowed type combinations _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) return Array._new(np.where(condition._array, x1._array, x2._array)) diff --git a/numpy/array_api/_set_functions.py b/numpy/array_api/_set_functions.py index 357f238f5..05ee7e555 100644 --- a/numpy/array_api/_set_functions.py +++ b/numpy/array_api/_set_functions.py @@ -2,19 +2,82 @@ from __future__ import annotations from ._array_object import Array -from typing import Tuple, Union +from typing import NamedTuple import numpy as np +# Note: np.unique() is split into four functions in the array API: +# unique_all, unique_counts, unique_inverse, and unique_values (this is done +# to remove polymorphic return types). -def unique( - x: Array, - /, - *, - return_counts: bool = False, - return_index: bool = False, - return_inverse: bool = False, -) -> Union[Array, Tuple[Array, ...]]: +# Note: The various unique() functions are supposed to return multiple NaNs. +# This does not match the NumPy behavior, however, this is currently left as a +# TODO in this implementation as this behavior may be reverted in np.unique(). +# See https://github.com/numpy/numpy/issues/20326. + +# Note: The functions here return a namedtuple (np.unique() returns a normal +# tuple). + +class UniqueAllResult(NamedTuple): + values: Array + indices: Array + inverse_indices: Array + counts: Array + + +class UniqueCountsResult(NamedTuple): + values: Array + counts: Array + + +class UniqueInverseResult(NamedTuple): + values: Array + inverse_indices: Array + + +def unique_all(x: Array, /) -> UniqueAllResult: + """ + Array API compatible wrapper for :py:func:`np.unique `. + + See its docstring for more information. + """ + res = np.unique( + x._array, + return_counts=True, + return_index=True, + return_inverse=True, + ) + + return UniqueAllResult(*[Array._new(i) for i in res]) + + +def unique_counts(x: Array, /) -> UniqueCountsResult: + res = np.unique( + x._array, + return_counts=True, + return_index=False, + return_inverse=False, + ) + + return UniqueCountsResult(*[Array._new(i) for i in res]) + + +def unique_inverse(x: Array, /) -> UniqueInverseResult: + """ + Array API compatible wrapper for :py:func:`np.unique `. + + See its docstring for more information. + """ + res = np.unique( + x._array, + return_counts=False, + return_index=False, + return_inverse=True, + ) + return UniqueInverseResult(*[Array._new(i) for i in res]) + + +def unique_values(x: Array, /) -> Array: """ Array API compatible wrapper for :py:func:`np.unique `. @@ -22,10 +85,8 @@ def unique( """ res = np.unique( x._array, - return_counts=return_counts, - return_index=return_index, - return_inverse=return_inverse, + return_counts=False, + return_index=False, + return_inverse=False, ) - if isinstance(res, tuple): - return tuple(Array._new(i) for i in res) return Array._new(res) diff --git a/numpy/array_api/tests/test_array_object.py b/numpy/array_api/tests/test_array_object.py index fb42cf621..12479d765 100644 --- a/numpy/array_api/tests/test_array_object.py +++ b/numpy/array_api/tests/test_array_object.py @@ -3,7 +3,7 @@ import operator from numpy.testing import assert_raises import numpy as np -from .. import ones, asarray, result_type +from .. import ones, asarray, result_type, all, equal from .._dtypes import ( _all_dtypes, _boolean_dtypes, @@ -39,18 +39,18 @@ def test_validate_index(): assert_raises(IndexError, lambda: a[:-4]) assert_raises(IndexError, lambda: a[:3:-1]) assert_raises(IndexError, lambda: a[:-5:-1]) - assert_raises(IndexError, lambda: a[3:]) + assert_raises(IndexError, lambda: a[4:]) assert_raises(IndexError, lambda: a[-4:]) - assert_raises(IndexError, lambda: a[3::-1]) + assert_raises(IndexError, lambda: a[4::-1]) assert_raises(IndexError, lambda: a[-4::-1]) assert_raises(IndexError, lambda: a[...,:5]) assert_raises(IndexError, lambda: a[...,:-5]) - assert_raises(IndexError, lambda: a[...,:4:-1]) + assert_raises(IndexError, lambda: a[...,:5:-1]) assert_raises(IndexError, lambda: a[...,:-6:-1]) - assert_raises(IndexError, lambda: a[...,4:]) + assert_raises(IndexError, lambda: a[...,5:]) assert_raises(IndexError, lambda: a[...,-5:]) - assert_raises(IndexError, lambda: a[...,4::-1]) + assert_raises(IndexError, lambda: a[...,5::-1]) assert_raises(IndexError, lambda: a[...,-5::-1]) # Boolean indices cannot be part of a larger tuple index @@ -74,6 +74,11 @@ def test_validate_index(): assert_raises(IndexError, lambda: a[None, ...]) assert_raises(IndexError, lambda: a[..., None]) + # Multiaxis indices must contain exactly as many indices as dimensions + assert_raises(IndexError, lambda: a[()]) + assert_raises(IndexError, lambda: a[0,]) + assert_raises(IndexError, lambda: a[0]) + assert_raises(IndexError, lambda: a[:]) def test_operators(): # For every operator, we test that it works for the required type @@ -291,8 +296,8 @@ def test_device_property(): a = ones((3, 4)) assert a.device == 'cpu' - assert np.array_equal(a.to_device('cpu'), a) + assert all(equal(a.to_device('cpu'), a)) assert_raises(ValueError, lambda: a.to_device('gpu')) - assert np.array_equal(asarray(a, device='cpu'), a) + assert all(equal(asarray(a, device='cpu'), a)) assert_raises(ValueError, lambda: asarray(a, device='gpu')) diff --git a/numpy/array_api/tests/test_creation_functions.py b/numpy/array_api/tests/test_creation_functions.py index 7b633eaf1..ebbb6aab3 100644 --- a/numpy/array_api/tests/test_creation_functions.py +++ b/numpy/array_api/tests/test_creation_functions.py @@ -11,11 +11,13 @@ from .._creation_functions import ( full, full_like, linspace, + meshgrid, ones, ones_like, zeros, zeros_like, ) +from .._dtypes import float32, float64 from .._array_object import Array @@ -124,3 +126,11 @@ def test_zeros_like_errors(): assert_raises(ValueError, lambda: zeros_like(asarray(1), device="gpu")) assert_raises(ValueError, lambda: zeros_like(asarray(1), dtype=int)) assert_raises(ValueError, lambda: zeros_like(asarray(1), dtype="i")) + +def test_meshgrid_dtype_errors(): + # Doesn't raise + meshgrid() + meshgrid(asarray([1.], dtype=float32)) + meshgrid(asarray([1.], dtype=float32), asarray([1.], dtype=float32)) + + assert_raises(ValueError, lambda: meshgrid(asarray([1.], dtype=float32), asarray([1.], dtype=float64))) -- cgit v1.2.1