content stringlengths 1 103k ⌀ | path stringlengths 8 216 | filename stringlengths 2 179 | language stringclasses 15
values | size_bytes int64 2 189k | quality_score float64 0.5 0.95 | complexity float64 0 1 | documentation_ratio float64 0 1 | repository stringclasses 5
values | stars int64 0 1k | created_date stringdate 2023-07-10 19:21:08 2025-07-09 19:11:45 | license stringclasses 4
values | is_test bool 2
classes | file_hash stringlengths 32 32 |
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import abc\nfrom collections.abc import Callable, Mapping, Sequence\nfrom threading import Lock\nfrom typing import (\n Any,\n ClassVar,\n Literal,\n NamedTuple,\n Self,\n TypeAlias,\n TypedDict,\n overload,\n type_check_only,\n)\n\nfrom _typeshed import Incomplete\nfrom typing_extensions import CapsuleType\n\nimport numpy as np\nfrom numpy._typing import (\n NDArray,\n _ArrayLikeInt_co,\n _DTypeLike,\n _ShapeLike,\n _UInt32Codes,\n _UInt64Codes,\n)\n\n__all__ = ["BitGenerator", "SeedSequence"]\n\n###\n\n_DTypeLikeUint_: TypeAlias = _DTypeLike[np.uint32 | np.uint64] | _UInt32Codes | _UInt64Codes\n\n@type_check_only\nclass _SeedSeqState(TypedDict):\n entropy: int | Sequence[int] | None\n spawn_key: tuple[int, ...]\n pool_size: int\n n_children_spawned: int\n\n@type_check_only\nclass _Interface(NamedTuple):\n state_address: Incomplete\n state: Incomplete\n next_uint64: Incomplete\n next_uint32: Incomplete\n next_double: Incomplete\n bit_generator: Incomplete\n\n@type_check_only\nclass _CythonMixin:\n def __setstate_cython__(self, pyx_state: object, /) -> None: ...\n def __reduce_cython__(self) -> Any: ... # noqa: ANN401\n\n@type_check_only\nclass _GenerateStateMixin(_CythonMixin):\n def generate_state(self, /, n_words: int, dtype: _DTypeLikeUint_ = ...) -> NDArray[np.uint32 | np.uint64]: ...\n\n###\n\nclass ISeedSequence(abc.ABC):\n @abc.abstractmethod\n def generate_state(self, /, n_words: int, dtype: _DTypeLikeUint_ = ...) -> NDArray[np.uint32 | np.uint64]: ...\n\nclass ISpawnableSeedSequence(ISeedSequence, abc.ABC):\n @abc.abstractmethod\n def spawn(self, /, n_children: int) -> list[Self]: ...\n\nclass SeedlessSeedSequence(_GenerateStateMixin, ISpawnableSeedSequence):\n def spawn(self, /, n_children: int) -> list[Self]: ...\n\nclass SeedSequence(_GenerateStateMixin, ISpawnableSeedSequence):\n __pyx_vtable__: ClassVar[CapsuleType] = ...\n\n entropy: int | Sequence[int] | None\n spawn_key: tuple[int, ...]\n pool_size: int\n n_children_spawned: int\n pool: NDArray[np.uint32]\n\n def __init__(\n self,\n /,\n entropy: _ArrayLikeInt_co | None = None,\n *,\n spawn_key: Sequence[int] = (),\n pool_size: int = 4,\n n_children_spawned: int = ...,\n ) -> None: ...\n def spawn(self, /, n_children: int) -> list[Self]: ...\n @property\n def state(self) -> _SeedSeqState: ...\n\nclass BitGenerator(_CythonMixin, abc.ABC):\n lock: Lock\n @property\n def state(self) -> Mapping[str, Any]: ...\n @state.setter\n def state(self, value: Mapping[str, Any], /) -> None: ...\n @property\n def seed_seq(self) -> ISeedSequence: ...\n @property\n def ctypes(self) -> _Interface: ...\n @property\n def cffi(self) -> _Interface: ...\n @property\n def capsule(self) -> CapsuleType: ...\n\n #\n def __init__(self, /, seed: _ArrayLikeInt_co | SeedSequence | None = None) -> None: ...\n def __reduce__(self) -> tuple[Callable[[str], Self], tuple[str], tuple[Mapping[str, Any], ISeedSequence]]: ...\n def spawn(self, /, n_children: int) -> list[Self]: ...\n def _benchmark(self, /, cnt: int, method: str = "uint64") -> None: ...\n\n #\n @overload\n def random_raw(self, /, size: None = None, output: Literal[True] = True) -> int: ...\n @overload\n def random_raw(self, /, size: _ShapeLike, output: Literal[True] = True) -> NDArray[np.uint64]: ...\n @overload\n def random_raw(self, /, size: _ShapeLike | None, output: Literal[False]) -> None: ...\n @overload\n def random_raw(self, /, size: _ShapeLike | None = None, *, output: Literal[False]) -> None: ...\n | .venv\Lib\site-packages\numpy\random\bit_generator.pyi | bit_generator.pyi | Other | 3,728 | 0.95 | 0.258065 | 0.047619 | node-utils | 585 | 2023-10-12T18:05:52.203518 | GPL-3.0 | false | 427ca6f8e04e8d9ee041d95ba2c543e5 |
#cython: wraparound=False, nonecheck=False, boundscheck=False, cdivision=True, language_level=3\nfrom numpy cimport npy_intp\n\nfrom libc.stdint cimport (uint64_t, int32_t, int64_t)\nfrom numpy.random cimport bitgen_t\n\ncdef extern from "numpy/random/distributions.h":\n\n struct s_binomial_t:\n int has_binomial\n double psave\n int64_t nsave\n double r\n double q\n double fm\n int64_t m\n double p1\n double xm\n double xl\n double xr\n double c\n double laml\n double lamr\n double p2\n double p3\n double p4\n\n ctypedef s_binomial_t binomial_t\n\n float random_standard_uniform_f(bitgen_t *bitgen_state) nogil\n double random_standard_uniform(bitgen_t *bitgen_state) nogil\n void random_standard_uniform_fill(bitgen_t* bitgen_state, npy_intp cnt, double *out) nogil\n void random_standard_uniform_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) nogil\n \n double random_standard_exponential(bitgen_t *bitgen_state) nogil\n float random_standard_exponential_f(bitgen_t *bitgen_state) nogil\n void random_standard_exponential_fill(bitgen_t *bitgen_state, npy_intp cnt, double *out) nogil\n void random_standard_exponential_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) nogil\n void random_standard_exponential_inv_fill(bitgen_t *bitgen_state, npy_intp cnt, double *out) nogil\n void random_standard_exponential_inv_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) nogil\n \n double random_standard_normal(bitgen_t* bitgen_state) nogil\n float random_standard_normal_f(bitgen_t *bitgen_state) nogil\n void random_standard_normal_fill(bitgen_t *bitgen_state, npy_intp count, double *out) nogil\n void random_standard_normal_fill_f(bitgen_t *bitgen_state, npy_intp count, float *out) nogil\n double random_standard_gamma(bitgen_t *bitgen_state, double shape) nogil\n float random_standard_gamma_f(bitgen_t *bitgen_state, float shape) nogil\n\n float random_standard_uniform_f(bitgen_t *bitgen_state) nogil\n void random_standard_uniform_fill_f(bitgen_t* bitgen_state, npy_intp cnt, float *out) nogil\n float random_standard_normal_f(bitgen_t* bitgen_state) nogil\n float random_standard_gamma_f(bitgen_t *bitgen_state, float shape) nogil\n\n int64_t random_positive_int64(bitgen_t *bitgen_state) nogil\n int32_t random_positive_int32(bitgen_t *bitgen_state) nogil\n int64_t random_positive_int(bitgen_t *bitgen_state) nogil\n uint64_t random_uint(bitgen_t *bitgen_state) nogil\n\n double random_normal(bitgen_t *bitgen_state, double loc, double scale) nogil\n\n double random_gamma(bitgen_t *bitgen_state, double shape, double scale) nogil\n float random_gamma_f(bitgen_t *bitgen_state, float shape, float scale) nogil\n\n double random_exponential(bitgen_t *bitgen_state, double scale) nogil\n double random_uniform(bitgen_t *bitgen_state, double lower, double range) nogil\n double random_beta(bitgen_t *bitgen_state, double a, double b) nogil\n double random_chisquare(bitgen_t *bitgen_state, double df) nogil\n double random_f(bitgen_t *bitgen_state, double dfnum, double dfden) nogil\n double random_standard_cauchy(bitgen_t *bitgen_state) nogil\n double random_pareto(bitgen_t *bitgen_state, double a) nogil\n double random_weibull(bitgen_t *bitgen_state, double a) nogil\n double random_power(bitgen_t *bitgen_state, double a) nogil\n double random_laplace(bitgen_t *bitgen_state, double loc, double scale) nogil\n double random_gumbel(bitgen_t *bitgen_state, double loc, double scale) nogil\n double random_logistic(bitgen_t *bitgen_state, double loc, double scale) nogil\n double random_lognormal(bitgen_t *bitgen_state, double mean, double sigma) nogil\n double random_rayleigh(bitgen_t *bitgen_state, double mode) nogil\n double random_standard_t(bitgen_t *bitgen_state, double df) nogil\n double random_noncentral_chisquare(bitgen_t *bitgen_state, double df,\n double nonc) nogil\n double random_noncentral_f(bitgen_t *bitgen_state, double dfnum,\n double dfden, double nonc) nogil\n double random_wald(bitgen_t *bitgen_state, double mean, double scale) nogil\n double random_vonmises(bitgen_t *bitgen_state, double mu, double kappa) nogil\n double random_triangular(bitgen_t *bitgen_state, double left, double mode,\n double right) nogil\n\n int64_t random_poisson(bitgen_t *bitgen_state, double lam) nogil\n int64_t random_negative_binomial(bitgen_t *bitgen_state, double n, double p) nogil\n int64_t random_binomial(bitgen_t *bitgen_state, double p, int64_t n, binomial_t *binomial) nogil\n int64_t random_logseries(bitgen_t *bitgen_state, double p) nogil\n int64_t random_geometric_search(bitgen_t *bitgen_state, double p) nogil\n int64_t random_geometric_inversion(bitgen_t *bitgen_state, double p) nogil\n int64_t random_geometric(bitgen_t *bitgen_state, double p) nogil\n int64_t random_zipf(bitgen_t *bitgen_state, double a) nogil\n int64_t random_hypergeometric(bitgen_t *bitgen_state, int64_t good, int64_t bad,\n int64_t sample) nogil\n\n uint64_t random_interval(bitgen_t *bitgen_state, uint64_t max) nogil\n\n # Generate random uint64 numbers in closed interval [off, off + rng].\n uint64_t random_bounded_uint64(bitgen_t *bitgen_state,\n uint64_t off, uint64_t rng,\n uint64_t mask, bint use_masked) nogil\n\n void random_multinomial(bitgen_t *bitgen_state, int64_t n, int64_t *mnix,\n double *pix, npy_intp d, binomial_t *binomial) nogil\n\n int random_multivariate_hypergeometric_count(bitgen_t *bitgen_state,\n int64_t total,\n size_t num_colors, int64_t *colors,\n int64_t nsample,\n size_t num_variates, int64_t *variates) nogil\n void random_multivariate_hypergeometric_marginals(bitgen_t *bitgen_state,\n int64_t total,\n size_t num_colors, int64_t *colors,\n int64_t nsample,\n size_t num_variates, int64_t *variates) nogil\n\n | .venv\Lib\site-packages\numpy\random\c_distributions.pxd | c_distributions.pxd | Other | 6,454 | 0.95 | 0 | 0.019802 | vue-tools | 717 | 2025-04-18T19:34:53.962094 | Apache-2.0 | false | 8a62bdbc28a7810c2aac8f65bac02250 |
**This software is dual-licensed under the The University of Illinois/NCSA\nOpen Source License (NCSA) and The 3-Clause BSD License**\n\n# NCSA Open Source License\n**Copyright (c) 2019 Kevin Sheppard. All rights reserved.**\n\nDeveloped by: Kevin Sheppard (<kevin.sheppard@economics.ox.ac.uk>,\n<kevin.k.sheppard@gmail.com>)\n[http://www.kevinsheppard.com](http://www.kevinsheppard.com)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of\nthis software and associated documentation files (the "Software"), to deal with\nthe Software without restriction, including without limitation the rights to\nuse, copy, modify, merge, publish, distribute, sublicense, and/or sell copies\nof the Software, and to permit persons to whom the Software is furnished to do\nso, subject to the following conditions:\n\nRedistributions of source code must retain the above copyright notice, this\nlist of conditions and the following disclaimers.\n\nRedistributions in binary form must reproduce the above copyright notice, this\nlist of conditions and the following disclaimers in the documentation and/or\nother materials provided with the distribution.\n\nNeither the names of Kevin Sheppard, nor the names of any contributors may be\nused to endorse or promote products derived from this Software without specific\nprior written permission.\n\n**THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nCONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH\nTHE SOFTWARE.**\n\n\n# 3-Clause BSD License\n**Copyright (c) 2019 Kevin Sheppard. All rights reserved.**\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n1. Redistributions of source code must retain the above copyright notice,\n this list of conditions and the following disclaimer.\n\n2. Redistributions in binary form must reproduce the above copyright notice,\n this list of conditions and the following disclaimer in the documentation\n and/or other materials provided with the distribution.\n\n3. Neither the name of the copyright holder nor the names of its contributors\n may be used to endorse or promote products derived from this software\n without specific prior written permission.\n\n**THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\nARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE\nLIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\nCONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\nSUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\nINTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\nCONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\nARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF\nTHE POSSIBILITY OF SUCH DAMAGE.**\n\n# Components\n\nMany parts of this module have been derived from original sources, \noften the algorithm's designer. Component licenses are located with \nthe component code.\n | .venv\Lib\site-packages\numpy\random\LICENSE.md | LICENSE.md | Markdown | 3,582 | 0.8 | 0 | 0.145455 | node-utils | 691 | 2024-01-16T13:08:07.636326 | MIT | false | 09b2aa3c0d32aea36fb330dc9c728fc4 |
!<arch>\n/ -1 0 3724 `\n | .venv\Lib\site-packages\numpy\random\mtrand.cp313-win_amd64.lib | mtrand.cp313-win_amd64.lib | Other | 17,122 | 0.8 | 0 | 0 | vue-tools | 909 | 2023-11-28T07:08:49.450477 | MIT | false | 70cd84db481fad6cc83698fcf6e6d09b |
import builtins\nfrom collections.abc import Callable\nfrom typing import Any, Literal, overload\n\nimport numpy as np\nfrom numpy import (\n dtype,\n float64,\n int8,\n int16,\n int32,\n int64,\n int_,\n long,\n uint,\n uint8,\n uint16,\n uint32,\n uint64,\n ulong,\n)\nfrom numpy._typing import (\n ArrayLike,\n NDArray,\n _ArrayLikeFloat_co,\n _ArrayLikeInt_co,\n _DTypeLikeBool,\n _Int8Codes,\n _Int16Codes,\n _Int32Codes,\n _Int64Codes,\n _IntCodes,\n _LongCodes,\n _ShapeLike,\n _SupportsDType,\n _UInt8Codes,\n _UInt16Codes,\n _UInt32Codes,\n _UInt64Codes,\n _UIntCodes,\n _ULongCodes,\n)\nfrom numpy.random.bit_generator import BitGenerator\n\nclass RandomState:\n _bit_generator: BitGenerator\n def __init__(self, seed: _ArrayLikeInt_co | BitGenerator | None = ...) -> None: ...\n def __repr__(self) -> str: ...\n def __str__(self) -> str: ...\n def __getstate__(self) -> dict[str, Any]: ...\n def __setstate__(self, state: dict[str, Any]) -> None: ...\n def __reduce__(self) -> tuple[Callable[[BitGenerator], RandomState], tuple[BitGenerator], dict[str, Any]]: ... # noqa: E501\n def seed(self, seed: _ArrayLikeFloat_co | None = ...) -> None: ...\n @overload\n def get_state(self, legacy: Literal[False] = ...) -> dict[str, Any]: ...\n @overload\n def get_state(\n self, legacy: Literal[True] = ...\n ) -> dict[str, Any] | tuple[str, NDArray[uint32], int, int, float]: ...\n def set_state(\n self, state: dict[str, Any] | tuple[str, NDArray[uint32], int, int, float]\n ) -> None: ...\n @overload\n def random_sample(self, size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def random_sample(self, size: _ShapeLike) -> NDArray[float64]: ...\n @overload\n def random(self, size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def random(self, size: _ShapeLike) -> NDArray[float64]: ...\n @overload\n def beta(self, a: float, b: float, size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def beta(\n self,\n a: _ArrayLikeFloat_co,\n b: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n @overload\n def exponential(self, scale: float = ..., size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def exponential(\n self, scale: _ArrayLikeFloat_co = ..., size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n @overload\n def standard_exponential(self, size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def standard_exponential(self, size: _ShapeLike) -> NDArray[float64]: ...\n @overload\n def tomaxint(self, size: None = ...) -> int: ... # type: ignore[misc]\n @overload\n # Generates long values, but stores it in a 64bit int:\n def tomaxint(self, size: _ShapeLike) -> NDArray[int64]: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: int,\n high: int | None = ...,\n size: None = ...,\n ) -> int: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: int,\n high: int | None = ...,\n size: None = ...,\n dtype: type[bool] = ...,\n ) -> bool: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: int,\n high: int | None = ...,\n size: None = ...,\n dtype: type[np.bool] = ...,\n ) -> np.bool: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: int,\n high: int | None = ...,\n size: None = ...,\n dtype: type[int] = ...,\n ) -> int: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: int,\n high: int | None = ...,\n size: None = ...,\n dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ..., # noqa: E501\n ) -> uint8: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: int,\n high: int | None = ...,\n size: None = ...,\n dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ..., # noqa: E501\n ) -> uint16: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: int,\n high: int | None = ...,\n size: None = ...,\n dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ..., # noqa: E501\n ) -> uint32: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: int,\n high: int | None = ...,\n size: None = ...,\n dtype: dtype[uint] | type[uint] | _UIntCodes | _SupportsDType[dtype[uint]] = ..., # noqa: E501\n ) -> uint: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: int,\n high: int | None = ...,\n size: None = ...,\n dtype: dtype[ulong] | type[ulong] | _ULongCodes | _SupportsDType[dtype[ulong]] = ..., # noqa: E501\n ) -> ulong: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: int,\n high: int | None = ...,\n size: None = ...,\n dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ..., # noqa: E501\n ) -> uint64: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: int,\n high: int | None = ...,\n size: None = ...,\n dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ..., # noqa: E501\n ) -> int8: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: int,\n high: int | None = ...,\n size: None = ...,\n dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ..., # noqa: E501\n ) -> int16: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: int,\n high: int | None = ...,\n size: None = ...,\n dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ..., # noqa: E501\n ) -> int32: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: int,\n high: int | None = ...,\n size: None = ...,\n dtype: dtype[int_] | type[int_] | _IntCodes | _SupportsDType[dtype[int_]] = ..., # noqa: E501\n ) -> int_: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: int,\n high: int | None = ...,\n size: None = ...,\n dtype: dtype[long] | type[long] | _LongCodes | _SupportsDType[dtype[long]] = ..., # noqa: E501\n ) -> long: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: int,\n high: int | None = ...,\n size: None = ...,\n dtype: dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ..., # noqa: E501\n ) -> int64: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = ...,\n size: _ShapeLike | None = ...,\n ) -> NDArray[long]: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = ...,\n size: _ShapeLike | None = ...,\n dtype: _DTypeLikeBool = ...,\n ) -> NDArray[np.bool]: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = ...,\n size: _ShapeLike | None = ...,\n dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ..., # noqa: E501\n ) -> NDArray[int8]: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = ...,\n size: _ShapeLike | None = ...,\n dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ..., # noqa: E501\n ) -> NDArray[int16]: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = ...,\n size: _ShapeLike | None = ...,\n dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ..., # noqa: E501\n ) -> NDArray[int32]: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = ...,\n size: _ShapeLike | None = ...,\n dtype: dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] | None = ..., # noqa: E501\n ) -> NDArray[int64]: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = ...,\n size: _ShapeLike | None = ...,\n dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ..., # noqa: E501\n ) -> NDArray[uint8]: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = ...,\n size: _ShapeLike | None = ...,\n dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ..., # noqa: E501\n ) -> NDArray[uint16]: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = ...,\n size: _ShapeLike | None = ...,\n dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ..., # noqa: E501\n ) -> NDArray[uint32]: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = ...,\n size: _ShapeLike | None = ...,\n dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ..., # noqa: E501\n ) -> NDArray[uint64]: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = ...,\n size: _ShapeLike | None = ...,\n dtype: dtype[long] | type[int] | type[long] | _LongCodes | _SupportsDType[dtype[long]] = ..., # noqa: E501\n ) -> NDArray[long]: ...\n @overload\n def randint( # type: ignore[misc]\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = ...,\n size: _ShapeLike | None = ...,\n dtype: dtype[ulong] | type[ulong] | _ULongCodes | _SupportsDType[dtype[ulong]] = ..., # noqa: E501\n ) -> NDArray[ulong]: ...\n def bytes(self, length: int) -> builtins.bytes: ...\n @overload\n def choice(\n self,\n a: int,\n size: None = ...,\n replace: bool = ...,\n p: _ArrayLikeFloat_co | None = ...,\n ) -> int: ...\n @overload\n def choice(\n self,\n a: int,\n size: _ShapeLike = ...,\n replace: bool = ...,\n p: _ArrayLikeFloat_co | None = ...,\n ) -> NDArray[long]: ...\n @overload\n def choice(\n self,\n a: ArrayLike,\n size: None = ...,\n replace: bool = ...,\n p: _ArrayLikeFloat_co | None = ...,\n ) -> Any: ...\n @overload\n def choice(\n self,\n a: ArrayLike,\n size: _ShapeLike = ...,\n replace: bool = ...,\n p: _ArrayLikeFloat_co | None = ...,\n ) -> NDArray[Any]: ...\n @overload\n def uniform(\n self, low: float = ..., high: float = ..., size: None = ...\n ) -> float: ... # type: ignore[misc]\n @overload\n def uniform(\n self,\n low: _ArrayLikeFloat_co = ...,\n high: _ArrayLikeFloat_co = ...,\n size: _ShapeLike | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def rand(self) -> float: ...\n @overload\n def rand(self, *args: int) -> NDArray[float64]: ...\n @overload\n def randn(self) -> float: ...\n @overload\n def randn(self, *args: int) -> NDArray[float64]: ...\n @overload\n def random_integers(\n self, low: int, high: int | None = ..., size: None = ...\n ) -> int: ... # type: ignore[misc]\n @overload\n def random_integers(\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = ...,\n size: _ShapeLike | None = ...,\n ) -> NDArray[long]: ...\n @overload\n def standard_normal(self, size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def standard_normal( # type: ignore[misc]\n self, size: _ShapeLike = ...\n ) -> NDArray[float64]: ...\n @overload\n def normal(\n self, loc: float = ..., scale: float = ..., size: None = ...\n ) -> float: ... # type: ignore[misc]\n @overload\n def normal(\n self,\n loc: _ArrayLikeFloat_co = ...,\n scale: _ArrayLikeFloat_co = ...,\n size: _ShapeLike | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def standard_gamma( # type: ignore[misc]\n self,\n shape: float,\n size: None = ...,\n ) -> float: ...\n @overload\n def standard_gamma(\n self,\n shape: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def gamma(self, shape: float, scale: float = ..., size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def gamma(\n self,\n shape: _ArrayLikeFloat_co,\n scale: _ArrayLikeFloat_co = ...,\n size: _ShapeLike | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def f(self, dfnum: float, dfden: float, size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def f(\n self,\n dfnum: _ArrayLikeFloat_co,\n dfden: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n @overload\n def noncentral_f(\n self, dfnum: float, dfden: float, nonc: float, size: None = ...\n ) -> float: ... # type: ignore[misc]\n @overload\n def noncentral_f(\n self,\n dfnum: _ArrayLikeFloat_co,\n dfden: _ArrayLikeFloat_co,\n nonc: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def chisquare(self, df: float, size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def chisquare(\n self, df: _ArrayLikeFloat_co, size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n @overload\n def noncentral_chisquare(\n self, df: float, nonc: float, size: None = ...\n ) -> float: ... # type: ignore[misc]\n @overload\n def noncentral_chisquare(\n self,\n df: _ArrayLikeFloat_co,\n nonc: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n @overload\n def standard_t(self, df: float, size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def standard_t(\n self, df: _ArrayLikeFloat_co, size: None = ...\n ) -> NDArray[float64]: ...\n @overload\n def standard_t(\n self, df: _ArrayLikeFloat_co, size: _ShapeLike = ...\n ) -> NDArray[float64]: ...\n @overload\n def vonmises(self, mu: float, kappa: float, size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def vonmises(\n self,\n mu: _ArrayLikeFloat_co,\n kappa: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n @overload\n def pareto(self, a: float, size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def pareto(\n self, a: _ArrayLikeFloat_co, size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n @overload\n def weibull(self, a: float, size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def weibull(\n self, a: _ArrayLikeFloat_co, size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n @overload\n def power(self, a: float, size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def power(\n self, a: _ArrayLikeFloat_co, size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n @overload\n def standard_cauchy(self, size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def standard_cauchy(self, size: _ShapeLike = ...) -> NDArray[float64]: ...\n @overload\n def laplace(\n self, loc: float = ..., scale: float = ..., size: None = ...\n ) -> float: ... # type: ignore[misc]\n @overload\n def laplace(\n self,\n loc: _ArrayLikeFloat_co = ...,\n scale: _ArrayLikeFloat_co = ...,\n size: _ShapeLike | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def gumbel(\n self, loc: float = ..., scale: float = ..., size: None = ...\n ) -> float: ... # type: ignore[misc]\n @overload\n def gumbel(\n self,\n loc: _ArrayLikeFloat_co = ...,\n scale: _ArrayLikeFloat_co = ...,\n size: _ShapeLike | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def logistic(\n self, loc: float = ..., scale: float = ..., size: None = ...\n ) -> float: ... # type: ignore[misc]\n @overload\n def logistic(\n self,\n loc: _ArrayLikeFloat_co = ...,\n scale: _ArrayLikeFloat_co = ...,\n size: _ShapeLike | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def lognormal(\n self, mean: float = ..., sigma: float = ..., size: None = ...\n ) -> float: ... # type: ignore[misc]\n @overload\n def lognormal(\n self,\n mean: _ArrayLikeFloat_co = ...,\n sigma: _ArrayLikeFloat_co = ...,\n size: _ShapeLike | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def rayleigh(self, scale: float = ..., size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def rayleigh(\n self, scale: _ArrayLikeFloat_co = ..., size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n @overload\n def wald(self, mean: float, scale: float, size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def wald(\n self,\n mean: _ArrayLikeFloat_co,\n scale: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n @overload\n def triangular(\n self, left: float, mode: float, right: float, size: None = ...\n ) -> float: ... # type: ignore[misc]\n @overload\n def triangular(\n self,\n left: _ArrayLikeFloat_co,\n mode: _ArrayLikeFloat_co,\n right: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def binomial(\n self, n: int, p: float, size: None = ...\n ) -> int: ... # type: ignore[misc]\n @overload\n def binomial(\n self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: _ShapeLike | None = ...\n ) -> NDArray[long]: ...\n @overload\n def negative_binomial(\n self, n: float, p: float, size: None = ...\n ) -> int: ... # type: ignore[misc]\n @overload\n def negative_binomial(\n self,\n n: _ArrayLikeFloat_co,\n p: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...\n ) -> NDArray[long]: ...\n @overload\n def poisson(\n self, lam: float = ..., size: None = ...\n ) -> int: ... # type: ignore[misc]\n @overload\n def poisson(\n self, lam: _ArrayLikeFloat_co = ..., size: _ShapeLike | None = ...\n ) -> NDArray[long]: ...\n @overload\n def zipf(self, a: float, size: None = ...) -> int: ... # type: ignore[misc]\n @overload\n def zipf(\n self, a: _ArrayLikeFloat_co, size: _ShapeLike | None = ...\n ) -> NDArray[long]: ...\n @overload\n def geometric(self, p: float, size: None = ...) -> int: ... # type: ignore[misc]\n @overload\n def geometric(\n self, p: _ArrayLikeFloat_co, size: _ShapeLike | None = ...\n ) -> NDArray[long]: ...\n @overload\n def hypergeometric(\n self, ngood: int, nbad: int, nsample: int, size: None = ...\n ) -> int: ... # type: ignore[misc]\n @overload\n def hypergeometric(\n self,\n ngood: _ArrayLikeInt_co,\n nbad: _ArrayLikeInt_co,\n nsample: _ArrayLikeInt_co,\n size: _ShapeLike | None = ...,\n ) -> NDArray[long]: ...\n @overload\n def logseries(self, p: float, size: None = ...) -> int: ... # type: ignore[misc]\n @overload\n def logseries(\n self, p: _ArrayLikeFloat_co, size: _ShapeLike | None = ...\n ) -> NDArray[long]: ...\n def multivariate_normal(\n self,\n mean: _ArrayLikeFloat_co,\n cov: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...,\n check_valid: Literal["warn", "raise", "ignore"] = ...,\n tol: float = ...,\n ) -> NDArray[float64]: ...\n def multinomial(\n self, n: _ArrayLikeInt_co,\n pvals: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...\n ) -> NDArray[long]: ...\n def dirichlet(\n self, alpha: _ArrayLikeFloat_co, size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n def shuffle(self, x: ArrayLike) -> None: ...\n @overload\n def permutation(self, x: int) -> NDArray[long]: ...\n @overload\n def permutation(self, x: ArrayLike) -> NDArray[Any]: ...\n\n_rand: RandomState\n\nbeta = _rand.beta\nbinomial = _rand.binomial\nbytes = _rand.bytes\nchisquare = _rand.chisquare\nchoice = _rand.choice\ndirichlet = _rand.dirichlet\nexponential = _rand.exponential\nf = _rand.f\ngamma = _rand.gamma\nget_state = _rand.get_state\ngeometric = _rand.geometric\ngumbel = _rand.gumbel\nhypergeometric = _rand.hypergeometric\nlaplace = _rand.laplace\nlogistic = _rand.logistic\nlognormal = _rand.lognormal\nlogseries = _rand.logseries\nmultinomial = _rand.multinomial\nmultivariate_normal = _rand.multivariate_normal\nnegative_binomial = _rand.negative_binomial\nnoncentral_chisquare = _rand.noncentral_chisquare\nnoncentral_f = _rand.noncentral_f\nnormal = _rand.normal\npareto = _rand.pareto\npermutation = _rand.permutation\npoisson = _rand.poisson\npower = _rand.power\nrand = _rand.rand\nrandint = _rand.randint\nrandn = _rand.randn\nrandom = _rand.random\nrandom_integers = _rand.random_integers\nrandom_sample = _rand.random_sample\nrayleigh = _rand.rayleigh\nseed = _rand.seed\nset_state = _rand.set_state\nshuffle = _rand.shuffle\nstandard_cauchy = _rand.standard_cauchy\nstandard_exponential = _rand.standard_exponential\nstandard_gamma = _rand.standard_gamma\nstandard_normal = _rand.standard_normal\nstandard_t = _rand.standard_t\ntriangular = _rand.triangular\nuniform = _rand.uniform\nvonmises = _rand.vonmises\nwald = _rand.wald\nweibull = _rand.weibull\nzipf = _rand.zipf\n# Two legacy that are trivial wrappers around random_sample\nsample = _rand.random_sample\nranf = _rand.random_sample\n\ndef set_bit_generator(bitgen: BitGenerator) -> None: ...\n\ndef get_bit_generator() -> BitGenerator: ...\n | .venv\Lib\site-packages\numpy\random\mtrand.pyi | mtrand.pyi | Other | 23,390 | 0.95 | 0.183499 | 0.002869 | python-kit | 381 | 2023-11-07T19:44:28.654460 | BSD-3-Clause | false | b928b1176ced7c8b5df500dabe20c004 |
!<arch>\n/ -1 0 3768 `\n | .venv\Lib\site-packages\numpy\random\_bounded_integers.cp313-win_amd64.lib | _bounded_integers.cp313-win_amd64.lib | Other | 18,000 | 0.8 | 0 | 0 | vue-tools | 81 | 2025-03-18T22:18:17.271608 | MIT | false | c9d78d91338aaeccbc2f1f7091102880 |
from libc.stdint cimport (uint8_t, uint16_t, uint32_t, uint64_t,\n\n int8_t, int16_t, int32_t, int64_t, intptr_t)\n\nimport numpy as np\n\ncimport numpy as np\n\nctypedef np.npy_bool bool_t\n\n\n\nfrom numpy.random cimport bitgen_t\n\n\n\ncdef inline uint64_t _gen_mask(uint64_t max_val) noexcept nogil:\n\n """Mask generator for use in bounded random numbers"""\n\n # Smallest bit mask >= max\n\n cdef uint64_t mask = max_val\n\n mask |= mask >> 1\n\n mask |= mask >> 2\n\n mask |= mask >> 4\n\n mask |= mask >> 8\n\n mask |= mask >> 16\n\n mask |= mask >> 32\n\n return mask\n\n\n\n\n\ncdef object _rand_uint64(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)\n\n\n\ncdef object _rand_uint32(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)\n\n\n\ncdef object _rand_uint16(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)\n\n\n\ncdef object _rand_uint8(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)\n\n\n\ncdef object _rand_bool(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)\n\n\n\ncdef object _rand_int64(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)\n\n\n\ncdef object _rand_int32(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)\n\n\n\ncdef object _rand_int16(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)\n\n\n\ncdef object _rand_int8(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)\n\n | .venv\Lib\site-packages\numpy\random\_bounded_integers.pxd | _bounded_integers.pxd | Other | 1,763 | 0.95 | 0.013158 | 0.038462 | python-kit | 755 | 2024-04-15T18:57:13.519133 | BSD-3-Clause | false | 4ef6dcf555273c7aacb8af62cbbe8e01 |
__all__: list[str] = []\n | .venv\Lib\site-packages\numpy\random\_bounded_integers.pyi | _bounded_integers.pyi | Other | 25 | 0.5 | 0 | 0 | vue-tools | 448 | 2024-04-30T07:38:34.977229 | BSD-3-Clause | false | 8cad7b52788a1f41859dbfd33f997a6b |
!<arch>\n/ -1 0 170 `\n | .venv\Lib\site-packages\numpy\random\_common.cp313-win_amd64.lib | _common.cp313-win_amd64.lib | Other | 2,012 | 0.8 | 0 | 0 | python-kit | 145 | 2024-07-09T13:57:07.542027 | BSD-3-Clause | false | d984d6f49aacaa23920d28206a34c8b6 |
#cython: language_level=3\n\nfrom libc.stdint cimport uint32_t, uint64_t, int32_t, int64_t\n\nimport numpy as np\ncimport numpy as np\n\nfrom numpy.random cimport bitgen_t\n\ncdef double POISSON_LAM_MAX\ncdef double LEGACY_POISSON_LAM_MAX\ncdef uint64_t MAXSIZE\n\ncdef enum ConstraintType:\n CONS_NONE\n CONS_NON_NEGATIVE\n CONS_POSITIVE\n CONS_POSITIVE_NOT_NAN\n CONS_BOUNDED_0_1\n CONS_BOUNDED_GT_0_1\n CONS_BOUNDED_LT_0_1\n CONS_GT_1\n CONS_GTE_1\n CONS_POISSON\n LEGACY_CONS_POISSON\n LEGACY_CONS_NON_NEGATIVE_INBOUNDS_LONG\n\nctypedef ConstraintType constraint_type\n\ncdef object benchmark(bitgen_t *bitgen, object lock, Py_ssize_t cnt, object method)\ncdef object random_raw(bitgen_t *bitgen, object lock, object size, object output)\ncdef object prepare_cffi(bitgen_t *bitgen)\ncdef object prepare_ctypes(bitgen_t *bitgen)\ncdef int check_constraint(double val, object name, constraint_type cons) except -1\ncdef int check_array_constraint(np.ndarray val, object name, constraint_type cons) except -1\n\ncdef extern from "include/aligned_malloc.h":\n cdef void *PyArray_realloc_aligned(void *p, size_t n)\n cdef void *PyArray_malloc_aligned(size_t n)\n cdef void *PyArray_calloc_aligned(size_t n, size_t s)\n cdef void PyArray_free_aligned(void *p)\n\nctypedef void (*random_double_fill)(bitgen_t *state, np.npy_intp count, double* out) noexcept nogil\nctypedef double (*random_double_0)(void *state) noexcept nogil\nctypedef double (*random_double_1)(void *state, double a) noexcept nogil\nctypedef double (*random_double_2)(void *state, double a, double b) noexcept nogil\nctypedef double (*random_double_3)(void *state, double a, double b, double c) noexcept nogil\n\nctypedef void (*random_float_fill)(bitgen_t *state, np.npy_intp count, float* out) noexcept nogil\nctypedef float (*random_float_0)(bitgen_t *state) noexcept nogil\nctypedef float (*random_float_1)(bitgen_t *state, float a) noexcept nogil\n\nctypedef int64_t (*random_uint_0)(void *state) noexcept nogil\nctypedef int64_t (*random_uint_d)(void *state, double a) noexcept nogil\nctypedef int64_t (*random_uint_dd)(void *state, double a, double b) noexcept nogil\nctypedef int64_t (*random_uint_di)(void *state, double a, uint64_t b) noexcept nogil\nctypedef int64_t (*random_uint_i)(void *state, int64_t a) noexcept nogil\nctypedef int64_t (*random_uint_iii)(void *state, int64_t a, int64_t b, int64_t c) noexcept nogil\n\nctypedef uint32_t (*random_uint_0_32)(bitgen_t *state) noexcept nogil\nctypedef uint32_t (*random_uint_1_i_32)(bitgen_t *state, uint32_t a) noexcept nogil\n\nctypedef int32_t (*random_int_2_i_32)(bitgen_t *state, int32_t a, int32_t b) noexcept nogil\nctypedef int64_t (*random_int_2_i)(bitgen_t *state, int64_t a, int64_t b) noexcept nogil\n\ncdef double kahan_sum(double *darr, np.npy_intp n) noexcept\n\ncdef inline double uint64_to_double(uint64_t rnd) noexcept nogil:\n return (rnd >> 11) * (1.0 / 9007199254740992.0)\n\ncdef object double_fill(void *func, bitgen_t *state, object size, object lock, object out)\n\ncdef object float_fill(void *func, bitgen_t *state, object size, object lock, object out)\n\ncdef object float_fill_from_double(void *func, bitgen_t *state, object size, object lock, object out)\n\ncdef object wrap_int(object val, object bits)\n\ncdef np.ndarray int_to_array(object value, object name, object bits, object uint_size)\n\ncdef validate_output_shape(iter_shape, np.ndarray output)\n\ncdef object cont(void *func, void *state, object size, object lock, int narg,\n object a, object a_name, constraint_type a_constraint,\n object b, object b_name, constraint_type b_constraint,\n object c, object c_name, constraint_type c_constraint,\n object out)\n\ncdef object disc(void *func, void *state, object size, object lock,\n int narg_double, int narg_int64,\n object a, object a_name, constraint_type a_constraint,\n object b, object b_name, constraint_type b_constraint,\n object c, object c_name, constraint_type c_constraint)\n\ncdef object cont_f(void *func, bitgen_t *state, object size, object lock,\n object a, object a_name, constraint_type a_constraint,\n object out)\n\ncdef object cont_broadcast_3(void *func, void *state, object size, object lock,\n np.ndarray a_arr, object a_name, constraint_type a_constraint,\n np.ndarray b_arr, object b_name, constraint_type b_constraint,\n np.ndarray c_arr, object c_name, constraint_type c_constraint)\n\ncdef object discrete_broadcast_iii(void *func, void *state, object size, object lock,\n np.ndarray a_arr, object a_name, constraint_type a_constraint,\n np.ndarray b_arr, object b_name, constraint_type b_constraint,\n np.ndarray c_arr, object c_name, constraint_type c_constraint)\n | .venv\Lib\site-packages\numpy\random\_common.pxd | _common.pxd | Other | 5,089 | 0.95 | 0 | 0.012346 | node-utils | 83 | 2025-01-14T04:41:02.940140 | GPL-3.0 | false | ebd5e04b37a24268cb423a6126e12d1a |
from collections.abc import Callable\nfrom typing import Any, NamedTuple, TypeAlias\n\nimport numpy as np\n\n__all__: list[str] = ["interface"]\n\n_CDataVoidPointer: TypeAlias = Any\n\nclass interface(NamedTuple):\n state_address: int\n state: _CDataVoidPointer\n next_uint64: Callable[..., np.uint64]\n next_uint32: Callable[..., np.uint32]\n next_double: Callable[..., np.float64]\n bit_generator: _CDataVoidPointer\n | .venv\Lib\site-packages\numpy\random\_common.pyi | _common.pyi | Other | 437 | 0.85 | 0.0625 | 0 | awesome-app | 292 | 2024-03-12T17:19:28.353817 | BSD-3-Clause | false | c37c7fb84d3126a2b40251b23a8acada |
!<arch>\n/ -1 0 3998 `\n | .venv\Lib\site-packages\numpy\random\_generator.cp313-win_amd64.lib | _generator.cp313-win_amd64.lib | Other | 18,400 | 0.8 | 0 | 0.008696 | react-lib | 145 | 2024-07-15T02:27:35.138762 | Apache-2.0 | false | 91f761e8e5fc09a340daf40e1d8f1551 |
from collections.abc import Callable\nfrom typing import Any, Literal, TypeAlias, TypeVar, overload\n\nimport numpy as np\nfrom numpy import dtype, float32, float64, int64\nfrom numpy._typing import (\n ArrayLike,\n DTypeLike,\n NDArray,\n _ArrayLikeFloat_co,\n _ArrayLikeInt_co,\n _BoolCodes,\n _DoubleCodes,\n _DTypeLike,\n _DTypeLikeBool,\n _Float32Codes,\n _Float64Codes,\n _FloatLike_co,\n _Int8Codes,\n _Int16Codes,\n _Int32Codes,\n _Int64Codes,\n _IntPCodes,\n _ShapeLike,\n _SingleCodes,\n _SupportsDType,\n _UInt8Codes,\n _UInt16Codes,\n _UInt32Codes,\n _UInt64Codes,\n _UIntPCodes,\n)\nfrom numpy.random import BitGenerator, RandomState, SeedSequence\n\n_IntegerT = TypeVar("_IntegerT", bound=np.integer)\n\n_DTypeLikeFloat32: TypeAlias = (\n dtype[float32]\n | _SupportsDType[dtype[float32]]\n | type[float32]\n | _Float32Codes\n | _SingleCodes\n)\n\n_DTypeLikeFloat64: TypeAlias = (\n dtype[float64]\n | _SupportsDType[dtype[float64]]\n | type[float]\n | type[float64]\n | _Float64Codes\n | _DoubleCodes\n)\n\nclass Generator:\n def __init__(self, bit_generator: BitGenerator) -> None: ...\n def __repr__(self) -> str: ...\n def __str__(self) -> str: ...\n def __getstate__(self) -> None: ...\n def __setstate__(self, state: dict[str, Any] | None) -> None: ...\n def __reduce__(self) -> tuple[\n Callable[[BitGenerator], Generator],\n tuple[BitGenerator],\n None]: ...\n @property\n def bit_generator(self) -> BitGenerator: ...\n def spawn(self, n_children: int) -> list[Generator]: ...\n def bytes(self, length: int) -> bytes: ...\n @overload\n def standard_normal( # type: ignore[misc]\n self,\n size: None = ...,\n dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,\n out: None = ...,\n ) -> float: ...\n @overload\n def standard_normal( # type: ignore[misc]\n self,\n size: _ShapeLike = ...,\n ) -> NDArray[float64]: ...\n @overload\n def standard_normal( # type: ignore[misc]\n self,\n *,\n out: NDArray[float64] = ...,\n ) -> NDArray[float64]: ...\n @overload\n def standard_normal( # type: ignore[misc]\n self,\n size: _ShapeLike = ...,\n dtype: _DTypeLikeFloat32 = ...,\n out: NDArray[float32] | None = ...,\n ) -> NDArray[float32]: ...\n @overload\n def standard_normal( # type: ignore[misc]\n self,\n size: _ShapeLike = ...,\n dtype: _DTypeLikeFloat64 = ...,\n out: NDArray[float64] | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def permutation(self, x: int, axis: int = ...) -> NDArray[int64]: ...\n @overload\n def permutation(self, x: ArrayLike, axis: int = ...) -> NDArray[Any]: ...\n @overload\n def standard_exponential( # type: ignore[misc]\n self,\n size: None = ...,\n dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,\n method: Literal["zig", "inv"] = ...,\n out: None = ...,\n ) -> float: ...\n @overload\n def standard_exponential(\n self,\n size: _ShapeLike = ...,\n ) -> NDArray[float64]: ...\n @overload\n def standard_exponential(\n self,\n *,\n out: NDArray[float64] = ...,\n ) -> NDArray[float64]: ...\n @overload\n def standard_exponential(\n self,\n size: _ShapeLike = ...,\n *,\n method: Literal["zig", "inv"] = ...,\n out: NDArray[float64] | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def standard_exponential(\n self,\n size: _ShapeLike = ...,\n dtype: _DTypeLikeFloat32 = ...,\n method: Literal["zig", "inv"] = ...,\n out: NDArray[float32] | None = ...,\n ) -> NDArray[float32]: ...\n @overload\n def standard_exponential(\n self,\n size: _ShapeLike = ...,\n dtype: _DTypeLikeFloat64 = ...,\n method: Literal["zig", "inv"] = ...,\n out: NDArray[float64] | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def random( # type: ignore[misc]\n self,\n size: None = ...,\n dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,\n out: None = ...,\n ) -> float: ...\n @overload\n def random(\n self,\n *,\n out: NDArray[float64] = ...,\n ) -> NDArray[float64]: ...\n @overload\n def random(\n self,\n size: _ShapeLike = ...,\n *,\n out: NDArray[float64] | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def random(\n self,\n size: _ShapeLike = ...,\n dtype: _DTypeLikeFloat32 = ...,\n out: NDArray[float32] | None = ...,\n ) -> NDArray[float32]: ...\n @overload\n def random(\n self,\n size: _ShapeLike = ...,\n dtype: _DTypeLikeFloat64 = ...,\n out: NDArray[float64] | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def beta(\n self,\n a: _FloatLike_co,\n b: _FloatLike_co,\n size: None = ...,\n ) -> float: ... # type: ignore[misc]\n @overload\n def beta(\n self,\n a: _ArrayLikeFloat_co,\n b: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n @overload\n def exponential(self, scale: _FloatLike_co = ..., size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def exponential(self, scale: _ArrayLikeFloat_co = ..., size: _ShapeLike | None = ...) -> NDArray[float64]: ...\n\n #\n @overload\n def integers(\n self,\n low: int,\n high: int | None = None,\n size: None = None,\n dtype: _DTypeLike[np.int64] | _Int64Codes = ...,\n endpoint: bool = False,\n ) -> np.int64: ...\n @overload\n def integers(\n self,\n low: int,\n high: int | None = None,\n size: None = None,\n *,\n dtype: type[bool],\n endpoint: bool = False,\n ) -> bool: ...\n @overload\n def integers(\n self,\n low: int,\n high: int | None = None,\n size: None = None,\n *,\n dtype: type[int],\n endpoint: bool = False,\n ) -> int: ...\n @overload\n def integers(\n self,\n low: int,\n high: int | None = None,\n size: None = None,\n *,\n dtype: _DTypeLike[np.bool] | _BoolCodes,\n endpoint: bool = False,\n ) -> np.bool: ...\n @overload\n def integers(\n self,\n low: int,\n high: int | None = None,\n size: None = None,\n *,\n dtype: _DTypeLike[_IntegerT],\n endpoint: bool = False,\n ) -> _IntegerT: ...\n @overload\n def integers(\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = None,\n size: _ShapeLike | None = None,\n dtype: _DTypeLike[np.int64] | _Int64Codes = ...,\n endpoint: bool = False,\n ) -> NDArray[np.int64]: ...\n @overload\n def integers(\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = None,\n size: _ShapeLike | None = None,\n *,\n dtype: _DTypeLikeBool,\n endpoint: bool = False,\n ) -> NDArray[np.bool]: ...\n @overload\n def integers(\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = None,\n size: _ShapeLike | None = None,\n *,\n dtype: _DTypeLike[_IntegerT],\n endpoint: bool = False,\n ) -> NDArray[_IntegerT]: ...\n @overload\n def integers(\n self,\n low: int,\n high: int | None = None,\n size: None = None,\n *,\n dtype: _Int8Codes,\n endpoint: bool = False,\n ) -> np.int8: ...\n @overload\n def integers(\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = None,\n size: _ShapeLike | None = None,\n *,\n dtype: _Int8Codes,\n endpoint: bool = False,\n ) -> NDArray[np.int8]: ...\n @overload\n def integers(\n self,\n low: int,\n high: int | None = None,\n size: None = None,\n *,\n dtype: _UInt8Codes,\n endpoint: bool = False,\n ) -> np.uint8: ...\n @overload\n def integers(\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = None,\n size: _ShapeLike | None = None,\n *,\n dtype: _UInt8Codes,\n endpoint: bool = False,\n ) -> NDArray[np.uint8]: ...\n @overload\n def integers(\n self,\n low: int,\n high: int | None = None,\n size: None = None,\n *,\n dtype: _Int16Codes,\n endpoint: bool = False,\n ) -> np.int16: ...\n @overload\n def integers(\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = None,\n size: _ShapeLike | None = None,\n *,\n dtype: _Int16Codes,\n endpoint: bool = False,\n ) -> NDArray[np.int16]: ...\n @overload\n def integers(\n self,\n low: int,\n high: int | None = None,\n size: None = None,\n *,\n dtype: _UInt16Codes,\n endpoint: bool = False,\n ) -> np.uint16: ...\n @overload\n def integers(\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = None,\n size: _ShapeLike | None = None,\n *,\n dtype: _UInt16Codes,\n endpoint: bool = False,\n ) -> NDArray[np.uint16]: ...\n @overload\n def integers(\n self,\n low: int,\n high: int | None = None,\n size: None = None,\n *,\n dtype: _Int32Codes,\n endpoint: bool = False,\n ) -> np.int32: ...\n @overload\n def integers(\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = None,\n size: _ShapeLike | None = None,\n *,\n dtype: _Int32Codes,\n endpoint: bool = False,\n ) -> NDArray[np.int32]: ...\n @overload\n def integers(\n self,\n low: int,\n high: int | None = None,\n size: None = None,\n *,\n dtype: _UInt32Codes,\n endpoint: bool = False,\n ) -> np.uint32: ...\n @overload\n def integers(\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = None,\n size: _ShapeLike | None = None,\n *,\n dtype: _UInt32Codes,\n endpoint: bool = False,\n ) -> NDArray[np.uint32]: ...\n @overload\n def integers(\n self,\n low: int,\n high: int | None = None,\n size: None = None,\n *,\n dtype: _UInt64Codes,\n endpoint: bool = False,\n ) -> np.uint64: ...\n @overload\n def integers(\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = None,\n size: _ShapeLike | None = None,\n *,\n dtype: _UInt64Codes,\n endpoint: bool = False,\n ) -> NDArray[np.uint64]: ...\n @overload\n def integers(\n self,\n low: int,\n high: int | None = None,\n size: None = None,\n *,\n dtype: _IntPCodes,\n endpoint: bool = False,\n ) -> np.intp: ...\n @overload\n def integers(\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = None,\n size: _ShapeLike | None = None,\n *,\n dtype: _IntPCodes,\n endpoint: bool = False,\n ) -> NDArray[np.intp]: ...\n @overload\n def integers(\n self,\n low: int,\n high: int | None = None,\n size: None = None,\n *,\n dtype: _UIntPCodes,\n endpoint: bool = False,\n ) -> np.uintp: ...\n @overload\n def integers(\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = None,\n size: _ShapeLike | None = None,\n *,\n dtype: _UIntPCodes,\n endpoint: bool = False,\n ) -> NDArray[np.uintp]: ...\n @overload\n def integers(\n self,\n low: int,\n high: int | None = None,\n size: None = None,\n dtype: DTypeLike = ...,\n endpoint: bool = False,\n ) -> Any: ...\n @overload\n def integers(\n self,\n low: _ArrayLikeInt_co,\n high: _ArrayLikeInt_co | None = None,\n size: _ShapeLike | None = None,\n dtype: DTypeLike = ...,\n endpoint: bool = False,\n ) -> NDArray[Any]: ...\n\n # TODO: Use a TypeVar _T here to get away from Any output?\n # Should be int->NDArray[int64], ArrayLike[_T] -> _T | NDArray[Any]\n @overload\n def choice(\n self,\n a: int,\n size: None = ...,\n replace: bool = ...,\n p: _ArrayLikeFloat_co | None = ...,\n axis: int = ...,\n shuffle: bool = ...,\n ) -> int: ...\n @overload\n def choice(\n self,\n a: int,\n size: _ShapeLike = ...,\n replace: bool = ...,\n p: _ArrayLikeFloat_co | None = ...,\n axis: int = ...,\n shuffle: bool = ...,\n ) -> NDArray[int64]: ...\n @overload\n def choice(\n self,\n a: ArrayLike,\n size: None = ...,\n replace: bool = ...,\n p: _ArrayLikeFloat_co | None = ...,\n axis: int = ...,\n shuffle: bool = ...,\n ) -> Any: ...\n @overload\n def choice(\n self,\n a: ArrayLike,\n size: _ShapeLike = ...,\n replace: bool = ...,\n p: _ArrayLikeFloat_co | None = ...,\n axis: int = ...,\n shuffle: bool = ...,\n ) -> NDArray[Any]: ...\n @overload\n def uniform(\n self,\n low: _FloatLike_co = ...,\n high: _FloatLike_co = ...,\n size: None = ...,\n ) -> float: ... # type: ignore[misc]\n @overload\n def uniform(\n self,\n low: _ArrayLikeFloat_co = ...,\n high: _ArrayLikeFloat_co = ...,\n size: _ShapeLike | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def normal(\n self,\n loc: _FloatLike_co = ...,\n scale: _FloatLike_co = ...,\n size: None = ...,\n ) -> float: ... # type: ignore[misc]\n @overload\n def normal(\n self,\n loc: _ArrayLikeFloat_co = ...,\n scale: _ArrayLikeFloat_co = ...,\n size: _ShapeLike | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def standard_gamma( # type: ignore[misc]\n self,\n shape: _FloatLike_co,\n size: None = ...,\n dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,\n out: None = ...,\n ) -> float: ...\n @overload\n def standard_gamma(\n self,\n shape: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def standard_gamma(\n self,\n shape: _ArrayLikeFloat_co,\n *,\n out: NDArray[float64] = ...,\n ) -> NDArray[float64]: ...\n @overload\n def standard_gamma(\n self,\n shape: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...,\n dtype: _DTypeLikeFloat32 = ...,\n out: NDArray[float32] | None = ...,\n ) -> NDArray[float32]: ...\n @overload\n def standard_gamma(\n self,\n shape: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...,\n dtype: _DTypeLikeFloat64 = ...,\n out: NDArray[float64] | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def gamma(\n self, shape: _FloatLike_co, scale: _FloatLike_co = ..., size: None = ...\n ) -> float: ... # type: ignore[misc]\n @overload\n def gamma(\n self,\n shape: _ArrayLikeFloat_co,\n scale: _ArrayLikeFloat_co = ...,\n size: _ShapeLike | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def f(\n self, dfnum: _FloatLike_co, dfden: _FloatLike_co, size: None = ...\n ) -> float: ... # type: ignore[misc]\n @overload\n def f(\n self,\n dfnum: _ArrayLikeFloat_co,\n dfden: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n @overload\n def noncentral_f(\n self,\n dfnum: _FloatLike_co,\n dfden: _FloatLike_co,\n nonc: _FloatLike_co, size: None = ...\n ) -> float: ... # type: ignore[misc]\n @overload\n def noncentral_f(\n self,\n dfnum: _ArrayLikeFloat_co,\n dfden: _ArrayLikeFloat_co,\n nonc: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def chisquare(self, df: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def chisquare(\n self, df: _ArrayLikeFloat_co, size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n @overload\n def noncentral_chisquare(\n self, df: _FloatLike_co, nonc: _FloatLike_co, size: None = ...\n ) -> float: ... # type: ignore[misc]\n @overload\n def noncentral_chisquare(\n self,\n df: _ArrayLikeFloat_co,\n nonc: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n @overload\n def standard_t(self, df: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def standard_t(\n self, df: _ArrayLikeFloat_co, size: None = ...\n ) -> NDArray[float64]: ...\n @overload\n def standard_t(\n self, df: _ArrayLikeFloat_co, size: _ShapeLike = ...\n ) -> NDArray[float64]: ...\n @overload\n def vonmises(\n self, mu: _FloatLike_co, kappa: _FloatLike_co, size: None = ...\n ) -> float: ... # type: ignore[misc]\n @overload\n def vonmises(\n self,\n mu: _ArrayLikeFloat_co,\n kappa: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n @overload\n def pareto(self, a: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def pareto(\n self, a: _ArrayLikeFloat_co, size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n @overload\n def weibull(self, a: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def weibull(\n self, a: _ArrayLikeFloat_co, size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n @overload\n def power(self, a: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def power(\n self, a: _ArrayLikeFloat_co, size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n @overload\n def standard_cauchy(self, size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def standard_cauchy(self, size: _ShapeLike = ...) -> NDArray[float64]: ...\n @overload\n def laplace(\n self,\n loc: _FloatLike_co = ...,\n scale: _FloatLike_co = ...,\n size: None = ...,\n ) -> float: ... # type: ignore[misc]\n @overload\n def laplace(\n self,\n loc: _ArrayLikeFloat_co = ...,\n scale: _ArrayLikeFloat_co = ...,\n size: _ShapeLike | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def gumbel(\n self,\n loc: _FloatLike_co = ...,\n scale: _FloatLike_co = ...,\n size: None = ...,\n ) -> float: ... # type: ignore[misc]\n @overload\n def gumbel(\n self,\n loc: _ArrayLikeFloat_co = ...,\n scale: _ArrayLikeFloat_co = ...,\n size: _ShapeLike | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def logistic(\n self,\n loc: _FloatLike_co = ...,\n scale: _FloatLike_co = ...,\n size: None = ...,\n ) -> float: ... # type: ignore[misc]\n @overload\n def logistic(\n self,\n loc: _ArrayLikeFloat_co = ...,\n scale: _ArrayLikeFloat_co = ...,\n size: _ShapeLike | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def lognormal(\n self,\n mean: _FloatLike_co = ...,\n sigma: _FloatLike_co = ...,\n size: None = ...,\n ) -> float: ... # type: ignore[misc]\n @overload\n def lognormal(\n self,\n mean: _ArrayLikeFloat_co = ...,\n sigma: _ArrayLikeFloat_co = ...,\n size: _ShapeLike | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def rayleigh(self, scale: _FloatLike_co = ..., size: None = ...) -> float: ... # type: ignore[misc]\n @overload\n def rayleigh(\n self, scale: _ArrayLikeFloat_co = ..., size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n @overload\n def wald(\n self, mean: _FloatLike_co, scale: _FloatLike_co, size: None = ...\n ) -> float: ... # type: ignore[misc]\n @overload\n def wald(\n self,\n mean: _ArrayLikeFloat_co,\n scale: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n @overload\n def triangular(\n self,\n left: _FloatLike_co,\n mode: _FloatLike_co,\n right: _FloatLike_co,\n size: None = ...,\n ) -> float: ... # type: ignore[misc]\n @overload\n def triangular(\n self,\n left: _ArrayLikeFloat_co,\n mode: _ArrayLikeFloat_co,\n right: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...,\n ) -> NDArray[float64]: ...\n @overload\n def binomial(self, n: int, p: _FloatLike_co, size: None = ...) -> int: ... # type: ignore[misc]\n @overload\n def binomial(\n self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: _ShapeLike | None = ...\n ) -> NDArray[int64]: ...\n @overload\n def negative_binomial(\n self, n: _FloatLike_co, p: _FloatLike_co, size: None = ...\n ) -> int: ... # type: ignore[misc]\n @overload\n def negative_binomial(\n self,\n n: _ArrayLikeFloat_co,\n p: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...\n ) -> NDArray[int64]: ...\n @overload\n def poisson(self, lam: _FloatLike_co = ..., size: None = ...) -> int: ... # type: ignore[misc]\n @overload\n def poisson(\n self, lam: _ArrayLikeFloat_co = ..., size: _ShapeLike | None = ...\n ) -> NDArray[int64]: ...\n @overload\n def zipf(self, a: _FloatLike_co, size: None = ...) -> int: ... # type: ignore[misc]\n @overload\n def zipf(\n self, a: _ArrayLikeFloat_co, size: _ShapeLike | None = ...\n ) -> NDArray[int64]: ...\n @overload\n def geometric(self, p: _FloatLike_co, size: None = ...) -> int: ... # type: ignore[misc]\n @overload\n def geometric(\n self, p: _ArrayLikeFloat_co, size: _ShapeLike | None = ...\n ) -> NDArray[int64]: ...\n @overload\n def hypergeometric(\n self, ngood: int, nbad: int, nsample: int, size: None = ...\n ) -> int: ... # type: ignore[misc]\n @overload\n def hypergeometric(\n self,\n ngood: _ArrayLikeInt_co,\n nbad: _ArrayLikeInt_co,\n nsample: _ArrayLikeInt_co,\n size: _ShapeLike | None = ...,\n ) -> NDArray[int64]: ...\n @overload\n def logseries(self, p: _FloatLike_co, size: None = ...) -> int: ... # type: ignore[misc]\n @overload\n def logseries(\n self, p: _ArrayLikeFloat_co, size: _ShapeLike | None = ...\n ) -> NDArray[int64]: ...\n def multivariate_normal(\n self,\n mean: _ArrayLikeFloat_co,\n cov: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...,\n check_valid: Literal["warn", "raise", "ignore"] = ...,\n tol: float = ...,\n *,\n method: Literal["svd", "eigh", "cholesky"] = ...,\n ) -> NDArray[float64]: ...\n def multinomial(\n self, n: _ArrayLikeInt_co,\n pvals: _ArrayLikeFloat_co,\n size: _ShapeLike | None = ...\n ) -> NDArray[int64]: ...\n def multivariate_hypergeometric(\n self,\n colors: _ArrayLikeInt_co,\n nsample: int,\n size: _ShapeLike | None = ...,\n method: Literal["marginals", "count"] = ...,\n ) -> NDArray[int64]: ...\n def dirichlet(\n self, alpha: _ArrayLikeFloat_co, size: _ShapeLike | None = ...\n ) -> NDArray[float64]: ...\n def permuted(\n self, x: ArrayLike, *, axis: int | None = ..., out: NDArray[Any] | None = ...\n ) -> NDArray[Any]: ...\n def shuffle(self, x: ArrayLike, axis: int = ...) -> None: ...\n\ndef default_rng(\n seed: _ArrayLikeInt_co | SeedSequence | BitGenerator | Generator | RandomState | None = ...\n) -> Generator: ...\n | .venv\Lib\site-packages\numpy\random\_generator.pyi | _generator.pyi | Other | 24,865 | 0.95 | 0.153037 | 0.040094 | awesome-app | 144 | 2023-07-12T02:50:02.374348 | Apache-2.0 | false | f2a1e6597396188b3e1136e7ddaad470 |
!<arch>\n/ -1 0 174 `\n | .venv\Lib\site-packages\numpy\random\_mt19937.cp313-win_amd64.lib | _mt19937.cp313-win_amd64.lib | Other | 2,032 | 0.8 | 0 | 0 | node-utils | 845 | 2024-09-22T18:25:56.351013 | Apache-2.0 | false | e03037797441852ee6f4da3874658726 |
MZ | .venv\Lib\site-packages\numpy\random\_mt19937.cp313-win_amd64.pyd | _mt19937.cp313-win_amd64.pyd | Other | 84,992 | 0.75 | 0.040816 | 0.011475 | vue-tools | 806 | 2024-12-03T00:28:27.110531 | GPL-3.0 | false | 642b2c6a2255bafb875f23bce92d6620 |
from typing import TypedDict, type_check_only\n\nfrom numpy import uint32\nfrom numpy._typing import _ArrayLikeInt_co\nfrom numpy.random.bit_generator import BitGenerator, SeedSequence\nfrom numpy.typing import NDArray\n\n@type_check_only\nclass _MT19937Internal(TypedDict):\n key: NDArray[uint32]\n pos: int\n\n@type_check_only\nclass _MT19937State(TypedDict):\n bit_generator: str\n state: _MT19937Internal\n\nclass MT19937(BitGenerator):\n def __init__(self, seed: _ArrayLikeInt_co | SeedSequence | None = ...) -> None: ...\n def _legacy_seeding(self, seed: _ArrayLikeInt_co) -> None: ...\n def jumped(self, jumps: int = ...) -> MT19937: ...\n @property\n def state(self) -> _MT19937State: ...\n @state.setter\n def state(self, value: _MT19937State) -> None: ...\n | .venv\Lib\site-packages\numpy\random\_mt19937.pyi | _mt19937.pyi | Other | 800 | 0.85 | 0.32 | 0 | react-lib | 701 | 2023-09-17T14:08:26.254375 | Apache-2.0 | false | ba66491d9dabea069fff09c3174351da |
!<arch>\n/ -1 0 166 `\n | .venv\Lib\site-packages\numpy\random\_pcg64.cp313-win_amd64.lib | _pcg64.cp313-win_amd64.lib | Other | 1,996 | 0.8 | 0 | 0 | vue-tools | 393 | 2023-08-18T12:55:17.252601 | MIT | false | 2f33dc87a5f28767d09bae43f06a27fb |
MZ | .venv\Lib\site-packages\numpy\random\_pcg64.cp313-win_amd64.pyd | _pcg64.cp313-win_amd64.pyd | Other | 95,232 | 0.75 | 0.044888 | 0.022758 | vue-tools | 691 | 2023-10-07T16:26:56.708348 | BSD-3-Clause | false | 19e8f41ad7d3b61460c0b3d61a59d222 |
from typing import TypedDict, type_check_only\n\nfrom numpy._typing import _ArrayLikeInt_co\nfrom numpy.random.bit_generator import BitGenerator, SeedSequence\n\n@type_check_only\nclass _PCG64Internal(TypedDict):\n state: int\n inc: int\n\n@type_check_only\nclass _PCG64State(TypedDict):\n bit_generator: str\n state: _PCG64Internal\n has_uint32: int\n uinteger: int\n\nclass PCG64(BitGenerator):\n def __init__(self, seed: _ArrayLikeInt_co | SeedSequence | None = ...) -> None: ...\n def jumped(self, jumps: int = ...) -> PCG64: ...\n @property\n def state(\n self,\n ) -> _PCG64State: ...\n @state.setter\n def state(\n self,\n value: _PCG64State,\n ) -> None: ...\n def advance(self, delta: int) -> PCG64: ...\n\nclass PCG64DXSM(BitGenerator):\n def __init__(self, seed: _ArrayLikeInt_co | SeedSequence | None = ...) -> None: ...\n def jumped(self, jumps: int = ...) -> PCG64DXSM: ...\n @property\n def state(\n self,\n ) -> _PCG64State: ...\n @state.setter\n def state(\n self,\n value: _PCG64State,\n ) -> None: ...\n def advance(self, delta: int) -> PCG64DXSM: ...\n | .venv\Lib\site-packages\numpy\random\_pcg64.pyi | _pcg64.pyi | Other | 1,186 | 0.85 | 0.318182 | 0 | python-kit | 236 | 2024-03-01T06:07:18.593678 | MIT | false | 53a5bee635fc55cac228edcf96eaa58b |
!<arch>\n/ -1 0 170 `\n | .venv\Lib\site-packages\numpy\random\_philox.cp313-win_amd64.lib | _philox.cp313-win_amd64.lib | Other | 2,012 | 0.8 | 0 | 0 | node-utils | 533 | 2024-02-17T07:16:27.644563 | GPL-3.0 | false | 8897f01476d02a410f4dd39ea4b5cc94 |
MZ | .venv\Lib\site-packages\numpy\random\_philox.cp313-win_amd64.pyd | _philox.cp313-win_amd64.pyd | Other | 78,848 | 0.75 | 0.048137 | 0.011475 | python-kit | 607 | 2025-03-21T02:52:44.386825 | BSD-3-Clause | false | 35b4d6d606ce6d06bcc8d946cdd367a1 |
from typing import TypedDict, type_check_only\n\nfrom numpy import uint64\nfrom numpy._typing import _ArrayLikeInt_co\nfrom numpy.random.bit_generator import BitGenerator, SeedSequence\nfrom numpy.typing import NDArray\n\n@type_check_only\nclass _PhiloxInternal(TypedDict):\n counter: NDArray[uint64]\n key: NDArray[uint64]\n\n@type_check_only\nclass _PhiloxState(TypedDict):\n bit_generator: str\n state: _PhiloxInternal\n buffer: NDArray[uint64]\n buffer_pos: int\n has_uint32: int\n uinteger: int\n\nclass Philox(BitGenerator):\n def __init__(\n self,\n seed: _ArrayLikeInt_co | SeedSequence | None = ...,\n counter: _ArrayLikeInt_co | None = ...,\n key: _ArrayLikeInt_co | None = ...,\n ) -> None: ...\n @property\n def state(\n self,\n ) -> _PhiloxState: ...\n @state.setter\n def state(\n self,\n value: _PhiloxState,\n ) -> None: ...\n def jumped(self, jumps: int = ...) -> Philox: ...\n def advance(self, delta: int) -> Philox: ...\n | .venv\Lib\site-packages\numpy\random\_philox.pyi | _philox.pyi | Other | 1,044 | 0.85 | 0.205128 | 0 | vue-tools | 532 | 2024-01-28T17:05:54.511766 | MIT | false | 87cd8aa221f80fdf6a196e43470c467b |
from ._generator import Generator\nfrom ._mt19937 import MT19937\nfrom ._pcg64 import PCG64, PCG64DXSM\nfrom ._philox import Philox\nfrom ._sfc64 import SFC64\nfrom .bit_generator import BitGenerator\nfrom .mtrand import RandomState\n\nBitGenerators = {'MT19937': MT19937,\n 'PCG64': PCG64,\n 'PCG64DXSM': PCG64DXSM,\n 'Philox': Philox,\n 'SFC64': SFC64,\n }\n\n\ndef __bit_generator_ctor(bit_generator: str | type[BitGenerator] = 'MT19937'):\n """\n Pickling helper function that returns a bit generator object\n\n Parameters\n ----------\n bit_generator : type[BitGenerator] or str\n BitGenerator class or string containing the name of the BitGenerator\n\n Returns\n -------\n BitGenerator\n BitGenerator instance\n """\n if isinstance(bit_generator, type):\n bit_gen_class = bit_generator\n elif bit_generator in BitGenerators:\n bit_gen_class = BitGenerators[bit_generator]\n else:\n raise ValueError(\n str(bit_generator) + ' is not a known BitGenerator module.'\n )\n\n return bit_gen_class()\n\n\ndef __generator_ctor(bit_generator_name="MT19937",\n bit_generator_ctor=__bit_generator_ctor):\n """\n Pickling helper function that returns a Generator object\n\n Parameters\n ----------\n bit_generator_name : str or BitGenerator\n String containing the core BitGenerator's name or a\n BitGenerator instance\n bit_generator_ctor : callable, optional\n Callable function that takes bit_generator_name as its only argument\n and returns an instantized bit generator.\n\n Returns\n -------\n rg : Generator\n Generator using the named core BitGenerator\n """\n if isinstance(bit_generator_name, BitGenerator):\n return Generator(bit_generator_name)\n # Legacy path that uses a bit generator name and ctor\n return Generator(bit_generator_ctor(bit_generator_name))\n\n\ndef __randomstate_ctor(bit_generator_name="MT19937",\n bit_generator_ctor=__bit_generator_ctor):\n """\n Pickling helper function that returns a legacy RandomState-like object\n\n Parameters\n ----------\n bit_generator_name : str\n String containing the core BitGenerator's name\n bit_generator_ctor : callable, optional\n Callable function that takes bit_generator_name as its only argument\n and returns an instantized bit generator.\n\n Returns\n -------\n rs : RandomState\n Legacy RandomState using the named core BitGenerator\n """\n if isinstance(bit_generator_name, BitGenerator):\n return RandomState(bit_generator_name)\n return RandomState(bit_generator_ctor(bit_generator_name))\n | .venv\Lib\site-packages\numpy\random\_pickle.py | _pickle.py | Python | 2,830 | 0.95 | 0.136364 | 0.013514 | react-lib | 795 | 2024-06-08T01:48:39.362268 | GPL-3.0 | false | 1751f6063a0834f57eda619999cd39be |
from collections.abc import Callable\nfrom typing import Final, Literal, TypedDict, TypeVar, overload, type_check_only\n\nfrom numpy.random._generator import Generator\nfrom numpy.random._mt19937 import MT19937\nfrom numpy.random._pcg64 import PCG64, PCG64DXSM\nfrom numpy.random._philox import Philox\nfrom numpy.random._sfc64 import SFC64\nfrom numpy.random.bit_generator import BitGenerator\nfrom numpy.random.mtrand import RandomState\n\n_T = TypeVar("_T", bound=BitGenerator)\n\n@type_check_only\nclass _BitGenerators(TypedDict):\n MT19937: type[MT19937]\n PCG64: type[PCG64]\n PCG64DXSM: type[PCG64DXSM]\n Philox: type[Philox]\n SFC64: type[SFC64]\n\nBitGenerators: Final[_BitGenerators] = ...\n\n@overload\ndef __bit_generator_ctor(bit_generator: Literal["MT19937"] = "MT19937") -> MT19937: ...\n@overload\ndef __bit_generator_ctor(bit_generator: Literal["PCG64"]) -> PCG64: ...\n@overload\ndef __bit_generator_ctor(bit_generator: Literal["PCG64DXSM"]) -> PCG64DXSM: ...\n@overload\ndef __bit_generator_ctor(bit_generator: Literal["Philox"]) -> Philox: ...\n@overload\ndef __bit_generator_ctor(bit_generator: Literal["SFC64"]) -> SFC64: ...\n@overload\ndef __bit_generator_ctor(bit_generator: type[_T]) -> _T: ...\ndef __generator_ctor(\n bit_generator_name: str | type[BitGenerator] | BitGenerator = "MT19937",\n bit_generator_ctor: Callable[[str | type[BitGenerator]], BitGenerator] = ...,\n) -> Generator: ...\ndef __randomstate_ctor(\n bit_generator_name: str | type[BitGenerator] | BitGenerator = "MT19937",\n bit_generator_ctor: Callable[[str | type[BitGenerator]], BitGenerator] = ...,\n) -> RandomState: ...\n | .venv\Lib\site-packages\numpy\random\_pickle.pyi | _pickle.pyi | Other | 1,651 | 0.85 | 0.209302 | 0 | awesome-app | 396 | 2023-09-29T06:03:36.325534 | GPL-3.0 | false | bda72cf22c0a868bdb9b348234de764b |
!<arch>\n/ -1 0 166 `\n | .venv\Lib\site-packages\numpy\random\_sfc64.cp313-win_amd64.lib | _sfc64.cp313-win_amd64.lib | Other | 1,996 | 0.8 | 0 | 0 | awesome-app | 497 | 2025-01-22T10:11:34.506441 | GPL-3.0 | false | 383ccca993215675ea053c4388d1d5ec |
MZ | .venv\Lib\site-packages\numpy\random\_sfc64.cp313-win_amd64.pyd | _sfc64.cp313-win_amd64.pyd | Other | 59,392 | 0.95 | 0.043578 | 0.009479 | awesome-app | 352 | 2023-12-01T10:56:56.593228 | Apache-2.0 | false | c14f296395b9de810c369d9e161f2d8f |
from typing import TypedDict, type_check_only\n\nfrom numpy import uint64\nfrom numpy._typing import NDArray, _ArrayLikeInt_co\nfrom numpy.random.bit_generator import BitGenerator, SeedSequence\n\n@type_check_only\nclass _SFC64Internal(TypedDict):\n state: NDArray[uint64]\n\n@type_check_only\nclass _SFC64State(TypedDict):\n bit_generator: str\n state: _SFC64Internal\n has_uint32: int\n uinteger: int\n\nclass SFC64(BitGenerator):\n def __init__(self, seed: _ArrayLikeInt_co | SeedSequence | None = ...) -> None: ...\n @property\n def state(\n self,\n ) -> _SFC64State: ...\n @state.setter\n def state(\n self,\n value: _SFC64State,\n ) -> None: ...\n | .venv\Lib\site-packages\numpy\random\_sfc64.pyi | _sfc64.pyi | Other | 710 | 0.85 | 0.214286 | 0 | react-lib | 539 | 2023-12-26T10:04:39.514681 | BSD-3-Clause | false | a4c053ca31f15ca8e8562b92f6945ee0 |
cimport numpy as np\nfrom libc.stdint cimport uint32_t, uint64_t\n\ncdef extern from "numpy/random/bitgen.h":\n struct bitgen:\n void *state\n uint64_t (*next_uint64)(void *st) nogil\n uint32_t (*next_uint32)(void *st) nogil\n double (*next_double)(void *st) nogil\n uint64_t (*next_raw)(void *st) nogil\n\n ctypedef bitgen bitgen_t\n\nfrom numpy.random.bit_generator cimport BitGenerator, SeedSequence\n | .venv\Lib\site-packages\numpy\random\__init__.pxd | __init__.pxd | Other | 445 | 0.85 | 0 | 0 | vue-tools | 161 | 2024-03-19T12:54:33.812231 | GPL-3.0 | false | b15e3c4ace18a97f03cad53e1f8d85a3 |
"""\n========================\nRandom Number Generation\n========================\n\nUse ``default_rng()`` to create a `Generator` and call its methods.\n\n=============== =========================================================\nGenerator\n--------------- ---------------------------------------------------------\nGenerator Class implementing all of the random number distributions\ndefault_rng Default constructor for ``Generator``\n=============== =========================================================\n\n============================================= ===\nBitGenerator Streams that work with Generator\n--------------------------------------------- ---\nMT19937\nPCG64\nPCG64DXSM\nPhilox\nSFC64\n============================================= ===\n\n============================================= ===\nGetting entropy to initialize a BitGenerator\n--------------------------------------------- ---\nSeedSequence\n============================================= ===\n\n\nLegacy\n------\n\nFor backwards compatibility with previous versions of numpy before 1.17, the\nvarious aliases to the global `RandomState` methods are left alone and do not\nuse the new `Generator` API.\n\n==================== =========================================================\nUtility functions\n-------------------- ---------------------------------------------------------\nrandom Uniformly distributed floats over ``[0, 1)``\nbytes Uniformly distributed random bytes.\npermutation Randomly permute a sequence / generate a random sequence.\nshuffle Randomly permute a sequence in place.\nchoice Random sample from 1-D array.\n==================== =========================================================\n\n==================== =========================================================\nCompatibility\nfunctions - removed\nin the new API\n-------------------- ---------------------------------------------------------\nrand Uniformly distributed values.\nrandn Normally distributed values.\nranf Uniformly distributed floating point numbers.\nrandom_integers Uniformly distributed integers in a given range.\n (deprecated, use ``integers(..., closed=True)`` instead)\nrandom_sample Alias for `random_sample`\nrandint Uniformly distributed integers in a given range\nseed Seed the legacy random number generator.\n==================== =========================================================\n\n==================== =========================================================\nUnivariate\ndistributions\n-------------------- ---------------------------------------------------------\nbeta Beta distribution over ``[0, 1]``.\nbinomial Binomial distribution.\nchisquare :math:`\\chi^2` distribution.\nexponential Exponential distribution.\nf F (Fisher-Snedecor) distribution.\ngamma Gamma distribution.\ngeometric Geometric distribution.\ngumbel Gumbel distribution.\nhypergeometric Hypergeometric distribution.\nlaplace Laplace distribution.\nlogistic Logistic distribution.\nlognormal Log-normal distribution.\nlogseries Logarithmic series distribution.\nnegative_binomial Negative binomial distribution.\nnoncentral_chisquare Non-central chi-square distribution.\nnoncentral_f Non-central F distribution.\nnormal Normal / Gaussian distribution.\npareto Pareto distribution.\npoisson Poisson distribution.\npower Power distribution.\nrayleigh Rayleigh distribution.\ntriangular Triangular distribution.\nuniform Uniform distribution.\nvonmises Von Mises circular distribution.\nwald Wald (inverse Gaussian) distribution.\nweibull Weibull distribution.\nzipf Zipf's distribution over ranked data.\n==================== =========================================================\n\n==================== ==========================================================\nMultivariate\ndistributions\n-------------------- ----------------------------------------------------------\ndirichlet Multivariate generalization of Beta distribution.\nmultinomial Multivariate generalization of the binomial distribution.\nmultivariate_normal Multivariate generalization of the normal distribution.\n==================== ==========================================================\n\n==================== =========================================================\nStandard\ndistributions\n-------------------- ---------------------------------------------------------\nstandard_cauchy Standard Cauchy-Lorentz distribution.\nstandard_exponential Standard exponential distribution.\nstandard_gamma Standard Gamma distribution.\nstandard_normal Standard normal distribution.\nstandard_t Standard Student's t-distribution.\n==================== =========================================================\n\n==================== =========================================================\nInternal functions\n-------------------- ---------------------------------------------------------\nget_state Get tuple representing internal state of generator.\nset_state Set state of generator.\n==================== =========================================================\n\n\n"""\n__all__ = [\n 'beta',\n 'binomial',\n 'bytes',\n 'chisquare',\n 'choice',\n 'dirichlet',\n 'exponential',\n 'f',\n 'gamma',\n 'geometric',\n 'get_state',\n 'gumbel',\n 'hypergeometric',\n 'laplace',\n 'logistic',\n 'lognormal',\n 'logseries',\n 'multinomial',\n 'multivariate_normal',\n 'negative_binomial',\n 'noncentral_chisquare',\n 'noncentral_f',\n 'normal',\n 'pareto',\n 'permutation',\n 'poisson',\n 'power',\n 'rand',\n 'randint',\n 'randn',\n 'random',\n 'random_integers',\n 'random_sample',\n 'ranf',\n 'rayleigh',\n 'sample',\n 'seed',\n 'set_state',\n 'shuffle',\n 'standard_cauchy',\n 'standard_exponential',\n 'standard_gamma',\n 'standard_normal',\n 'standard_t',\n 'triangular',\n 'uniform',\n 'vonmises',\n 'wald',\n 'weibull',\n 'zipf',\n]\n\n# add these for module-freeze analysis (like PyInstaller)\nfrom . import _bounded_integers, _common, _pickle\nfrom ._generator import Generator, default_rng\nfrom ._mt19937 import MT19937\nfrom ._pcg64 import PCG64, PCG64DXSM\nfrom ._philox import Philox\nfrom ._sfc64 import SFC64\nfrom .bit_generator import BitGenerator, SeedSequence\nfrom .mtrand import *\n\n__all__ += ['Generator', 'RandomState', 'SeedSequence', 'MT19937',\n 'Philox', 'PCG64', 'PCG64DXSM', 'SFC64', 'default_rng',\n 'BitGenerator']\n\n\ndef __RandomState_ctor():\n """Return a RandomState instance.\n\n This function exists solely to assist (un)pickling.\n\n Note that the state of the RandomState returned here is irrelevant, as this\n function's entire purpose is to return a newly allocated RandomState whose\n state pickle can set. Consequently the RandomState returned by this function\n is a freshly allocated copy with a seed=0.\n\n See https://github.com/numpy/numpy/issues/4763 for a detailed discussion\n\n """\n return RandomState(seed=0)\n\n\nfrom numpy._pytesttester import PytestTester\n\ntest = PytestTester(__name__)\ndel PytestTester\n | .venv\Lib\site-packages\numpy\random\__init__.py | __init__.py | Python | 7,693 | 0.95 | 0.037559 | 0.005348 | vue-tools | 121 | 2024-08-30T03:22:09.631342 | BSD-3-Clause | false | 798f9f8d2f877a2be63e9c915a124a65 |
from ._generator import Generator, default_rng\nfrom ._mt19937 import MT19937\nfrom ._pcg64 import PCG64, PCG64DXSM\nfrom ._philox import Philox\nfrom ._sfc64 import SFC64\nfrom .bit_generator import BitGenerator, SeedSequence\nfrom .mtrand import (\n RandomState,\n beta,\n binomial,\n bytes,\n chisquare,\n choice,\n dirichlet,\n exponential,\n f,\n gamma,\n geometric,\n get_bit_generator, # noqa: F401\n get_state,\n gumbel,\n hypergeometric,\n laplace,\n logistic,\n lognormal,\n logseries,\n multinomial,\n multivariate_normal,\n negative_binomial,\n noncentral_chisquare,\n noncentral_f,\n normal,\n pareto,\n permutation,\n poisson,\n power,\n rand,\n randint,\n randn,\n random,\n random_integers,\n random_sample,\n ranf,\n rayleigh,\n sample,\n seed,\n set_bit_generator, # noqa: F401\n set_state,\n shuffle,\n standard_cauchy,\n standard_exponential,\n standard_gamma,\n standard_normal,\n standard_t,\n triangular,\n uniform,\n vonmises,\n wald,\n weibull,\n zipf,\n)\n\n__all__ = [\n "beta",\n "binomial",\n "bytes",\n "chisquare",\n "choice",\n "dirichlet",\n "exponential",\n "f",\n "gamma",\n "geometric",\n "get_state",\n "gumbel",\n "hypergeometric",\n "laplace",\n "logistic",\n "lognormal",\n "logseries",\n "multinomial",\n "multivariate_normal",\n "negative_binomial",\n "noncentral_chisquare",\n "noncentral_f",\n "normal",\n "pareto",\n "permutation",\n "poisson",\n "power",\n "rand",\n "randint",\n "randn",\n "random",\n "random_integers",\n "random_sample",\n "ranf",\n "rayleigh",\n "sample",\n "seed",\n "set_state",\n "shuffle",\n "standard_cauchy",\n "standard_exponential",\n "standard_gamma",\n "standard_normal",\n "standard_t",\n "triangular",\n "uniform",\n "vonmises",\n "wald",\n "weibull",\n "zipf",\n "Generator",\n "RandomState",\n "SeedSequence",\n "MT19937",\n "Philox",\n "PCG64",\n "PCG64DXSM",\n "SFC64",\n "default_rng",\n "BitGenerator",\n]\n | .venv\Lib\site-packages\numpy\random\__init__.pyi | __init__.pyi | Other | 2,233 | 0.95 | 0 | 0 | vue-tools | 567 | 2023-08-05T22:39:56.487009 | BSD-3-Clause | false | 5e4c07cdd352a51101a78651222985a7 |
import os\nimport sys\nfrom os.path import join\n\nimport pytest\n\nimport numpy as np\nfrom numpy.random import (\n MT19937,\n PCG64,\n PCG64DXSM,\n SFC64,\n Generator,\n Philox,\n RandomState,\n SeedSequence,\n default_rng,\n)\nfrom numpy.random._common import interface\nfrom numpy.testing import (\n assert_allclose,\n assert_array_equal,\n assert_equal,\n assert_raises,\n)\n\ntry:\n import cffi # noqa: F401\n\n MISSING_CFFI = False\nexcept ImportError:\n MISSING_CFFI = True\n\ntry:\n import ctypes # noqa: F401\n\n MISSING_CTYPES = False\nexcept ImportError:\n MISSING_CTYPES = False\n\nif sys.flags.optimize > 1:\n # no docstrings present to inspect when PYTHONOPTIMIZE/Py_OptimizeFlag > 1\n # cffi cannot succeed\n MISSING_CFFI = True\n\n\npwd = os.path.dirname(os.path.abspath(__file__))\n\n\ndef assert_state_equal(actual, target):\n for key in actual:\n if isinstance(actual[key], dict):\n assert_state_equal(actual[key], target[key])\n elif isinstance(actual[key], np.ndarray):\n assert_array_equal(actual[key], target[key])\n else:\n assert actual[key] == target[key]\n\n\ndef uint32_to_float32(u):\n return ((u >> np.uint32(8)) * (1.0 / 2**24)).astype(np.float32)\n\n\ndef uniform32_from_uint64(x):\n x = np.uint64(x)\n upper = np.array(x >> np.uint64(32), dtype=np.uint32)\n lower = np.uint64(0xffffffff)\n lower = np.array(x & lower, dtype=np.uint32)\n joined = np.column_stack([lower, upper]).ravel()\n return uint32_to_float32(joined)\n\n\ndef uniform32_from_uint53(x):\n x = np.uint64(x) >> np.uint64(16)\n x = np.uint32(x & np.uint64(0xffffffff))\n return uint32_to_float32(x)\n\n\ndef uniform32_from_uint32(x):\n return uint32_to_float32(x)\n\n\ndef uniform32_from_uint(x, bits):\n if bits == 64:\n return uniform32_from_uint64(x)\n elif bits == 53:\n return uniform32_from_uint53(x)\n elif bits == 32:\n return uniform32_from_uint32(x)\n else:\n raise NotImplementedError\n\n\ndef uniform_from_uint(x, bits):\n if bits in (64, 63, 53):\n return uniform_from_uint64(x)\n elif bits == 32:\n return uniform_from_uint32(x)\n\n\ndef uniform_from_uint64(x):\n return (x >> np.uint64(11)) * (1.0 / 9007199254740992.0)\n\n\ndef uniform_from_uint32(x):\n out = np.empty(len(x) // 2)\n for i in range(0, len(x), 2):\n a = x[i] >> 5\n b = x[i + 1] >> 6\n out[i // 2] = (a * 67108864.0 + b) / 9007199254740992.0\n return out\n\n\ndef uniform_from_dsfmt(x):\n return x.view(np.double) - 1.0\n\n\ndef gauss_from_uint(x, n, bits):\n if bits in (64, 63):\n doubles = uniform_from_uint64(x)\n elif bits == 32:\n doubles = uniform_from_uint32(x)\n else: # bits == 'dsfmt'\n doubles = uniform_from_dsfmt(x)\n gauss = []\n loc = 0\n x1 = x2 = 0.0\n while len(gauss) < n:\n r2 = 2\n while r2 >= 1.0 or r2 == 0.0:\n x1 = 2.0 * doubles[loc] - 1.0\n x2 = 2.0 * doubles[loc + 1] - 1.0\n r2 = x1 * x1 + x2 * x2\n loc += 2\n\n f = np.sqrt(-2.0 * np.log(r2) / r2)\n gauss.append(f * x2)\n gauss.append(f * x1)\n\n return gauss[:n]\n\n\ndef test_seedsequence():\n from numpy.random.bit_generator import (\n ISeedSequence,\n ISpawnableSeedSequence,\n SeedlessSeedSequence,\n )\n\n s1 = SeedSequence(range(10), spawn_key=(1, 2), pool_size=6)\n s1.spawn(10)\n s2 = SeedSequence(**s1.state)\n assert_equal(s1.state, s2.state)\n assert_equal(s1.n_children_spawned, s2.n_children_spawned)\n\n # The interfaces cannot be instantiated themselves.\n assert_raises(TypeError, ISeedSequence)\n assert_raises(TypeError, ISpawnableSeedSequence)\n dummy = SeedlessSeedSequence()\n assert_raises(NotImplementedError, dummy.generate_state, 10)\n assert len(dummy.spawn(10)) == 10\n\n\ndef test_generator_spawning():\n """ Test spawning new generators and bit_generators directly.\n """\n rng = np.random.default_rng()\n seq = rng.bit_generator.seed_seq\n new_ss = seq.spawn(5)\n expected_keys = [seq.spawn_key + (i,) for i in range(5)]\n assert [c.spawn_key for c in new_ss] == expected_keys\n\n new_bgs = rng.bit_generator.spawn(5)\n expected_keys = [seq.spawn_key + (i,) for i in range(5, 10)]\n assert [bg.seed_seq.spawn_key for bg in new_bgs] == expected_keys\n\n new_rngs = rng.spawn(5)\n expected_keys = [seq.spawn_key + (i,) for i in range(10, 15)]\n found_keys = [rng.bit_generator.seed_seq.spawn_key for rng in new_rngs]\n assert found_keys == expected_keys\n\n # Sanity check that streams are actually different:\n assert new_rngs[0].uniform() != new_rngs[1].uniform()\n\n\ndef test_non_spawnable():\n from numpy.random.bit_generator import ISeedSequence\n\n class FakeSeedSequence:\n def generate_state(self, n_words, dtype=np.uint32):\n return np.zeros(n_words, dtype=dtype)\n\n ISeedSequence.register(FakeSeedSequence)\n\n rng = np.random.default_rng(FakeSeedSequence())\n\n with pytest.raises(TypeError, match="The underlying SeedSequence"):\n rng.spawn(5)\n\n with pytest.raises(TypeError, match="The underlying SeedSequence"):\n rng.bit_generator.spawn(5)\n\n\nclass Base:\n dtype = np.uint64\n data2 = data1 = {}\n\n @classmethod\n def setup_class(cls):\n cls.bit_generator = PCG64\n cls.bits = 64\n cls.dtype = np.uint64\n cls.seed_error_type = TypeError\n cls.invalid_init_types = []\n cls.invalid_init_values = []\n\n @classmethod\n def _read_csv(cls, filename):\n with open(filename) as csv:\n seed = csv.readline()\n seed = seed.split(',')\n seed = [int(s.strip(), 0) for s in seed[1:]]\n data = []\n for line in csv:\n data.append(int(line.split(',')[-1].strip(), 0))\n return {'seed': seed, 'data': np.array(data, dtype=cls.dtype)}\n\n def test_raw(self):\n bit_generator = self.bit_generator(*self.data1['seed'])\n uints = bit_generator.random_raw(1000)\n assert_equal(uints, self.data1['data'])\n\n bit_generator = self.bit_generator(*self.data1['seed'])\n uints = bit_generator.random_raw()\n assert_equal(uints, self.data1['data'][0])\n\n bit_generator = self.bit_generator(*self.data2['seed'])\n uints = bit_generator.random_raw(1000)\n assert_equal(uints, self.data2['data'])\n\n def test_random_raw(self):\n bit_generator = self.bit_generator(*self.data1['seed'])\n uints = bit_generator.random_raw(output=False)\n assert uints is None\n uints = bit_generator.random_raw(1000, output=False)\n assert uints is None\n\n def test_gauss_inv(self):\n n = 25\n rs = RandomState(self.bit_generator(*self.data1['seed']))\n gauss = rs.standard_normal(n)\n assert_allclose(gauss,\n gauss_from_uint(self.data1['data'], n, self.bits))\n\n rs = RandomState(self.bit_generator(*self.data2['seed']))\n gauss = rs.standard_normal(25)\n assert_allclose(gauss,\n gauss_from_uint(self.data2['data'], n, self.bits))\n\n def test_uniform_double(self):\n rs = Generator(self.bit_generator(*self.data1['seed']))\n vals = uniform_from_uint(self.data1['data'], self.bits)\n uniforms = rs.random(len(vals))\n assert_allclose(uniforms, vals)\n assert_equal(uniforms.dtype, np.float64)\n\n rs = Generator(self.bit_generator(*self.data2['seed']))\n vals = uniform_from_uint(self.data2['data'], self.bits)\n uniforms = rs.random(len(vals))\n assert_allclose(uniforms, vals)\n assert_equal(uniforms.dtype, np.float64)\n\n def test_uniform_float(self):\n rs = Generator(self.bit_generator(*self.data1['seed']))\n vals = uniform32_from_uint(self.data1['data'], self.bits)\n uniforms = rs.random(len(vals), dtype=np.float32)\n assert_allclose(uniforms, vals)\n assert_equal(uniforms.dtype, np.float32)\n\n rs = Generator(self.bit_generator(*self.data2['seed']))\n vals = uniform32_from_uint(self.data2['data'], self.bits)\n uniforms = rs.random(len(vals), dtype=np.float32)\n assert_allclose(uniforms, vals)\n assert_equal(uniforms.dtype, np.float32)\n\n def test_repr(self):\n rs = Generator(self.bit_generator(*self.data1['seed']))\n assert 'Generator' in repr(rs)\n assert f'{id(rs):#x}'.upper().replace('X', 'x') in repr(rs)\n\n def test_str(self):\n rs = Generator(self.bit_generator(*self.data1['seed']))\n assert 'Generator' in str(rs)\n assert str(self.bit_generator.__name__) in str(rs)\n assert f'{id(rs):#x}'.upper().replace('X', 'x') not in str(rs)\n\n def test_pickle(self):\n import pickle\n\n bit_generator = self.bit_generator(*self.data1['seed'])\n state = bit_generator.state\n bitgen_pkl = pickle.dumps(bit_generator)\n reloaded = pickle.loads(bitgen_pkl)\n reloaded_state = reloaded.state\n assert_array_equal(Generator(bit_generator).standard_normal(1000),\n Generator(reloaded).standard_normal(1000))\n assert bit_generator is not reloaded\n assert_state_equal(reloaded_state, state)\n\n ss = SeedSequence(100)\n aa = pickle.loads(pickle.dumps(ss))\n assert_equal(ss.state, aa.state)\n\n def test_pickle_preserves_seed_sequence(self):\n # GH 26234\n # Add explicit test that bit generators preserve seed sequences\n import pickle\n\n bit_generator = self.bit_generator(*self.data1['seed'])\n ss = bit_generator.seed_seq\n bg_plk = pickle.loads(pickle.dumps(bit_generator))\n ss_plk = bg_plk.seed_seq\n assert_equal(ss.state, ss_plk.state)\n assert_equal(ss.pool, ss_plk.pool)\n\n bit_generator.seed_seq.spawn(10)\n bg_plk = pickle.loads(pickle.dumps(bit_generator))\n ss_plk = bg_plk.seed_seq\n assert_equal(ss.state, ss_plk.state)\n assert_equal(ss.n_children_spawned, ss_plk.n_children_spawned)\n\n def test_invalid_state_type(self):\n bit_generator = self.bit_generator(*self.data1['seed'])\n with pytest.raises(TypeError):\n bit_generator.state = {'1'}\n\n def test_invalid_state_value(self):\n bit_generator = self.bit_generator(*self.data1['seed'])\n state = bit_generator.state\n state['bit_generator'] = 'otherBitGenerator'\n with pytest.raises(ValueError):\n bit_generator.state = state\n\n def test_invalid_init_type(self):\n bit_generator = self.bit_generator\n for st in self.invalid_init_types:\n with pytest.raises(TypeError):\n bit_generator(*st)\n\n def test_invalid_init_values(self):\n bit_generator = self.bit_generator\n for st in self.invalid_init_values:\n with pytest.raises((ValueError, OverflowError)):\n bit_generator(*st)\n\n def test_benchmark(self):\n bit_generator = self.bit_generator(*self.data1['seed'])\n bit_generator._benchmark(1)\n bit_generator._benchmark(1, 'double')\n with pytest.raises(ValueError):\n bit_generator._benchmark(1, 'int32')\n\n @pytest.mark.skipif(MISSING_CFFI, reason='cffi not available')\n def test_cffi(self):\n bit_generator = self.bit_generator(*self.data1['seed'])\n cffi_interface = bit_generator.cffi\n assert isinstance(cffi_interface, interface)\n other_cffi_interface = bit_generator.cffi\n assert other_cffi_interface is cffi_interface\n\n @pytest.mark.skipif(MISSING_CTYPES, reason='ctypes not available')\n def test_ctypes(self):\n bit_generator = self.bit_generator(*self.data1['seed'])\n ctypes_interface = bit_generator.ctypes\n assert isinstance(ctypes_interface, interface)\n other_ctypes_interface = bit_generator.ctypes\n assert other_ctypes_interface is ctypes_interface\n\n def test_getstate(self):\n bit_generator = self.bit_generator(*self.data1['seed'])\n state = bit_generator.state\n alt_state = bit_generator.__getstate__()\n assert isinstance(alt_state, tuple)\n assert_state_equal(state, alt_state[0])\n assert isinstance(alt_state[1], SeedSequence)\n\nclass TestPhilox(Base):\n @classmethod\n def setup_class(cls):\n cls.bit_generator = Philox\n cls.bits = 64\n cls.dtype = np.uint64\n cls.data1 = cls._read_csv(\n join(pwd, './data/philox-testset-1.csv'))\n cls.data2 = cls._read_csv(\n join(pwd, './data/philox-testset-2.csv'))\n cls.seed_error_type = TypeError\n cls.invalid_init_types = []\n cls.invalid_init_values = [(1, None, 1), (-1,), (None, None, 2 ** 257 + 1)]\n\n def test_set_key(self):\n bit_generator = self.bit_generator(*self.data1['seed'])\n state = bit_generator.state\n keyed = self.bit_generator(counter=state['state']['counter'],\n key=state['state']['key'])\n assert_state_equal(bit_generator.state, keyed.state)\n\n\nclass TestPCG64(Base):\n @classmethod\n def setup_class(cls):\n cls.bit_generator = PCG64\n cls.bits = 64\n cls.dtype = np.uint64\n cls.data1 = cls._read_csv(join(pwd, './data/pcg64-testset-1.csv'))\n cls.data2 = cls._read_csv(join(pwd, './data/pcg64-testset-2.csv'))\n cls.seed_error_type = (ValueError, TypeError)\n cls.invalid_init_types = [(3.2,), ([None],), (1, None)]\n cls.invalid_init_values = [(-1,)]\n\n def test_advance_symmetry(self):\n rs = Generator(self.bit_generator(*self.data1['seed']))\n state = rs.bit_generator.state\n step = -0x9e3779b97f4a7c150000000000000000\n rs.bit_generator.advance(step)\n val_neg = rs.integers(10)\n rs.bit_generator.state = state\n rs.bit_generator.advance(2**128 + step)\n val_pos = rs.integers(10)\n rs.bit_generator.state = state\n rs.bit_generator.advance(10 * 2**128 + step)\n val_big = rs.integers(10)\n assert val_neg == val_pos\n assert val_big == val_pos\n\n def test_advange_large(self):\n rs = Generator(self.bit_generator(38219308213743))\n pcg = rs.bit_generator\n state = pcg.state["state"]\n initial_state = 287608843259529770491897792873167516365\n assert state["state"] == initial_state\n pcg.advance(sum(2**i for i in (96, 64, 32, 16, 8, 4, 2, 1)))\n state = pcg.state["state"]\n advanced_state = 135275564607035429730177404003164635391\n assert state["state"] == advanced_state\n\n\nclass TestPCG64DXSM(Base):\n @classmethod\n def setup_class(cls):\n cls.bit_generator = PCG64DXSM\n cls.bits = 64\n cls.dtype = np.uint64\n cls.data1 = cls._read_csv(join(pwd, './data/pcg64dxsm-testset-1.csv'))\n cls.data2 = cls._read_csv(join(pwd, './data/pcg64dxsm-testset-2.csv'))\n cls.seed_error_type = (ValueError, TypeError)\n cls.invalid_init_types = [(3.2,), ([None],), (1, None)]\n cls.invalid_init_values = [(-1,)]\n\n def test_advance_symmetry(self):\n rs = Generator(self.bit_generator(*self.data1['seed']))\n state = rs.bit_generator.state\n step = -0x9e3779b97f4a7c150000000000000000\n rs.bit_generator.advance(step)\n val_neg = rs.integers(10)\n rs.bit_generator.state = state\n rs.bit_generator.advance(2**128 + step)\n val_pos = rs.integers(10)\n rs.bit_generator.state = state\n rs.bit_generator.advance(10 * 2**128 + step)\n val_big = rs.integers(10)\n assert val_neg == val_pos\n assert val_big == val_pos\n\n def test_advange_large(self):\n rs = Generator(self.bit_generator(38219308213743))\n pcg = rs.bit_generator\n state = pcg.state\n initial_state = 287608843259529770491897792873167516365\n assert state["state"]["state"] == initial_state\n pcg.advance(sum(2**i for i in (96, 64, 32, 16, 8, 4, 2, 1)))\n state = pcg.state["state"]\n advanced_state = 277778083536782149546677086420637664879\n assert state["state"] == advanced_state\n\n\nclass TestMT19937(Base):\n @classmethod\n def setup_class(cls):\n cls.bit_generator = MT19937\n cls.bits = 32\n cls.dtype = np.uint32\n cls.data1 = cls._read_csv(join(pwd, './data/mt19937-testset-1.csv'))\n cls.data2 = cls._read_csv(join(pwd, './data/mt19937-testset-2.csv'))\n cls.seed_error_type = ValueError\n cls.invalid_init_types = []\n cls.invalid_init_values = [(-1,)]\n\n def test_seed_float_array(self):\n assert_raises(TypeError, self.bit_generator, np.array([np.pi]))\n assert_raises(TypeError, self.bit_generator, np.array([-np.pi]))\n assert_raises(TypeError, self.bit_generator, np.array([np.pi, -np.pi]))\n assert_raises(TypeError, self.bit_generator, np.array([0, np.pi]))\n assert_raises(TypeError, self.bit_generator, [np.pi])\n assert_raises(TypeError, self.bit_generator, [0, np.pi])\n\n def test_state_tuple(self):\n rs = Generator(self.bit_generator(*self.data1['seed']))\n bit_generator = rs.bit_generator\n state = bit_generator.state\n desired = rs.integers(2 ** 16)\n tup = (state['bit_generator'], state['state']['key'],\n state['state']['pos'])\n bit_generator.state = tup\n actual = rs.integers(2 ** 16)\n assert_equal(actual, desired)\n tup = tup + (0, 0.0)\n bit_generator.state = tup\n actual = rs.integers(2 ** 16)\n assert_equal(actual, desired)\n\n\nclass TestSFC64(Base):\n @classmethod\n def setup_class(cls):\n cls.bit_generator = SFC64\n cls.bits = 64\n cls.dtype = np.uint64\n cls.data1 = cls._read_csv(\n join(pwd, './data/sfc64-testset-1.csv'))\n cls.data2 = cls._read_csv(\n join(pwd, './data/sfc64-testset-2.csv'))\n cls.seed_error_type = (ValueError, TypeError)\n cls.invalid_init_types = [(3.2,), ([None],), (1, None)]\n cls.invalid_init_values = [(-1,)]\n\n def test_legacy_pickle(self):\n # Pickling format was changed in 2.0.x\n import gzip\n import pickle\n\n expected_state = np.array(\n [\n 9957867060933711493,\n 532597980065565856,\n 14769588338631205282,\n 13\n ],\n dtype=np.uint64\n )\n\n base_path = os.path.split(os.path.abspath(__file__))[0]\n pkl_file = os.path.join(base_path, "data", "sfc64_np126.pkl.gz")\n with gzip.open(pkl_file) as gz:\n sfc = pickle.load(gz)\n\n assert isinstance(sfc, SFC64)\n assert_equal(sfc.state["state"]["state"], expected_state)\n\n\nclass TestDefaultRNG:\n def test_seed(self):\n for args in [(), (None,), (1234,), ([1234, 5678],)]:\n rg = default_rng(*args)\n assert isinstance(rg.bit_generator, PCG64)\n\n def test_passthrough(self):\n bg = Philox()\n rg = default_rng(bg)\n assert rg.bit_generator is bg\n rg2 = default_rng(rg)\n assert rg2 is rg\n assert rg2.bit_generator is bg\n\n def test_coercion_RandomState_Generator(self):\n # use default_rng to coerce RandomState to Generator\n rs = RandomState(1234)\n rg = default_rng(rs)\n assert isinstance(rg.bit_generator, MT19937)\n assert rg.bit_generator is rs._bit_generator\n\n # RandomState with a non MT19937 bit generator\n _original = np.random.get_bit_generator()\n bg = PCG64(12342298)\n np.random.set_bit_generator(bg)\n rs = np.random.mtrand._rand\n rg = default_rng(rs)\n assert rg.bit_generator is bg\n\n # vital to get global state back to original, otherwise\n # other tests start to fail.\n np.random.set_bit_generator(_original)\n | .venv\Lib\site-packages\numpy\random\tests\test_direct.py | test_direct.py | Python | 20,511 | 0.95 | 0.138514 | 0.022587 | python-kit | 702 | 2025-04-12T20:52:52.714377 | Apache-2.0 | true | dea4e9d60696603082b517b001ebca4b |
import os\nimport shutil\nimport subprocess\nimport sys\nimport sysconfig\nimport warnings\nfrom importlib.util import module_from_spec, spec_from_file_location\n\nimport pytest\n\nimport numpy as np\nfrom numpy.testing import IS_EDITABLE, IS_WASM\n\ntry:\n import cffi\nexcept ImportError:\n cffi = None\n\nif sys.flags.optimize > 1:\n # no docstrings present to inspect when PYTHONOPTIMIZE/Py_OptimizeFlag > 1\n # cffi cannot succeed\n cffi = None\n\ntry:\n with warnings.catch_warnings(record=True) as w:\n # numba issue gh-4733\n warnings.filterwarnings('always', '', DeprecationWarning)\n import numba\nexcept (ImportError, SystemError):\n # Certain numpy/numba versions trigger a SystemError due to a numba bug\n numba = None\n\ntry:\n import cython\n from Cython.Compiler.Version import version as cython_version\nexcept ImportError:\n cython = None\nelse:\n from numpy._utils import _pep440\n # Note: keep in sync with the one in pyproject.toml\n required_version = '3.0.6'\n if _pep440.parse(cython_version) < _pep440.Version(required_version):\n # too old or wrong cython, skip the test\n cython = None\n\n\n@pytest.mark.skipif(\n IS_EDITABLE,\n reason='Editable install cannot find .pxd headers'\n)\n@pytest.mark.skipif(\n sys.platform == "win32" and sys.maxsize < 2**32,\n reason="Failing in 32-bit Windows wheel build job, skip for now"\n)\n@pytest.mark.skipif(IS_WASM, reason="Can't start subprocess")\n@pytest.mark.skipif(cython is None, reason="requires cython")\n@pytest.mark.skipif(sysconfig.get_platform() == 'win-arm64',\n reason='Meson unable to find MSVC linker on win-arm64')\n@pytest.mark.slow\ndef test_cython(tmp_path):\n import glob\n # build the examples in a temporary directory\n srcdir = os.path.join(os.path.dirname(__file__), '..')\n shutil.copytree(srcdir, tmp_path / 'random')\n build_dir = tmp_path / 'random' / '_examples' / 'cython'\n target_dir = build_dir / "build"\n os.makedirs(target_dir, exist_ok=True)\n # Ensure we use the correct Python interpreter even when `meson` is\n # installed in a different Python environment (see gh-24956)\n native_file = str(build_dir / 'interpreter-native-file.ini')\n with open(native_file, 'w') as f:\n f.write("[binaries]\n")\n f.write(f"python = '{sys.executable}'\n")\n f.write(f"python3 = '{sys.executable}'")\n if sys.platform == "win32":\n subprocess.check_call(["meson", "setup",\n "--buildtype=release",\n "--vsenv", "--native-file", native_file,\n str(build_dir)],\n cwd=target_dir,\n )\n else:\n subprocess.check_call(["meson", "setup",\n "--native-file", native_file, str(build_dir)],\n cwd=target_dir\n )\n subprocess.check_call(["meson", "compile", "-vv"], cwd=target_dir)\n\n # gh-16162: make sure numpy's __init__.pxd was used for cython\n # not really part of this test, but it is a convenient place to check\n\n g = glob.glob(str(target_dir / "*" / "extending.pyx.c"))\n with open(g[0]) as fid:\n txt_to_find = 'NumPy API declarations from "numpy/__init__'\n for line in fid:\n if txt_to_find in line:\n break\n else:\n assert False, f"Could not find '{txt_to_find}' in C file, wrong pxd used"\n # import without adding the directory to sys.path\n suffix = sysconfig.get_config_var('EXT_SUFFIX')\n\n def load(modname):\n so = (target_dir / modname).with_suffix(suffix)\n spec = spec_from_file_location(modname, so)\n mod = module_from_spec(spec)\n spec.loader.exec_module(mod)\n return mod\n\n # test that the module can be imported\n load("extending")\n load("extending_cpp")\n # actually test the cython c-extension\n extending_distributions = load("extending_distributions")\n from numpy.random import PCG64\n values = extending_distributions.uniforms_ex(PCG64(0), 10, 'd')\n assert values.shape == (10,)\n assert values.dtype == np.float64\n\n@pytest.mark.skipif(numba is None or cffi is None,\n reason="requires numba and cffi")\ndef test_numba():\n from numpy.random._examples.numba import extending # noqa: F401\n\n@pytest.mark.skipif(cffi is None, reason="requires cffi")\ndef test_cffi():\n from numpy.random._examples.cffi import extending # noqa: F401\n | .venv\Lib\site-packages\numpy\random\tests\test_extending.py | test_extending.py | Python | 4,659 | 0.95 | 0.110236 | 0.123894 | vue-tools | 121 | 2023-11-10T04:50:52.512842 | BSD-3-Clause | true | 2f1e6515d9ff00e8111d3219d9134328 |
import pytest\n\nimport numpy as np\nfrom numpy.random import MT19937, Generator\nfrom numpy.testing import assert_, assert_array_equal\n\n\nclass TestRegression:\n\n def setup_method(self):\n self.mt19937 = Generator(MT19937(121263137472525314065))\n\n def test_vonmises_range(self):\n # Make sure generated random variables are in [-pi, pi].\n # Regression test for ticket #986.\n for mu in np.linspace(-7., 7., 5):\n r = self.mt19937.vonmises(mu, 1, 50)\n assert_(np.all(r > -np.pi) and np.all(r <= np.pi))\n\n def test_hypergeometric_range(self):\n # Test for ticket #921\n assert_(np.all(self.mt19937.hypergeometric(3, 18, 11, size=10) < 4))\n assert_(np.all(self.mt19937.hypergeometric(18, 3, 11, size=10) > 0))\n\n # Test for ticket #5623\n args = (2**20 - 2, 2**20 - 2, 2**20 - 2) # Check for 32-bit systems\n assert_(self.mt19937.hypergeometric(*args) > 0)\n\n def test_logseries_convergence(self):\n # Test for ticket #923\n N = 1000\n rvsn = self.mt19937.logseries(0.8, size=N)\n # these two frequency counts should be close to theoretical\n # numbers with this large sample\n # theoretical large N result is 0.49706795\n freq = np.sum(rvsn == 1) / N\n msg = f'Frequency was {freq:f}, should be > 0.45'\n assert_(freq > 0.45, msg)\n # theoretical large N result is 0.19882718\n freq = np.sum(rvsn == 2) / N\n msg = f'Frequency was {freq:f}, should be < 0.23'\n assert_(freq < 0.23, msg)\n\n def test_shuffle_mixed_dimension(self):\n # Test for trac ticket #2074\n for t in [[1, 2, 3, None],\n [(1, 1), (2, 2), (3, 3), None],\n [1, (2, 2), (3, 3), None],\n [(1, 1), 2, 3, None]]:\n mt19937 = Generator(MT19937(12345))\n shuffled = np.array(t, dtype=object)\n mt19937.shuffle(shuffled)\n expected = np.array([t[2], t[0], t[3], t[1]], dtype=object)\n assert_array_equal(np.array(shuffled, dtype=object), expected)\n\n def test_call_within_randomstate(self):\n # Check that custom BitGenerator does not call into global state\n res = np.array([1, 8, 0, 1, 5, 3, 3, 8, 1, 4])\n for i in range(3):\n mt19937 = Generator(MT19937(i))\n m = Generator(MT19937(4321))\n # If m.state is not honored, the result will change\n assert_array_equal(m.choice(10, size=10, p=np.ones(10) / 10.), res)\n\n def test_multivariate_normal_size_types(self):\n # Test for multivariate_normal issue with 'size' argument.\n # Check that the multivariate_normal size argument can be a\n # numpy integer.\n self.mt19937.multivariate_normal([0], [[0]], size=1)\n self.mt19937.multivariate_normal([0], [[0]], size=np.int_(1))\n self.mt19937.multivariate_normal([0], [[0]], size=np.int64(1))\n\n def test_beta_small_parameters(self):\n # Test that beta with small a and b parameters does not produce\n # NaNs due to roundoff errors causing 0 / 0, gh-5851\n x = self.mt19937.beta(0.0001, 0.0001, size=100)\n assert_(not np.any(np.isnan(x)), 'Nans in mt19937.beta')\n\n def test_beta_very_small_parameters(self):\n # gh-24203: beta would hang with very small parameters.\n self.mt19937.beta(1e-49, 1e-40)\n\n def test_beta_ridiculously_small_parameters(self):\n # gh-24266: beta would generate nan when the parameters\n # were subnormal or a small multiple of the smallest normal.\n tiny = np.finfo(1.0).tiny\n x = self.mt19937.beta(tiny / 32, tiny / 40, size=50)\n assert not np.any(np.isnan(x))\n\n def test_beta_expected_zero_frequency(self):\n # gh-24475: For small a and b (e.g. a=0.0025, b=0.0025), beta\n # would generate too many zeros.\n a = 0.0025\n b = 0.0025\n n = 1000000\n x = self.mt19937.beta(a, b, size=n)\n nzeros = np.count_nonzero(x == 0)\n # beta CDF at x = np.finfo(np.double).smallest_subnormal/2\n # is p = 0.0776169083131899, e.g,\n #\n # import numpy as np\n # from mpmath import mp\n # mp.dps = 160\n # x = mp.mpf(np.finfo(np.float64).smallest_subnormal)/2\n # # CDF of the beta distribution at x:\n # p = mp.betainc(a, b, x1=0, x2=x, regularized=True)\n # n = 1000000\n # exprected_freq = float(n*p)\n #\n expected_freq = 77616.90831318991\n assert 0.95 * expected_freq < nzeros < 1.05 * expected_freq\n\n def test_choice_sum_of_probs_tolerance(self):\n # The sum of probs should be 1.0 with some tolerance.\n # For low precision dtypes the tolerance was too tight.\n # See numpy github issue 6123.\n a = [1, 2, 3]\n counts = [4, 4, 2]\n for dt in np.float16, np.float32, np.float64:\n probs = np.array(counts, dtype=dt) / sum(counts)\n c = self.mt19937.choice(a, p=probs)\n assert_(c in a)\n with pytest.raises(ValueError):\n self.mt19937.choice(a, p=probs * 0.9)\n\n def test_shuffle_of_array_of_different_length_strings(self):\n # Test that permuting an array of different length strings\n # will not cause a segfault on garbage collection\n # Tests gh-7710\n\n a = np.array(['a', 'a' * 1000])\n\n for _ in range(100):\n self.mt19937.shuffle(a)\n\n # Force Garbage Collection - should not segfault.\n import gc\n gc.collect()\n\n def test_shuffle_of_array_of_objects(self):\n # Test that permuting an array of objects will not cause\n # a segfault on garbage collection.\n # See gh-7719\n a = np.array([np.arange(1), np.arange(4)], dtype=object)\n\n for _ in range(1000):\n self.mt19937.shuffle(a)\n\n # Force Garbage Collection - should not segfault.\n import gc\n gc.collect()\n\n def test_permutation_subclass(self):\n\n class N(np.ndarray):\n pass\n\n mt19937 = Generator(MT19937(1))\n orig = np.arange(3).view(N)\n perm = mt19937.permutation(orig)\n assert_array_equal(perm, np.array([2, 0, 1]))\n assert_array_equal(orig, np.arange(3).view(N))\n\n class M:\n a = np.arange(5)\n\n def __array__(self, dtype=None, copy=None):\n return self.a\n\n mt19937 = Generator(MT19937(1))\n m = M()\n perm = mt19937.permutation(m)\n assert_array_equal(perm, np.array([4, 1, 3, 0, 2]))\n assert_array_equal(m.__array__(), np.arange(5))\n\n def test_gamma_0(self):\n assert self.mt19937.standard_gamma(0.0) == 0.0\n assert_array_equal(self.mt19937.standard_gamma([0.0]), 0.0)\n\n actual = self.mt19937.standard_gamma([0.0], dtype='float')\n expected = np.array([0.], dtype=np.float32)\n assert_array_equal(actual, expected)\n\n def test_geometric_tiny_prob(self):\n # Regression test for gh-17007.\n # When p = 1e-30, the probability that a sample will exceed 2**63-1\n # is 0.9999999999907766, so we expect the result to be all 2**63-1.\n assert_array_equal(self.mt19937.geometric(p=1e-30, size=3),\n np.iinfo(np.int64).max)\n\n def test_zipf_large_parameter(self):\n # Regression test for part of gh-9829: a call such as rng.zipf(10000)\n # would hang.\n n = 8\n sample = self.mt19937.zipf(10000, size=n)\n assert_array_equal(sample, np.ones(n, dtype=np.int64))\n\n def test_zipf_a_near_1(self):\n # Regression test for gh-9829: a call such as rng.zipf(1.0000000000001)\n # would hang.\n n = 100000\n sample = self.mt19937.zipf(1.0000000000001, size=n)\n # Not much of a test, but let's do something more than verify that\n # it doesn't hang. Certainly for a monotonically decreasing\n # discrete distribution truncated to signed 64 bit integers, more\n # than half should be less than 2**62.\n assert np.count_nonzero(sample < 2**62) > n / 2\n | .venv\Lib\site-packages\numpy\random\tests\test_generator_mt19937_regressions.py | test_generator_mt19937_regressions.py | Python | 8,314 | 0.95 | 0.193237 | 0.323699 | node-utils | 340 | 2024-08-19T02:02:49.598919 | GPL-3.0 | true | 723e92f74136410c6445aeef8d8818a6 |
import sys\nimport warnings\n\nimport pytest\n\nimport numpy as np\nfrom numpy import random\nfrom numpy.testing import (\n IS_WASM,\n assert_,\n assert_array_almost_equal,\n assert_array_equal,\n assert_equal,\n assert_no_warnings,\n assert_raises,\n assert_warns,\n suppress_warnings,\n)\n\n\nclass TestSeed:\n def test_scalar(self):\n s = np.random.RandomState(0)\n assert_equal(s.randint(1000), 684)\n s = np.random.RandomState(4294967295)\n assert_equal(s.randint(1000), 419)\n\n def test_array(self):\n s = np.random.RandomState(range(10))\n assert_equal(s.randint(1000), 468)\n s = np.random.RandomState(np.arange(10))\n assert_equal(s.randint(1000), 468)\n s = np.random.RandomState([0])\n assert_equal(s.randint(1000), 973)\n s = np.random.RandomState([4294967295])\n assert_equal(s.randint(1000), 265)\n\n def test_invalid_scalar(self):\n # seed must be an unsigned 32 bit integer\n assert_raises(TypeError, np.random.RandomState, -0.5)\n assert_raises(ValueError, np.random.RandomState, -1)\n\n def test_invalid_array(self):\n # seed must be an unsigned 32 bit integer\n assert_raises(TypeError, np.random.RandomState, [-0.5])\n assert_raises(ValueError, np.random.RandomState, [-1])\n assert_raises(ValueError, np.random.RandomState, [4294967296])\n assert_raises(ValueError, np.random.RandomState, [1, 2, 4294967296])\n assert_raises(ValueError, np.random.RandomState, [1, -2, 4294967296])\n\n def test_invalid_array_shape(self):\n # gh-9832\n assert_raises(ValueError, np.random.RandomState,\n np.array([], dtype=np.int64))\n assert_raises(ValueError, np.random.RandomState, [[1, 2, 3]])\n assert_raises(ValueError, np.random.RandomState, [[1, 2, 3],\n [4, 5, 6]])\n\n\nclass TestBinomial:\n def test_n_zero(self):\n # Tests the corner case of n == 0 for the binomial distribution.\n # binomial(0, p) should be zero for any p in [0, 1].\n # This test addresses issue #3480.\n zeros = np.zeros(2, dtype='int')\n for p in [0, .5, 1]:\n assert_(random.binomial(0, p) == 0)\n assert_array_equal(random.binomial(zeros, p), zeros)\n\n def test_p_is_nan(self):\n # Issue #4571.\n assert_raises(ValueError, random.binomial, 1, np.nan)\n\n\nclass TestMultinomial:\n def test_basic(self):\n random.multinomial(100, [0.2, 0.8])\n\n def test_zero_probability(self):\n random.multinomial(100, [0.2, 0.8, 0.0, 0.0, 0.0])\n\n def test_int_negative_interval(self):\n assert_(-5 <= random.randint(-5, -1) < -1)\n x = random.randint(-5, -1, 5)\n assert_(np.all(-5 <= x))\n assert_(np.all(x < -1))\n\n def test_size(self):\n # gh-3173\n p = [0.5, 0.5]\n assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2))\n assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2))\n assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2))\n assert_equal(np.random.multinomial(1, p, [2, 2]).shape, (2, 2, 2))\n assert_equal(np.random.multinomial(1, p, (2, 2)).shape, (2, 2, 2))\n assert_equal(np.random.multinomial(1, p, np.array((2, 2))).shape,\n (2, 2, 2))\n\n assert_raises(TypeError, np.random.multinomial, 1, p,\n float(1))\n\n def test_multidimensional_pvals(self):\n assert_raises(ValueError, np.random.multinomial, 10, [[0, 1]])\n assert_raises(ValueError, np.random.multinomial, 10, [[0], [1]])\n assert_raises(ValueError, np.random.multinomial, 10, [[[0], [1]], [[1], [0]]])\n assert_raises(ValueError, np.random.multinomial, 10, np.array([[0, 1], [1, 0]]))\n\n\nclass TestSetState:\n def setup_method(self):\n self.seed = 1234567890\n self.prng = random.RandomState(self.seed)\n self.state = self.prng.get_state()\n\n def test_basic(self):\n old = self.prng.tomaxint(16)\n self.prng.set_state(self.state)\n new = self.prng.tomaxint(16)\n assert_(np.all(old == new))\n\n def test_gaussian_reset(self):\n # Make sure the cached every-other-Gaussian is reset.\n old = self.prng.standard_normal(size=3)\n self.prng.set_state(self.state)\n new = self.prng.standard_normal(size=3)\n assert_(np.all(old == new))\n\n def test_gaussian_reset_in_media_res(self):\n # When the state is saved with a cached Gaussian, make sure the\n # cached Gaussian is restored.\n\n self.prng.standard_normal()\n state = self.prng.get_state()\n old = self.prng.standard_normal(size=3)\n self.prng.set_state(state)\n new = self.prng.standard_normal(size=3)\n assert_(np.all(old == new))\n\n def test_backwards_compatibility(self):\n # Make sure we can accept old state tuples that do not have the\n # cached Gaussian value.\n old_state = self.state[:-2]\n x1 = self.prng.standard_normal(size=16)\n self.prng.set_state(old_state)\n x2 = self.prng.standard_normal(size=16)\n self.prng.set_state(self.state)\n x3 = self.prng.standard_normal(size=16)\n assert_(np.all(x1 == x2))\n assert_(np.all(x1 == x3))\n\n def test_negative_binomial(self):\n # Ensure that the negative binomial results take floating point\n # arguments without truncation.\n self.prng.negative_binomial(0.5, 0.5)\n\n def test_set_invalid_state(self):\n # gh-25402\n with pytest.raises(IndexError):\n self.prng.set_state(())\n\n\nclass TestRandint:\n\n rfunc = np.random.randint\n\n # valid integer/boolean types\n itype = [np.bool, np.int8, np.uint8, np.int16, np.uint16,\n np.int32, np.uint32, np.int64, np.uint64]\n\n def test_unsupported_type(self):\n assert_raises(TypeError, self.rfunc, 1, dtype=float)\n\n def test_bounds_checking(self):\n for dt in self.itype:\n lbnd = 0 if dt is np.bool else np.iinfo(dt).min\n ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1\n assert_raises(ValueError, self.rfunc, lbnd - 1, ubnd, dtype=dt)\n assert_raises(ValueError, self.rfunc, lbnd, ubnd + 1, dtype=dt)\n assert_raises(ValueError, self.rfunc, ubnd, lbnd, dtype=dt)\n assert_raises(ValueError, self.rfunc, 1, 0, dtype=dt)\n\n def test_rng_zero_and_extremes(self):\n for dt in self.itype:\n lbnd = 0 if dt is np.bool else np.iinfo(dt).min\n ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1\n\n tgt = ubnd - 1\n assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)\n\n tgt = lbnd\n assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)\n\n tgt = (lbnd + ubnd) // 2\n assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)\n\n def test_full_range(self):\n # Test for ticket #1690\n\n for dt in self.itype:\n lbnd = 0 if dt is np.bool else np.iinfo(dt).min\n ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1\n\n try:\n self.rfunc(lbnd, ubnd, dtype=dt)\n except Exception as e:\n raise AssertionError("No error should have been raised, "\n "but one was with the following "\n "message:\n\n%s" % str(e))\n\n def test_in_bounds_fuzz(self):\n # Don't use fixed seed\n np.random.seed()\n\n for dt in self.itype[1:]:\n for ubnd in [4, 8, 16]:\n vals = self.rfunc(2, ubnd, size=2**16, dtype=dt)\n assert_(vals.max() < ubnd)\n assert_(vals.min() >= 2)\n\n vals = self.rfunc(0, 2, size=2**16, dtype=np.bool)\n\n assert_(vals.max() < 2)\n assert_(vals.min() >= 0)\n\n def test_repeatability(self):\n import hashlib\n # We use a sha256 hash of generated sequences of 1000 samples\n # in the range [0, 6) for all but bool, where the range\n # is [0, 2). Hashes are for little endian numbers.\n tgt = {'bool': '509aea74d792fb931784c4b0135392c65aec64beee12b0cc167548a2c3d31e71', # noqa: E501\n 'int16': '7b07f1a920e46f6d0fe02314155a2330bcfd7635e708da50e536c5ebb631a7d4', # noqa: E501\n 'int32': 'e577bfed6c935de944424667e3da285012e741892dcb7051a8f1ce68ab05c92f', # noqa: E501\n 'int64': '0fbead0b06759df2cfb55e43148822d4a1ff953c7eb19a5b08445a63bb64fa9e', # noqa: E501\n 'int8': '001aac3a5acb935a9b186cbe14a1ca064b8bb2dd0b045d48abeacf74d0203404', # noqa: E501\n 'uint16': '7b07f1a920e46f6d0fe02314155a2330bcfd7635e708da50e536c5ebb631a7d4', # noqa: E501\n 'uint32': 'e577bfed6c935de944424667e3da285012e741892dcb7051a8f1ce68ab05c92f', # noqa: E501\n 'uint64': '0fbead0b06759df2cfb55e43148822d4a1ff953c7eb19a5b08445a63bb64fa9e', # noqa: E501\n 'uint8': '001aac3a5acb935a9b186cbe14a1ca064b8bb2dd0b045d48abeacf74d0203404'} # noqa: E501\n\n for dt in self.itype[1:]:\n np.random.seed(1234)\n\n # view as little endian for hash\n if sys.byteorder == 'little':\n val = self.rfunc(0, 6, size=1000, dtype=dt)\n else:\n val = self.rfunc(0, 6, size=1000, dtype=dt).byteswap()\n\n res = hashlib.sha256(val.view(np.int8)).hexdigest()\n assert_(tgt[np.dtype(dt).name] == res)\n\n # bools do not depend on endianness\n np.random.seed(1234)\n val = self.rfunc(0, 2, size=1000, dtype=bool).view(np.int8)\n res = hashlib.sha256(val).hexdigest()\n assert_(tgt[np.dtype(bool).name] == res)\n\n def test_int64_uint64_corner_case(self):\n # When stored in Numpy arrays, `lbnd` is casted\n # as np.int64, and `ubnd` is casted as np.uint64.\n # Checking whether `lbnd` >= `ubnd` used to be\n # done solely via direct comparison, which is incorrect\n # because when Numpy tries to compare both numbers,\n # it casts both to np.float64 because there is\n # no integer superset of np.int64 and np.uint64. However,\n # `ubnd` is too large to be represented in np.float64,\n # causing it be round down to np.iinfo(np.int64).max,\n # leading to a ValueError because `lbnd` now equals\n # the new `ubnd`.\n\n dt = np.int64\n tgt = np.iinfo(np.int64).max\n lbnd = np.int64(np.iinfo(np.int64).max)\n ubnd = np.uint64(np.iinfo(np.int64).max + 1)\n\n # None of these function calls should\n # generate a ValueError now.\n actual = np.random.randint(lbnd, ubnd, dtype=dt)\n assert_equal(actual, tgt)\n\n def test_respect_dtype_singleton(self):\n # See gh-7203\n for dt in self.itype:\n lbnd = 0 if dt is np.bool else np.iinfo(dt).min\n ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1\n\n sample = self.rfunc(lbnd, ubnd, dtype=dt)\n assert_equal(sample.dtype, np.dtype(dt))\n\n for dt in (bool, int):\n # The legacy rng uses "long" as the default integer:\n lbnd = 0 if dt is bool else np.iinfo("long").min\n ubnd = 2 if dt is bool else np.iinfo("long").max + 1\n\n # gh-7284: Ensure that we get Python data types\n sample = self.rfunc(lbnd, ubnd, dtype=dt)\n assert_(not hasattr(sample, 'dtype'))\n assert_equal(type(sample), dt)\n\n\nclass TestRandomDist:\n # Make sure the random distribution returns the correct value for a\n # given seed\n\n def setup_method(self):\n self.seed = 1234567890\n\n def test_rand(self):\n np.random.seed(self.seed)\n actual = np.random.rand(3, 2)\n desired = np.array([[0.61879477158567997, 0.59162362775974664],\n [0.88868358904449662, 0.89165480011560816],\n [0.4575674820298663, 0.7781880808593471]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_randn(self):\n np.random.seed(self.seed)\n actual = np.random.randn(3, 2)\n desired = np.array([[1.34016345771863121, 1.73759122771936081],\n [1.498988344300628, -0.2286433324536169],\n [2.031033998682787, 2.17032494605655257]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_randint(self):\n np.random.seed(self.seed)\n actual = np.random.randint(-99, 99, size=(3, 2))\n desired = np.array([[31, 3],\n [-52, 41],\n [-48, -66]])\n assert_array_equal(actual, desired)\n\n def test_random_integers(self):\n np.random.seed(self.seed)\n with suppress_warnings() as sup:\n w = sup.record(DeprecationWarning)\n actual = np.random.random_integers(-99, 99, size=(3, 2))\n assert_(len(w) == 1)\n desired = np.array([[31, 3],\n [-52, 41],\n [-48, -66]])\n assert_array_equal(actual, desired)\n\n def test_random_integers_max_int(self):\n # Tests whether random_integers can generate the\n # maximum allowed Python int that can be converted\n # into a C long. Previous implementations of this\n # method have thrown an OverflowError when attempting\n # to generate this integer.\n with suppress_warnings() as sup:\n w = sup.record(DeprecationWarning)\n actual = np.random.random_integers(np.iinfo('l').max,\n np.iinfo('l').max)\n assert_(len(w) == 1)\n\n desired = np.iinfo('l').max\n assert_equal(actual, desired)\n\n def test_random_integers_deprecated(self):\n with warnings.catch_warnings():\n warnings.simplefilter("error", DeprecationWarning)\n\n # DeprecationWarning raised with high == None\n assert_raises(DeprecationWarning,\n np.random.random_integers,\n np.iinfo('l').max)\n\n # DeprecationWarning raised with high != None\n assert_raises(DeprecationWarning,\n np.random.random_integers,\n np.iinfo('l').max, np.iinfo('l').max)\n\n def test_random(self):\n np.random.seed(self.seed)\n actual = np.random.random((3, 2))\n desired = np.array([[0.61879477158567997, 0.59162362775974664],\n [0.88868358904449662, 0.89165480011560816],\n [0.4575674820298663, 0.7781880808593471]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_choice_uniform_replace(self):\n np.random.seed(self.seed)\n actual = np.random.choice(4, 4)\n desired = np.array([2, 3, 2, 3])\n assert_array_equal(actual, desired)\n\n def test_choice_nonuniform_replace(self):\n np.random.seed(self.seed)\n actual = np.random.choice(4, 4, p=[0.4, 0.4, 0.1, 0.1])\n desired = np.array([1, 1, 2, 2])\n assert_array_equal(actual, desired)\n\n def test_choice_uniform_noreplace(self):\n np.random.seed(self.seed)\n actual = np.random.choice(4, 3, replace=False)\n desired = np.array([0, 1, 3])\n assert_array_equal(actual, desired)\n\n def test_choice_nonuniform_noreplace(self):\n np.random.seed(self.seed)\n actual = np.random.choice(4, 3, replace=False,\n p=[0.1, 0.3, 0.5, 0.1])\n desired = np.array([2, 3, 1])\n assert_array_equal(actual, desired)\n\n def test_choice_noninteger(self):\n np.random.seed(self.seed)\n actual = np.random.choice(['a', 'b', 'c', 'd'], 4)\n desired = np.array(['c', 'd', 'c', 'd'])\n assert_array_equal(actual, desired)\n\n def test_choice_exceptions(self):\n sample = np.random.choice\n assert_raises(ValueError, sample, -1, 3)\n assert_raises(ValueError, sample, 3., 3)\n assert_raises(ValueError, sample, [[1, 2], [3, 4]], 3)\n assert_raises(ValueError, sample, [], 3)\n assert_raises(ValueError, sample, [1, 2, 3, 4], 3,\n p=[[0.25, 0.25], [0.25, 0.25]])\n assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4, 0.2])\n assert_raises(ValueError, sample, [1, 2], 3, p=[1.1, -0.1])\n assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4])\n assert_raises(ValueError, sample, [1, 2, 3], 4, replace=False)\n # gh-13087\n assert_raises(ValueError, sample, [1, 2, 3], -2, replace=False)\n assert_raises(ValueError, sample, [1, 2, 3], (-1,), replace=False)\n assert_raises(ValueError, sample, [1, 2, 3], (-1, 1), replace=False)\n assert_raises(ValueError, sample, [1, 2, 3], 2,\n replace=False, p=[1, 0, 0])\n\n def test_choice_return_shape(self):\n p = [0.1, 0.9]\n # Check scalar\n assert_(np.isscalar(np.random.choice(2, replace=True)))\n assert_(np.isscalar(np.random.choice(2, replace=False)))\n assert_(np.isscalar(np.random.choice(2, replace=True, p=p)))\n assert_(np.isscalar(np.random.choice(2, replace=False, p=p)))\n assert_(np.isscalar(np.random.choice([1, 2], replace=True)))\n assert_(np.random.choice([None], replace=True) is None)\n a = np.array([1, 2])\n arr = np.empty(1, dtype=object)\n arr[0] = a\n assert_(np.random.choice(arr, replace=True) is a)\n\n # Check 0-d array\n s = ()\n assert_(not np.isscalar(np.random.choice(2, s, replace=True)))\n assert_(not np.isscalar(np.random.choice(2, s, replace=False)))\n assert_(not np.isscalar(np.random.choice(2, s, replace=True, p=p)))\n assert_(not np.isscalar(np.random.choice(2, s, replace=False, p=p)))\n assert_(not np.isscalar(np.random.choice([1, 2], s, replace=True)))\n assert_(np.random.choice([None], s, replace=True).ndim == 0)\n a = np.array([1, 2])\n arr = np.empty(1, dtype=object)\n arr[0] = a\n assert_(np.random.choice(arr, s, replace=True).item() is a)\n\n # Check multi dimensional array\n s = (2, 3)\n p = [0.1, 0.1, 0.1, 0.1, 0.4, 0.2]\n assert_equal(np.random.choice(6, s, replace=True).shape, s)\n assert_equal(np.random.choice(6, s, replace=False).shape, s)\n assert_equal(np.random.choice(6, s, replace=True, p=p).shape, s)\n assert_equal(np.random.choice(6, s, replace=False, p=p).shape, s)\n assert_equal(np.random.choice(np.arange(6), s, replace=True).shape, s)\n\n # Check zero-size\n assert_equal(np.random.randint(0, 0, size=(3, 0, 4)).shape, (3, 0, 4))\n assert_equal(np.random.randint(0, -10, size=0).shape, (0,))\n assert_equal(np.random.randint(10, 10, size=0).shape, (0,))\n assert_equal(np.random.choice(0, size=0).shape, (0,))\n assert_equal(np.random.choice([], size=(0,)).shape, (0,))\n assert_equal(np.random.choice(['a', 'b'], size=(3, 0, 4)).shape,\n (3, 0, 4))\n assert_raises(ValueError, np.random.choice, [], 10)\n\n def test_choice_nan_probabilities(self):\n a = np.array([42, 1, 2])\n p = [None, None, None]\n assert_raises(ValueError, np.random.choice, a, p=p)\n\n def test_bytes(self):\n np.random.seed(self.seed)\n actual = np.random.bytes(10)\n desired = b'\x82Ui\x9e\xff\x97+Wf\xa5'\n assert_equal(actual, desired)\n\n def test_shuffle(self):\n # Test lists, arrays (of various dtypes), and multidimensional versions\n # of both, c-contiguous or not:\n for conv in [lambda x: np.array([]),\n lambda x: x,\n lambda x: np.asarray(x).astype(np.int8),\n lambda x: np.asarray(x).astype(np.float32),\n lambda x: np.asarray(x).astype(np.complex64),\n lambda x: np.asarray(x).astype(object),\n lambda x: [(i, i) for i in x],\n lambda x: np.asarray([[i, i] for i in x]),\n lambda x: np.vstack([x, x]).T,\n # gh-11442\n lambda x: (np.asarray([(i, i) for i in x],\n [("a", int), ("b", int)])\n .view(np.recarray)),\n # gh-4270\n lambda x: np.asarray([(i, i) for i in x],\n [("a", object), ("b", np.int32)])]:\n np.random.seed(self.seed)\n alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])\n np.random.shuffle(alist)\n actual = alist\n desired = conv([0, 1, 9, 6, 2, 4, 5, 8, 7, 3])\n assert_array_equal(actual, desired)\n\n def test_shuffle_masked(self):\n # gh-3263\n a = np.ma.masked_values(np.reshape(range(20), (5, 4)) % 3 - 1, -1)\n b = np.ma.masked_values(np.arange(20) % 3 - 1, -1)\n a_orig = a.copy()\n b_orig = b.copy()\n for i in range(50):\n np.random.shuffle(a)\n assert_equal(\n sorted(a.data[~a.mask]), sorted(a_orig.data[~a_orig.mask]))\n np.random.shuffle(b)\n assert_equal(\n sorted(b.data[~b.mask]), sorted(b_orig.data[~b_orig.mask]))\n\n @pytest.mark.parametrize("random",\n [np.random, np.random.RandomState(), np.random.default_rng()])\n def test_shuffle_untyped_warning(self, random):\n # Create a dict works like a sequence but isn't one\n values = {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6}\n with pytest.warns(UserWarning,\n match="you are shuffling a 'dict' object") as rec:\n random.shuffle(values)\n assert "test_random" in rec[0].filename\n\n @pytest.mark.parametrize("random",\n [np.random, np.random.RandomState(), np.random.default_rng()])\n @pytest.mark.parametrize("use_array_like", [True, False])\n def test_shuffle_no_object_unpacking(self, random, use_array_like):\n class MyArr(np.ndarray):\n pass\n\n items = [\n None, np.array([3]), np.float64(3), np.array(10), np.float64(7)\n ]\n arr = np.array(items, dtype=object)\n item_ids = {id(i) for i in items}\n if use_array_like:\n arr = arr.view(MyArr)\n\n # The array was created fine, and did not modify any objects:\n assert all(id(i) in item_ids for i in arr)\n\n if use_array_like and not isinstance(random, np.random.Generator):\n # The old API gives incorrect results, but warns about it.\n with pytest.warns(UserWarning,\n match="Shuffling a one dimensional array.*"):\n random.shuffle(arr)\n else:\n random.shuffle(arr)\n assert all(id(i) in item_ids for i in arr)\n\n def test_shuffle_memoryview(self):\n # gh-18273\n # allow graceful handling of memoryviews\n # (treat the same as arrays)\n np.random.seed(self.seed)\n a = np.arange(5).data\n np.random.shuffle(a)\n assert_equal(np.asarray(a), [0, 1, 4, 3, 2])\n rng = np.random.RandomState(self.seed)\n rng.shuffle(a)\n assert_equal(np.asarray(a), [0, 1, 2, 3, 4])\n rng = np.random.default_rng(self.seed)\n rng.shuffle(a)\n assert_equal(np.asarray(a), [4, 1, 0, 3, 2])\n\n def test_shuffle_not_writeable(self):\n a = np.zeros(3)\n a.flags.writeable = False\n with pytest.raises(ValueError, match='read-only'):\n np.random.shuffle(a)\n\n def test_beta(self):\n np.random.seed(self.seed)\n actual = np.random.beta(.1, .9, size=(3, 2))\n desired = np.array(\n [[1.45341850513746058e-02, 5.31297615662868145e-04],\n [1.85366619058432324e-06, 4.19214516800110563e-03],\n [1.58405155108498093e-04, 1.26252891949397652e-04]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_binomial(self):\n np.random.seed(self.seed)\n actual = np.random.binomial(100, .456, size=(3, 2))\n desired = np.array([[37, 43],\n [42, 48],\n [46, 45]])\n assert_array_equal(actual, desired)\n\n def test_chisquare(self):\n np.random.seed(self.seed)\n actual = np.random.chisquare(50, size=(3, 2))\n desired = np.array([[63.87858175501090585, 68.68407748911370447],\n [65.77116116901505904, 47.09686762438974483],\n [72.3828403199695174, 74.18408615260374006]])\n assert_array_almost_equal(actual, desired, decimal=13)\n\n def test_dirichlet(self):\n np.random.seed(self.seed)\n alpha = np.array([51.72840233779265162, 39.74494232180943953])\n actual = np.random.mtrand.dirichlet(alpha, size=(3, 2))\n desired = np.array([[[0.54539444573611562, 0.45460555426388438],\n [0.62345816822039413, 0.37654183177960598]],\n [[0.55206000085785778, 0.44793999914214233],\n [0.58964023305154301, 0.41035976694845688]],\n [[0.59266909280647828, 0.40733090719352177],\n [0.56974431743975207, 0.43025568256024799]]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_dirichlet_size(self):\n # gh-3173\n p = np.array([51.72840233779265162, 39.74494232180943953])\n assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2))\n assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2))\n assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2))\n assert_equal(np.random.dirichlet(p, [2, 2]).shape, (2, 2, 2))\n assert_equal(np.random.dirichlet(p, (2, 2)).shape, (2, 2, 2))\n assert_equal(np.random.dirichlet(p, np.array((2, 2))).shape, (2, 2, 2))\n\n assert_raises(TypeError, np.random.dirichlet, p, float(1))\n\n def test_dirichlet_bad_alpha(self):\n # gh-2089\n alpha = np.array([5.4e-01, -1.0e-16])\n assert_raises(ValueError, np.random.mtrand.dirichlet, alpha)\n\n # gh-15876\n assert_raises(ValueError, random.dirichlet, [[5, 1]])\n assert_raises(ValueError, random.dirichlet, [[5], [1]])\n assert_raises(ValueError, random.dirichlet, [[[5], [1]], [[1], [5]]])\n assert_raises(ValueError, random.dirichlet, np.array([[5, 1], [1, 5]]))\n\n def test_exponential(self):\n np.random.seed(self.seed)\n actual = np.random.exponential(1.1234, size=(3, 2))\n desired = np.array([[1.08342649775011624, 1.00607889924557314],\n [2.46628830085216721, 2.49668106809923884],\n [0.68717433461363442, 1.69175666993575979]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_exponential_0(self):\n assert_equal(np.random.exponential(scale=0), 0)\n assert_raises(ValueError, np.random.exponential, scale=-0.)\n\n def test_f(self):\n np.random.seed(self.seed)\n actual = np.random.f(12, 77, size=(3, 2))\n desired = np.array([[1.21975394418575878, 1.75135759791559775],\n [1.44803115017146489, 1.22108959480396262],\n [1.02176975757740629, 1.34431827623300415]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_gamma(self):\n np.random.seed(self.seed)\n actual = np.random.gamma(5, 3, size=(3, 2))\n desired = np.array([[24.60509188649287182, 28.54993563207210627],\n [26.13476110204064184, 12.56988482927716078],\n [31.71863275789960568, 33.30143302795922011]])\n assert_array_almost_equal(actual, desired, decimal=14)\n\n def test_gamma_0(self):\n assert_equal(np.random.gamma(shape=0, scale=0), 0)\n assert_raises(ValueError, np.random.gamma, shape=-0., scale=-0.)\n\n def test_geometric(self):\n np.random.seed(self.seed)\n actual = np.random.geometric(.123456789, size=(3, 2))\n desired = np.array([[8, 7],\n [17, 17],\n [5, 12]])\n assert_array_equal(actual, desired)\n\n def test_gumbel(self):\n np.random.seed(self.seed)\n actual = np.random.gumbel(loc=.123456789, scale=2.0, size=(3, 2))\n desired = np.array([[0.19591898743416816, 0.34405539668096674],\n [-1.4492522252274278, -1.47374816298446865],\n [1.10651090478803416, -0.69535848626236174]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_gumbel_0(self):\n assert_equal(np.random.gumbel(scale=0), 0)\n assert_raises(ValueError, np.random.gumbel, scale=-0.)\n\n def test_hypergeometric(self):\n np.random.seed(self.seed)\n actual = np.random.hypergeometric(10, 5, 14, size=(3, 2))\n desired = np.array([[10, 10],\n [10, 10],\n [9, 9]])\n assert_array_equal(actual, desired)\n\n # Test nbad = 0\n actual = np.random.hypergeometric(5, 0, 3, size=4)\n desired = np.array([3, 3, 3, 3])\n assert_array_equal(actual, desired)\n\n actual = np.random.hypergeometric(15, 0, 12, size=4)\n desired = np.array([12, 12, 12, 12])\n assert_array_equal(actual, desired)\n\n # Test ngood = 0\n actual = np.random.hypergeometric(0, 5, 3, size=4)\n desired = np.array([0, 0, 0, 0])\n assert_array_equal(actual, desired)\n\n actual = np.random.hypergeometric(0, 15, 12, size=4)\n desired = np.array([0, 0, 0, 0])\n assert_array_equal(actual, desired)\n\n def test_laplace(self):\n np.random.seed(self.seed)\n actual = np.random.laplace(loc=.123456789, scale=2.0, size=(3, 2))\n desired = np.array([[0.66599721112760157, 0.52829452552221945],\n [3.12791959514407125, 3.18202813572992005],\n [-0.05391065675859356, 1.74901336242837324]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_laplace_0(self):\n assert_equal(np.random.laplace(scale=0), 0)\n assert_raises(ValueError, np.random.laplace, scale=-0.)\n\n def test_logistic(self):\n np.random.seed(self.seed)\n actual = np.random.logistic(loc=.123456789, scale=2.0, size=(3, 2))\n desired = np.array([[1.09232835305011444, 0.8648196662399954],\n [4.27818590694950185, 4.33897006346929714],\n [-0.21682183359214885, 2.63373365386060332]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_lognormal(self):\n np.random.seed(self.seed)\n actual = np.random.lognormal(mean=.123456789, sigma=2.0, size=(3, 2))\n desired = np.array([[16.50698631688883822, 36.54846706092654784],\n [22.67886599981281748, 0.71617561058995771],\n [65.72798501792723869, 86.84341601437161273]])\n assert_array_almost_equal(actual, desired, decimal=13)\n\n def test_lognormal_0(self):\n assert_equal(np.random.lognormal(sigma=0), 1)\n assert_raises(ValueError, np.random.lognormal, sigma=-0.)\n\n def test_logseries(self):\n np.random.seed(self.seed)\n actual = np.random.logseries(p=.923456789, size=(3, 2))\n desired = np.array([[2, 2],\n [6, 17],\n [3, 6]])\n assert_array_equal(actual, desired)\n\n def test_multinomial(self):\n np.random.seed(self.seed)\n actual = np.random.multinomial(20, [1 / 6.] * 6, size=(3, 2))\n desired = np.array([[[4, 3, 5, 4, 2, 2],\n [5, 2, 8, 2, 2, 1]],\n [[3, 4, 3, 6, 0, 4],\n [2, 1, 4, 3, 6, 4]],\n [[4, 4, 2, 5, 2, 3],\n [4, 3, 4, 2, 3, 4]]])\n assert_array_equal(actual, desired)\n\n def test_multivariate_normal(self):\n np.random.seed(self.seed)\n mean = (.123456789, 10)\n cov = [[1, 0], [0, 1]]\n size = (3, 2)\n actual = np.random.multivariate_normal(mean, cov, size)\n desired = np.array([[[1.463620246718631, 11.73759122771936],\n [1.622445133300628, 9.771356667546383]],\n [[2.154490787682787, 12.170324946056553],\n [1.719909438201865, 9.230548443648306]],\n [[0.689515026297799, 9.880729819607714],\n [-0.023054015651998, 9.201096623542879]]])\n\n assert_array_almost_equal(actual, desired, decimal=15)\n\n # Check for default size, was raising deprecation warning\n actual = np.random.multivariate_normal(mean, cov)\n desired = np.array([0.895289569463708, 9.17180864067987])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n # Check that non positive-semidefinite covariance warns with\n # RuntimeWarning\n mean = [0, 0]\n cov = [[1, 2], [2, 1]]\n assert_warns(RuntimeWarning, np.random.multivariate_normal, mean, cov)\n\n # and that it doesn't warn with RuntimeWarning check_valid='ignore'\n assert_no_warnings(np.random.multivariate_normal, mean, cov,\n check_valid='ignore')\n\n # and that it raises with RuntimeWarning check_valid='raises'\n assert_raises(ValueError, np.random.multivariate_normal, mean, cov,\n check_valid='raise')\n\n cov = np.array([[1, 0.1], [0.1, 1]], dtype=np.float32)\n with suppress_warnings() as sup:\n np.random.multivariate_normal(mean, cov)\n w = sup.record(RuntimeWarning)\n assert len(w) == 0\n\n def test_negative_binomial(self):\n np.random.seed(self.seed)\n actual = np.random.negative_binomial(n=100, p=.12345, size=(3, 2))\n desired = np.array([[848, 841],\n [892, 611],\n [779, 647]])\n assert_array_equal(actual, desired)\n\n def test_noncentral_chisquare(self):\n np.random.seed(self.seed)\n actual = np.random.noncentral_chisquare(df=5, nonc=5, size=(3, 2))\n desired = np.array([[23.91905354498517511, 13.35324692733826346],\n [31.22452661329736401, 16.60047399466177254],\n [5.03461598262724586, 17.94973089023519464]])\n assert_array_almost_equal(actual, desired, decimal=14)\n\n actual = np.random.noncentral_chisquare(df=.5, nonc=.2, size=(3, 2))\n desired = np.array([[1.47145377828516666, 0.15052899268012659],\n [0.00943803056963588, 1.02647251615666169],\n [0.332334982684171, 0.15451287602753125]])\n assert_array_almost_equal(actual, desired, decimal=14)\n\n np.random.seed(self.seed)\n actual = np.random.noncentral_chisquare(df=5, nonc=0, size=(3, 2))\n desired = np.array([[9.597154162763948, 11.725484450296079],\n [10.413711048138335, 3.694475922923986],\n [13.484222138963087, 14.377255424602957]])\n assert_array_almost_equal(actual, desired, decimal=14)\n\n def test_noncentral_f(self):\n np.random.seed(self.seed)\n actual = np.random.noncentral_f(dfnum=5, dfden=2, nonc=1,\n size=(3, 2))\n desired = np.array([[1.40598099674926669, 0.34207973179285761],\n [3.57715069265772545, 7.92632662577829805],\n [0.43741599463544162, 1.1774208752428319]])\n assert_array_almost_equal(actual, desired, decimal=14)\n\n def test_normal(self):\n np.random.seed(self.seed)\n actual = np.random.normal(loc=.123456789, scale=2.0, size=(3, 2))\n desired = np.array([[2.80378370443726244, 3.59863924443872163],\n [3.121433477601256, -0.33382987590723379],\n [4.18552478636557357, 4.46410668111310471]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_normal_0(self):\n assert_equal(np.random.normal(scale=0), 0)\n assert_raises(ValueError, np.random.normal, scale=-0.)\n\n def test_pareto(self):\n np.random.seed(self.seed)\n actual = np.random.pareto(a=.123456789, size=(3, 2))\n desired = np.array(\n [[2.46852460439034849e+03, 1.41286880810518346e+03],\n [5.28287797029485181e+07, 6.57720981047328785e+07],\n [1.40840323350391515e+02, 1.98390255135251704e+05]])\n # For some reason on 32-bit x86 Ubuntu 12.10 the [1, 0] entry in this\n # matrix differs by 24 nulps. Discussion:\n # https://mail.python.org/pipermail/numpy-discussion/2012-September/063801.html\n # Consensus is that this is probably some gcc quirk that affects\n # rounding but not in any important way, so we just use a looser\n # tolerance on this test:\n np.testing.assert_array_almost_equal_nulp(actual, desired, nulp=30)\n\n def test_poisson(self):\n np.random.seed(self.seed)\n actual = np.random.poisson(lam=.123456789, size=(3, 2))\n desired = np.array([[0, 0],\n [1, 0],\n [0, 0]])\n assert_array_equal(actual, desired)\n\n def test_poisson_exceptions(self):\n lambig = np.iinfo('l').max\n lamneg = -1\n assert_raises(ValueError, np.random.poisson, lamneg)\n assert_raises(ValueError, np.random.poisson, [lamneg] * 10)\n assert_raises(ValueError, np.random.poisson, lambig)\n assert_raises(ValueError, np.random.poisson, [lambig] * 10)\n\n def test_power(self):\n np.random.seed(self.seed)\n actual = np.random.power(a=.123456789, size=(3, 2))\n desired = np.array([[0.02048932883240791, 0.01424192241128213],\n [0.38446073748535298, 0.39499689943484395],\n [0.00177699707563439, 0.13115505880863756]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_rayleigh(self):\n np.random.seed(self.seed)\n actual = np.random.rayleigh(scale=10, size=(3, 2))\n desired = np.array([[13.8882496494248393, 13.383318339044731],\n [20.95413364294492098, 21.08285015800712614],\n [11.06066537006854311, 17.35468505778271009]])\n assert_array_almost_equal(actual, desired, decimal=14)\n\n def test_rayleigh_0(self):\n assert_equal(np.random.rayleigh(scale=0), 0)\n assert_raises(ValueError, np.random.rayleigh, scale=-0.)\n\n def test_standard_cauchy(self):\n np.random.seed(self.seed)\n actual = np.random.standard_cauchy(size=(3, 2))\n desired = np.array([[0.77127660196445336, -6.55601161955910605],\n [0.93582023391158309, -2.07479293013759447],\n [-4.74601644297011926, 0.18338989290760804]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_standard_exponential(self):\n np.random.seed(self.seed)\n actual = np.random.standard_exponential(size=(3, 2))\n desired = np.array([[0.96441739162374596, 0.89556604882105506],\n [2.1953785836319808, 2.22243285392490542],\n [0.6116915921431676, 1.50592546727413201]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_standard_gamma(self):\n np.random.seed(self.seed)\n actual = np.random.standard_gamma(shape=3, size=(3, 2))\n desired = np.array([[5.50841531318455058, 6.62953470301903103],\n [5.93988484943779227, 2.31044849402133989],\n [7.54838614231317084, 8.012756093271868]])\n assert_array_almost_equal(actual, desired, decimal=14)\n\n def test_standard_gamma_0(self):\n assert_equal(np.random.standard_gamma(shape=0), 0)\n assert_raises(ValueError, np.random.standard_gamma, shape=-0.)\n\n def test_standard_normal(self):\n np.random.seed(self.seed)\n actual = np.random.standard_normal(size=(3, 2))\n desired = np.array([[1.34016345771863121, 1.73759122771936081],\n [1.498988344300628, -0.2286433324536169],\n [2.031033998682787, 2.17032494605655257]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_standard_t(self):\n np.random.seed(self.seed)\n actual = np.random.standard_t(df=10, size=(3, 2))\n desired = np.array([[0.97140611862659965, -0.08830486548450577],\n [1.36311143689505321, -0.55317463909867071],\n [-0.18473749069684214, 0.61181537341755321]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_triangular(self):\n np.random.seed(self.seed)\n actual = np.random.triangular(left=5.12, mode=10.23, right=20.34,\n size=(3, 2))\n desired = np.array([[12.68117178949215784, 12.4129206149193152],\n [16.20131377335158263, 16.25692138747600524],\n [11.20400690911820263, 14.4978144835829923]])\n assert_array_almost_equal(actual, desired, decimal=14)\n\n def test_uniform(self):\n np.random.seed(self.seed)\n actual = np.random.uniform(low=1.23, high=10.54, size=(3, 2))\n desired = np.array([[6.99097932346268003, 6.73801597444323974],\n [9.50364421400426274, 9.53130618907631089],\n [5.48995325769805476, 8.47493103280052118]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_uniform_range_bounds(self):\n fmin = np.finfo('float').min\n fmax = np.finfo('float').max\n\n func = np.random.uniform\n assert_raises(OverflowError, func, -np.inf, 0)\n assert_raises(OverflowError, func, 0, np.inf)\n assert_raises(OverflowError, func, fmin, fmax)\n assert_raises(OverflowError, func, [-np.inf], [0])\n assert_raises(OverflowError, func, [0], [np.inf])\n\n # (fmax / 1e17) - fmin is within range, so this should not throw\n # account for i386 extended precision DBL_MAX / 1e17 + DBL_MAX >\n # DBL_MAX by increasing fmin a bit\n np.random.uniform(low=np.nextafter(fmin, 1), high=fmax / 1e17)\n\n def test_scalar_exception_propagation(self):\n # Tests that exceptions are correctly propagated in distributions\n # when called with objects that throw exceptions when converted to\n # scalars.\n #\n # Regression test for gh: 8865\n\n class ThrowingFloat(np.ndarray):\n def __float__(self):\n raise TypeError\n\n throwing_float = np.array(1.0).view(ThrowingFloat)\n assert_raises(TypeError, np.random.uniform, throwing_float,\n throwing_float)\n\n class ThrowingInteger(np.ndarray):\n def __int__(self):\n raise TypeError\n\n __index__ = __int__\n\n throwing_int = np.array(1).view(ThrowingInteger)\n assert_raises(TypeError, np.random.hypergeometric, throwing_int, 1, 1)\n\n def test_vonmises(self):\n np.random.seed(self.seed)\n actual = np.random.vonmises(mu=1.23, kappa=1.54, size=(3, 2))\n desired = np.array([[2.28567572673902042, 2.89163838442285037],\n [0.38198375564286025, 2.57638023113890746],\n [1.19153771588353052, 1.83509849681825354]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_vonmises_small(self):\n # check infinite loop, gh-4720\n np.random.seed(self.seed)\n r = np.random.vonmises(mu=0., kappa=1.1e-8, size=10**6)\n np.testing.assert_(np.isfinite(r).all())\n\n def test_wald(self):\n np.random.seed(self.seed)\n actual = np.random.wald(mean=1.23, scale=1.54, size=(3, 2))\n desired = np.array([[3.82935265715889983, 5.13125249184285526],\n [0.35045403618358717, 1.50832396872003538],\n [0.24124319895843183, 0.22031101461955038]])\n assert_array_almost_equal(actual, desired, decimal=14)\n\n def test_weibull(self):\n np.random.seed(self.seed)\n actual = np.random.weibull(a=1.23, size=(3, 2))\n desired = np.array([[0.97097342648766727, 0.91422896443565516],\n [1.89517770034962929, 1.91414357960479564],\n [0.67057783752390987, 1.39494046635066793]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_weibull_0(self):\n np.random.seed(self.seed)\n assert_equal(np.random.weibull(a=0, size=12), np.zeros(12))\n assert_raises(ValueError, np.random.weibull, a=-0.)\n\n def test_zipf(self):\n np.random.seed(self.seed)\n actual = np.random.zipf(a=1.23, size=(3, 2))\n desired = np.array([[66, 29],\n [1, 1],\n [3, 13]])\n assert_array_equal(actual, desired)\n\n\nclass TestBroadcast:\n # tests that functions that broadcast behave\n # correctly when presented with non-scalar arguments\n def setup_method(self):\n self.seed = 123456789\n\n def setSeed(self):\n np.random.seed(self.seed)\n\n # TODO: Include test for randint once it can broadcast\n # Can steal the test written in PR #6938\n\n def test_uniform(self):\n low = [0]\n high = [1]\n uniform = np.random.uniform\n desired = np.array([0.53283302478975902,\n 0.53413660089041659,\n 0.50955303552646702])\n\n self.setSeed()\n actual = uniform(low * 3, high)\n assert_array_almost_equal(actual, desired, decimal=14)\n\n self.setSeed()\n actual = uniform(low, high * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n\n def test_normal(self):\n loc = [0]\n scale = [1]\n bad_scale = [-1]\n normal = np.random.normal\n desired = np.array([2.2129019979039612,\n 2.1283977976520019,\n 1.8417114045748335])\n\n self.setSeed()\n actual = normal(loc * 3, scale)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, normal, loc * 3, bad_scale)\n\n self.setSeed()\n actual = normal(loc, scale * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, normal, loc, bad_scale * 3)\n\n def test_beta(self):\n a = [1]\n b = [2]\n bad_a = [-1]\n bad_b = [-2]\n beta = np.random.beta\n desired = np.array([0.19843558305989056,\n 0.075230336409423643,\n 0.24976865978980844])\n\n self.setSeed()\n actual = beta(a * 3, b)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, beta, bad_a * 3, b)\n assert_raises(ValueError, beta, a * 3, bad_b)\n\n self.setSeed()\n actual = beta(a, b * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, beta, bad_a, b * 3)\n assert_raises(ValueError, beta, a, bad_b * 3)\n\n def test_exponential(self):\n scale = [1]\n bad_scale = [-1]\n exponential = np.random.exponential\n desired = np.array([0.76106853658845242,\n 0.76386282278691653,\n 0.71243813125891797])\n\n self.setSeed()\n actual = exponential(scale * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, exponential, bad_scale * 3)\n\n def test_standard_gamma(self):\n shape = [1]\n bad_shape = [-1]\n std_gamma = np.random.standard_gamma\n desired = np.array([0.76106853658845242,\n 0.76386282278691653,\n 0.71243813125891797])\n\n self.setSeed()\n actual = std_gamma(shape * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, std_gamma, bad_shape * 3)\n\n def test_gamma(self):\n shape = [1]\n scale = [2]\n bad_shape = [-1]\n bad_scale = [-2]\n gamma = np.random.gamma\n desired = np.array([1.5221370731769048,\n 1.5277256455738331,\n 1.4248762625178359])\n\n self.setSeed()\n actual = gamma(shape * 3, scale)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, gamma, bad_shape * 3, scale)\n assert_raises(ValueError, gamma, shape * 3, bad_scale)\n\n self.setSeed()\n actual = gamma(shape, scale * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, gamma, bad_shape, scale * 3)\n assert_raises(ValueError, gamma, shape, bad_scale * 3)\n\n def test_f(self):\n dfnum = [1]\n dfden = [2]\n bad_dfnum = [-1]\n bad_dfden = [-2]\n f = np.random.f\n desired = np.array([0.80038951638264799,\n 0.86768719635363512,\n 2.7251095168386801])\n\n self.setSeed()\n actual = f(dfnum * 3, dfden)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, f, bad_dfnum * 3, dfden)\n assert_raises(ValueError, f, dfnum * 3, bad_dfden)\n\n self.setSeed()\n actual = f(dfnum, dfden * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, f, bad_dfnum, dfden * 3)\n assert_raises(ValueError, f, dfnum, bad_dfden * 3)\n\n def test_noncentral_f(self):\n dfnum = [2]\n dfden = [3]\n nonc = [4]\n bad_dfnum = [0]\n bad_dfden = [-1]\n bad_nonc = [-2]\n nonc_f = np.random.noncentral_f\n desired = np.array([9.1393943263705211,\n 13.025456344595602,\n 8.8018098359100545])\n\n self.setSeed()\n actual = nonc_f(dfnum * 3, dfden, nonc)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, nonc_f, bad_dfnum * 3, dfden, nonc)\n assert_raises(ValueError, nonc_f, dfnum * 3, bad_dfden, nonc)\n assert_raises(ValueError, nonc_f, dfnum * 3, dfden, bad_nonc)\n\n self.setSeed()\n actual = nonc_f(dfnum, dfden * 3, nonc)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, nonc_f, bad_dfnum, dfden * 3, nonc)\n assert_raises(ValueError, nonc_f, dfnum, bad_dfden * 3, nonc)\n assert_raises(ValueError, nonc_f, dfnum, dfden * 3, bad_nonc)\n\n self.setSeed()\n actual = nonc_f(dfnum, dfden, nonc * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, nonc_f, bad_dfnum, dfden, nonc * 3)\n assert_raises(ValueError, nonc_f, dfnum, bad_dfden, nonc * 3)\n assert_raises(ValueError, nonc_f, dfnum, dfden, bad_nonc * 3)\n\n def test_noncentral_f_small_df(self):\n self.setSeed()\n desired = np.array([6.869638627492048, 0.785880199263955])\n actual = np.random.noncentral_f(0.9, 0.9, 2, size=2)\n assert_array_almost_equal(actual, desired, decimal=14)\n\n def test_chisquare(self):\n df = [1]\n bad_df = [-1]\n chisquare = np.random.chisquare\n desired = np.array([0.57022801133088286,\n 0.51947702108840776,\n 0.1320969254923558])\n\n self.setSeed()\n actual = chisquare(df * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, chisquare, bad_df * 3)\n\n def test_noncentral_chisquare(self):\n df = [1]\n nonc = [2]\n bad_df = [-1]\n bad_nonc = [-2]\n nonc_chi = np.random.noncentral_chisquare\n desired = np.array([9.0015599467913763,\n 4.5804135049718742,\n 6.0872302432834564])\n\n self.setSeed()\n actual = nonc_chi(df * 3, nonc)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, nonc_chi, bad_df * 3, nonc)\n assert_raises(ValueError, nonc_chi, df * 3, bad_nonc)\n\n self.setSeed()\n actual = nonc_chi(df, nonc * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, nonc_chi, bad_df, nonc * 3)\n assert_raises(ValueError, nonc_chi, df, bad_nonc * 3)\n\n def test_standard_t(self):\n df = [1]\n bad_df = [-1]\n t = np.random.standard_t\n desired = np.array([3.0702872575217643,\n 5.8560725167361607,\n 1.0274791436474273])\n\n self.setSeed()\n actual = t(df * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, t, bad_df * 3)\n\n def test_vonmises(self):\n mu = [2]\n kappa = [1]\n bad_kappa = [-1]\n vonmises = np.random.vonmises\n desired = np.array([2.9883443664201312,\n -2.7064099483995943,\n -1.8672476700665914])\n\n self.setSeed()\n actual = vonmises(mu * 3, kappa)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, vonmises, mu * 3, bad_kappa)\n\n self.setSeed()\n actual = vonmises(mu, kappa * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, vonmises, mu, bad_kappa * 3)\n\n def test_pareto(self):\n a = [1]\n bad_a = [-1]\n pareto = np.random.pareto\n desired = np.array([1.1405622680198362,\n 1.1465519762044529,\n 1.0389564467453547])\n\n self.setSeed()\n actual = pareto(a * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, pareto, bad_a * 3)\n\n def test_weibull(self):\n a = [1]\n bad_a = [-1]\n weibull = np.random.weibull\n desired = np.array([0.76106853658845242,\n 0.76386282278691653,\n 0.71243813125891797])\n\n self.setSeed()\n actual = weibull(a * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, weibull, bad_a * 3)\n\n def test_power(self):\n a = [1]\n bad_a = [-1]\n power = np.random.power\n desired = np.array([0.53283302478975902,\n 0.53413660089041659,\n 0.50955303552646702])\n\n self.setSeed()\n actual = power(a * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, power, bad_a * 3)\n\n def test_laplace(self):\n loc = [0]\n scale = [1]\n bad_scale = [-1]\n laplace = np.random.laplace\n desired = np.array([0.067921356028507157,\n 0.070715642226971326,\n 0.019290950698972624])\n\n self.setSeed()\n actual = laplace(loc * 3, scale)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, laplace, loc * 3, bad_scale)\n\n self.setSeed()\n actual = laplace(loc, scale * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, laplace, loc, bad_scale * 3)\n\n def test_gumbel(self):\n loc = [0]\n scale = [1]\n bad_scale = [-1]\n gumbel = np.random.gumbel\n desired = np.array([0.2730318639556768,\n 0.26936705726291116,\n 0.33906220393037939])\n\n self.setSeed()\n actual = gumbel(loc * 3, scale)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, gumbel, loc * 3, bad_scale)\n\n self.setSeed()\n actual = gumbel(loc, scale * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, gumbel, loc, bad_scale * 3)\n\n def test_logistic(self):\n loc = [0]\n scale = [1]\n bad_scale = [-1]\n logistic = np.random.logistic\n desired = np.array([0.13152135837586171,\n 0.13675915696285773,\n 0.038216792802833396])\n\n self.setSeed()\n actual = logistic(loc * 3, scale)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, logistic, loc * 3, bad_scale)\n\n self.setSeed()\n actual = logistic(loc, scale * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, logistic, loc, bad_scale * 3)\n\n def test_lognormal(self):\n mean = [0]\n sigma = [1]\n bad_sigma = [-1]\n lognormal = np.random.lognormal\n desired = np.array([9.1422086044848427,\n 8.4013952870126261,\n 6.3073234116578671])\n\n self.setSeed()\n actual = lognormal(mean * 3, sigma)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, lognormal, mean * 3, bad_sigma)\n\n self.setSeed()\n actual = lognormal(mean, sigma * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, lognormal, mean, bad_sigma * 3)\n\n def test_rayleigh(self):\n scale = [1]\n bad_scale = [-1]\n rayleigh = np.random.rayleigh\n desired = np.array([1.2337491937897689,\n 1.2360119924878694,\n 1.1936818095781789])\n\n self.setSeed()\n actual = rayleigh(scale * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, rayleigh, bad_scale * 3)\n\n def test_wald(self):\n mean = [0.5]\n scale = [1]\n bad_mean = [0]\n bad_scale = [-2]\n wald = np.random.wald\n desired = np.array([0.11873681120271318,\n 0.12450084820795027,\n 0.9096122728408238])\n\n self.setSeed()\n actual = wald(mean * 3, scale)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, wald, bad_mean * 3, scale)\n assert_raises(ValueError, wald, mean * 3, bad_scale)\n\n self.setSeed()\n actual = wald(mean, scale * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, wald, bad_mean, scale * 3)\n assert_raises(ValueError, wald, mean, bad_scale * 3)\n assert_raises(ValueError, wald, 0.0, 1)\n assert_raises(ValueError, wald, 0.5, 0.0)\n\n def test_triangular(self):\n left = [1]\n right = [3]\n mode = [2]\n bad_left_one = [3]\n bad_mode_one = [4]\n bad_left_two, bad_mode_two = right * 2\n triangular = np.random.triangular\n desired = np.array([2.03339048710429,\n 2.0347400359389356,\n 2.0095991069536208])\n\n self.setSeed()\n actual = triangular(left * 3, mode, right)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, triangular, bad_left_one * 3, mode, right)\n assert_raises(ValueError, triangular, left * 3, bad_mode_one, right)\n assert_raises(ValueError, triangular, bad_left_two * 3, bad_mode_two,\n right)\n\n self.setSeed()\n actual = triangular(left, mode * 3, right)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, triangular, bad_left_one, mode * 3, right)\n assert_raises(ValueError, triangular, left, bad_mode_one * 3, right)\n assert_raises(ValueError, triangular, bad_left_two, bad_mode_two * 3,\n right)\n\n self.setSeed()\n actual = triangular(left, mode, right * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, triangular, bad_left_one, mode, right * 3)\n assert_raises(ValueError, triangular, left, bad_mode_one, right * 3)\n assert_raises(ValueError, triangular, bad_left_two, bad_mode_two,\n right * 3)\n\n def test_binomial(self):\n n = [1]\n p = [0.5]\n bad_n = [-1]\n bad_p_one = [-1]\n bad_p_two = [1.5]\n binom = np.random.binomial\n desired = np.array([1, 1, 1])\n\n self.setSeed()\n actual = binom(n * 3, p)\n assert_array_equal(actual, desired)\n assert_raises(ValueError, binom, bad_n * 3, p)\n assert_raises(ValueError, binom, n * 3, bad_p_one)\n assert_raises(ValueError, binom, n * 3, bad_p_two)\n\n self.setSeed()\n actual = binom(n, p * 3)\n assert_array_equal(actual, desired)\n assert_raises(ValueError, binom, bad_n, p * 3)\n assert_raises(ValueError, binom, n, bad_p_one * 3)\n assert_raises(ValueError, binom, n, bad_p_two * 3)\n\n def test_negative_binomial(self):\n n = [1]\n p = [0.5]\n bad_n = [-1]\n bad_p_one = [-1]\n bad_p_two = [1.5]\n neg_binom = np.random.negative_binomial\n desired = np.array([1, 0, 1])\n\n self.setSeed()\n actual = neg_binom(n * 3, p)\n assert_array_equal(actual, desired)\n assert_raises(ValueError, neg_binom, bad_n * 3, p)\n assert_raises(ValueError, neg_binom, n * 3, bad_p_one)\n assert_raises(ValueError, neg_binom, n * 3, bad_p_two)\n\n self.setSeed()\n actual = neg_binom(n, p * 3)\n assert_array_equal(actual, desired)\n assert_raises(ValueError, neg_binom, bad_n, p * 3)\n assert_raises(ValueError, neg_binom, n, bad_p_one * 3)\n assert_raises(ValueError, neg_binom, n, bad_p_two * 3)\n\n def test_poisson(self):\n max_lam = np.random.RandomState()._poisson_lam_max\n\n lam = [1]\n bad_lam_one = [-1]\n bad_lam_two = [max_lam * 2]\n poisson = np.random.poisson\n desired = np.array([1, 1, 0])\n\n self.setSeed()\n actual = poisson(lam * 3)\n assert_array_equal(actual, desired)\n assert_raises(ValueError, poisson, bad_lam_one * 3)\n assert_raises(ValueError, poisson, bad_lam_two * 3)\n\n def test_zipf(self):\n a = [2]\n bad_a = [0]\n zipf = np.random.zipf\n desired = np.array([2, 2, 1])\n\n self.setSeed()\n actual = zipf(a * 3)\n assert_array_equal(actual, desired)\n assert_raises(ValueError, zipf, bad_a * 3)\n with np.errstate(invalid='ignore'):\n assert_raises(ValueError, zipf, np.nan)\n assert_raises(ValueError, zipf, [0, 0, np.nan])\n\n def test_geometric(self):\n p = [0.5]\n bad_p_one = [-1]\n bad_p_two = [1.5]\n geom = np.random.geometric\n desired = np.array([2, 2, 2])\n\n self.setSeed()\n actual = geom(p * 3)\n assert_array_equal(actual, desired)\n assert_raises(ValueError, geom, bad_p_one * 3)\n assert_raises(ValueError, geom, bad_p_two * 3)\n\n def test_hypergeometric(self):\n ngood = [1]\n nbad = [2]\n nsample = [2]\n bad_ngood = [-1]\n bad_nbad = [-2]\n bad_nsample_one = [0]\n bad_nsample_two = [4]\n hypergeom = np.random.hypergeometric\n desired = np.array([1, 1, 1])\n\n self.setSeed()\n actual = hypergeom(ngood * 3, nbad, nsample)\n assert_array_equal(actual, desired)\n assert_raises(ValueError, hypergeom, bad_ngood * 3, nbad, nsample)\n assert_raises(ValueError, hypergeom, ngood * 3, bad_nbad, nsample)\n assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_one)\n assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_two)\n\n self.setSeed()\n actual = hypergeom(ngood, nbad * 3, nsample)\n assert_array_equal(actual, desired)\n assert_raises(ValueError, hypergeom, bad_ngood, nbad * 3, nsample)\n assert_raises(ValueError, hypergeom, ngood, bad_nbad * 3, nsample)\n assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_one)\n assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_two)\n\n self.setSeed()\n actual = hypergeom(ngood, nbad, nsample * 3)\n assert_array_equal(actual, desired)\n assert_raises(ValueError, hypergeom, bad_ngood, nbad, nsample * 3)\n assert_raises(ValueError, hypergeom, ngood, bad_nbad, nsample * 3)\n assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_one * 3)\n assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_two * 3)\n\n def test_logseries(self):\n p = [0.5]\n bad_p_one = [2]\n bad_p_two = [-1]\n logseries = np.random.logseries\n desired = np.array([1, 1, 1])\n\n self.setSeed()\n actual = logseries(p * 3)\n assert_array_equal(actual, desired)\n assert_raises(ValueError, logseries, bad_p_one * 3)\n assert_raises(ValueError, logseries, bad_p_two * 3)\n\n\n@pytest.mark.skipif(IS_WASM, reason="can't start thread")\nclass TestThread:\n # make sure each state produces the same sequence even in threads\n def setup_method(self):\n self.seeds = range(4)\n\n def check_function(self, function, sz):\n from threading import Thread\n\n out1 = np.empty((len(self.seeds),) + sz)\n out2 = np.empty((len(self.seeds),) + sz)\n\n # threaded generation\n t = [Thread(target=function, args=(np.random.RandomState(s), o))\n for s, o in zip(self.seeds, out1)]\n [x.start() for x in t]\n [x.join() for x in t]\n\n # the same serial\n for s, o in zip(self.seeds, out2):\n function(np.random.RandomState(s), o)\n\n # these platforms change x87 fpu precision mode in threads\n if np.intp().dtype.itemsize == 4 and sys.platform == "win32":\n assert_array_almost_equal(out1, out2)\n else:\n assert_array_equal(out1, out2)\n\n def test_normal(self):\n def gen_random(state, out):\n out[...] = state.normal(size=10000)\n self.check_function(gen_random, sz=(10000,))\n\n def test_exp(self):\n def gen_random(state, out):\n out[...] = state.exponential(scale=np.ones((100, 1000)))\n self.check_function(gen_random, sz=(100, 1000))\n\n def test_multinomial(self):\n def gen_random(state, out):\n out[...] = state.multinomial(10, [1 / 6.] * 6, size=10000)\n self.check_function(gen_random, sz=(10000, 6))\n\n\n# See Issue #4263\nclass TestSingleEltArrayInput:\n def setup_method(self):\n self.argOne = np.array([2])\n self.argTwo = np.array([3])\n self.argThree = np.array([4])\n self.tgtShape = (1,)\n\n def test_one_arg_funcs(self):\n funcs = (np.random.exponential, np.random.standard_gamma,\n np.random.chisquare, np.random.standard_t,\n np.random.pareto, np.random.weibull,\n np.random.power, np.random.rayleigh,\n np.random.poisson, np.random.zipf,\n np.random.geometric, np.random.logseries)\n\n probfuncs = (np.random.geometric, np.random.logseries)\n\n for func in funcs:\n if func in probfuncs: # p < 1.0\n out = func(np.array([0.5]))\n\n else:\n out = func(self.argOne)\n\n assert_equal(out.shape, self.tgtShape)\n\n def test_two_arg_funcs(self):\n funcs = (np.random.uniform, np.random.normal,\n np.random.beta, np.random.gamma,\n np.random.f, np.random.noncentral_chisquare,\n np.random.vonmises, np.random.laplace,\n np.random.gumbel, np.random.logistic,\n np.random.lognormal, np.random.wald,\n np.random.binomial, np.random.negative_binomial)\n\n probfuncs = (np.random.binomial, np.random.negative_binomial)\n\n for func in funcs:\n if func in probfuncs: # p <= 1\n argTwo = np.array([0.5])\n\n else:\n argTwo = self.argTwo\n\n out = func(self.argOne, argTwo)\n assert_equal(out.shape, self.tgtShape)\n\n out = func(self.argOne[0], argTwo)\n assert_equal(out.shape, self.tgtShape)\n\n out = func(self.argOne, argTwo[0])\n assert_equal(out.shape, self.tgtShape)\n\n def test_randint(self):\n itype = [bool, np.int8, np.uint8, np.int16, np.uint16,\n np.int32, np.uint32, np.int64, np.uint64]\n func = np.random.randint\n high = np.array([1])\n low = np.array([0])\n\n for dt in itype:\n out = func(low, high, dtype=dt)\n assert_equal(out.shape, self.tgtShape)\n\n out = func(low[0], high, dtype=dt)\n assert_equal(out.shape, self.tgtShape)\n\n out = func(low, high[0], dtype=dt)\n assert_equal(out.shape, self.tgtShape)\n\n def test_three_arg_funcs(self):\n funcs = [np.random.noncentral_f, np.random.triangular,\n np.random.hypergeometric]\n\n for func in funcs:\n out = func(self.argOne, self.argTwo, self.argThree)\n assert_equal(out.shape, self.tgtShape)\n\n out = func(self.argOne[0], self.argTwo, self.argThree)\n assert_equal(out.shape, self.tgtShape)\n\n out = func(self.argOne, self.argTwo[0], self.argThree)\n assert_equal(out.shape, self.tgtShape)\n | .venv\Lib\site-packages\numpy\random\tests\test_random.py | test_random.py | Python | 72,055 | 0.75 | 0.123506 | 0.066892 | awesome-app | 261 | 2025-03-01T20:45:03.007861 | Apache-2.0 | true | 7f311a88525df5ccc54291c36f898027 |
import hashlib\nimport pickle\nimport sys\nimport warnings\n\nimport pytest\n\nimport numpy as np\nfrom numpy import random\nfrom numpy.random import MT19937, PCG64\nfrom numpy.testing import (\n IS_WASM,\n assert_,\n assert_array_almost_equal,\n assert_array_equal,\n assert_equal,\n assert_no_warnings,\n assert_raises,\n assert_warns,\n suppress_warnings,\n)\n\nINT_FUNCS = {'binomial': (100.0, 0.6),\n 'geometric': (.5,),\n 'hypergeometric': (20, 20, 10),\n 'logseries': (.5,),\n 'multinomial': (20, np.ones(6) / 6.0),\n 'negative_binomial': (100, .5),\n 'poisson': (10.0,),\n 'zipf': (2,),\n }\n\nif np.iinfo(np.long).max < 2**32:\n # Windows and some 32-bit platforms, e.g., ARM\n INT_FUNC_HASHES = {'binomial': '2fbead005fc63942decb5326d36a1f32fe2c9d32c904ee61e46866b88447c263', # noqa: E501\n 'logseries': '23ead5dcde35d4cfd4ef2c105e4c3d43304b45dc1b1444b7823b9ee4fa144ebb', # noqa: E501\n 'geometric': '0d764db64f5c3bad48c8c33551c13b4d07a1e7b470f77629bef6c985cac76fcf', # noqa: E501\n 'hypergeometric': '7b59bf2f1691626c5815cdcd9a49e1dd68697251d4521575219e4d2a1b8b2c67', # noqa: E501\n 'multinomial': 'd754fa5b92943a38ec07630de92362dd2e02c43577fc147417dc5b9db94ccdd3', # noqa: E501\n 'negative_binomial': '8eb216f7cb2a63cf55605422845caaff002fddc64a7dc8b2d45acd477a49e824', # noqa: E501\n 'poisson': '70c891d76104013ebd6f6bcf30d403a9074b886ff62e4e6b8eb605bf1a4673b7', # noqa: E501\n 'zipf': '01f074f97517cd5d21747148ac6ca4074dde7fcb7acbaec0a936606fecacd93f', # noqa: E501\n }\nelse:\n INT_FUNC_HASHES = {'binomial': '8626dd9d052cb608e93d8868de0a7b347258b199493871a1dc56e2a26cacb112', # noqa: E501\n 'geometric': '8edd53d272e49c4fc8fbbe6c7d08d563d62e482921f3131d0a0e068af30f0db9', # noqa: E501\n 'hypergeometric': '83496cc4281c77b786c9b7ad88b74d42e01603a55c60577ebab81c3ba8d45657', # noqa: E501\n 'logseries': '65878a38747c176bc00e930ebafebb69d4e1e16cd3a704e264ea8f5e24f548db', # noqa: E501\n 'multinomial': '7a984ae6dca26fd25374479e118b22f55db0aedccd5a0f2584ceada33db98605', # noqa: E501\n 'negative_binomial': 'd636d968e6a24ae92ab52fe11c46ac45b0897e98714426764e820a7d77602a61', # noqa: E501\n 'poisson': '956552176f77e7c9cb20d0118fc9cf690be488d790ed4b4c4747b965e61b0bb4', # noqa: E501\n 'zipf': 'f84ba7feffda41e606e20b28dfc0f1ea9964a74574513d4a4cbc98433a8bfa45', # noqa: E501\n }\n\n\n@pytest.fixture(scope='module', params=INT_FUNCS)\ndef int_func(request):\n return (request.param, INT_FUNCS[request.param],\n INT_FUNC_HASHES[request.param])\n\n\n@pytest.fixture\ndef restore_singleton_bitgen():\n """Ensures that the singleton bitgen is restored after a test"""\n orig_bitgen = np.random.get_bit_generator()\n yield\n np.random.set_bit_generator(orig_bitgen)\n\n\ndef assert_mt19937_state_equal(a, b):\n assert_equal(a['bit_generator'], b['bit_generator'])\n assert_array_equal(a['state']['key'], b['state']['key'])\n assert_array_equal(a['state']['pos'], b['state']['pos'])\n assert_equal(a['has_gauss'], b['has_gauss'])\n assert_equal(a['gauss'], b['gauss'])\n\n\nclass TestSeed:\n def test_scalar(self):\n s = random.RandomState(0)\n assert_equal(s.randint(1000), 684)\n s = random.RandomState(4294967295)\n assert_equal(s.randint(1000), 419)\n\n def test_array(self):\n s = random.RandomState(range(10))\n assert_equal(s.randint(1000), 468)\n s = random.RandomState(np.arange(10))\n assert_equal(s.randint(1000), 468)\n s = random.RandomState([0])\n assert_equal(s.randint(1000), 973)\n s = random.RandomState([4294967295])\n assert_equal(s.randint(1000), 265)\n\n def test_invalid_scalar(self):\n # seed must be an unsigned 32 bit integer\n assert_raises(TypeError, random.RandomState, -0.5)\n assert_raises(ValueError, random.RandomState, -1)\n\n def test_invalid_array(self):\n # seed must be an unsigned 32 bit integer\n assert_raises(TypeError, random.RandomState, [-0.5])\n assert_raises(ValueError, random.RandomState, [-1])\n assert_raises(ValueError, random.RandomState, [4294967296])\n assert_raises(ValueError, random.RandomState, [1, 2, 4294967296])\n assert_raises(ValueError, random.RandomState, [1, -2, 4294967296])\n\n def test_invalid_array_shape(self):\n # gh-9832\n assert_raises(ValueError, random.RandomState, np.array([],\n dtype=np.int64))\n assert_raises(ValueError, random.RandomState, [[1, 2, 3]])\n assert_raises(ValueError, random.RandomState, [[1, 2, 3],\n [4, 5, 6]])\n\n def test_cannot_seed(self):\n rs = random.RandomState(PCG64(0))\n with assert_raises(TypeError):\n rs.seed(1234)\n\n def test_invalid_initialization(self):\n assert_raises(ValueError, random.RandomState, MT19937)\n\n\nclass TestBinomial:\n def test_n_zero(self):\n # Tests the corner case of n == 0 for the binomial distribution.\n # binomial(0, p) should be zero for any p in [0, 1].\n # This test addresses issue #3480.\n zeros = np.zeros(2, dtype='int')\n for p in [0, .5, 1]:\n assert_(random.binomial(0, p) == 0)\n assert_array_equal(random.binomial(zeros, p), zeros)\n\n def test_p_is_nan(self):\n # Issue #4571.\n assert_raises(ValueError, random.binomial, 1, np.nan)\n\n\nclass TestMultinomial:\n def test_basic(self):\n random.multinomial(100, [0.2, 0.8])\n\n def test_zero_probability(self):\n random.multinomial(100, [0.2, 0.8, 0.0, 0.0, 0.0])\n\n def test_int_negative_interval(self):\n assert_(-5 <= random.randint(-5, -1) < -1)\n x = random.randint(-5, -1, 5)\n assert_(np.all(-5 <= x))\n assert_(np.all(x < -1))\n\n def test_size(self):\n # gh-3173\n p = [0.5, 0.5]\n assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))\n assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))\n assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))\n assert_equal(random.multinomial(1, p, [2, 2]).shape, (2, 2, 2))\n assert_equal(random.multinomial(1, p, (2, 2)).shape, (2, 2, 2))\n assert_equal(random.multinomial(1, p, np.array((2, 2))).shape,\n (2, 2, 2))\n\n assert_raises(TypeError, random.multinomial, 1, p,\n float(1))\n\n def test_invalid_prob(self):\n assert_raises(ValueError, random.multinomial, 100, [1.1, 0.2])\n assert_raises(ValueError, random.multinomial, 100, [-.1, 0.9])\n\n def test_invalid_n(self):\n assert_raises(ValueError, random.multinomial, -1, [0.8, 0.2])\n\n def test_p_non_contiguous(self):\n p = np.arange(15.)\n p /= np.sum(p[1::3])\n pvals = p[1::3]\n random.seed(1432985819)\n non_contig = random.multinomial(100, pvals=pvals)\n random.seed(1432985819)\n contig = random.multinomial(100, pvals=np.ascontiguousarray(pvals))\n assert_array_equal(non_contig, contig)\n\n def test_multinomial_pvals_float32(self):\n x = np.array([9.9e-01, 9.9e-01, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09,\n 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09], dtype=np.float32)\n pvals = x / x.sum()\n match = r"[\w\s]*pvals array is cast to 64-bit floating"\n with pytest.raises(ValueError, match=match):\n random.multinomial(1, pvals)\n\n def test_multinomial_n_float(self):\n # Non-index integer types should gracefully truncate floats\n random.multinomial(100.5, [0.2, 0.8])\n\nclass TestSetState:\n def setup_method(self):\n self.seed = 1234567890\n self.random_state = random.RandomState(self.seed)\n self.state = self.random_state.get_state()\n\n def test_basic(self):\n old = self.random_state.tomaxint(16)\n self.random_state.set_state(self.state)\n new = self.random_state.tomaxint(16)\n assert_(np.all(old == new))\n\n def test_gaussian_reset(self):\n # Make sure the cached every-other-Gaussian is reset.\n old = self.random_state.standard_normal(size=3)\n self.random_state.set_state(self.state)\n new = self.random_state.standard_normal(size=3)\n assert_(np.all(old == new))\n\n def test_gaussian_reset_in_media_res(self):\n # When the state is saved with a cached Gaussian, make sure the\n # cached Gaussian is restored.\n\n self.random_state.standard_normal()\n state = self.random_state.get_state()\n old = self.random_state.standard_normal(size=3)\n self.random_state.set_state(state)\n new = self.random_state.standard_normal(size=3)\n assert_(np.all(old == new))\n\n def test_backwards_compatibility(self):\n # Make sure we can accept old state tuples that do not have the\n # cached Gaussian value.\n old_state = self.state[:-2]\n x1 = self.random_state.standard_normal(size=16)\n self.random_state.set_state(old_state)\n x2 = self.random_state.standard_normal(size=16)\n self.random_state.set_state(self.state)\n x3 = self.random_state.standard_normal(size=16)\n assert_(np.all(x1 == x2))\n assert_(np.all(x1 == x3))\n\n def test_negative_binomial(self):\n # Ensure that the negative binomial results take floating point\n # arguments without truncation.\n self.random_state.negative_binomial(0.5, 0.5)\n\n def test_get_state_warning(self):\n rs = random.RandomState(PCG64())\n with suppress_warnings() as sup:\n w = sup.record(RuntimeWarning)\n state = rs.get_state()\n assert_(len(w) == 1)\n assert isinstance(state, dict)\n assert state['bit_generator'] == 'PCG64'\n\n def test_invalid_legacy_state_setting(self):\n state = self.random_state.get_state()\n new_state = ('Unknown', ) + state[1:]\n assert_raises(ValueError, self.random_state.set_state, new_state)\n assert_raises(TypeError, self.random_state.set_state,\n np.array(new_state, dtype=object))\n state = self.random_state.get_state(legacy=False)\n del state['bit_generator']\n assert_raises(ValueError, self.random_state.set_state, state)\n\n def test_pickle(self):\n self.random_state.seed(0)\n self.random_state.random_sample(100)\n self.random_state.standard_normal()\n pickled = self.random_state.get_state(legacy=False)\n assert_equal(pickled['has_gauss'], 1)\n rs_unpick = pickle.loads(pickle.dumps(self.random_state))\n unpickled = rs_unpick.get_state(legacy=False)\n assert_mt19937_state_equal(pickled, unpickled)\n\n def test_state_setting(self):\n attr_state = self.random_state.__getstate__()\n self.random_state.standard_normal()\n self.random_state.__setstate__(attr_state)\n state = self.random_state.get_state(legacy=False)\n assert_mt19937_state_equal(attr_state, state)\n\n def test_repr(self):\n assert repr(self.random_state).startswith('RandomState(MT19937)')\n\n\nclass TestRandint:\n\n rfunc = random.randint\n\n # valid integer/boolean types\n itype = [np.bool, np.int8, np.uint8, np.int16, np.uint16,\n np.int32, np.uint32, np.int64, np.uint64]\n\n def test_unsupported_type(self):\n assert_raises(TypeError, self.rfunc, 1, dtype=float)\n\n def test_bounds_checking(self):\n for dt in self.itype:\n lbnd = 0 if dt is np.bool else np.iinfo(dt).min\n ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1\n assert_raises(ValueError, self.rfunc, lbnd - 1, ubnd, dtype=dt)\n assert_raises(ValueError, self.rfunc, lbnd, ubnd + 1, dtype=dt)\n assert_raises(ValueError, self.rfunc, ubnd, lbnd, dtype=dt)\n assert_raises(ValueError, self.rfunc, 1, 0, dtype=dt)\n\n def test_rng_zero_and_extremes(self):\n for dt in self.itype:\n lbnd = 0 if dt is np.bool else np.iinfo(dt).min\n ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1\n\n tgt = ubnd - 1\n assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)\n\n tgt = lbnd\n assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)\n\n tgt = (lbnd + ubnd) // 2\n assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)\n\n def test_full_range(self):\n # Test for ticket #1690\n\n for dt in self.itype:\n lbnd = 0 if dt is np.bool else np.iinfo(dt).min\n ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1\n\n try:\n self.rfunc(lbnd, ubnd, dtype=dt)\n except Exception as e:\n raise AssertionError("No error should have been raised, "\n "but one was with the following "\n "message:\n\n%s" % str(e))\n\n def test_in_bounds_fuzz(self):\n # Don't use fixed seed\n random.seed()\n\n for dt in self.itype[1:]:\n for ubnd in [4, 8, 16]:\n vals = self.rfunc(2, ubnd, size=2**16, dtype=dt)\n assert_(vals.max() < ubnd)\n assert_(vals.min() >= 2)\n\n vals = self.rfunc(0, 2, size=2**16, dtype=np.bool)\n\n assert_(vals.max() < 2)\n assert_(vals.min() >= 0)\n\n def test_repeatability(self):\n # We use a sha256 hash of generated sequences of 1000 samples\n # in the range [0, 6) for all but bool, where the range\n # is [0, 2). Hashes are for little endian numbers.\n tgt = {'bool': '509aea74d792fb931784c4b0135392c65aec64beee12b0cc167548a2c3d31e71', # noqa: E501\n 'int16': '7b07f1a920e46f6d0fe02314155a2330bcfd7635e708da50e536c5ebb631a7d4', # noqa: E501\n 'int32': 'e577bfed6c935de944424667e3da285012e741892dcb7051a8f1ce68ab05c92f', # noqa: E501\n 'int64': '0fbead0b06759df2cfb55e43148822d4a1ff953c7eb19a5b08445a63bb64fa9e', # noqa: E501\n 'int8': '001aac3a5acb935a9b186cbe14a1ca064b8bb2dd0b045d48abeacf74d0203404', # noqa: E501\n 'uint16': '7b07f1a920e46f6d0fe02314155a2330bcfd7635e708da50e536c5ebb631a7d4', # noqa: E501\n 'uint32': 'e577bfed6c935de944424667e3da285012e741892dcb7051a8f1ce68ab05c92f', # noqa: E501\n 'uint64': '0fbead0b06759df2cfb55e43148822d4a1ff953c7eb19a5b08445a63bb64fa9e', # noqa: E501\n 'uint8': '001aac3a5acb935a9b186cbe14a1ca064b8bb2dd0b045d48abeacf74d0203404'} # noqa: E501\n\n for dt in self.itype[1:]:\n random.seed(1234)\n\n # view as little endian for hash\n if sys.byteorder == 'little':\n val = self.rfunc(0, 6, size=1000, dtype=dt)\n else:\n val = self.rfunc(0, 6, size=1000, dtype=dt).byteswap()\n\n res = hashlib.sha256(val.view(np.int8)).hexdigest()\n assert_(tgt[np.dtype(dt).name] == res)\n\n # bools do not depend on endianness\n random.seed(1234)\n val = self.rfunc(0, 2, size=1000, dtype=bool).view(np.int8)\n res = hashlib.sha256(val).hexdigest()\n assert_(tgt[np.dtype(bool).name] == res)\n\n @pytest.mark.skipif(np.iinfo('l').max < 2**32,\n reason='Cannot test with 32-bit C long')\n def test_repeatability_32bit_boundary_broadcasting(self):\n desired = np.array([[[3992670689, 2438360420, 2557845020],\n [4107320065, 4142558326, 3216529513],\n [1605979228, 2807061240, 665605495]],\n [[3211410639, 4128781000, 457175120],\n [1712592594, 1282922662, 3081439808],\n [3997822960, 2008322436, 1563495165]],\n [[1398375547, 4269260146, 115316740],\n [3414372578, 3437564012, 2112038651],\n [3572980305, 2260248732, 3908238631]],\n [[2561372503, 223155946, 3127879445],\n [ 441282060, 3514786552, 2148440361],\n [1629275283, 3479737011, 3003195987]],\n [[ 412181688, 940383289, 3047321305],\n [2978368172, 764731833, 2282559898],\n [ 105711276, 720447391, 3596512484]]])\n for size in [None, (5, 3, 3)]:\n random.seed(12345)\n x = self.rfunc([[-1], [0], [1]], [2**32 - 1, 2**32, 2**32 + 1],\n size=size)\n assert_array_equal(x, desired if size is not None else desired[0])\n\n def test_int64_uint64_corner_case(self):\n # When stored in Numpy arrays, `lbnd` is casted\n # as np.int64, and `ubnd` is casted as np.uint64.\n # Checking whether `lbnd` >= `ubnd` used to be\n # done solely via direct comparison, which is incorrect\n # because when Numpy tries to compare both numbers,\n # it casts both to np.float64 because there is\n # no integer superset of np.int64 and np.uint64. However,\n # `ubnd` is too large to be represented in np.float64,\n # causing it be round down to np.iinfo(np.int64).max,\n # leading to a ValueError because `lbnd` now equals\n # the new `ubnd`.\n\n dt = np.int64\n tgt = np.iinfo(np.int64).max\n lbnd = np.int64(np.iinfo(np.int64).max)\n ubnd = np.uint64(np.iinfo(np.int64).max + 1)\n\n # None of these function calls should\n # generate a ValueError now.\n actual = random.randint(lbnd, ubnd, dtype=dt)\n assert_equal(actual, tgt)\n\n def test_respect_dtype_singleton(self):\n # See gh-7203\n for dt in self.itype:\n lbnd = 0 if dt is np.bool else np.iinfo(dt).min\n ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1\n\n sample = self.rfunc(lbnd, ubnd, dtype=dt)\n assert_equal(sample.dtype, np.dtype(dt))\n\n for dt in (bool, int):\n # The legacy random generation forces the use of "long" on this\n # branch even when the input is `int` and the default dtype\n # for int changed (dtype=int is also the functions default)\n op_dtype = "long" if dt is int else "bool"\n lbnd = 0 if dt is bool else np.iinfo(op_dtype).min\n ubnd = 2 if dt is bool else np.iinfo(op_dtype).max + 1\n\n sample = self.rfunc(lbnd, ubnd, dtype=dt)\n assert_(not hasattr(sample, 'dtype'))\n assert_equal(type(sample), dt)\n\n\nclass TestRandomDist:\n # Make sure the random distribution returns the correct value for a\n # given seed\n\n def setup_method(self):\n self.seed = 1234567890\n\n def test_rand(self):\n random.seed(self.seed)\n actual = random.rand(3, 2)\n desired = np.array([[0.61879477158567997, 0.59162362775974664],\n [0.88868358904449662, 0.89165480011560816],\n [0.4575674820298663, 0.7781880808593471]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_rand_singleton(self):\n random.seed(self.seed)\n actual = random.rand()\n desired = 0.61879477158567997\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_randn(self):\n random.seed(self.seed)\n actual = random.randn(3, 2)\n desired = np.array([[1.34016345771863121, 1.73759122771936081],\n [1.498988344300628, -0.2286433324536169],\n [2.031033998682787, 2.17032494605655257]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n random.seed(self.seed)\n actual = random.randn()\n assert_array_almost_equal(actual, desired[0, 0], decimal=15)\n\n def test_randint(self):\n random.seed(self.seed)\n actual = random.randint(-99, 99, size=(3, 2))\n desired = np.array([[31, 3],\n [-52, 41],\n [-48, -66]])\n assert_array_equal(actual, desired)\n\n def test_random_integers(self):\n random.seed(self.seed)\n with suppress_warnings() as sup:\n w = sup.record(DeprecationWarning)\n actual = random.random_integers(-99, 99, size=(3, 2))\n assert_(len(w) == 1)\n desired = np.array([[31, 3],\n [-52, 41],\n [-48, -66]])\n assert_array_equal(actual, desired)\n\n random.seed(self.seed)\n with suppress_warnings() as sup:\n w = sup.record(DeprecationWarning)\n actual = random.random_integers(198, size=(3, 2))\n assert_(len(w) == 1)\n assert_array_equal(actual, desired + 100)\n\n def test_tomaxint(self):\n random.seed(self.seed)\n rs = random.RandomState(self.seed)\n actual = rs.tomaxint(size=(3, 2))\n if np.iinfo(np.long).max == 2147483647:\n desired = np.array([[1328851649, 731237375],\n [1270502067, 320041495],\n [1908433478, 499156889]], dtype=np.int64)\n else:\n desired = np.array([[5707374374421908479, 5456764827585442327],\n [8196659375100692377, 8224063923314595285],\n [4220315081820346526, 7177518203184491332]],\n dtype=np.int64)\n\n assert_equal(actual, desired)\n\n rs.seed(self.seed)\n actual = rs.tomaxint()\n assert_equal(actual, desired[0, 0])\n\n def test_random_integers_max_int(self):\n # Tests whether random_integers can generate the\n # maximum allowed Python int that can be converted\n # into a C long. Previous implementations of this\n # method have thrown an OverflowError when attempting\n # to generate this integer.\n with suppress_warnings() as sup:\n w = sup.record(DeprecationWarning)\n actual = random.random_integers(np.iinfo('l').max,\n np.iinfo('l').max)\n assert_(len(w) == 1)\n\n desired = np.iinfo('l').max\n assert_equal(actual, desired)\n with suppress_warnings() as sup:\n w = sup.record(DeprecationWarning)\n typer = np.dtype('l').type\n actual = random.random_integers(typer(np.iinfo('l').max),\n typer(np.iinfo('l').max))\n assert_(len(w) == 1)\n assert_equal(actual, desired)\n\n def test_random_integers_deprecated(self):\n with warnings.catch_warnings():\n warnings.simplefilter("error", DeprecationWarning)\n\n # DeprecationWarning raised with high == None\n assert_raises(DeprecationWarning,\n random.random_integers,\n np.iinfo('l').max)\n\n # DeprecationWarning raised with high != None\n assert_raises(DeprecationWarning,\n random.random_integers,\n np.iinfo('l').max, np.iinfo('l').max)\n\n def test_random_sample(self):\n random.seed(self.seed)\n actual = random.random_sample((3, 2))\n desired = np.array([[0.61879477158567997, 0.59162362775974664],\n [0.88868358904449662, 0.89165480011560816],\n [0.4575674820298663, 0.7781880808593471]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n random.seed(self.seed)\n actual = random.random_sample()\n assert_array_almost_equal(actual, desired[0, 0], decimal=15)\n\n def test_choice_uniform_replace(self):\n random.seed(self.seed)\n actual = random.choice(4, 4)\n desired = np.array([2, 3, 2, 3])\n assert_array_equal(actual, desired)\n\n def test_choice_nonuniform_replace(self):\n random.seed(self.seed)\n actual = random.choice(4, 4, p=[0.4, 0.4, 0.1, 0.1])\n desired = np.array([1, 1, 2, 2])\n assert_array_equal(actual, desired)\n\n def test_choice_uniform_noreplace(self):\n random.seed(self.seed)\n actual = random.choice(4, 3, replace=False)\n desired = np.array([0, 1, 3])\n assert_array_equal(actual, desired)\n\n def test_choice_nonuniform_noreplace(self):\n random.seed(self.seed)\n actual = random.choice(4, 3, replace=False, p=[0.1, 0.3, 0.5, 0.1])\n desired = np.array([2, 3, 1])\n assert_array_equal(actual, desired)\n\n def test_choice_noninteger(self):\n random.seed(self.seed)\n actual = random.choice(['a', 'b', 'c', 'd'], 4)\n desired = np.array(['c', 'd', 'c', 'd'])\n assert_array_equal(actual, desired)\n\n def test_choice_exceptions(self):\n sample = random.choice\n assert_raises(ValueError, sample, -1, 3)\n assert_raises(ValueError, sample, 3., 3)\n assert_raises(ValueError, sample, [[1, 2], [3, 4]], 3)\n assert_raises(ValueError, sample, [], 3)\n assert_raises(ValueError, sample, [1, 2, 3, 4], 3,\n p=[[0.25, 0.25], [0.25, 0.25]])\n assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4, 0.2])\n assert_raises(ValueError, sample, [1, 2], 3, p=[1.1, -0.1])\n assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4])\n assert_raises(ValueError, sample, [1, 2, 3], 4, replace=False)\n # gh-13087\n assert_raises(ValueError, sample, [1, 2, 3], -2, replace=False)\n assert_raises(ValueError, sample, [1, 2, 3], (-1,), replace=False)\n assert_raises(ValueError, sample, [1, 2, 3], (-1, 1), replace=False)\n assert_raises(ValueError, sample, [1, 2, 3], 2,\n replace=False, p=[1, 0, 0])\n\n def test_choice_return_shape(self):\n p = [0.1, 0.9]\n # Check scalar\n assert_(np.isscalar(random.choice(2, replace=True)))\n assert_(np.isscalar(random.choice(2, replace=False)))\n assert_(np.isscalar(random.choice(2, replace=True, p=p)))\n assert_(np.isscalar(random.choice(2, replace=False, p=p)))\n assert_(np.isscalar(random.choice([1, 2], replace=True)))\n assert_(random.choice([None], replace=True) is None)\n a = np.array([1, 2])\n arr = np.empty(1, dtype=object)\n arr[0] = a\n assert_(random.choice(arr, replace=True) is a)\n\n # Check 0-d array\n s = ()\n assert_(not np.isscalar(random.choice(2, s, replace=True)))\n assert_(not np.isscalar(random.choice(2, s, replace=False)))\n assert_(not np.isscalar(random.choice(2, s, replace=True, p=p)))\n assert_(not np.isscalar(random.choice(2, s, replace=False, p=p)))\n assert_(not np.isscalar(random.choice([1, 2], s, replace=True)))\n assert_(random.choice([None], s, replace=True).ndim == 0)\n a = np.array([1, 2])\n arr = np.empty(1, dtype=object)\n arr[0] = a\n assert_(random.choice(arr, s, replace=True).item() is a)\n\n # Check multi dimensional array\n s = (2, 3)\n p = [0.1, 0.1, 0.1, 0.1, 0.4, 0.2]\n assert_equal(random.choice(6, s, replace=True).shape, s)\n assert_equal(random.choice(6, s, replace=False).shape, s)\n assert_equal(random.choice(6, s, replace=True, p=p).shape, s)\n assert_equal(random.choice(6, s, replace=False, p=p).shape, s)\n assert_equal(random.choice(np.arange(6), s, replace=True).shape, s)\n\n # Check zero-size\n assert_equal(random.randint(0, 0, size=(3, 0, 4)).shape, (3, 0, 4))\n assert_equal(random.randint(0, -10, size=0).shape, (0,))\n assert_equal(random.randint(10, 10, size=0).shape, (0,))\n assert_equal(random.choice(0, size=0).shape, (0,))\n assert_equal(random.choice([], size=(0,)).shape, (0,))\n assert_equal(random.choice(['a', 'b'], size=(3, 0, 4)).shape,\n (3, 0, 4))\n assert_raises(ValueError, random.choice, [], 10)\n\n def test_choice_nan_probabilities(self):\n a = np.array([42, 1, 2])\n p = [None, None, None]\n assert_raises(ValueError, random.choice, a, p=p)\n\n def test_choice_p_non_contiguous(self):\n p = np.ones(10) / 5\n p[1::2] = 3.0\n random.seed(self.seed)\n non_contig = random.choice(5, 3, p=p[::2])\n random.seed(self.seed)\n contig = random.choice(5, 3, p=np.ascontiguousarray(p[::2]))\n assert_array_equal(non_contig, contig)\n\n def test_bytes(self):\n random.seed(self.seed)\n actual = random.bytes(10)\n desired = b'\x82Ui\x9e\xff\x97+Wf\xa5'\n assert_equal(actual, desired)\n\n def test_shuffle(self):\n # Test lists, arrays (of various dtypes), and multidimensional versions\n # of both, c-contiguous or not:\n for conv in [lambda x: np.array([]),\n lambda x: x,\n lambda x: np.asarray(x).astype(np.int8),\n lambda x: np.asarray(x).astype(np.float32),\n lambda x: np.asarray(x).astype(np.complex64),\n lambda x: np.asarray(x).astype(object),\n lambda x: [(i, i) for i in x],\n lambda x: np.asarray([[i, i] for i in x]),\n lambda x: np.vstack([x, x]).T,\n # gh-11442\n lambda x: (np.asarray([(i, i) for i in x],\n [("a", int), ("b", int)])\n .view(np.recarray)),\n # gh-4270\n lambda x: np.asarray([(i, i) for i in x],\n [("a", object, (1,)),\n ("b", np.int32, (1,))])]:\n random.seed(self.seed)\n alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])\n random.shuffle(alist)\n actual = alist\n desired = conv([0, 1, 9, 6, 2, 4, 5, 8, 7, 3])\n assert_array_equal(actual, desired)\n\n def test_shuffle_masked(self):\n # gh-3263\n a = np.ma.masked_values(np.reshape(range(20), (5, 4)) % 3 - 1, -1)\n b = np.ma.masked_values(np.arange(20) % 3 - 1, -1)\n a_orig = a.copy()\n b_orig = b.copy()\n for i in range(50):\n random.shuffle(a)\n assert_equal(\n sorted(a.data[~a.mask]), sorted(a_orig.data[~a_orig.mask]))\n random.shuffle(b)\n assert_equal(\n sorted(b.data[~b.mask]), sorted(b_orig.data[~b_orig.mask]))\n\n def test_shuffle_invalid_objects(self):\n x = np.array(3)\n assert_raises(TypeError, random.shuffle, x)\n\n def test_permutation(self):\n random.seed(self.seed)\n alist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0]\n actual = random.permutation(alist)\n desired = [0, 1, 9, 6, 2, 4, 5, 8, 7, 3]\n assert_array_equal(actual, desired)\n\n random.seed(self.seed)\n arr_2d = np.atleast_2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 0]).T\n actual = random.permutation(arr_2d)\n assert_array_equal(actual, np.atleast_2d(desired).T)\n\n random.seed(self.seed)\n bad_x_str = "abcd"\n assert_raises(IndexError, random.permutation, bad_x_str)\n\n random.seed(self.seed)\n bad_x_float = 1.2\n assert_raises(IndexError, random.permutation, bad_x_float)\n\n integer_val = 10\n desired = [9, 0, 8, 5, 1, 3, 4, 7, 6, 2]\n\n random.seed(self.seed)\n actual = random.permutation(integer_val)\n assert_array_equal(actual, desired)\n\n def test_beta(self):\n random.seed(self.seed)\n actual = random.beta(.1, .9, size=(3, 2))\n desired = np.array(\n [[1.45341850513746058e-02, 5.31297615662868145e-04],\n [1.85366619058432324e-06, 4.19214516800110563e-03],\n [1.58405155108498093e-04, 1.26252891949397652e-04]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_binomial(self):\n random.seed(self.seed)\n actual = random.binomial(100.123, .456, size=(3, 2))\n desired = np.array([[37, 43],\n [42, 48],\n [46, 45]])\n assert_array_equal(actual, desired)\n\n random.seed(self.seed)\n actual = random.binomial(100.123, .456)\n desired = 37\n assert_array_equal(actual, desired)\n\n def test_chisquare(self):\n random.seed(self.seed)\n actual = random.chisquare(50, size=(3, 2))\n desired = np.array([[63.87858175501090585, 68.68407748911370447],\n [65.77116116901505904, 47.09686762438974483],\n [72.3828403199695174, 74.18408615260374006]])\n assert_array_almost_equal(actual, desired, decimal=13)\n\n def test_dirichlet(self):\n random.seed(self.seed)\n alpha = np.array([51.72840233779265162, 39.74494232180943953])\n actual = random.dirichlet(alpha, size=(3, 2))\n desired = np.array([[[0.54539444573611562, 0.45460555426388438],\n [0.62345816822039413, 0.37654183177960598]],\n [[0.55206000085785778, 0.44793999914214233],\n [0.58964023305154301, 0.41035976694845688]],\n [[0.59266909280647828, 0.40733090719352177],\n [0.56974431743975207, 0.43025568256024799]]])\n assert_array_almost_equal(actual, desired, decimal=15)\n bad_alpha = np.array([5.4e-01, -1.0e-16])\n assert_raises(ValueError, random.dirichlet, bad_alpha)\n\n random.seed(self.seed)\n alpha = np.array([51.72840233779265162, 39.74494232180943953])\n actual = random.dirichlet(alpha)\n assert_array_almost_equal(actual, desired[0, 0], decimal=15)\n\n def test_dirichlet_size(self):\n # gh-3173\n p = np.array([51.72840233779265162, 39.74494232180943953])\n assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))\n assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))\n assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))\n assert_equal(random.dirichlet(p, [2, 2]).shape, (2, 2, 2))\n assert_equal(random.dirichlet(p, (2, 2)).shape, (2, 2, 2))\n assert_equal(random.dirichlet(p, np.array((2, 2))).shape, (2, 2, 2))\n\n assert_raises(TypeError, random.dirichlet, p, float(1))\n\n def test_dirichlet_bad_alpha(self):\n # gh-2089\n alpha = np.array([5.4e-01, -1.0e-16])\n assert_raises(ValueError, random.dirichlet, alpha)\n\n def test_dirichlet_alpha_non_contiguous(self):\n a = np.array([51.72840233779265162, -1.0, 39.74494232180943953])\n alpha = a[::2]\n random.seed(self.seed)\n non_contig = random.dirichlet(alpha, size=(3, 2))\n random.seed(self.seed)\n contig = random.dirichlet(np.ascontiguousarray(alpha),\n size=(3, 2))\n assert_array_almost_equal(non_contig, contig)\n\n def test_exponential(self):\n random.seed(self.seed)\n actual = random.exponential(1.1234, size=(3, 2))\n desired = np.array([[1.08342649775011624, 1.00607889924557314],\n [2.46628830085216721, 2.49668106809923884],\n [0.68717433461363442, 1.69175666993575979]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_exponential_0(self):\n assert_equal(random.exponential(scale=0), 0)\n assert_raises(ValueError, random.exponential, scale=-0.)\n\n def test_f(self):\n random.seed(self.seed)\n actual = random.f(12, 77, size=(3, 2))\n desired = np.array([[1.21975394418575878, 1.75135759791559775],\n [1.44803115017146489, 1.22108959480396262],\n [1.02176975757740629, 1.34431827623300415]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_gamma(self):\n random.seed(self.seed)\n actual = random.gamma(5, 3, size=(3, 2))\n desired = np.array([[24.60509188649287182, 28.54993563207210627],\n [26.13476110204064184, 12.56988482927716078],\n [31.71863275789960568, 33.30143302795922011]])\n assert_array_almost_equal(actual, desired, decimal=14)\n\n def test_gamma_0(self):\n assert_equal(random.gamma(shape=0, scale=0), 0)\n assert_raises(ValueError, random.gamma, shape=-0., scale=-0.)\n\n def test_geometric(self):\n random.seed(self.seed)\n actual = random.geometric(.123456789, size=(3, 2))\n desired = np.array([[8, 7],\n [17, 17],\n [5, 12]])\n assert_array_equal(actual, desired)\n\n def test_geometric_exceptions(self):\n assert_raises(ValueError, random.geometric, 1.1)\n assert_raises(ValueError, random.geometric, [1.1] * 10)\n assert_raises(ValueError, random.geometric, -0.1)\n assert_raises(ValueError, random.geometric, [-0.1] * 10)\n with suppress_warnings() as sup:\n sup.record(RuntimeWarning)\n assert_raises(ValueError, random.geometric, np.nan)\n assert_raises(ValueError, random.geometric, [np.nan] * 10)\n\n def test_gumbel(self):\n random.seed(self.seed)\n actual = random.gumbel(loc=.123456789, scale=2.0, size=(3, 2))\n desired = np.array([[0.19591898743416816, 0.34405539668096674],\n [-1.4492522252274278, -1.47374816298446865],\n [1.10651090478803416, -0.69535848626236174]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_gumbel_0(self):\n assert_equal(random.gumbel(scale=0), 0)\n assert_raises(ValueError, random.gumbel, scale=-0.)\n\n def test_hypergeometric(self):\n random.seed(self.seed)\n actual = random.hypergeometric(10.1, 5.5, 14, size=(3, 2))\n desired = np.array([[10, 10],\n [10, 10],\n [9, 9]])\n assert_array_equal(actual, desired)\n\n # Test nbad = 0\n actual = random.hypergeometric(5, 0, 3, size=4)\n desired = np.array([3, 3, 3, 3])\n assert_array_equal(actual, desired)\n\n actual = random.hypergeometric(15, 0, 12, size=4)\n desired = np.array([12, 12, 12, 12])\n assert_array_equal(actual, desired)\n\n # Test ngood = 0\n actual = random.hypergeometric(0, 5, 3, size=4)\n desired = np.array([0, 0, 0, 0])\n assert_array_equal(actual, desired)\n\n actual = random.hypergeometric(0, 15, 12, size=4)\n desired = np.array([0, 0, 0, 0])\n assert_array_equal(actual, desired)\n\n def test_laplace(self):\n random.seed(self.seed)\n actual = random.laplace(loc=.123456789, scale=2.0, size=(3, 2))\n desired = np.array([[0.66599721112760157, 0.52829452552221945],\n [3.12791959514407125, 3.18202813572992005],\n [-0.05391065675859356, 1.74901336242837324]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_laplace_0(self):\n assert_equal(random.laplace(scale=0), 0)\n assert_raises(ValueError, random.laplace, scale=-0.)\n\n def test_logistic(self):\n random.seed(self.seed)\n actual = random.logistic(loc=.123456789, scale=2.0, size=(3, 2))\n desired = np.array([[1.09232835305011444, 0.8648196662399954],\n [4.27818590694950185, 4.33897006346929714],\n [-0.21682183359214885, 2.63373365386060332]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_lognormal(self):\n random.seed(self.seed)\n actual = random.lognormal(mean=.123456789, sigma=2.0, size=(3, 2))\n desired = np.array([[16.50698631688883822, 36.54846706092654784],\n [22.67886599981281748, 0.71617561058995771],\n [65.72798501792723869, 86.84341601437161273]])\n assert_array_almost_equal(actual, desired, decimal=13)\n\n def test_lognormal_0(self):\n assert_equal(random.lognormal(sigma=0), 1)\n assert_raises(ValueError, random.lognormal, sigma=-0.)\n\n def test_logseries(self):\n random.seed(self.seed)\n actual = random.logseries(p=.923456789, size=(3, 2))\n desired = np.array([[2, 2],\n [6, 17],\n [3, 6]])\n assert_array_equal(actual, desired)\n\n def test_logseries_zero(self):\n assert random.logseries(0) == 1\n\n @pytest.mark.parametrize("value", [np.nextafter(0., -1), 1., np.nan, 5.])\n def test_logseries_exceptions(self, value):\n with np.errstate(invalid="ignore"):\n with pytest.raises(ValueError):\n random.logseries(value)\n with pytest.raises(ValueError):\n # contiguous path:\n random.logseries(np.array([value] * 10))\n with pytest.raises(ValueError):\n # non-contiguous path:\n random.logseries(np.array([value] * 10)[::2])\n\n def test_multinomial(self):\n random.seed(self.seed)\n actual = random.multinomial(20, [1 / 6.] * 6, size=(3, 2))\n desired = np.array([[[4, 3, 5, 4, 2, 2],\n [5, 2, 8, 2, 2, 1]],\n [[3, 4, 3, 6, 0, 4],\n [2, 1, 4, 3, 6, 4]],\n [[4, 4, 2, 5, 2, 3],\n [4, 3, 4, 2, 3, 4]]])\n assert_array_equal(actual, desired)\n\n def test_multivariate_normal(self):\n random.seed(self.seed)\n mean = (.123456789, 10)\n cov = [[1, 0], [0, 1]]\n size = (3, 2)\n actual = random.multivariate_normal(mean, cov, size)\n desired = np.array([[[1.463620246718631, 11.73759122771936],\n [1.622445133300628, 9.771356667546383]],\n [[2.154490787682787, 12.170324946056553],\n [1.719909438201865, 9.230548443648306]],\n [[0.689515026297799, 9.880729819607714],\n [-0.023054015651998, 9.201096623542879]]])\n\n assert_array_almost_equal(actual, desired, decimal=15)\n\n # Check for default size, was raising deprecation warning\n actual = random.multivariate_normal(mean, cov)\n desired = np.array([0.895289569463708, 9.17180864067987])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n # Check that non positive-semidefinite covariance warns with\n # RuntimeWarning\n mean = [0, 0]\n cov = [[1, 2], [2, 1]]\n assert_warns(RuntimeWarning, random.multivariate_normal, mean, cov)\n\n # and that it doesn't warn with RuntimeWarning check_valid='ignore'\n assert_no_warnings(random.multivariate_normal, mean, cov,\n check_valid='ignore')\n\n # and that it raises with RuntimeWarning check_valid='raises'\n assert_raises(ValueError, random.multivariate_normal, mean, cov,\n check_valid='raise')\n\n cov = np.array([[1, 0.1], [0.1, 1]], dtype=np.float32)\n with suppress_warnings() as sup:\n random.multivariate_normal(mean, cov)\n w = sup.record(RuntimeWarning)\n assert len(w) == 0\n\n mu = np.zeros(2)\n cov = np.eye(2)\n assert_raises(ValueError, random.multivariate_normal, mean, cov,\n check_valid='other')\n assert_raises(ValueError, random.multivariate_normal,\n np.zeros((2, 1, 1)), cov)\n assert_raises(ValueError, random.multivariate_normal,\n mu, np.empty((3, 2)))\n assert_raises(ValueError, random.multivariate_normal,\n mu, np.eye(3))\n\n def test_negative_binomial(self):\n random.seed(self.seed)\n actual = random.negative_binomial(n=100, p=.12345, size=(3, 2))\n desired = np.array([[848, 841],\n [892, 611],\n [779, 647]])\n assert_array_equal(actual, desired)\n\n def test_negative_binomial_exceptions(self):\n with suppress_warnings() as sup:\n sup.record(RuntimeWarning)\n assert_raises(ValueError, random.negative_binomial, 100, np.nan)\n assert_raises(ValueError, random.negative_binomial, 100,\n [np.nan] * 10)\n\n def test_noncentral_chisquare(self):\n random.seed(self.seed)\n actual = random.noncentral_chisquare(df=5, nonc=5, size=(3, 2))\n desired = np.array([[23.91905354498517511, 13.35324692733826346],\n [31.22452661329736401, 16.60047399466177254],\n [5.03461598262724586, 17.94973089023519464]])\n assert_array_almost_equal(actual, desired, decimal=14)\n\n actual = random.noncentral_chisquare(df=.5, nonc=.2, size=(3, 2))\n desired = np.array([[1.47145377828516666, 0.15052899268012659],\n [0.00943803056963588, 1.02647251615666169],\n [0.332334982684171, 0.15451287602753125]])\n assert_array_almost_equal(actual, desired, decimal=14)\n\n random.seed(self.seed)\n actual = random.noncentral_chisquare(df=5, nonc=0, size=(3, 2))\n desired = np.array([[9.597154162763948, 11.725484450296079],\n [10.413711048138335, 3.694475922923986],\n [13.484222138963087, 14.377255424602957]])\n assert_array_almost_equal(actual, desired, decimal=14)\n\n def test_noncentral_f(self):\n random.seed(self.seed)\n actual = random.noncentral_f(dfnum=5, dfden=2, nonc=1,\n size=(3, 2))\n desired = np.array([[1.40598099674926669, 0.34207973179285761],\n [3.57715069265772545, 7.92632662577829805],\n [0.43741599463544162, 1.1774208752428319]])\n assert_array_almost_equal(actual, desired, decimal=14)\n\n def test_noncentral_f_nan(self):\n random.seed(self.seed)\n actual = random.noncentral_f(dfnum=5, dfden=2, nonc=np.nan)\n assert np.isnan(actual)\n\n def test_normal(self):\n random.seed(self.seed)\n actual = random.normal(loc=.123456789, scale=2.0, size=(3, 2))\n desired = np.array([[2.80378370443726244, 3.59863924443872163],\n [3.121433477601256, -0.33382987590723379],\n [4.18552478636557357, 4.46410668111310471]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_normal_0(self):\n assert_equal(random.normal(scale=0), 0)\n assert_raises(ValueError, random.normal, scale=-0.)\n\n def test_pareto(self):\n random.seed(self.seed)\n actual = random.pareto(a=.123456789, size=(3, 2))\n desired = np.array(\n [[2.46852460439034849e+03, 1.41286880810518346e+03],\n [5.28287797029485181e+07, 6.57720981047328785e+07],\n [1.40840323350391515e+02, 1.98390255135251704e+05]])\n # For some reason on 32-bit x86 Ubuntu 12.10 the [1, 0] entry in this\n # matrix differs by 24 nulps. Discussion:\n # https://mail.python.org/pipermail/numpy-discussion/2012-September/063801.html\n # Consensus is that this is probably some gcc quirk that affects\n # rounding but not in any important way, so we just use a looser\n # tolerance on this test:\n np.testing.assert_array_almost_equal_nulp(actual, desired, nulp=30)\n\n def test_poisson(self):\n random.seed(self.seed)\n actual = random.poisson(lam=.123456789, size=(3, 2))\n desired = np.array([[0, 0],\n [1, 0],\n [0, 0]])\n assert_array_equal(actual, desired)\n\n def test_poisson_exceptions(self):\n lambig = np.iinfo('l').max\n lamneg = -1\n assert_raises(ValueError, random.poisson, lamneg)\n assert_raises(ValueError, random.poisson, [lamneg] * 10)\n assert_raises(ValueError, random.poisson, lambig)\n assert_raises(ValueError, random.poisson, [lambig] * 10)\n with suppress_warnings() as sup:\n sup.record(RuntimeWarning)\n assert_raises(ValueError, random.poisson, np.nan)\n assert_raises(ValueError, random.poisson, [np.nan] * 10)\n\n def test_power(self):\n random.seed(self.seed)\n actual = random.power(a=.123456789, size=(3, 2))\n desired = np.array([[0.02048932883240791, 0.01424192241128213],\n [0.38446073748535298, 0.39499689943484395],\n [0.00177699707563439, 0.13115505880863756]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_rayleigh(self):\n random.seed(self.seed)\n actual = random.rayleigh(scale=10, size=(3, 2))\n desired = np.array([[13.8882496494248393, 13.383318339044731],\n [20.95413364294492098, 21.08285015800712614],\n [11.06066537006854311, 17.35468505778271009]])\n assert_array_almost_equal(actual, desired, decimal=14)\n\n def test_rayleigh_0(self):\n assert_equal(random.rayleigh(scale=0), 0)\n assert_raises(ValueError, random.rayleigh, scale=-0.)\n\n def test_standard_cauchy(self):\n random.seed(self.seed)\n actual = random.standard_cauchy(size=(3, 2))\n desired = np.array([[0.77127660196445336, -6.55601161955910605],\n [0.93582023391158309, -2.07479293013759447],\n [-4.74601644297011926, 0.18338989290760804]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_standard_exponential(self):\n random.seed(self.seed)\n actual = random.standard_exponential(size=(3, 2))\n desired = np.array([[0.96441739162374596, 0.89556604882105506],\n [2.1953785836319808, 2.22243285392490542],\n [0.6116915921431676, 1.50592546727413201]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_standard_gamma(self):\n random.seed(self.seed)\n actual = random.standard_gamma(shape=3, size=(3, 2))\n desired = np.array([[5.50841531318455058, 6.62953470301903103],\n [5.93988484943779227, 2.31044849402133989],\n [7.54838614231317084, 8.012756093271868]])\n assert_array_almost_equal(actual, desired, decimal=14)\n\n def test_standard_gamma_0(self):\n assert_equal(random.standard_gamma(shape=0), 0)\n assert_raises(ValueError, random.standard_gamma, shape=-0.)\n\n def test_standard_normal(self):\n random.seed(self.seed)\n actual = random.standard_normal(size=(3, 2))\n desired = np.array([[1.34016345771863121, 1.73759122771936081],\n [1.498988344300628, -0.2286433324536169],\n [2.031033998682787, 2.17032494605655257]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_randn_singleton(self):\n random.seed(self.seed)\n actual = random.randn()\n desired = np.array(1.34016345771863121)\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_standard_t(self):\n random.seed(self.seed)\n actual = random.standard_t(df=10, size=(3, 2))\n desired = np.array([[0.97140611862659965, -0.08830486548450577],\n [1.36311143689505321, -0.55317463909867071],\n [-0.18473749069684214, 0.61181537341755321]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_triangular(self):\n random.seed(self.seed)\n actual = random.triangular(left=5.12, mode=10.23, right=20.34,\n size=(3, 2))\n desired = np.array([[12.68117178949215784, 12.4129206149193152],\n [16.20131377335158263, 16.25692138747600524],\n [11.20400690911820263, 14.4978144835829923]])\n assert_array_almost_equal(actual, desired, decimal=14)\n\n def test_uniform(self):\n random.seed(self.seed)\n actual = random.uniform(low=1.23, high=10.54, size=(3, 2))\n desired = np.array([[6.99097932346268003, 6.73801597444323974],\n [9.50364421400426274, 9.53130618907631089],\n [5.48995325769805476, 8.47493103280052118]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_uniform_range_bounds(self):\n fmin = np.finfo('float').min\n fmax = np.finfo('float').max\n\n func = random.uniform\n assert_raises(OverflowError, func, -np.inf, 0)\n assert_raises(OverflowError, func, 0, np.inf)\n assert_raises(OverflowError, func, fmin, fmax)\n assert_raises(OverflowError, func, [-np.inf], [0])\n assert_raises(OverflowError, func, [0], [np.inf])\n\n # (fmax / 1e17) - fmin is within range, so this should not throw\n # account for i386 extended precision DBL_MAX / 1e17 + DBL_MAX >\n # DBL_MAX by increasing fmin a bit\n random.uniform(low=np.nextafter(fmin, 1), high=fmax / 1e17)\n\n def test_scalar_exception_propagation(self):\n # Tests that exceptions are correctly propagated in distributions\n # when called with objects that throw exceptions when converted to\n # scalars.\n #\n # Regression test for gh: 8865\n\n class ThrowingFloat(np.ndarray):\n def __float__(self):\n raise TypeError\n\n throwing_float = np.array(1.0).view(ThrowingFloat)\n assert_raises(TypeError, random.uniform, throwing_float,\n throwing_float)\n\n class ThrowingInteger(np.ndarray):\n def __int__(self):\n raise TypeError\n\n throwing_int = np.array(1).view(ThrowingInteger)\n assert_raises(TypeError, random.hypergeometric, throwing_int, 1, 1)\n\n def test_vonmises(self):\n random.seed(self.seed)\n actual = random.vonmises(mu=1.23, kappa=1.54, size=(3, 2))\n desired = np.array([[2.28567572673902042, 2.89163838442285037],\n [0.38198375564286025, 2.57638023113890746],\n [1.19153771588353052, 1.83509849681825354]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_vonmises_small(self):\n # check infinite loop, gh-4720\n random.seed(self.seed)\n r = random.vonmises(mu=0., kappa=1.1e-8, size=10**6)\n assert_(np.isfinite(r).all())\n\n def test_vonmises_large(self):\n # guard against changes in RandomState when Generator is fixed\n random.seed(self.seed)\n actual = random.vonmises(mu=0., kappa=1e7, size=3)\n desired = np.array([4.634253748521111e-04,\n 3.558873596114509e-04,\n -2.337119622577433e-04])\n assert_array_almost_equal(actual, desired, decimal=8)\n\n def test_vonmises_nan(self):\n random.seed(self.seed)\n r = random.vonmises(mu=0., kappa=np.nan)\n assert_(np.isnan(r))\n\n def test_wald(self):\n random.seed(self.seed)\n actual = random.wald(mean=1.23, scale=1.54, size=(3, 2))\n desired = np.array([[3.82935265715889983, 5.13125249184285526],\n [0.35045403618358717, 1.50832396872003538],\n [0.24124319895843183, 0.22031101461955038]])\n assert_array_almost_equal(actual, desired, decimal=14)\n\n def test_weibull(self):\n random.seed(self.seed)\n actual = random.weibull(a=1.23, size=(3, 2))\n desired = np.array([[0.97097342648766727, 0.91422896443565516],\n [1.89517770034962929, 1.91414357960479564],\n [0.67057783752390987, 1.39494046635066793]])\n assert_array_almost_equal(actual, desired, decimal=15)\n\n def test_weibull_0(self):\n random.seed(self.seed)\n assert_equal(random.weibull(a=0, size=12), np.zeros(12))\n assert_raises(ValueError, random.weibull, a=-0.)\n\n def test_zipf(self):\n random.seed(self.seed)\n actual = random.zipf(a=1.23, size=(3, 2))\n desired = np.array([[66, 29],\n [1, 1],\n [3, 13]])\n assert_array_equal(actual, desired)\n\n\nclass TestBroadcast:\n # tests that functions that broadcast behave\n # correctly when presented with non-scalar arguments\n def setup_method(self):\n self.seed = 123456789\n\n def set_seed(self):\n random.seed(self.seed)\n\n def test_uniform(self):\n low = [0]\n high = [1]\n uniform = random.uniform\n desired = np.array([0.53283302478975902,\n 0.53413660089041659,\n 0.50955303552646702])\n\n self.set_seed()\n actual = uniform(low * 3, high)\n assert_array_almost_equal(actual, desired, decimal=14)\n\n self.set_seed()\n actual = uniform(low, high * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n\n def test_normal(self):\n loc = [0]\n scale = [1]\n bad_scale = [-1]\n normal = random.normal\n desired = np.array([2.2129019979039612,\n 2.1283977976520019,\n 1.8417114045748335])\n\n self.set_seed()\n actual = normal(loc * 3, scale)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, normal, loc * 3, bad_scale)\n\n self.set_seed()\n actual = normal(loc, scale * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, normal, loc, bad_scale * 3)\n\n def test_beta(self):\n a = [1]\n b = [2]\n bad_a = [-1]\n bad_b = [-2]\n beta = random.beta\n desired = np.array([0.19843558305989056,\n 0.075230336409423643,\n 0.24976865978980844])\n\n self.set_seed()\n actual = beta(a * 3, b)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, beta, bad_a * 3, b)\n assert_raises(ValueError, beta, a * 3, bad_b)\n\n self.set_seed()\n actual = beta(a, b * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, beta, bad_a, b * 3)\n assert_raises(ValueError, beta, a, bad_b * 3)\n\n def test_exponential(self):\n scale = [1]\n bad_scale = [-1]\n exponential = random.exponential\n desired = np.array([0.76106853658845242,\n 0.76386282278691653,\n 0.71243813125891797])\n\n self.set_seed()\n actual = exponential(scale * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, exponential, bad_scale * 3)\n\n def test_standard_gamma(self):\n shape = [1]\n bad_shape = [-1]\n std_gamma = random.standard_gamma\n desired = np.array([0.76106853658845242,\n 0.76386282278691653,\n 0.71243813125891797])\n\n self.set_seed()\n actual = std_gamma(shape * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, std_gamma, bad_shape * 3)\n\n def test_gamma(self):\n shape = [1]\n scale = [2]\n bad_shape = [-1]\n bad_scale = [-2]\n gamma = random.gamma\n desired = np.array([1.5221370731769048,\n 1.5277256455738331,\n 1.4248762625178359])\n\n self.set_seed()\n actual = gamma(shape * 3, scale)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, gamma, bad_shape * 3, scale)\n assert_raises(ValueError, gamma, shape * 3, bad_scale)\n\n self.set_seed()\n actual = gamma(shape, scale * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, gamma, bad_shape, scale * 3)\n assert_raises(ValueError, gamma, shape, bad_scale * 3)\n\n def test_f(self):\n dfnum = [1]\n dfden = [2]\n bad_dfnum = [-1]\n bad_dfden = [-2]\n f = random.f\n desired = np.array([0.80038951638264799,\n 0.86768719635363512,\n 2.7251095168386801])\n\n self.set_seed()\n actual = f(dfnum * 3, dfden)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, f, bad_dfnum * 3, dfden)\n assert_raises(ValueError, f, dfnum * 3, bad_dfden)\n\n self.set_seed()\n actual = f(dfnum, dfden * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, f, bad_dfnum, dfden * 3)\n assert_raises(ValueError, f, dfnum, bad_dfden * 3)\n\n def test_noncentral_f(self):\n dfnum = [2]\n dfden = [3]\n nonc = [4]\n bad_dfnum = [0]\n bad_dfden = [-1]\n bad_nonc = [-2]\n nonc_f = random.noncentral_f\n desired = np.array([9.1393943263705211,\n 13.025456344595602,\n 8.8018098359100545])\n\n self.set_seed()\n actual = nonc_f(dfnum * 3, dfden, nonc)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert np.all(np.isnan(nonc_f(dfnum, dfden, [np.nan] * 3)))\n\n assert_raises(ValueError, nonc_f, bad_dfnum * 3, dfden, nonc)\n assert_raises(ValueError, nonc_f, dfnum * 3, bad_dfden, nonc)\n assert_raises(ValueError, nonc_f, dfnum * 3, dfden, bad_nonc)\n\n self.set_seed()\n actual = nonc_f(dfnum, dfden * 3, nonc)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, nonc_f, bad_dfnum, dfden * 3, nonc)\n assert_raises(ValueError, nonc_f, dfnum, bad_dfden * 3, nonc)\n assert_raises(ValueError, nonc_f, dfnum, dfden * 3, bad_nonc)\n\n self.set_seed()\n actual = nonc_f(dfnum, dfden, nonc * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, nonc_f, bad_dfnum, dfden, nonc * 3)\n assert_raises(ValueError, nonc_f, dfnum, bad_dfden, nonc * 3)\n assert_raises(ValueError, nonc_f, dfnum, dfden, bad_nonc * 3)\n\n def test_noncentral_f_small_df(self):\n self.set_seed()\n desired = np.array([6.869638627492048, 0.785880199263955])\n actual = random.noncentral_f(0.9, 0.9, 2, size=2)\n assert_array_almost_equal(actual, desired, decimal=14)\n\n def test_chisquare(self):\n df = [1]\n bad_df = [-1]\n chisquare = random.chisquare\n desired = np.array([0.57022801133088286,\n 0.51947702108840776,\n 0.1320969254923558])\n\n self.set_seed()\n actual = chisquare(df * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, chisquare, bad_df * 3)\n\n def test_noncentral_chisquare(self):\n df = [1]\n nonc = [2]\n bad_df = [-1]\n bad_nonc = [-2]\n nonc_chi = random.noncentral_chisquare\n desired = np.array([9.0015599467913763,\n 4.5804135049718742,\n 6.0872302432834564])\n\n self.set_seed()\n actual = nonc_chi(df * 3, nonc)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, nonc_chi, bad_df * 3, nonc)\n assert_raises(ValueError, nonc_chi, df * 3, bad_nonc)\n\n self.set_seed()\n actual = nonc_chi(df, nonc * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, nonc_chi, bad_df, nonc * 3)\n assert_raises(ValueError, nonc_chi, df, bad_nonc * 3)\n\n def test_standard_t(self):\n df = [1]\n bad_df = [-1]\n t = random.standard_t\n desired = np.array([3.0702872575217643,\n 5.8560725167361607,\n 1.0274791436474273])\n\n self.set_seed()\n actual = t(df * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, t, bad_df * 3)\n assert_raises(ValueError, random.standard_t, bad_df * 3)\n\n def test_vonmises(self):\n mu = [2]\n kappa = [1]\n bad_kappa = [-1]\n vonmises = random.vonmises\n desired = np.array([2.9883443664201312,\n -2.7064099483995943,\n -1.8672476700665914])\n\n self.set_seed()\n actual = vonmises(mu * 3, kappa)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, vonmises, mu * 3, bad_kappa)\n\n self.set_seed()\n actual = vonmises(mu, kappa * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, vonmises, mu, bad_kappa * 3)\n\n def test_pareto(self):\n a = [1]\n bad_a = [-1]\n pareto = random.pareto\n desired = np.array([1.1405622680198362,\n 1.1465519762044529,\n 1.0389564467453547])\n\n self.set_seed()\n actual = pareto(a * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, pareto, bad_a * 3)\n assert_raises(ValueError, random.pareto, bad_a * 3)\n\n def test_weibull(self):\n a = [1]\n bad_a = [-1]\n weibull = random.weibull\n desired = np.array([0.76106853658845242,\n 0.76386282278691653,\n 0.71243813125891797])\n\n self.set_seed()\n actual = weibull(a * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, weibull, bad_a * 3)\n assert_raises(ValueError, random.weibull, bad_a * 3)\n\n def test_power(self):\n a = [1]\n bad_a = [-1]\n power = random.power\n desired = np.array([0.53283302478975902,\n 0.53413660089041659,\n 0.50955303552646702])\n\n self.set_seed()\n actual = power(a * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, power, bad_a * 3)\n assert_raises(ValueError, random.power, bad_a * 3)\n\n def test_laplace(self):\n loc = [0]\n scale = [1]\n bad_scale = [-1]\n laplace = random.laplace\n desired = np.array([0.067921356028507157,\n 0.070715642226971326,\n 0.019290950698972624])\n\n self.set_seed()\n actual = laplace(loc * 3, scale)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, laplace, loc * 3, bad_scale)\n\n self.set_seed()\n actual = laplace(loc, scale * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, laplace, loc, bad_scale * 3)\n\n def test_gumbel(self):\n loc = [0]\n scale = [1]\n bad_scale = [-1]\n gumbel = random.gumbel\n desired = np.array([0.2730318639556768,\n 0.26936705726291116,\n 0.33906220393037939])\n\n self.set_seed()\n actual = gumbel(loc * 3, scale)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, gumbel, loc * 3, bad_scale)\n\n self.set_seed()\n actual = gumbel(loc, scale * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, gumbel, loc, bad_scale * 3)\n\n def test_logistic(self):\n loc = [0]\n scale = [1]\n bad_scale = [-1]\n logistic = random.logistic\n desired = np.array([0.13152135837586171,\n 0.13675915696285773,\n 0.038216792802833396])\n\n self.set_seed()\n actual = logistic(loc * 3, scale)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, logistic, loc * 3, bad_scale)\n\n self.set_seed()\n actual = logistic(loc, scale * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, logistic, loc, bad_scale * 3)\n assert_equal(random.logistic(1.0, 0.0), 1.0)\n\n def test_lognormal(self):\n mean = [0]\n sigma = [1]\n bad_sigma = [-1]\n lognormal = random.lognormal\n desired = np.array([9.1422086044848427,\n 8.4013952870126261,\n 6.3073234116578671])\n\n self.set_seed()\n actual = lognormal(mean * 3, sigma)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, lognormal, mean * 3, bad_sigma)\n assert_raises(ValueError, random.lognormal, mean * 3, bad_sigma)\n\n self.set_seed()\n actual = lognormal(mean, sigma * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, lognormal, mean, bad_sigma * 3)\n assert_raises(ValueError, random.lognormal, mean, bad_sigma * 3)\n\n def test_rayleigh(self):\n scale = [1]\n bad_scale = [-1]\n rayleigh = random.rayleigh\n desired = np.array([1.2337491937897689,\n 1.2360119924878694,\n 1.1936818095781789])\n\n self.set_seed()\n actual = rayleigh(scale * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, rayleigh, bad_scale * 3)\n\n def test_wald(self):\n mean = [0.5]\n scale = [1]\n bad_mean = [0]\n bad_scale = [-2]\n wald = random.wald\n desired = np.array([0.11873681120271318,\n 0.12450084820795027,\n 0.9096122728408238])\n\n self.set_seed()\n actual = wald(mean * 3, scale)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, wald, bad_mean * 3, scale)\n assert_raises(ValueError, wald, mean * 3, bad_scale)\n assert_raises(ValueError, random.wald, bad_mean * 3, scale)\n assert_raises(ValueError, random.wald, mean * 3, bad_scale)\n\n self.set_seed()\n actual = wald(mean, scale * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, wald, bad_mean, scale * 3)\n assert_raises(ValueError, wald, mean, bad_scale * 3)\n assert_raises(ValueError, wald, 0.0, 1)\n assert_raises(ValueError, wald, 0.5, 0.0)\n\n def test_triangular(self):\n left = [1]\n right = [3]\n mode = [2]\n bad_left_one = [3]\n bad_mode_one = [4]\n bad_left_two, bad_mode_two = right * 2\n triangular = random.triangular\n desired = np.array([2.03339048710429,\n 2.0347400359389356,\n 2.0095991069536208])\n\n self.set_seed()\n actual = triangular(left * 3, mode, right)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, triangular, bad_left_one * 3, mode, right)\n assert_raises(ValueError, triangular, left * 3, bad_mode_one, right)\n assert_raises(ValueError, triangular, bad_left_two * 3, bad_mode_two,\n right)\n\n self.set_seed()\n actual = triangular(left, mode * 3, right)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, triangular, bad_left_one, mode * 3, right)\n assert_raises(ValueError, triangular, left, bad_mode_one * 3, right)\n assert_raises(ValueError, triangular, bad_left_two, bad_mode_two * 3,\n right)\n\n self.set_seed()\n actual = triangular(left, mode, right * 3)\n assert_array_almost_equal(actual, desired, decimal=14)\n assert_raises(ValueError, triangular, bad_left_one, mode, right * 3)\n assert_raises(ValueError, triangular, left, bad_mode_one, right * 3)\n assert_raises(ValueError, triangular, bad_left_two, bad_mode_two,\n right * 3)\n\n assert_raises(ValueError, triangular, 10., 0., 20.)\n assert_raises(ValueError, triangular, 10., 25., 20.)\n assert_raises(ValueError, triangular, 10., 10., 10.)\n\n def test_binomial(self):\n n = [1]\n p = [0.5]\n bad_n = [-1]\n bad_p_one = [-1]\n bad_p_two = [1.5]\n binom = random.binomial\n desired = np.array([1, 1, 1])\n\n self.set_seed()\n actual = binom(n * 3, p)\n assert_array_equal(actual, desired)\n assert_raises(ValueError, binom, bad_n * 3, p)\n assert_raises(ValueError, binom, n * 3, bad_p_one)\n assert_raises(ValueError, binom, n * 3, bad_p_two)\n\n self.set_seed()\n actual = binom(n, p * 3)\n assert_array_equal(actual, desired)\n assert_raises(ValueError, binom, bad_n, p * 3)\n assert_raises(ValueError, binom, n, bad_p_one * 3)\n assert_raises(ValueError, binom, n, bad_p_two * 3)\n\n def test_negative_binomial(self):\n n = [1]\n p = [0.5]\n bad_n = [-1]\n bad_p_one = [-1]\n bad_p_two = [1.5]\n neg_binom = random.negative_binomial\n desired = np.array([1, 0, 1])\n\n self.set_seed()\n actual = neg_binom(n * 3, p)\n assert_array_equal(actual, desired)\n assert_raises(ValueError, neg_binom, bad_n * 3, p)\n assert_raises(ValueError, neg_binom, n * 3, bad_p_one)\n assert_raises(ValueError, neg_binom, n * 3, bad_p_two)\n\n self.set_seed()\n actual = neg_binom(n, p * 3)\n assert_array_equal(actual, desired)\n assert_raises(ValueError, neg_binom, bad_n, p * 3)\n assert_raises(ValueError, neg_binom, n, bad_p_one * 3)\n assert_raises(ValueError, neg_binom, n, bad_p_two * 3)\n\n def test_poisson(self):\n max_lam = random.RandomState()._poisson_lam_max\n\n lam = [1]\n bad_lam_one = [-1]\n bad_lam_two = [max_lam * 2]\n poisson = random.poisson\n desired = np.array([1, 1, 0])\n\n self.set_seed()\n actual = poisson(lam * 3)\n assert_array_equal(actual, desired)\n assert_raises(ValueError, poisson, bad_lam_one * 3)\n assert_raises(ValueError, poisson, bad_lam_two * 3)\n\n def test_zipf(self):\n a = [2]\n bad_a = [0]\n zipf = random.zipf\n desired = np.array([2, 2, 1])\n\n self.set_seed()\n actual = zipf(a * 3)\n assert_array_equal(actual, desired)\n assert_raises(ValueError, zipf, bad_a * 3)\n with np.errstate(invalid='ignore'):\n assert_raises(ValueError, zipf, np.nan)\n assert_raises(ValueError, zipf, [0, 0, np.nan])\n\n def test_geometric(self):\n p = [0.5]\n bad_p_one = [-1]\n bad_p_two = [1.5]\n geom = random.geometric\n desired = np.array([2, 2, 2])\n\n self.set_seed()\n actual = geom(p * 3)\n assert_array_equal(actual, desired)\n assert_raises(ValueError, geom, bad_p_one * 3)\n assert_raises(ValueError, geom, bad_p_two * 3)\n\n def test_hypergeometric(self):\n ngood = [1]\n nbad = [2]\n nsample = [2]\n bad_ngood = [-1]\n bad_nbad = [-2]\n bad_nsample_one = [0]\n bad_nsample_two = [4]\n hypergeom = random.hypergeometric\n desired = np.array([1, 1, 1])\n\n self.set_seed()\n actual = hypergeom(ngood * 3, nbad, nsample)\n assert_array_equal(actual, desired)\n assert_raises(ValueError, hypergeom, bad_ngood * 3, nbad, nsample)\n assert_raises(ValueError, hypergeom, ngood * 3, bad_nbad, nsample)\n assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_one)\n assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_two)\n\n self.set_seed()\n actual = hypergeom(ngood, nbad * 3, nsample)\n assert_array_equal(actual, desired)\n assert_raises(ValueError, hypergeom, bad_ngood, nbad * 3, nsample)\n assert_raises(ValueError, hypergeom, ngood, bad_nbad * 3, nsample)\n assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_one)\n assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_two)\n\n self.set_seed()\n actual = hypergeom(ngood, nbad, nsample * 3)\n assert_array_equal(actual, desired)\n assert_raises(ValueError, hypergeom, bad_ngood, nbad, nsample * 3)\n assert_raises(ValueError, hypergeom, ngood, bad_nbad, nsample * 3)\n assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_one * 3)\n assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_two * 3)\n\n assert_raises(ValueError, hypergeom, -1, 10, 20)\n assert_raises(ValueError, hypergeom, 10, -1, 20)\n assert_raises(ValueError, hypergeom, 10, 10, 0)\n assert_raises(ValueError, hypergeom, 10, 10, 25)\n\n def test_logseries(self):\n p = [0.5]\n bad_p_one = [2]\n bad_p_two = [-1]\n logseries = random.logseries\n desired = np.array([1, 1, 1])\n\n self.set_seed()\n actual = logseries(p * 3)\n assert_array_equal(actual, desired)\n assert_raises(ValueError, logseries, bad_p_one * 3)\n assert_raises(ValueError, logseries, bad_p_two * 3)\n\n\n@pytest.mark.skipif(IS_WASM, reason="can't start thread")\nclass TestThread:\n # make sure each state produces the same sequence even in threads\n def setup_method(self):\n self.seeds = range(4)\n\n def check_function(self, function, sz):\n from threading import Thread\n\n out1 = np.empty((len(self.seeds),) + sz)\n out2 = np.empty((len(self.seeds),) + sz)\n\n # threaded generation\n t = [Thread(target=function, args=(random.RandomState(s), o))\n for s, o in zip(self.seeds, out1)]\n [x.start() for x in t]\n [x.join() for x in t]\n\n # the same serial\n for s, o in zip(self.seeds, out2):\n function(random.RandomState(s), o)\n\n # these platforms change x87 fpu precision mode in threads\n if np.intp().dtype.itemsize == 4 and sys.platform == "win32":\n assert_array_almost_equal(out1, out2)\n else:\n assert_array_equal(out1, out2)\n\n def test_normal(self):\n def gen_random(state, out):\n out[...] = state.normal(size=10000)\n\n self.check_function(gen_random, sz=(10000,))\n\n def test_exp(self):\n def gen_random(state, out):\n out[...] = state.exponential(scale=np.ones((100, 1000)))\n\n self.check_function(gen_random, sz=(100, 1000))\n\n def test_multinomial(self):\n def gen_random(state, out):\n out[...] = state.multinomial(10, [1 / 6.] * 6, size=10000)\n\n self.check_function(gen_random, sz=(10000, 6))\n\n\n# See Issue #4263\nclass TestSingleEltArrayInput:\n def setup_method(self):\n self.argOne = np.array([2])\n self.argTwo = np.array([3])\n self.argThree = np.array([4])\n self.tgtShape = (1,)\n\n def test_one_arg_funcs(self):\n funcs = (random.exponential, random.standard_gamma,\n random.chisquare, random.standard_t,\n random.pareto, random.weibull,\n random.power, random.rayleigh,\n random.poisson, random.zipf,\n random.geometric, random.logseries)\n\n probfuncs = (random.geometric, random.logseries)\n\n for func in funcs:\n if func in probfuncs: # p < 1.0\n out = func(np.array([0.5]))\n\n else:\n out = func(self.argOne)\n\n assert_equal(out.shape, self.tgtShape)\n\n def test_two_arg_funcs(self):\n funcs = (random.uniform, random.normal,\n random.beta, random.gamma,\n random.f, random.noncentral_chisquare,\n random.vonmises, random.laplace,\n random.gumbel, random.logistic,\n random.lognormal, random.wald,\n random.binomial, random.negative_binomial)\n\n probfuncs = (random.binomial, random.negative_binomial)\n\n for func in funcs:\n if func in probfuncs: # p <= 1\n argTwo = np.array([0.5])\n\n else:\n argTwo = self.argTwo\n\n out = func(self.argOne, argTwo)\n assert_equal(out.shape, self.tgtShape)\n\n out = func(self.argOne[0], argTwo)\n assert_equal(out.shape, self.tgtShape)\n\n out = func(self.argOne, argTwo[0])\n assert_equal(out.shape, self.tgtShape)\n\n def test_three_arg_funcs(self):\n funcs = [random.noncentral_f, random.triangular,\n random.hypergeometric]\n\n for func in funcs:\n out = func(self.argOne, self.argTwo, self.argThree)\n assert_equal(out.shape, self.tgtShape)\n\n out = func(self.argOne[0], self.argTwo, self.argThree)\n assert_equal(out.shape, self.tgtShape)\n\n out = func(self.argOne, self.argTwo[0], self.argThree)\n assert_equal(out.shape, self.tgtShape)\n\n\n# Ensure returned array dtype is correct for platform\ndef test_integer_dtype(int_func):\n random.seed(123456789)\n fname, args, sha256 = int_func\n f = getattr(random, fname)\n actual = f(*args, size=2)\n assert_(actual.dtype == np.dtype('l'))\n\n\ndef test_integer_repeat(int_func):\n random.seed(123456789)\n fname, args, sha256 = int_func\n f = getattr(random, fname)\n val = f(*args, size=1000000)\n if sys.byteorder != 'little':\n val = val.byteswap()\n res = hashlib.sha256(val.view(np.int8)).hexdigest()\n assert_(res == sha256)\n\n\ndef test_broadcast_size_error():\n # GH-16833\n with pytest.raises(ValueError):\n random.binomial(1, [0.3, 0.7], size=(2, 1))\n with pytest.raises(ValueError):\n random.binomial([1, 2], 0.3, size=(2, 1))\n with pytest.raises(ValueError):\n random.binomial([1, 2], [0.3, 0.7], size=(2, 1))\n\n\ndef test_randomstate_ctor_old_style_pickle():\n rs = np.random.RandomState(MT19937(0))\n rs.standard_normal(1)\n # Directly call reduce which is used in pickling\n ctor, args, state_a = rs.__reduce__()\n # Simulate unpickling an old pickle that only has the name\n assert args[0].__class__.__name__ == "MT19937"\n b = ctor(*("MT19937",))\n b.set_state(state_a)\n state_b = b.get_state(legacy=False)\n\n assert_equal(state_a['bit_generator'], state_b['bit_generator'])\n assert_array_equal(state_a['state']['key'], state_b['state']['key'])\n assert_array_equal(state_a['state']['pos'], state_b['state']['pos'])\n assert_equal(state_a['has_gauss'], state_b['has_gauss'])\n assert_equal(state_a['gauss'], state_b['gauss'])\n\n\ndef test_hot_swap(restore_singleton_bitgen):\n # GH 21808\n def_bg = np.random.default_rng(0)\n bg = def_bg.bit_generator\n np.random.set_bit_generator(bg)\n assert isinstance(np.random.mtrand._rand._bit_generator, type(bg))\n\n second_bg = np.random.get_bit_generator()\n assert bg is second_bg\n\n\ndef test_seed_alt_bit_gen(restore_singleton_bitgen):\n # GH 21808\n bg = PCG64(0)\n np.random.set_bit_generator(bg)\n state = np.random.get_state(legacy=False)\n np.random.seed(1)\n new_state = np.random.get_state(legacy=False)\n print(state)\n print(new_state)\n assert state["bit_generator"] == "PCG64"\n assert state["state"]["state"] != new_state["state"]["state"]\n assert state["state"]["inc"] != new_state["state"]["inc"]\n\n\ndef test_state_error_alt_bit_gen(restore_singleton_bitgen):\n # GH 21808\n state = np.random.get_state()\n bg = PCG64(0)\n np.random.set_bit_generator(bg)\n with pytest.raises(ValueError, match="state must be for a PCG64"):\n np.random.set_state(state)\n\n\ndef test_swap_worked(restore_singleton_bitgen):\n # GH 21808\n np.random.seed(98765)\n vals = np.random.randint(0, 2 ** 30, 10)\n bg = PCG64(0)\n state = bg.state\n np.random.set_bit_generator(bg)\n state_direct = np.random.get_state(legacy=False)\n for field in state:\n assert state[field] == state_direct[field]\n np.random.seed(98765)\n pcg_vals = np.random.randint(0, 2 ** 30, 10)\n assert not np.all(vals == pcg_vals)\n new_state = bg.state\n assert new_state["state"]["state"] != state["state"]["state"]\n assert new_state["state"]["inc"] == new_state["state"]["inc"]\n\n\ndef test_swapped_singleton_against_direct(restore_singleton_bitgen):\n np.random.set_bit_generator(PCG64(98765))\n singleton_vals = np.random.randint(0, 2 ** 30, 10)\n rg = np.random.RandomState(PCG64(98765))\n non_singleton_vals = rg.randint(0, 2 ** 30, 10)\n assert_equal(non_singleton_vals, singleton_vals)\n | .venv\Lib\site-packages\numpy\random\tests\test_randomstate.py | test_randomstate.py | Python | 87,879 | 0.75 | 0.11784 | 0.05735 | vue-tools | 461 | 2024-12-30T04:39:58.421838 | MIT | true | 0205669666db5a09958c5e14ecbd4d90 |
import sys\n\nimport pytest\n\nimport numpy as np\nfrom numpy import random\nfrom numpy.testing import (\n assert_,\n assert_array_equal,\n assert_raises,\n)\n\n\nclass TestRegression:\n\n def test_VonMises_range(self):\n # Make sure generated random variables are in [-pi, pi].\n # Regression test for ticket #986.\n for mu in np.linspace(-7., 7., 5):\n r = random.vonmises(mu, 1, 50)\n assert_(np.all(r > -np.pi) and np.all(r <= np.pi))\n\n def test_hypergeometric_range(self):\n # Test for ticket #921\n assert_(np.all(random.hypergeometric(3, 18, 11, size=10) < 4))\n assert_(np.all(random.hypergeometric(18, 3, 11, size=10) > 0))\n\n # Test for ticket #5623\n args = [\n (2**20 - 2, 2**20 - 2, 2**20 - 2), # Check for 32-bit systems\n ]\n is_64bits = sys.maxsize > 2**32\n if is_64bits and sys.platform != 'win32':\n # Check for 64-bit systems\n args.append((2**40 - 2, 2**40 - 2, 2**40 - 2))\n for arg in args:\n assert_(random.hypergeometric(*arg) > 0)\n\n def test_logseries_convergence(self):\n # Test for ticket #923\n N = 1000\n random.seed(0)\n rvsn = random.logseries(0.8, size=N)\n # these two frequency counts should be close to theoretical\n # numbers with this large sample\n # theoretical large N result is 0.49706795\n freq = np.sum(rvsn == 1) / N\n msg = f'Frequency was {freq:f}, should be > 0.45'\n assert_(freq > 0.45, msg)\n # theoretical large N result is 0.19882718\n freq = np.sum(rvsn == 2) / N\n msg = f'Frequency was {freq:f}, should be < 0.23'\n assert_(freq < 0.23, msg)\n\n def test_shuffle_mixed_dimension(self):\n # Test for trac ticket #2074\n for t in [[1, 2, 3, None],\n [(1, 1), (2, 2), (3, 3), None],\n [1, (2, 2), (3, 3), None],\n [(1, 1), 2, 3, None]]:\n random.seed(12345)\n shuffled = list(t)\n random.shuffle(shuffled)\n expected = np.array([t[0], t[3], t[1], t[2]], dtype=object)\n assert_array_equal(np.array(shuffled, dtype=object), expected)\n\n def test_call_within_randomstate(self):\n # Check that custom RandomState does not call into global state\n m = random.RandomState()\n res = np.array([0, 8, 7, 2, 1, 9, 4, 7, 0, 3])\n for i in range(3):\n random.seed(i)\n m.seed(4321)\n # If m.state is not honored, the result will change\n assert_array_equal(m.choice(10, size=10, p=np.ones(10) / 10.), res)\n\n def test_multivariate_normal_size_types(self):\n # Test for multivariate_normal issue with 'size' argument.\n # Check that the multivariate_normal size argument can be a\n # numpy integer.\n random.multivariate_normal([0], [[0]], size=1)\n random.multivariate_normal([0], [[0]], size=np.int_(1))\n random.multivariate_normal([0], [[0]], size=np.int64(1))\n\n def test_beta_small_parameters(self):\n # Test that beta with small a and b parameters does not produce\n # NaNs due to roundoff errors causing 0 / 0, gh-5851\n random.seed(1234567890)\n x = random.beta(0.0001, 0.0001, size=100)\n assert_(not np.any(np.isnan(x)), 'Nans in random.beta')\n\n def test_choice_sum_of_probs_tolerance(self):\n # The sum of probs should be 1.0 with some tolerance.\n # For low precision dtypes the tolerance was too tight.\n # See numpy github issue 6123.\n random.seed(1234)\n a = [1, 2, 3]\n counts = [4, 4, 2]\n for dt in np.float16, np.float32, np.float64:\n probs = np.array(counts, dtype=dt) / sum(counts)\n c = random.choice(a, p=probs)\n assert_(c in a)\n assert_raises(ValueError, random.choice, a, p=probs * 0.9)\n\n def test_shuffle_of_array_of_different_length_strings(self):\n # Test that permuting an array of different length strings\n # will not cause a segfault on garbage collection\n # Tests gh-7710\n random.seed(1234)\n\n a = np.array(['a', 'a' * 1000])\n\n for _ in range(100):\n random.shuffle(a)\n\n # Force Garbage Collection - should not segfault.\n import gc\n gc.collect()\n\n def test_shuffle_of_array_of_objects(self):\n # Test that permuting an array of objects will not cause\n # a segfault on garbage collection.\n # See gh-7719\n random.seed(1234)\n a = np.array([np.arange(1), np.arange(4)], dtype=object)\n\n for _ in range(1000):\n random.shuffle(a)\n\n # Force Garbage Collection - should not segfault.\n import gc\n gc.collect()\n\n def test_permutation_subclass(self):\n class N(np.ndarray):\n pass\n\n random.seed(1)\n orig = np.arange(3).view(N)\n perm = random.permutation(orig)\n assert_array_equal(perm, np.array([0, 2, 1]))\n assert_array_equal(orig, np.arange(3).view(N))\n\n class M:\n a = np.arange(5)\n\n def __array__(self, dtype=None, copy=None):\n return self.a\n\n random.seed(1)\n m = M()\n perm = random.permutation(m)\n assert_array_equal(perm, np.array([2, 1, 4, 0, 3]))\n assert_array_equal(m.__array__(), np.arange(5))\n\n def test_warns_byteorder(self):\n # GH 13159\n other_byteord_dt = '<i4' if sys.byteorder == 'big' else '>i4'\n with pytest.deprecated_call(match='non-native byteorder is not'):\n random.randint(0, 200, size=10, dtype=other_byteord_dt)\n\n def test_named_argument_initialization(self):\n # GH 13669\n rs1 = np.random.RandomState(123456789)\n rs2 = np.random.RandomState(seed=123456789)\n assert rs1.randint(0, 100) == rs2.randint(0, 100)\n\n def test_choice_retun_dtype(self):\n # GH 9867, now long since the NumPy default changed.\n c = np.random.choice(10, p=[.1] * 10, size=2)\n assert c.dtype == np.dtype(np.long)\n c = np.random.choice(10, p=[.1] * 10, replace=False, size=2)\n assert c.dtype == np.dtype(np.long)\n c = np.random.choice(10, size=2)\n assert c.dtype == np.dtype(np.long)\n c = np.random.choice(10, replace=False, size=2)\n assert c.dtype == np.dtype(np.long)\n\n @pytest.mark.skipif(np.iinfo('l').max < 2**32,\n reason='Cannot test with 32-bit C long')\n def test_randint_117(self):\n # GH 14189\n random.seed(0)\n expected = np.array([2357136044, 2546248239, 3071714933, 3626093760,\n 2588848963, 3684848379, 2340255427, 3638918503,\n 1819583497, 2678185683], dtype='int64')\n actual = random.randint(2**32, size=10)\n assert_array_equal(actual, expected)\n\n def test_p_zero_stream(self):\n # Regression test for gh-14522. Ensure that future versions\n # generate the same variates as version 1.16.\n np.random.seed(12345)\n assert_array_equal(random.binomial(1, [0, 0.25, 0.5, 0.75, 1]),\n [0, 0, 0, 1, 1])\n\n def test_n_zero_stream(self):\n # Regression test for gh-14522. Ensure that future versions\n # generate the same variates as version 1.16.\n np.random.seed(8675309)\n expected = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 4, 2, 3, 3, 1, 5, 3, 1, 3]])\n assert_array_equal(random.binomial([[0], [10]], 0.25, size=(2, 10)),\n expected)\n\n\ndef test_multinomial_empty():\n # gh-20483\n # Ensure that empty p-vals are correctly handled\n assert random.multinomial(10, []).shape == (0,)\n assert random.multinomial(3, [], size=(7, 5, 3)).shape == (7, 5, 3, 0)\n\n\ndef test_multinomial_1d_pval():\n # gh-20483\n with pytest.raises(TypeError, match="pvals must be a 1-d"):\n random.multinomial(10, 0.3)\n | .venv\Lib\site-packages\numpy\random\tests\test_randomstate_regression.py | test_randomstate_regression.py | Python | 8,227 | 0.95 | 0.193548 | 0.21978 | vue-tools | 44 | 2024-03-13T02:09:46.628590 | MIT | true | 3e3f950c449da59c1cc95aab510d3913 |
import sys\n\nimport numpy as np\nfrom numpy import random\nfrom numpy.testing import (\n assert_,\n assert_array_equal,\n assert_raises,\n)\n\n\nclass TestRegression:\n\n def test_VonMises_range(self):\n # Make sure generated random variables are in [-pi, pi].\n # Regression test for ticket #986.\n for mu in np.linspace(-7., 7., 5):\n r = random.mtrand.vonmises(mu, 1, 50)\n assert_(np.all(r > -np.pi) and np.all(r <= np.pi))\n\n def test_hypergeometric_range(self):\n # Test for ticket #921\n assert_(np.all(np.random.hypergeometric(3, 18, 11, size=10) < 4))\n assert_(np.all(np.random.hypergeometric(18, 3, 11, size=10) > 0))\n\n # Test for ticket #5623\n args = [\n (2**20 - 2, 2**20 - 2, 2**20 - 2), # Check for 32-bit systems\n ]\n is_64bits = sys.maxsize > 2**32\n if is_64bits and sys.platform != 'win32':\n # Check for 64-bit systems\n args.append((2**40 - 2, 2**40 - 2, 2**40 - 2))\n for arg in args:\n assert_(np.random.hypergeometric(*arg) > 0)\n\n def test_logseries_convergence(self):\n # Test for ticket #923\n N = 1000\n np.random.seed(0)\n rvsn = np.random.logseries(0.8, size=N)\n # these two frequency counts should be close to theoretical\n # numbers with this large sample\n # theoretical large N result is 0.49706795\n freq = np.sum(rvsn == 1) / N\n msg = f'Frequency was {freq:f}, should be > 0.45'\n assert_(freq > 0.45, msg)\n # theoretical large N result is 0.19882718\n freq = np.sum(rvsn == 2) / N\n msg = f'Frequency was {freq:f}, should be < 0.23'\n assert_(freq < 0.23, msg)\n\n def test_shuffle_mixed_dimension(self):\n # Test for trac ticket #2074\n for t in [[1, 2, 3, None],\n [(1, 1), (2, 2), (3, 3), None],\n [1, (2, 2), (3, 3), None],\n [(1, 1), 2, 3, None]]:\n np.random.seed(12345)\n shuffled = list(t)\n random.shuffle(shuffled)\n expected = np.array([t[0], t[3], t[1], t[2]], dtype=object)\n assert_array_equal(np.array(shuffled, dtype=object), expected)\n\n def test_call_within_randomstate(self):\n # Check that custom RandomState does not call into global state\n m = np.random.RandomState()\n res = np.array([0, 8, 7, 2, 1, 9, 4, 7, 0, 3])\n for i in range(3):\n np.random.seed(i)\n m.seed(4321)\n # If m.state is not honored, the result will change\n assert_array_equal(m.choice(10, size=10, p=np.ones(10) / 10.), res)\n\n def test_multivariate_normal_size_types(self):\n # Test for multivariate_normal issue with 'size' argument.\n # Check that the multivariate_normal size argument can be a\n # numpy integer.\n np.random.multivariate_normal([0], [[0]], size=1)\n np.random.multivariate_normal([0], [[0]], size=np.int_(1))\n np.random.multivariate_normal([0], [[0]], size=np.int64(1))\n\n def test_beta_small_parameters(self):\n # Test that beta with small a and b parameters does not produce\n # NaNs due to roundoff errors causing 0 / 0, gh-5851\n np.random.seed(1234567890)\n x = np.random.beta(0.0001, 0.0001, size=100)\n assert_(not np.any(np.isnan(x)), 'Nans in np.random.beta')\n\n def test_choice_sum_of_probs_tolerance(self):\n # The sum of probs should be 1.0 with some tolerance.\n # For low precision dtypes the tolerance was too tight.\n # See numpy github issue 6123.\n np.random.seed(1234)\n a = [1, 2, 3]\n counts = [4, 4, 2]\n for dt in np.float16, np.float32, np.float64:\n probs = np.array(counts, dtype=dt) / sum(counts)\n c = np.random.choice(a, p=probs)\n assert_(c in a)\n assert_raises(ValueError, np.random.choice, a, p=probs * 0.9)\n\n def test_shuffle_of_array_of_different_length_strings(self):\n # Test that permuting an array of different length strings\n # will not cause a segfault on garbage collection\n # Tests gh-7710\n np.random.seed(1234)\n\n a = np.array(['a', 'a' * 1000])\n\n for _ in range(100):\n np.random.shuffle(a)\n\n # Force Garbage Collection - should not segfault.\n import gc\n gc.collect()\n\n def test_shuffle_of_array_of_objects(self):\n # Test that permuting an array of objects will not cause\n # a segfault on garbage collection.\n # See gh-7719\n np.random.seed(1234)\n a = np.array([np.arange(1), np.arange(4)], dtype=object)\n\n for _ in range(1000):\n np.random.shuffle(a)\n\n # Force Garbage Collection - should not segfault.\n import gc\n gc.collect()\n\n def test_permutation_subclass(self):\n class N(np.ndarray):\n pass\n\n np.random.seed(1)\n orig = np.arange(3).view(N)\n perm = np.random.permutation(orig)\n assert_array_equal(perm, np.array([0, 2, 1]))\n assert_array_equal(orig, np.arange(3).view(N))\n\n class M:\n a = np.arange(5)\n\n def __array__(self, dtype=None, copy=None):\n return self.a\n\n np.random.seed(1)\n m = M()\n perm = np.random.permutation(m)\n assert_array_equal(perm, np.array([2, 1, 4, 0, 3]))\n assert_array_equal(m.__array__(), np.arange(5))\n | .venv\Lib\site-packages\numpy\random\tests\test_regression.py | test_regression.py | Python | 5,623 | 0.95 | 0.203947 | 0.226563 | python-kit | 829 | 2024-01-29T09:28:55.550159 | MIT | true | 90c473a5557c7f2b5579bb5f89e85783 |
import numpy as np\nfrom numpy.random import SeedSequence\nfrom numpy.testing import assert_array_compare, assert_array_equal\n\n\ndef test_reference_data():\n """ Check that SeedSequence generates data the same as the C++ reference.\n\n https://gist.github.com/imneme/540829265469e673d045\n """\n inputs = [\n [3735928559, 195939070, 229505742, 305419896],\n [3668361503, 4165561550, 1661411377, 3634257570],\n [164546577, 4166754639, 1765190214, 1303880213],\n [446610472, 3941463886, 522937693, 1882353782],\n [1864922766, 1719732118, 3882010307, 1776744564],\n [4141682960, 3310988675, 553637289, 902896340],\n [1134851934, 2352871630, 3699409824, 2648159817],\n [1240956131, 3107113773, 1283198141, 1924506131],\n [2669565031, 579818610, 3042504477, 2774880435],\n [2766103236, 2883057919, 4029656435, 862374500],\n ]\n outputs = [\n [3914649087, 576849849, 3593928901, 2229911004],\n [2240804226, 3691353228, 1365957195, 2654016646],\n [3562296087, 3191708229, 1147942216, 3726991905],\n [1403443605, 3591372999, 1291086759, 441919183],\n [1086200464, 2191331643, 560336446, 3658716651],\n [3249937430, 2346751812, 847844327, 2996632307],\n [2584285912, 4034195531, 3523502488, 169742686],\n [959045797, 3875435559, 1886309314, 359682705],\n [3978441347, 432478529, 3223635119, 138903045],\n [296367413, 4262059219, 13109864, 3283683422],\n ]\n outputs64 = [\n [2477551240072187391, 9577394838764454085],\n [15854241394484835714, 11398914698975566411],\n [13708282465491374871, 16007308345579681096],\n [15424829579845884309, 1898028439751125927],\n [9411697742461147792, 15714068361935982142],\n [10079222287618677782, 12870437757549876199],\n [17326737873898640088, 729039288628699544],\n [16644868984619524261, 1544825456798124994],\n [1857481142255628931, 596584038813451439],\n [18305404959516669237, 14103312907920476776],\n ]\n for seed, expected, expected64 in zip(inputs, outputs, outputs64):\n expected = np.array(expected, dtype=np.uint32)\n ss = SeedSequence(seed)\n state = ss.generate_state(len(expected))\n assert_array_equal(state, expected)\n state64 = ss.generate_state(len(expected64), dtype=np.uint64)\n assert_array_equal(state64, expected64)\n\n\ndef test_zero_padding():\n """ Ensure that the implicit zero-padding does not cause problems.\n """\n # Ensure that large integers are inserted in little-endian fashion to avoid\n # trailing 0s.\n ss0 = SeedSequence(42)\n ss1 = SeedSequence(42 << 32)\n assert_array_compare(\n np.not_equal,\n ss0.generate_state(4),\n ss1.generate_state(4))\n\n # Ensure backwards compatibility with the original 0.17 release for small\n # integers and no spawn key.\n expected42 = np.array([3444837047, 2669555309, 2046530742, 3581440988],\n dtype=np.uint32)\n assert_array_equal(SeedSequence(42).generate_state(4), expected42)\n\n # Regression test for gh-16539 to ensure that the implicit 0s don't\n # conflict with spawn keys.\n assert_array_compare(\n np.not_equal,\n SeedSequence(42, spawn_key=(0,)).generate_state(4),\n expected42)\n | .venv\Lib\site-packages\numpy\random\tests\test_seed_sequence.py | test_seed_sequence.py | Python | 3,389 | 0.95 | 0.063291 | 0.083333 | node-utils | 529 | 2025-02-13T13:52:09.842535 | BSD-3-Clause | true | 730878b7352b21558d80608b164e2d02 |
import pickle\nfrom functools import partial\n\nimport pytest\n\nimport numpy as np\nfrom numpy.random import MT19937, PCG64, PCG64DXSM, SFC64, Generator, Philox\nfrom numpy.testing import assert_, assert_array_equal, assert_equal\n\n\n@pytest.fixture(scope='module',\n params=(np.bool, np.int8, np.int16, np.int32, np.int64,\n np.uint8, np.uint16, np.uint32, np.uint64))\ndef dtype(request):\n return request.param\n\n\ndef params_0(f):\n val = f()\n assert_(np.isscalar(val))\n val = f(10)\n assert_(val.shape == (10,))\n val = f((10, 10))\n assert_(val.shape == (10, 10))\n val = f((10, 10, 10))\n assert_(val.shape == (10, 10, 10))\n val = f(size=(5, 5))\n assert_(val.shape == (5, 5))\n\n\ndef params_1(f, bounded=False):\n a = 5.0\n b = np.arange(2.0, 12.0)\n c = np.arange(2.0, 102.0).reshape((10, 10))\n d = np.arange(2.0, 1002.0).reshape((10, 10, 10))\n e = np.array([2.0, 3.0])\n g = np.arange(2.0, 12.0).reshape((1, 10, 1))\n if bounded:\n a = 0.5\n b = b / (1.5 * b.max())\n c = c / (1.5 * c.max())\n d = d / (1.5 * d.max())\n e = e / (1.5 * e.max())\n g = g / (1.5 * g.max())\n\n # Scalar\n f(a)\n # Scalar - size\n f(a, size=(10, 10))\n # 1d\n f(b)\n # 2d\n f(c)\n # 3d\n f(d)\n # 1d size\n f(b, size=10)\n # 2d - size - broadcast\n f(e, size=(10, 2))\n # 3d - size\n f(g, size=(10, 10, 10))\n\n\ndef comp_state(state1, state2):\n identical = True\n if isinstance(state1, dict):\n for key in state1:\n identical &= comp_state(state1[key], state2[key])\n elif type(state1) != type(state2):\n identical &= type(state1) == type(state2)\n elif (isinstance(state1, (list, tuple, np.ndarray)) and isinstance(\n state2, (list, tuple, np.ndarray))):\n for s1, s2 in zip(state1, state2):\n identical &= comp_state(s1, s2)\n else:\n identical &= state1 == state2\n return identical\n\n\ndef warmup(rg, n=None):\n if n is None:\n n = 11 + np.random.randint(0, 20)\n rg.standard_normal(n)\n rg.standard_normal(n)\n rg.standard_normal(n, dtype=np.float32)\n rg.standard_normal(n, dtype=np.float32)\n rg.integers(0, 2 ** 24, n, dtype=np.uint64)\n rg.integers(0, 2 ** 48, n, dtype=np.uint64)\n rg.standard_gamma(11.0, n)\n rg.standard_gamma(11.0, n, dtype=np.float32)\n rg.random(n, dtype=np.float64)\n rg.random(n, dtype=np.float32)\n\n\nclass RNG:\n @classmethod\n def setup_class(cls):\n # Overridden in test classes. Place holder to silence IDE noise\n cls.bit_generator = PCG64\n cls.advance = None\n cls.seed = [12345]\n cls.rg = Generator(cls.bit_generator(*cls.seed))\n cls.initial_state = cls.rg.bit_generator.state\n cls.seed_vector_bits = 64\n cls._extra_setup()\n\n @classmethod\n def _extra_setup(cls):\n cls.vec_1d = np.arange(2.0, 102.0)\n cls.vec_2d = np.arange(2.0, 102.0)[None, :]\n cls.mat = np.arange(2.0, 102.0, 0.01).reshape((100, 100))\n cls.seed_error = TypeError\n\n def _reset_state(self):\n self.rg.bit_generator.state = self.initial_state\n\n def test_init(self):\n rg = Generator(self.bit_generator())\n state = rg.bit_generator.state\n rg.standard_normal(1)\n rg.standard_normal(1)\n rg.bit_generator.state = state\n new_state = rg.bit_generator.state\n assert_(comp_state(state, new_state))\n\n def test_advance(self):\n state = self.rg.bit_generator.state\n if hasattr(self.rg.bit_generator, 'advance'):\n self.rg.bit_generator.advance(self.advance)\n assert_(not comp_state(state, self.rg.bit_generator.state))\n else:\n bitgen_name = self.rg.bit_generator.__class__.__name__\n pytest.skip(f'Advance is not supported by {bitgen_name}')\n\n def test_jump(self):\n state = self.rg.bit_generator.state\n if hasattr(self.rg.bit_generator, 'jumped'):\n bit_gen2 = self.rg.bit_generator.jumped()\n jumped_state = bit_gen2.state\n assert_(not comp_state(state, jumped_state))\n self.rg.random(2 * 3 * 5 * 7 * 11 * 13 * 17)\n self.rg.bit_generator.state = state\n bit_gen3 = self.rg.bit_generator.jumped()\n rejumped_state = bit_gen3.state\n assert_(comp_state(jumped_state, rejumped_state))\n else:\n bitgen_name = self.rg.bit_generator.__class__.__name__\n if bitgen_name not in ('SFC64',):\n raise AttributeError(f'no "jumped" in {bitgen_name}')\n pytest.skip(f'Jump is not supported by {bitgen_name}')\n\n def test_uniform(self):\n r = self.rg.uniform(-1.0, 0.0, size=10)\n assert_(len(r) == 10)\n assert_((r > -1).all())\n assert_((r <= 0).all())\n\n def test_uniform_array(self):\n r = self.rg.uniform(np.array([-1.0] * 10), 0.0, size=10)\n assert_(len(r) == 10)\n assert_((r > -1).all())\n assert_((r <= 0).all())\n r = self.rg.uniform(np.array([-1.0] * 10),\n np.array([0.0] * 10), size=10)\n assert_(len(r) == 10)\n assert_((r > -1).all())\n assert_((r <= 0).all())\n r = self.rg.uniform(-1.0, np.array([0.0] * 10), size=10)\n assert_(len(r) == 10)\n assert_((r > -1).all())\n assert_((r <= 0).all())\n\n def test_random(self):\n assert_(len(self.rg.random(10)) == 10)\n params_0(self.rg.random)\n\n def test_standard_normal_zig(self):\n assert_(len(self.rg.standard_normal(10)) == 10)\n\n def test_standard_normal(self):\n assert_(len(self.rg.standard_normal(10)) == 10)\n params_0(self.rg.standard_normal)\n\n def test_standard_gamma(self):\n assert_(len(self.rg.standard_gamma(10, 10)) == 10)\n assert_(len(self.rg.standard_gamma(np.array([10] * 10), 10)) == 10)\n params_1(self.rg.standard_gamma)\n\n def test_standard_exponential(self):\n assert_(len(self.rg.standard_exponential(10)) == 10)\n params_0(self.rg.standard_exponential)\n\n def test_standard_exponential_float(self):\n randoms = self.rg.standard_exponential(10, dtype='float32')\n assert_(len(randoms) == 10)\n assert randoms.dtype == np.float32\n params_0(partial(self.rg.standard_exponential, dtype='float32'))\n\n def test_standard_exponential_float_log(self):\n randoms = self.rg.standard_exponential(10, dtype='float32',\n method='inv')\n assert_(len(randoms) == 10)\n assert randoms.dtype == np.float32\n params_0(partial(self.rg.standard_exponential, dtype='float32',\n method='inv'))\n\n def test_standard_cauchy(self):\n assert_(len(self.rg.standard_cauchy(10)) == 10)\n params_0(self.rg.standard_cauchy)\n\n def test_standard_t(self):\n assert_(len(self.rg.standard_t(10, 10)) == 10)\n params_1(self.rg.standard_t)\n\n def test_binomial(self):\n assert_(self.rg.binomial(10, .5) >= 0)\n assert_(self.rg.binomial(1000, .5) >= 0)\n\n def test_reset_state(self):\n state = self.rg.bit_generator.state\n int_1 = self.rg.integers(2**31)\n self.rg.bit_generator.state = state\n int_2 = self.rg.integers(2**31)\n assert_(int_1 == int_2)\n\n def test_entropy_init(self):\n rg = Generator(self.bit_generator())\n rg2 = Generator(self.bit_generator())\n assert_(not comp_state(rg.bit_generator.state,\n rg2.bit_generator.state))\n\n def test_seed(self):\n rg = Generator(self.bit_generator(*self.seed))\n rg2 = Generator(self.bit_generator(*self.seed))\n rg.random()\n rg2.random()\n assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))\n\n def test_reset_state_gauss(self):\n rg = Generator(self.bit_generator(*self.seed))\n rg.standard_normal()\n state = rg.bit_generator.state\n n1 = rg.standard_normal(size=10)\n rg2 = Generator(self.bit_generator())\n rg2.bit_generator.state = state\n n2 = rg2.standard_normal(size=10)\n assert_array_equal(n1, n2)\n\n def test_reset_state_uint32(self):\n rg = Generator(self.bit_generator(*self.seed))\n rg.integers(0, 2 ** 24, 120, dtype=np.uint32)\n state = rg.bit_generator.state\n n1 = rg.integers(0, 2 ** 24, 10, dtype=np.uint32)\n rg2 = Generator(self.bit_generator())\n rg2.bit_generator.state = state\n n2 = rg2.integers(0, 2 ** 24, 10, dtype=np.uint32)\n assert_array_equal(n1, n2)\n\n def test_reset_state_float(self):\n rg = Generator(self.bit_generator(*self.seed))\n rg.random(dtype='float32')\n state = rg.bit_generator.state\n n1 = rg.random(size=10, dtype='float32')\n rg2 = Generator(self.bit_generator())\n rg2.bit_generator.state = state\n n2 = rg2.random(size=10, dtype='float32')\n assert_((n1 == n2).all())\n\n def test_shuffle(self):\n original = np.arange(200, 0, -1)\n permuted = self.rg.permutation(original)\n assert_((original != permuted).any())\n\n def test_permutation(self):\n original = np.arange(200, 0, -1)\n permuted = self.rg.permutation(original)\n assert_((original != permuted).any())\n\n def test_beta(self):\n vals = self.rg.beta(2.0, 2.0, 10)\n assert_(len(vals) == 10)\n vals = self.rg.beta(np.array([2.0] * 10), 2.0)\n assert_(len(vals) == 10)\n vals = self.rg.beta(2.0, np.array([2.0] * 10))\n assert_(len(vals) == 10)\n vals = self.rg.beta(np.array([2.0] * 10), np.array([2.0] * 10))\n assert_(len(vals) == 10)\n vals = self.rg.beta(np.array([2.0] * 10), np.array([[2.0]] * 10))\n assert_(vals.shape == (10, 10))\n\n def test_bytes(self):\n vals = self.rg.bytes(10)\n assert_(len(vals) == 10)\n\n def test_chisquare(self):\n vals = self.rg.chisquare(2.0, 10)\n assert_(len(vals) == 10)\n params_1(self.rg.chisquare)\n\n def test_exponential(self):\n vals = self.rg.exponential(2.0, 10)\n assert_(len(vals) == 10)\n params_1(self.rg.exponential)\n\n def test_f(self):\n vals = self.rg.f(3, 1000, 10)\n assert_(len(vals) == 10)\n\n def test_gamma(self):\n vals = self.rg.gamma(3, 2, 10)\n assert_(len(vals) == 10)\n\n def test_geometric(self):\n vals = self.rg.geometric(0.5, 10)\n assert_(len(vals) == 10)\n params_1(self.rg.exponential, bounded=True)\n\n def test_gumbel(self):\n vals = self.rg.gumbel(2.0, 2.0, 10)\n assert_(len(vals) == 10)\n\n def test_laplace(self):\n vals = self.rg.laplace(2.0, 2.0, 10)\n assert_(len(vals) == 10)\n\n def test_logitic(self):\n vals = self.rg.logistic(2.0, 2.0, 10)\n assert_(len(vals) == 10)\n\n def test_logseries(self):\n vals = self.rg.logseries(0.5, 10)\n assert_(len(vals) == 10)\n\n def test_negative_binomial(self):\n vals = self.rg.negative_binomial(10, 0.2, 10)\n assert_(len(vals) == 10)\n\n def test_noncentral_chisquare(self):\n vals = self.rg.noncentral_chisquare(10, 2, 10)\n assert_(len(vals) == 10)\n\n def test_noncentral_f(self):\n vals = self.rg.noncentral_f(3, 1000, 2, 10)\n assert_(len(vals) == 10)\n vals = self.rg.noncentral_f(np.array([3] * 10), 1000, 2)\n assert_(len(vals) == 10)\n vals = self.rg.noncentral_f(3, np.array([1000] * 10), 2)\n assert_(len(vals) == 10)\n vals = self.rg.noncentral_f(3, 1000, np.array([2] * 10))\n assert_(len(vals) == 10)\n\n def test_normal(self):\n vals = self.rg.normal(10, 0.2, 10)\n assert_(len(vals) == 10)\n\n def test_pareto(self):\n vals = self.rg.pareto(3.0, 10)\n assert_(len(vals) == 10)\n\n def test_poisson(self):\n vals = self.rg.poisson(10, 10)\n assert_(len(vals) == 10)\n vals = self.rg.poisson(np.array([10] * 10))\n assert_(len(vals) == 10)\n params_1(self.rg.poisson)\n\n def test_power(self):\n vals = self.rg.power(0.2, 10)\n assert_(len(vals) == 10)\n\n def test_integers(self):\n vals = self.rg.integers(10, 20, 10)\n assert_(len(vals) == 10)\n\n def test_rayleigh(self):\n vals = self.rg.rayleigh(0.2, 10)\n assert_(len(vals) == 10)\n params_1(self.rg.rayleigh, bounded=True)\n\n def test_vonmises(self):\n vals = self.rg.vonmises(10, 0.2, 10)\n assert_(len(vals) == 10)\n\n def test_wald(self):\n vals = self.rg.wald(1.0, 1.0, 10)\n assert_(len(vals) == 10)\n\n def test_weibull(self):\n vals = self.rg.weibull(1.0, 10)\n assert_(len(vals) == 10)\n\n def test_zipf(self):\n vals = self.rg.zipf(10, 10)\n assert_(len(vals) == 10)\n vals = self.rg.zipf(self.vec_1d)\n assert_(len(vals) == 100)\n vals = self.rg.zipf(self.vec_2d)\n assert_(vals.shape == (1, 100))\n vals = self.rg.zipf(self.mat)\n assert_(vals.shape == (100, 100))\n\n def test_hypergeometric(self):\n vals = self.rg.hypergeometric(25, 25, 20)\n assert_(np.isscalar(vals))\n vals = self.rg.hypergeometric(np.array([25] * 10), 25, 20)\n assert_(vals.shape == (10,))\n\n def test_triangular(self):\n vals = self.rg.triangular(-5, 0, 5)\n assert_(np.isscalar(vals))\n vals = self.rg.triangular(-5, np.array([0] * 10), 5)\n assert_(vals.shape == (10,))\n\n def test_multivariate_normal(self):\n mean = [0, 0]\n cov = [[1, 0], [0, 100]] # diagonal covariance\n x = self.rg.multivariate_normal(mean, cov, 5000)\n assert_(x.shape == (5000, 2))\n x_zig = self.rg.multivariate_normal(mean, cov, 5000)\n assert_(x.shape == (5000, 2))\n x_inv = self.rg.multivariate_normal(mean, cov, 5000)\n assert_(x.shape == (5000, 2))\n assert_((x_zig != x_inv).any())\n\n def test_multinomial(self):\n vals = self.rg.multinomial(100, [1.0 / 3, 2.0 / 3])\n assert_(vals.shape == (2,))\n vals = self.rg.multinomial(100, [1.0 / 3, 2.0 / 3], size=10)\n assert_(vals.shape == (10, 2))\n\n def test_dirichlet(self):\n s = self.rg.dirichlet((10, 5, 3), 20)\n assert_(s.shape == (20, 3))\n\n def test_pickle(self):\n pick = pickle.dumps(self.rg)\n unpick = pickle.loads(pick)\n assert_(type(self.rg) == type(unpick))\n assert_(comp_state(self.rg.bit_generator.state,\n unpick.bit_generator.state))\n\n pick = pickle.dumps(self.rg)\n unpick = pickle.loads(pick)\n assert_(type(self.rg) == type(unpick))\n assert_(comp_state(self.rg.bit_generator.state,\n unpick.bit_generator.state))\n\n def test_seed_array(self):\n if self.seed_vector_bits is None:\n bitgen_name = self.bit_generator.__name__\n pytest.skip(f'Vector seeding is not supported by {bitgen_name}')\n\n if self.seed_vector_bits == 32:\n dtype = np.uint32\n else:\n dtype = np.uint64\n seed = np.array([1], dtype=dtype)\n bg = self.bit_generator(seed)\n state1 = bg.state\n bg = self.bit_generator(1)\n state2 = bg.state\n assert_(comp_state(state1, state2))\n\n seed = np.arange(4, dtype=dtype)\n bg = self.bit_generator(seed)\n state1 = bg.state\n bg = self.bit_generator(seed[0])\n state2 = bg.state\n assert_(not comp_state(state1, state2))\n\n seed = np.arange(1500, dtype=dtype)\n bg = self.bit_generator(seed)\n state1 = bg.state\n bg = self.bit_generator(seed[0])\n state2 = bg.state\n assert_(not comp_state(state1, state2))\n\n seed = 2 ** np.mod(np.arange(1500, dtype=dtype),\n self.seed_vector_bits - 1) + 1\n bg = self.bit_generator(seed)\n state1 = bg.state\n bg = self.bit_generator(seed[0])\n state2 = bg.state\n assert_(not comp_state(state1, state2))\n\n def test_uniform_float(self):\n rg = Generator(self.bit_generator(12345))\n warmup(rg)\n state = rg.bit_generator.state\n r1 = rg.random(11, dtype=np.float32)\n rg2 = Generator(self.bit_generator())\n warmup(rg2)\n rg2.bit_generator.state = state\n r2 = rg2.random(11, dtype=np.float32)\n assert_array_equal(r1, r2)\n assert_equal(r1.dtype, np.float32)\n assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))\n\n def test_gamma_floats(self):\n rg = Generator(self.bit_generator())\n warmup(rg)\n state = rg.bit_generator.state\n r1 = rg.standard_gamma(4.0, 11, dtype=np.float32)\n rg2 = Generator(self.bit_generator())\n warmup(rg2)\n rg2.bit_generator.state = state\n r2 = rg2.standard_gamma(4.0, 11, dtype=np.float32)\n assert_array_equal(r1, r2)\n assert_equal(r1.dtype, np.float32)\n assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))\n\n def test_normal_floats(self):\n rg = Generator(self.bit_generator())\n warmup(rg)\n state = rg.bit_generator.state\n r1 = rg.standard_normal(11, dtype=np.float32)\n rg2 = Generator(self.bit_generator())\n warmup(rg2)\n rg2.bit_generator.state = state\n r2 = rg2.standard_normal(11, dtype=np.float32)\n assert_array_equal(r1, r2)\n assert_equal(r1.dtype, np.float32)\n assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))\n\n def test_normal_zig_floats(self):\n rg = Generator(self.bit_generator())\n warmup(rg)\n state = rg.bit_generator.state\n r1 = rg.standard_normal(11, dtype=np.float32)\n rg2 = Generator(self.bit_generator())\n warmup(rg2)\n rg2.bit_generator.state = state\n r2 = rg2.standard_normal(11, dtype=np.float32)\n assert_array_equal(r1, r2)\n assert_equal(r1.dtype, np.float32)\n assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))\n\n def test_output_fill(self):\n rg = self.rg\n state = rg.bit_generator.state\n size = (31, 7, 97)\n existing = np.empty(size)\n rg.bit_generator.state = state\n rg.standard_normal(out=existing)\n rg.bit_generator.state = state\n direct = rg.standard_normal(size=size)\n assert_equal(direct, existing)\n\n sized = np.empty(size)\n rg.bit_generator.state = state\n rg.standard_normal(out=sized, size=sized.shape)\n\n existing = np.empty(size, dtype=np.float32)\n rg.bit_generator.state = state\n rg.standard_normal(out=existing, dtype=np.float32)\n rg.bit_generator.state = state\n direct = rg.standard_normal(size=size, dtype=np.float32)\n assert_equal(direct, existing)\n\n def test_output_filling_uniform(self):\n rg = self.rg\n state = rg.bit_generator.state\n size = (31, 7, 97)\n existing = np.empty(size)\n rg.bit_generator.state = state\n rg.random(out=existing)\n rg.bit_generator.state = state\n direct = rg.random(size=size)\n assert_equal(direct, existing)\n\n existing = np.empty(size, dtype=np.float32)\n rg.bit_generator.state = state\n rg.random(out=existing, dtype=np.float32)\n rg.bit_generator.state = state\n direct = rg.random(size=size, dtype=np.float32)\n assert_equal(direct, existing)\n\n def test_output_filling_exponential(self):\n rg = self.rg\n state = rg.bit_generator.state\n size = (31, 7, 97)\n existing = np.empty(size)\n rg.bit_generator.state = state\n rg.standard_exponential(out=existing)\n rg.bit_generator.state = state\n direct = rg.standard_exponential(size=size)\n assert_equal(direct, existing)\n\n existing = np.empty(size, dtype=np.float32)\n rg.bit_generator.state = state\n rg.standard_exponential(out=existing, dtype=np.float32)\n rg.bit_generator.state = state\n direct = rg.standard_exponential(size=size, dtype=np.float32)\n assert_equal(direct, existing)\n\n def test_output_filling_gamma(self):\n rg = self.rg\n state = rg.bit_generator.state\n size = (31, 7, 97)\n existing = np.zeros(size)\n rg.bit_generator.state = state\n rg.standard_gamma(1.0, out=existing)\n rg.bit_generator.state = state\n direct = rg.standard_gamma(1.0, size=size)\n assert_equal(direct, existing)\n\n existing = np.zeros(size, dtype=np.float32)\n rg.bit_generator.state = state\n rg.standard_gamma(1.0, out=existing, dtype=np.float32)\n rg.bit_generator.state = state\n direct = rg.standard_gamma(1.0, size=size, dtype=np.float32)\n assert_equal(direct, existing)\n\n def test_output_filling_gamma_broadcast(self):\n rg = self.rg\n state = rg.bit_generator.state\n size = (31, 7, 97)\n mu = np.arange(97.0) + 1.0\n existing = np.zeros(size)\n rg.bit_generator.state = state\n rg.standard_gamma(mu, out=existing)\n rg.bit_generator.state = state\n direct = rg.standard_gamma(mu, size=size)\n assert_equal(direct, existing)\n\n existing = np.zeros(size, dtype=np.float32)\n rg.bit_generator.state = state\n rg.standard_gamma(mu, out=existing, dtype=np.float32)\n rg.bit_generator.state = state\n direct = rg.standard_gamma(mu, size=size, dtype=np.float32)\n assert_equal(direct, existing)\n\n def test_output_fill_error(self):\n rg = self.rg\n size = (31, 7, 97)\n existing = np.empty(size)\n with pytest.raises(TypeError):\n rg.standard_normal(out=existing, dtype=np.float32)\n with pytest.raises(ValueError):\n rg.standard_normal(out=existing[::3])\n existing = np.empty(size, dtype=np.float32)\n with pytest.raises(TypeError):\n rg.standard_normal(out=existing, dtype=np.float64)\n\n existing = np.zeros(size, dtype=np.float32)\n with pytest.raises(TypeError):\n rg.standard_gamma(1.0, out=existing, dtype=np.float64)\n with pytest.raises(ValueError):\n rg.standard_gamma(1.0, out=existing[::3], dtype=np.float32)\n existing = np.zeros(size, dtype=np.float64)\n with pytest.raises(TypeError):\n rg.standard_gamma(1.0, out=existing, dtype=np.float32)\n with pytest.raises(ValueError):\n rg.standard_gamma(1.0, out=existing[::3])\n\n def test_integers_broadcast(self, dtype):\n if dtype == np.bool:\n upper = 2\n lower = 0\n else:\n info = np.iinfo(dtype)\n upper = int(info.max) + 1\n lower = info.min\n self._reset_state()\n a = self.rg.integers(lower, [upper] * 10, dtype=dtype)\n self._reset_state()\n b = self.rg.integers([lower] * 10, upper, dtype=dtype)\n assert_equal(a, b)\n self._reset_state()\n c = self.rg.integers(lower, upper, size=10, dtype=dtype)\n assert_equal(a, c)\n self._reset_state()\n d = self.rg.integers(np.array(\n [lower] * 10), np.array([upper], dtype=object), size=10,\n dtype=dtype)\n assert_equal(a, d)\n self._reset_state()\n e = self.rg.integers(\n np.array([lower] * 10), np.array([upper] * 10), size=10,\n dtype=dtype)\n assert_equal(a, e)\n\n self._reset_state()\n a = self.rg.integers(0, upper, size=10, dtype=dtype)\n self._reset_state()\n b = self.rg.integers([upper] * 10, dtype=dtype)\n assert_equal(a, b)\n\n def test_integers_numpy(self, dtype):\n high = np.array([1])\n low = np.array([0])\n\n out = self.rg.integers(low, high, dtype=dtype)\n assert out.shape == (1,)\n\n out = self.rg.integers(low[0], high, dtype=dtype)\n assert out.shape == (1,)\n\n out = self.rg.integers(low, high[0], dtype=dtype)\n assert out.shape == (1,)\n\n def test_integers_broadcast_errors(self, dtype):\n if dtype == np.bool:\n upper = 2\n lower = 0\n else:\n info = np.iinfo(dtype)\n upper = int(info.max) + 1\n lower = info.min\n with pytest.raises(ValueError):\n self.rg.integers(lower, [upper + 1] * 10, dtype=dtype)\n with pytest.raises(ValueError):\n self.rg.integers(lower - 1, [upper] * 10, dtype=dtype)\n with pytest.raises(ValueError):\n self.rg.integers([lower - 1], [upper] * 10, dtype=dtype)\n with pytest.raises(ValueError):\n self.rg.integers([0], [0], dtype=dtype)\n\n\nclass TestMT19937(RNG):\n @classmethod\n def setup_class(cls):\n cls.bit_generator = MT19937\n cls.advance = None\n cls.seed = [2 ** 21 + 2 ** 16 + 2 ** 5 + 1]\n cls.rg = Generator(cls.bit_generator(*cls.seed))\n cls.initial_state = cls.rg.bit_generator.state\n cls.seed_vector_bits = 32\n cls._extra_setup()\n cls.seed_error = ValueError\n\n def test_numpy_state(self):\n nprg = np.random.RandomState()\n nprg.standard_normal(99)\n state = nprg.get_state()\n self.rg.bit_generator.state = state\n state2 = self.rg.bit_generator.state\n assert_((state[1] == state2['state']['key']).all())\n assert_(state[2] == state2['state']['pos'])\n\n\nclass TestPhilox(RNG):\n @classmethod\n def setup_class(cls):\n cls.bit_generator = Philox\n cls.advance = 2**63 + 2**31 + 2**15 + 1\n cls.seed = [12345]\n cls.rg = Generator(cls.bit_generator(*cls.seed))\n cls.initial_state = cls.rg.bit_generator.state\n cls.seed_vector_bits = 64\n cls._extra_setup()\n\n\nclass TestSFC64(RNG):\n @classmethod\n def setup_class(cls):\n cls.bit_generator = SFC64\n cls.advance = None\n cls.seed = [12345]\n cls.rg = Generator(cls.bit_generator(*cls.seed))\n cls.initial_state = cls.rg.bit_generator.state\n cls.seed_vector_bits = 192\n cls._extra_setup()\n\n\nclass TestPCG64(RNG):\n @classmethod\n def setup_class(cls):\n cls.bit_generator = PCG64\n cls.advance = 2**63 + 2**31 + 2**15 + 1\n cls.seed = [12345]\n cls.rg = Generator(cls.bit_generator(*cls.seed))\n cls.initial_state = cls.rg.bit_generator.state\n cls.seed_vector_bits = 64\n cls._extra_setup()\n\n\nclass TestPCG64DXSM(RNG):\n @classmethod\n def setup_class(cls):\n cls.bit_generator = PCG64DXSM\n cls.advance = 2**63 + 2**31 + 2**15 + 1\n cls.seed = [12345]\n cls.rg = Generator(cls.bit_generator(*cls.seed))\n cls.initial_state = cls.rg.bit_generator.state\n cls.seed_vector_bits = 64\n cls._extra_setup()\n\n\nclass TestDefaultRNG(RNG):\n @classmethod\n def setup_class(cls):\n # This will duplicate some tests that directly instantiate a fresh\n # Generator(), but that's okay.\n cls.bit_generator = PCG64\n cls.advance = 2**63 + 2**31 + 2**15 + 1\n cls.seed = [12345]\n cls.rg = np.random.default_rng(*cls.seed)\n cls.initial_state = cls.rg.bit_generator.state\n cls.seed_vector_bits = 64\n cls._extra_setup()\n\n def test_default_is_pcg64(self):\n # In order to change the default BitGenerator, we'll go through\n # a deprecation cycle to move to a different function.\n assert_(isinstance(self.rg.bit_generator, PCG64))\n\n def test_seed(self):\n np.random.default_rng()\n np.random.default_rng(None)\n np.random.default_rng(12345)\n np.random.default_rng(0)\n np.random.default_rng(43660444402423911716352051725018508569)\n np.random.default_rng([43660444402423911716352051725018508569,\n 279705150948142787361475340226491943209])\n with pytest.raises(ValueError):\n np.random.default_rng(-1)\n with pytest.raises(ValueError):\n np.random.default_rng([12345, -1])\n | .venv\Lib\site-packages\numpy\random\tests\test_smoke.py | test_smoke.py | Python | 28,960 | 0.95 | 0.126984 | 0.018466 | node-utils | 82 | 2023-10-21T01:37:03.717240 | MIT | true | 8ed3c76cae1e2abc42e1239c85bc1e3d |
!fgenerator_pcg64_np121.pkl | .venv\Lib\site-packages\numpy\random\tests\data\generator_pcg64_np121.pkl.gz | generator_pcg64_np121.pkl.gz | Other | 203 | 0.7 | 0.1 | 0 | awesome-app | 60 | 2024-12-05T02:27:43.642329 | MIT | true | ee72d5f24ee531cb004ab725cc8af8f3 |
fgenerator_pcg64_126.pkl | .venv\Lib\site-packages\numpy\random\tests\data\generator_pcg64_np126.pkl.gz | generator_pcg64_np126.pkl.gz | Other | 208 | 0.8 | 0.1 | 0 | node-utils | 43 | 2024-10-25T21:16:24.269419 | Apache-2.0 | true | 0e05cc18ab9d6069de339f9cfd66e807 |
seed, 0xdeadbeaf\n0, 0xc816921f\n1, 0xb3623c6d\n2, 0x5fa391bb\n3, 0x40178d9\n4, 0x7dcc9811\n5, 0x548eb8e6\n6, 0x92ba3125\n7, 0x65fde68d\n8, 0x2f81ec95\n9, 0xbd94f7a2\n10, 0xdc4d9bcc\n11, 0xa672bf13\n12, 0xb41113e\n13, 0xec7e0066\n14, 0x50239372\n15, 0xd9d66b1d\n16, 0xab72a161\n17, 0xddc2e29f\n18, 0x7ea29ab4\n19, 0x80d141ba\n20, 0xb1c7edf1\n21, 0x44d29203\n22, 0xe224d98\n23, 0x5b3e9d26\n24, 0x14fd567c\n25, 0x27d98c96\n26, 0x838779fc\n27, 0x92a138a\n28, 0x5d08965b\n29, 0x531e0ad6\n30, 0x984ee8f4\n31, 0x1ed78539\n32, 0x32bd6d8d\n33, 0xc37c8516\n34, 0x9aef5c6b\n35, 0x3aacd139\n36, 0xd96ed154\n37, 0x489cd1ed\n38, 0x2cba4b3b\n39, 0x76c6ae72\n40, 0x2dae02b9\n41, 0x52ac5fd6\n42, 0xc2b5e265\n43, 0x630e6a28\n44, 0x3f560d5d\n45, 0x9315bdf3\n46, 0xf1055aba\n47, 0x840e42c6\n48, 0xf2099c6b\n49, 0x15ff7696\n50, 0x7948d146\n51, 0x97342961\n52, 0x7a7a21c\n53, 0xc66f4fb1\n54, 0x23c4103e\n55, 0xd7321f98\n56, 0xeb7efb75\n57, 0xe02490b5\n58, 0x2aa02de\n59, 0x8bee0bf7\n60, 0xfc2da059\n61, 0xae835034\n62, 0x678f2075\n63, 0x6d03094b\n64, 0x56455e05\n65, 0x18b32373\n66, 0x8ff0356b\n67, 0x1fe442fb\n68, 0x3f1ab6c3\n69, 0xb6fd21b\n70, 0xfc310eb2\n71, 0xb19e9a4d\n72, 0x17ddee72\n73, 0xfd534251\n74, 0x9e500564\n75, 0x9013a036\n76, 0xcf08f118\n77, 0x6b6d5969\n78, 0x3ccf1977\n79, 0x7cc11497\n80, 0x651c6ac9\n81, 0x4d6b104b\n82, 0x9a28314e\n83, 0x14c237be\n84, 0x9cfc8d52\n85, 0x2947fad5\n86, 0xd71eff49\n87, 0x5188730e\n88, 0x4b894614\n89, 0xf4fa2a34\n90, 0x42f7cc69\n91, 0x4089c9e8\n92, 0xbf0bbfe4\n93, 0x3cea65c\n94, 0xc6221207\n95, 0x1bb71a8f\n96, 0x54843fe7\n97, 0xbc59de4c\n98, 0x79c6ee64\n99, 0x14e57a26\n100, 0x68d88fe\n101, 0x2b86ef64\n102, 0x8ffff3c1\n103, 0x5bdd573f\n104, 0x85671813\n105, 0xefe32ca2\n106, 0x105ded1e\n107, 0x90ca2769\n108, 0xb33963ac\n109, 0x363fbbc3\n110, 0x3b3763ae\n111, 0x1d50ab88\n112, 0xc9ec01eb\n113, 0xc8bbeada\n114, 0x5d704692\n115, 0x5fd9e40\n116, 0xe61c125\n117, 0x2fe05792\n118, 0xda8afb72\n119, 0x4cbaa653\n120, 0xdd2243df\n121, 0x896fd3f5\n122, 0x5bc23db\n123, 0xa1c4e807\n124, 0x57d1a24d\n125, 0x66503ddc\n126, 0xcf7c0838\n127, 0x19e034fc\n128, 0x66807450\n129, 0xfc219b3b\n130, 0xe8a843e7\n131, 0x9ce61f08\n132, 0x92b950d6\n133, 0xce955ec4\n134, 0xda0d1f0d\n135, 0x960c6250\n136, 0x39552432\n137, 0xde845e84\n138, 0xff3b4b11\n139, 0x5d918e6f\n140, 0xbb930df2\n141, 0x7cfb0993\n142, 0x5400e1e9\n143, 0x3bfa0954\n144, 0x7e2605fb\n145, 0x11941591\n146, 0x887e6994\n147, 0xdc8bed45\n148, 0x45b3fb50\n149, 0xfbdf8358\n150, 0x41507468\n151, 0x34c87166\n152, 0x17f64d77\n153, 0x3bbaf4f8\n154, 0x4f26f37e\n155, 0x4a56ebf2\n156, 0x81100f1\n157, 0x96d94eae\n158, 0xca88fda5\n159, 0x2eef3a60\n160, 0x952afbd3\n161, 0x2bec88c7\n162, 0x52335c4b\n163, 0x8296db8e\n164, 0x4da7d00a\n165, 0xc00ac899\n166, 0xadff8c72\n167, 0xbecf26cf\n168, 0x8835c83c\n169, 0x1d13c804\n170, 0xaa940ddc\n171, 0x68222cfe\n172, 0x4569c0e1\n173, 0x29077976\n174, 0x32d4a5af\n175, 0xd31fcdef\n176, 0xdc60682b\n177, 0x7c95c368\n178, 0x75a70213\n179, 0x43021751\n180, 0x5e52e0a6\n181, 0xf7e190b5\n182, 0xee3e4bb\n183, 0x2fe3b150\n184, 0xcf419c07\n185, 0x478a4570\n186, 0xe5c3ea50\n187, 0x417f30a8\n188, 0xf0cfdaa0\n189, 0xd1f7f738\n190, 0x2c70fc23\n191, 0x54fc89f9\n192, 0x444dcf01\n193, 0xec2a002d\n194, 0xef0c3a88\n195, 0xde21be9\n196, 0x88ab3296\n197, 0x3028897c\n198, 0x264b200b\n199, 0xd8ae0706\n200, 0x9eef901a\n201, 0xbd1b96e0\n202, 0xea71366c\n203, 0x1465b694\n204, 0x5a794650\n205, 0x83df52d4\n206, 0x8262413d\n207, 0x5bc148c0\n208, 0xe0ecd80c\n209, 0x40649571\n210, 0xb4d2ee5f\n211, 0xedfd7d09\n212, 0xa082e25f\n213, 0xc62992d1\n214, 0xbc7e65ee\n215, 0x5499cf8a\n216, 0xac28f775\n217, 0x649840fb\n218, 0xd4c54805\n219, 0x1d166ba6\n220, 0xbeb1171f\n221, 0x45b66703\n222, 0x78c03349\n223, 0x38d2a6ff\n224, 0x935cae8b\n225, 0x1d07dc3f\n226, 0x6c1ed365\n227, 0x579fc585\n228, 0x1320c0ec\n229, 0x632757eb\n230, 0xd265a397\n231, 0x70e9b6c2\n232, 0xc81e322c\n233, 0xa27153cf\n234, 0x2118ba19\n235, 0x514ec400\n236, 0x2bd0ecd6\n237, 0xc3e7dae3\n238, 0xfa39355e\n239, 0x48f23cc1\n240, 0xbcf75948\n241, 0x53ccc70c\n242, 0x75346423\n243, 0x951181e0\n244, 0x348e90df\n245, 0x14365d7f\n246, 0xfbc95d7a\n247, 0xdc98a9e6\n248, 0xed202df7\n249, 0xa59ec913\n250, 0x6b6e9ae2\n251, 0x1697f265\n252, 0x15d322d0\n253, 0xa2e7ee0a\n254, 0x88860b7e\n255, 0x455d8b9d\n256, 0x2f5c59cb\n257, 0xac49c9f1\n258, 0xa6a6a039\n259, 0xc057f56b\n260, 0xf1ff1208\n261, 0x5eb8dc9d\n262, 0xe6702509\n263, 0xe238b0ed\n264, 0x5ae32e3d\n265, 0xa88ebbdf\n266, 0xef885ae7\n267, 0xafa6d49b\n268, 0xc94499e0\n269, 0x1a196325\n270, 0x88938da3\n271, 0x14f4345\n272, 0xd8e33637\n273, 0xa3551bd5\n274, 0x73fe35c7\n275, 0x9561e94b\n276, 0xd673bf68\n277, 0x16134872\n278, 0x68c42f9f\n279, 0xdf7574c8\n280, 0x8809bab9\n281, 0x1432cf69\n282, 0xafb66bf1\n283, 0xc184aa7b\n284, 0xedbf2007\n285, 0xbd420ce1\n286, 0x761033a0\n287, 0xff7e351f\n288, 0xd6c3780e\n289, 0x5844416f\n290, 0xc6c0ee1c\n291, 0xd2e147db\n292, 0x92ac601a\n293, 0x393e846b\n294, 0x18196cca\n295, 0x54a22be\n296, 0x32bab1c4\n297, 0x60365183\n298, 0x64fa342\n299, 0xca24a493\n300, 0xd8cc8b83\n301, 0x3faf102b\n302, 0x6e09bb58\n303, 0x812f0ea\n304, 0x592c95d8\n305, 0xe45ea4c5\n306, 0x23aebf83\n307, 0xbd9691d4\n308, 0xf47b4baa\n309, 0x4ac7b487\n310, 0xcce18803\n311, 0x3377556e\n312, 0x3ff8e6b6\n313, 0x99d22063\n314, 0x23250bec\n315, 0x4e1f9861\n316, 0x8554249b\n317, 0x8635c2fc\n318, 0xe8426e8a\n319, 0x966c29d8\n320, 0x270b6082\n321, 0x3180a8a1\n322, 0xe7e1668b\n323, 0x7f868dc\n324, 0xcf4c17cf\n325, 0xe31de4d1\n326, 0xc8c8aff4\n327, 0xae8db704\n328, 0x3c928cc2\n329, 0xe12cd48\n330, 0xb33ecd04\n331, 0xb93d7cbe\n332, 0x49c69d6a\n333, 0x7d3bce64\n334, 0x86bc219\n335, 0x8408233b\n336, 0x44dc7479\n337, 0xdf80d538\n338, 0xf3db02c3\n339, 0xbbbd31d7\n340, 0x121281f\n341, 0x7521e9a3\n342, 0x8859675a\n343, 0x75aa6502\n344, 0x430ed15b\n345, 0xecf0a28d\n346, 0x659774fd\n347, 0xd58a2311\n348, 0x512389a9\n349, 0xff65e1ff\n350, 0xb6ddf222\n351, 0xe3458895\n352, 0x8b13cd6e\n353, 0xd4a22870\n354, 0xe604c50c\n355, 0x27f54f26\n356, 0x8f7f422f\n357, 0x9735b4cf\n358, 0x414072b0\n359, 0x76a1c6d5\n360, 0xa2208c06\n361, 0x83cd0f61\n362, 0x6c4f7ead\n363, 0x6553cf76\n364, 0xeffcf44\n365, 0x7f434a3f\n366, 0x9dc364bd\n367, 0x3cdf52ed\n368, 0xad597594\n369, 0x9c3e211b\n370, 0x6c04a33f\n371, 0x885dafa6\n372, 0xbbdaca71\n373, 0x7ae5dd5c\n374, 0x37675644\n375, 0x251853c6\n376, 0x130b086b\n377, 0x143fa54b\n378, 0x54cdc282\n379, 0x9faff5b3\n380, 0x502a5c8b\n381, 0xd9524550\n382, 0xae221aa6\n383, 0x55cf759b\n384, 0x24782da4\n385, 0xd715d815\n386, 0x250ea09a\n387, 0x4e0744ac\n388, 0x11e15814\n389, 0xabe5f9df\n390, 0xc8146350\n391, 0xfba67d9b\n392, 0x2b82e42f\n393, 0xd4ea96fc\n394, 0x5ffc179e\n395, 0x1598bafe\n396, 0x7fb6d662\n397, 0x1a12a0db\n398, 0x450cee4a\n399, 0x85f8e12\n400, 0xce71b594\n401, 0xd4bb1d19\n402, 0x968f379d\n403, 0x54cc1d52\n404, 0x467e6066\n405, 0x7da5f9a9\n406, 0x70977034\n407, 0x49e65c4b\n408, 0xd08570d1\n409, 0x7acdf60b\n410, 0xdffa038b\n411, 0x9ce14e4c\n412, 0x107cbbf8\n413, 0xdd746ca0\n414, 0xc6370a46\n415, 0xe7f83312\n416, 0x373fa9ce\n417, 0xd822a2c6\n418, 0x1d4efea6\n419, 0xc53dcadb\n420, 0x9b4e898f\n421, 0x71daa6bf\n422, 0x7a0bc78b\n423, 0xd7b86f50\n424, 0x1b8b3286\n425, 0xcf9425dd\n426, 0xd5263220\n427, 0x4ea0b647\n428, 0xc767fe64\n429, 0xcfc5e67\n430, 0xcc6a2942\n431, 0xa51eff00\n432, 0x76092e1b\n433, 0xf606e80f\n434, 0x824b5e20\n435, 0xebb55e14\n436, 0x783d96a6\n437, 0x10696512\n438, 0x17ee510a\n439, 0x3ab70a1f\n440, 0xcce6b210\n441, 0x8f72f0fb\n442, 0xf0610b41\n443, 0x83d01fb5\n444, 0x6b3de36\n445, 0xe4c2e84f\n446, 0x9c43bb15\n447, 0xddf2905\n448, 0x7dd63556\n449, 0x3662ca09\n450, 0xfb81f35b\n451, 0xc2c8a72a\n452, 0x8e93c37\n453, 0xa93da2d4\n454, 0xa03af8f1\n455, 0x8d75159a\n456, 0x15f010b0\n457, 0xa296ab06\n458, 0xe55962ba\n459, 0xeae700a9\n460, 0xe388964a\n461, 0x917f2bec\n462, 0x1c203fea\n463, 0x792a01ba\n464, 0xa93a80ac\n465, 0x9eb8a197\n466, 0x56c0bc73\n467, 0xb8f05799\n468, 0xf429a8c8\n469, 0xb92cee42\n470, 0xf8864ec\n471, 0x62f2518a\n472, 0x3a7bfa3e\n473, 0x12e56e6d\n474, 0xd7a18313\n475, 0x41fa3899\n476, 0xa09c4956\n477, 0xebcfd94a\n478, 0xc485f90b\n479, 0x4391ce40\n480, 0x742a3333\n481, 0xc932f9e5\n482, 0x75c6c263\n483, 0x80937f0\n484, 0xcf21833c\n485, 0x16027520\n486, 0xd42e669f\n487, 0xb0f01fb7\n488, 0xb35896f1\n489, 0x763737a9\n490, 0x1bb20209\n491, 0x3551f189\n492, 0x56bc2602\n493, 0xb6eacf4\n494, 0x42ec4d11\n495, 0x245cc68\n496, 0xc27ac43b\n497, 0x9d903466\n498, 0xce3f0c05\n499, 0xb708c31c\n500, 0xc0fd37eb\n501, 0x95938b2c\n502, 0xf20175a7\n503, 0x4a86ee9b\n504, 0xbe039a58\n505, 0xd41cabe7\n506, 0x83bc99ba\n507, 0x761d60e1\n508, 0x7737cc2e\n509, 0x2b82fc4b\n510, 0x375aa401\n511, 0xfe9597a0\n512, 0x5543806a\n513, 0x44f31238\n514, 0x7df31538\n515, 0x74cfa770\n516, 0x8755d881\n517, 0x1fde665a\n518, 0xda8bf315\n519, 0x973d8e95\n520, 0x72205228\n521, 0x8fe59717\n522, 0x7bb90b34\n523, 0xef6ed945\n524, 0x16fd4a38\n525, 0x5db44de1\n526, 0xf09f93b3\n527, 0xe84824cc\n528, 0x945bb50e\n529, 0xd0be4aa5\n530, 0x47c277c2\n531, 0xd3800c28\n532, 0xac1c33ec\n533, 0xd3dacce\n534, 0x811c8387\n535, 0x6761b36\n536, 0x70d3882f\n537, 0xd6e62e3a\n538, 0xea25daa2\n539, 0xb07f39d1\n540, 0x391d89d7\n541, 0x84b6fb5e\n542, 0x3dda3fca\n543, 0x229e80a4\n544, 0x3d94a4b7\n545, 0x5d3d576a\n546, 0xad7818a0\n547, 0xce23b03a\n548, 0x7aa2079c\n549, 0x9a6be555\n550, 0x83f3b34a\n551, 0x1848f9d9\n552, 0xd8fefc1c\n553, 0x48e6ce48\n554, 0x52e55750\n555, 0xf41a71cf\n556, 0xba08e259\n557, 0xfaf06a15\n558, 0xeaaac0fb\n559, 0x34f90098\n560, 0xb1dfffbb\n561, 0x718daec2\n562, 0xab4dda21\n563, 0xd27cc1ee\n564, 0x4aafbc4c\n565, 0x356dfb4f\n566, 0x83fcdfd6\n567, 0x8f0bcde0\n568, 0x4363f844\n569, 0xadc0f4d5\n570, 0x3bde994e\n571, 0x3884d452\n572, 0x21876b4a\n573, 0x9c985398\n574, 0xca55a226\n575, 0x3a88c583\n576, 0x916dc33c\n577, 0x8f67d1d7\n578, 0x3b26a667\n579, 0xe4ddeb4b\n580, 0x1a9d8c33\n581, 0x81c9b74f\n582, 0x9ed1e9df\n583, 0x6e61aecf\n584, 0x95e95a5d\n585, 0x68864ff5\n586, 0xb8fa5b9\n587, 0x72b1b3de\n588, 0x5e18a86b\n589, 0xd7f2337d\n590, 0xd70e0925\n591, 0xb573a4c1\n592, 0xc77b3f8a\n593, 0x389b20de\n594, 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0 | 0 | python-kit | 265 | 2023-07-29T13:44:50.152956 | MIT | true | 42f930477266cc22c4a0bf3859ad0655 |
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0x88849be\n713, 0x69c02714\n714, 0x69d4a389\n715, 0x8f51854d\n716, 0xaf10bb82\n717, 0x4d5d1c77\n718, 0x53b53109\n719, 0xa0a92aa0\n720, 0x83ecb757\n721, 0x5325752a\n722, 0x114e466e\n723, 0x4b3f2780\n724, 0xa7a6a39c\n725, 0x5e723357\n726, 0xa6b8be9b\n727, 0x157c32ff\n728, 0x8b898012\n729, 0xd7ff2b1e\n730, 0x69cd8444\n731, 0x6ad8030c\n732, 0xa08a49ec\n733, 0xfbc055d3\n734, 0xedf17e46\n735, 0xc9526200\n736, 0x3849b88a\n737, 0x2746860b\n738, 0xae13d0c1\n739, 0x4f15154f\n740, 0xd65c3975\n741, 0x6a377278\n742, 0x54d501f7\n743, 0x81a054ea\n744, 0x143592ba\n745, 0x97714ad6\n746, 0x4f9926d9\n747, 0x4f7ac56d\n748, 0xe87ca939\n749, 0x58b76f6f\n750, 0x60901ad8\n751, 0x3e401bb6\n752, 0xa058468e\n753, 0xc0bb14f6\n754, 0x2cb8f02a\n755, 0x7c2cf756\n756, 0x34c31de5\n757, 0x9b243e83\n758, 0xa5c85ab4\n759, 0x2741e3b3\n760, 0x1249000e\n761, 0x3fc4e72b\n762, 0xa3e038a2\n763, 0x952dd92c\n764, 0x2b821966\n765, 0xfa81b365\n766, 0x530919b9\n767, 0x4486d66f\n768, 0xccf4f3c1\n769, 0xa8bddd1d\n770, 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16,826 | 0.7 | 0 | 0 | vue-tools | 363 | 2024-09-02T19:50:15.593861 | Apache-2.0 | true | e9af7389e46de3a6b804d773dd3fe28e |
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.venv\Lib\site-packages\numpy\random\tests\data\pcg64-testset-1.csv | pcg64-testset-1.csv | Other | 24,840 | 0.7 | 0 | 0 | node-utils | 124 | 2024-07-27T08:37:58.422479 | GPL-3.0 | true | 150bbd205f0a7808bd01187892f8f296 |
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.venv\Lib\site-packages\numpy\random\tests\data\pcg64-testset-2.csv | pcg64-testset-2.csv | Other | 24,846 | 0.7 | 0 | 0 | awesome-app | 649 | 2024-06-13T21:51:13.215593 | BSD-3-Clause | true | fa508f87d9882410ede29e647d69dd8c |
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.venv\Lib\site-packages\numpy\random\tests\data\pcg64dxsm-testset-1.csv | pcg64dxsm-testset-1.csv | Other | 24,834 | 0.7 | 0 | 0 | awesome-app | 9 | 2023-08-16T14:38:12.063035 | MIT | true | 4f8a0bb760030d130b3748a5140a2d96 |
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.venv\Lib\site-packages\numpy\random\tests\data\philox-testset-1.csv | philox-testset-1.csv | Other | 24,853 | 0.7 | 0 | 0 | react-lib | 107 | 2024-01-02T17:46:21.624427 | GPL-3.0 | true | 3183e7881d445adeee576ec328708a4c |
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.venv\Lib\site-packages\numpy\random\tests\data\philox-testset-2.csv | philox-testset-2.csv | Other | 24,839 | 0.7 | 0 | 0 | node-utils | 367 | 2024-05-01T18:12:44.290221 | Apache-2.0 | true | 1d4af19aa29c321ab03dfda4b5fd282d |
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\n\n | .venv\Lib\site-packages\numpy\random\tests\__pycache__\test_randomstate_regression.cpython-313.pyc | test_randomstate_regression.cpython-313.pyc | Other | 15,261 | 0.8 | 0 | 0.029412 | node-utils | 651 | 2025-02-16T12:46:25.567217 | BSD-3-Clause | true | 8d93b47ba305cacd352f0ff553d1a3ef |
\n\n | .venv\Lib\site-packages\numpy\random\tests\__pycache__\test_regression.cpython-313.pyc | test_regression.cpython-313.pyc | Other | 10,979 | 0.8 | 0 | 0 | python-kit | 472 | 2025-04-20T21:29:08.369623 | MIT | true | 286b40f03907b4b1209cfc755d4138eb |
\n\n | .venv\Lib\site-packages\numpy\random\tests\__pycache__\test_seed_sequence.cpython-313.pyc | test_seed_sequence.cpython-313.pyc | Other | 3,432 | 0.8 | 0 | 0.02439 | vue-tools | 107 | 2024-09-29T22:18:08.984557 | Apache-2.0 | true | 3f6687a932fcc5d6c8e173a2ee4c7bad |
\n\n | .venv\Lib\site-packages\numpy\random\tests\__pycache__\test_smoke.cpython-313.pyc | test_smoke.cpython-313.pyc | Other | 56,613 | 0.6 | 0 | 0.010989 | react-lib | 811 | 2024-01-18T16:24:41.008144 | Apache-2.0 | true | bc5f0c892f0797072ea8f932494b017a |
\n\n | .venv\Lib\site-packages\numpy\random\tests\__pycache__\__init__.cpython-313.pyc | __init__.cpython-313.pyc | Other | 193 | 0.7 | 0 | 0 | vue-tools | 753 | 2024-09-16T12:53:23.336843 | BSD-3-Clause | true | 5d7fda41415e9c1d02296597d4f9db46 |
"""\nUse cffi to access any of the underlying C functions from distributions.h\n"""\nimport os\n\nimport cffi\n\nimport numpy as np\n\nfrom .parse import parse_distributions_h\n\nffi = cffi.FFI()\n\ninc_dir = os.path.join(np.get_include(), 'numpy')\n\n# Basic numpy types\nffi.cdef('''\n typedef intptr_t npy_intp;\n typedef unsigned char npy_bool;\n\n''')\n\nparse_distributions_h(ffi, inc_dir)\n\nlib = ffi.dlopen(np.random._generator.__file__)\n\n# Compare the distributions.h random_standard_normal_fill to\n# Generator.standard_random\nbit_gen = np.random.PCG64()\nrng = np.random.Generator(bit_gen)\nstate = bit_gen.state\n\ninterface = rng.bit_generator.cffi\nn = 100\nvals_cffi = ffi.new('double[%d]' % n)\nlib.random_standard_normal_fill(interface.bit_generator, n, vals_cffi)\n\n# reset the state\nbit_gen.state = state\n\nvals = rng.standard_normal(n)\n\nfor i in range(n):\n assert vals[i] == vals_cffi[i]\n | .venv\Lib\site-packages\numpy\random\_examples\cffi\extending.py | extending.py | Python | 928 | 0.95 | 0.022727 | 0.133333 | react-lib | 692 | 2024-01-18T20:08:24.142362 | BSD-3-Clause | false | ea3856a5e2bda367a19965bccab94288 |
import os\n\n\ndef parse_distributions_h(ffi, inc_dir):\n """\n Parse distributions.h located in inc_dir for CFFI, filling in the ffi.cdef\n\n Read the function declarations without the "#define ..." macros that will\n be filled in when loading the library.\n """\n\n with open(os.path.join(inc_dir, 'random', 'bitgen.h')) as fid:\n s = []\n for line in fid:\n # massage the include file\n if line.strip().startswith('#'):\n continue\n s.append(line)\n ffi.cdef('\n'.join(s))\n\n with open(os.path.join(inc_dir, 'random', 'distributions.h')) as fid:\n s = []\n in_skip = 0\n ignoring = False\n for line in fid:\n # check for and remove extern "C" guards\n if ignoring:\n if line.strip().startswith('#endif'):\n ignoring = False\n continue\n if line.strip().startswith('#ifdef __cplusplus'):\n ignoring = True\n\n # massage the include file\n if line.strip().startswith('#'):\n continue\n\n # skip any inlined function definition\n # which starts with 'static inline xxx(...) {'\n # and ends with a closing '}'\n if line.strip().startswith('static inline'):\n in_skip += line.count('{')\n continue\n elif in_skip > 0:\n in_skip += line.count('{')\n in_skip -= line.count('}')\n continue\n\n # replace defines with their value or remove them\n line = line.replace('DECLDIR', '')\n line = line.replace('RAND_INT_TYPE', 'int64_t')\n s.append(line)\n ffi.cdef('\n'.join(s))\n | .venv\Lib\site-packages\numpy\random\_examples\cffi\parse.py | parse.py | Python | 1,803 | 0.95 | 0.245283 | 0.155556 | awesome-app | 465 | 2024-12-16T22:15:34.734391 | GPL-3.0 | false | 720d079af888e4ff2c5ad9afe3399aa6 |
\n\n | .venv\Lib\site-packages\numpy\random\_examples\cffi\__pycache__\extending.cpython-313.pyc | extending.cpython-313.pyc | Other | 1,665 | 0.95 | 0 | 0 | node-utils | 15 | 2024-04-05T17:35:51.981920 | MIT | false | b5bf0a1a599080e72398bbc6141b5d1f |
\n\n | .venv\Lib\site-packages\numpy\random\_examples\cffi\__pycache__\parse.cpython-313.pyc | parse.cpython-313.pyc | Other | 2,360 | 0.95 | 0.0625 | 0 | react-lib | 119 | 2024-12-15T15:35:31.171306 | MIT | false | ce9c551a4d6b28ce6c99a7bf17048abe |
#cython: language_level=3\n\nfrom libc.stdint cimport uint32_t\nfrom cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer\n\nimport numpy as np\ncimport numpy as np\ncimport cython\n\nfrom numpy.random cimport bitgen_t\nfrom numpy.random import PCG64\n\nnp.import_array()\n\n\n@cython.boundscheck(False)\n@cython.wraparound(False)\ndef uniform_mean(Py_ssize_t n):\n cdef Py_ssize_t i\n cdef bitgen_t *rng\n cdef const char *capsule_name = "BitGenerator"\n cdef double[::1] random_values\n cdef np.ndarray randoms\n\n x = PCG64()\n capsule = x.capsule\n if not PyCapsule_IsValid(capsule, capsule_name):\n raise ValueError("Invalid pointer to anon_func_state")\n rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name)\n random_values = np.empty(n)\n # Best practice is to acquire the lock whenever generating random values.\n # This prevents other threads from modifying the state. Acquiring the lock\n # is only necessary if the GIL is also released, as in this example.\n with x.lock, nogil:\n for i in range(n):\n random_values[i] = rng.next_double(rng.state)\n randoms = np.asarray(random_values)\n return randoms.mean()\n\n\n# This function is declared nogil so it can be used without the GIL below\ncdef uint32_t bounded_uint(uint32_t lb, uint32_t ub, bitgen_t *rng) nogil:\n cdef uint32_t mask, delta, val\n mask = delta = ub - lb\n mask |= mask >> 1\n mask |= mask >> 2\n mask |= mask >> 4\n mask |= mask >> 8\n mask |= mask >> 16\n\n val = rng.next_uint32(rng.state) & mask\n while val > delta:\n val = rng.next_uint32(rng.state) & mask\n\n return lb + val\n\n\n@cython.boundscheck(False)\n@cython.wraparound(False)\ndef bounded_uints(uint32_t lb, uint32_t ub, Py_ssize_t n):\n cdef Py_ssize_t i\n cdef bitgen_t *rng\n cdef uint32_t[::1] out\n cdef const char *capsule_name = "BitGenerator"\n\n x = PCG64()\n out = np.empty(n, dtype=np.uint32)\n capsule = x.capsule\n\n if not PyCapsule_IsValid(capsule, capsule_name):\n raise ValueError("Invalid pointer to anon_func_state")\n rng = <bitgen_t *>PyCapsule_GetPointer(capsule, capsule_name)\n\n with x.lock, nogil:\n for i in range(n):\n out[i] = bounded_uint(lb, ub, rng)\n return np.asarray(out)\n | .venv\Lib\site-packages\numpy\random\_examples\cython\extending.pyx | extending.pyx | Other | 2,344 | 0.95 | 0.116883 | 0.081967 | react-lib | 547 | 2023-12-23T15:15:32.779512 | MIT | false | 54d792dbf85996089a17048921769114 |
#cython: language_level=3\n"""\nThis file shows how the to use a BitGenerator to create a distribution.\n"""\nimport numpy as np\ncimport numpy as np\ncimport cython\nfrom cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer\nfrom libc.stdint cimport uint16_t, uint64_t\nfrom numpy.random cimport bitgen_t\nfrom numpy.random import PCG64\nfrom numpy.random.c_distributions cimport (\n random_standard_uniform_fill, random_standard_uniform_fill_f)\n\nnp.import_array()\n\n\n@cython.boundscheck(False)\n@cython.wraparound(False)\ndef uniforms(Py_ssize_t n):\n """\n Create an array of `n` uniformly distributed doubles.\n A 'real' distribution would want to process the values into\n some non-uniform distribution\n """\n cdef Py_ssize_t i\n cdef bitgen_t *rng\n cdef const char *capsule_name = "BitGenerator"\n cdef double[::1] random_values\n\n x = PCG64()\n capsule = x.capsule\n # Optional check that the capsule if from a BitGenerator\n if not PyCapsule_IsValid(capsule, capsule_name):\n raise ValueError("Invalid pointer to anon_func_state")\n # Cast the pointer\n rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name)\n random_values = np.empty(n, dtype='float64')\n with x.lock, nogil:\n for i in range(n):\n # Call the function\n random_values[i] = rng.next_double(rng.state)\n randoms = np.asarray(random_values)\n\n return randoms\n\n# cython example 2\n@cython.boundscheck(False)\n@cython.wraparound(False)\ndef uint10_uniforms(Py_ssize_t n):\n """Uniform 10 bit integers stored as 16-bit unsigned integers"""\n cdef Py_ssize_t i\n cdef bitgen_t *rng\n cdef const char *capsule_name = "BitGenerator"\n cdef uint16_t[::1] random_values\n cdef int bits_remaining\n cdef int width = 10\n cdef uint64_t buff, mask = 0x3FF\n\n x = PCG64()\n capsule = x.capsule\n if not PyCapsule_IsValid(capsule, capsule_name):\n raise ValueError("Invalid pointer to anon_func_state")\n rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name)\n random_values = np.empty(n, dtype='uint16')\n # Best practice is to release GIL and acquire the lock\n bits_remaining = 0\n with x.lock, nogil:\n for i in range(n):\n if bits_remaining < width:\n buff = rng.next_uint64(rng.state)\n random_values[i] = buff & mask\n buff >>= width\n\n randoms = np.asarray(random_values)\n return randoms\n\n# cython example 3\ndef uniforms_ex(bit_generator, Py_ssize_t n, dtype=np.float64):\n """\n Create an array of `n` uniformly distributed doubles via a "fill" function.\n\n A 'real' distribution would want to process the values into\n some non-uniform distribution\n\n Parameters\n ----------\n bit_generator: BitGenerator instance\n n: int\n Output vector length\n dtype: {str, dtype}, optional\n Desired dtype, either 'd' (or 'float64') or 'f' (or 'float32'). The\n default dtype value is 'd'\n """\n cdef Py_ssize_t i\n cdef bitgen_t *rng\n cdef const char *capsule_name = "BitGenerator"\n cdef np.ndarray randoms\n\n capsule = bit_generator.capsule\n # Optional check that the capsule if from a BitGenerator\n if not PyCapsule_IsValid(capsule, capsule_name):\n raise ValueError("Invalid pointer to anon_func_state")\n # Cast the pointer\n rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name)\n\n _dtype = np.dtype(dtype)\n randoms = np.empty(n, dtype=_dtype)\n if _dtype == np.float32:\n with bit_generator.lock:\n random_standard_uniform_fill_f(rng, n, <float*>np.PyArray_DATA(randoms))\n elif _dtype == np.float64:\n with bit_generator.lock:\n random_standard_uniform_fill(rng, n, <double*>np.PyArray_DATA(randoms))\n else:\n raise TypeError('Unsupported dtype %r for random' % _dtype)\n return randoms\n\n | .venv\Lib\site-packages\numpy\random\_examples\cython\extending_distributions.pyx | extending_distributions.pyx | Other | 3,984 | 0.95 | 0.127119 | 0.086538 | awesome-app | 87 | 2024-09-18T10:45:47.668230 | BSD-3-Clause | false | f500b1e8463a4abc526dc6885eb25230 |
project('random-build-examples', 'c', 'cpp', 'cython')\n\npy_mod = import('python')\npy3 = py_mod.find_installation(pure: false)\n\ncc = meson.get_compiler('c')\ncy = meson.get_compiler('cython')\n\n# Keep synced with pyproject.toml\nif not cy.version().version_compare('>=3.0.6')\n error('tests requires Cython >= 3.0.6')\nendif\n\nbase_cython_args = []\nif cy.version().version_compare('>=3.1.0')\n base_cython_args += ['-Xfreethreading_compatible=True']\nendif\n\n_numpy_abs = run_command(py3, ['-c',\n 'import os; os.chdir(".."); import numpy; print(os.path.abspath(numpy.get_include() + "../../.."))'],\n check: true).stdout().strip()\n\nnpymath_path = _numpy_abs / '_core' / 'lib'\nnpy_include_path = _numpy_abs / '_core' / 'include'\nnpyrandom_path = _numpy_abs / 'random' / 'lib'\nnpymath_lib = cc.find_library('npymath', dirs: npymath_path)\nnpyrandom_lib = cc.find_library('npyrandom', dirs: npyrandom_path)\n\npy3.extension_module(\n 'extending_distributions',\n 'extending_distributions.pyx',\n install: false,\n include_directories: [npy_include_path],\n dependencies: [npyrandom_lib, npymath_lib],\n cython_args: base_cython_args,\n)\npy3.extension_module(\n 'extending',\n 'extending.pyx',\n install: false,\n include_directories: [npy_include_path],\n dependencies: [npyrandom_lib, npymath_lib],\n cython_args: base_cython_args,\n)\npy3.extension_module(\n 'extending_cpp',\n 'extending_distributions.pyx',\n install: false,\n override_options : ['cython_language=cpp'],\n cython_args: base_cython_args + ['--module-name', 'extending_cpp'],\n include_directories: [npy_include_path],\n dependencies: [npyrandom_lib, npymath_lib],\n)\n | .venv\Lib\site-packages\numpy\random\_examples\cython\meson.build | meson.build | Other | 1,747 | 0.95 | 0.037736 | 0.021739 | vue-tools | 435 | 2023-11-27T13:50:02.943762 | GPL-3.0 | false | 3d70fb85443f979dc6e2031899453024 |
from timeit import timeit\n\nimport numba as nb\n\nimport numpy as np\nfrom numpy.random import PCG64\n\nbit_gen = PCG64()\nnext_d = bit_gen.cffi.next_double\nstate_addr = bit_gen.cffi.state_address\n\ndef normals(n, state):\n out = np.empty(n)\n for i in range((n + 1) // 2):\n x1 = 2.0 * next_d(state) - 1.0\n x2 = 2.0 * next_d(state) - 1.0\n r2 = x1 * x1 + x2 * x2\n while r2 >= 1.0 or r2 == 0.0:\n x1 = 2.0 * next_d(state) - 1.0\n x2 = 2.0 * next_d(state) - 1.0\n r2 = x1 * x1 + x2 * x2\n f = np.sqrt(-2.0 * np.log(r2) / r2)\n out[2 * i] = f * x1\n if 2 * i + 1 < n:\n out[2 * i + 1] = f * x2\n return out\n\n\n# Compile using Numba\nnormalsj = nb.jit(normals, nopython=True)\n# Must use state address not state with numba\nn = 10000\n\ndef numbacall():\n return normalsj(n, state_addr)\n\n\nrg = np.random.Generator(PCG64())\n\ndef numpycall():\n return rg.normal(size=n)\n\n\n# Check that the functions work\nr1 = numbacall()\nr2 = numpycall()\nassert r1.shape == (n,)\nassert r1.shape == r2.shape\n\nt1 = timeit(numbacall, number=1000)\nprint(f'{t1:.2f} secs for {n} PCG64 (Numba/PCG64) gaussian randoms')\nt2 = timeit(numpycall, number=1000)\nprint(f'{t2:.2f} secs for {n} PCG64 (NumPy/PCG64) gaussian randoms')\n\n# example 2\n\nnext_u32 = bit_gen.ctypes.next_uint32\nctypes_state = bit_gen.ctypes.state\n\n@nb.jit(nopython=True)\ndef bounded_uint(lb, ub, state):\n mask = delta = ub - lb\n mask |= mask >> 1\n mask |= mask >> 2\n mask |= mask >> 4\n mask |= mask >> 8\n mask |= mask >> 16\n\n val = next_u32(state) & mask\n while val > delta:\n val = next_u32(state) & mask\n\n return lb + val\n\n\nprint(bounded_uint(323, 2394691, ctypes_state.value))\n\n\n@nb.jit(nopython=True)\ndef bounded_uints(lb, ub, n, state):\n out = np.empty(n, dtype=np.uint32)\n for i in range(n):\n out[i] = bounded_uint(lb, ub, state)\n\n\nbounded_uints(323, 2394691, 10000000, ctypes_state.value)\n | .venv\Lib\site-packages\numpy\random\_examples\numba\extending.py | extending.py | Python | 2,045 | 0.95 | 0.139535 | 0.064516 | awesome-app | 790 | 2023-09-11T04:39:11.982220 | BSD-3-Clause | false | 32586ad5d8b22f0c91cb41088dce6b36 |
r"""\nBuilding the required library in this example requires a source distribution\nof NumPy or clone of the NumPy git repository since distributions.c is not\nincluded in binary distributions.\n\nOn *nix, execute in numpy/random/src/distributions\n\nexport ${PYTHON_VERSION}=3.8 # Python version\nexport PYTHON_INCLUDE=#path to Python's include folder, usually \\n ${PYTHON_HOME}/include/python${PYTHON_VERSION}m\nexport NUMPY_INCLUDE=#path to numpy's include folder, usually \\n ${PYTHON_HOME}/lib/python${PYTHON_VERSION}/site-packages/numpy/_core/include\ngcc -shared -o libdistributions.so -fPIC distributions.c \\n -I${NUMPY_INCLUDE} -I${PYTHON_INCLUDE}\nmv libdistributions.so ../../_examples/numba/\n\nOn Windows\n\nrem PYTHON_HOME and PYTHON_VERSION are setup dependent, this is an example\nset PYTHON_HOME=c:\Anaconda\nset PYTHON_VERSION=38\ncl.exe /LD .\distributions.c -DDLL_EXPORT \\n -I%PYTHON_HOME%\lib\site-packages\numpy\_core\include \\n -I%PYTHON_HOME%\include %PYTHON_HOME%\libs\python%PYTHON_VERSION%.lib\nmove distributions.dll ../../_examples/numba/\n"""\nimport os\n\nimport numba as nb\nfrom cffi import FFI\n\nimport numpy as np\nfrom numpy.random import PCG64\n\nffi = FFI()\nif os.path.exists('./distributions.dll'):\n lib = ffi.dlopen('./distributions.dll')\nelif os.path.exists('./libdistributions.so'):\n lib = ffi.dlopen('./libdistributions.so')\nelse:\n raise RuntimeError('Required DLL/so file was not found.')\n\nffi.cdef("""\ndouble random_standard_normal(void *bitgen_state);\n""")\nx = PCG64()\nxffi = x.cffi\nbit_generator = xffi.bit_generator\n\nrandom_standard_normal = lib.random_standard_normal\n\n\ndef normals(n, bit_generator):\n out = np.empty(n)\n for i in range(n):\n out[i] = random_standard_normal(bit_generator)\n return out\n\n\nnormalsj = nb.jit(normals, nopython=True)\n\n# Numba requires a memory address for void *\n# Can also get address from x.ctypes.bit_generator.value\nbit_generator_address = int(ffi.cast('uintptr_t', bit_generator))\n\nnorm = normalsj(1000, bit_generator_address)\nprint(norm[:12])\n | .venv\Lib\site-packages\numpy\random\_examples\numba\extending_distributions.py | extending_distributions.py | Python | 2,103 | 0.95 | 0.059701 | 0.038462 | awesome-app | 892 | 2025-03-28T12:21:01.523751 | MIT | false | 9e717425f68dcc06d204e2e209287c51 |
\n\n | .venv\Lib\site-packages\numpy\random\_examples\numba\__pycache__\extending.cpython-313.pyc | extending.cpython-313.pyc | Other | 3,789 | 0.8 | 0.020408 | 0 | node-utils | 222 | 2023-10-11T14:37:01.971053 | MIT | false | f6aed96c6a4b583440a33c9c58a2502c |
\n\n | .venv\Lib\site-packages\numpy\random\_examples\numba\__pycache__\extending_distributions.cpython-313.pyc | extending_distributions.cpython-313.pyc | Other | 2,747 | 0.95 | 0 | 0.044444 | python-kit | 829 | 2024-07-30T15:54:25.817227 | Apache-2.0 | false | b2143c294556860577a891042ef4ac24 |
\n\n | .venv\Lib\site-packages\numpy\random\__pycache__\_pickle.cpython-313.pyc | _pickle.cpython-313.pyc | Other | 2,824 | 0.95 | 0.088235 | 0 | node-utils | 381 | 2024-08-20T11:12:59.067259 | MIT | false | 5809fa72f93a75c31f29042f137fc572 |
\n\n | .venv\Lib\site-packages\numpy\random\__pycache__\__init__.cpython-313.pyc | __init__.cpython-313.pyc | Other | 7,471 | 0.95 | 0.039216 | 0 | awesome-app | 774 | 2025-03-08T02:47:13.980576 | MIT | false | c62ca79af13bd735bc39175698271779 |
from numpy._core.records import *\nfrom numpy._core.records import __all__, __doc__\n | .venv\Lib\site-packages\numpy\rec\__init__.py | __init__.py | Python | 85 | 0.65 | 0 | 0 | node-utils | 269 | 2024-03-24T20:25:47.804487 | Apache-2.0 | false | 3e73a2891c547eff1e02f83f7287f6dd |
from numpy._core.records import (\n array,\n find_duplicate,\n format_parser,\n fromarrays,\n fromfile,\n fromrecords,\n fromstring,\n recarray,\n record,\n)\n\n__all__ = [\n "record",\n "recarray",\n "format_parser",\n "fromarrays",\n "fromrecords",\n "fromstring",\n "fromfile",\n "array",\n "find_duplicate",\n]\n | .venv\Lib\site-packages\numpy\rec\__init__.pyi | __init__.pyi | Other | 370 | 0.85 | 0 | 0 | python-kit | 655 | 2025-04-27T21:34:47.110941 | Apache-2.0 | false | a67f474c34d9306c6ec906fe7a3336ba |
\n\n | .venv\Lib\site-packages\numpy\rec\__pycache__\__init__.cpython-313.pyc | __init__.cpython-313.pyc | Other | 277 | 0.7 | 0 | 0 | vue-tools | 81 | 2024-03-10T13:54:32.738150 | MIT | false | 0843caf2f6fa41eb9c1b8f363d8376a7 |
from numpy._core.strings import *\nfrom numpy._core.strings import __all__, __doc__\n | .venv\Lib\site-packages\numpy\strings\__init__.py | __init__.py | Python | 85 | 0.65 | 0 | 0 | node-utils | 745 | 2024-06-08T12:58:56.594893 | MIT | false | 185e6e43d6eb2c3db8d3acef9070f647 |
from numpy._core.strings import (\n add,\n capitalize,\n center,\n count,\n decode,\n encode,\n endswith,\n equal,\n expandtabs,\n find,\n greater,\n greater_equal,\n index,\n isalnum,\n isalpha,\n isdecimal,\n isdigit,\n islower,\n isnumeric,\n isspace,\n istitle,\n isupper,\n less,\n less_equal,\n ljust,\n lower,\n lstrip,\n mod,\n multiply,\n not_equal,\n partition,\n replace,\n rfind,\n rindex,\n rjust,\n rpartition,\n rstrip,\n slice,\n startswith,\n str_len,\n strip,\n swapcase,\n title,\n translate,\n upper,\n zfill,\n)\n\n__all__ = [\n "equal",\n "not_equal",\n "less",\n "less_equal",\n "greater",\n "greater_equal",\n "add",\n "multiply",\n "isalpha",\n "isdigit",\n "isspace",\n "isalnum",\n "islower",\n "isupper",\n "istitle",\n "isdecimal",\n "isnumeric",\n "str_len",\n "find",\n "rfind",\n "index",\n "rindex",\n "count",\n "startswith",\n "endswith",\n "lstrip",\n "rstrip",\n "strip",\n "replace",\n "expandtabs",\n "center",\n "ljust",\n "rjust",\n "zfill",\n "partition",\n "rpartition",\n "upper",\n "lower",\n "swapcase",\n "capitalize",\n "title",\n "mod",\n "decode",\n "encode",\n "translate",\n "slice",\n]\n | .venv\Lib\site-packages\numpy\strings\__init__.pyi | __init__.pyi | Other | 1,416 | 0.85 | 0 | 0 | vue-tools | 145 | 2025-01-08T23:22:19.496522 | MIT | false | cdeb05251e998b84248c94d7763c8d4a |
\n\n | .venv\Lib\site-packages\numpy\strings\__pycache__\__init__.cpython-313.pyc | __init__.cpython-313.pyc | Other | 281 | 0.7 | 0 | 0 | python-kit | 302 | 2024-01-27T14:34:20.204590 | MIT | false | 858764430c033ae2adb382f140d7335e |
"""Tools for testing implementations of __array_function__ and ufunc overrides\n\n\n"""\n\nimport numpy._core.umath as _umath\nfrom numpy import ufunc as _ufunc\nfrom numpy._core.overrides import ARRAY_FUNCTIONS as _array_functions\n\n\ndef get_overridable_numpy_ufuncs():\n """List all numpy ufuncs overridable via `__array_ufunc__`\n\n Parameters\n ----------\n None\n\n Returns\n -------\n set\n A set containing all overridable ufuncs in the public numpy API.\n """\n ufuncs = {obj for obj in _umath.__dict__.values()\n if isinstance(obj, _ufunc)}\n return ufuncs\n\n\ndef allows_array_ufunc_override(func):\n """Determine if a function can be overridden via `__array_ufunc__`\n\n Parameters\n ----------\n func : callable\n Function that may be overridable via `__array_ufunc__`\n\n Returns\n -------\n bool\n `True` if `func` is overridable via `__array_ufunc__` and\n `False` otherwise.\n\n Notes\n -----\n This function is equivalent to ``isinstance(func, np.ufunc)`` and\n will work correctly for ufuncs defined outside of Numpy.\n\n """\n return isinstance(func, _ufunc)\n\n\ndef get_overridable_numpy_array_functions():\n """List all numpy functions overridable via `__array_function__`\n\n Parameters\n ----------\n None\n\n Returns\n -------\n set\n A set containing all functions in the public numpy API that are\n overridable via `__array_function__`.\n\n """\n # 'import numpy' doesn't import recfunctions, so make sure it's imported\n # so ufuncs defined there show up in the ufunc listing\n from numpy.lib import recfunctions # noqa: F401\n return _array_functions.copy()\n\ndef allows_array_function_override(func):\n """Determine if a Numpy function can be overridden via `__array_function__`\n\n Parameters\n ----------\n func : callable\n Function that may be overridable via `__array_function__`\n\n Returns\n -------\n bool\n `True` if `func` is a function in the Numpy API that is\n overridable via `__array_function__` and `False` otherwise.\n """\n return func in _array_functions\n | .venv\Lib\site-packages\numpy\testing\overrides.py | overrides.py | Python | 2,218 | 0.95 | 0.190476 | 0.031746 | python-kit | 469 | 2025-05-28T10:54:22.211249 | Apache-2.0 | true | d7ac9353934af221306733106bf1d9cb |
from collections.abc import Callable, Hashable\nfrom typing import Any\n\nfrom typing_extensions import TypeIs\n\nimport numpy as np\n\ndef get_overridable_numpy_ufuncs() -> set[np.ufunc]: ...\ndef get_overridable_numpy_array_functions() -> set[Callable[..., Any]]: ...\ndef allows_array_ufunc_override(func: object) -> TypeIs[np.ufunc]: ...\ndef allows_array_function_override(func: Hashable) -> bool: ...\n | .venv\Lib\site-packages\numpy\testing\overrides.pyi | overrides.pyi | Other | 408 | 0.85 | 0.363636 | 0 | react-lib | 226 | 2024-07-18T11:41:17.487211 | Apache-2.0 | true | cf206e6b302f699afbab38d65bab4182 |
#!/usr/bin/env python3\n"""Prints type-coercion tables for the built-in NumPy types\n\n"""\nfrom collections import namedtuple\n\nimport numpy as np\nfrom numpy._core.numerictypes import obj2sctype\n\n\n# Generic object that can be added, but doesn't do anything else\nclass GenericObject:\n def __init__(self, v):\n self.v = v\n\n def __add__(self, other):\n return self\n\n def __radd__(self, other):\n return self\n\n dtype = np.dtype('O')\n\ndef print_cancast_table(ntypes):\n print('X', end=' ')\n for char in ntypes:\n print(char, end=' ')\n print()\n for row in ntypes:\n print(row, end=' ')\n for col in ntypes:\n if np.can_cast(row, col, "equiv"):\n cast = "#"\n elif np.can_cast(row, col, "safe"):\n cast = "="\n elif np.can_cast(row, col, "same_kind"):\n cast = "~"\n elif np.can_cast(row, col, "unsafe"):\n cast = "."\n else:\n cast = " "\n print(cast, end=' ')\n print()\n\ndef print_coercion_table(ntypes, inputfirstvalue, inputsecondvalue, firstarray,\n use_promote_types=False):\n print('+', end=' ')\n for char in ntypes:\n print(char, end=' ')\n print()\n for row in ntypes:\n if row == 'O':\n rowtype = GenericObject\n else:\n rowtype = obj2sctype(row)\n\n print(row, end=' ')\n for col in ntypes:\n if col == 'O':\n coltype = GenericObject\n else:\n coltype = obj2sctype(col)\n try:\n if firstarray:\n rowvalue = np.array([rowtype(inputfirstvalue)], dtype=rowtype)\n else:\n rowvalue = rowtype(inputfirstvalue)\n colvalue = coltype(inputsecondvalue)\n if use_promote_types:\n char = np.promote_types(rowvalue.dtype, colvalue.dtype).char\n else:\n value = np.add(rowvalue, colvalue)\n if isinstance(value, np.ndarray):\n char = value.dtype.char\n else:\n char = np.dtype(type(value)).char\n except ValueError:\n char = '!'\n except OverflowError:\n char = '@'\n except TypeError:\n char = '#'\n print(char, end=' ')\n print()\n\n\ndef print_new_cast_table(*, can_cast=True, legacy=False, flags=False):\n """Prints new casts, the values given are default "can-cast" values, not\n actual ones.\n """\n from numpy._core._multiarray_tests import get_all_cast_information\n\n cast_table = {\n -1: " ",\n 0: "#", # No cast (classify as equivalent here)\n 1: "#", # equivalent casting\n 2: "=", # safe casting\n 3: "~", # same-kind casting\n 4: ".", # unsafe casting\n }\n flags_table = {\n 0: "▗", 7: "█",\n 1: "▚", 2: "▐", 4: "▄",\n 3: "▜", 5: "▙",\n 6: "▟",\n }\n\n cast_info = namedtuple("cast_info", ["can_cast", "legacy", "flags"])\n no_cast_info = cast_info(" ", " ", " ")\n\n casts = get_all_cast_information()\n table = {}\n dtypes = set()\n for cast in casts:\n dtypes.add(cast["from"])\n dtypes.add(cast["to"])\n\n if cast["from"] not in table:\n table[cast["from"]] = {}\n to_dict = table[cast["from"]]\n\n can_cast = cast_table[cast["casting"]]\n legacy = "L" if cast["legacy"] else "."\n flags = 0\n if cast["requires_pyapi"]:\n flags |= 1\n if cast["supports_unaligned"]:\n flags |= 2\n if cast["no_floatingpoint_errors"]:\n flags |= 4\n\n flags = flags_table[flags]\n to_dict[cast["to"]] = cast_info(can_cast=can_cast, legacy=legacy, flags=flags)\n\n # The np.dtype(x.type) is a bit strange, because dtype classes do\n # not expose much yet.\n types = np.typecodes["All"]\n\n def sorter(x):\n # This is a bit weird hack, to get a table as close as possible to\n # the one printing all typecodes (but expecting user-dtypes).\n dtype = np.dtype(x.type)\n try:\n indx = types.index(dtype.char)\n except ValueError:\n indx = np.inf\n return (indx, dtype.char)\n\n dtypes = sorted(dtypes, key=sorter)\n\n def print_table(field="can_cast"):\n print('X', end=' ')\n for dt in dtypes:\n print(np.dtype(dt.type).char, end=' ')\n print()\n for from_dt in dtypes:\n print(np.dtype(from_dt.type).char, end=' ')\n row = table.get(from_dt, {})\n for to_dt in dtypes:\n print(getattr(row.get(to_dt, no_cast_info), field), end=' ')\n print()\n\n if can_cast:\n # Print the actual table:\n print()\n print("Casting: # is equivalent, = is safe, ~ is same-kind, and . is unsafe")\n print()\n print_table("can_cast")\n\n if legacy:\n print()\n print("L denotes a legacy cast . a non-legacy one.")\n print()\n print_table("legacy")\n\n if flags:\n print()\n print(f"{flags_table[0]}: no flags, "\n f"{flags_table[1]}: PyAPI, "\n f"{flags_table[2]}: supports unaligned, "\n f"{flags_table[4]}: no-float-errors")\n print()\n print_table("flags")\n\n\nif __name__ == '__main__':\n print("can cast")\n print_cancast_table(np.typecodes['All'])\n print()\n print("In these tables, ValueError is '!', OverflowError is '@', TypeError is '#'")\n print()\n print("scalar + scalar")\n print_coercion_table(np.typecodes['All'], 0, 0, False)\n print()\n print("scalar + neg scalar")\n print_coercion_table(np.typecodes['All'], 0, -1, False)\n print()\n print("array + scalar")\n print_coercion_table(np.typecodes['All'], 0, 0, True)\n print()\n print("array + neg scalar")\n print_coercion_table(np.typecodes['All'], 0, -1, True)\n print()\n print("promote_types")\n print_coercion_table(np.typecodes['All'], 0, 0, False, True)\n print("New casting type promotion:")\n print_new_cast_table(can_cast=True, legacy=True, flags=True)\n | .venv\Lib\site-packages\numpy\testing\print_coercion_tables.py | print_coercion_tables.py | Python | 6,493 | 0.95 | 0.178744 | 0.038889 | vue-tools | 615 | 2023-12-14T16:44:36.195237 | MIT | true | 00e25acabe3b046fb27fe2d4e4cfe21b |
from collections.abc import Iterable\nfrom typing import ClassVar, Generic, Self\n\nfrom typing_extensions import TypeVar\n\nimport numpy as np\n\n_VT_co = TypeVar("_VT_co", default=object, covariant=True)\n\n# undocumented\nclass GenericObject(Generic[_VT_co]):\n dtype: ClassVar[np.dtype[np.object_]] = ...\n v: _VT_co\n\n def __init__(self, /, v: _VT_co) -> None: ...\n def __add__(self, other: object, /) -> Self: ...\n def __radd__(self, other: object, /) -> Self: ...\n\ndef print_cancast_table(ntypes: Iterable[str]) -> None: ...\ndef print_coercion_table(\n ntypes: Iterable[str],\n inputfirstvalue: int,\n inputsecondvalue: int,\n firstarray: bool,\n use_promote_types: bool = False,\n) -> None: ...\ndef print_new_cast_table(*, can_cast: bool = True, legacy: bool = False, flags: bool = False) -> None: ...\n | .venv\Lib\site-packages\numpy\testing\print_coercion_tables.pyi | print_coercion_tables.pyi | Other | 848 | 0.95 | 0.259259 | 0.047619 | node-utils | 986 | 2024-11-09T01:19:25.889390 | BSD-3-Clause | true | 30cf349abb63e8665187c29aeea4a59d |
"""Common test support for all numpy test scripts.\n\nThis single module should provide all the common functionality for numpy tests\nin a single location, so that test scripts can just import it and work right\naway.\n\n"""\nfrom unittest import TestCase\n\nfrom . import _private, overrides\nfrom ._private import extbuild\nfrom ._private.utils import *\nfrom ._private.utils import _assert_valid_refcount, _gen_alignment_data\n\n__all__ = (\n _private.utils.__all__ + ['TestCase', 'overrides']\n)\n\nfrom numpy._pytesttester import PytestTester\n\ntest = PytestTester(__name__)\ndel PytestTester\n | .venv\Lib\site-packages\numpy\testing\__init__.py | __init__.py | Python | 603 | 0.85 | 0.090909 | 0 | python-kit | 854 | 2025-06-14T20:18:49.537083 | GPL-3.0 | true | 678e8ad60609e5c9d8190178478ad727 |
from unittest import TestCase\n\nfrom . import overrides\nfrom ._private.utils import (\n HAS_LAPACK64,\n HAS_REFCOUNT,\n IS_EDITABLE,\n IS_INSTALLED,\n IS_MUSL,\n IS_PYPY,\n IS_PYSTON,\n IS_WASM,\n NOGIL_BUILD,\n NUMPY_ROOT,\n IgnoreException,\n KnownFailureException,\n SkipTest,\n assert_,\n assert_allclose,\n assert_almost_equal,\n assert_approx_equal,\n assert_array_almost_equal,\n assert_array_almost_equal_nulp,\n assert_array_compare,\n assert_array_equal,\n assert_array_less,\n assert_array_max_ulp,\n assert_equal,\n assert_no_gc_cycles,\n assert_no_warnings,\n assert_raises,\n assert_raises_regex,\n assert_string_equal,\n assert_warns,\n break_cycles,\n build_err_msg,\n check_support_sve,\n clear_and_catch_warnings,\n decorate_methods,\n jiffies,\n measure,\n memusage,\n print_assert_equal,\n run_threaded,\n rundocs,\n runstring,\n suppress_warnings,\n tempdir,\n temppath,\n verbose,\n)\n\n__all__ = [\n "HAS_LAPACK64",\n "HAS_REFCOUNT",\n "IS_EDITABLE",\n "IS_INSTALLED",\n "IS_MUSL",\n "IS_PYPY",\n "IS_PYSTON",\n "IS_WASM",\n "NOGIL_BUILD",\n "NUMPY_ROOT",\n "IgnoreException",\n "KnownFailureException",\n "SkipTest",\n "TestCase",\n "assert_",\n "assert_allclose",\n "assert_almost_equal",\n "assert_approx_equal",\n "assert_array_almost_equal",\n "assert_array_almost_equal_nulp",\n "assert_array_compare",\n "assert_array_equal",\n "assert_array_less",\n "assert_array_max_ulp",\n "assert_equal",\n "assert_no_gc_cycles",\n "assert_no_warnings",\n "assert_raises",\n "assert_raises_regex",\n "assert_string_equal",\n "assert_warns",\n "break_cycles",\n "build_err_msg",\n "check_support_sve",\n "clear_and_catch_warnings",\n "decorate_methods",\n "jiffies",\n "measure",\n "memusage",\n "overrides",\n "print_assert_equal",\n "run_threaded",\n "rundocs",\n "runstring",\n "suppress_warnings",\n "tempdir",\n "temppath",\n "verbose",\n]\n | .venv\Lib\site-packages\numpy\testing\__init__.pyi | __init__.pyi | Other | 2,147 | 0.85 | 0 | 0 | node-utils | 355 | 2024-04-14T23:47:22.284445 | MIT | true | c1e86de418fd2d3a01dbf9699f75e920 |
import itertools\nimport os\nimport re\nimport sys\nimport warnings\nimport weakref\n\nimport pytest\n\nimport numpy as np\nimport numpy._core._multiarray_umath as ncu\nfrom numpy.testing import (\n HAS_REFCOUNT,\n assert_,\n assert_allclose,\n assert_almost_equal,\n assert_approx_equal,\n assert_array_almost_equal,\n assert_array_almost_equal_nulp,\n assert_array_equal,\n assert_array_less,\n assert_array_max_ulp,\n assert_equal,\n assert_no_gc_cycles,\n assert_no_warnings,\n assert_raises,\n assert_string_equal,\n assert_warns,\n build_err_msg,\n clear_and_catch_warnings,\n suppress_warnings,\n tempdir,\n temppath,\n)\n\n\nclass _GenericTest:\n\n def _test_equal(self, a, b):\n self._assert_func(a, b)\n\n def _test_not_equal(self, a, b):\n with assert_raises(AssertionError):\n self._assert_func(a, b)\n\n def test_array_rank1_eq(self):\n """Test two equal array of rank 1 are found equal."""\n a = np.array([1, 2])\n b = np.array([1, 2])\n\n self._test_equal(a, b)\n\n def test_array_rank1_noteq(self):\n """Test two different array of rank 1 are found not equal."""\n a = np.array([1, 2])\n b = np.array([2, 2])\n\n self._test_not_equal(a, b)\n\n def test_array_rank2_eq(self):\n """Test two equal array of rank 2 are found equal."""\n a = np.array([[1, 2], [3, 4]])\n b = np.array([[1, 2], [3, 4]])\n\n self._test_equal(a, b)\n\n def test_array_diffshape(self):\n """Test two arrays with different shapes are found not equal."""\n a = np.array([1, 2])\n b = np.array([[1, 2], [1, 2]])\n\n self._test_not_equal(a, b)\n\n def test_objarray(self):\n """Test object arrays."""\n a = np.array([1, 1], dtype=object)\n self._test_equal(a, 1)\n\n def test_array_likes(self):\n self._test_equal([1, 2, 3], (1, 2, 3))\n\n\nclass TestArrayEqual(_GenericTest):\n\n def setup_method(self):\n self._assert_func = assert_array_equal\n\n def test_generic_rank1(self):\n """Test rank 1 array for all dtypes."""\n def foo(t):\n a = np.empty(2, t)\n a.fill(1)\n b = a.copy()\n c = a.copy()\n c.fill(0)\n self._test_equal(a, b)\n self._test_not_equal(c, b)\n\n # Test numeric types and object\n for t in '?bhilqpBHILQPfdgFDG':\n foo(t)\n\n # Test strings\n for t in ['S1', 'U1']:\n foo(t)\n\n def test_0_ndim_array(self):\n x = np.array(473963742225900817127911193656584771)\n y = np.array(18535119325151578301457182298393896)\n\n with pytest.raises(AssertionError) as exc_info:\n self._assert_func(x, y)\n msg = str(exc_info.value)\n assert_('Mismatched elements: 1 / 1 (100%)\n'\n in msg)\n\n y = x\n self._assert_func(x, y)\n\n x = np.array(4395065348745.5643764887869876)\n y = np.array(0)\n expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'\n 'Max absolute difference among violations: '\n '4.39506535e+12\n'\n 'Max relative difference among violations: inf\n')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(x, y)\n\n x = y\n self._assert_func(x, y)\n\n def test_generic_rank3(self):\n """Test rank 3 array for all dtypes."""\n def foo(t):\n a = np.empty((4, 2, 3), t)\n a.fill(1)\n b = a.copy()\n c = a.copy()\n c.fill(0)\n self._test_equal(a, b)\n self._test_not_equal(c, b)\n\n # Test numeric types and object\n for t in '?bhilqpBHILQPfdgFDG':\n foo(t)\n\n # Test strings\n for t in ['S1', 'U1']:\n foo(t)\n\n def test_nan_array(self):\n """Test arrays with nan values in them."""\n a = np.array([1, 2, np.nan])\n b = np.array([1, 2, np.nan])\n\n self._test_equal(a, b)\n\n c = np.array([1, 2, 3])\n self._test_not_equal(c, b)\n\n def test_string_arrays(self):\n """Test two arrays with different shapes are found not equal."""\n a = np.array(['floupi', 'floupa'])\n b = np.array(['floupi', 'floupa'])\n\n self._test_equal(a, b)\n\n c = np.array(['floupipi', 'floupa'])\n\n self._test_not_equal(c, b)\n\n def test_recarrays(self):\n """Test record arrays."""\n a = np.empty(2, [('floupi', float), ('floupa', float)])\n a['floupi'] = [1, 2]\n a['floupa'] = [1, 2]\n b = a.copy()\n\n self._test_equal(a, b)\n\n c = np.empty(2, [('floupipi', float),\n ('floupi', float), ('floupa', float)])\n c['floupipi'] = a['floupi'].copy()\n c['floupa'] = a['floupa'].copy()\n\n with pytest.raises(TypeError):\n self._test_not_equal(c, b)\n\n def test_masked_nan_inf(self):\n # Regression test for gh-11121\n a = np.ma.MaskedArray([3., 4., 6.5], mask=[False, True, False])\n b = np.array([3., np.nan, 6.5])\n self._test_equal(a, b)\n self._test_equal(b, a)\n a = np.ma.MaskedArray([3., 4., 6.5], mask=[True, False, False])\n b = np.array([np.inf, 4., 6.5])\n self._test_equal(a, b)\n self._test_equal(b, a)\n\n def test_subclass_that_overrides_eq(self):\n # While we cannot guarantee testing functions will always work for\n # subclasses, the tests should ideally rely only on subclasses having\n # comparison operators, not on them being able to store booleans\n # (which, e.g., astropy Quantity cannot usefully do). See gh-8452.\n class MyArray(np.ndarray):\n def __eq__(self, other):\n return bool(np.equal(self, other).all())\n\n def __ne__(self, other):\n return not self == other\n\n a = np.array([1., 2.]).view(MyArray)\n b = np.array([2., 3.]).view(MyArray)\n assert_(type(a == a), bool)\n assert_(a == a)\n assert_(a != b)\n self._test_equal(a, a)\n self._test_not_equal(a, b)\n self._test_not_equal(b, a)\n\n expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'\n 'Max absolute difference among violations: 1.\n'\n 'Max relative difference among violations: 0.5')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._test_equal(a, b)\n\n c = np.array([0., 2.9]).view(MyArray)\n expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'\n 'Max absolute difference among violations: 2.\n'\n 'Max relative difference among violations: inf')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._test_equal(b, c)\n\n def test_subclass_that_does_not_implement_npall(self):\n class MyArray(np.ndarray):\n def __array_function__(self, *args, **kwargs):\n return NotImplemented\n\n a = np.array([1., 2.]).view(MyArray)\n b = np.array([2., 3.]).view(MyArray)\n with assert_raises(TypeError):\n np.all(a)\n self._test_equal(a, a)\n self._test_not_equal(a, b)\n self._test_not_equal(b, a)\n\n def test_suppress_overflow_warnings(self):\n # Based on issue #18992\n with pytest.raises(AssertionError):\n with np.errstate(all="raise"):\n np.testing.assert_array_equal(\n np.array([1, 2, 3], np.float32),\n np.array([1, 1e-40, 3], np.float32))\n\n def test_array_vs_scalar_is_equal(self):\n """Test comparing an array with a scalar when all values are equal."""\n a = np.array([1., 1., 1.])\n b = 1.\n\n self._test_equal(a, b)\n\n def test_array_vs_array_not_equal(self):\n """Test comparing an array with a scalar when not all values equal."""\n a = np.array([34986, 545676, 439655, 563766])\n b = np.array([34986, 545676, 439655, 0])\n\n expected_msg = ('Mismatched elements: 1 / 4 (25%)\n'\n 'Max absolute difference among violations: 563766\n'\n 'Max relative difference among violations: inf')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(a, b)\n\n a = np.array([34986, 545676, 439655.2, 563766])\n expected_msg = ('Mismatched elements: 2 / 4 (50%)\n'\n 'Max absolute difference among violations: '\n '563766.\n'\n 'Max relative difference among violations: '\n '4.54902139e-07')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(a, b)\n\n def test_array_vs_scalar_strict(self):\n """Test comparing an array with a scalar with strict option."""\n a = np.array([1., 1., 1.])\n b = 1.\n\n with pytest.raises(AssertionError):\n self._assert_func(a, b, strict=True)\n\n def test_array_vs_array_strict(self):\n """Test comparing two arrays with strict option."""\n a = np.array([1., 1., 1.])\n b = np.array([1., 1., 1.])\n\n self._assert_func(a, b, strict=True)\n\n def test_array_vs_float_array_strict(self):\n """Test comparing two arrays with strict option."""\n a = np.array([1, 1, 1])\n b = np.array([1., 1., 1.])\n\n with pytest.raises(AssertionError):\n self._assert_func(a, b, strict=True)\n\n\nclass TestBuildErrorMessage:\n\n def test_build_err_msg_defaults(self):\n x = np.array([1.00001, 2.00002, 3.00003])\n y = np.array([1.00002, 2.00003, 3.00004])\n err_msg = 'There is a mismatch'\n\n a = build_err_msg([x, y], err_msg)\n b = ('\nItems are not equal: There is a mismatch\n ACTUAL: array(['\n '1.00001, 2.00002, 3.00003])\n DESIRED: array([1.00002, '\n '2.00003, 3.00004])')\n assert_equal(a, b)\n\n def test_build_err_msg_no_verbose(self):\n x = np.array([1.00001, 2.00002, 3.00003])\n y = np.array([1.00002, 2.00003, 3.00004])\n err_msg = 'There is a mismatch'\n\n a = build_err_msg([x, y], err_msg, verbose=False)\n b = '\nItems are not equal: There is a mismatch'\n assert_equal(a, b)\n\n def test_build_err_msg_custom_names(self):\n x = np.array([1.00001, 2.00002, 3.00003])\n y = np.array([1.00002, 2.00003, 3.00004])\n err_msg = 'There is a mismatch'\n\n a = build_err_msg([x, y], err_msg, names=('FOO', 'BAR'))\n b = ('\nItems are not equal: There is a mismatch\n FOO: array(['\n '1.00001, 2.00002, 3.00003])\n BAR: array([1.00002, 2.00003, '\n '3.00004])')\n assert_equal(a, b)\n\n def test_build_err_msg_custom_precision(self):\n x = np.array([1.000000001, 2.00002, 3.00003])\n y = np.array([1.000000002, 2.00003, 3.00004])\n err_msg = 'There is a mismatch'\n\n a = build_err_msg([x, y], err_msg, precision=10)\n b = ('\nItems are not equal: There is a mismatch\n ACTUAL: array(['\n '1.000000001, 2.00002 , 3.00003 ])\n DESIRED: array(['\n '1.000000002, 2.00003 , 3.00004 ])')\n assert_equal(a, b)\n\n\nclass TestEqual(TestArrayEqual):\n\n def setup_method(self):\n self._assert_func = assert_equal\n\n def test_nan_items(self):\n self._assert_func(np.nan, np.nan)\n self._assert_func([np.nan], [np.nan])\n self._test_not_equal(np.nan, [np.nan])\n self._test_not_equal(np.nan, 1)\n\n def test_inf_items(self):\n self._assert_func(np.inf, np.inf)\n self._assert_func([np.inf], [np.inf])\n self._test_not_equal(np.inf, [np.inf])\n\n def test_datetime(self):\n self._test_equal(\n np.datetime64("2017-01-01", "s"),\n np.datetime64("2017-01-01", "s")\n )\n self._test_equal(\n np.datetime64("2017-01-01", "s"),\n np.datetime64("2017-01-01", "m")\n )\n\n # gh-10081\n self._test_not_equal(\n np.datetime64("2017-01-01", "s"),\n np.datetime64("2017-01-02", "s")\n )\n self._test_not_equal(\n np.datetime64("2017-01-01", "s"),\n np.datetime64("2017-01-02", "m")\n )\n\n def test_nat_items(self):\n # not a datetime\n nadt_no_unit = np.datetime64("NaT")\n nadt_s = np.datetime64("NaT", "s")\n nadt_d = np.datetime64("NaT", "ns")\n # not a timedelta\n natd_no_unit = np.timedelta64("NaT")\n natd_s = np.timedelta64("NaT", "s")\n natd_d = np.timedelta64("NaT", "ns")\n\n dts = [nadt_no_unit, nadt_s, nadt_d]\n tds = [natd_no_unit, natd_s, natd_d]\n for a, b in itertools.product(dts, dts):\n self._assert_func(a, b)\n self._assert_func([a], [b])\n self._test_not_equal([a], b)\n\n for a, b in itertools.product(tds, tds):\n self._assert_func(a, b)\n self._assert_func([a], [b])\n self._test_not_equal([a], b)\n\n for a, b in itertools.product(tds, dts):\n self._test_not_equal(a, b)\n self._test_not_equal(a, [b])\n self._test_not_equal([a], [b])\n self._test_not_equal([a], np.datetime64("2017-01-01", "s"))\n self._test_not_equal([b], np.datetime64("2017-01-01", "s"))\n self._test_not_equal([a], np.timedelta64(123, "s"))\n self._test_not_equal([b], np.timedelta64(123, "s"))\n\n def test_non_numeric(self):\n self._assert_func('ab', 'ab')\n self._test_not_equal('ab', 'abb')\n\n def test_complex_item(self):\n self._assert_func(complex(1, 2), complex(1, 2))\n self._assert_func(complex(1, np.nan), complex(1, np.nan))\n self._test_not_equal(complex(1, np.nan), complex(1, 2))\n self._test_not_equal(complex(np.nan, 1), complex(1, np.nan))\n self._test_not_equal(complex(np.nan, np.inf), complex(np.nan, 2))\n\n def test_negative_zero(self):\n self._test_not_equal(ncu.PZERO, ncu.NZERO)\n\n def test_complex(self):\n x = np.array([complex(1, 2), complex(1, np.nan)])\n y = np.array([complex(1, 2), complex(1, 2)])\n self._assert_func(x, x)\n self._test_not_equal(x, y)\n\n def test_object(self):\n # gh-12942\n import datetime\n a = np.array([datetime.datetime(2000, 1, 1),\n datetime.datetime(2000, 1, 2)])\n self._test_not_equal(a, a[::-1])\n\n\nclass TestArrayAlmostEqual(_GenericTest):\n\n def setup_method(self):\n self._assert_func = assert_array_almost_equal\n\n def test_closeness(self):\n # Note that in the course of time we ended up with\n # `abs(x - y) < 1.5 * 10**(-decimal)`\n # instead of the previously documented\n # `abs(x - y) < 0.5 * 10**(-decimal)`\n # so this check serves to preserve the wrongness.\n\n # test scalars\n expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'\n 'Max absolute difference among violations: 1.5\n'\n 'Max relative difference among violations: inf')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(1.5, 0.0, decimal=0)\n\n # test arrays\n self._assert_func([1.499999], [0.0], decimal=0)\n\n expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'\n 'Max absolute difference among violations: 1.5\n'\n 'Max relative difference among violations: inf')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func([1.5], [0.0], decimal=0)\n\n a = [1.4999999, 0.00003]\n b = [1.49999991, 0]\n expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'\n 'Max absolute difference among violations: 3.e-05\n'\n 'Max relative difference among violations: inf')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(a, b, decimal=7)\n\n expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'\n 'Max absolute difference among violations: 3.e-05\n'\n 'Max relative difference among violations: 1.')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(b, a, decimal=7)\n\n def test_simple(self):\n x = np.array([1234.2222])\n y = np.array([1234.2223])\n\n self._assert_func(x, y, decimal=3)\n self._assert_func(x, y, decimal=4)\n\n expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'\n 'Max absolute difference among violations: '\n '1.e-04\n'\n 'Max relative difference among violations: '\n '8.10226812e-08')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(x, y, decimal=5)\n\n def test_array_vs_scalar(self):\n a = [5498.42354, 849.54345, 0.00]\n b = 5498.42354\n expected_msg = ('Mismatched elements: 2 / 3 (66.7%)\n'\n 'Max absolute difference among violations: '\n '5498.42354\n'\n 'Max relative difference among violations: 1.')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(a, b, decimal=9)\n\n expected_msg = ('Mismatched elements: 2 / 3 (66.7%)\n'\n 'Max absolute difference among violations: '\n '5498.42354\n'\n 'Max relative difference among violations: 5.4722099')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(b, a, decimal=9)\n\n a = [5498.42354, 0.00]\n expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'\n 'Max absolute difference among violations: '\n '5498.42354\n'\n 'Max relative difference among violations: inf')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(b, a, decimal=7)\n\n b = 0\n expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'\n 'Max absolute difference among violations: '\n '5498.42354\n'\n 'Max relative difference among violations: inf')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(a, b, decimal=7)\n\n def test_nan(self):\n anan = np.array([np.nan])\n aone = np.array([1])\n ainf = np.array([np.inf])\n self._assert_func(anan, anan)\n assert_raises(AssertionError,\n lambda: self._assert_func(anan, aone))\n assert_raises(AssertionError,\n lambda: self._assert_func(anan, ainf))\n assert_raises(AssertionError,\n lambda: self._assert_func(ainf, anan))\n\n def test_inf(self):\n a = np.array([[1., 2.], [3., 4.]])\n b = a.copy()\n a[0, 0] = np.inf\n assert_raises(AssertionError,\n lambda: self._assert_func(a, b))\n b[0, 0] = -np.inf\n assert_raises(AssertionError,\n lambda: self._assert_func(a, b))\n\n def test_subclass(self):\n a = np.array([[1., 2.], [3., 4.]])\n b = np.ma.masked_array([[1., 2.], [0., 4.]],\n [[False, False], [True, False]])\n self._assert_func(a, b)\n self._assert_func(b, a)\n self._assert_func(b, b)\n\n # Test fully masked as well (see gh-11123).\n a = np.ma.MaskedArray(3.5, mask=True)\n b = np.array([3., 4., 6.5])\n self._test_equal(a, b)\n self._test_equal(b, a)\n a = np.ma.masked\n b = np.array([3., 4., 6.5])\n self._test_equal(a, b)\n self._test_equal(b, a)\n a = np.ma.MaskedArray([3., 4., 6.5], mask=[True, True, True])\n b = np.array([1., 2., 3.])\n self._test_equal(a, b)\n self._test_equal(b, a)\n a = np.ma.MaskedArray([3., 4., 6.5], mask=[True, True, True])\n b = np.array(1.)\n self._test_equal(a, b)\n self._test_equal(b, a)\n\n def test_subclass_2(self):\n # While we cannot guarantee testing functions will always work for\n # subclasses, the tests should ideally rely only on subclasses having\n # comparison operators, not on them being able to store booleans\n # (which, e.g., astropy Quantity cannot usefully do). See gh-8452.\n class MyArray(np.ndarray):\n def __eq__(self, other):\n return super().__eq__(other).view(np.ndarray)\n\n def __lt__(self, other):\n return super().__lt__(other).view(np.ndarray)\n\n def all(self, *args, **kwargs):\n return all(self)\n\n a = np.array([1., 2.]).view(MyArray)\n self._assert_func(a, a)\n\n z = np.array([True, True]).view(MyArray)\n all(z)\n b = np.array([1., 202]).view(MyArray)\n expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'\n 'Max absolute difference among violations: 200.\n'\n 'Max relative difference among violations: 0.99009')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(a, b)\n\n def test_subclass_that_cannot_be_bool(self):\n # While we cannot guarantee testing functions will always work for\n # subclasses, the tests should ideally rely only on subclasses having\n # comparison operators, not on them being able to store booleans\n # (which, e.g., astropy Quantity cannot usefully do). See gh-8452.\n class MyArray(np.ndarray):\n def __eq__(self, other):\n return super().__eq__(other).view(np.ndarray)\n\n def __lt__(self, other):\n return super().__lt__(other).view(np.ndarray)\n\n def all(self, *args, **kwargs):\n raise NotImplementedError\n\n a = np.array([1., 2.]).view(MyArray)\n self._assert_func(a, a)\n\n\nclass TestAlmostEqual(_GenericTest):\n\n def setup_method(self):\n self._assert_func = assert_almost_equal\n\n def test_closeness(self):\n # Note that in the course of time we ended up with\n # `abs(x - y) < 1.5 * 10**(-decimal)`\n # instead of the previously documented\n # `abs(x - y) < 0.5 * 10**(-decimal)`\n # so this check serves to preserve the wrongness.\n\n # test scalars\n self._assert_func(1.499999, 0.0, decimal=0)\n assert_raises(AssertionError,\n lambda: self._assert_func(1.5, 0.0, decimal=0))\n\n # test arrays\n self._assert_func([1.499999], [0.0], decimal=0)\n assert_raises(AssertionError,\n lambda: self._assert_func([1.5], [0.0], decimal=0))\n\n def test_nan_item(self):\n self._assert_func(np.nan, np.nan)\n assert_raises(AssertionError,\n lambda: self._assert_func(np.nan, 1))\n assert_raises(AssertionError,\n lambda: self._assert_func(np.nan, np.inf))\n assert_raises(AssertionError,\n lambda: self._assert_func(np.inf, np.nan))\n\n def test_inf_item(self):\n self._assert_func(np.inf, np.inf)\n self._assert_func(-np.inf, -np.inf)\n assert_raises(AssertionError,\n lambda: self._assert_func(np.inf, 1))\n assert_raises(AssertionError,\n lambda: self._assert_func(-np.inf, np.inf))\n\n def test_simple_item(self):\n self._test_not_equal(1, 2)\n\n def test_complex_item(self):\n self._assert_func(complex(1, 2), complex(1, 2))\n self._assert_func(complex(1, np.nan), complex(1, np.nan))\n self._assert_func(complex(np.inf, np.nan), complex(np.inf, np.nan))\n self._test_not_equal(complex(1, np.nan), complex(1, 2))\n self._test_not_equal(complex(np.nan, 1), complex(1, np.nan))\n self._test_not_equal(complex(np.nan, np.inf), complex(np.nan, 2))\n\n def test_complex(self):\n x = np.array([complex(1, 2), complex(1, np.nan)])\n z = np.array([complex(1, 2), complex(np.nan, 1)])\n y = np.array([complex(1, 2), complex(1, 2)])\n self._assert_func(x, x)\n self._test_not_equal(x, y)\n self._test_not_equal(x, z)\n\n def test_error_message(self):\n """Check the message is formatted correctly for the decimal value.\n Also check the message when input includes inf or nan (gh12200)"""\n x = np.array([1.00000000001, 2.00000000002, 3.00003])\n y = np.array([1.00000000002, 2.00000000003, 3.00004])\n\n # Test with a different amount of decimal digits\n expected_msg = ('Mismatched elements: 3 / 3 (100%)\n'\n 'Max absolute difference among violations: 1.e-05\n'\n 'Max relative difference among violations: '\n '3.33328889e-06\n'\n ' ACTUAL: array([1.00000000001, '\n '2.00000000002, '\n '3.00003 ])\n'\n ' DESIRED: array([1.00000000002, 2.00000000003, '\n '3.00004 ])')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(x, y, decimal=12)\n\n # With the default value of decimal digits, only the 3rd element\n # differs. Note that we only check for the formatting of the arrays\n # themselves.\n expected_msg = ('Mismatched elements: 1 / 3 (33.3%)\n'\n 'Max absolute difference among violations: 1.e-05\n'\n 'Max relative difference among violations: '\n '3.33328889e-06\n'\n ' ACTUAL: array([1. , 2. , 3.00003])\n'\n ' DESIRED: array([1. , 2. , 3.00004])')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(x, y)\n\n # Check the error message when input includes inf\n x = np.array([np.inf, 0])\n y = np.array([np.inf, 1])\n expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'\n 'Max absolute difference among violations: 1.\n'\n 'Max relative difference among violations: 1.\n'\n ' ACTUAL: array([inf, 0.])\n'\n ' DESIRED: array([inf, 1.])')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(x, y)\n\n # Check the error message when dividing by zero\n x = np.array([1, 2])\n y = np.array([0, 0])\n expected_msg = ('Mismatched elements: 2 / 2 (100%)\n'\n 'Max absolute difference among violations: 2\n'\n 'Max relative difference among violations: inf')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(x, y)\n\n def test_error_message_2(self):\n """Check the message is formatted correctly """\n """when either x or y is a scalar."""\n x = 2\n y = np.ones(20)\n expected_msg = ('Mismatched elements: 20 / 20 (100%)\n'\n 'Max absolute difference among violations: 1.\n'\n 'Max relative difference among violations: 1.')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(x, y)\n\n y = 2\n x = np.ones(20)\n expected_msg = ('Mismatched elements: 20 / 20 (100%)\n'\n 'Max absolute difference among violations: 1.\n'\n 'Max relative difference among violations: 0.5')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(x, y)\n\n def test_subclass_that_cannot_be_bool(self):\n # While we cannot guarantee testing functions will always work for\n # subclasses, the tests should ideally rely only on subclasses having\n # comparison operators, not on them being able to store booleans\n # (which, e.g., astropy Quantity cannot usefully do). See gh-8452.\n class MyArray(np.ndarray):\n def __eq__(self, other):\n return super().__eq__(other).view(np.ndarray)\n\n def __lt__(self, other):\n return super().__lt__(other).view(np.ndarray)\n\n def all(self, *args, **kwargs):\n raise NotImplementedError\n\n a = np.array([1., 2.]).view(MyArray)\n self._assert_func(a, a)\n\n\nclass TestApproxEqual:\n\n def setup_method(self):\n self._assert_func = assert_approx_equal\n\n def test_simple_0d_arrays(self):\n x = np.array(1234.22)\n y = np.array(1234.23)\n\n self._assert_func(x, y, significant=5)\n self._assert_func(x, y, significant=6)\n assert_raises(AssertionError,\n lambda: self._assert_func(x, y, significant=7))\n\n def test_simple_items(self):\n x = 1234.22\n y = 1234.23\n\n self._assert_func(x, y, significant=4)\n self._assert_func(x, y, significant=5)\n self._assert_func(x, y, significant=6)\n assert_raises(AssertionError,\n lambda: self._assert_func(x, y, significant=7))\n\n def test_nan_array(self):\n anan = np.array(np.nan)\n aone = np.array(1)\n ainf = np.array(np.inf)\n self._assert_func(anan, anan)\n assert_raises(AssertionError, lambda: self._assert_func(anan, aone))\n assert_raises(AssertionError, lambda: self._assert_func(anan, ainf))\n assert_raises(AssertionError, lambda: self._assert_func(ainf, anan))\n\n def test_nan_items(self):\n anan = np.array(np.nan)\n aone = np.array(1)\n ainf = np.array(np.inf)\n self._assert_func(anan, anan)\n assert_raises(AssertionError, lambda: self._assert_func(anan, aone))\n assert_raises(AssertionError, lambda: self._assert_func(anan, ainf))\n assert_raises(AssertionError, lambda: self._assert_func(ainf, anan))\n\n\nclass TestArrayAssertLess:\n\n def setup_method(self):\n self._assert_func = assert_array_less\n\n def test_simple_arrays(self):\n x = np.array([1.1, 2.2])\n y = np.array([1.2, 2.3])\n\n self._assert_func(x, y)\n assert_raises(AssertionError, lambda: self._assert_func(y, x))\n\n y = np.array([1.0, 2.3])\n\n assert_raises(AssertionError, lambda: self._assert_func(x, y))\n assert_raises(AssertionError, lambda: self._assert_func(y, x))\n\n a = np.array([1, 3, 6, 20])\n b = np.array([2, 4, 6, 8])\n\n expected_msg = ('Mismatched elements: 2 / 4 (50%)\n'\n 'Max absolute difference among violations: 12\n'\n 'Max relative difference among violations: 1.5')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(a, b)\n\n def test_rank2(self):\n x = np.array([[1.1, 2.2], [3.3, 4.4]])\n y = np.array([[1.2, 2.3], [3.4, 4.5]])\n\n self._assert_func(x, y)\n expected_msg = ('Mismatched elements: 4 / 4 (100%)\n'\n 'Max absolute difference among violations: 0.1\n'\n 'Max relative difference among violations: 0.09090909')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(y, x)\n\n y = np.array([[1.0, 2.3], [3.4, 4.5]])\n assert_raises(AssertionError, lambda: self._assert_func(x, y))\n assert_raises(AssertionError, lambda: self._assert_func(y, x))\n\n def test_rank3(self):\n x = np.ones(shape=(2, 2, 2))\n y = np.ones(shape=(2, 2, 2)) + 1\n\n self._assert_func(x, y)\n assert_raises(AssertionError, lambda: self._assert_func(y, x))\n\n y[0, 0, 0] = 0\n expected_msg = ('Mismatched elements: 1 / 8 (12.5%)\n'\n 'Max absolute difference among violations: 1.\n'\n 'Max relative difference among violations: inf')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(x, y)\n\n assert_raises(AssertionError, lambda: self._assert_func(y, x))\n\n def test_simple_items(self):\n x = 1.1\n y = 2.2\n\n self._assert_func(x, y)\n expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'\n 'Max absolute difference among violations: 1.1\n'\n 'Max relative difference among violations: 1.')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(y, x)\n\n y = np.array([2.2, 3.3])\n\n self._assert_func(x, y)\n assert_raises(AssertionError, lambda: self._assert_func(y, x))\n\n y = np.array([1.0, 3.3])\n\n assert_raises(AssertionError, lambda: self._assert_func(x, y))\n\n def test_simple_items_and_array(self):\n x = np.array([[621.345454, 390.5436, 43.54657, 626.4535],\n [54.54, 627.3399, 13., 405.5435],\n [543.545, 8.34, 91.543, 333.3]])\n y = 627.34\n self._assert_func(x, y)\n\n y = 8.339999\n self._assert_func(y, x)\n\n x = np.array([[3.4536, 2390.5436, 435.54657, 324525.4535],\n [5449.54, 999090.54, 130303.54, 405.5435],\n [543.545, 8.34, 91.543, 999090.53999]])\n y = 999090.54\n\n expected_msg = ('Mismatched elements: 1 / 12 (8.33%)\n'\n 'Max absolute difference among violations: 0.\n'\n 'Max relative difference among violations: 0.')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(x, y)\n\n expected_msg = ('Mismatched elements: 12 / 12 (100%)\n'\n 'Max absolute difference among violations: '\n '999087.0864\n'\n 'Max relative difference among violations: '\n '289288.5934676')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(y, x)\n\n def test_zeroes(self):\n x = np.array([546456., 0, 15.455])\n y = np.array(87654.)\n\n expected_msg = ('Mismatched elements: 1 / 3 (33.3%)\n'\n 'Max absolute difference among violations: 458802.\n'\n 'Max relative difference among violations: 5.23423917')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(x, y)\n\n expected_msg = ('Mismatched elements: 2 / 3 (66.7%)\n'\n 'Max absolute difference among violations: 87654.\n'\n 'Max relative difference among violations: '\n '5670.5626011')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(y, x)\n\n y = 0\n\n expected_msg = ('Mismatched elements: 3 / 3 (100%)\n'\n 'Max absolute difference among violations: 546456.\n'\n 'Max relative difference among violations: inf')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(x, y)\n\n expected_msg = ('Mismatched elements: 1 / 3 (33.3%)\n'\n 'Max absolute difference among violations: 0.\n'\n 'Max relative difference among violations: inf')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n self._assert_func(y, x)\n\n def test_nan_noncompare(self):\n anan = np.array(np.nan)\n aone = np.array(1)\n ainf = np.array(np.inf)\n self._assert_func(anan, anan)\n assert_raises(AssertionError, lambda: self._assert_func(aone, anan))\n assert_raises(AssertionError, lambda: self._assert_func(anan, aone))\n assert_raises(AssertionError, lambda: self._assert_func(anan, ainf))\n assert_raises(AssertionError, lambda: self._assert_func(ainf, anan))\n\n def test_nan_noncompare_array(self):\n x = np.array([1.1, 2.2, 3.3])\n anan = np.array(np.nan)\n\n assert_raises(AssertionError, lambda: self._assert_func(x, anan))\n assert_raises(AssertionError, lambda: self._assert_func(anan, x))\n\n x = np.array([1.1, 2.2, np.nan])\n\n assert_raises(AssertionError, lambda: self._assert_func(x, anan))\n assert_raises(AssertionError, lambda: self._assert_func(anan, x))\n\n y = np.array([1.0, 2.0, np.nan])\n\n self._assert_func(y, x)\n assert_raises(AssertionError, lambda: self._assert_func(x, y))\n\n def test_inf_compare(self):\n aone = np.array(1)\n ainf = np.array(np.inf)\n\n self._assert_func(aone, ainf)\n self._assert_func(-ainf, aone)\n self._assert_func(-ainf, ainf)\n assert_raises(AssertionError, lambda: self._assert_func(ainf, aone))\n assert_raises(AssertionError, lambda: self._assert_func(aone, -ainf))\n assert_raises(AssertionError, lambda: self._assert_func(ainf, ainf))\n assert_raises(AssertionError, lambda: self._assert_func(ainf, -ainf))\n assert_raises(AssertionError, lambda: self._assert_func(-ainf, -ainf))\n\n def test_inf_compare_array(self):\n x = np.array([1.1, 2.2, np.inf])\n ainf = np.array(np.inf)\n\n assert_raises(AssertionError, lambda: self._assert_func(x, ainf))\n assert_raises(AssertionError, lambda: self._assert_func(ainf, x))\n assert_raises(AssertionError, lambda: self._assert_func(x, -ainf))\n assert_raises(AssertionError, lambda: self._assert_func(-x, -ainf))\n assert_raises(AssertionError, lambda: self._assert_func(-ainf, -x))\n self._assert_func(-ainf, x)\n\n def test_strict(self):\n """Test the behavior of the `strict` option."""\n x = np.zeros(3)\n y = np.ones(())\n self._assert_func(x, y)\n with pytest.raises(AssertionError):\n self._assert_func(x, y, strict=True)\n y = np.broadcast_to(y, x.shape)\n self._assert_func(x, y)\n with pytest.raises(AssertionError):\n self._assert_func(x, y.astype(np.float32), strict=True)\n\n\nclass TestWarns:\n\n def test_warn(self):\n def f():\n warnings.warn("yo")\n return 3\n\n before_filters = sys.modules['warnings'].filters[:]\n assert_equal(assert_warns(UserWarning, f), 3)\n after_filters = sys.modules['warnings'].filters\n\n assert_raises(AssertionError, assert_no_warnings, f)\n assert_equal(assert_no_warnings(lambda x: x, 1), 1)\n\n # Check that the warnings state is unchanged\n assert_equal(before_filters, after_filters,\n "assert_warns does not preserver warnings state")\n\n def test_context_manager(self):\n\n before_filters = sys.modules['warnings'].filters[:]\n with assert_warns(UserWarning):\n warnings.warn("yo")\n after_filters = sys.modules['warnings'].filters\n\n def no_warnings():\n with assert_no_warnings():\n warnings.warn("yo")\n\n assert_raises(AssertionError, no_warnings)\n assert_equal(before_filters, after_filters,\n "assert_warns does not preserver warnings state")\n\n def test_args(self):\n def f(a=0, b=1):\n warnings.warn("yo")\n return a + b\n\n assert assert_warns(UserWarning, f, b=20) == 20\n\n with pytest.raises(RuntimeError) as exc:\n # assert_warns cannot do regexp matching, use pytest.warns\n with assert_warns(UserWarning, match="A"):\n warnings.warn("B", UserWarning)\n assert "assert_warns" in str(exc)\n assert "pytest.warns" in str(exc)\n\n with pytest.raises(RuntimeError) as exc:\n # assert_warns cannot do regexp matching, use pytest.warns\n with assert_warns(UserWarning, wrong="A"):\n warnings.warn("B", UserWarning)\n assert "assert_warns" in str(exc)\n assert "pytest.warns" not in str(exc)\n\n def test_warn_wrong_warning(self):\n def f():\n warnings.warn("yo", DeprecationWarning)\n\n failed = False\n with warnings.catch_warnings():\n warnings.simplefilter("error", DeprecationWarning)\n try:\n # Should raise a DeprecationWarning\n assert_warns(UserWarning, f)\n failed = True\n except DeprecationWarning:\n pass\n\n if failed:\n raise AssertionError("wrong warning caught by assert_warn")\n\n\nclass TestAssertAllclose:\n\n def test_simple(self):\n x = 1e-3\n y = 1e-9\n\n assert_allclose(x, y, atol=1)\n assert_raises(AssertionError, assert_allclose, x, y)\n\n expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'\n 'Max absolute difference among violations: 0.001\n'\n 'Max relative difference among violations: 999999.')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n assert_allclose(x, y)\n\n z = 0\n expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'\n 'Max absolute difference among violations: 1.e-09\n'\n 'Max relative difference among violations: inf')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n assert_allclose(y, z)\n\n expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'\n 'Max absolute difference among violations: 1.e-09\n'\n 'Max relative difference among violations: 1.')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n assert_allclose(z, y)\n\n a = np.array([x, y, x, y])\n b = np.array([x, y, x, x])\n\n assert_allclose(a, b, atol=1)\n assert_raises(AssertionError, assert_allclose, a, b)\n\n b[-1] = y * (1 + 1e-8)\n assert_allclose(a, b)\n assert_raises(AssertionError, assert_allclose, a, b, rtol=1e-9)\n\n assert_allclose(6, 10, rtol=0.5)\n assert_raises(AssertionError, assert_allclose, 10, 6, rtol=0.5)\n\n b = np.array([x, y, x, x])\n c = np.array([x, y, x, z])\n expected_msg = ('Mismatched elements: 1 / 4 (25%)\n'\n 'Max absolute difference among violations: 0.001\n'\n 'Max relative difference among violations: inf')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n assert_allclose(b, c)\n\n expected_msg = ('Mismatched elements: 1 / 4 (25%)\n'\n 'Max absolute difference among violations: 0.001\n'\n 'Max relative difference among violations: 1.')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n assert_allclose(c, b)\n\n def test_min_int(self):\n a = np.array([np.iinfo(np.int_).min], dtype=np.int_)\n # Should not raise:\n assert_allclose(a, a)\n\n def test_report_fail_percentage(self):\n a = np.array([1, 1, 1, 1])\n b = np.array([1, 1, 1, 2])\n\n expected_msg = ('Mismatched elements: 1 / 4 (25%)\n'\n 'Max absolute difference among violations: 1\n'\n 'Max relative difference among violations: 0.5')\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n assert_allclose(a, b)\n\n def test_equal_nan(self):\n a = np.array([np.nan])\n b = np.array([np.nan])\n # Should not raise:\n assert_allclose(a, b, equal_nan=True)\n\n def test_not_equal_nan(self):\n a = np.array([np.nan])\n b = np.array([np.nan])\n assert_raises(AssertionError, assert_allclose, a, b, equal_nan=False)\n\n def test_equal_nan_default(self):\n # Make sure equal_nan default behavior remains unchanged. (All\n # of these functions use assert_array_compare under the hood.)\n # None of these should raise.\n a = np.array([np.nan])\n b = np.array([np.nan])\n assert_array_equal(a, b)\n assert_array_almost_equal(a, b)\n assert_array_less(a, b)\n assert_allclose(a, b)\n\n def test_report_max_relative_error(self):\n a = np.array([0, 1])\n b = np.array([0, 2])\n\n expected_msg = 'Max relative difference among violations: 0.5'\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n assert_allclose(a, b)\n\n def test_timedelta(self):\n # see gh-18286\n a = np.array([[1, 2, 3, "NaT"]], dtype="m8[ns]")\n assert_allclose(a, a)\n\n def test_error_message_unsigned(self):\n """Check the message is formatted correctly when overflow can occur\n (gh21768)"""\n # Ensure to test for potential overflow in the case of:\n # x - y\n # and\n # y - x\n x = np.asarray([0, 1, 8], dtype='uint8')\n y = np.asarray([4, 4, 4], dtype='uint8')\n expected_msg = 'Max absolute difference among violations: 4'\n with pytest.raises(AssertionError, match=re.escape(expected_msg)):\n assert_allclose(x, y, atol=3)\n\n def test_strict(self):\n """Test the behavior of the `strict` option."""\n x = np.ones(3)\n y = np.ones(())\n assert_allclose(x, y)\n with pytest.raises(AssertionError):\n assert_allclose(x, y, strict=True)\n assert_allclose(x, x)\n with pytest.raises(AssertionError):\n assert_allclose(x, x.astype(np.float32), strict=True)\n\n\nclass TestArrayAlmostEqualNulp:\n\n def test_float64_pass(self):\n # The number of units of least precision\n # In this case, use a few places above the lowest level (ie nulp=1)\n nulp = 5\n x = np.linspace(-20, 20, 50, dtype=np.float64)\n x = 10**x\n x = np.r_[-x, x]\n\n # Addition\n eps = np.finfo(x.dtype).eps\n y = x + x * eps * nulp / 2.\n assert_array_almost_equal_nulp(x, y, nulp)\n\n # Subtraction\n epsneg = np.finfo(x.dtype).epsneg\n y = x - x * epsneg * nulp / 2.\n assert_array_almost_equal_nulp(x, y, nulp)\n\n def test_float64_fail(self):\n nulp = 5\n x = np.linspace(-20, 20, 50, dtype=np.float64)\n x = 10**x\n x = np.r_[-x, x]\n\n eps = np.finfo(x.dtype).eps\n y = x + x * eps * nulp * 2.\n assert_raises(AssertionError, assert_array_almost_equal_nulp,\n x, y, nulp)\n\n epsneg = np.finfo(x.dtype).epsneg\n y = x - x * epsneg * nulp * 2.\n assert_raises(AssertionError, assert_array_almost_equal_nulp,\n x, y, nulp)\n\n def test_float64_ignore_nan(self):\n # Ignore ULP differences between various NAN's\n # Note that MIPS may reverse quiet and signaling nans\n # so we use the builtin version as a base.\n offset = np.uint64(0xffffffff)\n nan1_i64 = np.array(np.nan, dtype=np.float64).view(np.uint64)\n nan2_i64 = nan1_i64 ^ offset # nan payload on MIPS is all ones.\n nan1_f64 = nan1_i64.view(np.float64)\n nan2_f64 = nan2_i64.view(np.float64)\n assert_array_max_ulp(nan1_f64, nan2_f64, 0)\n\n def test_float32_pass(self):\n nulp = 5\n x = np.linspace(-20, 20, 50, dtype=np.float32)\n x = 10**x\n x = np.r_[-x, x]\n\n eps = np.finfo(x.dtype).eps\n y = x + x * eps * nulp / 2.\n assert_array_almost_equal_nulp(x, y, nulp)\n\n epsneg = np.finfo(x.dtype).epsneg\n y = x - x * epsneg * nulp / 2.\n assert_array_almost_equal_nulp(x, y, nulp)\n\n def test_float32_fail(self):\n nulp = 5\n x = np.linspace(-20, 20, 50, dtype=np.float32)\n x = 10**x\n x = np.r_[-x, x]\n\n eps = np.finfo(x.dtype).eps\n y = x + x * eps * nulp * 2.\n assert_raises(AssertionError, assert_array_almost_equal_nulp,\n x, y, nulp)\n\n epsneg = np.finfo(x.dtype).epsneg\n y = x - x * epsneg * nulp * 2.\n assert_raises(AssertionError, assert_array_almost_equal_nulp,\n x, y, nulp)\n\n def test_float32_ignore_nan(self):\n # Ignore ULP differences between various NAN's\n # Note that MIPS may reverse quiet and signaling nans\n # so we use the builtin version as a base.\n offset = np.uint32(0xffff)\n nan1_i32 = np.array(np.nan, dtype=np.float32).view(np.uint32)\n nan2_i32 = nan1_i32 ^ offset # nan payload on MIPS is all ones.\n nan1_f32 = nan1_i32.view(np.float32)\n nan2_f32 = nan2_i32.view(np.float32)\n assert_array_max_ulp(nan1_f32, nan2_f32, 0)\n\n def test_float16_pass(self):\n nulp = 5\n x = np.linspace(-4, 4, 10, dtype=np.float16)\n x = 10**x\n x = np.r_[-x, x]\n\n eps = np.finfo(x.dtype).eps\n y = x + x * eps * nulp / 2.\n assert_array_almost_equal_nulp(x, y, nulp)\n\n epsneg = np.finfo(x.dtype).epsneg\n y = x - x * epsneg * nulp / 2.\n assert_array_almost_equal_nulp(x, y, nulp)\n\n def test_float16_fail(self):\n nulp = 5\n x = np.linspace(-4, 4, 10, dtype=np.float16)\n x = 10**x\n x = np.r_[-x, x]\n\n eps = np.finfo(x.dtype).eps\n y = x + x * eps * nulp * 2.\n assert_raises(AssertionError, assert_array_almost_equal_nulp,\n x, y, nulp)\n\n epsneg = np.finfo(x.dtype).epsneg\n y = x - x * epsneg * nulp * 2.\n assert_raises(AssertionError, assert_array_almost_equal_nulp,\n x, y, nulp)\n\n def test_float16_ignore_nan(self):\n # Ignore ULP differences between various NAN's\n # Note that MIPS may reverse quiet and signaling nans\n # so we use the builtin version as a base.\n offset = np.uint16(0xff)\n nan1_i16 = np.array(np.nan, dtype=np.float16).view(np.uint16)\n nan2_i16 = nan1_i16 ^ offset # nan payload on MIPS is all ones.\n nan1_f16 = nan1_i16.view(np.float16)\n nan2_f16 = nan2_i16.view(np.float16)\n assert_array_max_ulp(nan1_f16, nan2_f16, 0)\n\n def test_complex128_pass(self):\n nulp = 5\n x = np.linspace(-20, 20, 50, dtype=np.float64)\n x = 10**x\n x = np.r_[-x, x]\n xi = x + x * 1j\n\n eps = np.finfo(x.dtype).eps\n y = x + x * eps * nulp / 2.\n assert_array_almost_equal_nulp(xi, x + y * 1j, nulp)\n assert_array_almost_equal_nulp(xi, y + x * 1j, nulp)\n # The test condition needs to be at least a factor of sqrt(2) smaller\n # because the real and imaginary parts both change\n y = x + x * eps * nulp / 4.\n assert_array_almost_equal_nulp(xi, y + y * 1j, nulp)\n\n epsneg = np.finfo(x.dtype).epsneg\n y = x - x * epsneg * nulp / 2.\n assert_array_almost_equal_nulp(xi, x + y * 1j, nulp)\n assert_array_almost_equal_nulp(xi, y + x * 1j, nulp)\n y = x - x * epsneg * nulp / 4.\n assert_array_almost_equal_nulp(xi, y + y * 1j, nulp)\n\n def test_complex128_fail(self):\n nulp = 5\n x = np.linspace(-20, 20, 50, dtype=np.float64)\n x = 10**x\n x = np.r_[-x, x]\n xi = x + x * 1j\n\n eps = np.finfo(x.dtype).eps\n y = x + x * eps * nulp * 2.\n assert_raises(AssertionError, assert_array_almost_equal_nulp,\n xi, x + y * 1j, nulp)\n assert_raises(AssertionError, assert_array_almost_equal_nulp,\n xi, y + x * 1j, nulp)\n # The test condition needs to be at least a factor of sqrt(2) smaller\n # because the real and imaginary parts both change\n y = x + x * eps * nulp\n assert_raises(AssertionError, assert_array_almost_equal_nulp,\n xi, y + y * 1j, nulp)\n\n epsneg = np.finfo(x.dtype).epsneg\n y = x - x * epsneg * nulp * 2.\n assert_raises(AssertionError, assert_array_almost_equal_nulp,\n xi, x + y * 1j, nulp)\n assert_raises(AssertionError, assert_array_almost_equal_nulp,\n xi, y + x * 1j, nulp)\n y = x - x * epsneg * nulp\n assert_raises(AssertionError, assert_array_almost_equal_nulp,\n xi, y + y * 1j, nulp)\n\n def test_complex64_pass(self):\n nulp = 5\n x = np.linspace(-20, 20, 50, dtype=np.float32)\n x = 10**x\n x = np.r_[-x, x]\n xi = x + x * 1j\n\n eps = np.finfo(x.dtype).eps\n y = x + x * eps * nulp / 2.\n assert_array_almost_equal_nulp(xi, x + y * 1j, nulp)\n assert_array_almost_equal_nulp(xi, y + x * 1j, nulp)\n y = x + x * eps * nulp / 4.\n assert_array_almost_equal_nulp(xi, y + y * 1j, nulp)\n\n epsneg = np.finfo(x.dtype).epsneg\n y = x - x * epsneg * nulp / 2.\n assert_array_almost_equal_nulp(xi, x + y * 1j, nulp)\n assert_array_almost_equal_nulp(xi, y + x * 1j, nulp)\n y = x - x * epsneg * nulp / 4.\n assert_array_almost_equal_nulp(xi, y + y * 1j, nulp)\n\n def test_complex64_fail(self):\n nulp = 5\n x = np.linspace(-20, 20, 50, dtype=np.float32)\n x = 10**x\n x = np.r_[-x, x]\n xi = x + x * 1j\n\n eps = np.finfo(x.dtype).eps\n y = x + x * eps * nulp * 2.\n assert_raises(AssertionError, assert_array_almost_equal_nulp,\n xi, x + y * 1j, nulp)\n assert_raises(AssertionError, assert_array_almost_equal_nulp,\n xi, y + x * 1j, nulp)\n y = x + x * eps * nulp\n assert_raises(AssertionError, assert_array_almost_equal_nulp,\n xi, y + y * 1j, nulp)\n\n epsneg = np.finfo(x.dtype).epsneg\n y = x - x * epsneg * nulp * 2.\n assert_raises(AssertionError, assert_array_almost_equal_nulp,\n xi, x + y * 1j, nulp)\n assert_raises(AssertionError, assert_array_almost_equal_nulp,\n xi, y + x * 1j, nulp)\n y = x - x * epsneg * nulp\n assert_raises(AssertionError, assert_array_almost_equal_nulp,\n xi, y + y * 1j, nulp)\n\n\nclass TestULP:\n\n def test_equal(self):\n x = np.random.randn(10)\n assert_array_max_ulp(x, x, maxulp=0)\n\n def test_single(self):\n # Generate 1 + small deviation, check that adding eps gives a few UNL\n x = np.ones(10).astype(np.float32)\n x += 0.01 * np.random.randn(10).astype(np.float32)\n eps = np.finfo(np.float32).eps\n assert_array_max_ulp(x, x + eps, maxulp=20)\n\n def test_double(self):\n # Generate 1 + small deviation, check that adding eps gives a few UNL\n x = np.ones(10).astype(np.float64)\n x += 0.01 * np.random.randn(10).astype(np.float64)\n eps = np.finfo(np.float64).eps\n assert_array_max_ulp(x, x + eps, maxulp=200)\n\n def test_inf(self):\n for dt in [np.float32, np.float64]:\n inf = np.array([np.inf]).astype(dt)\n big = np.array([np.finfo(dt).max])\n assert_array_max_ulp(inf, big, maxulp=200)\n\n def test_nan(self):\n # Test that nan is 'far' from small, tiny, inf, max and min\n for dt in [np.float32, np.float64]:\n if dt == np.float32:\n maxulp = 1e6\n else:\n maxulp = 1e12\n inf = np.array([np.inf]).astype(dt)\n nan = np.array([np.nan]).astype(dt)\n big = np.array([np.finfo(dt).max])\n tiny = np.array([np.finfo(dt).tiny])\n zero = np.array([0.0]).astype(dt)\n nzero = np.array([-0.0]).astype(dt)\n assert_raises(AssertionError,\n lambda: assert_array_max_ulp(nan, inf,\n maxulp=maxulp))\n assert_raises(AssertionError,\n lambda: assert_array_max_ulp(nan, big,\n maxulp=maxulp))\n assert_raises(AssertionError,\n lambda: assert_array_max_ulp(nan, tiny,\n maxulp=maxulp))\n assert_raises(AssertionError,\n lambda: assert_array_max_ulp(nan, zero,\n maxulp=maxulp))\n assert_raises(AssertionError,\n lambda: assert_array_max_ulp(nan, nzero,\n maxulp=maxulp))\n\n\nclass TestStringEqual:\n def test_simple(self):\n assert_string_equal("hello", "hello")\n assert_string_equal("hello\nmultiline", "hello\nmultiline")\n\n with pytest.raises(AssertionError) as exc_info:\n assert_string_equal("foo\nbar", "hello\nbar")\n msg = str(exc_info.value)\n assert_equal(msg, "Differences in strings:\n- foo\n+ hello")\n\n assert_raises(AssertionError,\n lambda: assert_string_equal("foo", "hello"))\n\n def test_regex(self):\n assert_string_equal("a+*b", "a+*b")\n\n assert_raises(AssertionError,\n lambda: assert_string_equal("aaa", "a+b"))\n\n\ndef assert_warn_len_equal(mod, n_in_context):\n try:\n mod_warns = mod.__warningregistry__\n except AttributeError:\n # the lack of a __warningregistry__\n # attribute means that no warning has\n # occurred; this can be triggered in\n # a parallel test scenario, while in\n # a serial test scenario an initial\n # warning (and therefore the attribute)\n # are always created first\n mod_warns = {}\n\n num_warns = len(mod_warns)\n\n if 'version' in mod_warns:\n # Python adds a 'version' entry to the registry,\n # do not count it.\n num_warns -= 1\n\n assert_equal(num_warns, n_in_context)\n\n\ndef test_warn_len_equal_call_scenarios():\n # assert_warn_len_equal is called under\n # varying circumstances depending on serial\n # vs. parallel test scenarios; this test\n # simply aims to probe both code paths and\n # check that no assertion is uncaught\n\n # parallel scenario -- no warning issued yet\n class mod:\n pass\n\n mod_inst = mod()\n\n assert_warn_len_equal(mod=mod_inst,\n n_in_context=0)\n\n # serial test scenario -- the __warningregistry__\n # attribute should be present\n class mod:\n def __init__(self):\n self.__warningregistry__ = {'warning1': 1,\n 'warning2': 2}\n\n mod_inst = mod()\n assert_warn_len_equal(mod=mod_inst,\n n_in_context=2)\n\n\ndef _get_fresh_mod():\n # Get this module, with warning registry empty\n my_mod = sys.modules[__name__]\n try:\n my_mod.__warningregistry__.clear()\n except AttributeError:\n # will not have a __warningregistry__ unless warning has been\n # raised in the module at some point\n pass\n return my_mod\n\n\ndef test_clear_and_catch_warnings():\n # Initial state of module, no warnings\n my_mod = _get_fresh_mod()\n assert_equal(getattr(my_mod, '__warningregistry__', {}), {})\n with clear_and_catch_warnings(modules=[my_mod]):\n warnings.simplefilter('ignore')\n warnings.warn('Some warning')\n assert_equal(my_mod.__warningregistry__, {})\n # Without specified modules, don't clear warnings during context.\n # catch_warnings doesn't make an entry for 'ignore'.\n with clear_and_catch_warnings():\n warnings.simplefilter('ignore')\n warnings.warn('Some warning')\n assert_warn_len_equal(my_mod, 0)\n\n # Manually adding two warnings to the registry:\n my_mod.__warningregistry__ = {'warning1': 1,\n 'warning2': 2}\n\n # Confirm that specifying module keeps old warning, does not add new\n with clear_and_catch_warnings(modules=[my_mod]):\n warnings.simplefilter('ignore')\n warnings.warn('Another warning')\n assert_warn_len_equal(my_mod, 2)\n\n # Another warning, no module spec it clears up registry\n with clear_and_catch_warnings():\n warnings.simplefilter('ignore')\n warnings.warn('Another warning')\n assert_warn_len_equal(my_mod, 0)\n\n\ndef test_suppress_warnings_module():\n # Initial state of module, no warnings\n my_mod = _get_fresh_mod()\n assert_equal(getattr(my_mod, '__warningregistry__', {}), {})\n\n def warn_other_module():\n # Apply along axis is implemented in python; stacklevel=2 means\n # we end up inside its module, not ours.\n def warn(arr):\n warnings.warn("Some warning 2", stacklevel=2)\n return arr\n np.apply_along_axis(warn, 0, [0])\n\n # Test module based warning suppression:\n assert_warn_len_equal(my_mod, 0)\n with suppress_warnings() as sup:\n sup.record(UserWarning)\n # suppress warning from other module (may have .pyc ending),\n # if apply_along_axis is moved, had to be changed.\n sup.filter(module=np.lib._shape_base_impl)\n warnings.warn("Some warning")\n warn_other_module()\n # Check that the suppression did test the file correctly (this module\n # got filtered)\n assert_equal(len(sup.log), 1)\n assert_equal(sup.log[0].message.args[0], "Some warning")\n assert_warn_len_equal(my_mod, 0)\n sup = suppress_warnings()\n # Will have to be changed if apply_along_axis is moved:\n sup.filter(module=my_mod)\n with sup:\n warnings.warn('Some warning')\n assert_warn_len_equal(my_mod, 0)\n # And test repeat works:\n sup.filter(module=my_mod)\n with sup:\n warnings.warn('Some warning')\n assert_warn_len_equal(my_mod, 0)\n\n # Without specified modules\n with suppress_warnings():\n warnings.simplefilter('ignore')\n warnings.warn('Some warning')\n assert_warn_len_equal(my_mod, 0)\n\n\ndef test_suppress_warnings_type():\n # Initial state of module, no warnings\n my_mod = _get_fresh_mod()\n assert_equal(getattr(my_mod, '__warningregistry__', {}), {})\n\n # Test module based warning suppression:\n with suppress_warnings() as sup:\n sup.filter(UserWarning)\n warnings.warn('Some warning')\n assert_warn_len_equal(my_mod, 0)\n sup = suppress_warnings()\n sup.filter(UserWarning)\n with sup:\n warnings.warn('Some warning')\n assert_warn_len_equal(my_mod, 0)\n # And test repeat works:\n sup.filter(module=my_mod)\n with sup:\n warnings.warn('Some warning')\n assert_warn_len_equal(my_mod, 0)\n\n # Without specified modules\n with suppress_warnings():\n warnings.simplefilter('ignore')\n warnings.warn('Some warning')\n assert_warn_len_equal(my_mod, 0)\n\n\ndef test_suppress_warnings_decorate_no_record():\n sup = suppress_warnings()\n sup.filter(UserWarning)\n\n @sup\n def warn(category):\n warnings.warn('Some warning', category)\n\n with warnings.catch_warnings(record=True) as w:\n warnings.simplefilter("always")\n warn(UserWarning) # should be suppressed\n warn(RuntimeWarning)\n assert_equal(len(w), 1)\n\n\ndef test_suppress_warnings_record():\n sup = suppress_warnings()\n log1 = sup.record()\n\n with sup:\n log2 = sup.record(message='Some other warning 2')\n sup.filter(message='Some warning')\n warnings.warn('Some warning')\n warnings.warn('Some other warning')\n warnings.warn('Some other warning 2')\n\n assert_equal(len(sup.log), 2)\n assert_equal(len(log1), 1)\n assert_equal(len(log2), 1)\n assert_equal(log2[0].message.args[0], 'Some other warning 2')\n\n # Do it again, with the same context to see if some warnings survived:\n with sup:\n log2 = sup.record(message='Some other warning 2')\n sup.filter(message='Some warning')\n warnings.warn('Some warning')\n warnings.warn('Some other warning')\n warnings.warn('Some other warning 2')\n\n assert_equal(len(sup.log), 2)\n assert_equal(len(log1), 1)\n assert_equal(len(log2), 1)\n assert_equal(log2[0].message.args[0], 'Some other warning 2')\n\n # Test nested:\n with suppress_warnings() as sup:\n sup.record()\n with suppress_warnings() as sup2:\n sup2.record(message='Some warning')\n warnings.warn('Some warning')\n warnings.warn('Some other warning')\n assert_equal(len(sup2.log), 1)\n assert_equal(len(sup.log), 1)\n\n\ndef test_suppress_warnings_forwarding():\n def warn_other_module():\n # Apply along axis is implemented in python; stacklevel=2 means\n # we end up inside its module, not ours.\n def warn(arr):\n warnings.warn("Some warning", stacklevel=2)\n return arr\n np.apply_along_axis(warn, 0, [0])\n\n with suppress_warnings() as sup:\n sup.record()\n with suppress_warnings("always"):\n for i in range(2):\n warnings.warn("Some warning")\n\n assert_equal(len(sup.log), 2)\n\n with suppress_warnings() as sup:\n sup.record()\n with suppress_warnings("location"):\n for i in range(2):\n warnings.warn("Some warning")\n warnings.warn("Some warning")\n\n assert_equal(len(sup.log), 2)\n\n with suppress_warnings() as sup:\n sup.record()\n with suppress_warnings("module"):\n for i in range(2):\n warnings.warn("Some warning")\n warnings.warn("Some warning")\n warn_other_module()\n\n assert_equal(len(sup.log), 2)\n\n with suppress_warnings() as sup:\n sup.record()\n with suppress_warnings("once"):\n for i in range(2):\n warnings.warn("Some warning")\n warnings.warn("Some other warning")\n warn_other_module()\n\n assert_equal(len(sup.log), 2)\n\n\ndef test_tempdir():\n with tempdir() as tdir:\n fpath = os.path.join(tdir, 'tmp')\n with open(fpath, 'w'):\n pass\n assert_(not os.path.isdir(tdir))\n\n raised = False\n try:\n with tempdir() as tdir:\n raise ValueError\n except ValueError:\n raised = True\n assert_(raised)\n assert_(not os.path.isdir(tdir))\n\n\ndef test_temppath():\n with temppath() as fpath:\n with open(fpath, 'w'):\n pass\n assert_(not os.path.isfile(fpath))\n\n raised = False\n try:\n with temppath() as fpath:\n raise ValueError\n except ValueError:\n raised = True\n assert_(raised)\n assert_(not os.path.isfile(fpath))\n\n\nclass my_cacw(clear_and_catch_warnings):\n\n class_modules = (sys.modules[__name__],)\n\n\ndef test_clear_and_catch_warnings_inherit():\n # Test can subclass and add default modules\n my_mod = _get_fresh_mod()\n with my_cacw():\n warnings.simplefilter('ignore')\n warnings.warn('Some warning')\n assert_equal(my_mod.__warningregistry__, {})\n\n\n@pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")\nclass TestAssertNoGcCycles:\n """ Test assert_no_gc_cycles """\n\n def test_passes(self):\n def no_cycle():\n b = []\n b.append([])\n return b\n\n with assert_no_gc_cycles():\n no_cycle()\n\n assert_no_gc_cycles(no_cycle)\n\n def test_asserts(self):\n def make_cycle():\n a = []\n a.append(a)\n a.append(a)\n return a\n\n with assert_raises(AssertionError):\n with assert_no_gc_cycles():\n make_cycle()\n\n with assert_raises(AssertionError):\n assert_no_gc_cycles(make_cycle)\n\n @pytest.mark.slow\n def test_fails(self):\n """\n Test that in cases where the garbage cannot be collected, we raise an\n error, instead of hanging forever trying to clear it.\n """\n\n class ReferenceCycleInDel:\n """\n An object that not only contains a reference cycle, but creates new\n cycles whenever it's garbage-collected and its __del__ runs\n """\n make_cycle = True\n\n def __init__(self):\n self.cycle = self\n\n def __del__(self):\n # break the current cycle so that `self` can be freed\n self.cycle = None\n\n if ReferenceCycleInDel.make_cycle:\n # but create a new one so that the garbage collector (GC) has more\n # work to do.\n ReferenceCycleInDel()\n\n try:\n w = weakref.ref(ReferenceCycleInDel())\n try:\n with assert_raises(RuntimeError):\n # this will be unable to get a baseline empty garbage\n assert_no_gc_cycles(lambda: None)\n except AssertionError:\n # the above test is only necessary if the GC actually tried to free\n # our object anyway.\n if w() is not None:\n pytest.skip("GC does not call __del__ on cyclic objects")\n raise\n\n finally:\n # make sure that we stop creating reference cycles\n ReferenceCycleInDel.make_cycle = False\n | .venv\Lib\site-packages\numpy\testing\tests\test_utils.py | test_utils.py | Python | 71,492 | 0.75 | 0.112154 | 0.085842 | node-utils | 859 | 2024-04-28T11:02:38.228297 | Apache-2.0 | true | 3e3365e9ccc54194bbb0d02a6a17161e |
\n\n | .venv\Lib\site-packages\numpy\testing\tests\__pycache__\__init__.cpython-313.pyc | __init__.cpython-313.pyc | Other | 194 | 0.7 | 0 | 0 | python-kit | 322 | 2025-02-02T19:19:42.753777 | BSD-3-Clause | true | b902805e968d14fc5b169d91f9fde218 |
"""\nBuild a c-extension module on-the-fly in tests.\nSee build_and_import_extensions for usage hints\n\n"""\n\nimport os\nimport pathlib\nimport subprocess\nimport sys\nimport sysconfig\nimport textwrap\n\n__all__ = ['build_and_import_extension', 'compile_extension_module']\n\n\ndef build_and_import_extension(\n modname, functions, *, prologue="", build_dir=None,\n include_dirs=None, more_init=""):\n """\n Build and imports a c-extension module `modname` from a list of function\n fragments `functions`.\n\n\n Parameters\n ----------\n functions : list of fragments\n Each fragment is a sequence of func_name, calling convention, snippet.\n prologue : string\n Code to precede the rest, usually extra ``#include`` or ``#define``\n macros.\n build_dir : pathlib.Path\n Where to build the module, usually a temporary directory\n include_dirs : list\n Extra directories to find include files when compiling\n more_init : string\n Code to appear in the module PyMODINIT_FUNC\n\n Returns\n -------\n out: module\n The module will have been loaded and is ready for use\n\n Examples\n --------\n >>> functions = [("test_bytes", "METH_O", \"\"\"\n if ( !PyBytesCheck(args)) {\n Py_RETURN_FALSE;\n }\n Py_RETURN_TRUE;\n \"\"\")]\n >>> mod = build_and_import_extension("testme", functions)\n >>> assert not mod.test_bytes('abc')\n >>> assert mod.test_bytes(b'abc')\n """\n if include_dirs is None:\n include_dirs = []\n body = prologue + _make_methods(functions, modname)\n init = """\n PyObject *mod = PyModule_Create(&moduledef);\n #ifdef Py_GIL_DISABLED\n PyUnstable_Module_SetGIL(mod, Py_MOD_GIL_NOT_USED);\n #endif\n """\n if not build_dir:\n build_dir = pathlib.Path('.')\n if more_init:\n init += """#define INITERROR return NULL\n """\n init += more_init\n init += "\nreturn mod;"\n source_string = _make_source(modname, init, body)\n mod_so = compile_extension_module(\n modname, build_dir, include_dirs, source_string)\n import importlib.util\n spec = importlib.util.spec_from_file_location(modname, mod_so)\n foo = importlib.util.module_from_spec(spec)\n spec.loader.exec_module(foo)\n return foo\n\n\ndef compile_extension_module(\n name, builddir, include_dirs,\n source_string, libraries=None, library_dirs=None):\n """\n Build an extension module and return the filename of the resulting\n native code file.\n\n Parameters\n ----------\n name : string\n name of the module, possibly including dots if it is a module inside a\n package.\n builddir : pathlib.Path\n Where to build the module, usually a temporary directory\n include_dirs : list\n Extra directories to find include files when compiling\n libraries : list\n Libraries to link into the extension module\n library_dirs: list\n Where to find the libraries, ``-L`` passed to the linker\n """\n modname = name.split('.')[-1]\n dirname = builddir / name\n dirname.mkdir(exist_ok=True)\n cfile = _convert_str_to_file(source_string, dirname)\n include_dirs = include_dirs or []\n libraries = libraries or []\n library_dirs = library_dirs or []\n\n return _c_compile(\n cfile, outputfilename=dirname / modname,\n include_dirs=include_dirs, libraries=libraries,\n library_dirs=library_dirs,\n )\n\n\ndef _convert_str_to_file(source, dirname):\n """Helper function to create a file ``source.c`` in `dirname` that contains\n the string in `source`. Returns the file name\n """\n filename = dirname / 'source.c'\n with filename.open('w') as f:\n f.write(str(source))\n return filename\n\n\ndef _make_methods(functions, modname):\n """ Turns the name, signature, code in functions into complete functions\n and lists them in a methods_table. Then turns the methods_table into a\n ``PyMethodDef`` structure and returns the resulting code fragment ready\n for compilation\n """\n methods_table = []\n codes = []\n for funcname, flags, code in functions:\n cfuncname = f"{modname}_{funcname}"\n if 'METH_KEYWORDS' in flags:\n signature = '(PyObject *self, PyObject *args, PyObject *kwargs)'\n else:\n signature = '(PyObject *self, PyObject *args)'\n methods_table.append(\n "{\"%s\", (PyCFunction)%s, %s}," % (funcname, cfuncname, flags))\n func_code = f"""\n static PyObject* {cfuncname}{signature}\n {{\n {code}\n }}\n """\n codes.append(func_code)\n\n body = "\n".join(codes) + """\n static PyMethodDef methods[] = {\n %(methods)s\n { NULL }\n };\n static struct PyModuleDef moduledef = {\n PyModuleDef_HEAD_INIT,\n "%(modname)s", /* m_name */\n NULL, /* m_doc */\n -1, /* m_size */\n methods, /* m_methods */\n };\n """ % {'methods': '\n'.join(methods_table), 'modname': modname}\n return body\n\n\ndef _make_source(name, init, body):\n """ Combines the code fragments into source code ready to be compiled\n """\n code = """\n #include <Python.h>\n\n %(body)s\n\n PyMODINIT_FUNC\n PyInit_%(name)s(void) {\n %(init)s\n }\n """ % {\n 'name': name, 'init': init, 'body': body,\n }\n return code\n\n\ndef _c_compile(cfile, outputfilename, include_dirs, libraries,\n library_dirs):\n link_extra = []\n if sys.platform == 'win32':\n compile_extra = ["/we4013"]\n link_extra.append('/DEBUG') # generate .pdb file\n elif sys.platform.startswith('linux'):\n compile_extra = [\n "-O0", "-g", "-Werror=implicit-function-declaration", "-fPIC"]\n else:\n compile_extra = []\n\n return build(\n cfile, outputfilename,\n compile_extra, link_extra,\n include_dirs, libraries, library_dirs)\n\n\ndef build(cfile, outputfilename, compile_extra, link_extra,\n include_dirs, libraries, library_dirs):\n "use meson to build"\n\n build_dir = cfile.parent / "build"\n os.makedirs(build_dir, exist_ok=True)\n with open(cfile.parent / "meson.build", "wt") as fid:\n link_dirs = ['-L' + d for d in library_dirs]\n fid.write(textwrap.dedent(f"""\\n project('foo', 'c')\n py = import('python').find_installation(pure: false)\n py.extension_module(\n '{outputfilename.parts[-1]}',\n '{cfile.parts[-1]}',\n c_args: {compile_extra},\n link_args: {link_dirs},\n include_directories: {include_dirs},\n )\n """))\n native_file_name = cfile.parent / ".mesonpy-native-file.ini"\n with open(native_file_name, "wt") as fid:\n fid.write(textwrap.dedent(f"""\\n [binaries]\n python = '{sys.executable}'\n """))\n if sys.platform == "win32":\n subprocess.check_call(["meson", "setup",\n "--buildtype=release",\n "--vsenv", ".."],\n cwd=build_dir,\n )\n else:\n subprocess.check_call(["meson", "setup", "--vsenv",\n "..", f'--native-file={os.fspath(native_file_name)}'],\n cwd=build_dir\n )\n\n so_name = outputfilename.parts[-1] + get_so_suffix()\n subprocess.check_call(["meson", "compile"], cwd=build_dir)\n os.rename(str(build_dir / so_name), cfile.parent / so_name)\n return cfile.parent / so_name\n\n\ndef get_so_suffix():\n ret = sysconfig.get_config_var('EXT_SUFFIX')\n assert ret\n return ret\n | .venv\Lib\site-packages\numpy\testing\_private\extbuild.py | extbuild.py | Python | 7,966 | 0.95 | 0.096 | 0.013699 | react-lib | 698 | 2025-06-06T02:59:09.357970 | Apache-2.0 | true | 563cf82ebd77e164b13a3ca9cb537b6b |
import pathlib\nimport types\nfrom collections.abc import Sequence\n\n__all__ = ["build_and_import_extension", "compile_extension_module"]\n\ndef build_and_import_extension(\n modname: str,\n functions: Sequence[tuple[str, str, str]],\n *,\n prologue: str = "",\n build_dir: pathlib.Path | None = None,\n include_dirs: Sequence[str] = [],\n more_init: str = "",\n) -> types.ModuleType: ...\n\n#\ndef compile_extension_module(\n name: str,\n builddir: pathlib.Path,\n include_dirs: Sequence[str],\n source_string: str,\n libraries: Sequence[str] = [],\n library_dirs: Sequence[str] = [],\n) -> pathlib.Path: ...\n | .venv\Lib\site-packages\numpy\testing\_private\extbuild.pyi | extbuild.pyi | Other | 651 | 0.95 | 0.08 | 0.090909 | react-lib | 503 | 2024-01-22T17:55:30.560495 | Apache-2.0 | true | b724641bf7e68caf8a4cafe267db7b3c |
"""\nUtility function to facilitate testing.\n\n"""\nimport concurrent.futures\nimport contextlib\nimport gc\nimport importlib.metadata\nimport operator\nimport os\nimport pathlib\nimport platform\nimport pprint\nimport re\nimport shutil\nimport sys\nimport sysconfig\nimport threading\nimport warnings\nfrom functools import partial, wraps\nfrom io import StringIO\nfrom tempfile import mkdtemp, mkstemp\nfrom unittest.case import SkipTest\nfrom warnings import WarningMessage\n\nimport numpy as np\nimport numpy.linalg._umath_linalg\nfrom numpy import isfinite, isinf, isnan\nfrom numpy._core import arange, array, array_repr, empty, float32, intp, isnat, ndarray\n\n__all__ = [\n 'assert_equal', 'assert_almost_equal', 'assert_approx_equal',\n 'assert_array_equal', 'assert_array_less', 'assert_string_equal',\n 'assert_array_almost_equal', 'assert_raises', 'build_err_msg',\n 'decorate_methods', 'jiffies', 'memusage', 'print_assert_equal',\n 'rundocs', 'runstring', 'verbose', 'measure',\n 'assert_', 'assert_array_almost_equal_nulp', 'assert_raises_regex',\n 'assert_array_max_ulp', 'assert_warns', 'assert_no_warnings',\n 'assert_allclose', 'IgnoreException', 'clear_and_catch_warnings',\n 'SkipTest', 'KnownFailureException', 'temppath', 'tempdir', 'IS_PYPY',\n 'HAS_REFCOUNT', "IS_WASM", 'suppress_warnings', 'assert_array_compare',\n 'assert_no_gc_cycles', 'break_cycles', 'HAS_LAPACK64', 'IS_PYSTON',\n 'IS_MUSL', 'check_support_sve', 'NOGIL_BUILD',\n 'IS_EDITABLE', 'IS_INSTALLED', 'NUMPY_ROOT', 'run_threaded', 'IS_64BIT',\n 'BLAS_SUPPORTS_FPE',\n ]\n\n\nclass KnownFailureException(Exception):\n '''Raise this exception to mark a test as a known failing test.'''\n pass\n\n\nKnownFailureTest = KnownFailureException # backwards compat\nverbose = 0\n\nNUMPY_ROOT = pathlib.Path(np.__file__).parent\n\ntry:\n np_dist = importlib.metadata.distribution('numpy')\nexcept importlib.metadata.PackageNotFoundError:\n IS_INSTALLED = IS_EDITABLE = False\nelse:\n IS_INSTALLED = True\n try:\n if sys.version_info >= (3, 13):\n IS_EDITABLE = np_dist.origin.dir_info.editable\n else:\n # Backport importlib.metadata.Distribution.origin\n import json # noqa: E401\n import types\n origin = json.loads(\n np_dist.read_text('direct_url.json') or '{}',\n object_hook=lambda data: types.SimpleNamespace(**data),\n )\n IS_EDITABLE = origin.dir_info.editable\n except AttributeError:\n IS_EDITABLE = False\n\n # spin installs numpy directly via meson, instead of using meson-python, and\n # runs the module by setting PYTHONPATH. This is problematic because the\n # resulting installation lacks the Python metadata (.dist-info), and numpy\n # might already be installed on the environment, causing us to find its\n # metadata, even though we are not actually loading that package.\n # Work around this issue by checking if the numpy root matches.\n if not IS_EDITABLE and np_dist.locate_file('numpy') != NUMPY_ROOT:\n IS_INSTALLED = False\n\nIS_WASM = platform.machine() in ["wasm32", "wasm64"]\nIS_PYPY = sys.implementation.name == 'pypy'\nIS_PYSTON = hasattr(sys, "pyston_version_info")\nHAS_REFCOUNT = getattr(sys, 'getrefcount', None) is not None and not IS_PYSTON\nBLAS_SUPPORTS_FPE = True\nif platform.system() == 'Darwin' or platform.machine() == 'arm64':\n try:\n blas = np.__config__.CONFIG['Build Dependencies']['blas']\n if blas['name'] == 'accelerate':\n BLAS_SUPPORTS_FPE = False\n except KeyError:\n pass\n\nHAS_LAPACK64 = numpy.linalg._umath_linalg._ilp64\n\nIS_MUSL = False\n# alternate way is\n# from packaging.tags import sys_tags\n# _tags = list(sys_tags())\n# if 'musllinux' in _tags[0].platform:\n_v = sysconfig.get_config_var('HOST_GNU_TYPE') or ''\nif 'musl' in _v:\n IS_MUSL = True\n\nNOGIL_BUILD = bool(sysconfig.get_config_var("Py_GIL_DISABLED"))\nIS_64BIT = np.dtype(np.intp).itemsize == 8\n\ndef assert_(val, msg=''):\n """\n Assert that works in release mode.\n Accepts callable msg to allow deferring evaluation until failure.\n\n The Python built-in ``assert`` does not work when executing code in\n optimized mode (the ``-O`` flag) - no byte-code is generated for it.\n\n For documentation on usage, refer to the Python documentation.\n\n """\n __tracebackhide__ = True # Hide traceback for py.test\n if not val:\n try:\n smsg = msg()\n except TypeError:\n smsg = msg\n raise AssertionError(smsg)\n\n\nif os.name == 'nt':\n # Code "stolen" from enthought/debug/memusage.py\n def GetPerformanceAttributes(object, counter, instance=None,\n inum=-1, format=None, machine=None):\n # NOTE: Many counters require 2 samples to give accurate results,\n # including "% Processor Time" (as by definition, at any instant, a\n # thread's CPU usage is either 0 or 100). To read counters like this,\n # you should copy this function, but keep the counter open, and call\n # CollectQueryData() each time you need to know.\n # See http://msdn.microsoft.com/library/en-us/dnperfmo/html/perfmonpt2.asp\n # (dead link)\n # My older explanation for this was that the "AddCounter" process\n # forced the CPU to 100%, but the above makes more sense :)\n import win32pdh\n if format is None:\n format = win32pdh.PDH_FMT_LONG\n path = win32pdh.MakeCounterPath((machine, object, instance, None,\n inum, counter))\n hq = win32pdh.OpenQuery()\n try:\n hc = win32pdh.AddCounter(hq, path)\n try:\n win32pdh.CollectQueryData(hq)\n type, val = win32pdh.GetFormattedCounterValue(hc, format)\n return val\n finally:\n win32pdh.RemoveCounter(hc)\n finally:\n win32pdh.CloseQuery(hq)\n\n def memusage(processName="python", instance=0):\n # from win32pdhutil, part of the win32all package\n import win32pdh\n return GetPerformanceAttributes("Process", "Virtual Bytes",\n processName, instance,\n win32pdh.PDH_FMT_LONG, None)\nelif sys.platform[:5] == 'linux':\n\n def memusage(_proc_pid_stat=None):\n """\n Return virtual memory size in bytes of the running python.\n\n """\n _proc_pid_stat = _proc_pid_stat or f'/proc/{os.getpid()}/stat'\n try:\n with open(_proc_pid_stat) as f:\n l = f.readline().split(' ')\n return int(l[22])\n except Exception:\n return\nelse:\n def memusage():\n """\n Return memory usage of running python. [Not implemented]\n\n """\n raise NotImplementedError\n\n\nif sys.platform[:5] == 'linux':\n def jiffies(_proc_pid_stat=None, _load_time=None):\n """\n Return number of jiffies elapsed.\n\n Return number of jiffies (1/100ths of a second) that this\n process has been scheduled in user mode. See man 5 proc.\n\n """\n _proc_pid_stat = _proc_pid_stat or f'/proc/{os.getpid()}/stat'\n _load_time = _load_time or []\n import time\n if not _load_time:\n _load_time.append(time.time())\n try:\n with open(_proc_pid_stat) as f:\n l = f.readline().split(' ')\n return int(l[13])\n except Exception:\n return int(100 * (time.time() - _load_time[0]))\nelse:\n # os.getpid is not in all platforms available.\n # Using time is safe but inaccurate, especially when process\n # was suspended or sleeping.\n def jiffies(_load_time=[]):\n """\n Return number of jiffies elapsed.\n\n Return number of jiffies (1/100ths of a second) that this\n process has been scheduled in user mode. See man 5 proc.\n\n """\n import time\n if not _load_time:\n _load_time.append(time.time())\n return int(100 * (time.time() - _load_time[0]))\n\n\ndef build_err_msg(arrays, err_msg, header='Items are not equal:',\n verbose=True, names=('ACTUAL', 'DESIRED'), precision=8):\n msg = ['\n' + header]\n err_msg = str(err_msg)\n if err_msg:\n if err_msg.find('\n') == -1 and len(err_msg) < 79 - len(header):\n msg = [msg[0] + ' ' + err_msg]\n else:\n msg.append(err_msg)\n if verbose:\n for i, a in enumerate(arrays):\n\n if isinstance(a, ndarray):\n # precision argument is only needed if the objects are ndarrays\n r_func = partial(array_repr, precision=precision)\n else:\n r_func = repr\n\n try:\n r = r_func(a)\n except Exception as exc:\n r = f'[repr failed for <{type(a).__name__}>: {exc}]'\n if r.count('\n') > 3:\n r = '\n'.join(r.splitlines()[:3])\n r += '...'\n msg.append(f' {names[i]}: {r}')\n return '\n'.join(msg)\n\n\ndef assert_equal(actual, desired, err_msg='', verbose=True, *, strict=False):\n """\n Raises an AssertionError if two objects are not equal.\n\n Given two objects (scalars, lists, tuples, dictionaries or numpy arrays),\n check that all elements of these objects are equal. An exception is raised\n at the first conflicting values.\n\n This function handles NaN comparisons as if NaN was a "normal" number.\n That is, AssertionError is not raised if both objects have NaNs in the same\n positions. This is in contrast to the IEEE standard on NaNs, which says\n that NaN compared to anything must return False.\n\n Parameters\n ----------\n actual : array_like\n The object to check.\n desired : array_like\n The expected object.\n err_msg : str, optional\n The error message to be printed in case of failure.\n verbose : bool, optional\n If True, the conflicting values are appended to the error message.\n strict : bool, optional\n If True and either of the `actual` and `desired` arguments is an array,\n raise an ``AssertionError`` when either the shape or the data type of\n the arguments does not match. If neither argument is an array, this\n parameter has no effect.\n\n .. versionadded:: 2.0.0\n\n Raises\n ------\n AssertionError\n If actual and desired are not equal.\n\n See Also\n --------\n assert_allclose\n assert_array_almost_equal_nulp,\n assert_array_max_ulp,\n\n Notes\n -----\n By default, when one of `actual` and `desired` is a scalar and the other is\n an array, the function checks that each element of the array is equal to\n the scalar. This behaviour can be disabled by setting ``strict==True``.\n\n Examples\n --------\n >>> np.testing.assert_equal([4, 5], [4, 6])\n Traceback (most recent call last):\n ...\n AssertionError:\n Items are not equal:\n item=1\n ACTUAL: 5\n DESIRED: 6\n\n The following comparison does not raise an exception. There are NaNs\n in the inputs, but they are in the same positions.\n\n >>> np.testing.assert_equal(np.array([1.0, 2.0, np.nan]), [1, 2, np.nan])\n\n As mentioned in the Notes section, `assert_equal` has special\n handling for scalars when one of the arguments is an array.\n Here, the test checks that each value in `x` is 3:\n\n >>> x = np.full((2, 5), fill_value=3)\n >>> np.testing.assert_equal(x, 3)\n\n Use `strict` to raise an AssertionError when comparing a scalar with an\n array of a different shape:\n\n >>> np.testing.assert_equal(x, 3, strict=True)\n Traceback (most recent call last):\n ...\n AssertionError:\n Arrays are not equal\n <BLANKLINE>\n (shapes (2, 5), () mismatch)\n ACTUAL: array([[3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3]])\n DESIRED: array(3)\n\n The `strict` parameter also ensures that the array data types match:\n\n >>> x = np.array([2, 2, 2])\n >>> y = np.array([2., 2., 2.], dtype=np.float32)\n >>> np.testing.assert_equal(x, y, strict=True)\n Traceback (most recent call last):\n ...\n AssertionError:\n Arrays are not equal\n <BLANKLINE>\n (dtypes int64, float32 mismatch)\n ACTUAL: array([2, 2, 2])\n DESIRED: array([2., 2., 2.], dtype=float32)\n """\n __tracebackhide__ = True # Hide traceback for py.test\n if isinstance(desired, dict):\n if not isinstance(actual, dict):\n raise AssertionError(repr(type(actual)))\n assert_equal(len(actual), len(desired), err_msg, verbose)\n for k, i in desired.items():\n if k not in actual:\n raise AssertionError(repr(k))\n assert_equal(actual[k], desired[k], f'key={k!r}\n{err_msg}',\n verbose)\n return\n if isinstance(desired, (list, tuple)) and isinstance(actual, (list, tuple)):\n assert_equal(len(actual), len(desired), err_msg, verbose)\n for k in range(len(desired)):\n assert_equal(actual[k], desired[k], f'item={k!r}\n{err_msg}',\n verbose)\n return\n from numpy import imag, iscomplexobj, real\n from numpy._core import isscalar, ndarray, signbit\n if isinstance(actual, ndarray) or isinstance(desired, ndarray):\n return assert_array_equal(actual, desired, err_msg, verbose,\n strict=strict)\n msg = build_err_msg([actual, desired], err_msg, verbose=verbose)\n\n # Handle complex numbers: separate into real/imag to handle\n # nan/inf/negative zero correctly\n # XXX: catch ValueError for subclasses of ndarray where iscomplex fail\n try:\n usecomplex = iscomplexobj(actual) or iscomplexobj(desired)\n except (ValueError, TypeError):\n usecomplex = False\n\n if usecomplex:\n if iscomplexobj(actual):\n actualr = real(actual)\n actuali = imag(actual)\n else:\n actualr = actual\n actuali = 0\n if iscomplexobj(desired):\n desiredr = real(desired)\n desiredi = imag(desired)\n else:\n desiredr = desired\n desiredi = 0\n try:\n assert_equal(actualr, desiredr)\n assert_equal(actuali, desiredi)\n except AssertionError:\n raise AssertionError(msg)\n\n # isscalar test to check cases such as [np.nan] != np.nan\n if isscalar(desired) != isscalar(actual):\n raise AssertionError(msg)\n\n try:\n isdesnat = isnat(desired)\n isactnat = isnat(actual)\n dtypes_match = (np.asarray(desired).dtype.type ==\n np.asarray(actual).dtype.type)\n if isdesnat and isactnat:\n # If both are NaT (and have the same dtype -- datetime or\n # timedelta) they are considered equal.\n if dtypes_match:\n return\n else:\n raise AssertionError(msg)\n\n except (TypeError, ValueError, NotImplementedError):\n pass\n\n # Inf/nan/negative zero handling\n try:\n isdesnan = isnan(desired)\n isactnan = isnan(actual)\n if isdesnan and isactnan:\n return # both nan, so equal\n\n # handle signed zero specially for floats\n array_actual = np.asarray(actual)\n array_desired = np.asarray(desired)\n if (array_actual.dtype.char in 'Mm' or\n array_desired.dtype.char in 'Mm'):\n # version 1.18\n # until this version, isnan failed for datetime64 and timedelta64.\n # Now it succeeds but comparison to scalar with a different type\n # emits a DeprecationWarning.\n # Avoid that by skipping the next check\n raise NotImplementedError('cannot compare to a scalar '\n 'with a different type')\n\n if desired == 0 and actual == 0:\n if not signbit(desired) == signbit(actual):\n raise AssertionError(msg)\n\n except (TypeError, ValueError, NotImplementedError):\n pass\n\n try:\n # Explicitly use __eq__ for comparison, gh-2552\n if not (desired == actual):\n raise AssertionError(msg)\n\n except (DeprecationWarning, FutureWarning) as e:\n # this handles the case when the two types are not even comparable\n if 'elementwise == comparison' in e.args[0]:\n raise AssertionError(msg)\n else:\n raise\n\n\ndef print_assert_equal(test_string, actual, desired):\n """\n Test if two objects are equal, and print an error message if test fails.\n\n The test is performed with ``actual == desired``.\n\n Parameters\n ----------\n test_string : str\n The message supplied to AssertionError.\n actual : object\n The object to test for equality against `desired`.\n desired : object\n The expected result.\n\n Examples\n --------\n >>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 1])\n >>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 2])\n Traceback (most recent call last):\n ...\n AssertionError: Test XYZ of func xyz failed\n ACTUAL:\n [0, 1]\n DESIRED:\n [0, 2]\n\n """\n __tracebackhide__ = True # Hide traceback for py.test\n import pprint\n\n if not (actual == desired):\n msg = StringIO()\n msg.write(test_string)\n msg.write(' failed\nACTUAL: \n')\n pprint.pprint(actual, msg)\n msg.write('DESIRED: \n')\n pprint.pprint(desired, msg)\n raise AssertionError(msg.getvalue())\n\n\ndef assert_almost_equal(actual, desired, decimal=7, err_msg='', verbose=True):\n """\n Raises an AssertionError if two items are not equal up to desired\n precision.\n\n .. note:: It is recommended to use one of `assert_allclose`,\n `assert_array_almost_equal_nulp` or `assert_array_max_ulp`\n instead of this function for more consistent floating point\n comparisons.\n\n The test verifies that the elements of `actual` and `desired` satisfy::\n\n abs(desired-actual) < float64(1.5 * 10**(-decimal))\n\n That is a looser test than originally documented, but agrees with what the\n actual implementation in `assert_array_almost_equal` did up to rounding\n vagaries. An exception is raised at conflicting values. For ndarrays this\n delegates to assert_array_almost_equal\n\n Parameters\n ----------\n actual : array_like\n The object to check.\n desired : array_like\n The expected object.\n decimal : int, optional\n Desired precision, default is 7.\n err_msg : str, optional\n The error message to be printed in case of failure.\n verbose : bool, optional\n If True, the conflicting values are appended to the error message.\n\n Raises\n ------\n AssertionError\n If actual and desired are not equal up to specified precision.\n\n See Also\n --------\n assert_allclose: Compare two array_like objects for equality with desired\n relative and/or absolute precision.\n assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal\n\n Examples\n --------\n >>> from numpy.testing import assert_almost_equal\n >>> assert_almost_equal(2.3333333333333, 2.33333334)\n >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10)\n Traceback (most recent call last):\n ...\n AssertionError:\n Arrays are not almost equal to 10 decimals\n ACTUAL: 2.3333333333333\n DESIRED: 2.33333334\n\n >>> assert_almost_equal(np.array([1.0,2.3333333333333]),\n ... np.array([1.0,2.33333334]), decimal=9)\n Traceback (most recent call last):\n ...\n AssertionError:\n Arrays are not almost equal to 9 decimals\n <BLANKLINE>\n Mismatched elements: 1 / 2 (50%)\n Max absolute difference among violations: 6.66669964e-09\n Max relative difference among violations: 2.85715698e-09\n ACTUAL: array([1. , 2.333333333])\n DESIRED: array([1. , 2.33333334])\n\n """\n __tracebackhide__ = True # Hide traceback for py.test\n from numpy import imag, iscomplexobj, real\n from numpy._core import ndarray\n\n # Handle complex numbers: separate into real/imag to handle\n # nan/inf/negative zero correctly\n # XXX: catch ValueError for subclasses of ndarray where iscomplex fail\n try:\n usecomplex = iscomplexobj(actual) or iscomplexobj(desired)\n except ValueError:\n usecomplex = False\n\n def _build_err_msg():\n header = ('Arrays are not almost equal to %d decimals' % decimal)\n return build_err_msg([actual, desired], err_msg, verbose=verbose,\n header=header)\n\n if usecomplex:\n if iscomplexobj(actual):\n actualr = real(actual)\n actuali = imag(actual)\n else:\n actualr = actual\n actuali = 0\n if iscomplexobj(desired):\n desiredr = real(desired)\n desiredi = imag(desired)\n else:\n desiredr = desired\n desiredi = 0\n try:\n assert_almost_equal(actualr, desiredr, decimal=decimal)\n assert_almost_equal(actuali, desiredi, decimal=decimal)\n except AssertionError:\n raise AssertionError(_build_err_msg())\n\n if isinstance(actual, (ndarray, tuple, list)) \\n or isinstance(desired, (ndarray, tuple, list)):\n return assert_array_almost_equal(actual, desired, decimal, err_msg)\n try:\n # If one of desired/actual is not finite, handle it specially here:\n # check that both are nan if any is a nan, and test for equality\n # otherwise\n if not (isfinite(desired) and isfinite(actual)):\n if isnan(desired) or isnan(actual):\n if not (isnan(desired) and isnan(actual)):\n raise AssertionError(_build_err_msg())\n elif not desired == actual:\n raise AssertionError(_build_err_msg())\n return\n except (NotImplementedError, TypeError):\n pass\n if abs(desired - actual) >= np.float64(1.5 * 10.0**(-decimal)):\n raise AssertionError(_build_err_msg())\n\n\ndef assert_approx_equal(actual, desired, significant=7, err_msg='',\n verbose=True):\n """\n Raises an AssertionError if two items are not equal up to significant\n digits.\n\n .. note:: It is recommended to use one of `assert_allclose`,\n `assert_array_almost_equal_nulp` or `assert_array_max_ulp`\n instead of this function for more consistent floating point\n comparisons.\n\n Given two numbers, check that they are approximately equal.\n Approximately equal is defined as the number of significant digits\n that agree.\n\n Parameters\n ----------\n actual : scalar\n The object to check.\n desired : scalar\n The expected object.\n significant : int, optional\n Desired precision, default is 7.\n err_msg : str, optional\n The error message to be printed in case of failure.\n verbose : bool, optional\n If True, the conflicting values are appended to the error message.\n\n Raises\n ------\n AssertionError\n If actual and desired are not equal up to specified precision.\n\n See Also\n --------\n assert_allclose: Compare two array_like objects for equality with desired\n relative and/or absolute precision.\n assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal\n\n Examples\n --------\n >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20)\n >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20,\n ... significant=8)\n >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20,\n ... significant=8)\n Traceback (most recent call last):\n ...\n AssertionError:\n Items are not equal to 8 significant digits:\n ACTUAL: 1.234567e-21\n DESIRED: 1.2345672e-21\n\n the evaluated condition that raises the exception is\n\n >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1)\n True\n\n """\n __tracebackhide__ = True # Hide traceback for py.test\n import numpy as np\n\n (actual, desired) = map(float, (actual, desired))\n if desired == actual:\n return\n # Normalized the numbers to be in range (-10.0,10.0)\n # scale = float(pow(10,math.floor(math.log10(0.5*(abs(desired)+abs(actual))))))\n with np.errstate(invalid='ignore'):\n scale = 0.5 * (np.abs(desired) + np.abs(actual))\n scale = np.power(10, np.floor(np.log10(scale)))\n try:\n sc_desired = desired / scale\n except ZeroDivisionError:\n sc_desired = 0.0\n try:\n sc_actual = actual / scale\n except ZeroDivisionError:\n sc_actual = 0.0\n msg = build_err_msg(\n [actual, desired], err_msg,\n header='Items are not equal to %d significant digits:' % significant,\n verbose=verbose)\n try:\n # If one of desired/actual is not finite, handle it specially here:\n # check that both are nan if any is a nan, and test for equality\n # otherwise\n if not (isfinite(desired) and isfinite(actual)):\n if isnan(desired) or isnan(actual):\n if not (isnan(desired) and isnan(actual)):\n raise AssertionError(msg)\n elif not desired == actual:\n raise AssertionError(msg)\n return\n except (TypeError, NotImplementedError):\n pass\n if np.abs(sc_desired - sc_actual) >= np.power(10., -(significant - 1)):\n raise AssertionError(msg)\n\n\ndef assert_array_compare(comparison, x, y, err_msg='', verbose=True, header='',\n precision=6, equal_nan=True, equal_inf=True,\n *, strict=False, names=('ACTUAL', 'DESIRED')):\n __tracebackhide__ = True # Hide traceback for py.test\n from numpy._core import all, array2string, errstate, inf, isnan, max, object_\n\n x = np.asanyarray(x)\n y = np.asanyarray(y)\n\n # original array for output formatting\n ox, oy = x, y\n\n def isnumber(x):\n return x.dtype.char in '?bhilqpBHILQPefdgFDG'\n\n def istime(x):\n return x.dtype.char in "Mm"\n\n def isvstring(x):\n return x.dtype.char == "T"\n\n def func_assert_same_pos(x, y, func=isnan, hasval='nan'):\n """Handling nan/inf.\n\n Combine results of running func on x and y, checking that they are True\n at the same locations.\n\n """\n __tracebackhide__ = True # Hide traceback for py.test\n\n x_id = func(x)\n y_id = func(y)\n # We include work-arounds here to handle three types of slightly\n # pathological ndarray subclasses:\n # (1) all() on `masked` array scalars can return masked arrays, so we\n # use != True\n # (2) __eq__ on some ndarray subclasses returns Python booleans\n # instead of element-wise comparisons, so we cast to np.bool() and\n # use isinstance(..., bool) checks\n # (3) subclasses with bare-bones __array_function__ implementations may\n # not implement np.all(), so favor using the .all() method\n # We are not committed to supporting such subclasses, but it's nice to\n # support them if possible.\n if np.bool(x_id == y_id).all() != True:\n msg = build_err_msg(\n [x, y],\n err_msg + '\n%s location mismatch:'\n % (hasval), verbose=verbose, header=header,\n names=names,\n precision=precision)\n raise AssertionError(msg)\n # If there is a scalar, then here we know the array has the same\n # flag as it everywhere, so we should return the scalar flag.\n if isinstance(x_id, bool) or x_id.ndim == 0:\n return np.bool(x_id)\n elif isinstance(y_id, bool) or y_id.ndim == 0:\n return np.bool(y_id)\n else:\n return y_id\n\n try:\n if strict:\n cond = x.shape == y.shape and x.dtype == y.dtype\n else:\n cond = (x.shape == () or y.shape == ()) or x.shape == y.shape\n if not cond:\n if x.shape != y.shape:\n reason = f'\n(shapes {x.shape}, {y.shape} mismatch)'\n else:\n reason = f'\n(dtypes {x.dtype}, {y.dtype} mismatch)'\n msg = build_err_msg([x, y],\n err_msg\n + reason,\n verbose=verbose, header=header,\n names=names,\n precision=precision)\n raise AssertionError(msg)\n\n flagged = np.bool(False)\n if isnumber(x) and isnumber(y):\n if equal_nan:\n flagged = func_assert_same_pos(x, y, func=isnan, hasval='nan')\n\n if equal_inf:\n flagged |= func_assert_same_pos(x, y,\n func=lambda xy: xy == +inf,\n hasval='+inf')\n flagged |= func_assert_same_pos(x, y,\n func=lambda xy: xy == -inf,\n hasval='-inf')\n\n elif istime(x) and istime(y):\n # If one is datetime64 and the other timedelta64 there is no point\n if equal_nan and x.dtype.type == y.dtype.type:\n flagged = func_assert_same_pos(x, y, func=isnat, hasval="NaT")\n\n elif isvstring(x) and isvstring(y):\n dt = x.dtype\n if equal_nan and dt == y.dtype and hasattr(dt, 'na_object'):\n is_nan = (isinstance(dt.na_object, float) and\n np.isnan(dt.na_object))\n bool_errors = 0\n try:\n bool(dt.na_object)\n except TypeError:\n bool_errors = 1\n if is_nan or bool_errors:\n # nan-like NA object\n flagged = func_assert_same_pos(\n x, y, func=isnan, hasval=x.dtype.na_object)\n\n if flagged.ndim > 0:\n x, y = x[~flagged], y[~flagged]\n # Only do the comparison if actual values are left\n if x.size == 0:\n return\n elif flagged:\n # no sense doing comparison if everything is flagged.\n return\n\n val = comparison(x, y)\n invalids = np.logical_not(val)\n\n if isinstance(val, bool):\n cond = val\n reduced = array([val])\n else:\n reduced = val.ravel()\n cond = reduced.all()\n\n # The below comparison is a hack to ensure that fully masked\n # results, for which val.ravel().all() returns np.ma.masked,\n # do not trigger a failure (np.ma.masked != True evaluates as\n # np.ma.masked, which is falsy).\n if cond != True:\n n_mismatch = reduced.size - reduced.sum(dtype=intp)\n n_elements = flagged.size if flagged.ndim != 0 else reduced.size\n percent_mismatch = 100 * n_mismatch / n_elements\n remarks = [f'Mismatched elements: {n_mismatch} / {n_elements} '\n f'({percent_mismatch:.3g}%)']\n\n with errstate(all='ignore'):\n # ignore errors for non-numeric types\n with contextlib.suppress(TypeError):\n error = abs(x - y)\n if np.issubdtype(x.dtype, np.unsignedinteger):\n error2 = abs(y - x)\n np.minimum(error, error2, out=error)\n\n reduced_error = error[invalids]\n max_abs_error = max(reduced_error)\n if getattr(error, 'dtype', object_) == object_:\n remarks.append(\n 'Max absolute difference among violations: '\n + str(max_abs_error))\n else:\n remarks.append(\n 'Max absolute difference among violations: '\n + array2string(max_abs_error))\n\n # note: this definition of relative error matches that one\n # used by assert_allclose (found in np.isclose)\n # Filter values where the divisor would be zero\n nonzero = np.bool(y != 0)\n nonzero_and_invalid = np.logical_and(invalids, nonzero)\n\n if all(~nonzero_and_invalid):\n max_rel_error = array(inf)\n else:\n nonzero_invalid_error = error[nonzero_and_invalid]\n broadcasted_y = np.broadcast_to(y, error.shape)\n nonzero_invalid_y = broadcasted_y[nonzero_and_invalid]\n max_rel_error = max(nonzero_invalid_error\n / abs(nonzero_invalid_y))\n\n if getattr(error, 'dtype', object_) == object_:\n remarks.append(\n 'Max relative difference among violations: '\n + str(max_rel_error))\n else:\n remarks.append(\n 'Max relative difference among violations: '\n + array2string(max_rel_error))\n err_msg = str(err_msg)\n err_msg += '\n' + '\n'.join(remarks)\n msg = build_err_msg([ox, oy], err_msg,\n verbose=verbose, header=header,\n names=names,\n precision=precision)\n raise AssertionError(msg)\n except ValueError:\n import traceback\n efmt = traceback.format_exc()\n header = f'error during assertion:\n\n{efmt}\n\n{header}'\n\n msg = build_err_msg([x, y], err_msg, verbose=verbose, header=header,\n names=names, precision=precision)\n raise ValueError(msg)\n\n\ndef assert_array_equal(actual, desired, err_msg='', verbose=True, *,\n strict=False):\n """\n Raises an AssertionError if two array_like objects are not equal.\n\n Given two array_like objects, check that the shape is equal and all\n elements of these objects are equal (but see the Notes for the special\n handling of a scalar). An exception is raised at shape mismatch or\n conflicting values. In contrast to the standard usage in numpy, NaNs\n are compared like numbers, no assertion is raised if both objects have\n NaNs in the same positions.\n\n The usual caution for verifying equality with floating point numbers is\n advised.\n\n .. note:: When either `actual` or `desired` is already an instance of\n `numpy.ndarray` and `desired` is not a ``dict``, the behavior of\n ``assert_equal(actual, desired)`` is identical to the behavior of this\n function. Otherwise, this function performs `np.asanyarray` on the\n inputs before comparison, whereas `assert_equal` defines special\n comparison rules for common Python types. For example, only\n `assert_equal` can be used to compare nested Python lists. In new code,\n consider using only `assert_equal`, explicitly converting either\n `actual` or `desired` to arrays if the behavior of `assert_array_equal`\n is desired.\n\n Parameters\n ----------\n actual : array_like\n The actual object to check.\n desired : array_like\n The desired, expected object.\n err_msg : str, optional\n The error message to be printed in case of failure.\n verbose : bool, optional\n If True, the conflicting values are appended to the error message.\n strict : bool, optional\n If True, raise an AssertionError when either the shape or the data\n type of the array_like objects does not match. The special\n handling for scalars mentioned in the Notes section is disabled.\n\n .. versionadded:: 1.24.0\n\n Raises\n ------\n AssertionError\n If actual and desired objects are not equal.\n\n See Also\n --------\n assert_allclose: Compare two array_like objects for equality with desired\n relative and/or absolute precision.\n assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal\n\n Notes\n -----\n When one of `actual` and `desired` is a scalar and the other is array_like,\n the function checks that each element of the array_like object is equal to\n the scalar. This behaviour can be disabled with the `strict` parameter.\n\n Examples\n --------\n The first assert does not raise an exception:\n\n >>> np.testing.assert_array_equal([1.0,2.33333,np.nan],\n ... [np.exp(0),2.33333, np.nan])\n\n Assert fails with numerical imprecision with floats:\n\n >>> np.testing.assert_array_equal([1.0,np.pi,np.nan],\n ... [1, np.sqrt(np.pi)**2, np.nan])\n Traceback (most recent call last):\n ...\n AssertionError:\n Arrays are not equal\n <BLANKLINE>\n Mismatched elements: 1 / 3 (33.3%)\n Max absolute difference among violations: 4.4408921e-16\n Max relative difference among violations: 1.41357986e-16\n ACTUAL: array([1. , 3.141593, nan])\n DESIRED: array([1. , 3.141593, nan])\n\n Use `assert_allclose` or one of the nulp (number of floating point values)\n functions for these cases instead:\n\n >>> np.testing.assert_allclose([1.0,np.pi,np.nan],\n ... [1, np.sqrt(np.pi)**2, np.nan],\n ... rtol=1e-10, atol=0)\n\n As mentioned in the Notes section, `assert_array_equal` has special\n handling for scalars. Here the test checks that each value in `x` is 3:\n\n >>> x = np.full((2, 5), fill_value=3)\n >>> np.testing.assert_array_equal(x, 3)\n\n Use `strict` to raise an AssertionError when comparing a scalar with an\n array:\n\n >>> np.testing.assert_array_equal(x, 3, strict=True)\n Traceback (most recent call last):\n ...\n AssertionError:\n Arrays are not equal\n <BLANKLINE>\n (shapes (2, 5), () mismatch)\n ACTUAL: array([[3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3]])\n DESIRED: array(3)\n\n The `strict` parameter also ensures that the array data types match:\n\n >>> x = np.array([2, 2, 2])\n >>> y = np.array([2., 2., 2.], dtype=np.float32)\n >>> np.testing.assert_array_equal(x, y, strict=True)\n Traceback (most recent call last):\n ...\n AssertionError:\n Arrays are not equal\n <BLANKLINE>\n (dtypes int64, float32 mismatch)\n ACTUAL: array([2, 2, 2])\n DESIRED: array([2., 2., 2.], dtype=float32)\n """\n __tracebackhide__ = True # Hide traceback for py.test\n assert_array_compare(operator.__eq__, actual, desired, err_msg=err_msg,\n verbose=verbose, header='Arrays are not equal',\n strict=strict)\n\n\ndef assert_array_almost_equal(actual, desired, decimal=6, err_msg='',\n verbose=True):\n """\n Raises an AssertionError if two objects are not equal up to desired\n precision.\n\n .. note:: It is recommended to use one of `assert_allclose`,\n `assert_array_almost_equal_nulp` or `assert_array_max_ulp`\n instead of this function for more consistent floating point\n comparisons.\n\n The test verifies identical shapes and that the elements of ``actual`` and\n ``desired`` satisfy::\n\n abs(desired-actual) < 1.5 * 10**(-decimal)\n\n That is a looser test than originally documented, but agrees with what the\n actual implementation did up to rounding vagaries. An exception is raised\n at shape mismatch or conflicting values. In contrast to the standard usage\n in numpy, NaNs are compared like numbers, no assertion is raised if both\n objects have NaNs in the same positions.\n\n Parameters\n ----------\n actual : array_like\n The actual object to check.\n desired : array_like\n The desired, expected object.\n decimal : int, optional\n Desired precision, default is 6.\n err_msg : str, optional\n The error message to be printed in case of failure.\n verbose : bool, optional\n If True, the conflicting values are appended to the error message.\n\n Raises\n ------\n AssertionError\n If actual and desired are not equal up to specified precision.\n\n See Also\n --------\n assert_allclose: Compare two array_like objects for equality with desired\n relative and/or absolute precision.\n assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal\n\n Examples\n --------\n the first assert does not raise an exception\n\n >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan],\n ... [1.0,2.333,np.nan])\n\n >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],\n ... [1.0,2.33339,np.nan], decimal=5)\n Traceback (most recent call last):\n ...\n AssertionError:\n Arrays are not almost equal to 5 decimals\n <BLANKLINE>\n Mismatched elements: 1 / 3 (33.3%)\n Max absolute difference among violations: 6.e-05\n Max relative difference among violations: 2.57136612e-05\n ACTUAL: array([1. , 2.33333, nan])\n DESIRED: array([1. , 2.33339, nan])\n\n >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],\n ... [1.0,2.33333, 5], decimal=5)\n Traceback (most recent call last):\n ...\n AssertionError:\n Arrays are not almost equal to 5 decimals\n <BLANKLINE>\n nan location mismatch:\n ACTUAL: array([1. , 2.33333, nan])\n DESIRED: array([1. , 2.33333, 5. ])\n\n """\n __tracebackhide__ = True # Hide traceback for py.test\n from numpy._core import number, result_type\n from numpy._core.fromnumeric import any as npany\n from numpy._core.numerictypes import issubdtype\n\n def compare(x, y):\n try:\n if npany(isinf(x)) or npany(isinf(y)):\n xinfid = isinf(x)\n yinfid = isinf(y)\n if not (xinfid == yinfid).all():\n return False\n # if one item, x and y is +- inf\n if x.size == y.size == 1:\n return x == y\n x = x[~xinfid]\n y = y[~yinfid]\n except (TypeError, NotImplementedError):\n pass\n\n # make sure y is an inexact type to avoid abs(MIN_INT); will cause\n # casting of x later.\n dtype = result_type(y, 1.)\n y = np.asanyarray(y, dtype)\n z = abs(x - y)\n\n if not issubdtype(z.dtype, number):\n z = z.astype(np.float64) # handle object arrays\n\n return z < 1.5 * 10.0**(-decimal)\n\n assert_array_compare(compare, actual, desired, err_msg=err_msg,\n verbose=verbose,\n header=('Arrays are not almost equal to %d decimals' % decimal),\n precision=decimal)\n\n\ndef assert_array_less(x, y, err_msg='', verbose=True, *, strict=False):\n """\n Raises an AssertionError if two array_like objects are not ordered by less\n than.\n\n Given two array_like objects `x` and `y`, check that the shape is equal and\n all elements of `x` are strictly less than the corresponding elements of\n `y` (but see the Notes for the special handling of a scalar). An exception\n is raised at shape mismatch or values that are not correctly ordered. In\n contrast to the standard usage in NumPy, no assertion is raised if both\n objects have NaNs in the same positions.\n\n Parameters\n ----------\n x : array_like\n The smaller object to check.\n y : array_like\n The larger object to compare.\n err_msg : string\n The error message to be printed in case of failure.\n verbose : bool\n If True, the conflicting values are appended to the error message.\n strict : bool, optional\n If True, raise an AssertionError when either the shape or the data\n type of the array_like objects does not match. The special\n handling for scalars mentioned in the Notes section is disabled.\n\n .. versionadded:: 2.0.0\n\n Raises\n ------\n AssertionError\n If x is not strictly smaller than y, element-wise.\n\n See Also\n --------\n assert_array_equal: tests objects for equality\n assert_array_almost_equal: test objects for equality up to precision\n\n Notes\n -----\n When one of `x` and `y` is a scalar and the other is array_like, the\n function performs the comparison as though the scalar were broadcasted\n to the shape of the array. This behaviour can be disabled with the `strict`\n parameter.\n\n Examples\n --------\n The following assertion passes because each finite element of `x` is\n strictly less than the corresponding element of `y`, and the NaNs are in\n corresponding locations.\n\n >>> x = [1.0, 1.0, np.nan]\n >>> y = [1.1, 2.0, np.nan]\n >>> np.testing.assert_array_less(x, y)\n\n The following assertion fails because the zeroth element of `x` is no\n longer strictly less than the zeroth element of `y`.\n\n >>> y[0] = 1\n >>> np.testing.assert_array_less(x, y)\n Traceback (most recent call last):\n ...\n AssertionError:\n Arrays are not strictly ordered `x < y`\n <BLANKLINE>\n Mismatched elements: 1 / 3 (33.3%)\n Max absolute difference among violations: 0.\n Max relative difference among violations: 0.\n x: array([ 1., 1., nan])\n y: array([ 1., 2., nan])\n\n Here, `y` is a scalar, so each element of `x` is compared to `y`, and\n the assertion passes.\n\n >>> x = [1.0, 4.0]\n >>> y = 5.0\n >>> np.testing.assert_array_less(x, y)\n\n However, with ``strict=True``, the assertion will fail because the shapes\n do not match.\n\n >>> np.testing.assert_array_less(x, y, strict=True)\n Traceback (most recent call last):\n ...\n AssertionError:\n Arrays are not strictly ordered `x < y`\n <BLANKLINE>\n (shapes (2,), () mismatch)\n x: array([1., 4.])\n y: array(5.)\n\n With ``strict=True``, the assertion also fails if the dtypes of the two\n arrays do not match.\n\n >>> y = [5, 5]\n >>> np.testing.assert_array_less(x, y, strict=True)\n Traceback (most recent call last):\n ...\n AssertionError:\n Arrays are not strictly ordered `x < y`\n <BLANKLINE>\n (dtypes float64, int64 mismatch)\n x: array([1., 4.])\n y: array([5, 5])\n """\n __tracebackhide__ = True # Hide traceback for py.test\n assert_array_compare(operator.__lt__, x, y, err_msg=err_msg,\n verbose=verbose,\n header='Arrays are not strictly ordered `x < y`',\n equal_inf=False,\n strict=strict,\n names=('x', 'y'))\n\n\ndef runstring(astr, dict):\n exec(astr, dict)\n\n\ndef assert_string_equal(actual, desired):\n """\n Test if two strings are equal.\n\n If the given strings are equal, `assert_string_equal` does nothing.\n If they are not equal, an AssertionError is raised, and the diff\n between the strings is shown.\n\n Parameters\n ----------\n actual : str\n The string to test for equality against the expected string.\n desired : str\n The expected string.\n\n Examples\n --------\n >>> np.testing.assert_string_equal('abc', 'abc')\n >>> np.testing.assert_string_equal('abc', 'abcd')\n Traceback (most recent call last):\n File "<stdin>", line 1, in <module>\n ...\n AssertionError: Differences in strings:\n - abc+ abcd? +\n\n """\n # delay import of difflib to reduce startup time\n __tracebackhide__ = True # Hide traceback for py.test\n import difflib\n\n if not isinstance(actual, str):\n raise AssertionError(repr(type(actual)))\n if not isinstance(desired, str):\n raise AssertionError(repr(type(desired)))\n if desired == actual:\n return\n\n diff = list(difflib.Differ().compare(actual.splitlines(True),\n desired.splitlines(True)))\n diff_list = []\n while diff:\n d1 = diff.pop(0)\n if d1.startswith(' '):\n continue\n if d1.startswith('- '):\n l = [d1]\n d2 = diff.pop(0)\n if d2.startswith('? '):\n l.append(d2)\n d2 = diff.pop(0)\n if not d2.startswith('+ '):\n raise AssertionError(repr(d2))\n l.append(d2)\n if diff:\n d3 = diff.pop(0)\n if d3.startswith('? '):\n l.append(d3)\n else:\n diff.insert(0, d3)\n if d2[2:] == d1[2:]:\n continue\n diff_list.extend(l)\n continue\n raise AssertionError(repr(d1))\n if not diff_list:\n return\n msg = f"Differences in strings:\n{''.join(diff_list).rstrip()}"\n if actual != desired:\n raise AssertionError(msg)\n\n\ndef rundocs(filename=None, raise_on_error=True):\n """\n Run doctests found in the given file.\n\n By default `rundocs` raises an AssertionError on failure.\n\n Parameters\n ----------\n filename : str\n The path to the file for which the doctests are run.\n raise_on_error : bool\n Whether to raise an AssertionError when a doctest fails. Default is\n True.\n\n Notes\n -----\n The doctests can be run by the user/developer by adding the ``doctests``\n argument to the ``test()`` call. For example, to run all tests (including\n doctests) for ``numpy.lib``:\n\n >>> np.lib.test(doctests=True) # doctest: +SKIP\n """\n import doctest\n\n from numpy.distutils.misc_util import exec_mod_from_location\n if filename is None:\n f = sys._getframe(1)\n filename = f.f_globals['__file__']\n name = os.path.splitext(os.path.basename(filename))[0]\n m = exec_mod_from_location(name, filename)\n\n tests = doctest.DocTestFinder().find(m)\n runner = doctest.DocTestRunner(verbose=False)\n\n msg = []\n if raise_on_error:\n out = msg.append\n else:\n out = None\n\n for test in tests:\n runner.run(test, out=out)\n\n if runner.failures > 0 and raise_on_error:\n raise AssertionError("Some doctests failed:\n%s" % "\n".join(msg))\n\n\ndef check_support_sve(__cache=[]):\n """\n gh-22982\n """\n\n if __cache:\n return __cache[0]\n\n import subprocess\n cmd = 'lscpu'\n try:\n output = subprocess.run(cmd, capture_output=True, text=True)\n result = 'sve' in output.stdout\n except (OSError, subprocess.SubprocessError):\n result = False\n __cache.append(result)\n return __cache[0]\n\n\n#\n# assert_raises and assert_raises_regex are taken from unittest.\n#\nimport unittest\n\n\nclass _Dummy(unittest.TestCase):\n def nop(self):\n pass\n\n\n_d = _Dummy('nop')\n\n\ndef assert_raises(*args, **kwargs):\n """\n assert_raises(exception_class, callable, *args, **kwargs)\n assert_raises(exception_class)\n\n Fail unless an exception of class exception_class is thrown\n by callable when invoked with arguments args and keyword\n arguments kwargs. If a different type of exception is\n thrown, it will not be caught, and the test case will be\n deemed to have suffered an error, exactly as for an\n unexpected exception.\n\n Alternatively, `assert_raises` can be used as a context manager:\n\n >>> from numpy.testing import assert_raises\n >>> with assert_raises(ZeroDivisionError):\n ... 1 / 0\n\n is equivalent to\n\n >>> def div(x, y):\n ... return x / y\n >>> assert_raises(ZeroDivisionError, div, 1, 0)\n\n """\n __tracebackhide__ = True # Hide traceback for py.test\n return _d.assertRaises(*args, **kwargs)\n\n\ndef assert_raises_regex(exception_class, expected_regexp, *args, **kwargs):\n """\n assert_raises_regex(exception_class, expected_regexp, callable, *args,\n **kwargs)\n assert_raises_regex(exception_class, expected_regexp)\n\n Fail unless an exception of class exception_class and with message that\n matches expected_regexp is thrown by callable when invoked with arguments\n args and keyword arguments kwargs.\n\n Alternatively, can be used as a context manager like `assert_raises`.\n """\n __tracebackhide__ = True # Hide traceback for py.test\n return _d.assertRaisesRegex(exception_class, expected_regexp, *args, **kwargs)\n\n\ndef decorate_methods(cls, decorator, testmatch=None):\n """\n Apply a decorator to all methods in a class matching a regular expression.\n\n The given decorator is applied to all public methods of `cls` that are\n matched by the regular expression `testmatch`\n (``testmatch.search(methodname)``). Methods that are private, i.e. start\n with an underscore, are ignored.\n\n Parameters\n ----------\n cls : class\n Class whose methods to decorate.\n decorator : function\n Decorator to apply to methods\n testmatch : compiled regexp or str, optional\n The regular expression. Default value is None, in which case the\n nose default (``re.compile(r'(?:^|[\\b_\\.%s-])[Tt]est' % os.sep)``)\n is used.\n If `testmatch` is a string, it is compiled to a regular expression\n first.\n\n """\n if testmatch is None:\n testmatch = re.compile(r'(?:^|[\\b_\\.%s-])[Tt]est' % os.sep)\n else:\n testmatch = re.compile(testmatch)\n cls_attr = cls.__dict__\n\n # delayed import to reduce startup time\n from inspect import isfunction\n\n methods = [_m for _m in cls_attr.values() if isfunction(_m)]\n for function in methods:\n try:\n if hasattr(function, 'compat_func_name'):\n funcname = function.compat_func_name\n else:\n funcname = function.__name__\n except AttributeError:\n # not a function\n continue\n if testmatch.search(funcname) and not funcname.startswith('_'):\n setattr(cls, funcname, decorator(function))\n\n\ndef measure(code_str, times=1, label=None):\n """\n Return elapsed time for executing code in the namespace of the caller.\n\n The supplied code string is compiled with the Python builtin ``compile``.\n The precision of the timing is 10 milli-seconds. If the code will execute\n fast on this timescale, it can be executed many times to get reasonable\n timing accuracy.\n\n Parameters\n ----------\n code_str : str\n The code to be timed.\n times : int, optional\n The number of times the code is executed. Default is 1. The code is\n only compiled once.\n label : str, optional\n A label to identify `code_str` with. This is passed into ``compile``\n as the second argument (for run-time error messages).\n\n Returns\n -------\n elapsed : float\n Total elapsed time in seconds for executing `code_str` `times` times.\n\n Examples\n --------\n >>> times = 10\n >>> etime = np.testing.measure('for i in range(1000): np.sqrt(i**2)', times=times)\n >>> print("Time for a single execution : ", etime / times, "s") # doctest: +SKIP\n Time for a single execution : 0.005 s\n\n """\n frame = sys._getframe(1)\n locs, globs = frame.f_locals, frame.f_globals\n\n code = compile(code_str, f'Test name: {label} ', 'exec')\n i = 0\n elapsed = jiffies()\n while i < times:\n i += 1\n exec(code, globs, locs)\n elapsed = jiffies() - elapsed\n return 0.01 * elapsed\n\n\ndef _assert_valid_refcount(op):\n """\n Check that ufuncs don't mishandle refcount of object `1`.\n Used in a few regression tests.\n """\n if not HAS_REFCOUNT:\n return True\n\n import gc\n\n import numpy as np\n\n b = np.arange(100 * 100).reshape(100, 100)\n c = b\n i = 1\n\n gc.disable()\n try:\n rc = sys.getrefcount(i)\n for j in range(15):\n d = op(b, c)\n assert_(sys.getrefcount(i) >= rc)\n finally:\n gc.enable()\n\n\ndef assert_allclose(actual, desired, rtol=1e-7, atol=0, equal_nan=True,\n err_msg='', verbose=True, *, strict=False):\n """\n Raises an AssertionError if two objects are not equal up to desired\n tolerance.\n\n Given two array_like objects, check that their shapes and all elements\n are equal (but see the Notes for the special handling of a scalar). An\n exception is raised if the shapes mismatch or any values conflict. In\n contrast to the standard usage in numpy, NaNs are compared like numbers,\n no assertion is raised if both objects have NaNs in the same positions.\n\n The test is equivalent to ``allclose(actual, desired, rtol, atol)`` (note\n that ``allclose`` has different default values). It compares the difference\n between `actual` and `desired` to ``atol + rtol * abs(desired)``.\n\n Parameters\n ----------\n actual : array_like\n Array obtained.\n desired : array_like\n Array desired.\n rtol : float, optional\n Relative tolerance.\n atol : float, optional\n Absolute tolerance.\n equal_nan : bool, optional.\n If True, NaNs will compare equal.\n err_msg : str, optional\n The error message to be printed in case of failure.\n verbose : bool, optional\n If True, the conflicting values are appended to the error message.\n strict : bool, optional\n If True, raise an ``AssertionError`` when either the shape or the data\n type of the arguments does not match. The special handling of scalars\n mentioned in the Notes section is disabled.\n\n .. versionadded:: 2.0.0\n\n Raises\n ------\n AssertionError\n If actual and desired are not equal up to specified precision.\n\n See Also\n --------\n assert_array_almost_equal_nulp, assert_array_max_ulp\n\n Notes\n -----\n When one of `actual` and `desired` is a scalar and the other is\n array_like, the function performs the comparison as if the scalar were\n broadcasted to the shape of the array.\n This behaviour can be disabled with the `strict` parameter.\n\n Examples\n --------\n >>> x = [1e-5, 1e-3, 1e-1]\n >>> y = np.arccos(np.cos(x))\n >>> np.testing.assert_allclose(x, y, rtol=1e-5, atol=0)\n\n As mentioned in the Notes section, `assert_allclose` has special\n handling for scalars. Here, the test checks that the value of `numpy.sin`\n is nearly zero at integer multiples of π.\n\n >>> x = np.arange(3) * np.pi\n >>> np.testing.assert_allclose(np.sin(x), 0, atol=1e-15)\n\n Use `strict` to raise an ``AssertionError`` when comparing an array\n with one or more dimensions against a scalar.\n\n >>> np.testing.assert_allclose(np.sin(x), 0, atol=1e-15, strict=True)\n Traceback (most recent call last):\n ...\n AssertionError:\n Not equal to tolerance rtol=1e-07, atol=1e-15\n <BLANKLINE>\n (shapes (3,), () mismatch)\n ACTUAL: array([ 0.000000e+00, 1.224647e-16, -2.449294e-16])\n DESIRED: array(0)\n\n The `strict` parameter also ensures that the array data types match:\n\n >>> y = np.zeros(3, dtype=np.float32)\n >>> np.testing.assert_allclose(np.sin(x), y, atol=1e-15, strict=True)\n Traceback (most recent call last):\n ...\n AssertionError:\n Not equal to tolerance rtol=1e-07, atol=1e-15\n <BLANKLINE>\n (dtypes float64, float32 mismatch)\n ACTUAL: array([ 0.000000e+00, 1.224647e-16, -2.449294e-16])\n DESIRED: array([0., 0., 0.], dtype=float32)\n\n """\n __tracebackhide__ = True # Hide traceback for py.test\n import numpy as np\n\n def compare(x, y):\n return np._core.numeric.isclose(x, y, rtol=rtol, atol=atol,\n equal_nan=equal_nan)\n\n actual, desired = np.asanyarray(actual), np.asanyarray(desired)\n header = f'Not equal to tolerance rtol={rtol:g}, atol={atol:g}'\n assert_array_compare(compare, actual, desired, err_msg=str(err_msg),\n verbose=verbose, header=header, equal_nan=equal_nan,\n strict=strict)\n\n\ndef assert_array_almost_equal_nulp(x, y, nulp=1):\n """\n Compare two arrays relatively to their spacing.\n\n This is a relatively robust method to compare two arrays whose amplitude\n is variable.\n\n Parameters\n ----------\n x, y : array_like\n Input arrays.\n nulp : int, optional\n The maximum number of unit in the last place for tolerance (see Notes).\n Default is 1.\n\n Returns\n -------\n None\n\n Raises\n ------\n AssertionError\n If the spacing between `x` and `y` for one or more elements is larger\n than `nulp`.\n\n See Also\n --------\n assert_array_max_ulp : Check that all items of arrays differ in at most\n N Units in the Last Place.\n spacing : Return the distance between x and the nearest adjacent number.\n\n Notes\n -----\n An assertion is raised if the following condition is not met::\n\n abs(x - y) <= nulp * spacing(maximum(abs(x), abs(y)))\n\n Examples\n --------\n >>> x = np.array([1., 1e-10, 1e-20])\n >>> eps = np.finfo(x.dtype).eps\n >>> np.testing.assert_array_almost_equal_nulp(x, x*eps/2 + x)\n\n >>> np.testing.assert_array_almost_equal_nulp(x, x*eps + x)\n Traceback (most recent call last):\n ...\n AssertionError: Arrays are not equal to 1 ULP (max is 2)\n\n """\n __tracebackhide__ = True # Hide traceback for py.test\n import numpy as np\n ax = np.abs(x)\n ay = np.abs(y)\n ref = nulp * np.spacing(np.where(ax > ay, ax, ay))\n if not np.all(np.abs(x - y) <= ref):\n if np.iscomplexobj(x) or np.iscomplexobj(y):\n msg = f"Arrays are not equal to {nulp} ULP"\n else:\n max_nulp = np.max(nulp_diff(x, y))\n msg = f"Arrays are not equal to {nulp} ULP (max is {max_nulp:g})"\n raise AssertionError(msg)\n\n\ndef assert_array_max_ulp(a, b, maxulp=1, dtype=None):\n """\n Check that all items of arrays differ in at most N Units in the Last Place.\n\n Parameters\n ----------\n a, b : array_like\n Input arrays to be compared.\n maxulp : int, optional\n The maximum number of units in the last place that elements of `a` and\n `b` can differ. Default is 1.\n dtype : dtype, optional\n Data-type to convert `a` and `b` to if given. Default is None.\n\n Returns\n -------\n ret : ndarray\n Array containing number of representable floating point numbers between\n items in `a` and `b`.\n\n Raises\n ------\n AssertionError\n If one or more elements differ by more than `maxulp`.\n\n Notes\n -----\n For computing the ULP difference, this API does not differentiate between\n various representations of NAN (ULP difference between 0x7fc00000 and 0xffc00000\n is zero).\n\n See Also\n --------\n assert_array_almost_equal_nulp : Compare two arrays relatively to their\n spacing.\n\n Examples\n --------\n >>> a = np.linspace(0., 1., 100)\n >>> res = np.testing.assert_array_max_ulp(a, np.arcsin(np.sin(a)))\n\n """\n __tracebackhide__ = True # Hide traceback for py.test\n import numpy as np\n ret = nulp_diff(a, b, dtype)\n if not np.all(ret <= maxulp):\n raise AssertionError("Arrays are not almost equal up to %g "\n "ULP (max difference is %g ULP)" %\n (maxulp, np.max(ret)))\n return ret\n\n\ndef nulp_diff(x, y, dtype=None):\n """For each item in x and y, return the number of representable floating\n points between them.\n\n Parameters\n ----------\n x : array_like\n first input array\n y : array_like\n second input array\n dtype : dtype, optional\n Data-type to convert `x` and `y` to if given. Default is None.\n\n Returns\n -------\n nulp : array_like\n number of representable floating point numbers between each item in x\n and y.\n\n Notes\n -----\n For computing the ULP difference, this API does not differentiate between\n various representations of NAN (ULP difference between 0x7fc00000 and 0xffc00000\n is zero).\n\n Examples\n --------\n # By definition, epsilon is the smallest number such as 1 + eps != 1, so\n # there should be exactly one ULP between 1 and 1 + eps\n >>> nulp_diff(1, 1 + np.finfo(x.dtype).eps)\n 1.0\n """\n import numpy as np\n if dtype:\n x = np.asarray(x, dtype=dtype)\n y = np.asarray(y, dtype=dtype)\n else:\n x = np.asarray(x)\n y = np.asarray(y)\n\n t = np.common_type(x, y)\n if np.iscomplexobj(x) or np.iscomplexobj(y):\n raise NotImplementedError("_nulp not implemented for complex array")\n\n x = np.array([x], dtype=t)\n y = np.array([y], dtype=t)\n\n x[np.isnan(x)] = np.nan\n y[np.isnan(y)] = np.nan\n\n if not x.shape == y.shape:\n raise ValueError(f"Arrays do not have the same shape: {x.shape} - {y.shape}")\n\n def _diff(rx, ry, vdt):\n diff = np.asarray(rx - ry, dtype=vdt)\n return np.abs(diff)\n\n rx = integer_repr(x)\n ry = integer_repr(y)\n return _diff(rx, ry, t)\n\n\ndef _integer_repr(x, vdt, comp):\n # Reinterpret binary representation of the float as sign-magnitude:\n # take into account two-complement representation\n # See also\n # https://randomascii.wordpress.com/2012/02/25/comparing-floating-point-numbers-2012-edition/\n rx = x.view(vdt)\n if not (rx.size == 1):\n rx[rx < 0] = comp - rx[rx < 0]\n elif rx < 0:\n rx = comp - rx\n\n return rx\n\n\ndef integer_repr(x):\n """Return the signed-magnitude interpretation of the binary representation\n of x."""\n import numpy as np\n if x.dtype == np.float16:\n return _integer_repr(x, np.int16, np.int16(-2**15))\n elif x.dtype == np.float32:\n return _integer_repr(x, np.int32, np.int32(-2**31))\n elif x.dtype == np.float64:\n return _integer_repr(x, np.int64, np.int64(-2**63))\n else:\n raise ValueError(f'Unsupported dtype {x.dtype}')\n\n\n@contextlib.contextmanager\ndef _assert_warns_context(warning_class, name=None):\n __tracebackhide__ = True # Hide traceback for py.test\n with suppress_warnings() as sup:\n l = sup.record(warning_class)\n yield\n if not len(l) > 0:\n name_str = f' when calling {name}' if name is not None else ''\n raise AssertionError("No warning raised" + name_str)\n\n\ndef assert_warns(warning_class, *args, **kwargs):\n """\n Fail unless the given callable throws the specified warning.\n\n A warning of class warning_class should be thrown by the callable when\n invoked with arguments args and keyword arguments kwargs.\n If a different type of warning is thrown, it will not be caught.\n\n If called with all arguments other than the warning class omitted, may be\n used as a context manager::\n\n with assert_warns(SomeWarning):\n do_something()\n\n The ability to be used as a context manager is new in NumPy v1.11.0.\n\n Parameters\n ----------\n warning_class : class\n The class defining the warning that `func` is expected to throw.\n func : callable, optional\n Callable to test\n *args : Arguments\n Arguments for `func`.\n **kwargs : Kwargs\n Keyword arguments for `func`.\n\n Returns\n -------\n The value returned by `func`.\n\n Examples\n --------\n >>> import warnings\n >>> def deprecated_func(num):\n ... warnings.warn("Please upgrade", DeprecationWarning)\n ... return num*num\n >>> with np.testing.assert_warns(DeprecationWarning):\n ... assert deprecated_func(4) == 16\n >>> # or passing a func\n >>> ret = np.testing.assert_warns(DeprecationWarning, deprecated_func, 4)\n >>> assert ret == 16\n """\n if not args and not kwargs:\n return _assert_warns_context(warning_class)\n elif len(args) < 1:\n if "match" in kwargs:\n raise RuntimeError(\n "assert_warns does not use 'match' kwarg, "\n "use pytest.warns instead"\n )\n raise RuntimeError("assert_warns(...) needs at least one arg")\n\n func = args[0]\n args = args[1:]\n with _assert_warns_context(warning_class, name=func.__name__):\n return func(*args, **kwargs)\n\n\n@contextlib.contextmanager\ndef _assert_no_warnings_context(name=None):\n __tracebackhide__ = True # Hide traceback for py.test\n with warnings.catch_warnings(record=True) as l:\n warnings.simplefilter('always')\n yield\n if len(l) > 0:\n name_str = f' when calling {name}' if name is not None else ''\n raise AssertionError(f'Got warnings{name_str}: {l}')\n\n\ndef assert_no_warnings(*args, **kwargs):\n """\n Fail if the given callable produces any warnings.\n\n If called with all arguments omitted, may be used as a context manager::\n\n with assert_no_warnings():\n do_something()\n\n The ability to be used as a context manager is new in NumPy v1.11.0.\n\n Parameters\n ----------\n func : callable\n The callable to test.\n \\*args : Arguments\n Arguments passed to `func`.\n \\*\\*kwargs : Kwargs\n Keyword arguments passed to `func`.\n\n Returns\n -------\n The value returned by `func`.\n\n """\n if not args:\n return _assert_no_warnings_context()\n\n func = args[0]\n args = args[1:]\n with _assert_no_warnings_context(name=func.__name__):\n return func(*args, **kwargs)\n\n\ndef _gen_alignment_data(dtype=float32, type='binary', max_size=24):\n """\n generator producing data with different alignment and offsets\n to test simd vectorization\n\n Parameters\n ----------\n dtype : dtype\n data type to produce\n type : string\n 'unary': create data for unary operations, creates one input\n and output array\n 'binary': create data for unary operations, creates two input\n and output array\n max_size : integer\n maximum size of data to produce\n\n Returns\n -------\n if type is 'unary' yields one output, one input array and a message\n containing information on the data\n if type is 'binary' yields one output array, two input array and a message\n containing information on the data\n\n """\n ufmt = 'unary offset=(%d, %d), size=%d, dtype=%r, %s'\n bfmt = 'binary offset=(%d, %d, %d), size=%d, dtype=%r, %s'\n for o in range(3):\n for s in range(o + 2, max(o + 3, max_size)):\n if type == 'unary':\n inp = lambda: arange(s, dtype=dtype)[o:]\n out = empty((s,), dtype=dtype)[o:]\n yield out, inp(), ufmt % (o, o, s, dtype, 'out of place')\n d = inp()\n yield d, d, ufmt % (o, o, s, dtype, 'in place')\n yield out[1:], inp()[:-1], ufmt % \\n (o + 1, o, s - 1, dtype, 'out of place')\n yield out[:-1], inp()[1:], ufmt % \\n (o, o + 1, s - 1, dtype, 'out of place')\n yield inp()[:-1], inp()[1:], ufmt % \\n (o, o + 1, s - 1, dtype, 'aliased')\n yield inp()[1:], inp()[:-1], ufmt % \\n (o + 1, o, s - 1, dtype, 'aliased')\n if type == 'binary':\n inp1 = lambda: arange(s, dtype=dtype)[o:]\n inp2 = lambda: arange(s, dtype=dtype)[o:]\n out = empty((s,), dtype=dtype)[o:]\n yield out, inp1(), inp2(), bfmt % \\n (o, o, o, s, dtype, 'out of place')\n d = inp1()\n yield d, d, inp2(), bfmt % \\n (o, o, o, s, dtype, 'in place1')\n d = inp2()\n yield d, inp1(), d, bfmt % \\n (o, o, o, s, dtype, 'in place2')\n yield out[1:], inp1()[:-1], inp2()[:-1], bfmt % \\n (o + 1, o, o, s - 1, dtype, 'out of place')\n yield out[:-1], inp1()[1:], inp2()[:-1], bfmt % \\n (o, o + 1, o, s - 1, dtype, 'out of place')\n yield out[:-1], inp1()[:-1], inp2()[1:], bfmt % \\n (o, o, o + 1, s - 1, dtype, 'out of place')\n yield inp1()[1:], inp1()[:-1], inp2()[:-1], bfmt % \\n (o + 1, o, o, s - 1, dtype, 'aliased')\n yield inp1()[:-1], inp1()[1:], inp2()[:-1], bfmt % \\n (o, o + 1, o, s - 1, dtype, 'aliased')\n yield inp1()[:-1], inp1()[:-1], inp2()[1:], bfmt % \\n (o, o, o + 1, s - 1, dtype, 'aliased')\n\n\nclass IgnoreException(Exception):\n "Ignoring this exception due to disabled feature"\n pass\n\n\n@contextlib.contextmanager\ndef tempdir(*args, **kwargs):\n """Context manager to provide a temporary test folder.\n\n All arguments are passed as this to the underlying tempfile.mkdtemp\n function.\n\n """\n tmpdir = mkdtemp(*args, **kwargs)\n try:\n yield tmpdir\n finally:\n shutil.rmtree(tmpdir)\n\n\n@contextlib.contextmanager\ndef temppath(*args, **kwargs):\n """Context manager for temporary files.\n\n Context manager that returns the path to a closed temporary file. Its\n parameters are the same as for tempfile.mkstemp and are passed directly\n to that function. The underlying file is removed when the context is\n exited, so it should be closed at that time.\n\n Windows does not allow a temporary file to be opened if it is already\n open, so the underlying file must be closed after opening before it\n can be opened again.\n\n """\n fd, path = mkstemp(*args, **kwargs)\n os.close(fd)\n try:\n yield path\n finally:\n os.remove(path)\n\n\nclass clear_and_catch_warnings(warnings.catch_warnings):\n """ Context manager that resets warning registry for catching warnings\n\n Warnings can be slippery, because, whenever a warning is triggered, Python\n adds a ``__warningregistry__`` member to the *calling* module. This makes\n it impossible to retrigger the warning in this module, whatever you put in\n the warnings filters. This context manager accepts a sequence of `modules`\n as a keyword argument to its constructor and:\n\n * stores and removes any ``__warningregistry__`` entries in given `modules`\n on entry;\n * resets ``__warningregistry__`` to its previous state on exit.\n\n This makes it possible to trigger any warning afresh inside the context\n manager without disturbing the state of warnings outside.\n\n For compatibility with Python, please consider all arguments to be\n keyword-only.\n\n Parameters\n ----------\n record : bool, optional\n Specifies whether warnings should be captured by a custom\n implementation of ``warnings.showwarning()`` and be appended to a list\n returned by the context manager. Otherwise None is returned by the\n context manager. The objects appended to the list are arguments whose\n attributes mirror the arguments to ``showwarning()``.\n modules : sequence, optional\n Sequence of modules for which to reset warnings registry on entry and\n restore on exit. To work correctly, all 'ignore' filters should\n filter by one of these modules.\n\n Examples\n --------\n >>> import warnings\n >>> with np.testing.clear_and_catch_warnings(\n ... modules=[np._core.fromnumeric]):\n ... warnings.simplefilter('always')\n ... warnings.filterwarnings('ignore', module='np._core.fromnumeric')\n ... # do something that raises a warning but ignore those in\n ... # np._core.fromnumeric\n """\n class_modules = ()\n\n def __init__(self, record=False, modules=()):\n self.modules = set(modules).union(self.class_modules)\n self._warnreg_copies = {}\n super().__init__(record=record)\n\n def __enter__(self):\n for mod in self.modules:\n if hasattr(mod, '__warningregistry__'):\n mod_reg = mod.__warningregistry__\n self._warnreg_copies[mod] = mod_reg.copy()\n mod_reg.clear()\n return super().__enter__()\n\n def __exit__(self, *exc_info):\n super().__exit__(*exc_info)\n for mod in self.modules:\n if hasattr(mod, '__warningregistry__'):\n mod.__warningregistry__.clear()\n if mod in self._warnreg_copies:\n mod.__warningregistry__.update(self._warnreg_copies[mod])\n\n\nclass suppress_warnings:\n """\n Context manager and decorator doing much the same as\n ``warnings.catch_warnings``.\n\n However, it also provides a filter mechanism to work around\n https://bugs.python.org/issue4180.\n\n This bug causes Python before 3.4 to not reliably show warnings again\n after they have been ignored once (even within catch_warnings). It\n means that no "ignore" filter can be used easily, since following\n tests might need to see the warning. Additionally it allows easier\n specificity for testing warnings and can be nested.\n\n Parameters\n ----------\n forwarding_rule : str, optional\n One of "always", "once", "module", or "location". Analogous to\n the usual warnings module filter mode, it is useful to reduce\n noise mostly on the outmost level. Unsuppressed and unrecorded\n warnings will be forwarded based on this rule. Defaults to "always".\n "location" is equivalent to the warnings "default", match by exact\n location the warning warning originated from.\n\n Notes\n -----\n Filters added inside the context manager will be discarded again\n when leaving it. Upon entering all filters defined outside a\n context will be applied automatically.\n\n When a recording filter is added, matching warnings are stored in the\n ``log`` attribute as well as in the list returned by ``record``.\n\n If filters are added and the ``module`` keyword is given, the\n warning registry of this module will additionally be cleared when\n applying it, entering the context, or exiting it. This could cause\n warnings to appear a second time after leaving the context if they\n were configured to be printed once (default) and were already\n printed before the context was entered.\n\n Nesting this context manager will work as expected when the\n forwarding rule is "always" (default). Unfiltered and unrecorded\n warnings will be passed out and be matched by the outer level.\n On the outmost level they will be printed (or caught by another\n warnings context). The forwarding rule argument can modify this\n behaviour.\n\n Like ``catch_warnings`` this context manager is not threadsafe.\n\n Examples\n --------\n\n With a context manager::\n\n with np.testing.suppress_warnings() as sup:\n sup.filter(DeprecationWarning, "Some text")\n sup.filter(module=np.ma.core)\n log = sup.record(FutureWarning, "Does this occur?")\n command_giving_warnings()\n # The FutureWarning was given once, the filtered warnings were\n # ignored. All other warnings abide outside settings (may be\n # printed/error)\n assert_(len(log) == 1)\n assert_(len(sup.log) == 1) # also stored in log attribute\n\n Or as a decorator::\n\n sup = np.testing.suppress_warnings()\n sup.filter(module=np.ma.core) # module must match exactly\n @sup\n def some_function():\n # do something which causes a warning in np.ma.core\n pass\n """\n def __init__(self, forwarding_rule="always"):\n self._entered = False\n\n # Suppressions are either instance or defined inside one with block:\n self._suppressions = []\n\n if forwarding_rule not in {"always", "module", "once", "location"}:\n raise ValueError("unsupported forwarding rule.")\n self._forwarding_rule = forwarding_rule\n\n def _clear_registries(self):\n if hasattr(warnings, "_filters_mutated"):\n # clearing the registry should not be necessary on new pythons,\n # instead the filters should be mutated.\n warnings._filters_mutated()\n return\n # Simply clear the registry, this should normally be harmless,\n # note that on new pythons it would be invalidated anyway.\n for module in self._tmp_modules:\n if hasattr(module, "__warningregistry__"):\n module.__warningregistry__.clear()\n\n def _filter(self, category=Warning, message="", module=None, record=False):\n if record:\n record = [] # The log where to store warnings\n else:\n record = None\n if self._entered:\n if module is None:\n warnings.filterwarnings(\n "always", category=category, message=message)\n else:\n module_regex = module.__name__.replace('.', r'\.') + '$'\n warnings.filterwarnings(\n "always", category=category, message=message,\n module=module_regex)\n self._tmp_modules.add(module)\n self._clear_registries()\n\n self._tmp_suppressions.append(\n (category, message, re.compile(message, re.I), module, record))\n else:\n self._suppressions.append(\n (category, message, re.compile(message, re.I), module, record))\n\n return record\n\n def filter(self, category=Warning, message="", module=None):\n """\n Add a new suppressing filter or apply it if the state is entered.\n\n Parameters\n ----------\n category : class, optional\n Warning class to filter\n message : string, optional\n Regular expression matching the warning message.\n module : module, optional\n Module to filter for. Note that the module (and its file)\n must match exactly and cannot be a submodule. This may make\n it unreliable for external modules.\n\n Notes\n -----\n When added within a context, filters are only added inside\n the context and will be forgotten when the context is exited.\n """\n self._filter(category=category, message=message, module=module,\n record=False)\n\n def record(self, category=Warning, message="", module=None):\n """\n Append a new recording filter or apply it if the state is entered.\n\n All warnings matching will be appended to the ``log`` attribute.\n\n Parameters\n ----------\n category : class, optional\n Warning class to filter\n message : string, optional\n Regular expression matching the warning message.\n module : module, optional\n Module to filter for. Note that the module (and its file)\n must match exactly and cannot be a submodule. This may make\n it unreliable for external modules.\n\n Returns\n -------\n log : list\n A list which will be filled with all matched warnings.\n\n Notes\n -----\n When added within a context, filters are only added inside\n the context and will be forgotten when the context is exited.\n """\n return self._filter(category=category, message=message, module=module,\n record=True)\n\n def __enter__(self):\n if self._entered:\n raise RuntimeError("cannot enter suppress_warnings twice.")\n\n self._orig_show = warnings.showwarning\n self._filters = warnings.filters\n warnings.filters = self._filters[:]\n\n self._entered = True\n self._tmp_suppressions = []\n self._tmp_modules = set()\n self._forwarded = set()\n\n self.log = [] # reset global log (no need to keep same list)\n\n for cat, mess, _, mod, log in self._suppressions:\n if log is not None:\n del log[:] # clear the log\n if mod is None:\n warnings.filterwarnings(\n "always", category=cat, message=mess)\n else:\n module_regex = mod.__name__.replace('.', r'\.') + '$'\n warnings.filterwarnings(\n "always", category=cat, message=mess,\n module=module_regex)\n self._tmp_modules.add(mod)\n warnings.showwarning = self._showwarning\n self._clear_registries()\n\n return self\n\n def __exit__(self, *exc_info):\n warnings.showwarning = self._orig_show\n warnings.filters = self._filters\n self._clear_registries()\n self._entered = False\n del self._orig_show\n del self._filters\n\n def _showwarning(self, message, category, filename, lineno,\n *args, use_warnmsg=None, **kwargs):\n for cat, _, pattern, mod, rec in (\n self._suppressions + self._tmp_suppressions)[::-1]:\n if (issubclass(category, cat) and\n pattern.match(message.args[0]) is not None):\n if mod is None:\n # Message and category match, either recorded or ignored\n if rec is not None:\n msg = WarningMessage(message, category, filename,\n lineno, **kwargs)\n self.log.append(msg)\n rec.append(msg)\n return\n # Use startswith, because warnings strips the c or o from\n # .pyc/.pyo files.\n elif mod.__file__.startswith(filename):\n # The message and module (filename) match\n if rec is not None:\n msg = WarningMessage(message, category, filename,\n lineno, **kwargs)\n self.log.append(msg)\n rec.append(msg)\n return\n\n # There is no filter in place, so pass to the outside handler\n # unless we should only pass it once\n if self._forwarding_rule == "always":\n if use_warnmsg is None:\n self._orig_show(message, category, filename, lineno,\n *args, **kwargs)\n else:\n self._orig_showmsg(use_warnmsg)\n return\n\n if self._forwarding_rule == "once":\n signature = (message.args, category)\n elif self._forwarding_rule == "module":\n signature = (message.args, category, filename)\n elif self._forwarding_rule == "location":\n signature = (message.args, category, filename, lineno)\n\n if signature in self._forwarded:\n return\n self._forwarded.add(signature)\n if use_warnmsg is None:\n self._orig_show(message, category, filename, lineno, *args,\n **kwargs)\n else:\n self._orig_showmsg(use_warnmsg)\n\n def __call__(self, func):\n """\n Function decorator to apply certain suppressions to a whole\n function.\n """\n @wraps(func)\n def new_func(*args, **kwargs):\n with self:\n return func(*args, **kwargs)\n\n return new_func\n\n\n@contextlib.contextmanager\ndef _assert_no_gc_cycles_context(name=None):\n __tracebackhide__ = True # Hide traceback for py.test\n\n # not meaningful to test if there is no refcounting\n if not HAS_REFCOUNT:\n yield\n return\n\n assert_(gc.isenabled())\n gc.disable()\n gc_debug = gc.get_debug()\n try:\n for i in range(100):\n if gc.collect() == 0:\n break\n else:\n raise RuntimeError(\n "Unable to fully collect garbage - perhaps a __del__ method "\n "is creating more reference cycles?")\n\n gc.set_debug(gc.DEBUG_SAVEALL)\n yield\n # gc.collect returns the number of unreachable objects in cycles that\n # were found -- we are checking that no cycles were created in the context\n n_objects_in_cycles = gc.collect()\n objects_in_cycles = gc.garbage[:]\n finally:\n del gc.garbage[:]\n gc.set_debug(gc_debug)\n gc.enable()\n\n if n_objects_in_cycles:\n name_str = f' when calling {name}' if name is not None else ''\n raise AssertionError(\n "Reference cycles were found{}: {} objects were collected, "\n "of which {} are shown below:{}"\n .format(\n name_str,\n n_objects_in_cycles,\n len(objects_in_cycles),\n ''.join(\n "\n {} object with id={}:\n {}".format(\n type(o).__name__,\n id(o),\n pprint.pformat(o).replace('\n', '\n ')\n ) for o in objects_in_cycles\n )\n )\n )\n\n\ndef assert_no_gc_cycles(*args, **kwargs):\n """\n Fail if the given callable produces any reference cycles.\n\n If called with all arguments omitted, may be used as a context manager::\n\n with assert_no_gc_cycles():\n do_something()\n\n Parameters\n ----------\n func : callable\n The callable to test.\n \\*args : Arguments\n Arguments passed to `func`.\n \\*\\*kwargs : Kwargs\n Keyword arguments passed to `func`.\n\n Returns\n -------\n Nothing. The result is deliberately discarded to ensure that all cycles\n are found.\n\n """\n if not args:\n return _assert_no_gc_cycles_context()\n\n func = args[0]\n args = args[1:]\n with _assert_no_gc_cycles_context(name=func.__name__):\n func(*args, **kwargs)\n\n\ndef break_cycles():\n """\n Break reference cycles by calling gc.collect\n Objects can call other objects' methods (for instance, another object's\n __del__) inside their own __del__. On PyPy, the interpreter only runs\n between calls to gc.collect, so multiple calls are needed to completely\n release all cycles.\n """\n\n gc.collect()\n if IS_PYPY:\n # a few more, just to make sure all the finalizers are called\n gc.collect()\n gc.collect()\n gc.collect()\n gc.collect()\n\n\ndef requires_memory(free_bytes):\n """Decorator to skip a test if not enough memory is available"""\n import pytest\n\n def decorator(func):\n @wraps(func)\n def wrapper(*a, **kw):\n msg = check_free_memory(free_bytes)\n if msg is not None:\n pytest.skip(msg)\n\n try:\n return func(*a, **kw)\n except MemoryError:\n # Probably ran out of memory regardless: don't regard as failure\n pytest.xfail("MemoryError raised")\n\n return wrapper\n\n return decorator\n\n\ndef check_free_memory(free_bytes):\n """\n Check whether `free_bytes` amount of memory is currently free.\n Returns: None if enough memory available, otherwise error message\n """\n env_var = 'NPY_AVAILABLE_MEM'\n env_value = os.environ.get(env_var)\n if env_value is not None:\n try:\n mem_free = _parse_size(env_value)\n except ValueError as exc:\n raise ValueError(f'Invalid environment variable {env_var}: {exc}')\n\n msg = (f'{free_bytes / 1e9} GB memory required, but environment variable '\n f'NPY_AVAILABLE_MEM={env_value} set')\n else:\n mem_free = _get_mem_available()\n\n if mem_free is None:\n msg = ("Could not determine available memory; set NPY_AVAILABLE_MEM "\n "environment variable (e.g. NPY_AVAILABLE_MEM=16GB) to run "\n "the test.")\n mem_free = -1\n else:\n free_bytes_gb = free_bytes / 1e9\n mem_free_gb = mem_free / 1e9\n msg = f'{free_bytes_gb} GB memory required, but {mem_free_gb} GB available'\n\n return msg if mem_free < free_bytes else None\n\n\ndef _parse_size(size_str):\n """Convert memory size strings ('12 GB' etc.) to float"""\n suffixes = {'': 1, 'b': 1,\n 'k': 1000, 'm': 1000**2, 'g': 1000**3, 't': 1000**4,\n 'kb': 1000, 'mb': 1000**2, 'gb': 1000**3, 'tb': 1000**4,\n 'kib': 1024, 'mib': 1024**2, 'gib': 1024**3, 'tib': 1024**4}\n\n pipe_suffixes = "|".join(suffixes.keys())\n\n size_re = re.compile(fr'^\s*(\d+|\d+\.\d+)\s*({pipe_suffixes})\s*$', re.I)\n\n m = size_re.match(size_str.lower())\n if not m or m.group(2) not in suffixes:\n raise ValueError(f'value {size_str!r} not a valid size')\n return int(float(m.group(1)) * suffixes[m.group(2)])\n\n\ndef _get_mem_available():\n """Return available memory in bytes, or None if unknown."""\n try:\n import psutil\n return psutil.virtual_memory().available\n except (ImportError, AttributeError):\n pass\n\n if sys.platform.startswith('linux'):\n info = {}\n with open('/proc/meminfo') as f:\n for line in f:\n p = line.split()\n info[p[0].strip(':').lower()] = int(p[1]) * 1024\n\n if 'memavailable' in info:\n # Linux >= 3.14\n return info['memavailable']\n else:\n return info['memfree'] + info['cached']\n\n return None\n\n\ndef _no_tracing(func):\n """\n Decorator to temporarily turn off tracing for the duration of a test.\n Needed in tests that check refcounting, otherwise the tracing itself\n influences the refcounts\n """\n if not hasattr(sys, 'gettrace'):\n return func\n else:\n @wraps(func)\n def wrapper(*args, **kwargs):\n original_trace = sys.gettrace()\n try:\n sys.settrace(None)\n return func(*args, **kwargs)\n finally:\n sys.settrace(original_trace)\n return wrapper\n\n\ndef _get_glibc_version():\n try:\n ver = os.confstr('CS_GNU_LIBC_VERSION').rsplit(' ')[1]\n except Exception:\n ver = '0.0'\n\n return ver\n\n\n_glibcver = _get_glibc_version()\n_glibc_older_than = lambda x: (_glibcver != '0.0' and _glibcver < x)\n\n\ndef run_threaded(func, max_workers=8, pass_count=False,\n pass_barrier=False, outer_iterations=1,\n prepare_args=None):\n """Runs a function many times in parallel"""\n for _ in range(outer_iterations):\n with (concurrent.futures.ThreadPoolExecutor(max_workers=max_workers)\n as tpe):\n if prepare_args is None:\n args = []\n else:\n args = prepare_args()\n if pass_barrier:\n barrier = threading.Barrier(max_workers)\n args.append(barrier)\n if pass_count:\n all_args = [(func, i, *args) for i in range(max_workers)]\n else:\n all_args = [(func, *args) for i in range(max_workers)]\n try:\n futures = []\n for arg in all_args:\n futures.append(tpe.submit(*arg))\n except RuntimeError as e:\n import pytest\n pytest.skip(f"Spawning {max_workers} threads failed with "\n f"error {e!r} (likely due to resource limits on the "\n "system running the tests)")\n finally:\n if len(futures) < max_workers and pass_barrier:\n barrier.abort()\n for f in futures:\n f.result()\n | .venv\Lib\site-packages\numpy\testing\_private\utils.py | utils.py | Python | 98,466 | 0.75 | 0.163103 | 0.053109 | python-kit | 442 | 2024-09-08T13:07:45.770998 | Apache-2.0 | true | 081c63b859493703e8e034ad03007be6 |
import ast\nimport sys\nimport types\nimport unittest\nimport warnings\nfrom collections.abc import Callable, Iterable, Sequence\nfrom contextlib import _GeneratorContextManager\nfrom pathlib import Path\nfrom re import Pattern\nfrom typing import (\n Any,\n AnyStr,\n ClassVar,\n Final,\n Generic,\n NoReturn,\n ParamSpec,\n Self,\n SupportsIndex,\n TypeAlias,\n TypeVarTuple,\n overload,\n type_check_only,\n)\nfrom typing import Literal as L\nfrom unittest.case import SkipTest\n\nfrom _typeshed import ConvertibleToFloat, GenericPath, StrOrBytesPath, StrPath\nfrom typing_extensions import TypeVar\n\nimport numpy as np\nfrom numpy._typing import (\n ArrayLike,\n DTypeLike,\n NDArray,\n _ArrayLikeDT64_co,\n _ArrayLikeNumber_co,\n _ArrayLikeObject_co,\n _ArrayLikeTD64_co,\n)\n\n__all__ = [ # noqa: RUF022\n "IS_EDITABLE",\n "IS_MUSL",\n "IS_PYPY",\n "IS_PYSTON",\n "IS_WASM",\n "HAS_LAPACK64",\n "HAS_REFCOUNT",\n "NOGIL_BUILD",\n "assert_",\n "assert_array_almost_equal_nulp",\n "assert_raises_regex",\n "assert_array_max_ulp",\n "assert_warns",\n "assert_no_warnings",\n "assert_allclose",\n "assert_equal",\n "assert_almost_equal",\n "assert_approx_equal",\n "assert_array_equal",\n "assert_array_less",\n "assert_string_equal",\n "assert_array_almost_equal",\n "assert_raises",\n "build_err_msg",\n "decorate_methods",\n "jiffies",\n "memusage",\n "print_assert_equal",\n "rundocs",\n "runstring",\n "verbose",\n "measure",\n "IgnoreException",\n "clear_and_catch_warnings",\n "SkipTest",\n "KnownFailureException",\n "temppath",\n "tempdir",\n "suppress_warnings",\n "assert_array_compare",\n "assert_no_gc_cycles",\n "break_cycles",\n "check_support_sve",\n "run_threaded",\n]\n\n###\n\n_T = TypeVar("_T")\n_Ts = TypeVarTuple("_Ts")\n_Tss = ParamSpec("_Tss")\n_ET = TypeVar("_ET", bound=BaseException, default=BaseException)\n_FT = TypeVar("_FT", bound=Callable[..., Any])\n_W_co = TypeVar("_W_co", bound=_WarnLog | None, default=_WarnLog | None, covariant=True)\n_T_or_bool = TypeVar("_T_or_bool", default=bool)\n\n_StrLike: TypeAlias = str | bytes\n_RegexLike: TypeAlias = _StrLike | Pattern[Any]\n_NumericArrayLike: TypeAlias = _ArrayLikeNumber_co | _ArrayLikeObject_co\n\n_ExceptionSpec: TypeAlias = type[_ET] | tuple[type[_ET], ...]\n_WarningSpec: TypeAlias = type[Warning]\n_WarnLog: TypeAlias = list[warnings.WarningMessage]\n_ToModules: TypeAlias = Iterable[types.ModuleType]\n\n# Must return a bool or an ndarray/generic type that is supported by `np.logical_and.reduce`\n_ComparisonFunc: TypeAlias = Callable[\n [NDArray[Any], NDArray[Any]],\n bool | np.bool | np.number | NDArray[np.bool | np.number | np.object_],\n]\n\n# Type-check only `clear_and_catch_warnings` subclasses for both values of the\n# `record` parameter. Copied from the stdlib `warnings` stubs.\n@type_check_only\nclass _clear_and_catch_warnings_with_records(clear_and_catch_warnings):\n def __enter__(self) -> list[warnings.WarningMessage]: ...\n\n@type_check_only\nclass _clear_and_catch_warnings_without_records(clear_and_catch_warnings):\n def __enter__(self) -> None: ...\n\n###\n\nverbose: int = 0\nNUMPY_ROOT: Final[Path] = ...\nIS_INSTALLED: Final[bool] = ...\nIS_EDITABLE: Final[bool] = ...\nIS_MUSL: Final[bool] = ...\nIS_PYPY: Final[bool] = ...\nIS_PYSTON: Final[bool] = ...\nIS_WASM: Final[bool] = ...\nHAS_REFCOUNT: Final[bool] = ...\nHAS_LAPACK64: Final[bool] = ...\nNOGIL_BUILD: Final[bool] = ...\n\nclass KnownFailureException(Exception): ...\nclass IgnoreException(Exception): ...\n\n# NOTE: `warnings.catch_warnings` is incorrectly defined as invariant in typeshed\nclass clear_and_catch_warnings(warnings.catch_warnings[_W_co], Generic[_W_co]): # type: ignore[type-var] # pyright: ignore[reportInvalidTypeArguments]\n class_modules: ClassVar[tuple[types.ModuleType, ...]] = ()\n modules: Final[set[types.ModuleType]]\n @overload # record: True\n def __init__(self: clear_and_catch_warnings[_WarnLog], /, record: L[True], modules: _ToModules = ()) -> None: ...\n @overload # record: False (default)\n def __init__(self: clear_and_catch_warnings[None], /, record: L[False] = False, modules: _ToModules = ()) -> None: ...\n @overload # record; bool\n def __init__(self, /, record: bool, modules: _ToModules = ()) -> None: ...\n\nclass suppress_warnings:\n log: Final[_WarnLog]\n def __init__(self, /, forwarding_rule: L["always", "module", "once", "location"] = "always") -> None: ...\n def __enter__(self) -> Self: ...\n def __exit__(self, cls: type[BaseException] | None, exc: BaseException | None, tb: types.TracebackType | None, /) -> None: ...\n def __call__(self, /, func: _FT) -> _FT: ...\n\n #\n def filter(self, /, category: type[Warning] = ..., message: str = "", module: types.ModuleType | None = None) -> None: ...\n def record(self, /, category: type[Warning] = ..., message: str = "", module: types.ModuleType | None = None) -> _WarnLog: ...\n\n# Contrary to runtime we can't do `os.name` checks while type checking,\n# only `sys.platform` checks\nif sys.platform == "win32" or sys.platform == "cygwin":\n def memusage(processName: str = ..., instance: int = ...) -> int: ...\nelif sys.platform == "linux":\n def memusage(_proc_pid_stat: StrOrBytesPath = ...) -> int | None: ...\nelse:\n def memusage() -> NoReturn: ...\n\nif sys.platform == "linux":\n def jiffies(_proc_pid_stat: StrOrBytesPath = ..., _load_time: list[float] = []) -> int: ...\nelse:\n def jiffies(_load_time: list[float] = []) -> int: ...\n\n#\ndef build_err_msg(\n arrays: Iterable[object],\n err_msg: object,\n header: str = ...,\n verbose: bool = ...,\n names: Sequence[str] = ...,\n precision: SupportsIndex | None = ...,\n) -> str: ...\n\n#\ndef print_assert_equal(test_string: str, actual: object, desired: object) -> None: ...\n\n#\ndef assert_(val: object, msg: str | Callable[[], str] = "") -> None: ...\n\n#\ndef assert_equal(\n actual: object,\n desired: object,\n err_msg: object = "",\n verbose: bool = True,\n *,\n strict: bool = False,\n) -> None: ...\n\ndef assert_almost_equal(\n actual: _NumericArrayLike,\n desired: _NumericArrayLike,\n decimal: int = 7,\n err_msg: object = "",\n verbose: bool = True,\n) -> None: ...\n\n#\ndef assert_approx_equal(\n actual: ConvertibleToFloat,\n desired: ConvertibleToFloat,\n significant: int = 7,\n err_msg: object = "",\n verbose: bool = True,\n) -> None: ...\n\n#\ndef assert_array_compare(\n comparison: _ComparisonFunc,\n x: ArrayLike,\n y: ArrayLike,\n err_msg: object = "",\n verbose: bool = True,\n header: str = "",\n precision: SupportsIndex = 6,\n equal_nan: bool = True,\n equal_inf: bool = True,\n *,\n strict: bool = False,\n names: tuple[str, str] = ("ACTUAL", "DESIRED"),\n) -> None: ...\n\n#\ndef assert_array_equal(\n actual: object,\n desired: object,\n err_msg: object = "",\n verbose: bool = True,\n *,\n strict: bool = False,\n) -> None: ...\n\n#\ndef assert_array_almost_equal(\n actual: _NumericArrayLike,\n desired: _NumericArrayLike,\n decimal: float = 6,\n err_msg: object = "",\n verbose: bool = True,\n) -> None: ...\n\n@overload\ndef assert_array_less(\n x: _ArrayLikeDT64_co,\n y: _ArrayLikeDT64_co,\n err_msg: object = "",\n verbose: bool = True,\n *,\n strict: bool = False,\n) -> None: ...\n@overload\ndef assert_array_less(\n x: _ArrayLikeTD64_co,\n y: _ArrayLikeTD64_co,\n err_msg: object = "",\n verbose: bool = True,\n *,\n strict: bool = False,\n) -> None: ...\n@overload\ndef assert_array_less(\n x: _NumericArrayLike,\n y: _NumericArrayLike,\n err_msg: object = "",\n verbose: bool = True,\n *,\n strict: bool = False,\n) -> None: ...\n\n#\ndef assert_string_equal(actual: str, desired: str) -> None: ...\n\n#\n@overload\ndef assert_raises(\n exception_class: _ExceptionSpec[_ET],\n /,\n *,\n msg: str | None = None,\n) -> unittest.case._AssertRaisesContext[_ET]: ...\n@overload\ndef assert_raises(\n exception_class: _ExceptionSpec,\n callable: Callable[_Tss, Any],\n /,\n *args: _Tss.args,\n **kwargs: _Tss.kwargs,\n) -> None: ...\n\n#\n@overload\ndef assert_raises_regex(\n exception_class: _ExceptionSpec[_ET],\n expected_regexp: _RegexLike,\n *,\n msg: str | None = None,\n) -> unittest.case._AssertRaisesContext[_ET]: ...\n@overload\ndef assert_raises_regex(\n exception_class: _ExceptionSpec,\n expected_regexp: _RegexLike,\n callable: Callable[_Tss, Any],\n *args: _Tss.args,\n **kwargs: _Tss.kwargs,\n) -> None: ...\n\n#\n@overload\ndef assert_allclose(\n actual: _ArrayLikeTD64_co,\n desired: _ArrayLikeTD64_co,\n rtol: float = 1e-7,\n atol: float = 0,\n equal_nan: bool = True,\n err_msg: object = "",\n verbose: bool = True,\n *,\n strict: bool = False,\n) -> None: ...\n@overload\ndef assert_allclose(\n actual: _NumericArrayLike,\n desired: _NumericArrayLike,\n rtol: float = 1e-7,\n atol: float = 0,\n equal_nan: bool = True,\n err_msg: object = "",\n verbose: bool = True,\n *,\n strict: bool = False,\n) -> None: ...\n\n#\ndef assert_array_almost_equal_nulp(\n x: _ArrayLikeNumber_co,\n y: _ArrayLikeNumber_co,\n nulp: float = 1,\n) -> None: ...\n\n#\ndef assert_array_max_ulp(\n a: _ArrayLikeNumber_co,\n b: _ArrayLikeNumber_co,\n maxulp: float = 1,\n dtype: DTypeLike | None = None,\n) -> NDArray[Any]: ...\n\n#\n@overload\ndef assert_warns(warning_class: _WarningSpec) -> _GeneratorContextManager[None]: ...\n@overload\ndef assert_warns(warning_class: _WarningSpec, func: Callable[_Tss, _T], *args: _Tss.args, **kwargs: _Tss.kwargs) -> _T: ...\n\n#\n@overload\ndef assert_no_warnings() -> _GeneratorContextManager[None]: ...\n@overload\ndef assert_no_warnings(func: Callable[_Tss, _T], /, *args: _Tss.args, **kwargs: _Tss.kwargs) -> _T: ...\n\n#\n@overload\ndef assert_no_gc_cycles() -> _GeneratorContextManager[None]: ...\n@overload\ndef assert_no_gc_cycles(func: Callable[_Tss, Any], /, *args: _Tss.args, **kwargs: _Tss.kwargs) -> None: ...\n\n###\n\n#\n@overload\ndef tempdir(\n suffix: None = None,\n prefix: None = None,\n dir: None = None,\n) -> _GeneratorContextManager[str]: ...\n@overload\ndef tempdir(\n suffix: AnyStr | None = None,\n prefix: AnyStr | None = None,\n *,\n dir: GenericPath[AnyStr],\n) -> _GeneratorContextManager[AnyStr]: ...\n@overload\ndef tempdir(\n suffix: AnyStr | None = None,\n *,\n prefix: AnyStr,\n dir: GenericPath[AnyStr] | None = None,\n) -> _GeneratorContextManager[AnyStr]: ...\n@overload\ndef tempdir(\n suffix: AnyStr,\n prefix: AnyStr | None = None,\n dir: GenericPath[AnyStr] | None = None,\n) -> _GeneratorContextManager[AnyStr]: ...\n\n#\n@overload\ndef temppath(\n suffix: None = None,\n prefix: None = None,\n dir: None = None,\n text: bool = False,\n) -> _GeneratorContextManager[str]: ...\n@overload\ndef temppath(\n suffix: AnyStr | None,\n prefix: AnyStr | None,\n dir: GenericPath[AnyStr],\n text: bool = False,\n) -> _GeneratorContextManager[AnyStr]: ...\n@overload\ndef temppath(\n suffix: AnyStr | None = None,\n prefix: AnyStr | None = None,\n *,\n dir: GenericPath[AnyStr],\n text: bool = False,\n) -> _GeneratorContextManager[AnyStr]: ...\n@overload\ndef temppath(\n suffix: AnyStr | None,\n prefix: AnyStr,\n dir: GenericPath[AnyStr] | None = None,\n text: bool = False,\n) -> _GeneratorContextManager[AnyStr]: ...\n@overload\ndef temppath(\n suffix: AnyStr | None = None,\n *,\n prefix: AnyStr,\n dir: GenericPath[AnyStr] | None = None,\n text: bool = False,\n) -> _GeneratorContextManager[AnyStr]: ...\n@overload\ndef temppath(\n suffix: AnyStr,\n prefix: AnyStr | None = None,\n dir: GenericPath[AnyStr] | None = None,\n text: bool = False,\n) -> _GeneratorContextManager[AnyStr]: ...\n\n#\ndef check_support_sve(__cache: list[_T_or_bool] = []) -> _T_or_bool: ... # noqa: PYI063\n\n#\ndef decorate_methods(\n cls: type,\n decorator: Callable[[Callable[..., Any]], Any],\n testmatch: _RegexLike | None = None,\n) -> None: ...\n\n#\n@overload\ndef run_threaded(\n func: Callable[[], None],\n max_workers: int = 8,\n pass_count: bool = False,\n pass_barrier: bool = False,\n outer_iterations: int = 1,\n prepare_args: None = None,\n) -> None: ...\n@overload\ndef run_threaded(\n func: Callable[[*_Ts], None],\n max_workers: int,\n pass_count: bool,\n pass_barrier: bool,\n outer_iterations: int,\n prepare_args: tuple[*_Ts],\n) -> None: ...\n@overload\ndef run_threaded(\n func: Callable[[*_Ts], None],\n max_workers: int = 8,\n pass_count: bool = False,\n pass_barrier: bool = False,\n outer_iterations: int = 1,\n *,\n prepare_args: tuple[*_Ts],\n) -> None: ...\n\n#\ndef runstring(astr: _StrLike | types.CodeType, dict: dict[str, Any] | None) -> Any: ... # noqa: ANN401\ndef rundocs(filename: StrPath | None = None, raise_on_error: bool = True) -> None: ...\ndef measure(code_str: _StrLike | ast.AST, times: int = 1, label: str | None = None) -> float: ...\ndef break_cycles() -> None: ...\n | .venv\Lib\site-packages\numpy\testing\_private\utils.pyi | utils.pyi | Other | 13,442 | 0.95 | 0.144289 | 0.114286 | react-lib | 240 | 2024-04-20T11:47:58.875116 | MIT | true | 3d4fff6345d98e520ffda1ece103d3c4 |
\n\n | .venv\Lib\site-packages\numpy\testing\_private\__pycache__\extbuild.cpython-313.pyc | extbuild.cpython-313.pyc | Other | 9,230 | 0.95 | 0.041237 | 0.021622 | awesome-app | 476 | 2024-12-02T13:46:22.625901 | BSD-3-Clause | true | 1a4e3e78eff9bb3ae601a2ecf30e6820 |
\n\n | .venv\Lib\site-packages\numpy\testing\_private\__pycache__\utils.cpython-313.pyc | utils.cpython-313.pyc | Other | 103,798 | 0.75 | 0.061872 | 0.008521 | vue-tools | 692 | 2024-10-19T11:46:15.488624 | BSD-3-Clause | true | 594f7f12bb6845605824f859b5e88c65 |
\n\n | .venv\Lib\site-packages\numpy\testing\_private\__pycache__\__init__.cpython-313.pyc | __init__.cpython-313.pyc | Other | 197 | 0.7 | 0 | 0 | node-utils | 411 | 2024-01-26T18:56:15.815074 | MIT | true | 03557a1f1ddc56df68338eeba42ec213 |
\n\n | .venv\Lib\site-packages\numpy\testing\__pycache__\overrides.cpython-313.pyc | overrides.cpython-313.pyc | Other | 2,582 | 0.95 | 0.147059 | 0 | python-kit | 527 | 2023-12-04T20:43:57.535362 | MIT | true | a1cfb7c0582d249288bf55c177fd8544 |
\n\n | .venv\Lib\site-packages\numpy\testing\__pycache__\print_coercion_tables.cpython-313.pyc | print_coercion_tables.cpython-313.pyc | Other | 8,232 | 0.95 | 0.009804 | 0 | python-kit | 127 | 2023-07-29T21:18:52.357021 | BSD-3-Clause | true | 9e0750492577e6e256c5781fb6e6e96e |
\n\n | .venv\Lib\site-packages\numpy\testing\__pycache__\__init__.cpython-313.pyc | __init__.cpython-313.pyc | Other | 883 | 0.85 | 0.133333 | 0 | react-lib | 157 | 2024-04-12T06:16:09.089848 | BSD-3-Clause | true | 84758e1781ba8f234c5c70acc214c810 |
import importlib\nimport importlib.metadata\nimport os\nimport pathlib\nimport subprocess\n\nimport pytest\n\nimport numpy as np\nimport numpy._core.include\nimport numpy._core.lib.pkgconfig\nfrom numpy.testing import IS_EDITABLE, IS_INSTALLED, IS_WASM, NUMPY_ROOT\n\nINCLUDE_DIR = NUMPY_ROOT / '_core' / 'include'\nPKG_CONFIG_DIR = NUMPY_ROOT / '_core' / 'lib' / 'pkgconfig'\n\n\n@pytest.mark.skipif(not IS_INSTALLED, reason="`numpy-config` not expected to be installed")\n@pytest.mark.skipif(IS_WASM, reason="wasm interpreter cannot start subprocess")\nclass TestNumpyConfig:\n def check_numpyconfig(self, arg):\n p = subprocess.run(['numpy-config', arg], capture_output=True, text=True)\n p.check_returncode()\n return p.stdout.strip()\n\n def test_configtool_version(self):\n stdout = self.check_numpyconfig('--version')\n assert stdout == np.__version__\n\n def test_configtool_cflags(self):\n stdout = self.check_numpyconfig('--cflags')\n assert f'-I{os.fspath(INCLUDE_DIR)}' in stdout\n\n def test_configtool_pkgconfigdir(self):\n stdout = self.check_numpyconfig('--pkgconfigdir')\n assert pathlib.Path(stdout) == PKG_CONFIG_DIR\n\n\n@pytest.mark.skipif(not IS_INSTALLED, reason="numpy must be installed to check its entrypoints")\ndef test_pkg_config_entrypoint():\n (entrypoint,) = importlib.metadata.entry_points(group='pkg_config', name='numpy')\n assert entrypoint.value == numpy._core.lib.pkgconfig.__name__\n\n\n@pytest.mark.skipif(not IS_INSTALLED, reason="numpy.pc is only available when numpy is installed")\n@pytest.mark.skipif(IS_EDITABLE, reason="editable installs don't have a numpy.pc")\ndef test_pkg_config_config_exists():\n assert PKG_CONFIG_DIR.joinpath('numpy.pc').is_file()\n | .venv\Lib\site-packages\numpy\tests\test_configtool.py | test_configtool.py | Python | 1,787 | 0.85 | 0.145833 | 0 | react-lib | 634 | 2024-11-09T07:15:18.862604 | MIT | true | 014b99a4e225871100af178392560353 |
import sys\nimport sysconfig\nimport weakref\nfrom pathlib import Path\n\nimport pytest\n\nimport numpy as np\nfrom numpy.ctypeslib import as_array, load_library, ndpointer\nfrom numpy.testing import assert_, assert_array_equal, assert_equal, assert_raises\n\ntry:\n import ctypes\nexcept ImportError:\n ctypes = None\nelse:\n cdll = None\n test_cdll = None\n if hasattr(sys, 'gettotalrefcount'):\n try:\n cdll = load_library(\n '_multiarray_umath_d', np._core._multiarray_umath.__file__\n )\n except OSError:\n pass\n try:\n test_cdll = load_library(\n '_multiarray_tests', np._core._multiarray_tests.__file__\n )\n except OSError:\n pass\n if cdll is None:\n cdll = load_library(\n '_multiarray_umath', np._core._multiarray_umath.__file__)\n if test_cdll is None:\n test_cdll = load_library(\n '_multiarray_tests', np._core._multiarray_tests.__file__\n )\n\n c_forward_pointer = test_cdll.forward_pointer\n\n\n@pytest.mark.skipif(ctypes is None,\n reason="ctypes not available in this python")\n@pytest.mark.skipif(sys.platform == 'cygwin',\n reason="Known to fail on cygwin")\nclass TestLoadLibrary:\n def test_basic(self):\n loader_path = np._core._multiarray_umath.__file__\n\n out1 = load_library('_multiarray_umath', loader_path)\n out2 = load_library(Path('_multiarray_umath'), loader_path)\n out3 = load_library('_multiarray_umath', Path(loader_path))\n out4 = load_library(b'_multiarray_umath', loader_path)\n\n assert isinstance(out1, ctypes.CDLL)\n assert out1 is out2 is out3 is out4\n\n def test_basic2(self):\n # Regression for #801: load_library with a full library name\n # (including extension) does not work.\n try:\n so_ext = sysconfig.get_config_var('EXT_SUFFIX')\n load_library(f'_multiarray_umath{so_ext}',\n np._core._multiarray_umath.__file__)\n except ImportError as e:\n msg = ("ctypes is not available on this python: skipping the test"\n " (import error was: %s)" % str(e))\n print(msg)\n\n\nclass TestNdpointer:\n def test_dtype(self):\n dt = np.intc\n p = ndpointer(dtype=dt)\n assert_(p.from_param(np.array([1], dt)))\n dt = '<i4'\n p = ndpointer(dtype=dt)\n assert_(p.from_param(np.array([1], dt)))\n dt = np.dtype('>i4')\n p = ndpointer(dtype=dt)\n p.from_param(np.array([1], dt))\n assert_raises(TypeError, p.from_param,\n np.array([1], dt.newbyteorder('swap')))\n dtnames = ['x', 'y']\n dtformats = [np.intc, np.float64]\n dtdescr = {'names': dtnames, 'formats': dtformats}\n dt = np.dtype(dtdescr)\n p = ndpointer(dtype=dt)\n assert_(p.from_param(np.zeros((10,), dt)))\n samedt = np.dtype(dtdescr)\n p = ndpointer(dtype=samedt)\n assert_(p.from_param(np.zeros((10,), dt)))\n dt2 = np.dtype(dtdescr, align=True)\n if dt.itemsize != dt2.itemsize:\n assert_raises(TypeError, p.from_param, np.zeros((10,), dt2))\n else:\n assert_(p.from_param(np.zeros((10,), dt2)))\n\n def test_ndim(self):\n p = ndpointer(ndim=0)\n assert_(p.from_param(np.array(1)))\n assert_raises(TypeError, p.from_param, np.array([1]))\n p = ndpointer(ndim=1)\n assert_raises(TypeError, p.from_param, np.array(1))\n assert_(p.from_param(np.array([1])))\n p = ndpointer(ndim=2)\n assert_(p.from_param(np.array([[1]])))\n\n def test_shape(self):\n p = ndpointer(shape=(1, 2))\n assert_(p.from_param(np.array([[1, 2]])))\n assert_raises(TypeError, p.from_param, np.array([[1], [2]]))\n p = ndpointer(shape=())\n assert_(p.from_param(np.array(1)))\n\n def test_flags(self):\n x = np.array([[1, 2], [3, 4]], order='F')\n p = ndpointer(flags='FORTRAN')\n assert_(p.from_param(x))\n p = ndpointer(flags='CONTIGUOUS')\n assert_raises(TypeError, p.from_param, x)\n p = ndpointer(flags=x.flags.num)\n assert_(p.from_param(x))\n assert_raises(TypeError, p.from_param, np.array([[1, 2], [3, 4]]))\n\n def test_cache(self):\n assert_(ndpointer(dtype=np.float64) is ndpointer(dtype=np.float64))\n\n # shapes are normalized\n assert_(ndpointer(shape=2) is ndpointer(shape=(2,)))\n\n # 1.12 <= v < 1.16 had a bug that made these fail\n assert_(ndpointer(shape=2) is not ndpointer(ndim=2))\n assert_(ndpointer(ndim=2) is not ndpointer(shape=2))\n\n@pytest.mark.skipif(ctypes is None,\n reason="ctypes not available on this python installation")\nclass TestNdpointerCFunc:\n def test_arguments(self):\n """ Test that arguments are coerced from arrays """\n c_forward_pointer.restype = ctypes.c_void_p\n c_forward_pointer.argtypes = (ndpointer(ndim=2),)\n\n c_forward_pointer(np.zeros((2, 3)))\n # too many dimensions\n assert_raises(\n ctypes.ArgumentError, c_forward_pointer, np.zeros((2, 3, 4)))\n\n @pytest.mark.parametrize(\n 'dt', [\n float,\n np.dtype({\n 'formats': ['<i4', '<i4'],\n 'names': ['a', 'b'],\n 'offsets': [0, 2],\n 'itemsize': 6\n })\n ], ids=[\n 'float',\n 'overlapping-fields'\n ]\n )\n def test_return(self, dt):\n """ Test that return values are coerced to arrays """\n arr = np.zeros((2, 3), dt)\n ptr_type = ndpointer(shape=arr.shape, dtype=arr.dtype)\n\n c_forward_pointer.restype = ptr_type\n c_forward_pointer.argtypes = (ptr_type,)\n\n # check that the arrays are equivalent views on the same data\n arr2 = c_forward_pointer(arr)\n assert_equal(arr2.dtype, arr.dtype)\n assert_equal(arr2.shape, arr.shape)\n assert_equal(\n arr2.__array_interface__['data'],\n arr.__array_interface__['data']\n )\n\n def test_vague_return_value(self):\n """ Test that vague ndpointer return values do not promote to arrays """\n arr = np.zeros((2, 3))\n ptr_type = ndpointer(dtype=arr.dtype)\n\n c_forward_pointer.restype = ptr_type\n c_forward_pointer.argtypes = (ptr_type,)\n\n ret = c_forward_pointer(arr)\n assert_(isinstance(ret, ptr_type))\n\n\n@pytest.mark.skipif(ctypes is None,\n reason="ctypes not available on this python installation")\nclass TestAsArray:\n def test_array(self):\n from ctypes import c_int\n\n pair_t = c_int * 2\n a = as_array(pair_t(1, 2))\n assert_equal(a.shape, (2,))\n assert_array_equal(a, np.array([1, 2]))\n a = as_array((pair_t * 3)(pair_t(1, 2), pair_t(3, 4), pair_t(5, 6)))\n assert_equal(a.shape, (3, 2))\n assert_array_equal(a, np.array([[1, 2], [3, 4], [5, 6]]))\n\n def test_pointer(self):\n from ctypes import POINTER, c_int, cast\n\n p = cast((c_int * 10)(*range(10)), POINTER(c_int))\n\n a = as_array(p, shape=(10,))\n assert_equal(a.shape, (10,))\n assert_array_equal(a, np.arange(10))\n\n a = as_array(p, shape=(2, 5))\n assert_equal(a.shape, (2, 5))\n assert_array_equal(a, np.arange(10).reshape((2, 5)))\n\n # shape argument is required\n assert_raises(TypeError, as_array, p)\n\n @pytest.mark.skipif(\n sys.version_info[:2] == (3, 12),\n reason="Broken in 3.12.0rc1, see gh-24399",\n )\n def test_struct_array_pointer(self):\n from ctypes import Structure, c_int16, pointer\n\n class Struct(Structure):\n _fields_ = [('a', c_int16)]\n\n Struct3 = 3 * Struct\n\n c_array = (2 * Struct3)(\n Struct3(Struct(a=1), Struct(a=2), Struct(a=3)),\n Struct3(Struct(a=4), Struct(a=5), Struct(a=6))\n )\n\n expected = np.array([\n [(1,), (2,), (3,)],\n [(4,), (5,), (6,)],\n ], dtype=[('a', np.int16)])\n\n def check(x):\n assert_equal(x.dtype, expected.dtype)\n assert_equal(x, expected)\n\n # all of these should be equivalent\n check(as_array(c_array))\n check(as_array(pointer(c_array), shape=()))\n check(as_array(pointer(c_array[0]), shape=(2,)))\n check(as_array(pointer(c_array[0][0]), shape=(2, 3)))\n\n def test_reference_cycles(self):\n # related to gh-6511\n import ctypes\n\n # create array to work with\n # don't use int/long to avoid running into bpo-10746\n N = 100\n a = np.arange(N, dtype=np.short)\n\n # get pointer to array\n pnt = np.ctypeslib.as_ctypes(a)\n\n with np.testing.assert_no_gc_cycles():\n # decay the array above to a pointer to its first element\n newpnt = ctypes.cast(pnt, ctypes.POINTER(ctypes.c_short))\n # and construct an array using this data\n b = np.ctypeslib.as_array(newpnt, (N,))\n # now delete both, which should cleanup both objects\n del newpnt, b\n\n def test_segmentation_fault(self):\n arr = np.zeros((224, 224, 3))\n c_arr = np.ctypeslib.as_ctypes(arr)\n arr_ref = weakref.ref(arr)\n del arr\n\n # check the reference wasn't cleaned up\n assert_(arr_ref() is not None)\n\n # check we avoid the segfault\n c_arr[0][0][0]\n\n\n@pytest.mark.skipif(ctypes is None,\n reason="ctypes not available on this python installation")\nclass TestAsCtypesType:\n """ Test conversion from dtypes to ctypes types """\n def test_scalar(self):\n dt = np.dtype('<u2')\n ct = np.ctypeslib.as_ctypes_type(dt)\n assert_equal(ct, ctypes.c_uint16.__ctype_le__)\n\n dt = np.dtype('>u2')\n ct = np.ctypeslib.as_ctypes_type(dt)\n assert_equal(ct, ctypes.c_uint16.__ctype_be__)\n\n dt = np.dtype('u2')\n ct = np.ctypeslib.as_ctypes_type(dt)\n assert_equal(ct, ctypes.c_uint16)\n\n def test_subarray(self):\n dt = np.dtype((np.int32, (2, 3)))\n ct = np.ctypeslib.as_ctypes_type(dt)\n assert_equal(ct, 2 * (3 * ctypes.c_int32))\n\n def test_structure(self):\n dt = np.dtype([\n ('a', np.uint16),\n ('b', np.uint32),\n ])\n\n ct = np.ctypeslib.as_ctypes_type(dt)\n assert_(issubclass(ct, ctypes.Structure))\n assert_equal(ctypes.sizeof(ct), dt.itemsize)\n assert_equal(ct._fields_, [\n ('a', ctypes.c_uint16),\n ('b', ctypes.c_uint32),\n ])\n\n def test_structure_aligned(self):\n dt = np.dtype([\n ('a', np.uint16),\n ('b', np.uint32),\n ], align=True)\n\n ct = np.ctypeslib.as_ctypes_type(dt)\n assert_(issubclass(ct, ctypes.Structure))\n assert_equal(ctypes.sizeof(ct), dt.itemsize)\n assert_equal(ct._fields_, [\n ('a', ctypes.c_uint16),\n ('', ctypes.c_char * 2), # padding\n ('b', ctypes.c_uint32),\n ])\n\n def test_union(self):\n dt = np.dtype({\n 'names': ['a', 'b'],\n 'offsets': [0, 0],\n 'formats': [np.uint16, np.uint32]\n })\n\n ct = np.ctypeslib.as_ctypes_type(dt)\n assert_(issubclass(ct, ctypes.Union))\n assert_equal(ctypes.sizeof(ct), dt.itemsize)\n assert_equal(ct._fields_, [\n ('a', ctypes.c_uint16),\n ('b', ctypes.c_uint32),\n ])\n\n def test_padded_union(self):\n dt = np.dtype({\n 'names': ['a', 'b'],\n 'offsets': [0, 0],\n 'formats': [np.uint16, np.uint32],\n 'itemsize': 5,\n })\n\n ct = np.ctypeslib.as_ctypes_type(dt)\n assert_(issubclass(ct, ctypes.Union))\n assert_equal(ctypes.sizeof(ct), dt.itemsize)\n assert_equal(ct._fields_, [\n ('a', ctypes.c_uint16),\n ('b', ctypes.c_uint32),\n ('', ctypes.c_char * 5), # padding\n ])\n\n def test_overlapping(self):\n dt = np.dtype({\n 'names': ['a', 'b'],\n 'offsets': [0, 2],\n 'formats': [np.uint32, np.uint32]\n })\n assert_raises(NotImplementedError, np.ctypeslib.as_ctypes_type, dt)\n | .venv\Lib\site-packages\numpy\tests\test_ctypeslib.py | test_ctypeslib.py | Python | 12,752 | 0.95 | 0.100796 | 0.053797 | vue-tools | 992 | 2025-03-16T11:00:11.448689 | BSD-3-Clause | true | df4065ee9eba47d229a6e1f21b3a213e |
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