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repo_id stringlengths 12 110 | file_path stringlengths 24 164 | content stringlengths 3 89.3M | __index_level_0__ int64 0 0 |
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public_repos | public_repos/numpy/doc_requirements.txt | # doxygen required, use apt-get or dnf
sphinx>=4.5.0
numpydoc==1.4
pydata-sphinx-theme==0.13.3
sphinx-design
ipython!=8.1.0
scipy
matplotlib
pandas
breathe>4.33.0
# needed to build release notes
towncrier
toml
| 0 |
public_repos | public_repos/numpy/building_with_meson.md | # Building with Meson
_Note: this is for early adopters. It has been tested on Linux and macOS, and
with Python 3.9-3.12. Windows will be tested soon. There is one CI job to keep
the build stable. This may have rough edges, please open an issue if you run
into a problem._
### Developer build
**Install build tools:**... | 0 |
public_repos/numpy | public_repos/numpy/numpy/__init__.pyi | import builtins
import sys
import os
import mmap
import ctypes as ct
import array as _array
import datetime as dt
import enum
from abc import abstractmethod
from types import TracebackType, MappingProxyType, GenericAlias
from contextlib import contextmanager
from numpy._pytesttester import PytestTester
from numpy._cor... | 0 |
public_repos/numpy | public_repos/numpy/numpy/_distributor_init.py | """ Distributor init file
Distributors: you can add custom code here to support particular distributions
of numpy.
For example, this is a good place to put any BLAS/LAPACK initialization code.
The numpy standard source distribution will not put code in this file, so you
can safely replace this file with your own ver... | 0 |
public_repos/numpy | public_repos/numpy/numpy/dtypes.pyi | import numpy as np
__all__: list[str]
# Boolean:
BoolDType = np.dtype[np.bool_]
# Sized integers:
Int8DType = np.dtype[np.int8]
UInt8DType = np.dtype[np.uint8]
Int16DType = np.dtype[np.int16]
UInt16DType = np.dtype[np.uint16]
Int32DType = np.dtype[np.int32]
UInt32DType = np.dtype[np.uint32]
Int64DType = np.dtype[np.... | 0 |
public_repos/numpy | public_repos/numpy/numpy/meson.build | # We need -lm for all C code (assuming it uses math functions, which is safe to
# assume for NumPy). For C++ it isn't needed, because libstdc++/libc++ is
# guaranteed to depend on it.
m_dep = cc.find_library('m', required : false)
mlib_linkflag = ''
if m_dep.found()
mlib_linkflag = '-lm'
add_project_link_arguments(... | 0 |
public_repos/numpy | public_repos/numpy/numpy/exceptions.py | """
Exceptions and Warnings (:mod:`numpy.exceptions`)
=================================================
General exceptions used by NumPy. Note that some exceptions may be module
specific, such as linear algebra errors.
.. versionadded:: NumPy 1.25
The exceptions module is new in NumPy 1.25. Older exceptions re... | 0 |
public_repos/numpy | public_repos/numpy/numpy/matlib.py | import warnings
# 2018-05-29, PendingDeprecationWarning added to matrix.__new__
# 2020-01-23, numpy 1.19.0 PendingDeprecatonWarning
warnings.warn("Importing from numpy.matlib is deprecated since 1.19.0. "
"The matrix subclass is not the recommended way to represent "
"matrices or deal with ... | 0 |
public_repos/numpy | public_repos/numpy/numpy/conftest.py | """
Pytest configuration and fixtures for the Numpy test suite.
"""
import os
import tempfile
import hypothesis
import pytest
import numpy
from numpy._core._multiarray_tests import get_fpu_mode
_old_fpu_mode = None
_collect_results = {}
# Use a known and persistent tmpdir for hypothesis' caches, which
# can be aut... | 0 |
public_repos/numpy | public_repos/numpy/numpy/__init__.pxd | # NumPy static imports for Cython < 3.0
#
# If any of the PyArray_* functions are called, import_array must be
# called first.
#
# Author: Dag Sverre Seljebotn
#
DEF _buffer_format_string_len = 255
cimport cpython.buffer as pybuf
from cpython.ref cimport Py_INCREF
from cpython.mem cimport PyObject_Malloc, PyObject_Fr... | 0 |
public_repos/numpy | public_repos/numpy/numpy/__config__.py.in | # This file is generated by numpy's build process
# It contains system_info results at the time of building this package.
from enum import Enum
from numpy._core._multiarray_umath import (
__cpu_features__,
__cpu_baseline__,
__cpu_dispatch__,
)
__all__ = ["show"]
_built_with_meson = True
class DisplayMode... | 0 |
public_repos/numpy | public_repos/numpy/numpy/ctypeslib.pyi | # NOTE: Numpy's mypy plugin is used for importing the correct
# platform-specific `ctypes._SimpleCData[int]` sub-type
from ctypes import c_int64 as _c_intp
import os
import ctypes
from collections.abc import Iterable, Sequence
from typing import (
Literal as L,
Any,
TypeVar,
Generic,
overload,
... | 0 |
public_repos/numpy | public_repos/numpy/numpy/ctypeslib.py | """
============================
``ctypes`` Utility Functions
============================
See Also
--------
load_library : Load a C library.
ndpointer : Array restype/argtype with verification.
as_ctypes : Create a ctypes array from an ndarray.
as_array : Create an ndarray from a ctypes array.
References
----------
... | 0 |
public_repos/numpy | public_repos/numpy/numpy/__init__.cython-30.pxd | # NumPy static imports for Cython >= 3.0
#
# If any of the PyArray_* functions are called, import_array must be
# called first. This is done automatically by Cython 3.0+ if a call
# is not detected inside of the module.
#
# Author: Dag Sverre Seljebotn
#
from cpython.ref cimport Py_INCREF
from cpython.object cimport ... | 0 |
public_repos/numpy | public_repos/numpy/numpy/_pytesttester.py | """
Pytest test running.
This module implements the ``test()`` function for NumPy modules. The usual
boiler plate for doing that is to put the following in the module
``__init__.py`` file::
from numpy._pytesttester import PytestTester
test = PytestTester(__name__)
del PytestTester
Warnings filtering and... | 0 |
public_repos/numpy | public_repos/numpy/numpy/_expired_attrs_2_0.py | """
Dict of expired attributes that are discontinued since 2.0 release.
Each item is associated with a migration note.
"""
__expired_attributes__ = {
"geterrobj": "Use the np.errstate context manager instead.",
"seterrobj": "Use the np.errstate context manager instead.",
"cast": "Use `np.asarray(arr, dtype... | 0 |
public_repos/numpy | public_repos/numpy/numpy/_globals.py | """
Module defining global singleton classes.
This module raises a RuntimeError if an attempt to reload it is made. In that
way the identities of the classes defined here are fixed and will remain so
even if numpy itself is reloaded. In particular, a function like the following
will still work correctly after numpy is... | 0 |
public_repos/numpy | public_repos/numpy/numpy/exceptions.pyi | from typing import overload
__all__: list[str]
class ComplexWarning(RuntimeWarning): ...
class ModuleDeprecationWarning(DeprecationWarning): ...
class VisibleDeprecationWarning(UserWarning): ...
class RankWarning(RuntimeWarning): ...
class TooHardError(RuntimeError): ...
class DTypePromotionError(TypeError): ...
cla... | 0 |
public_repos/numpy | public_repos/numpy/numpy/_pytesttester.pyi | from collections.abc import Iterable
from typing import Literal as L
__all__: list[str]
class PytestTester:
module_name: str
def __init__(self, module_name: str) -> None: ...
def __call__(
self,
label: L["fast", "full"] = ...,
verbose: int = ...,
extra_argv: None | Iterable... | 0 |
public_repos/numpy | public_repos/numpy/numpy/__init__.py | """
NumPy
=====
Provides
1. An array object of arbitrary homogeneous items
2. Fast mathematical operations over arrays
3. Linear Algebra, Fourier Transforms, Random Number Generation
How to use the documentation
----------------------------
Documentation is available in two forms: docstrings provided
with the c... | 0 |
public_repos/numpy | public_repos/numpy/numpy/dtypes.py | """
DType classes and utility (:mod:`numpy.dtypes`)
===============================================
This module is home to specific dtypes related functionality and their classes.
For more general information about dtypes, also see `numpy.dtype` and
:ref:`arrays.dtypes`.
Similar to the builtin ``types`` module, this ... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/char/__init__.pyi | from numpy._core.defchararray import (
equal as equal,
not_equal as not_equal,
greater_equal as greater_equal,
less_equal as less_equal,
greater as greater,
less as less,
str_len as str_len,
add as add,
multiply as multiply,
mod as mod,
capitalize as capitalize,
center as... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/char/__init__.py | from numpy._core.defchararray import __all__, __doc__
from numpy._core.defchararray import *
| 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/_typing/_array_like.py | from __future__ import annotations
import sys
from collections.abc import Collection, Callable, Sequence
from typing import Any, Protocol, Union, TypeVar, runtime_checkable
from numpy import (
ndarray,
dtype,
generic,
bool_,
unsignedinteger,
integer,
floating,
complexfloating,
numb... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/_typing/_callable.pyi | """
A module with various ``typing.Protocol`` subclasses that implement
the ``__call__`` magic method.
See the `Mypy documentation`_ on protocols for more details.
.. _`Mypy documentation`: https://mypy.readthedocs.io/en/stable/protocols.html#callback-protocols
"""
from __future__ import annotations
from typing im... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/_typing/_dtype_like.py | from collections.abc import Sequence
from typing import (
Any,
Sequence,
Union,
TypeVar,
Protocol,
TypedDict,
runtime_checkable,
)
import numpy as np
from ._shape import _ShapeLike
from ._char_codes import (
_BoolCodes,
_UInt8Codes,
_UInt16Codes,
_UInt32Codes,
_UInt64C... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/_typing/_nested_sequence.py | """A module containing the `_NestedSequence` protocol."""
from __future__ import annotations
from collections.abc import Iterator
from typing import (
Any,
TypeVar,
Protocol,
runtime_checkable,
)
__all__ = ["_NestedSequence"]
_T_co = TypeVar("_T_co", covariant=True)
@runtime_checkable
class _Neste... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/_typing/_nbit.py | """A module with the precisions of platform-specific `~numpy.number`s."""
from typing import Any
# To-be replaced with a `npt.NBitBase` subclass by numpy's mypy plugin
_NBitByte = Any
_NBitShort = Any
_NBitIntC = Any
_NBitIntP = Any
_NBitInt = Any
_NBitLong = Any
_NBitLongLong = Any
_NBitHalf = Any
_NBitSingle = Any... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/_typing/_scalars.py | from typing import Union, Any
import numpy as np
# NOTE: `_StrLike_co` and `_BytesLike_co` are pointless, as `np.str_` and
# `np.bytes_` are already subclasses of their builtin counterpart
_CharLike_co = Union[str, bytes]
# The 6 `<X>Like_co` type-aliases below represent all scalars that can be
# coerced into `<X>`... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/_typing/_extended_precision.py | """A module with platform-specific extended precision
`numpy.number` subclasses.
The subclasses are defined here (instead of ``__init__.pyi``) such
that they can be imported conditionally via the numpy's mypy plugin.
"""
import numpy as np
from . import (
_80Bit,
_96Bit,
_128Bit,
_256Bit,
)
uint128 =... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/_typing/_char_codes.py | from typing import Literal
_BoolCodes = Literal["?", "=?", "<?", ">?", "bool", "bool_"]
_UInt8Codes = Literal["uint8", "u1", "=u1", "<u1", ">u1"]
_UInt16Codes = Literal["uint16", "u2", "=u2", "<u2", ">u2"]
_UInt32Codes = Literal["uint32", "u4", "=u4", "<u4", ">u4"]
_UInt64Codes = Literal["uint64", "u8", "=u8", "<u8",... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/_typing/_add_docstring.py | """A module for creating docstrings for sphinx ``data`` domains."""
import re
import textwrap
from ._array_like import NDArray
_docstrings_list = []
def add_newdoc(name: str, value: str, doc: str) -> None:
"""Append ``_docstrings_list`` with a docstring for `name`.
Parameters
----------
name : str... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/_typing/_shape.py | from collections.abc import Sequence
from typing import Union, SupportsIndex
_Shape = tuple[int, ...]
# Anything that can be coerced to a shape tuple
_ShapeLike = Union[SupportsIndex, Sequence[SupportsIndex]]
| 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/_typing/_ufunc.pyi | """A module with private type-check-only `numpy.ufunc` subclasses.
The signatures of the ufuncs are too varied to reasonably type
with a single class. So instead, `ufunc` has been expanded into
four private subclasses, one for each combination of
`~ufunc.nin` and `~ufunc.nout`.
"""
from typing import (
Any,
... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/_typing/__init__.py | """Private counterpart of ``numpy.typing``."""
from __future__ import annotations
from .. import ufunc
from .._utils import set_module
from typing import TYPE_CHECKING, final
@final # Disallow the creation of arbitrary `NBitBase` subclasses
@set_module("numpy.typing")
class NBitBase:
"""
A type representin... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/rec/__init__.pyi | from numpy._core.records import (
record as record,
recarray as recarray,
format_parser as format_parser,
fromarrays as fromarrays,
fromrecords as fromrecords,
fromstring as fromstring,
fromfile as fromfile,
array as array
)
__all__: list[str]
__path__: list[str]
| 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/rec/__init__.py | from numpy._core.records import __all__, __doc__
from numpy._core.records import *
| 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/array_api/_dtypes.py | import numpy as np
# Note: we use dtype objects instead of dtype classes. The spec does not
# require any behavior on dtypes other than equality.
int8 = np.dtype("int8")
int16 = np.dtype("int16")
int32 = np.dtype("int32")
int64 = np.dtype("int64")
uint8 = np.dtype("uint8")
uint16 = np.dtype("uint16")
uint32 = np.dtype... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/array_api/_set_functions.py | from __future__ import annotations
from ._array_object import Array
from typing import NamedTuple
import numpy as np
# Note: np.unique() is split into four functions in the array API:
# unique_all, unique_counts, unique_inverse, and unique_values (this is done
# to remove polymorphic return types).
# Note: The var... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/array_api/_sorting_functions.py | from __future__ import annotations
from ._array_object import Array
from ._dtypes import _real_numeric_dtypes
import numpy as np
# Note: the descending keyword argument is new in this function
def argsort(
x: Array, /, *, axis: int = -1, descending: bool = False, stable: bool = True
) -> Array:
"""
Arra... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/array_api/_constants.py | import numpy as np
e = np.e
inf = np.inf
nan = np.nan
pi = np.pi
newaxis = np.newaxis
| 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/array_api/_typing.py | """
This file defines the types for type annotations.
These names aren't part of the module namespace, but they are used in the
annotations in the function signatures. The functions in the module are only
valid for inputs that match the given type annotations.
"""
from __future__ import annotations
__all__ = [
"... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/array_api/_manipulation_functions.py | from __future__ import annotations
from ._array_object import Array
from ._data_type_functions import result_type
from typing import List, Optional, Tuple, Union
import numpy as np
# Note: the function name is different here
def concat(
arrays: Union[Tuple[Array, ...], List[Array]], /, *, axis: Optional[int] = ... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/array_api/_indexing_functions.py | from __future__ import annotations
from ._array_object import Array
from ._dtypes import _integer_dtypes
import numpy as np
def take(x: Array, indices: Array, /, *, axis: Optional[int] = None) -> Array:
"""
Array API compatible wrapper for :py:func:`np.take <numpy.take>`.
See its docstring for more info... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/array_api/_utility_functions.py | from __future__ import annotations
from ._array_object import Array
from typing import Optional, Tuple, Union
import numpy as np
def all(
x: Array,
/,
*,
axis: Optional[Union[int, Tuple[int, ...]]] = None,
keepdims: bool = False,
) -> Array:
"""
Array API compatible wrapper for :py:func... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/array_api/_elementwise_functions.py | from __future__ import annotations
from ._dtypes import (
_boolean_dtypes,
_floating_dtypes,
_real_floating_dtypes,
_complex_floating_dtypes,
_integer_dtypes,
_integer_or_boolean_dtypes,
_real_numeric_dtypes,
_numeric_dtypes,
_result_type,
)
from ._array_object import Array
import ... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/array_api/_searching_functions.py | from __future__ import annotations
from ._array_object import Array
from ._dtypes import _result_type, _real_numeric_dtypes
from typing import Optional, Tuple
import numpy as np
def argmax(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -> Array:
"""
Array API compatible wrapper for :py... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/array_api/_array_object.py | """
Wrapper class around the ndarray object for the array API standard.
The array API standard defines some behaviors differently than ndarray, in
particular, type promotion rules are different (the standard has no
value-based casting). The standard also specifies a more limited subset of
array methods and functionali... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/array_api/_statistical_functions.py | from __future__ import annotations
from ._dtypes import (
_real_floating_dtypes,
_real_numeric_dtypes,
_numeric_dtypes,
)
from ._array_object import Array
from ._dtypes import float32, float64, complex64, complex128
from typing import TYPE_CHECKING, Optional, Tuple, Union
if TYPE_CHECKING:
from ._typ... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/array_api/__init__.py | """
A NumPy sub-namespace that conforms to the Python array API standard.
This submodule accompanies NEP 47, which proposes its inclusion in NumPy. It
is still considered experimental, and will issue a warning when imported.
This is a proof-of-concept namespace that wraps the corresponding NumPy
functions to give a c... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/array_api/linalg.py | from __future__ import annotations
from ._dtypes import (
_floating_dtypes,
_numeric_dtypes,
float32,
float64,
complex64,
complex128
)
from ._manipulation_functions import reshape
from ._array_object import Array
from .._core.numeric import normalize_axis_tuple
from typing import TYPE_CHECKIN... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/array_api/_creation_functions.py | from __future__ import annotations
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
if TYPE_CHECKING:
from ._typing import (
Array,
Device,
Dtype,
NestedSequence,
SupportsBufferProtocol,
)
from collections.abc import Sequence
from ._dtypes import _all... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/array_api/_data_type_functions.py | from __future__ import annotations
from ._array_object import Array
from ._dtypes import (
_all_dtypes,
_boolean_dtypes,
_signed_integer_dtypes,
_unsigned_integer_dtypes,
_integer_dtypes,
_real_floating_dtypes,
_complex_floating_dtypes,
_numeric_dtypes,
_result_type,
)
from datacla... | 0 |
public_repos/numpy/numpy/array_api | public_repos/numpy/numpy/array_api/tests/test_elementwise_functions.py | from inspect import getfullargspec
from numpy.testing import assert_raises
from .. import asarray, _elementwise_functions
from .._elementwise_functions import bitwise_left_shift, bitwise_right_shift
from .._dtypes import (
_dtype_categories,
_boolean_dtypes,
_floating_dtypes,
_integer_dtypes,
)
def ... | 0 |
public_repos/numpy/numpy/array_api | public_repos/numpy/numpy/array_api/tests/test_manipulation_functions.py | from numpy.testing import assert_raises
import numpy as np
from .. import all
from .._creation_functions import asarray
from .._dtypes import float64, int8
from .._manipulation_functions import (
concat,
reshape,
stack
)
def test_concat_errors():
assert_raises(TypeError, lambda: concat((1... | 0 |
public_repos/numpy/numpy/array_api | public_repos/numpy/numpy/array_api/tests/test_validation.py | from typing import Callable
import pytest
from numpy import array_api as xp
def p(func: Callable, *args, **kwargs):
f_sig = ", ".join(
[str(a) for a in args] + [f"{k}={v}" for k, v in kwargs.items()]
)
id_ = f"{func.__name__}({f_sig})"
return pytest.param(func, args, kwargs, id=id_)
@pytes... | 0 |
public_repos/numpy/numpy/array_api | public_repos/numpy/numpy/array_api/tests/test_indexing_functions.py | import pytest
from numpy import array_api as xp
@pytest.mark.parametrize(
"x, indices, axis, expected",
[
([2, 3], [1, 1, 0], 0, [3, 3, 2]),
([2, 3], [1, 1, 0], -1, [3, 3, 2]),
([[2, 3]], [1], -1, [[3]]),
([[2, 3]], [0, 0], 0, [[2, 3], [2, 3]]),
],
)
def test_take_functio... | 0 |
public_repos/numpy/numpy/array_api | public_repos/numpy/numpy/array_api/tests/test_array_object.py | import operator
from numpy.testing import assert_raises, suppress_warnings
import numpy as np
import pytest
from .. import ones, asarray, reshape, result_type, all, equal
from .._array_object import Array
from .._dtypes import (
_all_dtypes,
_boolean_dtypes,
_real_floating_dtypes,
_floating_dtypes,
... | 0 |
public_repos/numpy/numpy/array_api | public_repos/numpy/numpy/array_api/tests/test_set_functions.py | import pytest
from hypothesis import given
from hypothesis.extra.array_api import make_strategies_namespace
from numpy import array_api as xp
xps = make_strategies_namespace(xp)
@pytest.mark.parametrize("func", [xp.unique_all, xp.unique_inverse])
@given(xps.arrays(dtype=xps.scalar_dtypes(), shape=xps.array_shapes()... | 0 |
public_repos/numpy/numpy/array_api | public_repos/numpy/numpy/array_api/tests/test_data_type_functions.py | import pytest
from numpy.testing import assert_raises
from numpy import array_api as xp
import numpy as np
@pytest.mark.parametrize(
"from_, to, expected",
[
(xp.int8, xp.int16, True),
(xp.int16, xp.int8, False),
(xp.bool, xp.int8, False),
(xp.asarray(0, dtype=xp.uint8), xp.int... | 0 |
public_repos/numpy/numpy/array_api | public_repos/numpy/numpy/array_api/tests/test_creation_functions.py | from numpy.testing import assert_raises
import numpy as np
from .. import all
from .._creation_functions import (
asarray,
arange,
empty,
empty_like,
eye,
full,
full_like,
linspace,
meshgrid,
ones,
ones_like,
zeros,
zeros_like,
)
from .._dtypes import float32, float6... | 0 |
public_repos/numpy/numpy/array_api | public_repos/numpy/numpy/array_api/tests/__init__.py | """
Tests for the array API namespace.
Note, full compliance with the array API can be tested with the official array API test
suite https://github.com/data-apis/array-api-tests. This test suite primarily
focuses on those things that are not tested by the official test suite.
"""
| 0 |
public_repos/numpy/numpy/array_api | public_repos/numpy/numpy/array_api/tests/test_sorting_functions.py | import pytest
from numpy import array_api as xp
@pytest.mark.parametrize(
"obj, axis, expected",
[
([0, 0], -1, [0, 1]),
([0, 1, 0], -1, [1, 0, 2]),
([[0, 1], [1, 1]], 0, [[1, 0], [0, 1]]),
([[0, 1], [1, 1]], 1, [[1, 0], [0, 1]]),
],
)
def test_stable_desc_argsort(obj, axi... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/_pcg64.pyx | #cython: binding=True
import numpy as np
cimport numpy as np
from libc.stdint cimport uint32_t, uint64_t
from ._common cimport uint64_to_double, wrap_int
from numpy.random cimport BitGenerator
__all__ = ['PCG64']
cdef extern from "src/pcg64/pcg64.h":
# Use int as generic type, actual type read from pcg64.h and ... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/__init__.pyi | from numpy._pytesttester import PytestTester
from numpy.random._generator import Generator as Generator
from numpy.random._generator import default_rng as default_rng
from numpy.random._mt19937 import MT19937 as MT19937
from numpy.random._pcg64 import (
PCG64 as PCG64,
PCG64DXSM as PCG64DXSM,
)
from numpy.rand... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/bit_generator.pyi | import abc
from threading import Lock
from collections.abc import Callable, Mapping, Sequence
from typing import (
Any,
NamedTuple,
TypedDict,
TypeVar,
overload,
Literal,
)
from numpy import dtype, uint32, uint64
from numpy._typing import (
NDArray,
_ArrayLikeInt_co,
_ShapeLike,
... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/bit_generator.pxd | cimport numpy as np
from libc.stdint cimport uint32_t, uint64_t
cdef extern from "numpy/random/bitgen.h":
struct bitgen:
void *state
uint64_t (*next_uint64)(void *st) nogil
uint32_t (*next_uint32)(void *st) nogil
double (*next_double)(void *st) nogil
uint64_t (*next_raw)(voi... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/_bounded_integers.pxd.in | from libc.stdint cimport (uint8_t, uint16_t, uint32_t, uint64_t,
int8_t, int16_t, int32_t, int64_t, intptr_t)
import numpy as np
cimport numpy as np
ctypedef np.npy_bool bool_t
from numpy.random cimport bitgen_t
cdef inline uint64_t _gen_mask(uint64_t max_val) nogil:
"""Mask generator fo... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/meson.build | # Build npyrandom library
# -----------------------
npyrandom_sources = [
'src/distributions/logfactorial.c',
'src/distributions/distributions.c',
'src/distributions/random_mvhg_count.c',
'src/distributions/random_mvhg_marginals.c',
'src/distributions/random_hypergeometric.c',
]
npyrandom_lib = static_librar... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/_generator.pyx | #!python
#cython: wraparound=False, nonecheck=False, boundscheck=False, cdivision=True, language_level=3, binding=True
import operator
import warnings
from collections.abc import Sequence
from cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer
from cpython cimport (Py_INCREF, PyFloat_AsDouble)
from cpyt... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/mtrand.pyx | #!python
#cython: wraparound=False, nonecheck=False, boundscheck=False, cdivision=True, language_level=3, binding=True
import operator
import warnings
from collections.abc import Sequence
import numpy as np
from cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer
from cpython cimport (Py_INCREF, PyFloat... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/_sfc64.pyi | from typing import TypedDict
from numpy import uint64
from numpy.random.bit_generator import BitGenerator, SeedSequence
from numpy._typing import NDArray, _ArrayLikeInt_co
class _SFC64Internal(TypedDict):
state: NDArray[uint64]
class _SFC64State(TypedDict):
bit_generator: str
state: _SFC64Internal
ha... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/_common.pyx | #!python
#cython: wraparound=False, nonecheck=False, boundscheck=False, cdivision=True, language_level=3
from collections import namedtuple
from cpython cimport PyFloat_AsDouble
import sys
import numpy as np
cimport numpy as np
cimport numpy.math as npmath
from libc.stdint cimport uintptr_t
cdef extern from "limits.h... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/_philox.pyx | #cython: binding=True
from cpython.pycapsule cimport PyCapsule_New
import numpy as np
cimport numpy as np
from libc.stdint cimport uint32_t, uint64_t
from ._common cimport uint64_to_double, int_to_array, wrap_int
from numpy.random cimport BitGenerator
__all__ = ['Philox']
np.import_array()
cdef int PHILOX_BUFFER_... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/bit_generator.pyx | #cython: binding=True
"""
BitGenerator base class and SeedSequence used to seed the BitGenerators.
SeedSequence is derived from Melissa E. O'Neill's C++11 `std::seed_seq`
implementation, as it has a lot of nice properties that we want.
https://gist.github.com/imneme/540829265469e673d045
https://www.pcg-random.org/po... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/__init__.pxd | cimport numpy as np
from libc.stdint cimport uint32_t, uint64_t
cdef extern from "numpy/random/bitgen.h":
struct bitgen:
void *state
uint64_t (*next_uint64)(void *st) nogil
uint32_t (*next_uint32)(void *st) nogil
double (*next_double)(void *st) nogil
uint64_t (*next_raw)(voi... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/c_distributions.pxd | #!python
#cython: wraparound=False, nonecheck=False, boundscheck=False, cdivision=True, language_level=3
from numpy cimport npy_intp
from libc.stdint cimport (uint64_t, int32_t, int64_t)
from numpy.random cimport bitgen_t
cdef extern from "numpy/random/distributions.h":
struct s_binomial_t:
int has_binom... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/_mt19937.pyx | #cython: binding=True
import operator
import numpy as np
cimport numpy as np
from libc.stdint cimport uint32_t, uint64_t
from numpy.random cimport BitGenerator, SeedSequence
__all__ = ['MT19937']
np.import_array()
cdef extern from "src/mt19937/mt19937.h":
struct s_mt19937_state:
uint32_t key[624]
... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/_pickle.py | from .mtrand import RandomState
from ._philox import Philox
from ._pcg64 import PCG64, PCG64DXSM
from ._sfc64 import SFC64
from ._generator import Generator
from ._mt19937 import MT19937
BitGenerators = {'MT19937': MT19937,
'PCG64': PCG64,
'PCG64DXSM': PCG64DXSM,
'Ph... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/_philox.pyi | from typing import TypedDict
from numpy import uint64
from numpy.typing import NDArray
from numpy.random.bit_generator import BitGenerator, SeedSequence
from numpy._typing import _ArrayLikeInt_co
class _PhiloxInternal(TypedDict):
counter: NDArray[uint64]
key: NDArray[uint64]
class _PhiloxState(TypedDict):
... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/mtrand.pyi | import builtins
from collections.abc import Callable
from typing import Any, overload, Literal
from numpy import (
bool_,
dtype,
float32,
float64,
int8,
int16,
int32,
int64,
long,
ulong,
uint8,
uint16,
uint32,
uint64,
)
from numpy.random.bit_generator import BitG... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/_pcg64.pyi | from typing import TypedDict
from numpy.random.bit_generator import BitGenerator, SeedSequence
from numpy._typing import _ArrayLikeInt_co
class _PCG64Internal(TypedDict):
state: int
inc: int
class _PCG64State(TypedDict):
bit_generator: str
state: _PCG64Internal
has_uint32: int
uinteger: int
... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/_sfc64.pyx | #cython: binding=True
import numpy as np
cimport numpy as np
from libc.stdint cimport uint32_t, uint64_t
from ._common cimport uint64_to_double
from numpy.random cimport BitGenerator
__all__ = ['SFC64']
cdef extern from "src/sfc64/sfc64.h":
struct s_sfc64_state:
uint64_t s[4]
int has_uint32
... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/_bounded_integers.pyx.in | #!python
#cython: wraparound=False, nonecheck=False, boundscheck=False, cdivision=True
import numpy as np
cimport numpy as np
__all__ = []
np.import_array()
cdef extern from "numpy/random/distributions.h":
# Generate random numbers in closed interval [off, off + rng].
uint64_t random_bounded_uint64(bitgen_... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/LICENSE.md | **This software is dual-licensed under the The University of Illinois/NCSA
Open Source License (NCSA) and The 3-Clause BSD License**
# NCSA Open Source License
**Copyright (c) 2019 Kevin Sheppard. All rights reserved.**
Developed by: Kevin Sheppard (<kevin.sheppard@economics.ox.ac.uk>,
<kevin.k.sheppard@gmail.com>)
[... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/_generator.pyi | from collections.abc import Callable
from typing import Any, overload, TypeVar, Literal
from numpy import (
bool_,
dtype,
float32,
float64,
int8,
int16,
int32,
int64,
int_,
uint,
uint8,
uint16,
uint32,
uint64,
)
from numpy.random import BitGenerator, SeedSequence... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/_mt19937.pyi | from typing import TypedDict
from numpy import uint32
from numpy.typing import NDArray
from numpy.random.bit_generator import BitGenerator, SeedSequence
from numpy._typing import _ArrayLikeInt_co
class _MT19937Internal(TypedDict):
key: NDArray[uint32]
pos: int
class _MT19937State(TypedDict):
bit_generato... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/_common.pxd | #cython: language_level=3
from libc.stdint cimport uint32_t, uint64_t, int32_t, int64_t
import numpy as np
cimport numpy as np
from numpy.random cimport bitgen_t
cdef double POISSON_LAM_MAX
cdef double LEGACY_POISSON_LAM_MAX
cdef uint64_t MAXSIZE
cdef enum ConstraintType:
CONS_NONE
CONS_NON_NEGATIVE
CO... | 0 |
public_repos/numpy/numpy | public_repos/numpy/numpy/random/__init__.py | """
========================
Random Number Generation
========================
Use ``default_rng()`` to create a `Generator` and call its methods.
=============== =========================================================
Generator
--------------- ---------------------------------------------------------
Generator ... | 0 |
public_repos/numpy/numpy/random | public_repos/numpy/numpy/random/include/aligned_malloc.h | #ifndef _RANDOMDGEN__ALIGNED_MALLOC_H_
#define _RANDOMDGEN__ALIGNED_MALLOC_H_
#include <Python.h>
#include "numpy/npy_common.h"
#define NPY_MEMALIGN 16 /* 16 for SSE2, 32 for AVX, 64 for Xeon Phi */
static inline void *PyArray_realloc_aligned(void *p, size_t n)
{
void *p1, **p2, *base;
size_t old_offs, offs ... | 0 |
public_repos/numpy/numpy/random | public_repos/numpy/numpy/random/include/legacy-distributions.h | #ifndef _RANDOMDGEN__DISTRIBUTIONS_LEGACY_H_
#define _RANDOMDGEN__DISTRIBUTIONS_LEGACY_H_
#include "numpy/random/distributions.h"
typedef struct aug_bitgen {
bitgen_t *bit_generator;
int has_gauss;
double gauss;
} aug_bitgen_t;
extern double legacy_gauss(aug_bitgen_t *aug_state);
extern double legacy_standard... | 0 |
public_repos/numpy/numpy/random/_examples | public_repos/numpy/numpy/random/_examples/cffi/extending.py | """
Use cffi to access any of the underlying C functions from distributions.h
"""
import os
import numpy as np
import cffi
from .parse import parse_distributions_h
ffi = cffi.FFI()
inc_dir = os.path.join(np.get_include(), 'numpy')
# Basic numpy types
ffi.cdef('''
typedef intptr_t npy_intp;
typedef unsigned ch... | 0 |
public_repos/numpy/numpy/random/_examples | public_repos/numpy/numpy/random/_examples/cffi/parse.py | import os
def parse_distributions_h(ffi, inc_dir):
"""
Parse distributions.h located in inc_dir for CFFI, filling in the ffi.cdef
Read the function declarations without the "#define ..." macros that will
be filled in when loading the library.
"""
with open(os.path.join(inc_dir, 'random', 'bi... | 0 |
public_repos/numpy/numpy/random/_examples | public_repos/numpy/numpy/random/_examples/cython/meson.build | project('random-build-examples', 'c', 'cpp', 'cython')
py_mod = import('python')
py3 = py_mod.find_installation(pure: false)
cc = meson.get_compiler('c')
cy = meson.get_compiler('cython')
if not cy.version().version_compare('>=0.29.35')
error('tests requires Cython >= 0.29.35')
endif
_numpy_abs = run_command(py3,... | 0 |
public_repos/numpy/numpy/random/_examples | public_repos/numpy/numpy/random/_examples/cython/extending.pyx | #!/usr/bin/env python3
#cython: language_level=3
from libc.stdint cimport uint32_t
from cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer
import numpy as np
cimport numpy as np
cimport cython
from numpy.random cimport bitgen_t
from numpy.random import PCG64
np.import_array()
@cython.boundscheck(Fa... | 0 |
public_repos/numpy/numpy/random/_examples | public_repos/numpy/numpy/random/_examples/cython/extending_distributions.pyx | #!/usr/bin/env python3
#cython: language_level=3
"""
This file shows how the to use a BitGenerator to create a distribution.
"""
import numpy as np
cimport numpy as np
cimport cython
from cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer
from libc.stdint cimport uint16_t, uint64_t
from numpy.random cimp... | 0 |
public_repos/numpy/numpy/random/_examples | public_repos/numpy/numpy/random/_examples/numba/extending.py | import numpy as np
import numba as nb
from numpy.random import PCG64
from timeit import timeit
bit_gen = PCG64()
next_d = bit_gen.cffi.next_double
state_addr = bit_gen.cffi.state_address
def normals(n, state):
out = np.empty(n)
for i in range((n + 1) // 2):
x1 = 2.0 * next_d(state) - 1.0
x2 =... | 0 |
public_repos/numpy/numpy/random/_examples | public_repos/numpy/numpy/random/_examples/numba/extending_distributions.py | r"""
Building the required library in this example requires a source distribution
of NumPy or clone of the NumPy git repository since distributions.c is not
included in binary distributions.
On *nix, execute in numpy/random/src/distributions
export ${PYTHON_VERSION}=3.8 # Python version
export PYTHON_INCLUDE=#path to... | 0 |
public_repos/numpy/numpy/random/src | public_repos/numpy/numpy/random/src/philox/philox.c | #include "philox.h"
extern inline uint64_t philox_next64(philox_state *state);
extern inline uint32_t philox_next32(philox_state *state);
extern void philox_jump(philox_state *state) {
/* Advances state as-if 2^128 draws were made */
state->ctr->v[2]++;
if (state->ctr->v[2] == 0) {
state->ctr->v[3]++;
}
... | 0 |
public_repos/numpy/numpy/random/src | public_repos/numpy/numpy/random/src/philox/philox.h | #ifndef _RANDOMDGEN__PHILOX_H_
#define _RANDOMDGEN__PHILOX_H_
#include "numpy/npy_common.h"
#include <inttypes.h>
#define PHILOX_BUFFER_SIZE 4L
struct r123array2x64 {
uint64_t v[2];
};
struct r123array4x64 {
uint64_t v[4];
};
enum r123_enum_philox4x64 { philox4x64_rounds = 10 };
typedef struct r123array4x64 phi... | 0 |
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