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repo_id stringlengths 12 110 | file_path stringlengths 24 164 | content stringlengths 3 89.3M | __index_level_0__ int64 0 0 |
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public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/tests/test_ccompiler_opt.py | import re, textwrap, os
from os import sys, path
from distutils.errors import DistutilsError
is_standalone = __name__ == '__main__' and __package__ is None
if is_standalone:
import unittest, contextlib, tempfile, shutil
sys.path.append(path.abspath(path.join(path.dirname(__file__), "..")))
from ccompiler_o... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/tests/test_ccompiler_opt_conf.py | import unittest
from os import sys, path
is_standalone = __name__ == '__main__' and __package__ is None
if is_standalone:
sys.path.append(path.abspath(path.join(path.dirname(__file__), "..")))
from ccompiler_opt import CCompilerOpt
else:
from numpy.distutils.ccompiler_opt import CCompilerOpt
arch_compiler... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/tests/test_fcompiler_gnu.py | from numpy.testing import assert_
import numpy.distutils.fcompiler
g77_version_strings = [
('GNU Fortran 0.5.25 20010319 (prerelease)', '0.5.25'),
('GNU Fortran (GCC 3.2) 3.2 20020814 (release)', '3.2'),
('GNU Fortran (GCC) 3.3.3 20040110 (prerelease) (Debian)', '3.3.3'),
('GNU Fortran (GCC) 3.3.3 (De... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/mingw/gfortran_vs2003_hack.c | int _get_output_format(void)
{
return 0;
}
int _imp____lc_codepage = 0;
| 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_popcnt.c | #if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
/*
* Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
* whether or not the build options for those features are specified.
* Therefore, we must test #definitions of CPU features when option native/host
* is enabled v... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/extra_avx512f_reduce.c | #include <immintrin.h>
/**
* The following intrinsics don't have direct native support but compilers
* tend to emulate them.
* They're usually supported by gcc >= 7.1, clang >= 4 and icc >= 19
*/
int main(void)
{
__m512 one_ps = _mm512_set1_ps(1.0f);
__m512d one_pd = _mm512_set1_pd(1.0);
__m512i one_i6... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_avx2.c | #if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
/*
* Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
* whether or not the build options for those features are specified.
* Therefore, we must test #definitions of CPU features when option native/host
* is enabled v... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_asimd.c | #ifdef _MSC_VER
#include <Intrin.h>
#endif
#include <arm_neon.h>
int main(int argc, char **argv)
{
float *src = (float*)argv[argc-1];
float32x4_t v1 = vdupq_n_f32(src[0]), v2 = vdupq_n_f32(src[1]);
/* MAXMIN */
int ret = (int)vgetq_lane_f32(vmaxnmq_f32(v1, v2), 0);
ret += (int)vgetq_lane_f... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_vxe.c | #if (__VEC__ < 10302) || (__ARCH__ < 12)
#error VXE not supported
#endif
#include <vecintrin.h>
int main(int argc, char **argv)
{
__vector float x = vec_nabs(vec_xl(argc, (float*)argv));
__vector float y = vec_load_len((float*)argv, (unsigned int)argc);
x = vec_round(vec_ceil(x) + vec_floor(y));
... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_ssse3.c | #if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
/*
* Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
* whether or not the build options for those features are specified.
* Therefore, we must test #definitions of CPU features when option native/host
* is enabled v... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_avx512_knl.c | #if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
/*
* Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
* whether or not the build options for those features are specified.
* Therefore, we must test #definitions of CPU features when option native/host
* is enabled v... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_avx512_icl.c | #if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
/*
* Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
* whether or not the build options for those features are specified.
* Therefore, we must test #definitions of CPU features when option native/host
* is enabled v... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_neon.c | #ifdef _MSC_VER
#include <Intrin.h>
#endif
#include <arm_neon.h>
int main(int argc, char **argv)
{
// passing from untraced pointers to avoid optimizing out any constants
// so we can test against the linker.
float *src = (float*)argv[argc-1];
float32x4_t v1 = vdupq_n_f32(src[0]), v2 = vdupq_n_f32(... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/test_flags.c | int test_flags;
| 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_neon_fp16.c | #ifdef _MSC_VER
#include <Intrin.h>
#endif
#include <arm_neon.h>
int main(int argc, char **argv)
{
short *src = (short*)argv[argc-1];
float32x4_t v_z4 = vcvt_f32_f16((float16x4_t)vld1_s16(src));
return (int)vgetq_lane_f32(v_z4, 0);
}
| 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/extra_vsx3_half_double.c | /**
* Assembler may not fully support the following VSX3 scalar
* instructions, even though compilers report VSX3 support.
*/
int main(void)
{
unsigned short bits = 0xFF;
double f;
__asm__ __volatile__("xscvhpdp %x0,%x1" : "=wa"(f) : "wa"(bits));
__asm__ __volatile__ ("xscvdphp %x0,%x1" : "=wa" (bits... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_vsx4.c | #ifndef __VSX__
#error "VSX is not supported"
#endif
#include <altivec.h>
typedef __vector unsigned int v_uint32x4;
int main(void)
{
v_uint32x4 v1 = (v_uint32x4){2, 4, 8, 16};
v_uint32x4 v2 = (v_uint32x4){2, 2, 2, 2};
v_uint32x4 v3 = vec_mod(v1, v2);
return (int)vec_extractm(v3);
}
| 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_vsx3.c | #ifndef __VSX__
#error "VSX is not supported"
#endif
#include <altivec.h>
typedef __vector unsigned int v_uint32x4;
int main(void)
{
v_uint32x4 z4 = (v_uint32x4){0, 0, 0, 0};
z4 = vec_absd(z4, z4);
return (int)vec_extract(z4, 0);
}
| 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_asimdhp.c | #ifdef _MSC_VER
#include <Intrin.h>
#endif
#include <arm_neon.h>
int main(int argc, char **argv)
{
float16_t *src = (float16_t*)argv[argc-1];
float16x8_t vhp = vdupq_n_f16(src[0]);
float16x4_t vlhp = vdup_n_f16(src[1]);
int ret = (int)vgetq_lane_f16(vabdq_f16(vhp, vhp), 0);
ret += (int... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/extra_vsx_asm.c | /**
* Testing ASM VSX register number fixer '%x<n>'
*
* old versions of CLANG doesn't support %x<n> in the inline asm template
* which fixes register number when using any of the register constraints wa, wd, wf.
*
* xref:
* - https://bugs.llvm.org/show_bug.cgi?id=31837
* - https://gcc.gnu.org/onlinedocs/gcc/Mac... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_asimdfhm.c | #ifdef _MSC_VER
#include <Intrin.h>
#endif
#include <arm_neon.h>
int main(int argc, char **argv)
{
float16_t *src = (float16_t*)argv[argc-1];
float *src2 = (float*)argv[argc-2];
float16x8_t vhp = vdupq_n_f16(src[0]);
float16x4_t vlhp = vdup_n_f16(src[1]);
float32x4_t vf = vdupq_n_f32(src2[0]... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_avx512_clx.c | #if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
/*
* Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
* whether or not the build options for those features are specified.
* Therefore, we must test #definitions of CPU features when option native/host
* is enabled v... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_avx512f.c | #if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
/*
* Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
* whether or not the build options for those features are specified.
* Therefore, we must test #definitions of CPU features when option native/host
* is enabled v... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_sse42.c | #if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
/*
* Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
* whether or not the build options for those features are specified.
* Therefore, we must test #definitions of CPU features when option native/host
* is enabled v... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_avx512_cnl.c | #if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
/*
* Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
* whether or not the build options for those features are specified.
* Therefore, we must test #definitions of CPU features when option native/host
* is enabled v... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_vx.c | #if (__VEC__ < 10301) || (__ARCH__ < 11)
#error VX not supported
#endif
#include <vecintrin.h>
int main(int argc, char **argv)
{
__vector double x = vec_abs(vec_xl(argc, (double*)argv));
__vector double y = vec_load_len((double*)argv, (unsigned int)argc);
x = vec_round(vec_ceil(x) + vec_floor(y));
... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/extra_avx512bw_mask.c | #include <immintrin.h>
/**
* Test BW mask operations due to:
* - MSVC has supported it since vs2019 see,
* https://developercommunity.visualstudio.com/content/problem/518298/missing-avx512bw-mask-intrinsics.html
* - Clang >= v8.0
* - GCC >= v7.1
*/
int main(void)
{
__mmask64 m64 = _mm512_cmpeq_epi8_mask... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_sse41.c | #if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
/*
* Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
* whether or not the build options for those features are specified.
* Therefore, we must test #definitions of CPU features when option native/host
* is enabled v... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_f16c.c | #if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
/*
* Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
* whether or not the build options for those features are specified.
* Therefore, we must test #definitions of CPU features when option native/host
* is enabled v... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_avx512_spr.c | #if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
/*
* Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
* whether or not the build options for those features are specified.
* Therefore, we must test #definitions of CPU features when option native/host
* is enabled v... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_asimddp.c | #ifdef _MSC_VER
#include <Intrin.h>
#endif
#include <arm_neon.h>
int main(int argc, char **argv)
{
unsigned char *src = (unsigned char*)argv[argc-1];
uint8x16_t v1 = vdupq_n_u8(src[0]), v2 = vdupq_n_u8(src[1]);
uint32x4_t va = vdupq_n_u32(3);
int ret = (int)vgetq_lane_u32(vdotq_u32(va, v1, v2), 0);... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/extra_vsx4_mma.c | #ifndef __VSX__
#error "VSX is not supported"
#endif
#include <altivec.h>
typedef __vector float fv4sf_t;
typedef __vector unsigned char vec_t;
int main(void)
{
__vector_quad acc0;
float a[4] = {0,1,2,3};
float b[4] = {0,1,2,3};
vec_t *va = (vec_t *) a;
vec_t *vb = (vec_t *) b;
__builtin_m... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_vsx.c | #ifndef __VSX__
#error "VSX is not supported"
#endif
#include <altivec.h>
#if (defined(__GNUC__) && !defined(vec_xl)) || (defined(__clang__) && !defined(__IBMC__))
#define vsx_ld vec_vsx_ld
#define vsx_st vec_vsx_st
#else
#define vsx_ld vec_xl
#define vsx_st vec_xst
#endif
int main(void)
{
... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_vxe2.c | #if (__VEC__ < 10303) || (__ARCH__ < 13)
#error VXE2 not supported
#endif
#include <vecintrin.h>
int main(int argc, char **argv)
{
int val;
__vector signed short large = { 'a', 'b', 'c', 'a', 'g', 'h', 'g', 'o' };
__vector signed short search = { 'g', 'h', 'g', 'o' };
__vector unsigned char len = ... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_avx512_skx.c | #if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
/*
* Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
* whether or not the build options for those features are specified.
* Therefore, we must test #definitions of CPU features when option native/host
* is enabled v... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_avx512cd.c | #if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
/*
* Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
* whether or not the build options for those features are specified.
* Therefore, we must test #definitions of CPU features when option native/host
* is enabled v... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_sse2.c | #if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
/*
* Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
* whether or not the build options for those features are specified.
* Therefore, we must test #definitions of CPU features when option native/host
* is enabled v... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_vsx2.c | #ifndef __VSX__
#error "VSX is not supported"
#endif
#include <altivec.h>
typedef __vector unsigned long long v_uint64x2;
int main(void)
{
v_uint64x2 z2 = (v_uint64x2){0, 0};
z2 = (v_uint64x2)vec_cmpeq(z2, z2);
return (int)vec_extract(z2, 0);
}
| 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/extra_avx512dq_mask.c | #include <immintrin.h>
/**
* Test DQ mask operations due to:
* - MSVC has supported it since vs2019 see,
* https://developercommunity.visualstudio.com/content/problem/518298/missing-avx512bw-mask-intrinsics.html
* - Clang >= v8.0
* - GCC >= v7.1
*/
int main(void)
{
__mmask8 m8 = _mm512_cmpeq_epi64_mask(... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_avx512_knm.c | #if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
/*
* Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
* whether or not the build options for those features are specified.
* Therefore, we must test #definitions of CPU features when option native/host
* is enabled v... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_sse.c | #if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
/*
* Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
* whether or not the build options for those features are specified.
* Therefore, we must test #definitions of CPU features when option native/host
* is enabled v... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_sse3.c | #if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
/*
* Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
* whether or not the build options for those features are specified.
* Therefore, we must test #definitions of CPU features when option native/host
* is enabled v... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_neon_vfpv4.c | #ifdef _MSC_VER
#include <Intrin.h>
#endif
#include <arm_neon.h>
int main(int argc, char **argv)
{
float *src = (float*)argv[argc-1];
float32x4_t v1 = vdupq_n_f32(src[0]);
float32x4_t v2 = vdupq_n_f32(src[1]);
float32x4_t v3 = vdupq_n_f32(src[2]);
int ret = (int)vgetq_lane_f32(vfmaq_f32(v1, v2,... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_fma4.c | #include <immintrin.h>
#ifdef _MSC_VER
#include <ammintrin.h>
#else
#include <x86intrin.h>
#endif
int main(int argc, char **argv)
{
__m256 a = _mm256_loadu_ps((const float*)argv[argc-1]);
a = _mm256_macc_ps(a, a, a);
return (int)_mm_cvtss_f32(_mm256_castps256_ps128(a));
}
| 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_fma3.c | #if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
/*
* Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
* whether or not the build options for those features are specified.
* Therefore, we must test #definitions of CPU features when option native/host
* is enabled v... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_avx.c | #if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
/*
* Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
* whether or not the build options for those features are specified.
* Therefore, we must test #definitions of CPU features when option native/host
* is enabled v... | 0 |
public_repos/numpy/numpy/distutils | public_repos/numpy/numpy/distutils/checks/cpu_xop.c | #include <immintrin.h>
#ifdef _MSC_VER
#include <ammintrin.h>
#else
#include <x86intrin.h>
#endif
int main(void)
{
__m128i a = _mm_comge_epu32(_mm_setzero_si128(), _mm_setzero_si128());
return _mm_cvtsi128_si32(a);
}
| 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/typing/mypy_plugin.py | """A mypy_ plugin for managing a number of platform-specific annotations.
Its functionality can be split into three distinct parts:
* Assigning the (platform-dependent) precisions of certain `~numpy.number`
subclasses, including the likes of `~numpy.int_`, `~numpy.intp` and
`~numpy.longlong`. See the documentation... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/typing/__init__.py | """
============================
Typing (:mod:`numpy.typing`)
============================
.. versionadded:: 1.20
Large parts of the NumPy API have :pep:`484`-style type annotations. In
addition a number of type aliases are available to users, most prominently
the two below:
- `ArrayLike`: objects that can be conver... | 0 |
public_repos/numpy/numpy/typing | public_repos/numpy/numpy/typing/tests/test_typing.py | from __future__ import annotations
import importlib.util
import os
import re
import shutil
from collections import defaultdict
from collections.abc import Iterator
from typing import TYPE_CHECKING
import pytest
from numpy.typing.mypy_plugin import _EXTENDED_PRECISION_LIST
# Only trigger a full `mypy` run if this en... | 0 |
public_repos/numpy/numpy/typing | public_repos/numpy/numpy/typing/tests/test_isfile.py | import os
import sys
from pathlib import Path
import numpy as np
from numpy.testing import assert_
ROOT = Path(np.__file__).parents[0]
FILES = [
ROOT / "py.typed",
ROOT / "__init__.pyi",
ROOT / "ctypeslib.pyi",
ROOT / "_core" / "__init__.pyi",
ROOT / "f2py" / "__init__.pyi",
ROOT / "fft" / "__... | 0 |
public_repos/numpy/numpy/typing | public_repos/numpy/numpy/typing/tests/test_runtime.py | """Test the runtime usage of `numpy.typing`."""
from __future__ import annotations
from typing import (
get_type_hints,
Union,
NamedTuple,
get_args,
get_origin,
Any,
)
import pytest
import numpy as np
import numpy.typing as npt
import numpy._typing as _npt
class TypeTup(NamedTuple):
typ... | 0 |
public_repos/numpy/numpy/typing/tests | public_repos/numpy/numpy/typing/tests/data/mypy.ini | [mypy]
plugins = numpy.typing.mypy_plugin
show_absolute_path = True
implicit_reexport = False
pretty = True
disallow_any_unimported = True
disallow_any_generics = True
| 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/histograms.pyi | import numpy as np
import numpy.typing as npt
AR_i8: npt.NDArray[np.int64]
AR_f8: npt.NDArray[np.float64]
np.histogram_bin_edges(AR_i8, range=(0, 1, 2)) # E: incompatible type
np.histogram(AR_i8, range=(0, 1, 2)) # E: incompatible type
np.histogramdd(AR_i8, range=(0, 1)) # E: incompatible type
np.histogramdd(AR_... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/lib_utils.pyi | import numpy.lib.array_utils as array_utils
array_utils.byte_bounds(1) # E: incompatible type
| 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/lib_function_base.pyi | from typing import Any
import numpy as np
import numpy.typing as npt
AR_f8: npt.NDArray[np.float64]
AR_c16: npt.NDArray[np.complex128]
AR_m: npt.NDArray[np.timedelta64]
AR_M: npt.NDArray[np.datetime64]
AR_O: npt.NDArray[np.object_]
def func(a: int) -> None: ...
np.average(AR_m) # E: incompatible type
np.select(1, ... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/flatiter.pyi | from typing import Any
import numpy as np
import numpy._typing as npt
class Index:
def __index__(self) -> int:
...
a: np.flatiter[npt.NDArray[np.float64]]
supports_array: npt._SupportsArray[np.dtype[np.float64]]
a.base = Any # E: Property "base" defined in "flatiter" is read-only
a.coords = Any # E:... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/arrayterator.pyi | import numpy as np
import numpy.typing as npt
AR_i8: npt.NDArray[np.int64]
ar_iter = np.lib.Arrayterator(AR_i8)
np.lib.Arrayterator(np.int64()) # E: incompatible type
ar_iter.shape = (10, 5) # E: is read-only
ar_iter[None] # E: Invalid index type
ar_iter[None, 1] # E: Invalid index type
ar_iter[np.intp()] # E: I... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/array_constructors.pyi | import numpy as np
import numpy.typing as npt
a: npt.NDArray[np.float64]
generator = (i for i in range(10))
np.require(a, requirements=1) # E: No overload variant
np.require(a, requirements="TEST") # E: incompatible type
np.zeros("test") # E: incompatible type
np.zeros() # E: require at least one argument
np.on... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/chararray.pyi | import numpy as np
from typing import Any
AR_U: np.char.chararray[Any, np.dtype[np.str_]]
AR_S: np.char.chararray[Any, np.dtype[np.bytes_]]
AR_S.encode() # E: Invalid self argument
AR_U.decode() # E: Invalid self argument
AR_U.join(b"_") # E: incompatible type
AR_S.join("_") # E: incompatible type
AR_U.ljust(5,... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/index_tricks.pyi | import numpy as np
AR_LIKE_i: list[int]
AR_LIKE_f: list[float]
np.ndindex([1, 2, 3]) # E: No overload variant
np.unravel_index(AR_LIKE_f, (1, 2, 3)) # E: incompatible type
np.ravel_multi_index(AR_LIKE_i, (1, 2, 3), mode="bob") # E: No overload variant
np.mgrid[1] # E: Invalid index type
np.mgrid[...] # E: Invali... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/scalars.pyi | import sys
import numpy as np
f2: np.float16
f8: np.float64
c8: np.complex64
# Construction
np.float32(3j) # E: incompatible type
# Technically the following examples are valid NumPy code. But they
# are not considered a best practice, and people who wish to use the
# stubs should instead do
#
# np.array([1.0, 0.0... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/comparisons.pyi | import numpy as np
import numpy.typing as npt
AR_i: npt.NDArray[np.int64]
AR_f: npt.NDArray[np.float64]
AR_c: npt.NDArray[np.complex128]
AR_m: npt.NDArray[np.timedelta64]
AR_M: npt.NDArray[np.datetime64]
AR_f > AR_m # E: Unsupported operand types
AR_c > AR_m # E: Unsupported operand types
AR_m > AR_f # E: Unsuppo... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/lib_version.pyi | from numpy.lib import NumpyVersion
version: NumpyVersion
NumpyVersion(b"1.8.0") # E: incompatible type
version >= b"1.8.0" # E: Unsupported operand types
| 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/constants.pyi | import numpy as np
np.little_endian = np.little_endian # E: Cannot assign to final
| 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/linalg.pyi | import numpy as np
import numpy.typing as npt
AR_f8: npt.NDArray[np.float64]
AR_O: npt.NDArray[np.object_]
AR_M: npt.NDArray[np.datetime64]
np.linalg.tensorsolve(AR_O, AR_O) # E: incompatible type
np.linalg.solve(AR_O, AR_O) # E: incompatible type
np.linalg.tensorinv(AR_O) # E: incompatible type
np.linalg.inv(A... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/ufunc_config.pyi | """Typing tests for `numpy._core._ufunc_config`."""
import numpy as np
def func1(a: str, b: int, c: float) -> None: ...
def func2(a: str, *, b: int) -> None: ...
class Write1:
def write1(self, a: str) -> None: ...
class Write2:
def write(self, a: str, b: str) -> None: ...
class Write3:
def write(self, ... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/fromnumeric.pyi | """Tests for :mod:`numpy._core.fromnumeric`."""
import numpy as np
import numpy.typing as npt
A = np.array(True, ndmin=2, dtype=bool)
A.setflags(write=False)
AR_U: npt.NDArray[np.str_]
a = np.bool_(True)
np.take(a, None) # E: No overload variant
np.take(a, axis=1.0) # E: No overload variant
np.take(A, out=1) # E... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/bitwise_ops.pyi | import numpy as np
i8 = np.int64()
i4 = np.int32()
u8 = np.uint64()
b_ = np.bool_()
i = int()
f8 = np.float64()
b_ >> f8 # E: No overload variant
i8 << f8 # E: No overload variant
i | f8 # E: Unsupported operand types
i8 ^ f8 # E: No overload variant
u8 & f8 # E: No overload variant
~f8 # E: Unsupported operan... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/numerictypes.pyi | import numpy as np
np.issubdtype(1, np.int64) # E: incompatible type
| 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/memmap.pyi | import numpy as np
with open("file.txt", "r") as f:
np.memmap(f) # E: No overload variant
np.memmap("test.txt", shape=[10, 5]) # E: No overload variant
| 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/nditer.pyi | import numpy as np
class Test(np.nditer): ... # E: Cannot inherit from final class
np.nditer([0, 1], flags=["test"]) # E: incompatible type
np.nditer([0, 1], op_flags=[["test"]]) # E: incompatible type
np.nditer([0, 1], itershape=(1.0,)) # E: incompatible type
np.nditer([0, 1], buffersize=1.0) # E: incompatible ... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/type_check.pyi | import numpy as np
import numpy.typing as npt
DTYPE_i8: np.dtype[np.int64]
np.mintypecode(DTYPE_i8) # E: incompatible type
np.iscomplexobj(DTYPE_i8) # E: incompatible type
np.isrealobj(DTYPE_i8) # E: incompatible type
np.typename(DTYPE_i8) # E: No overload variant
np.typename("invalid") # E: No overload variant... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/random.pyi | import numpy as np
import numpy.typing as npt
SEED_FLOAT: float = 457.3
SEED_ARR_FLOAT: npt.NDArray[np.float64] = np.array([1.0, 2, 3, 4])
SEED_ARRLIKE_FLOAT: list[float] = [1.0, 2.0, 3.0, 4.0]
SEED_SEED_SEQ: np.random.SeedSequence = np.random.SeedSequence(0)
SEED_STR: str = "String seeding not allowed"
# default rng... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/shape_base.pyi | import numpy as np
class DTypeLike:
dtype: np.dtype[np.int_]
dtype_like: DTypeLike
np.expand_dims(dtype_like, (5, 10)) # E: No overload variant
| 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/char.pyi | import numpy as np
import numpy.typing as npt
AR_U: npt.NDArray[np.str_]
AR_S: npt.NDArray[np.bytes_]
np.char.equal(AR_U, AR_S) # E: incompatible type
np.char.not_equal(AR_U, AR_S) # E: incompatible type
np.char.greater_equal(AR_U, AR_S) # E: incompatible type
np.char.less_equal(AR_U, AR_S) # E: incompatible t... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/einsumfunc.pyi | import numpy as np
import numpy.typing as npt
AR_i: npt.NDArray[np.int64]
AR_f: npt.NDArray[np.float64]
AR_m: npt.NDArray[np.timedelta64]
AR_U: npt.NDArray[np.str_]
np.einsum("i,i->i", AR_i, AR_m) # E: incompatible type
np.einsum("i,i->i", AR_f, AR_f, dtype=np.int32) # E: incompatible type
np.einsum("i,i->i", AR_i,... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/npyio.pyi | import pathlib
from typing import IO
import numpy.typing as npt
import numpy as np
str_path: str
bytes_path: bytes
pathlib_path: pathlib.Path
str_file: IO[str]
AR_i8: npt.NDArray[np.int64]
np.load(str_file) # E: incompatible type
np.save(bytes_path, AR_i8) # E: incompatible type
np.savez(bytes_path, AR_i8) # E:... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/ndarray.pyi | import numpy as np
# Ban setting dtype since mutating the type of the array in place
# makes having ndarray be generic over dtype impossible. Generally
# users should use `ndarray.view` in this situation anyway. See
#
# https://github.com/numpy/numpy-stubs/issues/7
#
# for more context.
float_array = np.array([1.0])
f... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/warnings_and_errors.pyi | import numpy.exceptions as ex
ex.AxisError(1.0) # E: No overload variant
ex.AxisError(1, ndim=2.0) # E: No overload variant
ex.AxisError(2, msg_prefix=404) # E: No overload variant
| 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/array_like.pyi | import numpy as np
from numpy._typing import ArrayLike
class A:
pass
x1: ArrayLike = (i for i in range(10)) # E: Incompatible types in assignment
x2: ArrayLike = A() # E: Incompatible types in assignment
x3: ArrayLike = {1: "foo", 2: "bar"} # E: Incompatible types in assignment
scalar = np.int64(1)
scalar._... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/stride_tricks.pyi | import numpy as np
import numpy.typing as npt
AR_f8: npt.NDArray[np.float64]
np.lib.stride_tricks.as_strided(AR_f8, shape=8) # E: No overload variant
np.lib.stride_tricks.as_strided(AR_f8, strides=8) # E: No overload variant
np.lib.stride_tricks.sliding_window_view(AR_f8, axis=(1,)) # E: No overload variant
| 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/ufunclike.pyi | import numpy as np
import numpy.typing as npt
AR_c: npt.NDArray[np.complex128]
AR_m: npt.NDArray[np.timedelta64]
AR_M: npt.NDArray[np.datetime64]
AR_O: npt.NDArray[np.object_]
np.fix(AR_c) # E: incompatible type
np.fix(AR_m) # E: incompatible type
np.fix(AR_M) # E: incompatible type
np.isposinf(AR_c) # E: incomp... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/nested_sequence.pyi | from collections.abc import Sequence
from numpy._typing import _NestedSequence
a: Sequence[float]
b: list[complex]
c: tuple[str, ...]
d: int
e: str
def func(a: _NestedSequence[int]) -> None:
...
reveal_type(func(a)) # E: incompatible type
reveal_type(func(b)) # E: incompatible type
reveal_type(func(c)) # E: i... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/false_positives.pyi | import numpy as np
import numpy.typing as npt
AR_f8: npt.NDArray[np.float64]
# NOTE: Mypy bug presumably due to the special-casing of heterogeneous tuples;
# xref numpy/numpy#20901
#
# The expected output should be no different than, e.g., when using a
# list instead of a tuple
np.concatenate(([1], AR_f8)) # E: Argu... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/ndarray_misc.pyi | """
Tests for miscellaneous (non-magic) ``np.ndarray``/``np.generic`` methods.
More extensive tests are performed for the methods'
function-based counterpart in `../from_numeric.py`.
"""
import numpy as np
import numpy.typing as npt
f8: np.float64
AR_f8: npt.NDArray[np.float64]
AR_M: npt.NDArray[np.datetime64]
AR_b... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/modules.pyi | import numpy as np
np.testing.bob # E: Module has no attribute
np.bob # E: Module has no attribute
# Stdlib modules in the namespace by accident
np.warnings # E: Module has no attribute
np.sys # E: Module has no attribute
np.os # E: Module "numpy" does not explicitly export
np.math # E: Module has no attribute
... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/twodim_base.pyi | from typing import Any, TypeVar
import numpy as np
import numpy.typing as npt
def func1(ar: npt.NDArray[Any], a: int) -> npt.NDArray[np.str_]:
pass
def func2(ar: npt.NDArray[Any], a: float) -> float:
pass
AR_b: npt.NDArray[np.bool_]
AR_m: npt.NDArray[np.timedelta64]
AR_LIKE_b: list[bool]
np.eye(10, M=2... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/rec.pyi | import numpy as np
import numpy.typing as npt
AR_i8: npt.NDArray[np.int64]
np.rec.fromarrays(1) # E: No overload variant
np.rec.fromarrays([1, 2, 3], dtype=[("f8", "f8")], formats=["f8", "f8"]) # E: No overload variant
np.rec.fromrecords(AR_i8) # E: incompatible type
np.rec.fromrecords([(1.5,)], dtype=[("f8", "f8... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/multiarray.pyi | import numpy as np
import numpy.typing as npt
i8: np.int64
AR_b: npt.NDArray[np.bool_]
AR_u1: npt.NDArray[np.uint8]
AR_i8: npt.NDArray[np.int64]
AR_f8: npt.NDArray[np.float64]
AR_M: npt.NDArray[np.datetime64]
M: np.datetime64
AR_LIKE_f: list[float]
def func(a: int) -> None: ...
np.where(AR_b, 1) # E: No overload... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/lib_polynomial.pyi | import numpy as np
import numpy.typing as npt
AR_f8: npt.NDArray[np.float64]
AR_c16: npt.NDArray[np.complex128]
AR_O: npt.NDArray[np.object_]
AR_U: npt.NDArray[np.str_]
poly_obj: np.poly1d
np.polymul(AR_f8, AR_U) # E: incompatible type
np.polydiv(AR_f8, AR_U) # E: incompatible type
5**poly_obj # E: No overload v... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/arrayprint.pyi | from collections.abc import Callable
from typing import Any
import numpy as np
import numpy.typing as npt
AR: npt.NDArray[np.float64]
func1: Callable[[Any], str]
func2: Callable[[np.integer[Any]], str]
np.array2string(AR, style=None) # E: Unexpected keyword argument
np.array2string(AR, legacy="1.14") # E: incompat... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/dtype.pyi | import numpy as np
class Test1:
not_dtype = np.dtype(float)
class Test2:
dtype = float
np.dtype(Test1()) # E: No overload variant of "dtype" matches
np.dtype(Test2()) # E: incompatible type
np.dtype( # E: No overload variant of "dtype" matches
{
"field1": (float, 1),
"field2": (int... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/datasource.pyi | from pathlib import Path
import numpy as np
path: Path
d1: np.lib.npyio.DataSource
d1.abspath(path) # E: incompatible type
d1.abspath(b"...") # E: incompatible type
d1.exists(path) # E: incompatible type
d1.exists(b"...") # E: incompatible type
d1.open(path, "r") # E: incompatible type
d1.open(b"...", encoding... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/array_pad.pyi | import numpy as np
import numpy.typing as npt
AR_i8: npt.NDArray[np.int64]
np.pad(AR_i8, 2, mode="bob") # E: No overload variant
| 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/ufuncs.pyi | import numpy as np
import numpy.typing as npt
AR_f8: npt.NDArray[np.float64]
np.sin.nin + "foo" # E: Unsupported operand types
np.sin(1, foo="bar") # E: No overload variant
np.abs(None) # E: No overload variant
np.add(1, 1, 1) # E: No overload variant
np.add(1, 1, axis=0) # E: No overload variant
np.matmul(AR... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/arithmetic.pyi | from typing import Any
import numpy as np
import numpy.typing as npt
b_ = np.bool_()
dt = np.datetime64(0, "D")
td = np.timedelta64(0, "D")
AR_b: npt.NDArray[np.bool_]
AR_u: npt.NDArray[np.uint32]
AR_i: npt.NDArray[np.int64]
AR_f: npt.NDArray[np.float64]
AR_c: npt.NDArray[np.complex128]
AR_m: npt.NDArray[np.timedelt... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/fail/testing.pyi | import numpy as np
import numpy.typing as npt
AR_U: npt.NDArray[np.str_]
def func() -> bool: ...
np.testing.assert_(True, msg=1) # E: incompatible type
np.testing.build_err_msg(1, "test") # E: incompatible type
np.testing.assert_almost_equal(AR_U, AR_U) # E: incompatible type
np.testing.assert_approx_equal([1, 2,... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/misc/extended_precision.pyi | import sys
import numpy as np
from numpy._typing import _80Bit, _96Bit, _128Bit, _256Bit
if sys.version_info >= (3, 11):
from typing import assert_type
else:
from typing_extensions import assert_type
assert_type(np.uint128(), np.unsignedinteger[_128Bit])
assert_type(np.uint256(), np.unsignedinteger[_256Bit])... | 0 |
public_repos/numpy/numpy/typing/tests/data | public_repos/numpy/numpy/typing/tests/data/pass/einsumfunc.py | from __future__ import annotations
from typing import Any
import numpy as np
AR_LIKE_b = [True, True, True]
AR_LIKE_u = [np.uint32(1), np.uint32(2), np.uint32(3)]
AR_LIKE_i = [1, 2, 3]
AR_LIKE_f = [1.0, 2.0, 3.0]
AR_LIKE_c = [1j, 2j, 3j]
AR_LIKE_U = ["1", "2", "3"]
OUT_f: np.ndarray[Any, np.dtype[np.float64]] = np.... | 0 |
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