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remove venv

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Tykayn 2025-08-30 18:57:59 +02:00 • committed by tykayn
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170
venv/lib/python3.13/site-packages/numpy/__config__.py
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# 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_config"]
_built_with_meson = True
class DisplayModes(Enum):
stdout = "stdout"
dicts = "dicts"
def _cleanup(d):
"""
Removes empty values in a `dict` recursively
This ensures we remove values that Meson could not provide to CONFIG
"""
if isinstance(d, dict):
return {k: _cleanup(v) for k, v in d.items() if v and _cleanup(v)}
else:
return d
CONFIG = _cleanup(
{
"Compilers": {
"c": {
"name": "gcc",
"linker": r"ld.bfd",
"version": "14.2.1",
"commands": r"cc",
"args": r"",
"linker args": r"",
},
"cython": {
"name": "cython",
"linker": r"cython",
"version": "3.1.2",
"commands": r"cython",
"args": r"",
"linker args": r"",
},
"c++": {
"name": "gcc",
"linker": r"ld.bfd",
"version": "14.2.1",
"commands": r"c++",
"args": r"",
"linker args": r"",
},
},
"Machine Information": {
"host": {
"cpu": "x86_64",
"family": "x86_64",
"endian": "little",
"system": "linux",
},
"build": {
"cpu": "x86_64",
"family": "x86_64",
"endian": "little",
"system": "linux",
},
"cross-compiled": bool("False".lower().replace("false", "")),
},
"Build Dependencies": {
"blas": {
"name": "scipy-openblas",
"found": bool("True".lower().replace("false", "")),
"version": "0.3.30",
"detection method": "pkgconfig",
"include directory": r"/opt/_internal/cpython-3.13.5/lib/python3.13/site-packages/scipy_openblas64/include",
"lib directory": r"/opt/_internal/cpython-3.13.5/lib/python3.13/site-packages/scipy_openblas64/lib",
"openblas configuration": r"OpenBLAS 0.3.30 USE64BITINT DYNAMIC_ARCH NO_AFFINITY Haswell MAX_THREADS=64",
"pc file directory": r"/project/.openblas",
},
"lapack": {
"name": "scipy-openblas",
"found": bool("True".lower().replace("false", "")),
"version": "0.3.30",
"detection method": "pkgconfig",
"include directory": r"/opt/_internal/cpython-3.13.5/lib/python3.13/site-packages/scipy_openblas64/include",
"lib directory": r"/opt/_internal/cpython-3.13.5/lib/python3.13/site-packages/scipy_openblas64/lib",
"openblas configuration": r"OpenBLAS 0.3.30 USE64BITINT DYNAMIC_ARCH NO_AFFINITY Haswell MAX_THREADS=64",
"pc file directory": r"/project/.openblas",
},
},
"Python Information": {
"path": r"/tmp/build-env-7ps6a76l/bin/python",
"version": "3.13",
},
"SIMD Extensions": {
"baseline": __cpu_baseline__,
"found": [
feature for feature in __cpu_dispatch__ if __cpu_features__[feature]
],
"not found": [
feature for feature in __cpu_dispatch__ if not __cpu_features__[feature]
],
},
}
)
def _check_pyyaml():
import yaml
return yaml
def show(mode=DisplayModes.stdout.value):
"""
Show libraries and system information on which NumPy was built
and is being used
Parameters
----------
mode : {`'stdout'`, `'dicts'`}, optional.
Indicates how to display the config information.
`'stdout'` prints to console, `'dicts'` returns a dictionary
of the configuration.
Returns
-------
out : {`dict`, `None`}
If mode is `'dicts'`, a dict is returned, else None
See Also
--------
get_include : Returns the directory containing NumPy C
header files.
Notes
-----
1. The `'stdout'` mode will give more readable
output if ``pyyaml`` is installed
"""
if mode == DisplayModes.stdout.value:
try: # Non-standard library, check import
yaml = _check_pyyaml()
print(yaml.dump(CONFIG))
except ModuleNotFoundError:
import warnings
import json
warnings.warn("Install `pyyaml` for better output", stacklevel=1)
print(json.dumps(CONFIG, indent=2))
elif mode == DisplayModes.dicts.value:
return CONFIG
else:
raise AttributeError(
f"Invalid `mode`, use one of: {', '.join([e.value for e in DisplayModes])}"
)
def show_config(mode=DisplayModes.stdout.value):
return show(mode)
show_config.__doc__ = show.__doc__
show_config.__module__ = "numpy"

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venv/lib/python3.13/site-packages/numpy/__config__.pyi
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from enum import Enum
from types import ModuleType
from typing import Final, NotRequired, TypedDict, overload, type_check_only
from typing import Literal as L
_CompilerConfigDictValue = TypedDict(
"_CompilerConfigDictValue",
{
"name": str,
"linker": str,
"version": str,
"commands": str,
"args": str,
"linker args": str,
},
)
_CompilerConfigDict = TypedDict(
"_CompilerConfigDict",
{
"c": _CompilerConfigDictValue,
"cython": _CompilerConfigDictValue,
"c++": _CompilerConfigDictValue,
},
)
_MachineInformationDict = TypedDict(
"_MachineInformationDict",
{
"host": _MachineInformationDictValue,
"build": _MachineInformationDictValue,
"cross-compiled": NotRequired[L[True]],
},
)
@type_check_only
class _MachineInformationDictValue(TypedDict):
cpu: str
family: str
endian: L["little", "big"]
system: str
_BuildDependenciesDictValue = TypedDict(
"_BuildDependenciesDictValue",
{
"name": str,
"found": NotRequired[L[True]],
"version": str,
"include directory": str,
"lib directory": str,
"openblas configuration": str,
"pc file directory": str,
},
)
class _BuildDependenciesDict(TypedDict):
blas: _BuildDependenciesDictValue
lapack: _BuildDependenciesDictValue
class _PythonInformationDict(TypedDict):
path: str
version: str
_SIMDExtensionsDict = TypedDict(
"_SIMDExtensionsDict",
{
"baseline": list[str],
"found": list[str],
"not found": list[str],
},
)
_ConfigDict = TypedDict(
"_ConfigDict",
{
"Compilers": _CompilerConfigDict,
"Machine Information": _MachineInformationDict,
"Build Dependencies": _BuildDependenciesDict,
"Python Information": _PythonInformationDict,
"SIMD Extensions": _SIMDExtensionsDict,
},
)
###
__all__ = ["show_config"]
CONFIG: Final[_ConfigDict] = ...
class DisplayModes(Enum):
stdout = "stdout"
dicts = "dicts"
def _check_pyyaml() -> ModuleType: ...
@overload
def show(mode: L["stdout"] = "stdout") -> None: ...
@overload
def show(mode: L["dicts"]) -> _ConfigDict: ...
@overload
def show_config(mode: L["stdout"] = "stdout") -> None: ...
@overload
def show_config(mode: L["dicts"]) -> _ConfigDict: ...

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venv/lib/python3.13/site-packages/numpy/__init__.py
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"""
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 code, and a loose standing reference guide, available from
`the NumPy homepage <https://numpy.org>`_.
We recommend exploring the docstrings using
`IPython <https://ipython.org>`_, an advanced Python shell with
TAB-completion and introspection capabilities. See below for further
instructions.
The docstring examples assume that `numpy` has been imported as ``np``::
>>> import numpy as np
Code snippets are indicated by three greater-than signs::
>>> x = 42
>>> x = x + 1
Use the built-in ``help`` function to view a function's docstring::
>>> help(np.sort)
... # doctest: +SKIP
For some objects, ``np.info(obj)`` may provide additional help. This is
particularly true if you see the line "Help on ufunc object:" at the top
of the help() page. Ufuncs are implemented in C, not Python, for speed.
The native Python help() does not know how to view their help, but our
np.info() function does.
Available subpackages
---------------------
lib
Basic functions used by several sub-packages.
random
Core Random Tools
linalg
Core Linear Algebra Tools
fft
Core FFT routines
polynomial
Polynomial tools
testing
NumPy testing tools
distutils
Enhancements to distutils with support for
Fortran compilers support and more (for Python <= 3.11)
Utilities
---------
test
Run numpy unittests
show_config
Show numpy build configuration
__version__
NumPy version string
Viewing documentation using IPython
-----------------------------------
Start IPython and import `numpy` usually under the alias ``np``: `import
numpy as np`. Then, directly past or use the ``%cpaste`` magic to paste
examples into the shell. To see which functions are available in `numpy`,
type ``np.<TAB>`` (where ``<TAB>`` refers to the TAB key), or use
``np.*cos*?<ENTER>`` (where ``<ENTER>`` refers to the ENTER key) to narrow
down the list. To view the docstring for a function, use
``np.cos?<ENTER>`` (to view the docstring) and ``np.cos??<ENTER>`` (to view
the source code).
Copies vs. in-place operation
-----------------------------
Most of the functions in `numpy` return a copy of the array argument
(e.g., `np.sort`). In-place versions of these functions are often
available as array methods, i.e. ``x = np.array([1,2,3]); x.sort()``.
Exceptions to this rule are documented.
"""
import os
import sys
import warnings
# If a version with git hash was stored, use that instead
from . import version
from ._expired_attrs_2_0 import __expired_attributes__
from ._globals import _CopyMode, _NoValue
from .version import __version__
# We first need to detect if we're being called as part of the numpy setup
# procedure itself in a reliable manner.
try:
__NUMPY_SETUP__ # noqa: B018
except NameError:
__NUMPY_SETUP__ = False
if __NUMPY_SETUP__:
sys.stderr.write('Running from numpy source directory.\n')
else:
# Allow distributors to run custom init code before importing numpy._core
from . import _distributor_init
try:
from numpy.__config__ import show_config
except ImportError as e:
msg = """Error importing numpy: you should not try to import numpy from
its source directory; please exit the numpy source tree, and relaunch
your python interpreter from there."""
raise ImportError(msg) from e
from . import _core
from ._core import (
False_,
ScalarType,
True_,
abs,
absolute,
acos,
acosh,
add,
all,
allclose,
amax,
amin,
any,
arange,
arccos,
arccosh,
arcsin,
arcsinh,
arctan,
arctan2,
arctanh,
argmax,
argmin,
argpartition,
argsort,
argwhere,
around,
array,
array2string,
array_equal,
array_equiv,
array_repr,
array_str,
asanyarray,
asarray,
ascontiguousarray,
asfortranarray,
asin,
asinh,
astype,
atan,
atan2,
atanh,
atleast_1d,
atleast_2d,
atleast_3d,
base_repr,
binary_repr,
bitwise_and,
bitwise_count,
bitwise_invert,
bitwise_left_shift,
bitwise_not,
bitwise_or,
bitwise_right_shift,
bitwise_xor,
block,
bool,
bool_,
broadcast,
busday_count,
busday_offset,
busdaycalendar,
byte,
bytes_,
can_cast,
cbrt,
cdouble,
ceil,
character,
choose,
clip,
clongdouble,
complex64,
complex128,
complexfloating,
compress,
concat,
concatenate,
conj,
conjugate,
convolve,
copysign,
copyto,
correlate,
cos,
cosh,
count_nonzero,
cross,
csingle,
cumprod,
cumsum,
cumulative_prod,
cumulative_sum,
datetime64,
datetime_as_string,
datetime_data,
deg2rad,
degrees,
diagonal,
divide,
divmod,
dot,
double,
dtype,
e,
einsum,
einsum_path,
empty,
empty_like,
equal,
errstate,
euler_gamma,
exp,
exp2,
expm1,
fabs,
finfo,
flatiter,
flatnonzero,
flexible,
float16,
float32,
float64,
float_power,
floating,
floor,
floor_divide,
fmax,
fmin,
fmod,
format_float_positional,
format_float_scientific,
frexp,
from_dlpack,
frombuffer,
fromfile,
fromfunction,
fromiter,
frompyfunc,
fromstring,
full,
full_like,
gcd,
generic,
geomspace,
get_printoptions,
getbufsize,
geterr,
geterrcall,
greater,
greater_equal,
half,
heaviside,
hstack,
hypot,
identity,
iinfo,
indices,
inexact,
inf,
inner,
int8,
int16,
int32,
int64,
int_,
intc,
integer,
intp,
invert,
is_busday,
isclose,
isdtype,
isfinite,
isfortran,
isinf,
isnan,
isnat,
isscalar,
issubdtype,
lcm,
ldexp,
left_shift,
less,
less_equal,
lexsort,
linspace,
little_endian,
log,
log1p,
log2,
log10,
logaddexp,
logaddexp2,
logical_and,
logical_not,
logical_or,
logical_xor,
logspace,
long,
longdouble,
longlong,
matmul,
matrix_transpose,
matvec,
max,
maximum,
may_share_memory,
mean,
memmap,
min,
min_scalar_type,
minimum,
mod,
modf,
moveaxis,
multiply,
nan,
ndarray,
ndim,
nditer,
negative,
nested_iters,
newaxis,
nextafter,
nonzero,
not_equal,
number,
object_,
ones,
ones_like,
outer,
partition,
permute_dims,
pi,
positive,
pow,
power,
printoptions,
prod,
promote_types,
ptp,
put,
putmask,
rad2deg,
radians,
ravel,
recarray,
reciprocal,
record,
remainder,
repeat,
require,
reshape,
resize,
result_type,
right_shift,
rint,
roll,
rollaxis,
round,
sctypeDict,
searchsorted,
set_printoptions,
setbufsize,
seterr,
seterrcall,
shape,
shares_memory,
short,
sign,
signbit,
signedinteger,
sin,
single,
sinh,
size,
sort,
spacing,
sqrt,
square,
squeeze,
stack,
std,
str_,
subtract,
sum,
swapaxes,
take,
tan,
tanh,
tensordot,
timedelta64,
trace,
transpose,
true_divide,
trunc,
typecodes,
ubyte,
ufunc,
uint,
uint8,
uint16,
uint32,
uint64,
uintc,
uintp,
ulong,
ulonglong,
unsignedinteger,
unstack,
ushort,
var,
vdot,
vecdot,
vecmat,
void,
vstack,
where,
zeros,
zeros_like,
)
# NOTE: It's still under discussion whether these aliases
# should be removed.
for ta in ["float96", "float128", "complex192", "complex256"]:
try:
globals()[ta] = getattr(_core, ta)
except AttributeError:
pass
del ta
from . import lib
from . import matrixlib as _mat
from .lib import scimath as emath
from .lib._arraypad_impl import pad
from .lib._arraysetops_impl import (
ediff1d,
in1d,
intersect1d,
isin,
setdiff1d,
setxor1d,
union1d,
unique,
unique_all,
unique_counts,
unique_inverse,
unique_values,
)
from .lib._function_base_impl import (
angle,
append,
asarray_chkfinite,
average,
bartlett,
bincount,
blackman,
copy,
corrcoef,
cov,
delete,
diff,
digitize,
extract,
flip,
gradient,
hamming,
hanning,
i0,
insert,
interp,
iterable,
kaiser,
median,
meshgrid,
percentile,
piecewise,
place,
quantile,
rot90,
select,
sinc,
sort_complex,
trapezoid,
trapz,
trim_zeros,
unwrap,
vectorize,
)
from .lib._histograms_impl import histogram, histogram_bin_edges, histogramdd
from .lib._index_tricks_impl import (
c_,
diag_indices,
diag_indices_from,
fill_diagonal,
index_exp,
ix_,
mgrid,
ndenumerate,
ndindex,
ogrid,
r_,
ravel_multi_index,
s_,
unravel_index,
)
from .lib._nanfunctions_impl import (
nanargmax,
nanargmin,
nancumprod,
nancumsum,
nanmax,
nanmean,
nanmedian,
nanmin,
nanpercentile,
nanprod,
nanquantile,
nanstd,
nansum,
nanvar,
)
from .lib._npyio_impl import (
fromregex,
genfromtxt,
load,
loadtxt,
packbits,
save,
savetxt,
savez,
savez_compressed,
unpackbits,
)
from .lib._polynomial_impl import (
poly,
poly1d,
polyadd,
polyder,
polydiv,
polyfit,
polyint,
polymul,
polysub,
polyval,
roots,
)
from .lib._shape_base_impl import (
apply_along_axis,
apply_over_axes,
array_split,
column_stack,
dsplit,
dstack,
expand_dims,
hsplit,
kron,
put_along_axis,
row_stack,
split,
take_along_axis,
tile,
vsplit,
)
from .lib._stride_tricks_impl import (
broadcast_arrays,
broadcast_shapes,
broadcast_to,
)
from .lib._twodim_base_impl import (
diag,
diagflat,
eye,
fliplr,
flipud,
histogram2d,
mask_indices,
tri,
tril,
tril_indices,
tril_indices_from,
triu,
triu_indices,
triu_indices_from,
vander,
)
from .lib._type_check_impl import (
common_type,
imag,
iscomplex,
iscomplexobj,
isreal,
isrealobj,
mintypecode,
nan_to_num,
real,
real_if_close,
typename,
)
from .lib._ufunclike_impl import fix, isneginf, isposinf
from .lib._utils_impl import get_include, info, show_runtime
from .matrixlib import asmatrix, bmat, matrix
# public submodules are imported lazily, therefore are accessible from
# __getattr__. Note that `distutils` (deprecated) and `array_api`
# (experimental label) are not added here, because `from numpy import *`
# must not raise any warnings - that's too disruptive.
__numpy_submodules__ = {
"linalg", "fft", "dtypes", "random", "polynomial", "ma",
"exceptions", "lib", "ctypeslib", "testing", "typing",
"f2py", "test", "rec", "char", "core", "strings",
}
# We build warning messages for former attributes
_msg = (
"module 'numpy' has no attribute '{n}'.\n"
"`np.{n}` was a deprecated alias for the builtin `{n}`. "
"To avoid this error in existing code, use `{n}` by itself. "
"Doing this will not modify any behavior and is safe. {extended_msg}\n"
"The aliases was originally deprecated in NumPy 1.20; for more "
"details and guidance see the original release note at:\n"
" https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations")
_specific_msg = (
"If you specifically wanted the numpy scalar type, use `np.{}` here.")
_int_extended_msg = (
"When replacing `np.{}`, you may wish to use e.g. `np.int64` "
"or `np.int32` to specify the precision. If you wish to review "
"your current use, check the release note link for "
"additional information.")
_type_info = [
("object", ""), # The NumPy scalar only exists by name.
("float", _specific_msg.format("float64")),
("complex", _specific_msg.format("complex128")),
("str", _specific_msg.format("str_")),
("int", _int_extended_msg.format("int"))]
__former_attrs__ = {
n: _msg.format(n=n, extended_msg=extended_msg)
for n, extended_msg in _type_info
}
# Some of these could be defined right away, but most were aliases to
# the Python objects and only removed in NumPy 1.24. Defining them should
# probably wait for NumPy 1.26 or 2.0.
# When defined, these should possibly not be added to `__all__` to avoid
# import with `from numpy import *`.
__future_scalars__ = {"str", "bytes", "object"}
__array_api_version__ = "2024.12"
from ._array_api_info import __array_namespace_info__
# now that numpy core module is imported, can initialize limits
_core.getlimits._register_known_types()
__all__ = list(
__numpy_submodules__ |
set(_core.__all__) |
set(_mat.__all__) |
set(lib._histograms_impl.__all__) |
set(lib._nanfunctions_impl.__all__) |
set(lib._function_base_impl.__all__) |
set(lib._twodim_base_impl.__all__) |
set(lib._shape_base_impl.__all__) |
set(lib._type_check_impl.__all__) |
set(lib._arraysetops_impl.__all__) |
set(lib._ufunclike_impl.__all__) |
set(lib._arraypad_impl.__all__) |
set(lib._utils_impl.__all__) |
set(lib._stride_tricks_impl.__all__) |
set(lib._polynomial_impl.__all__) |
set(lib._npyio_impl.__all__) |
set(lib._index_tricks_impl.__all__) |
{"emath", "show_config", "__version__", "__array_namespace_info__"}
)
# Filter out Cython harmless warnings
warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
warnings.filterwarnings("ignore", message="numpy.ndarray size changed")
def __getattr__(attr):
# Warn for expired attributes
import warnings
if attr == "linalg":
import numpy.linalg as linalg
return linalg
elif attr == "fft":
import numpy.fft as fft
return fft
elif attr == "dtypes":
import numpy.dtypes as dtypes
return dtypes
elif attr == "random":
import numpy.random as random
return random
elif attr == "polynomial":
import numpy.polynomial as polynomial
return polynomial
elif attr == "ma":
import numpy.ma as ma
return ma
elif attr == "ctypeslib":
import numpy.ctypeslib as ctypeslib
return ctypeslib
elif attr == "exceptions":
import numpy.exceptions as exceptions
return exceptions
elif attr == "testing":
import numpy.testing as testing
return testing
elif attr == "matlib":
import numpy.matlib as matlib
return matlib
elif attr == "f2py":
import numpy.f2py as f2py
return f2py
elif attr == "typing":
import numpy.typing as typing
return typing
elif attr == "rec":
import numpy.rec as rec
return rec
elif attr == "char":
import numpy.char as char
return char
elif attr == "array_api":
raise AttributeError("`numpy.array_api` is not available from "
"numpy 2.0 onwards", name=None)
elif attr == "core":
import numpy.core as core
return core
elif attr == "strings":
import numpy.strings as strings
return strings
elif attr == "distutils":
if 'distutils' in __numpy_submodules__:
import numpy.distutils as distutils
return distutils
else:
raise AttributeError("`numpy.distutils` is not available from "
"Python 3.12 onwards", name=None)
if attr in __future_scalars__:
# And future warnings for those that will change, but also give
# the AttributeError
warnings.warn(
f"In the future `np.{attr}` will be defined as the "
"corresponding NumPy scalar.", FutureWarning, stacklevel=2)
if attr in __former_attrs__:
raise AttributeError(__former_attrs__[attr], name=None)
if attr in __expired_attributes__:
raise AttributeError(
f"`np.{attr}` was removed in the NumPy 2.0 release. "
f"{__expired_attributes__[attr]}",
name=None
)
if attr == "chararray":
warnings.warn(
"`np.chararray` is deprecated and will be removed from "
"the main namespace in the future. Use an array with a string "
"or bytes dtype instead.", DeprecationWarning, stacklevel=2)
import numpy.char as char
return char.chararray
raise AttributeError(f"module {__name__!r} has no attribute {attr!r}")
def __dir__():
public_symbols = (
globals().keys() | __numpy_submodules__
)
public_symbols -= {
"matrixlib", "matlib", "tests", "conftest", "version",
"distutils", "array_api"
}
return list(public_symbols)
# Pytest testing
from numpy._pytesttester import PytestTester
test = PytestTester(__name__)
del PytestTester
def _sanity_check():
"""
Quick sanity checks for common bugs caused by environment.
There are some cases e.g. with wrong BLAS ABI that cause wrong
results under specific runtime conditions that are not necessarily
achieved during test suite runs, and it is useful to catch those early.
See https://github.com/numpy/numpy/issues/8577 and other
similar bug reports.
"""
try:
x = ones(2, dtype=float32)
if not abs(x.dot(x) - float32(2.0)) < 1e-5:
raise AssertionError
except AssertionError:
msg = ("The current Numpy installation ({!r}) fails to "
"pass simple sanity checks. This can be caused for example "
"by incorrect BLAS library being linked in, or by mixing "
"package managers (pip, conda, apt, ...). Search closed "
"numpy issues for similar problems.")
raise RuntimeError(msg.format(__file__)) from None
_sanity_check()
del _sanity_check
def _mac_os_check():
"""
Quick Sanity check for Mac OS look for accelerate build bugs.
Testing numpy polyfit calls init_dgelsd(LAPACK)
"""
try:
c = array([3., 2., 1.])
x = linspace(0, 2, 5)
y = polyval(c, x)
_ = polyfit(x, y, 2, cov=True)
except ValueError:
pass
if sys.platform == "darwin":
from . import exceptions
with warnings.catch_warnings(record=True) as w:
_mac_os_check()
# Throw runtime error, if the test failed
# Check for warning and report the error_message
if len(w) > 0:
for _wn in w:
if _wn.category is exceptions.RankWarning:
# Ignore other warnings, they may not be relevant (see gh-25433)
error_message = (
f"{_wn.category.__name__}: {_wn.message}"
)
msg = (
"Polyfit sanity test emitted a warning, most likely due "
"to using a buggy Accelerate backend."
"\nIf you compiled yourself, more information is available at:" # noqa: E501
"\nhttps://numpy.org/devdocs/building/index.html"
"\nOtherwise report this to the vendor "
f"that provided NumPy.\n\n{error_message}\n")
raise RuntimeError(msg)
del _wn
del w
del _mac_os_check
def hugepage_setup():
"""
We usually use madvise hugepages support, but on some old kernels it
is slow and thus better avoided. Specifically kernel version 4.6
had a bug fix which probably fixed this:
https://github.com/torvalds/linux/commit/7cf91a98e607c2f935dbcc177d70011e95b8faff
"""
use_hugepage = os.environ.get("NUMPY_MADVISE_HUGEPAGE", None)
if sys.platform == "linux" and use_hugepage is None:
# If there is an issue with parsing the kernel version,
# set use_hugepage to 0. Usage of LooseVersion will handle
# the kernel version parsing better, but avoided since it
# will increase the import time.
# See: #16679 for related discussion.
try:
use_hugepage = 1
kernel_version = os.uname().release.split(".")[:2]
kernel_version = tuple(int(v) for v in kernel_version)
if kernel_version < (4, 6):
use_hugepage = 0
except ValueError:
use_hugepage = 0
elif use_hugepage is None:
# This is not Linux, so it should not matter, just enable anyway
use_hugepage = 1
else:
use_hugepage = int(use_hugepage)
return use_hugepage
# Note that this will currently only make a difference on Linux
_core.multiarray._set_madvise_hugepage(hugepage_setup())
del hugepage_setup
# Give a warning if NumPy is reloaded or imported on a sub-interpreter
# We do this from python, since the C-module may not be reloaded and
# it is tidier organized.
_core.multiarray._multiarray_umath._reload_guard()
# TODO: Remove the environment variable entirely now that it is "weak"
if (os.environ.get("NPY_PROMOTION_STATE", "weak") != "weak"):
warnings.warn(
"NPY_PROMOTION_STATE was a temporary feature for NumPy 2.0 "
"transition and is ignored after NumPy 2.2.",
UserWarning, stacklevel=2)
# Tell PyInstaller where to find hook-numpy.py
def _pyinstaller_hooks_dir():
from pathlib import Path
return [str(Path(__file__).with_name("_pyinstaller").resolve())]
# Remove symbols imported for internal use
del os, sys, warnings

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"""
Array API Inspection namespace
This is the namespace for inspection functions as defined by the array API
standard. See
https://data-apis.org/array-api/latest/API_specification/inspection.html for
more details.
"""
from numpy._core import (
bool,
complex64,
complex128,
dtype,
float32,
float64,
int8,
int16,
int32,
int64,
intp,
uint8,
uint16,
uint32,
uint64,
)
class __array_namespace_info__:
"""
Get the array API inspection namespace for NumPy.
The array API inspection namespace defines the following functions:
- capabilities()
- default_device()
- default_dtypes()
- dtypes()
- devices()
See
https://data-apis.org/array-api/latest/API_specification/inspection.html
for more details.
Returns
-------
info : ModuleType
The array API inspection namespace for NumPy.
Examples
--------
>>> info = np.__array_namespace_info__()
>>> info.default_dtypes()
{'real floating': numpy.float64,
'complex floating': numpy.complex128,
'integral': numpy.int64,
'indexing': numpy.int64}
"""
__module__ = 'numpy'
def capabilities(self):
"""
Return a dictionary of array API library capabilities.
The resulting dictionary has the following keys:
- **"boolean indexing"**: boolean indicating whether an array library
supports boolean indexing. Always ``True`` for NumPy.
- **"data-dependent shapes"**: boolean indicating whether an array
library supports data-dependent output shapes. Always ``True`` for
NumPy.
See
https://data-apis.org/array-api/latest/API_specification/generated/array_api.info.capabilities.html
for more details.
See Also
--------
__array_namespace_info__.default_device,
__array_namespace_info__.default_dtypes,
__array_namespace_info__.dtypes,
__array_namespace_info__.devices
Returns
-------
capabilities : dict
A dictionary of array API library capabilities.
Examples
--------
>>> info = np.__array_namespace_info__()
>>> info.capabilities()
{'boolean indexing': True,
'data-dependent shapes': True,
'max dimensions': 64}
"""
return {
"boolean indexing": True,
"data-dependent shapes": True,
"max dimensions": 64,
}
def default_device(self):
"""
The default device used for new NumPy arrays.
For NumPy, this always returns ``'cpu'``.
See Also
--------
__array_namespace_info__.capabilities,
__array_namespace_info__.default_dtypes,
__array_namespace_info__.dtypes,
__array_namespace_info__.devices
Returns
-------
device : str
The default device used for new NumPy arrays.
Examples
--------
>>> info = np.__array_namespace_info__()
>>> info.default_device()
'cpu'
"""
return "cpu"
def default_dtypes(self, *, device=None):
"""
The default data types used for new NumPy arrays.
For NumPy, this always returns the following dictionary:
- **"real floating"**: ``numpy.float64``
- **"complex floating"**: ``numpy.complex128``
- **"integral"**: ``numpy.intp``
- **"indexing"**: ``numpy.intp``
Parameters
----------
device : str, optional
The device to get the default data types for. For NumPy, only
``'cpu'`` is allowed.
Returns
-------
dtypes : dict
A dictionary describing the default data types used for new NumPy
arrays.
See Also
--------
__array_namespace_info__.capabilities,
__array_namespace_info__.default_device,
__array_namespace_info__.dtypes,
__array_namespace_info__.devices
Examples
--------
>>> info = np.__array_namespace_info__()
>>> info.default_dtypes()
{'real floating': numpy.float64,
'complex floating': numpy.complex128,
'integral': numpy.int64,
'indexing': numpy.int64}
"""
if device not in ["cpu", None]:
raise ValueError(
'Device not understood. Only "cpu" is allowed, but received:'
f' {device}'
)
return {
"real floating": dtype(float64),
"complex floating": dtype(complex128),
"integral": dtype(intp),
"indexing": dtype(intp),
}
def dtypes(self, *, device=None, kind=None):
"""
The array API data types supported by NumPy.
Note that this function only returns data types that are defined by
the array API.
Parameters
----------
device : str, optional
The device to get the data types for. For NumPy, only ``'cpu'`` is
allowed.
kind : str or tuple of str, optional
The kind of data types to return. If ``None``, all data types are
returned. If a string, only data types of that kind are returned.
If a tuple, a dictionary containing the union of the given kinds
is returned. The following kinds are supported:
- ``'bool'``: boolean data types (i.e., ``bool``).
- ``'signed integer'``: signed integer data types (i.e., ``int8``,
``int16``, ``int32``, ``int64``).
- ``'unsigned integer'``: unsigned integer data types (i.e.,
``uint8``, ``uint16``, ``uint32``, ``uint64``).
- ``'integral'``: integer data types. Shorthand for ``('signed
integer', 'unsigned integer')``.
- ``'real floating'``: real-valued floating-point data types
(i.e., ``float32``, ``float64``).
- ``'complex floating'``: complex floating-point data types (i.e.,
``complex64``, ``complex128``).
- ``'numeric'``: numeric data types. Shorthand for ``('integral',
'real floating', 'complex floating')``.
Returns
-------
dtypes : dict
A dictionary mapping the names of data types to the corresponding
NumPy data types.
See Also
--------
__array_namespace_info__.capabilities,
__array_namespace_info__.default_device,
__array_namespace_info__.default_dtypes,
__array_namespace_info__.devices
Examples
--------
>>> info = np.__array_namespace_info__()
>>> info.dtypes(kind='signed integer')
{'int8': numpy.int8,
'int16': numpy.int16,
'int32': numpy.int32,
'int64': numpy.int64}
"""
if device not in ["cpu", None]:
raise ValueError(
'Device not understood. Only "cpu" is allowed, but received:'
f' {device}'
)
if kind is None:
return {
"bool": dtype(bool),
"int8": dtype(int8),
"int16": dtype(int16),
"int32": dtype(int32),
"int64": dtype(int64),
"uint8": dtype(uint8),
"uint16": dtype(uint16),
"uint32": dtype(uint32),
"uint64": dtype(uint64),
"float32": dtype(float32),
"float64": dtype(float64),
"complex64": dtype(complex64),
"complex128": dtype(complex128),
}
if kind == "bool":
return {"bool": bool}
if kind == "signed integer":
return {
"int8": dtype(int8),
"int16": dtype(int16),
"int32": dtype(int32),
"int64": dtype(int64),
}
if kind == "unsigned integer":
return {
"uint8": dtype(uint8),
"uint16": dtype(uint16),
"uint32": dtype(uint32),
"uint64": dtype(uint64),
}
if kind == "integral":
return {
"int8": dtype(int8),
"int16": dtype(int16),
"int32": dtype(int32),
"int64": dtype(int64),
"uint8": dtype(uint8),
"uint16": dtype(uint16),
"uint32": dtype(uint32),
"uint64": dtype(uint64),
}
if kind == "real floating":
return {
"float32": dtype(float32),
"float64": dtype(float64),
}
if kind == "complex floating":
return {
"complex64": dtype(complex64),
"complex128": dtype(complex128),
}
if kind == "numeric":
return {
"int8": dtype(int8),
"int16": dtype(int16),
"int32": dtype(int32),
"int64": dtype(int64),
"uint8": dtype(uint8),
"uint16": dtype(uint16),
"uint32": dtype(uint32),
"uint64": dtype(uint64),
"float32": dtype(float32),
"float64": dtype(float64),
"complex64": dtype(complex64),
"complex128": dtype(complex128),
}
if isinstance(kind, tuple):
res = {}
for k in kind:
res.update(self.dtypes(kind=k))
return res
raise ValueError(f"unsupported kind: {kind!r}")
def devices(self):
"""
The devices supported by NumPy.
For NumPy, this always returns ``['cpu']``.
Returns
-------
devices : list of str
The devices supported by NumPy.
See Also
--------
__array_namespace_info__.capabilities,
__array_namespace_info__.default_device,
__array_namespace_info__.default_dtypes,
__array_namespace_info__.dtypes
Examples
--------
>>> info = np.__array_namespace_info__()
>>> info.devices()
['cpu']
"""
return ["cpu"]

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from typing import (
ClassVar,
Literal,
Never,
TypeAlias,
TypedDict,
TypeVar,
final,
overload,
type_check_only,
)
import numpy as np
_Device: TypeAlias = Literal["cpu"]
_DeviceLike: TypeAlias = _Device | None
_Capabilities = TypedDict(
"_Capabilities",
{
"boolean indexing": Literal[True],
"data-dependent shapes": Literal[True],
},
)
_DefaultDTypes = TypedDict(
"_DefaultDTypes",
{
"real floating": np.dtype[np.float64],
"complex floating": np.dtype[np.complex128],
"integral": np.dtype[np.intp],
"indexing": np.dtype[np.intp],
},
)
_KindBool: TypeAlias = Literal["bool"]
_KindInt: TypeAlias = Literal["signed integer"]
_KindUInt: TypeAlias = Literal["unsigned integer"]
_KindInteger: TypeAlias = Literal["integral"]
_KindFloat: TypeAlias = Literal["real floating"]
_KindComplex: TypeAlias = Literal["complex floating"]
_KindNumber: TypeAlias = Literal["numeric"]
_Kind: TypeAlias = (
_KindBool
| _KindInt
| _KindUInt
| _KindInteger
| _KindFloat
| _KindComplex
| _KindNumber
)
_T1 = TypeVar("_T1")
_T2 = TypeVar("_T2")
_T3 = TypeVar("_T3")
_Permute1: TypeAlias = _T1 | tuple[_T1]
_Permute2: TypeAlias = tuple[_T1, _T2] | tuple[_T2, _T1]
_Permute3: TypeAlias = (
tuple[_T1, _T2, _T3] | tuple[_T1, _T3, _T2]
| tuple[_T2, _T1, _T3] | tuple[_T2, _T3, _T1]
| tuple[_T3, _T1, _T2] | tuple[_T3, _T2, _T1]
)
@type_check_only
class _DTypesBool(TypedDict):
bool: np.dtype[np.bool]
@type_check_only
class _DTypesInt(TypedDict):
int8: np.dtype[np.int8]
int16: np.dtype[np.int16]
int32: np.dtype[np.int32]
int64: np.dtype[np.int64]
@type_check_only
class _DTypesUInt(TypedDict):
uint8: np.dtype[np.uint8]
uint16: np.dtype[np.uint16]
uint32: np.dtype[np.uint32]
uint64: np.dtype[np.uint64]
@type_check_only
class _DTypesInteger(_DTypesInt, _DTypesUInt): ...
@type_check_only
class _DTypesFloat(TypedDict):
float32: np.dtype[np.float32]
float64: np.dtype[np.float64]
@type_check_only
class _DTypesComplex(TypedDict):
complex64: np.dtype[np.complex64]
complex128: np.dtype[np.complex128]
@type_check_only
class _DTypesNumber(_DTypesInteger, _DTypesFloat, _DTypesComplex): ...
@type_check_only
class _DTypes(_DTypesBool, _DTypesNumber): ...
@type_check_only
class _DTypesUnion(TypedDict, total=False):
bool: np.dtype[np.bool]
int8: np.dtype[np.int8]
int16: np.dtype[np.int16]
int32: np.dtype[np.int32]
int64: np.dtype[np.int64]
uint8: np.dtype[np.uint8]
uint16: np.dtype[np.uint16]
uint32: np.dtype[np.uint32]
uint64: np.dtype[np.uint64]
float32: np.dtype[np.float32]
float64: np.dtype[np.float64]
complex64: np.dtype[np.complex64]
complex128: np.dtype[np.complex128]
_EmptyDict: TypeAlias = dict[Never, Never]
@final
class __array_namespace_info__:
__module__: ClassVar[Literal['numpy']]
def capabilities(self) -> _Capabilities: ...
def default_device(self) -> _Device: ...
def default_dtypes(
self,
*,
device: _DeviceLike = ...,
) -> _DefaultDTypes: ...
def devices(self) -> list[_Device]: ...
@overload
def dtypes(
self,
*,
device: _DeviceLike = ...,
kind: None = ...,
) -> _DTypes: ...
@overload
def dtypes(
self,
*,
device: _DeviceLike = ...,
kind: _Permute1[_KindBool],
) -> _DTypesBool: ...
@overload
def dtypes(
self,
*,
device: _DeviceLike = ...,
kind: _Permute1[_KindInt],
) -> _DTypesInt: ...
@overload
def dtypes(
self,
*,
device: _DeviceLike = ...,
kind: _Permute1[_KindUInt],
) -> _DTypesUInt: ...
@overload
def dtypes(
self,
*,
device: _DeviceLike = ...,
kind: _Permute1[_KindFloat],
) -> _DTypesFloat: ...
@overload
def dtypes(
self,
*,
device: _DeviceLike = ...,
kind: _Permute1[_KindComplex],
) -> _DTypesComplex: ...
@overload
def dtypes(
self,
*,
device: _DeviceLike = ...,
kind: (
_Permute1[_KindInteger]
| _Permute2[_KindInt, _KindUInt]
),
) -> _DTypesInteger: ...
@overload
def dtypes(
self,
*,
device: _DeviceLike = ...,
kind: (
_Permute1[_KindNumber]
| _Permute3[_KindInteger, _KindFloat, _KindComplex]
),
) -> _DTypesNumber: ...
@overload
def dtypes(
self,
*,
device: _DeviceLike = ...,
kind: tuple[()],
) -> _EmptyDict: ...
@overload
def dtypes(
self,
*,
device: _DeviceLike = ...,
kind: tuple[_Kind, ...],
) -> _DTypesUnion: ...

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import argparse
import sys
from pathlib import Path
from .lib._utils_impl import get_include
from .version import __version__
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument(
"--version",
action="version",
version=__version__,
help="Print the version and exit.",
)
parser.add_argument(
"--cflags",
action="store_true",
help="Compile flag needed when using the NumPy headers.",
)
parser.add_argument(
"--pkgconfigdir",
action="store_true",
help=("Print the pkgconfig directory in which `numpy.pc` is stored "
"(useful for setting $PKG_CONFIG_PATH)."),
)
args = parser.parse_args()
if not sys.argv[1:]:
parser.print_help()
if args.cflags:
print("-I" + get_include())
if args.pkgconfigdir:
_path = Path(get_include()) / '..' / 'lib' / 'pkgconfig'
print(_path.resolve())
if __name__ == "__main__":
main()

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def main() -> None: ...

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venv/lib/python3.13/site-packages/numpy/_core/__init__.py
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"""
Contains the core of NumPy: ndarray, ufuncs, dtypes, etc.
Please note that this module is private. All functions and objects
are available in the main ``numpy`` namespace - use that instead.
"""
import os
from numpy.version import version as __version__
# disables OpenBLAS affinity setting of the main thread that limits
# python threads or processes to one core
env_added = []
for envkey in ['OPENBLAS_MAIN_FREE', 'GOTOBLAS_MAIN_FREE']:
if envkey not in os.environ:
os.environ[envkey] = '1'
env_added.append(envkey)
try:
from . import multiarray
except ImportError as exc:
import sys
msg = """
IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!
Importing the numpy C-extensions failed. This error can happen for
many reasons, often due to issues with your setup or how NumPy was
installed.
We have compiled some common reasons and troubleshooting tips at:
https://numpy.org/devdocs/user/troubleshooting-importerror.html
Please note and check the following:
* The Python version is: Python%d.%d from "%s"
* The NumPy version is: "%s"
and make sure that they are the versions you expect.
Please carefully study the documentation linked above for further help.
Original error was: %s
""" % (sys.version_info[0], sys.version_info[1], sys.executable,
__version__, exc)
raise ImportError(msg) from exc
finally:
for envkey in env_added:
del os.environ[envkey]
del envkey
del env_added
del os
from . import umath
# Check that multiarray,umath are pure python modules wrapping
# _multiarray_umath and not either of the old c-extension modules
if not (hasattr(multiarray, '_multiarray_umath') and
hasattr(umath, '_multiarray_umath')):
import sys
path = sys.modules['numpy'].__path__
msg = ("Something is wrong with the numpy installation. "
"While importing we detected an older version of "
"numpy in {}. One method of fixing this is to repeatedly uninstall "
"numpy until none is found, then reinstall this version.")
raise ImportError(msg.format(path))
from . import numerictypes as nt
from .numerictypes import sctypeDict, sctypes
multiarray.set_typeDict(nt.sctypeDict)
from . import (
_machar,
einsumfunc,
fromnumeric,
function_base,
getlimits,
numeric,
shape_base,
)
from .einsumfunc import *
from .fromnumeric import *
from .function_base import *
from .getlimits import *
# Note: module name memmap is overwritten by a class with same name
from .memmap import *
from .numeric import *
from .records import recarray, record
from .shape_base import *
del nt
# do this after everything else, to minimize the chance of this misleadingly
# appearing in an import-time traceback
# add these for module-freeze analysis (like PyInstaller)
from . import (
_add_newdocs,
_add_newdocs_scalars,
_dtype,
_dtype_ctypes,
_internal,
_methods,
)
from .numeric import absolute as abs
acos = numeric.arccos
acosh = numeric.arccosh
asin = numeric.arcsin
asinh = numeric.arcsinh
atan = numeric.arctan
atanh = numeric.arctanh
atan2 = numeric.arctan2
concat = numeric.concatenate
bitwise_left_shift = numeric.left_shift
bitwise_invert = numeric.invert
bitwise_right_shift = numeric.right_shift
permute_dims = numeric.transpose
pow = numeric.power
__all__ = [
"abs", "acos", "acosh", "asin", "asinh", "atan", "atanh", "atan2",
"bitwise_invert", "bitwise_left_shift", "bitwise_right_shift", "concat",
"pow", "permute_dims", "memmap", "sctypeDict", "record", "recarray"
]
__all__ += numeric.__all__
__all__ += function_base.__all__
__all__ += getlimits.__all__
__all__ += shape_base.__all__
__all__ += einsumfunc.__all__
def _ufunc_reduce(func):
# Report the `__name__`. pickle will try to find the module. Note that
# pickle supports for this `__name__` to be a `__qualname__`. It may
# make sense to add a `__qualname__` to ufuncs, to allow this more
# explicitly (Numba has ufuncs as attributes).
# See also: https://github.com/dask/distributed/issues/3450
return func.__name__
def _DType_reconstruct(scalar_type):
# This is a work-around to pickle type(np.dtype(np.float64)), etc.
# and it should eventually be replaced with a better solution, e.g. when
# DTypes become HeapTypes.
return type(dtype(scalar_type))
def _DType_reduce(DType):
# As types/classes, most DTypes can simply be pickled by their name:
if not DType._legacy or DType.__module__ == "numpy.dtypes":
return DType.__name__
# However, user defined legacy dtypes (like rational) do not end up in
# `numpy.dtypes` as module and do not have a public class at all.
# For these, we pickle them by reconstructing them from the scalar type:
scalar_type = DType.type
return _DType_reconstruct, (scalar_type,)
def __getattr__(name):
# Deprecated 2022-11-22, NumPy 1.25.
if name == "MachAr":
import warnings
warnings.warn(
"The `np._core.MachAr` is considered private API (NumPy 1.24)",
DeprecationWarning, stacklevel=2,
)
return _machar.MachAr
raise AttributeError(f"Module {__name__!r} has no attribute {name!r}")
import copyreg
copyreg.pickle(ufunc, _ufunc_reduce)
copyreg.pickle(type(dtype), _DType_reduce, _DType_reconstruct)
# Unclutter namespace (must keep _*_reconstruct for unpickling)
del copyreg, _ufunc_reduce, _DType_reduce
from numpy._pytesttester import PytestTester
test = PytestTester(__name__)
del PytestTester

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from .overrides import get_array_function_like_doc as get_array_function_like_doc
def refer_to_array_attribute(attr: str, method: bool = True) -> tuple[str, str]: ...

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"""
This file is separate from ``_add_newdocs.py`` so that it can be mocked out by
our sphinx ``conf.py`` during doc builds, where we want to avoid showing
platform-dependent information.
"""
import os
import sys
from numpy._core import dtype
from numpy._core import numerictypes as _numerictypes
from numpy._core.function_base import add_newdoc
##############################################################################
#
# Documentation for concrete scalar classes
#
##############################################################################
def numeric_type_aliases(aliases):
def type_aliases_gen():
for alias, doc in aliases:
try:
alias_type = getattr(_numerictypes, alias)
except AttributeError:
# The set of aliases that actually exist varies between platforms
pass
else:
yield (alias_type, alias, doc)
return list(type_aliases_gen())
possible_aliases = numeric_type_aliases([
('int8', '8-bit signed integer (``-128`` to ``127``)'),
('int16', '16-bit signed integer (``-32_768`` to ``32_767``)'),
('int32', '32-bit signed integer (``-2_147_483_648`` to ``2_147_483_647``)'),
('int64', '64-bit signed integer (``-9_223_372_036_854_775_808`` to ``9_223_372_036_854_775_807``)'),
('intp', 'Signed integer large enough to fit pointer, compatible with C ``intptr_t``'),
('uint8', '8-bit unsigned integer (``0`` to ``255``)'),
('uint16', '16-bit unsigned integer (``0`` to ``65_535``)'),
('uint32', '32-bit unsigned integer (``0`` to ``4_294_967_295``)'),
('uint64', '64-bit unsigned integer (``0`` to ``18_446_744_073_709_551_615``)'),
('uintp', 'Unsigned integer large enough to fit pointer, compatible with C ``uintptr_t``'),
('float16', '16-bit-precision floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa'),
('float32', '32-bit-precision floating-point number type: sign bit, 8 bits exponent, 23 bits mantissa'),
('float64', '64-bit precision floating-point number type: sign bit, 11 bits exponent, 52 bits mantissa'),
('float96', '96-bit extended-precision floating-point number type'),
('float128', '128-bit extended-precision floating-point number type'),
('complex64', 'Complex number type composed of 2 32-bit-precision floating-point numbers'),
('complex128', 'Complex number type composed of 2 64-bit-precision floating-point numbers'),
('complex192', 'Complex number type composed of 2 96-bit extended-precision floating-point numbers'),
('complex256', 'Complex number type composed of 2 128-bit extended-precision floating-point numbers'),
])
def _get_platform_and_machine():
try:
system, _, _, _, machine = os.uname()
except AttributeError:
system = sys.platform
if system == 'win32':
machine = os.environ.get('PROCESSOR_ARCHITEW6432', '') \
or os.environ.get('PROCESSOR_ARCHITECTURE', '')
else:
machine = 'unknown'
return system, machine
_system, _machine = _get_platform_and_machine()
_doc_alias_string = f":Alias on this platform ({_system} {_machine}):"
def add_newdoc_for_scalar_type(obj, fixed_aliases, doc):
# note: `:field: value` is rST syntax which renders as field lists.
o = getattr(_numerictypes, obj)
character_code = dtype(o).char
canonical_name_doc = "" if obj == o.__name__ else \
f":Canonical name: `numpy.{obj}`\n "
if fixed_aliases:
alias_doc = ''.join(f":Alias: `numpy.{alias}`\n "
for alias in fixed_aliases)
else:
alias_doc = ''
alias_doc += ''.join(f"{_doc_alias_string} `numpy.{alias}`: {doc}.\n "
for (alias_type, alias, doc) in possible_aliases if alias_type is o)
docstring = f"""
{doc.strip()}
:Character code: ``'{character_code}'``
{canonical_name_doc}{alias_doc}
"""
add_newdoc('numpy._core.numerictypes', obj, docstring)
_bool_docstring = (
"""
Boolean type (True or False), stored as a byte.
.. warning::
The :class:`bool` type is not a subclass of the :class:`int_` type
(the :class:`bool` is not even a number type). This is different
than Python's default implementation of :class:`bool` as a
sub-class of :class:`int`.
"""
)
add_newdoc_for_scalar_type('bool', [], _bool_docstring)
add_newdoc_for_scalar_type('bool_', [], _bool_docstring)
add_newdoc_for_scalar_type('byte', [],
"""
Signed integer type, compatible with C ``char``.
""")
add_newdoc_for_scalar_type('short', [],
"""
Signed integer type, compatible with C ``short``.
""")
add_newdoc_for_scalar_type('intc', [],
"""
Signed integer type, compatible with C ``int``.
""")
# TODO: These docs probably need an if to highlight the default rather than
# the C-types (and be correct).
add_newdoc_for_scalar_type('int_', [],
"""
Default signed integer type, 64bit on 64bit systems and 32bit on 32bit
systems.
""")
add_newdoc_for_scalar_type('longlong', [],
"""
Signed integer type, compatible with C ``long long``.
""")
add_newdoc_for_scalar_type('ubyte', [],
"""
Unsigned integer type, compatible with C ``unsigned char``.
""")
add_newdoc_for_scalar_type('ushort', [],
"""
Unsigned integer type, compatible with C ``unsigned short``.
""")
add_newdoc_for_scalar_type('uintc', [],
"""
Unsigned integer type, compatible with C ``unsigned int``.
""")
add_newdoc_for_scalar_type('uint', [],
"""
Unsigned signed integer type, 64bit on 64bit systems and 32bit on 32bit
systems.
""")
add_newdoc_for_scalar_type('ulonglong', [],
"""
Signed integer type, compatible with C ``unsigned long long``.
""")
add_newdoc_for_scalar_type('half', [],
"""
Half-precision floating-point number type.
""")
add_newdoc_for_scalar_type('single', [],
"""
Single-precision floating-point number type, compatible with C ``float``.
""")
add_newdoc_for_scalar_type('double', [],
"""
Double-precision floating-point number type, compatible with Python
:class:`float` and C ``double``.
""")
add_newdoc_for_scalar_type('longdouble', [],
"""
Extended-precision floating-point number type, compatible with C
``long double`` but not necessarily with IEEE 754 quadruple-precision.
""")
add_newdoc_for_scalar_type('csingle', [],
"""
Complex number type composed of two single-precision floating-point
numbers.
""")
add_newdoc_for_scalar_type('cdouble', [],
"""
Complex number type composed of two double-precision floating-point
numbers, compatible with Python :class:`complex`.
""")
add_newdoc_for_scalar_type('clongdouble', [],
"""
Complex number type composed of two extended-precision floating-point
numbers.
""")
add_newdoc_for_scalar_type('object_', [],
"""
Any Python object.
""")
add_newdoc_for_scalar_type('str_', [],
r"""
A unicode string.
This type strips trailing null codepoints.
>>> s = np.str_("abc\x00")
>>> s
'abc'
Unlike the builtin :class:`str`, this supports the
:ref:`python:bufferobjects`, exposing its contents as UCS4:
>>> m = memoryview(np.str_("abc"))
>>> m.format
'3w'
>>> m.tobytes()
b'a\x00\x00\x00b\x00\x00\x00c\x00\x00\x00'
""")
add_newdoc_for_scalar_type('bytes_', [],
r"""
A byte string.
When used in arrays, this type strips trailing null bytes.
""")
add_newdoc_for_scalar_type('void', [],
r"""
np.void(length_or_data, /, dtype=None)
Create a new structured or unstructured void scalar.
Parameters
----------
length_or_data : int, array-like, bytes-like, object
One of multiple meanings (see notes). The length or
bytes data of an unstructured void. Or alternatively,
the data to be stored in the new scalar when `dtype`
is provided.
This can be an array-like, in which case an array may
be returned.
dtype : dtype, optional
If provided the dtype of the new scalar. This dtype must
be "void" dtype (i.e. a structured or unstructured void,
see also :ref:`defining-structured-types`).
.. versionadded:: 1.24
Notes
-----
For historical reasons and because void scalars can represent both
arbitrary byte data and structured dtypes, the void constructor
has three calling conventions:
1. ``np.void(5)`` creates a ``dtype="V5"`` scalar filled with five
``\0`` bytes. The 5 can be a Python or NumPy integer.
2. ``np.void(b"bytes-like")`` creates a void scalar from the byte string.
The dtype itemsize will match the byte string length, here ``"V10"``.
3. When a ``dtype=`` is passed the call is roughly the same as an
array creation. However, a void scalar rather than array is returned.
Please see the examples which show all three different conventions.
Examples
--------
>>> np.void(5)
np.void(b'\x00\x00\x00\x00\x00')
>>> np.void(b'abcd')
np.void(b'\x61\x62\x63\x64')
>>> np.void((3.2, b'eggs'), dtype="d,S5")
np.void((3.2, b'eggs'), dtype=[('f0', '<f8'), ('f1', 'S5')])
>>> np.void(3, dtype=[('x', np.int8), ('y', np.int8)])
np.void((3, 3), dtype=[('x', 'i1'), ('y', 'i1')])
""")
add_newdoc_for_scalar_type('datetime64', [],
"""
If created from a 64-bit integer, it represents an offset from
``1970-01-01T00:00:00``.
If created from string, the string can be in ISO 8601 date
or datetime format.
When parsing a string to create a datetime object, if the string contains
a trailing timezone (A 'Z' or a timezone offset), the timezone will be
dropped and a User Warning is given.
Datetime64 objects should be considered to be UTC and therefore have an
offset of +0000.
>>> np.datetime64(10, 'Y')
np.datetime64('1980')
>>> np.datetime64('1980', 'Y')
np.datetime64('1980')
>>> np.datetime64(10, 'D')
np.datetime64('1970-01-11')
See :ref:`arrays.datetime` for more information.
""")
add_newdoc_for_scalar_type('timedelta64', [],
"""
A timedelta stored as a 64-bit integer.
See :ref:`arrays.datetime` for more information.
""")
add_newdoc('numpy._core.numerictypes', "integer", ('is_integer',
"""
integer.is_integer() -> bool
Return ``True`` if the number is finite with integral value.
.. versionadded:: 1.22
Examples
--------
>>> import numpy as np
>>> np.int64(-2).is_integer()
True
>>> np.uint32(5).is_integer()
True
"""))
# TODO: work out how to put this on the base class, np.floating
for float_name in ('half', 'single', 'double', 'longdouble'):
add_newdoc('numpy._core.numerictypes', float_name, ('as_integer_ratio',
f"""
{float_name}.as_integer_ratio() -> (int, int)
Return a pair of integers, whose ratio is exactly equal to the original
floating point number, and with a positive denominator.
Raise `OverflowError` on infinities and a `ValueError` on NaNs.
>>> np.{float_name}(10.0).as_integer_ratio()
(10, 1)
>>> np.{float_name}(0.0).as_integer_ratio()
(0, 1)
>>> np.{float_name}(-.25).as_integer_ratio()
(-1, 4)
"""))
add_newdoc('numpy._core.numerictypes', float_name, ('is_integer',
f"""
{float_name}.is_integer() -> bool
Return ``True`` if the floating point number is finite with integral
value, and ``False`` otherwise.
.. versionadded:: 1.22
Examples
--------
>>> np.{float_name}(-2.0).is_integer()
True
>>> np.{float_name}(3.2).is_integer()
False
"""))
for int_name in ('int8', 'uint8', 'int16', 'uint16', 'int32', 'uint32',
'int64', 'uint64', 'int64', 'uint64', 'int64', 'uint64'):
# Add negative examples for signed cases by checking typecode
add_newdoc('numpy._core.numerictypes', int_name, ('bit_count',
f"""
{int_name}.bit_count() -> int
Computes the number of 1-bits in the absolute value of the input.
Analogous to the builtin `int.bit_count` or ``popcount`` in C++.
Examples
--------
>>> np.{int_name}(127).bit_count()
7""" +
(f"""
>>> np.{int_name}(-127).bit_count()
7
""" if dtype(int_name).char.islower() else "")))

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from collections.abc import Iterable
from typing import Final
import numpy as np
possible_aliases: Final[list[tuple[type[np.number], str, str]]] = ...
_system: Final[str] = ...
_machine: Final[str] = ...
_doc_alias_string: Final[str] = ...
_bool_docstring: Final[str] = ...
int_name: str = ...
float_name: str = ...
def numeric_type_aliases(aliases: list[tuple[str, str]]) -> list[tuple[type[np.number], str, str]]: ...
def add_newdoc_for_scalar_type(obj: str, fixed_aliases: Iterable[str], doc: str) -> None: ...
def _get_platform_and_machine() -> tuple[str, str]: ...

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"""
Functions in the ``as*array`` family that promote array-likes into arrays.
`require` fits this category despite its name not matching this pattern.
"""
from .multiarray import array, asanyarray
from .overrides import (
array_function_dispatch,
finalize_array_function_like,
set_module,
)
__all__ = ["require"]
POSSIBLE_FLAGS = {
'C': 'C', 'C_CONTIGUOUS': 'C', 'CONTIGUOUS': 'C',
'F': 'F', 'F_CONTIGUOUS': 'F', 'FORTRAN': 'F',
'A': 'A', 'ALIGNED': 'A',
'W': 'W', 'WRITEABLE': 'W',
'O': 'O', 'OWNDATA': 'O',
'E': 'E', 'ENSUREARRAY': 'E'
}
@finalize_array_function_like
@set_module('numpy')
def require(a, dtype=None, requirements=None, *, like=None):
"""
Return an ndarray of the provided type that satisfies requirements.
This function is useful to be sure that an array with the correct flags
is returned for passing to compiled code (perhaps through ctypes).
Parameters
----------
a : array_like
The object to be converted to a type-and-requirement-satisfying array.
dtype : data-type
The required data-type. If None preserve the current dtype. If your
application requires the data to be in native byteorder, include
a byteorder specification as a part of the dtype specification.
requirements : str or sequence of str
The requirements list can be any of the following
* 'F_CONTIGUOUS' ('F') - ensure a Fortran-contiguous array
* 'C_CONTIGUOUS' ('C') - ensure a C-contiguous array
* 'ALIGNED' ('A') - ensure a data-type aligned array
* 'WRITEABLE' ('W') - ensure a writable array
* 'OWNDATA' ('O') - ensure an array that owns its own data
* 'ENSUREARRAY', ('E') - ensure a base array, instead of a subclass
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
Returns
-------
out : ndarray
Array with specified requirements and type if given.
See Also
--------
asarray : Convert input to an ndarray.
asanyarray : Convert to an ndarray, but pass through ndarray subclasses.
ascontiguousarray : Convert input to a contiguous array.
asfortranarray : Convert input to an ndarray with column-major
memory order.
ndarray.flags : Information about the memory layout of the array.
Notes
-----
The returned array will be guaranteed to have the listed requirements
by making a copy if needed.
Examples
--------
>>> import numpy as np
>>> x = np.arange(6).reshape(2,3)
>>> x.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
>>> y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F'])
>>> y.flags
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
"""
if like is not None:
return _require_with_like(
like,
a,
dtype=dtype,
requirements=requirements,
)
if not requirements:
return asanyarray(a, dtype=dtype)
requirements = {POSSIBLE_FLAGS[x.upper()] for x in requirements}
if 'E' in requirements:
requirements.remove('E')
subok = False
else:
subok = True
order = 'A'
if requirements >= {'C', 'F'}:
raise ValueError('Cannot specify both "C" and "F" order')
elif 'F' in requirements:
order = 'F'
requirements.remove('F')
elif 'C' in requirements:
order = 'C'
requirements.remove('C')
arr = array(a, dtype=dtype, order=order, copy=None, subok=subok)
for prop in requirements:
if not arr.flags[prop]:
return arr.copy(order)
return arr
_require_with_like = array_function_dispatch()(require)

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from collections.abc import Iterable
from typing import Any, Literal, TypeAlias, TypeVar, overload
from numpy._typing import DTypeLike, NDArray, _SupportsArrayFunc
_ArrayT = TypeVar("_ArrayT", bound=NDArray[Any])
_Requirements: TypeAlias = Literal[
"C", "C_CONTIGUOUS", "CONTIGUOUS",
"F", "F_CONTIGUOUS", "FORTRAN",
"A", "ALIGNED",
"W", "WRITEABLE",
"O", "OWNDATA"
]
_E: TypeAlias = Literal["E", "ENSUREARRAY"]
_RequirementsWithE: TypeAlias = _Requirements | _E
@overload
def require(
a: _ArrayT,
dtype: None = ...,
requirements: _Requirements | Iterable[_Requirements] | None = ...,
*,
like: _SupportsArrayFunc = ...
) -> _ArrayT: ...
@overload
def require(
a: object,
dtype: DTypeLike = ...,
requirements: _E | Iterable[_RequirementsWithE] = ...,
*,
like: _SupportsArrayFunc = ...
) -> NDArray[Any]: ...
@overload
def require(
a: object,
dtype: DTypeLike = ...,
requirements: _Requirements | Iterable[_Requirements] | None = ...,
*,
like: _SupportsArrayFunc = ...
) -> NDArray[Any]: ...

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"""
A place for code to be called from the implementation of np.dtype
String handling is much easier to do correctly in python.
"""
import numpy as np
_kind_to_stem = {
'u': 'uint',
'i': 'int',
'c': 'complex',
'f': 'float',
'b': 'bool',
'V': 'void',
'O': 'object',
'M': 'datetime',
'm': 'timedelta',
'S': 'bytes',
'U': 'str',
}
def _kind_name(dtype):
try:
return _kind_to_stem[dtype.kind]
except KeyError as e:
raise RuntimeError(
f"internal dtype error, unknown kind {dtype.kind!r}"
) from None
def __str__(dtype):
if dtype.fields is not None:
return _struct_str(dtype, include_align=True)
elif dtype.subdtype:
return _subarray_str(dtype)
elif issubclass(dtype.type, np.flexible) or not dtype.isnative:
return dtype.str
else:
return dtype.name
def __repr__(dtype):
arg_str = _construction_repr(dtype, include_align=False)
if dtype.isalignedstruct:
arg_str = arg_str + ", align=True"
return f"dtype({arg_str})"
def _unpack_field(dtype, offset, title=None):
"""
Helper function to normalize the items in dtype.fields.
Call as:
dtype, offset, title = _unpack_field(*dtype.fields[name])
"""
return dtype, offset, title
def _isunsized(dtype):
# PyDataType_ISUNSIZED
return dtype.itemsize == 0
def _construction_repr(dtype, include_align=False, short=False):
"""
Creates a string repr of the dtype, excluding the 'dtype()' part
surrounding the object. This object may be a string, a list, or
a dict depending on the nature of the dtype. This
is the object passed as the first parameter to the dtype
constructor, and if no additional constructor parameters are
given, will reproduce the exact memory layout.
Parameters
----------
short : bool
If true, this creates a shorter repr using 'kind' and 'itemsize',
instead of the longer type name.
include_align : bool
If true, this includes the 'align=True' parameter
inside the struct dtype construction dict when needed. Use this flag
if you want a proper repr string without the 'dtype()' part around it.
If false, this does not preserve the
'align=True' parameter or sticky NPY_ALIGNED_STRUCT flag for
struct arrays like the regular repr does, because the 'align'
flag is not part of first dtype constructor parameter. This
mode is intended for a full 'repr', where the 'align=True' is
provided as the second parameter.
"""
if dtype.fields is not None:
return _struct_str(dtype, include_align=include_align)
elif dtype.subdtype:
return _subarray_str(dtype)
else:
return _scalar_str(dtype, short=short)
def _scalar_str(dtype, short):
byteorder = _byte_order_str(dtype)
if dtype.type == np.bool:
if short:
return "'?'"
else:
return "'bool'"
elif dtype.type == np.object_:
# The object reference may be different sizes on different
# platforms, so it should never include the itemsize here.
return "'O'"
elif dtype.type == np.bytes_:
if _isunsized(dtype):
return "'S'"
else:
return "'S%d'" % dtype.itemsize
elif dtype.type == np.str_:
if _isunsized(dtype):
return f"'{byteorder}U'"
else:
return "'%sU%d'" % (byteorder, dtype.itemsize / 4)
elif dtype.type == str:
return "'T'"
elif not type(dtype)._legacy:
return f"'{byteorder}{type(dtype).__name__}{dtype.itemsize * 8}'"
# unlike the other types, subclasses of void are preserved - but
# historically the repr does not actually reveal the subclass
elif issubclass(dtype.type, np.void):
if _isunsized(dtype):
return "'V'"
else:
return "'V%d'" % dtype.itemsize
elif dtype.type == np.datetime64:
return f"'{byteorder}M8{_datetime_metadata_str(dtype)}'"
elif dtype.type == np.timedelta64:
return f"'{byteorder}m8{_datetime_metadata_str(dtype)}'"
elif dtype.isbuiltin == 2:
return dtype.type.__name__
elif np.issubdtype(dtype, np.number):
# Short repr with endianness, like '<f8'
if short or dtype.byteorder not in ('=', '|'):
return "'%s%c%d'" % (byteorder, dtype.kind, dtype.itemsize)
# Longer repr, like 'float64'
else:
return "'%s%d'" % (_kind_name(dtype), 8 * dtype.itemsize)
else:
raise RuntimeError(
"Internal error: NumPy dtype unrecognized type number")
def _byte_order_str(dtype):
""" Normalize byteorder to '<' or '>' """
# hack to obtain the native and swapped byte order characters
swapped = np.dtype(int).newbyteorder('S')
native = swapped.newbyteorder('S')
byteorder = dtype.byteorder
if byteorder == '=':
return native.byteorder
if byteorder == 'S':
# TODO: this path can never be reached
return swapped.byteorder
elif byteorder == '|':
return ''
else:
return byteorder
def _datetime_metadata_str(dtype):
# TODO: this duplicates the C metastr_to_unicode functionality
unit, count = np.datetime_data(dtype)
if unit == 'generic':
return ''
elif count == 1:
return f'[{unit}]'
else:
return f'[{count}{unit}]'
def _struct_dict_str(dtype, includealignedflag):
# unpack the fields dictionary into ls
names = dtype.names
fld_dtypes = []
offsets = []
titles = []
for name in names:
fld_dtype, offset, title = _unpack_field(*dtype.fields[name])
fld_dtypes.append(fld_dtype)
offsets.append(offset)
titles.append(title)
# Build up a string to make the dictionary
if np._core.arrayprint._get_legacy_print_mode() <= 121:
colon = ":"
fieldsep = ","
else:
colon = ": "
fieldsep = ", "
# First, the names
ret = "{'names'%s[" % colon
ret += fieldsep.join(repr(name) for name in names)
# Second, the formats
ret += f"], 'formats'{colon}["
ret += fieldsep.join(
_construction_repr(fld_dtype, short=True) for fld_dtype in fld_dtypes)
# Third, the offsets
ret += f"], 'offsets'{colon}["
ret += fieldsep.join("%d" % offset for offset in offsets)
# Fourth, the titles
if any(title is not None for title in titles):
ret += f"], 'titles'{colon}["
ret += fieldsep.join(repr(title) for title in titles)
# Fifth, the itemsize
ret += "], 'itemsize'%s%d" % (colon, dtype.itemsize)
if (includealignedflag and dtype.isalignedstruct):
# Finally, the aligned flag
ret += ", 'aligned'%sTrue}" % colon
else:
ret += "}"
return ret
def _aligned_offset(offset, alignment):
# round up offset:
return - (-offset // alignment) * alignment
def _is_packed(dtype):
"""
Checks whether the structured data type in 'dtype'
has a simple layout, where all the fields are in order,
and follow each other with no alignment padding.
When this returns true, the dtype can be reconstructed
from a list of the field names and dtypes with no additional
dtype parameters.
Duplicates the C `is_dtype_struct_simple_unaligned_layout` function.
"""
align = dtype.isalignedstruct
max_alignment = 1
total_offset = 0
for name in dtype.names:
fld_dtype, fld_offset, title = _unpack_field(*dtype.fields[name])
if align:
total_offset = _aligned_offset(total_offset, fld_dtype.alignment)
max_alignment = max(max_alignment, fld_dtype.alignment)
if fld_offset != total_offset:
return False
total_offset += fld_dtype.itemsize
if align:
total_offset = _aligned_offset(total_offset, max_alignment)
return total_offset == dtype.itemsize
def _struct_list_str(dtype):
items = []
for name in dtype.names:
fld_dtype, fld_offset, title = _unpack_field(*dtype.fields[name])
item = "("
if title is not None:
item += f"({title!r}, {name!r}), "
else:
item += f"{name!r}, "
# Special case subarray handling here
if fld_dtype.subdtype is not None:
base, shape = fld_dtype.subdtype
item += f"{_construction_repr(base, short=True)}, {shape}"
else:
item += _construction_repr(fld_dtype, short=True)
item += ")"
items.append(item)
return "[" + ", ".join(items) + "]"
def _struct_str(dtype, include_align):
# The list str representation can't include the 'align=' flag,
# so if it is requested and the struct has the aligned flag set,
# we must use the dict str instead.
if not (include_align and dtype.isalignedstruct) and _is_packed(dtype):
sub = _struct_list_str(dtype)
else:
sub = _struct_dict_str(dtype, include_align)
# If the data type isn't the default, void, show it
if dtype.type != np.void:
return f"({dtype.type.__module__}.{dtype.type.__name__}, {sub})"
else:
return sub
def _subarray_str(dtype):
base, shape = dtype.subdtype
return f"({_construction_repr(base, short=True)}, {shape})"
def _name_includes_bit_suffix(dtype):
if dtype.type == np.object_:
# pointer size varies by system, best to omit it
return False
elif dtype.type == np.bool:
# implied
return False
elif dtype.type is None:
return True
elif np.issubdtype(dtype, np.flexible) and _isunsized(dtype):
# unspecified
return False
else:
return True
def _name_get(dtype):
# provides dtype.name.__get__, documented as returning a "bit name"
if dtype.isbuiltin == 2:
# user dtypes don't promise to do anything special
return dtype.type.__name__
if not type(dtype)._legacy:
name = type(dtype).__name__
elif issubclass(dtype.type, np.void):
# historically, void subclasses preserve their name, eg `record64`
name = dtype.type.__name__
else:
name = _kind_name(dtype)
# append bit counts
if _name_includes_bit_suffix(dtype):
name += f"{dtype.itemsize * 8}"
# append metadata to datetimes
if dtype.type in (np.datetime64, np.timedelta64):
name += _datetime_metadata_str(dtype)
return name

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from typing import Final, TypeAlias, TypedDict, overload, type_check_only
from typing import Literal as L
from typing_extensions import ReadOnly, TypeVar
import numpy as np
###
_T = TypeVar("_T")
_Name: TypeAlias = L["uint", "int", "complex", "float", "bool", "void", "object", "datetime", "timedelta", "bytes", "str"]
@type_check_only
class _KindToStemType(TypedDict):
u: ReadOnly[L["uint"]]
i: ReadOnly[L["int"]]
c: ReadOnly[L["complex"]]
f: ReadOnly[L["float"]]
b: ReadOnly[L["bool"]]
V: ReadOnly[L["void"]]
O: ReadOnly[L["object"]]
M: ReadOnly[L["datetime"]]
m: ReadOnly[L["timedelta"]]
S: ReadOnly[L["bytes"]]
U: ReadOnly[L["str"]]
###
_kind_to_stem: Final[_KindToStemType] = ...
#
def _kind_name(dtype: np.dtype) -> _Name: ...
def __str__(dtype: np.dtype) -> str: ...
def __repr__(dtype: np.dtype) -> str: ...
#
def _isunsized(dtype: np.dtype) -> bool: ...
def _is_packed(dtype: np.dtype) -> bool: ...
def _name_includes_bit_suffix(dtype: np.dtype) -> bool: ...
#
def _construction_repr(dtype: np.dtype, include_align: bool = False, short: bool = False) -> str: ...
def _scalar_str(dtype: np.dtype, short: bool) -> str: ...
def _byte_order_str(dtype: np.dtype) -> str: ...
def _datetime_metadata_str(dtype: np.dtype) -> str: ...
def _struct_dict_str(dtype: np.dtype, includealignedflag: bool) -> str: ...
def _struct_list_str(dtype: np.dtype) -> str: ...
def _struct_str(dtype: np.dtype, include_align: bool) -> str: ...
def _subarray_str(dtype: np.dtype) -> str: ...
def _name_get(dtype: np.dtype) -> str: ...
#
@overload
def _unpack_field(dtype: np.dtype, offset: int, title: _T) -> tuple[np.dtype, int, _T]: ...
@overload
def _unpack_field(dtype: np.dtype, offset: int, title: None = None) -> tuple[np.dtype, int, None]: ...
def _aligned_offset(offset: int, alignment: int) -> int: ...

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"""
Conversion from ctypes to dtype.
In an ideal world, we could achieve this through the PEP3118 buffer protocol,
something like::
def dtype_from_ctypes_type(t):
# needed to ensure that the shape of `t` is within memoryview.format
class DummyStruct(ctypes.Structure):
_fields_ = [('a', t)]
# empty to avoid memory allocation
ctype_0 = (DummyStruct * 0)()
mv = memoryview(ctype_0)
# convert the struct, and slice back out the field
return _dtype_from_pep3118(mv.format)['a']
Unfortunately, this fails because:
* ctypes cannot handle length-0 arrays with PEP3118 (bpo-32782)
* PEP3118 cannot represent unions, but both numpy and ctypes can
* ctypes cannot handle big-endian structs with PEP3118 (bpo-32780)
"""
# We delay-import ctypes for distributions that do not include it.
# While this module is not used unless the user passes in ctypes
# members, it is eagerly imported from numpy/_core/__init__.py.
import numpy as np
def _from_ctypes_array(t):
return np.dtype((dtype_from_ctypes_type(t._type_), (t._length_,)))
def _from_ctypes_structure(t):
for item in t._fields_:
if len(item) > 2:
raise TypeError(
"ctypes bitfields have no dtype equivalent")
if hasattr(t, "_pack_"):
import ctypes
formats = []
offsets = []
names = []
current_offset = 0
for fname, ftyp in t._fields_:
names.append(fname)
formats.append(dtype_from_ctypes_type(ftyp))
# Each type has a default offset, this is platform dependent
# for some types.
effective_pack = min(t._pack_, ctypes.alignment(ftyp))
current_offset = (
(current_offset + effective_pack - 1) // effective_pack
) * effective_pack
offsets.append(current_offset)
current_offset += ctypes.sizeof(ftyp)
return np.dtype({
"formats": formats,
"offsets": offsets,
"names": names,
"itemsize": ctypes.sizeof(t)})
else:
fields = []
for fname, ftyp in t._fields_:
fields.append((fname, dtype_from_ctypes_type(ftyp)))
# by default, ctypes structs are aligned
return np.dtype(fields, align=True)
def _from_ctypes_scalar(t):
"""
Return the dtype type with endianness included if it's the case
"""
if getattr(t, '__ctype_be__', None) is t:
return np.dtype('>' + t._type_)
elif getattr(t, '__ctype_le__', None) is t:
return np.dtype('<' + t._type_)
else:
return np.dtype(t._type_)
def _from_ctypes_union(t):
import ctypes
formats = []
offsets = []
names = []
for fname, ftyp in t._fields_:
names.append(fname)
formats.append(dtype_from_ctypes_type(ftyp))
offsets.append(0) # Union fields are offset to 0
return np.dtype({
"formats": formats,
"offsets": offsets,
"names": names,
"itemsize": ctypes.sizeof(t)})
def dtype_from_ctypes_type(t):
"""
Construct a dtype object from a ctypes type
"""
import _ctypes
if issubclass(t, _ctypes.Array):
return _from_ctypes_array(t)
elif issubclass(t, _ctypes._Pointer):
raise TypeError("ctypes pointers have no dtype equivalent")
elif issubclass(t, _ctypes.Structure):
return _from_ctypes_structure(t)
elif issubclass(t, _ctypes.Union):
return _from_ctypes_union(t)
elif isinstance(getattr(t, '_type_', None), str):
return _from_ctypes_scalar(t)
else:
raise NotImplementedError(
f"Unknown ctypes type {t.__name__}")

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import _ctypes
import ctypes as ct
from typing import Any, overload
import numpy as np
#
@overload
def dtype_from_ctypes_type(t: type[_ctypes.Array[Any] | _ctypes.Structure]) -> np.dtype[np.void]: ...
@overload
def dtype_from_ctypes_type(t: type[ct.c_bool]) -> np.dtype[np.bool]: ...
@overload
def dtype_from_ctypes_type(t: type[ct.c_int8 | ct.c_byte]) -> np.dtype[np.int8]: ...
@overload
def dtype_from_ctypes_type(t: type[ct.c_uint8 | ct.c_ubyte]) -> np.dtype[np.uint8]: ...
@overload
def dtype_from_ctypes_type(t: type[ct.c_int16 | ct.c_short]) -> np.dtype[np.int16]: ...
@overload
def dtype_from_ctypes_type(t: type[ct.c_uint16 | ct.c_ushort]) -> np.dtype[np.uint16]: ...
@overload
def dtype_from_ctypes_type(t: type[ct.c_int32 | ct.c_int]) -> np.dtype[np.int32]: ...
@overload
def dtype_from_ctypes_type(t: type[ct.c_uint32 | ct.c_uint]) -> np.dtype[np.uint32]: ...
@overload
def dtype_from_ctypes_type(t: type[ct.c_ssize_t | ct.c_long]) -> np.dtype[np.int32 | np.int64]: ...
@overload
def dtype_from_ctypes_type(t: type[ct.c_size_t | ct.c_ulong]) -> np.dtype[np.uint32 | np.uint64]: ...
@overload
def dtype_from_ctypes_type(t: type[ct.c_int64 | ct.c_longlong]) -> np.dtype[np.int64]: ...
@overload
def dtype_from_ctypes_type(t: type[ct.c_uint64 | ct.c_ulonglong]) -> np.dtype[np.uint64]: ...
@overload
def dtype_from_ctypes_type(t: type[ct.c_float]) -> np.dtype[np.float32]: ...
@overload
def dtype_from_ctypes_type(t: type[ct.c_double]) -> np.dtype[np.float64]: ...
@overload
def dtype_from_ctypes_type(t: type[ct.c_longdouble]) -> np.dtype[np.longdouble]: ...
@overload
def dtype_from_ctypes_type(t: type[ct.c_char]) -> np.dtype[np.bytes_]: ...
@overload
def dtype_from_ctypes_type(t: type[ct.py_object[Any]]) -> np.dtype[np.object_]: ...
# NOTE: the complex ctypes on python>=3.14 are not yet supported at runtim, see
# https://github.com/numpy/numpy/issues/28360
#
def _from_ctypes_array(t: type[_ctypes.Array[Any]]) -> np.dtype[np.void]: ...
def _from_ctypes_structure(t: type[_ctypes.Structure]) -> np.dtype[np.void]: ...
def _from_ctypes_union(t: type[_ctypes.Union]) -> np.dtype[np.void]: ...
# keep in sync with `dtype_from_ctypes_type` (minus the first overload)
@overload
def _from_ctypes_scalar(t: type[ct.c_bool]) -> np.dtype[np.bool]: ...
@overload
def _from_ctypes_scalar(t: type[ct.c_int8 | ct.c_byte]) -> np.dtype[np.int8]: ...
@overload
def _from_ctypes_scalar(t: type[ct.c_uint8 | ct.c_ubyte]) -> np.dtype[np.uint8]: ...
@overload
def _from_ctypes_scalar(t: type[ct.c_int16 | ct.c_short]) -> np.dtype[np.int16]: ...
@overload
def _from_ctypes_scalar(t: type[ct.c_uint16 | ct.c_ushort]) -> np.dtype[np.uint16]: ...
@overload
def _from_ctypes_scalar(t: type[ct.c_int32 | ct.c_int]) -> np.dtype[np.int32]: ...
@overload
def _from_ctypes_scalar(t: type[ct.c_uint32 | ct.c_uint]) -> np.dtype[np.uint32]: ...
@overload
def _from_ctypes_scalar(t: type[ct.c_ssize_t | ct.c_long]) -> np.dtype[np.int32 | np.int64]: ...
@overload
def _from_ctypes_scalar(t: type[ct.c_size_t | ct.c_ulong]) -> np.dtype[np.uint32 | np.uint64]: ...
@overload
def _from_ctypes_scalar(t: type[ct.c_int64 | ct.c_longlong]) -> np.dtype[np.int64]: ...
@overload
def _from_ctypes_scalar(t: type[ct.c_uint64 | ct.c_ulonglong]) -> np.dtype[np.uint64]: ...
@overload
def _from_ctypes_scalar(t: type[ct.c_float]) -> np.dtype[np.float32]: ...
@overload
def _from_ctypes_scalar(t: type[ct.c_double]) -> np.dtype[np.float64]: ...
@overload
def _from_ctypes_scalar(t: type[ct.c_longdouble]) -> np.dtype[np.longdouble]: ...
@overload
def _from_ctypes_scalar(t: type[ct.c_char]) -> np.dtype[np.bytes_]: ...
@overload
def _from_ctypes_scalar(t: type[ct.py_object[Any]]) -> np.dtype[np.object_]: ...

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"""
Various richly-typed exceptions, that also help us deal with string formatting
in python where it's easier.
By putting the formatting in `__str__`, we also avoid paying the cost for
users who silence the exceptions.
"""
def _unpack_tuple(tup):
if len(tup) == 1:
return tup[0]
else:
return tup
def _display_as_base(cls):
"""
A decorator that makes an exception class look like its base.
We use this to hide subclasses that are implementation details - the user
should catch the base type, which is what the traceback will show them.
Classes decorated with this decorator are subject to removal without a
deprecation warning.
"""
assert issubclass(cls, Exception)
cls.__name__ = cls.__base__.__name__
return cls
class UFuncTypeError(TypeError):
""" Base class for all ufunc exceptions """
def __init__(self, ufunc):
self.ufunc = ufunc
@_display_as_base
class _UFuncNoLoopError(UFuncTypeError):
""" Thrown when a ufunc loop cannot be found """
def __init__(self, ufunc, dtypes):
super().__init__(ufunc)
self.dtypes = tuple(dtypes)
def __str__(self):
return (
f"ufunc {self.ufunc.__name__!r} did not contain a loop with signature "
f"matching types {_unpack_tuple(self.dtypes[:self.ufunc.nin])!r} "
f"-> {_unpack_tuple(self.dtypes[self.ufunc.nin:])!r}"
)
@_display_as_base
class _UFuncBinaryResolutionError(_UFuncNoLoopError):
""" Thrown when a binary resolution fails """
def __init__(self, ufunc, dtypes):
super().__init__(ufunc, dtypes)
assert len(self.dtypes) == 2
def __str__(self):
return (
"ufunc {!r} cannot use operands with types {!r} and {!r}"
).format(
self.ufunc.__name__, *self.dtypes
)
@_display_as_base
class _UFuncCastingError(UFuncTypeError):
def __init__(self, ufunc, casting, from_, to):
super().__init__(ufunc)
self.casting = casting
self.from_ = from_
self.to = to
@_display_as_base
class _UFuncInputCastingError(_UFuncCastingError):
""" Thrown when a ufunc input cannot be casted """
def __init__(self, ufunc, casting, from_, to, i):
super().__init__(ufunc, casting, from_, to)
self.in_i = i
def __str__(self):
# only show the number if more than one input exists
i_str = f"{self.in_i} " if self.ufunc.nin != 1 else ""
return (
f"Cannot cast ufunc {self.ufunc.__name__!r} input {i_str}from "
f"{self.from_!r} to {self.to!r} with casting rule {self.casting!r}"
)
@_display_as_base
class _UFuncOutputCastingError(_UFuncCastingError):
""" Thrown when a ufunc output cannot be casted """
def __init__(self, ufunc, casting, from_, to, i):
super().__init__(ufunc, casting, from_, to)
self.out_i = i
def __str__(self):
# only show the number if more than one output exists
i_str = f"{self.out_i} " if self.ufunc.nout != 1 else ""
return (
f"Cannot cast ufunc {self.ufunc.__name__!r} output {i_str}from "
f"{self.from_!r} to {self.to!r} with casting rule {self.casting!r}"
)
@_display_as_base
class _ArrayMemoryError(MemoryError):
""" Thrown when an array cannot be allocated"""
def __init__(self, shape, dtype):
self.shape = shape
self.dtype = dtype
@property
def _total_size(self):
num_bytes = self.dtype.itemsize
for dim in self.shape:
num_bytes *= dim
return num_bytes
@staticmethod
def _size_to_string(num_bytes):
""" Convert a number of bytes into a binary size string """
# https://en.wikipedia.org/wiki/Binary_prefix
LOG2_STEP = 10
STEP = 1024
units = ['bytes', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB', 'EiB']
unit_i = max(num_bytes.bit_length() - 1, 1) // LOG2_STEP
unit_val = 1 << (unit_i * LOG2_STEP)
n_units = num_bytes / unit_val
del unit_val
# ensure we pick a unit that is correct after rounding
if round(n_units) == STEP:
unit_i += 1
n_units /= STEP
# deal with sizes so large that we don't have units for them
if unit_i >= len(units):
new_unit_i = len(units) - 1
n_units *= 1 << ((unit_i - new_unit_i) * LOG2_STEP)
unit_i = new_unit_i
unit_name = units[unit_i]
# format with a sensible number of digits
if unit_i == 0:
# no decimal point on bytes
return f'{n_units:.0f} {unit_name}'
elif round(n_units) < 1000:
# 3 significant figures, if none are dropped to the left of the .
return f'{n_units:#.3g} {unit_name}'
else:
# just give all the digits otherwise
return f'{n_units:#.0f} {unit_name}'
def __str__(self):
size_str = self._size_to_string(self._total_size)
return (f"Unable to allocate {size_str} for an array with shape "
f"{self.shape} and data type {self.dtype}")

55
venv/lib/python3.13/site-packages/numpy/_core/_exceptions.pyi
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@ -1,55 +0,0 @@
from collections.abc import Iterable
from typing import Any, Final, TypeVar, overload
import numpy as np
from numpy import _CastingKind
from numpy._utils import set_module as set_module
###
_T = TypeVar("_T")
_TupleT = TypeVar("_TupleT", bound=tuple[()] | tuple[Any, Any, *tuple[Any, ...]])
_ExceptionT = TypeVar("_ExceptionT", bound=Exception)
###
class UFuncTypeError(TypeError):
ufunc: Final[np.ufunc]
def __init__(self, /, ufunc: np.ufunc) -> None: ...
class _UFuncNoLoopError(UFuncTypeError):
dtypes: tuple[np.dtype, ...]
def __init__(self, /, ufunc: np.ufunc, dtypes: Iterable[np.dtype]) -> None: ...
class _UFuncBinaryResolutionError(_UFuncNoLoopError):
dtypes: tuple[np.dtype, np.dtype]
def __init__(self, /, ufunc: np.ufunc, dtypes: Iterable[np.dtype]) -> None: ...
class _UFuncCastingError(UFuncTypeError):
casting: Final[_CastingKind]
from_: Final[np.dtype]
to: Final[np.dtype]
def __init__(self, /, ufunc: np.ufunc, casting: _CastingKind, from_: np.dtype, to: np.dtype) -> None: ...
class _UFuncInputCastingError(_UFuncCastingError):
in_i: Final[int]
def __init__(self, /, ufunc: np.ufunc, casting: _CastingKind, from_: np.dtype, to: np.dtype, i: int) -> None: ...
class _UFuncOutputCastingError(_UFuncCastingError):
out_i: Final[int]
def __init__(self, /, ufunc: np.ufunc, casting: _CastingKind, from_: np.dtype, to: np.dtype, i: int) -> None: ...
class _ArrayMemoryError(MemoryError):
shape: tuple[int, ...]
dtype: np.dtype
def __init__(self, /, shape: tuple[int, ...], dtype: np.dtype) -> None: ...
@property
def _total_size(self) -> int: ...
@staticmethod
def _size_to_string(num_bytes: int) -> str: ...
@overload
def _unpack_tuple(tup: tuple[_T]) -> _T: ...
@overload
def _unpack_tuple(tup: _TupleT) -> _TupleT: ...
def _display_as_base(cls: type[_ExceptionT]) -> type[_ExceptionT]: ...

958
venv/lib/python3.13/site-packages/numpy/_core/_internal.py
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@ -1,958 +0,0 @@
"""
A place for internal code
Some things are more easily handled Python.
"""
import ast
import math
import re
import sys
import warnings
from numpy import _NoValue
from numpy.exceptions import DTypePromotionError
from .multiarray import StringDType, array, dtype, promote_types
try:
import ctypes
except ImportError:
ctypes = None
IS_PYPY = sys.implementation.name == 'pypy'
if sys.byteorder == 'little':
_nbo = '<'
else:
_nbo = '>'
def _makenames_list(adict, align):
allfields = []
for fname, obj in adict.items():
n = len(obj)
if not isinstance(obj, tuple) or n not in (2, 3):
raise ValueError("entry not a 2- or 3- tuple")
if n > 2 and obj[2] == fname:
continue
num = int(obj[1])
if num < 0:
raise ValueError("invalid offset.")
format = dtype(obj[0], align=align)
if n > 2:
title = obj[2]
else:
title = None
allfields.append((fname, format, num, title))
# sort by offsets
allfields.sort(key=lambda x: x[2])
names = [x[0] for x in allfields]
formats = [x[1] for x in allfields]
offsets = [x[2] for x in allfields]
titles = [x[3] for x in allfields]
return names, formats, offsets, titles
# Called in PyArray_DescrConverter function when
# a dictionary without "names" and "formats"
# fields is used as a data-type descriptor.
def _usefields(adict, align):
try:
names = adict[-1]
except KeyError:
names = None
if names is None:
names, formats, offsets, titles = _makenames_list(adict, align)
else:
formats = []
offsets = []
titles = []
for name in names:
res = adict[name]
formats.append(res[0])
offsets.append(res[1])
if len(res) > 2:
titles.append(res[2])
else:
titles.append(None)
return dtype({"names": names,
"formats": formats,
"offsets": offsets,
"titles": titles}, align)
# construct an array_protocol descriptor list
# from the fields attribute of a descriptor
# This calls itself recursively but should eventually hit
# a descriptor that has no fields and then return
# a simple typestring
def _array_descr(descriptor):
fields = descriptor.fields
if fields is None:
subdtype = descriptor.subdtype
if subdtype is None:
if descriptor.metadata is None:
return descriptor.str
else:
new = descriptor.metadata.copy()
if new:
return (descriptor.str, new)
else:
return descriptor.str
else:
return (_array_descr(subdtype[0]), subdtype[1])
names = descriptor.names
ordered_fields = [fields[x] + (x,) for x in names]
result = []
offset = 0
for field in ordered_fields:
if field[1] > offset:
num = field[1] - offset
result.append(('', f'|V{num}'))
offset += num
elif field[1] < offset:
raise ValueError(
"dtype.descr is not defined for types with overlapping or "
"out-of-order fields")
if len(field) > 3:
name = (field[2], field[3])
else:
name = field[2]
if field[0].subdtype:
tup = (name, _array_descr(field[0].subdtype[0]),
field[0].subdtype[1])
else:
tup = (name, _array_descr(field[0]))
offset += field[0].itemsize
result.append(tup)
if descriptor.itemsize > offset:
num = descriptor.itemsize - offset
result.append(('', f'|V{num}'))
return result
# format_re was originally from numarray by J. Todd Miller
format_re = re.compile(r'(?P<order1>[<>|=]?)'
r'(?P<repeats> *[(]?[ ,0-9]*[)]? *)'
r'(?P<order2>[<>|=]?)'
r'(?P<dtype>[A-Za-z0-9.?]*(?:\[[a-zA-Z0-9,.]+\])?)')
sep_re = re.compile(r'\s*,\s*')
space_re = re.compile(r'\s+$')
# astr is a string (perhaps comma separated)
_convorder = {'=': _nbo}
def _commastring(astr):
startindex = 0
result = []
islist = False
while startindex < len(astr):
mo = format_re.match(astr, pos=startindex)
try:
(order1, repeats, order2, dtype) = mo.groups()
except (TypeError, AttributeError):
raise ValueError(
f'format number {len(result) + 1} of "{astr}" is not recognized'
) from None
startindex = mo.end()
# Separator or ending padding
if startindex < len(astr):
if space_re.match(astr, pos=startindex):
startindex = len(astr)
else:
mo = sep_re.match(astr, pos=startindex)
if not mo:
raise ValueError(
'format number %d of "%s" is not recognized' %
(len(result) + 1, astr))
startindex = mo.end()
islist = True
if order2 == '':
order = order1
elif order1 == '':
order = order2
else:
order1 = _convorder.get(order1, order1)
order2 = _convorder.get(order2, order2)
if (order1 != order2):
raise ValueError(
f'inconsistent byte-order specification {order1} and {order2}')
order = order1
if order in ('|', '=', _nbo):
order = ''
dtype = order + dtype
if repeats == '':
newitem = dtype
else:
if (repeats[0] == "(" and repeats[-1] == ")"
and repeats[1:-1].strip() != ""
and "," not in repeats):
warnings.warn(
'Passing in a parenthesized single number for repeats '
'is deprecated; pass either a single number or indicate '
'a tuple with a comma, like "(2,)".', DeprecationWarning,
stacklevel=2)
newitem = (dtype, ast.literal_eval(repeats))
result.append(newitem)
return result if islist else result[0]
class dummy_ctype:
def __init__(self, cls):
self._cls = cls
def __mul__(self, other):
return self
def __call__(self, *other):
return self._cls(other)
def __eq__(self, other):
return self._cls == other._cls
def __ne__(self, other):
return self._cls != other._cls
def _getintp_ctype():
val = _getintp_ctype.cache
if val is not None:
return val
if ctypes is None:
import numpy as np
val = dummy_ctype(np.intp)
else:
char = dtype('n').char
if char == 'i':
val = ctypes.c_int
elif char == 'l':
val = ctypes.c_long
elif char == 'q':
val = ctypes.c_longlong
else:
val = ctypes.c_long
_getintp_ctype.cache = val
return val
_getintp_ctype.cache = None
# Used for .ctypes attribute of ndarray
class _missing_ctypes:
def cast(self, num, obj):
return num.value
class c_void_p:
def __init__(self, ptr):
self.value = ptr
class _ctypes:
def __init__(self, array, ptr=None):
self._arr = array
if ctypes:
self._ctypes = ctypes
self._data = self._ctypes.c_void_p(ptr)
else:
# fake a pointer-like object that holds onto the reference
self._ctypes = _missing_ctypes()
self._data = self._ctypes.c_void_p(ptr)
self._data._objects = array
if self._arr.ndim == 0:
self._zerod = True
else:
self._zerod = False
def data_as(self, obj):
"""
Return the data pointer cast to a particular c-types object.
For example, calling ``self._as_parameter_`` is equivalent to
``self.data_as(ctypes.c_void_p)``. Perhaps you want to use
the data as a pointer to a ctypes array of floating-point data:
``self.data_as(ctypes.POINTER(ctypes.c_double))``.
The returned pointer will keep a reference to the array.
"""
# _ctypes.cast function causes a circular reference of self._data in
# self._data._objects. Attributes of self._data cannot be released
# until gc.collect is called. Make a copy of the pointer first then
# let it hold the array reference. This is a workaround to circumvent
# the CPython bug https://bugs.python.org/issue12836.
ptr = self._ctypes.cast(self._data, obj)
ptr._arr = self._arr
return ptr
def shape_as(self, obj):
"""
Return the shape tuple as an array of some other c-types
type. For example: ``self.shape_as(ctypes.c_short)``.
"""
if self._zerod:
return None
return (obj * self._arr.ndim)(*self._arr.shape)
def strides_as(self, obj):
"""
Return the strides tuple as an array of some other
c-types type. For example: ``self.strides_as(ctypes.c_longlong)``.
"""
if self._zerod:
return None
return (obj * self._arr.ndim)(*self._arr.strides)
@property
def data(self):
"""
A pointer to the memory area of the array as a Python integer.
This memory area may contain data that is not aligned, or not in
correct byte-order. The memory area may not even be writeable.
The array flags and data-type of this array should be respected
when passing this attribute to arbitrary C-code to avoid trouble
that can include Python crashing. User Beware! The value of this
attribute is exactly the same as:
``self._array_interface_['data'][0]``.
Note that unlike ``data_as``, a reference won't be kept to the array:
code like ``ctypes.c_void_p((a + b).ctypes.data)`` will result in a
pointer to a deallocated array, and should be spelt
``(a + b).ctypes.data_as(ctypes.c_void_p)``
"""
return self._data.value
@property
def shape(self):
"""
(c_intp*self.ndim): A ctypes array of length self.ndim where
the basetype is the C-integer corresponding to ``dtype('p')`` on this
platform (see `~numpy.ctypeslib.c_intp`). This base-type could be
`ctypes.c_int`, `ctypes.c_long`, or `ctypes.c_longlong` depending on
the platform. The ctypes array contains the shape of
the underlying array.
"""
return self.shape_as(_getintp_ctype())
@property
def strides(self):
"""
(c_intp*self.ndim): A ctypes array of length self.ndim where
the basetype is the same as for the shape attribute. This ctypes
array contains the strides information from the underlying array.
This strides information is important for showing how many bytes
must be jumped to get to the next element in the array.
"""
return self.strides_as(_getintp_ctype())
@property
def _as_parameter_(self):
"""
Overrides the ctypes semi-magic method
Enables `c_func(some_array.ctypes)`
"""
return self.data_as(ctypes.c_void_p)
# Numpy 1.21.0, 2021-05-18
def get_data(self):
"""Deprecated getter for the `_ctypes.data` property.
.. deprecated:: 1.21
"""
warnings.warn('"get_data" is deprecated. Use "data" instead',
DeprecationWarning, stacklevel=2)
return self.data
def get_shape(self):
"""Deprecated getter for the `_ctypes.shape` property.
.. deprecated:: 1.21
"""
warnings.warn('"get_shape" is deprecated. Use "shape" instead',
DeprecationWarning, stacklevel=2)
return self.shape
def get_strides(self):
"""Deprecated getter for the `_ctypes.strides` property.
.. deprecated:: 1.21
"""
warnings.warn('"get_strides" is deprecated. Use "strides" instead',
DeprecationWarning, stacklevel=2)
return self.strides
def get_as_parameter(self):
"""Deprecated getter for the `_ctypes._as_parameter_` property.
.. deprecated:: 1.21
"""
warnings.warn(
'"get_as_parameter" is deprecated. Use "_as_parameter_" instead',
DeprecationWarning, stacklevel=2,
)
return self._as_parameter_
def _newnames(datatype, order):
"""
Given a datatype and an order object, return a new names tuple, with the
order indicated
"""
oldnames = datatype.names
nameslist = list(oldnames)
if isinstance(order, str):
order = [order]
seen = set()
if isinstance(order, (list, tuple)):
for name in order:
try:
nameslist.remove(name)
except ValueError:
if name in seen:
raise ValueError(f"duplicate field name: {name}") from None
else:
raise ValueError(f"unknown field name: {name}") from None
seen.add(name)
return tuple(list(order) + nameslist)
raise ValueError(f"unsupported order value: {order}")
def _copy_fields(ary):
"""Return copy of structured array with padding between fields removed.
Parameters
----------
ary : ndarray
Structured array from which to remove padding bytes
Returns
-------
ary_copy : ndarray
Copy of ary with padding bytes removed
"""
dt = ary.dtype
copy_dtype = {'names': dt.names,
'formats': [dt.fields[name][0] for name in dt.names]}
return array(ary, dtype=copy_dtype, copy=True)
def _promote_fields(dt1, dt2):
""" Perform type promotion for two structured dtypes.
Parameters
----------
dt1 : structured dtype
First dtype.
dt2 : structured dtype
Second dtype.
Returns
-------
out : dtype
The promoted dtype
Notes
-----
If one of the inputs is aligned, the result will be. The titles of
both descriptors must match (point to the same field).
"""
# Both must be structured and have the same names in the same order
if (dt1.names is None or dt2.names is None) or dt1.names != dt2.names:
raise DTypePromotionError(
f"field names `{dt1.names}` and `{dt2.names}` mismatch.")
# if both are identical, we can (maybe!) just return the same dtype.
identical = dt1 is dt2
new_fields = []
for name in dt1.names:
field1 = dt1.fields[name]
field2 = dt2.fields[name]
new_descr = promote_types(field1[0], field2[0])
identical = identical and new_descr is field1[0]
# Check that the titles match (if given):
if field1[2:] != field2[2:]:
raise DTypePromotionError(
f"field titles of field '{name}' mismatch")
if len(field1) == 2:
new_fields.append((name, new_descr))
else:
new_fields.append(((field1[2], name), new_descr))
res = dtype(new_fields, align=dt1.isalignedstruct or dt2.isalignedstruct)
# Might as well preserve identity (and metadata) if the dtype is identical
# and the itemsize, offsets are also unmodified. This could probably be
# sped up, but also probably just be removed entirely.
if identical and res.itemsize == dt1.itemsize:
for name in dt1.names:
if dt1.fields[name][1] != res.fields[name][1]:
return res # the dtype changed.
return dt1
return res
def _getfield_is_safe(oldtype, newtype, offset):
""" Checks safety of getfield for object arrays.
As in _view_is_safe, we need to check that memory containing objects is not
reinterpreted as a non-object datatype and vice versa.
Parameters
----------
oldtype : data-type
Data type of the original ndarray.
newtype : data-type
Data type of the field being accessed by ndarray.getfield
offset : int
Offset of the field being accessed by ndarray.getfield
Raises
------
TypeError
If the field access is invalid
"""
if newtype.hasobject or oldtype.hasobject:
if offset == 0 and newtype == oldtype:
return
if oldtype.names is not None:
for name in oldtype.names:
if (oldtype.fields[name][1] == offset and
oldtype.fields[name][0] == newtype):
return
raise TypeError("Cannot get/set field of an object array")
return
def _view_is_safe(oldtype, newtype):
""" Checks safety of a view involving object arrays, for example when
doing::
np.zeros(10, dtype=oldtype).view(newtype)
Parameters
----------
oldtype : data-type
Data type of original ndarray
newtype : data-type
Data type of the view
Raises
------
TypeError
If the new type is incompatible with the old type.
"""
# if the types are equivalent, there is no problem.
# for example: dtype((np.record, 'i4,i4')) == dtype((np.void, 'i4,i4'))
if oldtype == newtype:
return
if newtype.hasobject or oldtype.hasobject:
raise TypeError("Cannot change data-type for array of references.")
return
# Given a string containing a PEP 3118 format specifier,
# construct a NumPy dtype
_pep3118_native_map = {
'?': '?',
'c': 'S1',
'b': 'b',
'B': 'B',
'h': 'h',
'H': 'H',
'i': 'i',
'I': 'I',
'l': 'l',
'L': 'L',
'q': 'q',
'Q': 'Q',
'e': 'e',
'f': 'f',
'd': 'd',
'g': 'g',
'Zf': 'F',
'Zd': 'D',
'Zg': 'G',
's': 'S',
'w': 'U',
'O': 'O',
'x': 'V', # padding
}
_pep3118_native_typechars = ''.join(_pep3118_native_map.keys())
_pep3118_standard_map = {
'?': '?',
'c': 'S1',
'b': 'b',
'B': 'B',
'h': 'i2',
'H': 'u2',
'i': 'i4',
'I': 'u4',
'l': 'i4',
'L': 'u4',
'q': 'i8',
'Q': 'u8',
'e': 'f2',
'f': 'f',
'd': 'd',
'Zf': 'F',
'Zd': 'D',
's': 'S',
'w': 'U',
'O': 'O',
'x': 'V', # padding
}
_pep3118_standard_typechars = ''.join(_pep3118_standard_map.keys())
_pep3118_unsupported_map = {
'u': 'UCS-2 strings',
'&': 'pointers',
't': 'bitfields',
'X': 'function pointers',
}
class _Stream:
def __init__(self, s):
self.s = s
self.byteorder = '@'
def advance(self, n):
res = self.s[:n]
self.s = self.s[n:]
return res
def consume(self, c):
if self.s[:len(c)] == c:
self.advance(len(c))
return True
return False
def consume_until(self, c):
if callable(c):
i = 0
while i < len(self.s) and not c(self.s[i]):
i = i + 1
return self.advance(i)
else:
i = self.s.index(c)
res = self.advance(i)
self.advance(len(c))
return res
@property
def next(self):
return self.s[0]
def __bool__(self):
return bool(self.s)
def _dtype_from_pep3118(spec):
stream = _Stream(spec)
dtype, align = __dtype_from_pep3118(stream, is_subdtype=False)
return dtype
def __dtype_from_pep3118(stream, is_subdtype):
field_spec = {
'names': [],
'formats': [],
'offsets': [],
'itemsize': 0
}
offset = 0
common_alignment = 1
is_padding = False
# Parse spec
while stream:
value = None
# End of structure, bail out to upper level
if stream.consume('}'):
break
# Sub-arrays (1)
shape = None
if stream.consume('('):
shape = stream.consume_until(')')
shape = tuple(map(int, shape.split(',')))
# Byte order
if stream.next in ('@', '=', '<', '>', '^', '!'):
byteorder = stream.advance(1)
if byteorder == '!':
byteorder = '>'
stream.byteorder = byteorder
# Byte order characters also control native vs. standard type sizes
if stream.byteorder in ('@', '^'):
type_map = _pep3118_native_map
type_map_chars = _pep3118_native_typechars
else:
type_map = _pep3118_standard_map
type_map_chars = _pep3118_standard_typechars
# Item sizes
itemsize_str = stream.consume_until(lambda c: not c.isdigit())
if itemsize_str:
itemsize = int(itemsize_str)
else:
itemsize = 1
# Data types
is_padding = False
if stream.consume('T{'):
value, align = __dtype_from_pep3118(
stream, is_subdtype=True)
elif stream.next in type_map_chars:
if stream.next == 'Z':
typechar = stream.advance(2)
else:
typechar = stream.advance(1)
is_padding = (typechar == 'x')
dtypechar = type_map[typechar]
if dtypechar in 'USV':
dtypechar += '%d' % itemsize
itemsize = 1
numpy_byteorder = {'@': '=', '^': '='}.get(
stream.byteorder, stream.byteorder)
value = dtype(numpy_byteorder + dtypechar)
align = value.alignment
elif stream.next in _pep3118_unsupported_map:
desc = _pep3118_unsupported_map[stream.next]
raise NotImplementedError(
f"Unrepresentable PEP 3118 data type {stream.next!r} ({desc})")
else:
raise ValueError(
f"Unknown PEP 3118 data type specifier {stream.s!r}"
)
#
# Native alignment may require padding
#
# Here we assume that the presence of a '@' character implicitly
# implies that the start of the array is *already* aligned.
#
extra_offset = 0
if stream.byteorder == '@':
start_padding = (-offset) % align
intra_padding = (-value.itemsize) % align
offset += start_padding
if intra_padding != 0:
if itemsize > 1 or (shape is not None and _prod(shape) > 1):
# Inject internal padding to the end of the sub-item
value = _add_trailing_padding(value, intra_padding)
else:
# We can postpone the injection of internal padding,
# as the item appears at most once
extra_offset += intra_padding
# Update common alignment
common_alignment = _lcm(align, common_alignment)
# Convert itemsize to sub-array
if itemsize != 1:
value = dtype((value, (itemsize,)))
# Sub-arrays (2)
if shape is not None:
value = dtype((value, shape))
# Field name
if stream.consume(':'):
name = stream.consume_until(':')
else:
name = None
if not (is_padding and name is None):
if name is not None and name in field_spec['names']:
raise RuntimeError(
f"Duplicate field name '{name}' in PEP3118 format"
)
field_spec['names'].append(name)
field_spec['formats'].append(value)
field_spec['offsets'].append(offset)
offset += value.itemsize
offset += extra_offset
field_spec['itemsize'] = offset
# extra final padding for aligned types
if stream.byteorder == '@':
field_spec['itemsize'] += (-offset) % common_alignment
# Check if this was a simple 1-item type, and unwrap it
if (field_spec['names'] == [None]
and field_spec['offsets'][0] == 0
and field_spec['itemsize'] == field_spec['formats'][0].itemsize
and not is_subdtype):
ret = field_spec['formats'][0]
else:
_fix_names(field_spec)
ret = dtype(field_spec)
# Finished
return ret, common_alignment
def _fix_names(field_spec):
""" Replace names which are None with the next unused f%d name """
names = field_spec['names']
for i, name in enumerate(names):
if name is not None:
continue
j = 0
while True:
name = f'f{j}'
if name not in names:
break
j = j + 1
names[i] = name
def _add_trailing_padding(value, padding):
"""Inject the specified number of padding bytes at the end of a dtype"""
if value.fields is None:
field_spec = {
'names': ['f0'],
'formats': [value],
'offsets': [0],
'itemsize': value.itemsize
}
else:
fields = value.fields
names = value.names
field_spec = {
'names': names,
'formats': [fields[name][0] for name in names],
'offsets': [fields[name][1] for name in names],
'itemsize': value.itemsize
}
field_spec['itemsize'] += padding
return dtype(field_spec)
def _prod(a):
p = 1
for x in a:
p *= x
return p
def _gcd(a, b):
"""Calculate the greatest common divisor of a and b"""
if not (math.isfinite(a) and math.isfinite(b)):
raise ValueError('Can only find greatest common divisor of '
f'finite arguments, found "{a}" and "{b}"')
while b:
a, b = b, a % b
return a
def _lcm(a, b):
return a // _gcd(a, b) * b
def array_ufunc_errmsg_formatter(dummy, ufunc, method, *inputs, **kwargs):
""" Format the error message for when __array_ufunc__ gives up. """
args_string = ', '.join([f'{arg!r}' for arg in inputs] +
[f'{k}={v!r}'
for k, v in kwargs.items()])
args = inputs + kwargs.get('out', ())
types_string = ', '.join(repr(type(arg).__name__) for arg in args)
return ('operand type(s) all returned NotImplemented from '
f'__array_ufunc__({ufunc!r}, {method!r}, {args_string}): {types_string}'
)
def array_function_errmsg_formatter(public_api, types):
""" Format the error message for when __array_ufunc__ gives up. """
func_name = f'{public_api.__module__}.{public_api.__name__}'
return (f"no implementation found for '{func_name}' on types that implement "
f'__array_function__: {list(types)}')
def _ufunc_doc_signature_formatter(ufunc):
"""
Builds a signature string which resembles PEP 457
This is used to construct the first line of the docstring
"""
# input arguments are simple
if ufunc.nin == 1:
in_args = 'x'
else:
in_args = ', '.join(f'x{i + 1}' for i in range(ufunc.nin))
# output arguments are both keyword or positional
if ufunc.nout == 0:
out_args = ', /, out=()'
elif ufunc.nout == 1:
out_args = ', /, out=None'
else:
out_args = '[, {positional}], / [, out={default}]'.format(
positional=', '.join(
f'out{i + 1}' for i in range(ufunc.nout)),
default=repr((None,) * ufunc.nout)
)
# keyword only args depend on whether this is a gufunc
kwargs = (
", casting='same_kind'"
", order='K'"
", dtype=None"
", subok=True"
)
# NOTE: gufuncs may or may not support the `axis` parameter
if ufunc.signature is None:
kwargs = f", where=True{kwargs}[, signature]"
else:
kwargs += "[, signature, axes, axis]"
# join all the parts together
return f'{ufunc.__name__}({in_args}{out_args}, *{kwargs})'
def npy_ctypes_check(cls):
# determine if a class comes from ctypes, in order to work around
# a bug in the buffer protocol for those objects, bpo-10746
try:
# ctypes class are new-style, so have an __mro__. This probably fails
# for ctypes classes with multiple inheritance.
if IS_PYPY:
# (..., _ctypes.basics._CData, Bufferable, object)
ctype_base = cls.__mro__[-3]
else:
# # (..., _ctypes._CData, object)
ctype_base = cls.__mro__[-2]
# right now, they're part of the _ctypes module
return '_ctypes' in ctype_base.__module__
except Exception:
return False
# used to handle the _NoValue default argument for na_object
# in the C implementation of the __reduce__ method for stringdtype
def _convert_to_stringdtype_kwargs(coerce, na_object=_NoValue):
if na_object is _NoValue:
return StringDType(coerce=coerce)
return StringDType(coerce=coerce, na_object=na_object)

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import ctypes as ct
import re
from collections.abc import Callable, Iterable
from typing import Any, Final, Generic, Self, overload
from typing_extensions import TypeVar, deprecated
import numpy as np
import numpy.typing as npt
from numpy.ctypeslib import c_intp
_CastT = TypeVar("_CastT", bound=ct._CanCastTo)
_T_co = TypeVar("_T_co", covariant=True)
_CT = TypeVar("_CT", bound=ct._CData)
_PT_co = TypeVar("_PT_co", bound=int | None, default=None, covariant=True)
###
IS_PYPY: Final[bool] = ...
format_re: Final[re.Pattern[str]] = ...
sep_re: Final[re.Pattern[str]] = ...
space_re: Final[re.Pattern[str]] = ...
###
# TODO: Let the likes of `shape_as` and `strides_as` return `None`
# for 0D arrays once we've got shape-support
class _ctypes(Generic[_PT_co]):
@overload
def __init__(self: _ctypes[None], /, array: npt.NDArray[Any], ptr: None = None) -> None: ...
@overload
def __init__(self, /, array: npt.NDArray[Any], ptr: _PT_co) -> None: ...
#
@property
def data(self) -> _PT_co: ...
@property
def shape(self) -> ct.Array[c_intp]: ...
@property
def strides(self) -> ct.Array[c_intp]: ...
@property
def _as_parameter_(self) -> ct.c_void_p: ...
#
def data_as(self, /, obj: type[_CastT]) -> _CastT: ...
def shape_as(self, /, obj: type[_CT]) -> ct.Array[_CT]: ...
def strides_as(self, /, obj: type[_CT]) -> ct.Array[_CT]: ...
#
@deprecated('"get_data" is deprecated. Use "data" instead')
def get_data(self, /) -> _PT_co: ...
@deprecated('"get_shape" is deprecated. Use "shape" instead')
def get_shape(self, /) -> ct.Array[c_intp]: ...
@deprecated('"get_strides" is deprecated. Use "strides" instead')
def get_strides(self, /) -> ct.Array[c_intp]: ...
@deprecated('"get_as_parameter" is deprecated. Use "_as_parameter_" instead')
def get_as_parameter(self, /) -> ct.c_void_p: ...
class dummy_ctype(Generic[_T_co]):
_cls: type[_T_co]
def __init__(self, /, cls: type[_T_co]) -> None: ...
def __eq__(self, other: Self, /) -> bool: ... # type: ignore[override] # pyright: ignore[reportIncompatibleMethodOverride]
def __ne__(self, other: Self, /) -> bool: ... # type: ignore[override] # pyright: ignore[reportIncompatibleMethodOverride]
def __mul__(self, other: object, /) -> Self: ...
def __call__(self, /, *other: object) -> _T_co: ...
def array_ufunc_errmsg_formatter(dummy: object, ufunc: np.ufunc, method: str, *inputs: object, **kwargs: object) -> str: ...
def array_function_errmsg_formatter(public_api: Callable[..., object], types: Iterable[str]) -> str: ...
def npy_ctypes_check(cls: type) -> bool: ...

355
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@ -1,355 +0,0 @@
"""
Machine arithmetic - determine the parameters of the
floating-point arithmetic system
Author: Pearu Peterson, September 2003
"""
__all__ = ['MachAr']
from ._ufunc_config import errstate
from .fromnumeric import any
# Need to speed this up...especially for longdouble
# Deprecated 2021-10-20, NumPy 1.22
class MachAr:
"""
Diagnosing machine parameters.
Attributes
----------
ibeta : int
Radix in which numbers are represented.
it : int
Number of base-`ibeta` digits in the floating point mantissa M.
machep : int
Exponent of the smallest (most negative) power of `ibeta` that,
added to 1.0, gives something different from 1.0
eps : float
Floating-point number ``beta**machep`` (floating point precision)
negep : int
Exponent of the smallest power of `ibeta` that, subtracted
from 1.0, gives something different from 1.0.
epsneg : float
Floating-point number ``beta**negep``.
iexp : int
Number of bits in the exponent (including its sign and bias).
minexp : int
Smallest (most negative) power of `ibeta` consistent with there
being no leading zeros in the mantissa.
xmin : float
Floating-point number ``beta**minexp`` (the smallest [in
magnitude] positive floating point number with full precision).
maxexp : int
Smallest (positive) power of `ibeta` that causes overflow.
xmax : float
``(1-epsneg) * beta**maxexp`` (the largest [in magnitude]
usable floating value).
irnd : int
In ``range(6)``, information on what kind of rounding is done
in addition, and on how underflow is handled.
ngrd : int
Number of 'guard digits' used when truncating the product
of two mantissas to fit the representation.
epsilon : float
Same as `eps`.
tiny : float
An alias for `smallest_normal`, kept for backwards compatibility.
huge : float
Same as `xmax`.
precision : float
``- int(-log10(eps))``
resolution : float
``- 10**(-precision)``
smallest_normal : float
The smallest positive floating point number with 1 as leading bit in
the mantissa following IEEE-754. Same as `xmin`.
smallest_subnormal : float
The smallest positive floating point number with 0 as leading bit in
the mantissa following IEEE-754.
Parameters
----------
float_conv : function, optional
Function that converts an integer or integer array to a float
or float array. Default is `float`.
int_conv : function, optional
Function that converts a float or float array to an integer or
integer array. Default is `int`.
float_to_float : function, optional
Function that converts a float array to float. Default is `float`.
Note that this does not seem to do anything useful in the current
implementation.
float_to_str : function, optional
Function that converts a single float to a string. Default is
``lambda v:'%24.16e' %v``.
title : str, optional
Title that is printed in the string representation of `MachAr`.
See Also
--------
finfo : Machine limits for floating point types.
iinfo : Machine limits for integer types.
References
----------
.. [1] Press, Teukolsky, Vetterling and Flannery,
"Numerical Recipes in C++," 2nd ed,
Cambridge University Press, 2002, p. 31.
"""
def __init__(self, float_conv=float, int_conv=int,
float_to_float=float,
float_to_str=lambda v: f'{v:24.16e}',
title='Python floating point number'):
"""
float_conv - convert integer to float (array)
int_conv - convert float (array) to integer
float_to_float - convert float array to float
float_to_str - convert array float to str
title - description of used floating point numbers
"""
# We ignore all errors here because we are purposely triggering
# underflow to detect the properties of the running arch.
with errstate(under='ignore'):
self._do_init(float_conv, int_conv, float_to_float, float_to_str, title)
def _do_init(self, float_conv, int_conv, float_to_float, float_to_str, title):
max_iterN = 10000
msg = "Did not converge after %d tries with %s"
one = float_conv(1)
two = one + one
zero = one - one
# Do we really need to do this? Aren't they 2 and 2.0?
# Determine ibeta and beta
a = one
for _ in range(max_iterN):
a = a + a
temp = a + one
temp1 = temp - a
if any(temp1 - one != zero):
break
else:
raise RuntimeError(msg % (_, one.dtype))
b = one
for _ in range(max_iterN):
b = b + b
temp = a + b
itemp = int_conv(temp - a)
if any(itemp != 0):
break
else:
raise RuntimeError(msg % (_, one.dtype))
ibeta = itemp
beta = float_conv(ibeta)
# Determine it and irnd
it = -1
b = one
for _ in range(max_iterN):
it = it + 1
b = b * beta
temp = b + one
temp1 = temp - b
if any(temp1 - one != zero):
break
else:
raise RuntimeError(msg % (_, one.dtype))
betah = beta / two
a = one
for _ in range(max_iterN):
a = a + a
temp = a + one
temp1 = temp - a
if any(temp1 - one != zero):
break
else:
raise RuntimeError(msg % (_, one.dtype))
temp = a + betah
irnd = 0
if any(temp - a != zero):
irnd = 1
tempa = a + beta
temp = tempa + betah
if irnd == 0 and any(temp - tempa != zero):
irnd = 2
# Determine negep and epsneg
negep = it + 3
betain = one / beta
a = one
for i in range(negep):
a = a * betain
b = a
for _ in range(max_iterN):
temp = one - a
if any(temp - one != zero):
break
a = a * beta
negep = negep - 1
# Prevent infinite loop on PPC with gcc 4.0:
if negep < 0:
raise RuntimeError("could not determine machine tolerance "
"for 'negep', locals() -> %s" % (locals()))
else:
raise RuntimeError(msg % (_, one.dtype))
negep = -negep
epsneg = a
# Determine machep and eps
machep = - it - 3
a = b
for _ in range(max_iterN):
temp = one + a
if any(temp - one != zero):
break
a = a * beta
machep = machep + 1
else:
raise RuntimeError(msg % (_, one.dtype))
eps = a
# Determine ngrd
ngrd = 0
temp = one + eps
if irnd == 0 and any(temp * one - one != zero):
ngrd = 1
# Determine iexp
i = 0
k = 1
z = betain
t = one + eps
nxres = 0
for _ in range(max_iterN):
y = z
z = y * y
a = z * one # Check here for underflow
temp = z * t
if any(a + a == zero) or any(abs(z) >= y):
break
temp1 = temp * betain
if any(temp1 * beta == z):
break
i = i + 1
k = k + k
else:
raise RuntimeError(msg % (_, one.dtype))
if ibeta != 10:
iexp = i + 1
mx = k + k
else:
iexp = 2
iz = ibeta
while k >= iz:
iz = iz * ibeta
iexp = iexp + 1
mx = iz + iz - 1
# Determine minexp and xmin
for _ in range(max_iterN):
xmin = y
y = y * betain
a = y * one
temp = y * t
if any((a + a) != zero) and any(abs(y) < xmin):
k = k + 1
temp1 = temp * betain
if any(temp1 * beta == y) and any(temp != y):
nxres = 3
xmin = y
break
else:
break
else:
raise RuntimeError(msg % (_, one.dtype))
minexp = -k
# Determine maxexp, xmax
if mx <= k + k - 3 and ibeta != 10:
mx = mx + mx
iexp = iexp + 1
maxexp = mx + minexp
irnd = irnd + nxres
if irnd >= 2:
maxexp = maxexp - 2
i = maxexp + minexp
if ibeta == 2 and not i:
maxexp = maxexp - 1
if i > 20:
maxexp = maxexp - 1
if any(a != y):
maxexp = maxexp - 2
xmax = one - epsneg
if any(xmax * one != xmax):
xmax = one - beta * epsneg
xmax = xmax / (xmin * beta * beta * beta)
i = maxexp + minexp + 3
for j in range(i):
if ibeta == 2:
xmax = xmax + xmax
else:
xmax = xmax * beta
smallest_subnormal = abs(xmin / beta ** (it))
self.ibeta = ibeta
self.it = it
self.negep = negep
self.epsneg = float_to_float(epsneg)
self._str_epsneg = float_to_str(epsneg)
self.machep = machep
self.eps = float_to_float(eps)
self._str_eps = float_to_str(eps)
self.ngrd = ngrd
self.iexp = iexp
self.minexp = minexp
self.xmin = float_to_float(xmin)
self._str_xmin = float_to_str(xmin)
self.maxexp = maxexp
self.xmax = float_to_float(xmax)
self._str_xmax = float_to_str(xmax)
self.irnd = irnd
self.title = title
# Commonly used parameters
self.epsilon = self.eps
self.tiny = self.xmin
self.huge = self.xmax
self.smallest_normal = self.xmin
self._str_smallest_normal = float_to_str(self.xmin)
self.smallest_subnormal = float_to_float(smallest_subnormal)
self._str_smallest_subnormal = float_to_str(smallest_subnormal)
import math
self.precision = int(-math.log10(float_to_float(self.eps)))
ten = two + two + two + two + two
resolution = ten ** (-self.precision)
self.resolution = float_to_float(resolution)
self._str_resolution = float_to_str(resolution)
def __str__(self):
fmt = (
'Machine parameters for %(title)s\n'
'---------------------------------------------------------------------\n'
'ibeta=%(ibeta)s it=%(it)s iexp=%(iexp)s ngrd=%(ngrd)s irnd=%(irnd)s\n'
'machep=%(machep)s eps=%(_str_eps)s (beta**machep == epsilon)\n'
'negep =%(negep)s epsneg=%(_str_epsneg)s (beta**epsneg)\n'
'minexp=%(minexp)s xmin=%(_str_xmin)s (beta**minexp == tiny)\n'
'maxexp=%(maxexp)s xmax=%(_str_xmax)s ((1-epsneg)*beta**maxexp == huge)\n'
'smallest_normal=%(smallest_normal)s '
'smallest_subnormal=%(smallest_subnormal)s\n'
'---------------------------------------------------------------------\n'
)
return fmt % self.__dict__
if __name__ == '__main__':
print(MachAr())

55
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@ -1,55 +0,0 @@
from collections.abc import Iterable
from typing import Any, Final, TypeVar, overload
import numpy as np
from numpy import _CastingKind
from numpy._utils import set_module as set_module
###
_T = TypeVar("_T")
_TupleT = TypeVar("_TupleT", bound=tuple[()] | tuple[Any, Any, *tuple[Any, ...]])
_ExceptionT = TypeVar("_ExceptionT", bound=Exception)
###
class UFuncTypeError(TypeError):
ufunc: Final[np.ufunc]
def __init__(self, /, ufunc: np.ufunc) -> None: ...
class _UFuncNoLoopError(UFuncTypeError):
dtypes: tuple[np.dtype, ...]
def __init__(self, /, ufunc: np.ufunc, dtypes: Iterable[np.dtype]) -> None: ...
class _UFuncBinaryResolutionError(_UFuncNoLoopError):
dtypes: tuple[np.dtype, np.dtype]
def __init__(self, /, ufunc: np.ufunc, dtypes: Iterable[np.dtype]) -> None: ...
class _UFuncCastingError(UFuncTypeError):
casting: Final[_CastingKind]
from_: Final[np.dtype]
to: Final[np.dtype]
def __init__(self, /, ufunc: np.ufunc, casting: _CastingKind, from_: np.dtype, to: np.dtype) -> None: ...
class _UFuncInputCastingError(_UFuncCastingError):
in_i: Final[int]
def __init__(self, /, ufunc: np.ufunc, casting: _CastingKind, from_: np.dtype, to: np.dtype, i: int) -> None: ...
class _UFuncOutputCastingError(_UFuncCastingError):
out_i: Final[int]
def __init__(self, /, ufunc: np.ufunc, casting: _CastingKind, from_: np.dtype, to: np.dtype, i: int) -> None: ...
class _ArrayMemoryError(MemoryError):
shape: tuple[int, ...]
dtype: np.dtype
def __init__(self, /, shape: tuple[int, ...], dtype: np.dtype) -> None: ...
@property
def _total_size(self) -> int: ...
@staticmethod
def _size_to_string(num_bytes: int) -> str: ...
@overload
def _unpack_tuple(tup: tuple[_T]) -> _T: ...
@overload
def _unpack_tuple(tup: _TupleT) -> _TupleT: ...
def _display_as_base(cls: type[_ExceptionT]) -> type[_ExceptionT]: ...

255
venv/lib/python3.13/site-packages/numpy/_core/_methods.py
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@ -1,255 +0,0 @@
"""
Array methods which are called by both the C-code for the method
and the Python code for the NumPy-namespace function
"""
import os
import pickle
import warnings
from contextlib import nullcontext
import numpy as np
from numpy._core import multiarray as mu
from numpy._core import numerictypes as nt
from numpy._core import umath as um
from numpy._core.multiarray import asanyarray
from numpy._globals import _NoValue
# save those O(100) nanoseconds!
bool_dt = mu.dtype("bool")
umr_maximum = um.maximum.reduce
umr_minimum = um.minimum.reduce
umr_sum = um.add.reduce
umr_prod = um.multiply.reduce
umr_bitwise_count = um.bitwise_count
umr_any = um.logical_or.reduce
umr_all = um.logical_and.reduce
# Complex types to -> (2,)float view for fast-path computation in _var()
_complex_to_float = {
nt.dtype(nt.csingle): nt.dtype(nt.single),
nt.dtype(nt.cdouble): nt.dtype(nt.double),
}
# Special case for windows: ensure double takes precedence
if nt.dtype(nt.longdouble) != nt.dtype(nt.double):
_complex_to_float.update({
nt.dtype(nt.clongdouble): nt.dtype(nt.longdouble),
})
# avoid keyword arguments to speed up parsing, saves about 15%-20% for very
# small reductions
def _amax(a, axis=None, out=None, keepdims=False,
initial=_NoValue, where=True):
return umr_maximum(a, axis, None, out, keepdims, initial, where)
def _amin(a, axis=None, out=None, keepdims=False,
initial=_NoValue, where=True):
return umr_minimum(a, axis, None, out, keepdims, initial, where)
def _sum(a, axis=None, dtype=None, out=None, keepdims=False,
initial=_NoValue, where=True):
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
def _prod(a, axis=None, dtype=None, out=None, keepdims=False,
initial=_NoValue, where=True):
return umr_prod(a, axis, dtype, out, keepdims, initial, where)
def _any(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
# By default, return a boolean for any and all
if dtype is None:
dtype = bool_dt
# Parsing keyword arguments is currently fairly slow, so avoid it for now
if where is True:
return umr_any(a, axis, dtype, out, keepdims)
return umr_any(a, axis, dtype, out, keepdims, where=where)
def _all(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
# By default, return a boolean for any and all
if dtype is None:
dtype = bool_dt
# Parsing keyword arguments is currently fairly slow, so avoid it for now
if where is True:
return umr_all(a, axis, dtype, out, keepdims)
return umr_all(a, axis, dtype, out, keepdims, where=where)
def _count_reduce_items(arr, axis, keepdims=False, where=True):
# fast-path for the default case
if where is True:
# no boolean mask given, calculate items according to axis
if axis is None:
axis = tuple(range(arr.ndim))
elif not isinstance(axis, tuple):
axis = (axis,)
items = 1
for ax in axis:
items *= arr.shape[mu.normalize_axis_index(ax, arr.ndim)]
items = nt.intp(items)
else:
# TODO: Optimize case when `where` is broadcast along a non-reduction
# axis and full sum is more excessive than needed.
# guarded to protect circular imports
from numpy.lib._stride_tricks_impl import broadcast_to
# count True values in (potentially broadcasted) boolean mask
items = umr_sum(broadcast_to(where, arr.shape), axis, nt.intp, None,
keepdims)
return items
def _clip(a, min=None, max=None, out=None, **kwargs):
if a.dtype.kind in "iu":
# If min/max is a Python integer, deal with out-of-bound values here.
# (This enforces NEP 50 rules as no value based promotion is done.)
if type(min) is int and min <= np.iinfo(a.dtype).min:
min = None
if type(max) is int and max >= np.iinfo(a.dtype).max:
max = None
if min is None and max is None:
# return identity
return um.positive(a, out=out, **kwargs)
elif min is None:
return um.minimum(a, max, out=out, **kwargs)
elif max is None:
return um.maximum(a, min, out=out, **kwargs)
else:
return um.clip(a, min, max, out=out, **kwargs)
def _mean(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
arr = asanyarray(a)
is_float16_result = False
rcount = _count_reduce_items(arr, axis, keepdims=keepdims, where=where)
if rcount == 0 if where is True else umr_any(rcount == 0, axis=None):
warnings.warn("Mean of empty slice.", RuntimeWarning, stacklevel=2)
# Cast bool, unsigned int, and int to float64 by default
if dtype is None:
if issubclass(arr.dtype.type, (nt.integer, nt.bool)):
dtype = mu.dtype('f8')
elif issubclass(arr.dtype.type, nt.float16):
dtype = mu.dtype('f4')
is_float16_result = True
ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
if isinstance(ret, mu.ndarray):
ret = um.true_divide(
ret, rcount, out=ret, casting='unsafe', subok=False)
if is_float16_result and out is None:
ret = arr.dtype.type(ret)
elif hasattr(ret, 'dtype'):
if is_float16_result:
ret = arr.dtype.type(ret / rcount)
else:
ret = ret.dtype.type(ret / rcount)
else:
ret = ret / rcount
return ret
def _var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, *,
where=True, mean=None):
arr = asanyarray(a)
rcount = _count_reduce_items(arr, axis, keepdims=keepdims, where=where)
# Make this warning show up on top.
if ddof >= rcount if where is True else umr_any(ddof >= rcount, axis=None):
warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning,
stacklevel=2)
# Cast bool, unsigned int, and int to float64 by default
if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool)):
dtype = mu.dtype('f8')
if mean is not None:
arrmean = mean
else:
# Compute the mean.
# Note that if dtype is not of inexact type then arraymean will
# not be either.
arrmean = umr_sum(arr, axis, dtype, keepdims=True, where=where)
# The shape of rcount has to match arrmean to not change the shape of
# out in broadcasting. Otherwise, it cannot be stored back to arrmean.
if rcount.ndim == 0:
# fast-path for default case when where is True
div = rcount
else:
# matching rcount to arrmean when where is specified as array
div = rcount.reshape(arrmean.shape)
if isinstance(arrmean, mu.ndarray):
arrmean = um.true_divide(arrmean, div, out=arrmean,
casting='unsafe', subok=False)
elif hasattr(arrmean, "dtype"):
arrmean = arrmean.dtype.type(arrmean / rcount)
else:
arrmean = arrmean / rcount
# Compute sum of squared deviations from mean
# Note that x may not be inexact and that we need it to be an array,
# not a scalar.
x = asanyarray(arr - arrmean)
if issubclass(arr.dtype.type, (nt.floating, nt.integer)):
x = um.multiply(x, x, out=x)
# Fast-paths for built-in complex types
elif x.dtype in _complex_to_float:
xv = x.view(dtype=(_complex_to_float[x.dtype], (2,)))
um.multiply(xv, xv, out=xv)
x = um.add(xv[..., 0], xv[..., 1], out=x.real).real
# Most general case; includes handling object arrays containing imaginary
# numbers and complex types with non-native byteorder
else:
x = um.multiply(x, um.conjugate(x), out=x).real
ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where)
# Compute degrees of freedom and make sure it is not negative.
rcount = um.maximum(rcount - ddof, 0)
# divide by degrees of freedom
if isinstance(ret, mu.ndarray):
ret = um.true_divide(
ret, rcount, out=ret, casting='unsafe', subok=False)
elif hasattr(ret, 'dtype'):
ret = ret.dtype.type(ret / rcount)
else:
ret = ret / rcount
return ret
def _std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, *,
where=True, mean=None):
ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
keepdims=keepdims, where=where, mean=mean)
if isinstance(ret, mu.ndarray):
ret = um.sqrt(ret, out=ret)
elif hasattr(ret, 'dtype'):
ret = ret.dtype.type(um.sqrt(ret))
else:
ret = um.sqrt(ret)
return ret
def _ptp(a, axis=None, out=None, keepdims=False):
return um.subtract(
umr_maximum(a, axis, None, out, keepdims),
umr_minimum(a, axis, None, None, keepdims),
out
)
def _dump(self, file, protocol=2):
if hasattr(file, 'write'):
ctx = nullcontext(file)
else:
ctx = open(os.fspath(file), "wb")
with ctx as f:
pickle.dump(self, f, protocol=protocol)
def _dumps(self, protocol=2):
return pickle.dumps(self, protocol=protocol)
def _bitwise_count(a, out=None, *, where=True, casting='same_kind',
order='K', dtype=None, subok=True):
return umr_bitwise_count(a, out, where=where, casting=casting,
order=order, dtype=dtype, subok=subok)

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from collections.abc import Callable
from typing import Any, Concatenate, TypeAlias
import numpy as np
from . import _exceptions as _exceptions
###
_Reduce2: TypeAlias = Callable[Concatenate[object, ...], Any]
###
bool_dt: np.dtype[np.bool] = ...
umr_maximum: _Reduce2 = ...
umr_minimum: _Reduce2 = ...
umr_sum: _Reduce2 = ...
umr_prod: _Reduce2 = ...
umr_bitwise_count = np.bitwise_count
umr_any: _Reduce2 = ...
umr_all: _Reduce2 = ...
_complex_to_float: dict[np.dtype[np.complexfloating], np.dtype[np.floating]] = ...

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from types import ModuleType
from typing import TypedDict, type_check_only
# NOTE: these 5 are only defined on systems with an intel processor
SSE42: ModuleType | None = ...
FMA3: ModuleType | None = ...
AVX2: ModuleType | None = ...
AVX512F: ModuleType | None = ...
AVX512_SKX: ModuleType | None = ...
baseline: ModuleType | None = ...
@type_check_only
class SimdTargets(TypedDict):
SSE42: ModuleType | None
AVX2: ModuleType | None
FMA3: ModuleType | None
AVX512F: ModuleType | None
AVX512_SKX: ModuleType | None
baseline: ModuleType | None
targets: SimdTargets = ...
def clear_floatstatus() -> None: ...
def get_floatstatus() -> int: ...

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"""
String-handling utilities to avoid locale-dependence.
Used primarily to generate type name aliases.
"""
# "import string" is costly to import!
# Construct the translation tables directly
# "A" = chr(65), "a" = chr(97)
_all_chars = tuple(map(chr, range(256)))
_ascii_upper = _all_chars[65:65 + 26]
_ascii_lower = _all_chars[97:97 + 26]
LOWER_TABLE = _all_chars[:65] + _ascii_lower + _all_chars[65 + 26:]
UPPER_TABLE = _all_chars[:97] + _ascii_upper + _all_chars[97 + 26:]
def english_lower(s):
""" Apply English case rules to convert ASCII strings to all lower case.
This is an internal utility function to replace calls to str.lower() such
that we can avoid changing behavior with changing locales. In particular,
Turkish has distinct dotted and dotless variants of the Latin letter "I" in
both lowercase and uppercase. Thus, "I".lower() != "i" in a "tr" locale.
Parameters
----------
s : str
Returns
-------
lowered : str
Examples
--------
>>> from numpy._core.numerictypes import english_lower
>>> english_lower('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789_')
'abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz0123456789_'
>>> english_lower('')
''
"""
lowered = s.translate(LOWER_TABLE)
return lowered
def english_upper(s):
""" Apply English case rules to convert ASCII strings to all upper case.
This is an internal utility function to replace calls to str.upper() such
that we can avoid changing behavior with changing locales. In particular,
Turkish has distinct dotted and dotless variants of the Latin letter "I" in
both lowercase and uppercase. Thus, "i".upper() != "I" in a "tr" locale.
Parameters
----------
s : str
Returns
-------
uppered : str
Examples
--------
>>> from numpy._core.numerictypes import english_upper
>>> english_upper('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789_')
'ABCDEFGHIJKLMNOPQRSTUVWXYZABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_'
>>> english_upper('')
''
"""
uppered = s.translate(UPPER_TABLE)
return uppered
def english_capitalize(s):
""" Apply English case rules to convert the first character of an ASCII
string to upper case.
This is an internal utility function to replace calls to str.capitalize()
such that we can avoid changing behavior with changing locales.
Parameters
----------
s : str
Returns
-------
capitalized : str
Examples
--------
>>> from numpy._core.numerictypes import english_capitalize
>>> english_capitalize('int8')
'Int8'
>>> english_capitalize('Int8')
'Int8'
>>> english_capitalize('')
''
"""
if s:
return english_upper(s[0]) + s[1:]
else:
return s

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from typing import Final
_all_chars: Final[tuple[str, ...]] = ...
_ascii_upper: Final[tuple[str, ...]] = ...
_ascii_lower: Final[tuple[str, ...]] = ...
LOWER_TABLE: Final[tuple[str, ...]] = ...
UPPER_TABLE: Final[tuple[str, ...]] = ...
def english_lower(s: str) -> str: ...
def english_upper(s: str) -> str: ...
def english_capitalize(s: str) -> str: ...

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"""
Due to compatibility, numpy has a very large number of different naming
conventions for the scalar types (those subclassing from `numpy.generic`).
This file produces a convoluted set of dictionaries mapping names to types,
and sometimes other mappings too.
.. data:: allTypes
A dictionary of names to types that will be exposed as attributes through
``np._core.numerictypes.*``
.. data:: sctypeDict
Similar to `allTypes`, but maps a broader set of aliases to their types.
.. data:: sctypes
A dictionary keyed by a "type group" string, providing a list of types
under that group.
"""
import numpy._core.multiarray as ma
from numpy._core.multiarray import dtype, typeinfo
######################################
# Building `sctypeDict` and `allTypes`
######################################
sctypeDict = {}
allTypes = {}
c_names_dict = {}
_abstract_type_names = {
"generic", "integer", "inexact", "floating", "number",
"flexible", "character", "complexfloating", "unsignedinteger",
"signedinteger"
}
for _abstract_type_name in _abstract_type_names:
allTypes[_abstract_type_name] = getattr(ma, _abstract_type_name)
for k, v in typeinfo.items():
if k.startswith("NPY_") and v not in c_names_dict:
c_names_dict[k[4:]] = v
else:
concrete_type = v.type
allTypes[k] = concrete_type
sctypeDict[k] = concrete_type
_aliases = {
"double": "float64",
"cdouble": "complex128",
"single": "float32",
"csingle": "complex64",
"half": "float16",
"bool_": "bool",
# Default integer:
"int_": "intp",
"uint": "uintp",
}
for k, v in _aliases.items():
sctypeDict[k] = allTypes[v]
allTypes[k] = allTypes[v]
# extra aliases are added only to `sctypeDict`
# to support dtype name access, such as`np.dtype("float")`
_extra_aliases = {
"float": "float64",
"complex": "complex128",
"object": "object_",
"bytes": "bytes_",
"a": "bytes_",
"int": "int_",
"str": "str_",
"unicode": "str_",
}
for k, v in _extra_aliases.items():
sctypeDict[k] = allTypes[v]
# include extended precision sized aliases
for is_complex, full_name in [(False, "longdouble"), (True, "clongdouble")]:
longdouble_type: type = allTypes[full_name]
bits: int = dtype(longdouble_type).itemsize * 8
base_name: str = "complex" if is_complex else "float"
extended_prec_name: str = f"{base_name}{bits}"
if extended_prec_name not in allTypes:
sctypeDict[extended_prec_name] = longdouble_type
allTypes[extended_prec_name] = longdouble_type
####################
# Building `sctypes`
####################
sctypes = {"int": set(), "uint": set(), "float": set(),
"complex": set(), "others": set()}
for type_info in typeinfo.values():
if type_info.kind in ["M", "m"]: # exclude timedelta and datetime
continue
concrete_type = type_info.type
# find proper group for each concrete type
for type_group, abstract_type in [
("int", ma.signedinteger), ("uint", ma.unsignedinteger),
("float", ma.floating), ("complex", ma.complexfloating),
("others", ma.generic)
]:
if issubclass(concrete_type, abstract_type):
sctypes[type_group].add(concrete_type)
break
# sort sctype groups by bitsize
for sctype_key in sctypes.keys():
sctype_list = list(sctypes[sctype_key])
sctype_list.sort(key=lambda x: dtype(x).itemsize)
sctypes[sctype_key] = sctype_list

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from collections.abc import Collection
from typing import Final, TypeAlias, TypedDict, type_check_only
from typing import Literal as L
import numpy as np
__all__ = (
"_abstract_type_names",
"_aliases",
"_extra_aliases",
"allTypes",
"c_names_dict",
"sctypeDict",
"sctypes",
)
sctypeDict: Final[dict[str, type[np.generic]]]
allTypes: Final[dict[str, type[np.generic]]]
@type_check_only
class _CNamesDict(TypedDict):
BOOL: np.dtype[np.bool]
HALF: np.dtype[np.half]
FLOAT: np.dtype[np.single]
DOUBLE: np.dtype[np.double]
LONGDOUBLE: np.dtype[np.longdouble]
CFLOAT: np.dtype[np.csingle]
CDOUBLE: np.dtype[np.cdouble]
CLONGDOUBLE: np.dtype[np.clongdouble]
STRING: np.dtype[np.bytes_]
UNICODE: np.dtype[np.str_]
VOID: np.dtype[np.void]
OBJECT: np.dtype[np.object_]
DATETIME: np.dtype[np.datetime64]
TIMEDELTA: np.dtype[np.timedelta64]
BYTE: np.dtype[np.byte]
UBYTE: np.dtype[np.ubyte]
SHORT: np.dtype[np.short]
USHORT: np.dtype[np.ushort]
INT: np.dtype[np.intc]
UINT: np.dtype[np.uintc]
LONG: np.dtype[np.long]
ULONG: np.dtype[np.ulong]
LONGLONG: np.dtype[np.longlong]
ULONGLONG: np.dtype[np.ulonglong]
c_names_dict: Final[_CNamesDict]
_AbstractTypeName: TypeAlias = L[
"generic",
"flexible",
"character",
"number",
"integer",
"inexact",
"unsignedinteger",
"signedinteger",
"floating",
"complexfloating",
]
_abstract_type_names: Final[set[_AbstractTypeName]]
@type_check_only
class _AliasesType(TypedDict):
double: L["float64"]
cdouble: L["complex128"]
single: L["float32"]
csingle: L["complex64"]
half: L["float16"]
bool_: L["bool"]
int_: L["intp"]
uint: L["intp"]
_aliases: Final[_AliasesType]
@type_check_only
class _ExtraAliasesType(TypedDict):
float: L["float64"]
complex: L["complex128"]
object: L["object_"]
bytes: L["bytes_"]
a: L["bytes_"]
int: L["int_"]
str: L["str_"]
unicode: L["str_"]
_extra_aliases: Final[_ExtraAliasesType]
@type_check_only
class _SCTypes(TypedDict):
int: Collection[type[np.signedinteger]]
uint: Collection[type[np.unsignedinteger]]
float: Collection[type[np.floating]]
complex: Collection[type[np.complexfloating]]
others: Collection[type[np.flexible | np.bool | np.object_]]
sctypes: Final[_SCTypes]

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"""
Functions for changing global ufunc configuration
This provides helpers which wrap `_get_extobj_dict` and `_make_extobj`, and
`_extobj_contextvar` from umath.
"""
import functools
from numpy._utils import set_module
from .umath import _extobj_contextvar, _get_extobj_dict, _make_extobj
__all__ = [
"seterr", "geterr", "setbufsize", "getbufsize", "seterrcall", "geterrcall",
"errstate"
]
@set_module('numpy')
def seterr(all=None, divide=None, over=None, under=None, invalid=None):
"""
Set how floating-point errors are handled.
Note that operations on integer scalar types (such as `int16`) are
handled like floating point, and are affected by these settings.
Parameters
----------
all : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Set treatment for all types of floating-point errors at once:
- ignore: Take no action when the exception occurs.
- warn: Print a :exc:`RuntimeWarning` (via the Python `warnings`
module).
- raise: Raise a :exc:`FloatingPointError`.
- call: Call a function specified using the `seterrcall` function.
- print: Print a warning directly to ``stdout``.
- log: Record error in a Log object specified by `seterrcall`.
The default is not to change the current behavior.
divide : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for division by zero.
over : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for floating-point overflow.
under : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for floating-point underflow.
invalid : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for invalid floating-point operation.
Returns
-------
old_settings : dict
Dictionary containing the old settings.
See also
--------
seterrcall : Set a callback function for the 'call' mode.
geterr, geterrcall, errstate
Notes
-----
The floating-point exceptions are defined in the IEEE 754 standard [1]_:
- Division by zero: infinite result obtained from finite numbers.
- Overflow: result too large to be expressed.
- Underflow: result so close to zero that some precision
was lost.
- Invalid operation: result is not an expressible number, typically
indicates that a NaN was produced.
.. [1] https://en.wikipedia.org/wiki/IEEE_754
Examples
--------
>>> import numpy as np
>>> orig_settings = np.seterr(all='ignore') # seterr to known value
>>> np.int16(32000) * np.int16(3)
np.int16(30464)
>>> np.seterr(over='raise')
{'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}
>>> old_settings = np.seterr(all='warn', over='raise')
>>> np.int16(32000) * np.int16(3)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
FloatingPointError: overflow encountered in scalar multiply
>>> old_settings = np.seterr(all='print')
>>> np.geterr()
{'divide': 'print', 'over': 'print', 'under': 'print', 'invalid': 'print'}
>>> np.int16(32000) * np.int16(3)
np.int16(30464)
>>> np.seterr(**orig_settings) # restore original
{'divide': 'print', 'over': 'print', 'under': 'print', 'invalid': 'print'}
"""
old = _get_extobj_dict()
# The errstate doesn't include call and bufsize, so pop them:
old.pop("call", None)
old.pop("bufsize", None)
extobj = _make_extobj(
all=all, divide=divide, over=over, under=under, invalid=invalid)
_extobj_contextvar.set(extobj)
return old
@set_module('numpy')
def geterr():
"""
Get the current way of handling floating-point errors.
Returns
-------
res : dict
A dictionary with keys "divide", "over", "under", and "invalid",
whose values are from the strings "ignore", "print", "log", "warn",
"raise", and "call". The keys represent possible floating-point
exceptions, and the values define how these exceptions are handled.
See Also
--------
geterrcall, seterr, seterrcall
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> import numpy as np
>>> np.geterr()
{'divide': 'warn', 'over': 'warn', 'under': 'ignore', 'invalid': 'warn'}
>>> np.arange(3.) / np.arange(3.) # doctest: +SKIP
array([nan, 1., 1.])
RuntimeWarning: invalid value encountered in divide
>>> oldsettings = np.seterr(all='warn', invalid='raise')
>>> np.geterr()
{'divide': 'warn', 'over': 'warn', 'under': 'warn', 'invalid': 'raise'}
>>> np.arange(3.) / np.arange(3.)
Traceback (most recent call last):
...
FloatingPointError: invalid value encountered in divide
>>> oldsettings = np.seterr(**oldsettings) # restore original
"""
res = _get_extobj_dict()
# The "geterr" doesn't include call and bufsize,:
res.pop("call", None)
res.pop("bufsize", None)
return res
@set_module('numpy')
def setbufsize(size):
"""
Set the size of the buffer used in ufuncs.
.. versionchanged:: 2.0
The scope of setting the buffer is tied to the `numpy.errstate`
context. Exiting a ``with errstate():`` will also restore the bufsize.
Parameters
----------
size : int
Size of buffer.
Returns
-------
bufsize : int
Previous size of ufunc buffer in bytes.
Examples
--------
When exiting a `numpy.errstate` context manager the bufsize is restored:
>>> import numpy as np
>>> with np.errstate():
... np.setbufsize(4096)
... print(np.getbufsize())
...
8192
4096
>>> np.getbufsize()
8192
"""
old = _get_extobj_dict()["bufsize"]
extobj = _make_extobj(bufsize=size)
_extobj_contextvar.set(extobj)
return old
@set_module('numpy')
def getbufsize():
"""
Return the size of the buffer used in ufuncs.
Returns
-------
getbufsize : int
Size of ufunc buffer in bytes.
Examples
--------
>>> import numpy as np
>>> np.getbufsize()
8192
"""
return _get_extobj_dict()["bufsize"]
@set_module('numpy')
def seterrcall(func):
"""
Set the floating-point error callback function or log object.
There are two ways to capture floating-point error messages. The first
is to set the error-handler to 'call', using `seterr`. Then, set
the function to call using this function.
The second is to set the error-handler to 'log', using `seterr`.
Floating-point errors then trigger a call to the 'write' method of
the provided object.
Parameters
----------
func : callable f(err, flag) or object with write method
Function to call upon floating-point errors ('call'-mode) or
object whose 'write' method is used to log such message ('log'-mode).
The call function takes two arguments. The first is a string describing
the type of error (such as "divide by zero", "overflow", "underflow",
or "invalid value"), and the second is the status flag. The flag is a
byte, whose four least-significant bits indicate the type of error, one
of "divide", "over", "under", "invalid"::
[0 0 0 0 divide over under invalid]
In other words, ``flags = divide + 2*over + 4*under + 8*invalid``.
If an object is provided, its write method should take one argument,
a string.
Returns
-------
h : callable, log instance or None
The old error handler.
See Also
--------
seterr, geterr, geterrcall
Examples
--------
Callback upon error:
>>> def err_handler(type, flag):
... print("Floating point error (%s), with flag %s" % (type, flag))
...
>>> import numpy as np
>>> orig_handler = np.seterrcall(err_handler)
>>> orig_err = np.seterr(all='call')
>>> np.array([1, 2, 3]) / 0.0
Floating point error (divide by zero), with flag 1
array([inf, inf, inf])
>>> np.seterrcall(orig_handler)
<function err_handler at 0x...>
>>> np.seterr(**orig_err)
{'divide': 'call', 'over': 'call', 'under': 'call', 'invalid': 'call'}
Log error message:
>>> class Log:
... def write(self, msg):
... print("LOG: %s" % msg)
...
>>> log = Log()
>>> saved_handler = np.seterrcall(log)
>>> save_err = np.seterr(all='log')
>>> np.array([1, 2, 3]) / 0.0
LOG: Warning: divide by zero encountered in divide
array([inf, inf, inf])
>>> np.seterrcall(orig_handler)
<numpy.Log object at 0x...>
>>> np.seterr(**orig_err)
{'divide': 'log', 'over': 'log', 'under': 'log', 'invalid': 'log'}
"""
old = _get_extobj_dict()["call"]
extobj = _make_extobj(call=func)
_extobj_contextvar.set(extobj)
return old
@set_module('numpy')
def geterrcall():
"""
Return the current callback function used on floating-point errors.
When the error handling for a floating-point error (one of "divide",
"over", "under", or "invalid") is set to 'call' or 'log', the function
that is called or the log instance that is written to is returned by
`geterrcall`. This function or log instance has been set with
`seterrcall`.
Returns
-------
errobj : callable, log instance or None
The current error handler. If no handler was set through `seterrcall`,
``None`` is returned.
See Also
--------
seterrcall, seterr, geterr
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> import numpy as np
>>> np.geterrcall() # we did not yet set a handler, returns None
>>> orig_settings = np.seterr(all='call')
>>> def err_handler(type, flag):
... print("Floating point error (%s), with flag %s" % (type, flag))
>>> old_handler = np.seterrcall(err_handler)
>>> np.array([1, 2, 3]) / 0.0
Floating point error (divide by zero), with flag 1
array([inf, inf, inf])
>>> cur_handler = np.geterrcall()
>>> cur_handler is err_handler
True
>>> old_settings = np.seterr(**orig_settings) # restore original
>>> old_handler = np.seterrcall(None) # restore original
"""
return _get_extobj_dict()["call"]
class _unspecified:
pass
_Unspecified = _unspecified()
@set_module('numpy')
class errstate:
"""
errstate(**kwargs)
Context manager for floating-point error handling.
Using an instance of `errstate` as a context manager allows statements in
that context to execute with a known error handling behavior. Upon entering
the context the error handling is set with `seterr` and `seterrcall`, and
upon exiting it is reset to what it was before.
.. versionchanged:: 1.17.0
`errstate` is also usable as a function decorator, saving
a level of indentation if an entire function is wrapped.
.. versionchanged:: 2.0
`errstate` is now fully thread and asyncio safe, but may not be
entered more than once.
It is not safe to decorate async functions using ``errstate``.
Parameters
----------
kwargs : {divide, over, under, invalid}
Keyword arguments. The valid keywords are the possible floating-point
exceptions. Each keyword should have a string value that defines the
treatment for the particular error. Possible values are
{'ignore', 'warn', 'raise', 'call', 'print', 'log'}.
See Also
--------
seterr, geterr, seterrcall, geterrcall
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> import numpy as np
>>> olderr = np.seterr(all='ignore') # Set error handling to known state.
>>> np.arange(3) / 0.
array([nan, inf, inf])
>>> with np.errstate(divide='ignore'):
... np.arange(3) / 0.
array([nan, inf, inf])
>>> np.sqrt(-1)
np.float64(nan)
>>> with np.errstate(invalid='raise'):
... np.sqrt(-1)
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
FloatingPointError: invalid value encountered in sqrt
Outside the context the error handling behavior has not changed:
>>> np.geterr()
{'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}
>>> olderr = np.seterr(**olderr) # restore original state
"""
__slots__ = (
"_all",
"_call",
"_divide",
"_invalid",
"_over",
"_token",
"_under",
)
def __init__(self, *, call=_Unspecified,
all=None, divide=None, over=None, under=None, invalid=None):
self._token = None
self._call = call
self._all = all
self._divide = divide
self._over = over
self._under = under
self._invalid = invalid
def __enter__(self):
# Note that __call__ duplicates much of this logic
if self._token is not None:
raise TypeError("Cannot enter `np.errstate` twice.")
if self._call is _Unspecified:
extobj = _make_extobj(
all=self._all, divide=self._divide, over=self._over,
under=self._under, invalid=self._invalid)
else:
extobj = _make_extobj(
call=self._call,
all=self._all, divide=self._divide, over=self._over,
under=self._under, invalid=self._invalid)
self._token = _extobj_contextvar.set(extobj)
def __exit__(self, *exc_info):
_extobj_contextvar.reset(self._token)
def __call__(self, func):
# We need to customize `__call__` compared to `ContextDecorator`
# because we must store the token per-thread so cannot store it on
# the instance (we could create a new instance for this).
# This duplicates the code from `__enter__`.
@functools.wraps(func)
def inner(*args, **kwargs):
if self._call is _Unspecified:
extobj = _make_extobj(
all=self._all, divide=self._divide, over=self._over,
under=self._under, invalid=self._invalid)
else:
extobj = _make_extobj(
call=self._call,
all=self._all, divide=self._divide, over=self._over,
under=self._under, invalid=self._invalid)
_token = _extobj_contextvar.set(extobj)
try:
# Call the original, decorated, function:
return func(*args, **kwargs)
finally:
_extobj_contextvar.reset(_token)
return inner

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from collections.abc import Callable
from typing import Any, Literal, TypeAlias, TypedDict, type_check_only
from _typeshed import SupportsWrite
from numpy import errstate as errstate
_ErrKind: TypeAlias = Literal["ignore", "warn", "raise", "call", "print", "log"]
_ErrFunc: TypeAlias = Callable[[str, int], Any]
_ErrCall: TypeAlias = _ErrFunc | SupportsWrite[str]
@type_check_only
class _ErrDict(TypedDict):
divide: _ErrKind
over: _ErrKind
under: _ErrKind
invalid: _ErrKind
def seterr(
all: _ErrKind | None = ...,
divide: _ErrKind | None = ...,
over: _ErrKind | None = ...,
under: _ErrKind | None = ...,
invalid: _ErrKind | None = ...,
) -> _ErrDict: ...
def geterr() -> _ErrDict: ...
def setbufsize(size: int) -> int: ...
def getbufsize() -> int: ...
def seterrcall(func: _ErrCall | None) -> _ErrCall | None: ...
def geterrcall() -> _ErrCall | None: ...
# See `numpy/__init__.pyi` for the `errstate` class and `no_nep5_warnings`

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from collections.abc import Callable
# Using a private class is by no means ideal, but it is simply a consequence
# of a `contextlib.context` returning an instance of aforementioned class
from contextlib import _GeneratorContextManager
from typing import (
Any,
Final,
Literal,
SupportsIndex,
TypeAlias,
TypedDict,
overload,
type_check_only,
)
from typing_extensions import deprecated
import numpy as np
from numpy._globals import _NoValueType
from numpy._typing import NDArray, _CharLike_co, _FloatLike_co
__all__ = [
"array2string",
"array_repr",
"array_str",
"format_float_positional",
"format_float_scientific",
"get_printoptions",
"printoptions",
"set_printoptions",
]
###
_FloatMode: TypeAlias = Literal["fixed", "unique", "maxprec", "maxprec_equal"]
_LegacyNoStyle: TypeAlias = Literal["1.21", "1.25", "2.1", False]
_Legacy: TypeAlias = Literal["1.13", _LegacyNoStyle]
_Sign: TypeAlias = Literal["-", "+", " "]
_Trim: TypeAlias = Literal["k", ".", "0", "-"]
_ReprFunc: TypeAlias = Callable[[NDArray[Any]], str]
@type_check_only
class _FormatDict(TypedDict, total=False):
bool: Callable[[np.bool], str]
int: Callable[[np.integer], str]
timedelta: Callable[[np.timedelta64], str]
datetime: Callable[[np.datetime64], str]
float: Callable[[np.floating], str]
longfloat: Callable[[np.longdouble], str]
complexfloat: Callable[[np.complexfloating], str]
longcomplexfloat: Callable[[np.clongdouble], str]
void: Callable[[np.void], str]
numpystr: Callable[[_CharLike_co], str]
object: Callable[[object], str]
all: Callable[[object], str]
int_kind: Callable[[np.integer], str]
float_kind: Callable[[np.floating], str]
complex_kind: Callable[[np.complexfloating], str]
str_kind: Callable[[_CharLike_co], str]
@type_check_only
class _FormatOptions(TypedDict):
precision: int
threshold: int
edgeitems: int
linewidth: int
suppress: bool
nanstr: str
infstr: str
formatter: _FormatDict | None
sign: _Sign
floatmode: _FloatMode
legacy: _Legacy
###
__docformat__: Final = "restructuredtext" # undocumented
def set_printoptions(
precision: SupportsIndex | None = ...,
threshold: int | None = ...,
edgeitems: int | None = ...,
linewidth: int | None = ...,
suppress: bool | None = ...,
nanstr: str | None = ...,
infstr: str | None = ...,
formatter: _FormatDict | None = ...,
sign: _Sign | None = None,
floatmode: _FloatMode | None = None,
*,
legacy: _Legacy | None = None,
override_repr: _ReprFunc | None = None,
) -> None: ...
def get_printoptions() -> _FormatOptions: ...
# public numpy export
@overload # no style
def array2string(
a: NDArray[Any],
max_line_width: int | None = None,
precision: SupportsIndex | None = None,
suppress_small: bool | None = None,
separator: str = " ",
prefix: str = "",
style: _NoValueType = ...,
formatter: _FormatDict | None = None,
threshold: int | None = None,
edgeitems: int | None = None,
sign: _Sign | None = None,
floatmode: _FloatMode | None = None,
suffix: str = "",
*,
legacy: _Legacy | None = None,
) -> str: ...
@overload # style=<given> (positional), legacy="1.13"
def array2string(
a: NDArray[Any],
max_line_width: int | None,
precision: SupportsIndex | None,
suppress_small: bool | None,
separator: str,
prefix: str,
style: _ReprFunc,
formatter: _FormatDict | None = None,
threshold: int | None = None,
edgeitems: int | None = None,
sign: _Sign | None = None,
floatmode: _FloatMode | None = None,
suffix: str = "",
*,
legacy: Literal["1.13"],
) -> str: ...
@overload # style=<given> (keyword), legacy="1.13"
def array2string(
a: NDArray[Any],
max_line_width: int | None = None,
precision: SupportsIndex | None = None,
suppress_small: bool | None = None,
separator: str = " ",
prefix: str = "",
*,
style: _ReprFunc,
formatter: _FormatDict | None = None,
threshold: int | None = None,
edgeitems: int | None = None,
sign: _Sign | None = None,
floatmode: _FloatMode | None = None,
suffix: str = "",
legacy: Literal["1.13"],
) -> str: ...
@overload # style=<given> (positional), legacy!="1.13"
@deprecated("'style' argument is deprecated and no longer functional except in 1.13 'legacy' mode")
def array2string(
a: NDArray[Any],
max_line_width: int | None,
precision: SupportsIndex | None,
suppress_small: bool | None,
separator: str,
prefix: str,
style: _ReprFunc,
formatter: _FormatDict | None = None,
threshold: int | None = None,
edgeitems: int | None = None,
sign: _Sign | None = None,
floatmode: _FloatMode | None = None,
suffix: str = "",
*,
legacy: _LegacyNoStyle | None = None,
) -> str: ...
@overload # style=<given> (keyword), legacy="1.13"
@deprecated("'style' argument is deprecated and no longer functional except in 1.13 'legacy' mode")
def array2string(
a: NDArray[Any],
max_line_width: int | None = None,
precision: SupportsIndex | None = None,
suppress_small: bool | None = None,
separator: str = " ",
prefix: str = "",
*,
style: _ReprFunc,
formatter: _FormatDict | None = None,
threshold: int | None = None,
edgeitems: int | None = None,
sign: _Sign | None = None,
floatmode: _FloatMode | None = None,
suffix: str = "",
legacy: _LegacyNoStyle | None = None,
) -> str: ...
def format_float_scientific(
x: _FloatLike_co,
precision: int | None = ...,
unique: bool = ...,
trim: _Trim = "k",
sign: bool = ...,
pad_left: int | None = ...,
exp_digits: int | None = ...,
min_digits: int | None = ...,
) -> str: ...
def format_float_positional(
x: _FloatLike_co,
precision: int | None = ...,
unique: bool = ...,
fractional: bool = ...,
trim: _Trim = "k",
sign: bool = ...,
pad_left: int | None = ...,
pad_right: int | None = ...,
min_digits: int | None = ...,
) -> str: ...
def array_repr(
arr: NDArray[Any],
max_line_width: int | None = ...,
precision: SupportsIndex | None = ...,
suppress_small: bool | None = ...,
) -> str: ...
def array_str(
a: NDArray[Any],
max_line_width: int | None = ...,
precision: SupportsIndex | None = ...,
suppress_small: bool | None = ...,
) -> str: ...
def printoptions(
precision: SupportsIndex | None = ...,
threshold: int | None = ...,
edgeitems: int | None = ...,
linewidth: int | None = ...,
suppress: bool | None = ...,
nanstr: str | None = ...,
infstr: str | None = ...,
formatter: _FormatDict | None = ...,
sign: _Sign | None = None,
floatmode: _FloatMode | None = None,
*,
legacy: _Legacy | None = None,
override_repr: _ReprFunc | None = None,
) -> _GeneratorContextManager[_FormatOptions]: ...

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"""Simple script to compute the api hash of the current API.
The API has is defined by numpy_api_order and ufunc_api_order.
"""
from os.path import dirname
from code_generators.genapi import fullapi_hash
from code_generators.numpy_api import full_api
if __name__ == '__main__':
curdir = dirname(__file__)
print(fullapi_hash(full_api))

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from collections.abc import Sequence
from typing import Any, Literal, TypeAlias, TypeVar, overload
import numpy as np
from numpy import _OrderKACF, number
from numpy._typing import (
NDArray,
_ArrayLikeBool_co,
_ArrayLikeComplex_co,
_ArrayLikeFloat_co,
_ArrayLikeInt_co,
_ArrayLikeObject_co,
_ArrayLikeUInt_co,
_DTypeLikeBool,
_DTypeLikeComplex,
_DTypeLikeComplex_co,
_DTypeLikeFloat,
_DTypeLikeInt,
_DTypeLikeObject,
_DTypeLikeUInt,
)
__all__ = ["einsum", "einsum_path"]
_ArrayT = TypeVar(
"_ArrayT",
bound=NDArray[np.bool | number],
)
_OptimizeKind: TypeAlias = bool | Literal["greedy", "optimal"] | Sequence[Any] | None
_CastingSafe: TypeAlias = Literal["no", "equiv", "safe", "same_kind"]
_CastingUnsafe: TypeAlias = Literal["unsafe"]
# TODO: Properly handle the `casting`-based combinatorics
# TODO: We need to evaluate the content `__subscripts` in order
# to identify whether or an array or scalar is returned. At a cursory
# glance this seems like something that can quite easily be done with
# a mypy plugin.
# Something like `is_scalar = bool(__subscripts.partition("->")[-1])`
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeBool_co,
out: None = ...,
dtype: _DTypeLikeBool | None = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeUInt_co,
out: None = ...,
dtype: _DTypeLikeUInt | None = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeInt_co,
out: None = ...,
dtype: _DTypeLikeInt | None = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeFloat_co,
out: None = ...,
dtype: _DTypeLikeFloat | None = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeComplex_co,
out: None = ...,
dtype: _DTypeLikeComplex | None = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: Any,
casting: _CastingUnsafe,
dtype: _DTypeLikeComplex_co | None = ...,
out: None = ...,
order: _OrderKACF = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeComplex_co,
out: _ArrayT,
dtype: _DTypeLikeComplex_co | None = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> _ArrayT: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: Any,
out: _ArrayT,
casting: _CastingUnsafe,
dtype: _DTypeLikeComplex_co | None = ...,
order: _OrderKACF = ...,
optimize: _OptimizeKind = ...,
) -> _ArrayT: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeObject_co,
out: None = ...,
dtype: _DTypeLikeObject | None = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: Any,
casting: _CastingUnsafe,
dtype: _DTypeLikeObject | None = ...,
out: None = ...,
order: _OrderKACF = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeObject_co,
out: _ArrayT,
dtype: _DTypeLikeObject | None = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> _ArrayT: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: Any,
out: _ArrayT,
casting: _CastingUnsafe,
dtype: _DTypeLikeObject | None = ...,
order: _OrderKACF = ...,
optimize: _OptimizeKind = ...,
) -> _ArrayT: ...
# NOTE: `einsum_call` is a hidden kwarg unavailable for public use.
# It is therefore excluded from the signatures below.
# NOTE: In practice the list consists of a `str` (first element)
# and a variable number of integer tuples.
def einsum_path(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeComplex_co | _DTypeLikeObject,
optimize: _OptimizeKind = "greedy",
einsum_call: Literal[False] = False,
) -> tuple[list[Any], str]: ...

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import functools
import operator
import types
import warnings
import numpy as np
from numpy._core import overrides
from numpy._core._multiarray_umath import _array_converter
from numpy._core.multiarray import add_docstring
from . import numeric as _nx
from .numeric import asanyarray, nan, ndim, result_type
__all__ = ['logspace', 'linspace', 'geomspace']
array_function_dispatch = functools.partial(
overrides.array_function_dispatch, module='numpy')
def _linspace_dispatcher(start, stop, num=None, endpoint=None, retstep=None,
dtype=None, axis=None, *, device=None):
return (start, stop)
@array_function_dispatch(_linspace_dispatcher)
def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None,
axis=0, *, device=None):
"""
Return evenly spaced numbers over a specified interval.
Returns `num` evenly spaced samples, calculated over the
interval [`start`, `stop`].
The endpoint of the interval can optionally be excluded.
.. versionchanged:: 1.20.0
Values are rounded towards ``-inf`` instead of ``0`` when an
integer ``dtype`` is specified. The old behavior can
still be obtained with ``np.linspace(start, stop, num).astype(int)``
Parameters
----------
start : array_like
The starting value of the sequence.
stop : array_like
The end value of the sequence, unless `endpoint` is set to False.
In that case, the sequence consists of all but the last of ``num + 1``
evenly spaced samples, so that `stop` is excluded. Note that the step
size changes when `endpoint` is False.
num : int, optional
Number of samples to generate. Default is 50. Must be non-negative.
endpoint : bool, optional
If True, `stop` is the last sample. Otherwise, it is not included.
Default is True.
retstep : bool, optional
If True, return (`samples`, `step`), where `step` is the spacing
between samples.
dtype : dtype, optional
The type of the output array. If `dtype` is not given, the data type
is inferred from `start` and `stop`. The inferred dtype will never be
an integer; `float` is chosen even if the arguments would produce an
array of integers.
axis : int, optional
The axis in the result to store the samples. Relevant only if start
or stop are array-like. By default (0), the samples will be along a
new axis inserted at the beginning. Use -1 to get an axis at the end.
device : str, optional
The device on which to place the created array. Default: None.
For Array-API interoperability only, so must be ``"cpu"`` if passed.
.. versionadded:: 2.0.0
Returns
-------
samples : ndarray
There are `num` equally spaced samples in the closed interval
``[start, stop]`` or the half-open interval ``[start, stop)``
(depending on whether `endpoint` is True or False).
step : float, optional
Only returned if `retstep` is True
Size of spacing between samples.
See Also
--------
arange : Similar to `linspace`, but uses a step size (instead of the
number of samples).
geomspace : Similar to `linspace`, but with numbers spaced evenly on a log
scale (a geometric progression).
logspace : Similar to `geomspace`, but with the end points specified as
logarithms.
:ref:`how-to-partition`
Examples
--------
>>> import numpy as np
>>> np.linspace(2.0, 3.0, num=5)
array([2. , 2.25, 2.5 , 2.75, 3. ])
>>> np.linspace(2.0, 3.0, num=5, endpoint=False)
array([2. , 2.2, 2.4, 2.6, 2.8])
>>> np.linspace(2.0, 3.0, num=5, retstep=True)
(array([2. , 2.25, 2.5 , 2.75, 3. ]), 0.25)
Graphical illustration:
>>> import matplotlib.pyplot as plt
>>> N = 8
>>> y = np.zeros(N)
>>> x1 = np.linspace(0, 10, N, endpoint=True)
>>> x2 = np.linspace(0, 10, N, endpoint=False)
>>> plt.plot(x1, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(x2, y + 0.5, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()
"""
num = operator.index(num)
if num < 0:
raise ValueError(
f"Number of samples, {num}, must be non-negative."
)
div = (num - 1) if endpoint else num
conv = _array_converter(start, stop)
start, stop = conv.as_arrays()
dt = conv.result_type(ensure_inexact=True)
if dtype is None:
dtype = dt
integer_dtype = False
else:
integer_dtype = _nx.issubdtype(dtype, _nx.integer)
# Use `dtype=type(dt)` to enforce a floating point evaluation:
delta = np.subtract(stop, start, dtype=type(dt))
y = _nx.arange(
0, num, dtype=dt, device=device
).reshape((-1,) + (1,) * ndim(delta))
# In-place multiplication y *= delta/div is faster, but prevents
# the multiplicant from overriding what class is produced, and thus
# prevents, e.g. use of Quantities, see gh-7142. Hence, we multiply
# in place only for standard scalar types.
if div > 0:
_mult_inplace = _nx.isscalar(delta)
step = delta / div
any_step_zero = (
step == 0 if _mult_inplace else _nx.asanyarray(step == 0).any())
if any_step_zero:
# Special handling for denormal numbers, gh-5437
y /= div
if _mult_inplace:
y *= delta
else:
y = y * delta
elif _mult_inplace:
y *= step
else:
y = y * step
else:
# sequences with 0 items or 1 item with endpoint=True (i.e. div <= 0)
# have an undefined step
step = nan
# Multiply with delta to allow possible override of output class.
y = y * delta
y += start
if endpoint and num > 1:
y[-1, ...] = stop
if axis != 0:
y = _nx.moveaxis(y, 0, axis)
if integer_dtype:
_nx.floor(y, out=y)
y = conv.wrap(y.astype(dtype, copy=False))
if retstep:
return y, step
else:
return y
def _logspace_dispatcher(start, stop, num=None, endpoint=None, base=None,
dtype=None, axis=None):
return (start, stop, base)
@array_function_dispatch(_logspace_dispatcher)
def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None,
axis=0):
"""
Return numbers spaced evenly on a log scale.
In linear space, the sequence starts at ``base ** start``
(`base` to the power of `start`) and ends with ``base ** stop``
(see `endpoint` below).
.. versionchanged:: 1.25.0
Non-scalar 'base` is now supported
Parameters
----------
start : array_like
``base ** start`` is the starting value of the sequence.
stop : array_like
``base ** stop`` is the final value of the sequence, unless `endpoint`
is False. In that case, ``num + 1`` values are spaced over the
interval in log-space, of which all but the last (a sequence of
length `num`) are returned.
num : integer, optional
Number of samples to generate. Default is 50.
endpoint : boolean, optional
If true, `stop` is the last sample. Otherwise, it is not included.
Default is True.
base : array_like, optional
The base of the log space. The step size between the elements in
``ln(samples) / ln(base)`` (or ``log_base(samples)``) is uniform.
Default is 10.0.
dtype : dtype
The type of the output array. If `dtype` is not given, the data type
is inferred from `start` and `stop`. The inferred type will never be
an integer; `float` is chosen even if the arguments would produce an
array of integers.
axis : int, optional
The axis in the result to store the samples. Relevant only if start,
stop, or base are array-like. By default (0), the samples will be
along a new axis inserted at the beginning. Use -1 to get an axis at
the end.
Returns
-------
samples : ndarray
`num` samples, equally spaced on a log scale.
See Also
--------
arange : Similar to linspace, with the step size specified instead of the
number of samples. Note that, when used with a float endpoint, the
endpoint may or may not be included.
linspace : Similar to logspace, but with the samples uniformly distributed
in linear space, instead of log space.
geomspace : Similar to logspace, but with endpoints specified directly.
:ref:`how-to-partition`
Notes
-----
If base is a scalar, logspace is equivalent to the code
>>> y = np.linspace(start, stop, num=num, endpoint=endpoint)
... # doctest: +SKIP
>>> power(base, y).astype(dtype)
... # doctest: +SKIP
Examples
--------
>>> import numpy as np
>>> np.logspace(2.0, 3.0, num=4)
array([ 100. , 215.443469 , 464.15888336, 1000. ])
>>> np.logspace(2.0, 3.0, num=4, endpoint=False)
array([100. , 177.827941 , 316.22776602, 562.34132519])
>>> np.logspace(2.0, 3.0, num=4, base=2.0)
array([4. , 5.0396842 , 6.34960421, 8. ])
>>> np.logspace(2.0, 3.0, num=4, base=[2.0, 3.0], axis=-1)
array([[ 4. , 5.0396842 , 6.34960421, 8. ],
[ 9. , 12.98024613, 18.72075441, 27. ]])
Graphical illustration:
>>> import matplotlib.pyplot as plt
>>> N = 10
>>> x1 = np.logspace(0.1, 1, N, endpoint=True)
>>> x2 = np.logspace(0.1, 1, N, endpoint=False)
>>> y = np.zeros(N)
>>> plt.plot(x1, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(x2, y + 0.5, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()
"""
if not isinstance(base, (float, int)) and np.ndim(base):
# If base is non-scalar, broadcast it with the others, since it
# may influence how axis is interpreted.
ndmax = np.broadcast(start, stop, base).ndim
start, stop, base = (
np.array(a, copy=None, subok=True, ndmin=ndmax)
for a in (start, stop, base)
)
base = np.expand_dims(base, axis=axis)
y = linspace(start, stop, num=num, endpoint=endpoint, axis=axis)
if dtype is None:
return _nx.power(base, y)
return _nx.power(base, y).astype(dtype, copy=False)
def _geomspace_dispatcher(start, stop, num=None, endpoint=None, dtype=None,
axis=None):
return (start, stop)
@array_function_dispatch(_geomspace_dispatcher)
def geomspace(start, stop, num=50, endpoint=True, dtype=None, axis=0):
"""
Return numbers spaced evenly on a log scale (a geometric progression).
This is similar to `logspace`, but with endpoints specified directly.
Each output sample is a constant multiple of the previous.
Parameters
----------
start : array_like
The starting value of the sequence.
stop : array_like
The final value of the sequence, unless `endpoint` is False.
In that case, ``num + 1`` values are spaced over the
interval in log-space, of which all but the last (a sequence of
length `num`) are returned.
num : integer, optional
Number of samples to generate. Default is 50.
endpoint : boolean, optional
If true, `stop` is the last sample. Otherwise, it is not included.
Default is True.
dtype : dtype
The type of the output array. If `dtype` is not given, the data type
is inferred from `start` and `stop`. The inferred dtype will never be
an integer; `float` is chosen even if the arguments would produce an
array of integers.
axis : int, optional
The axis in the result to store the samples. Relevant only if start
or stop are array-like. By default (0), the samples will be along a
new axis inserted at the beginning. Use -1 to get an axis at the end.
Returns
-------
samples : ndarray
`num` samples, equally spaced on a log scale.
See Also
--------
logspace : Similar to geomspace, but with endpoints specified using log
and base.
linspace : Similar to geomspace, but with arithmetic instead of geometric
progression.
arange : Similar to linspace, with the step size specified instead of the
number of samples.
:ref:`how-to-partition`
Notes
-----
If the inputs or dtype are complex, the output will follow a logarithmic
spiral in the complex plane. (There are an infinite number of spirals
passing through two points; the output will follow the shortest such path.)
Examples
--------
>>> import numpy as np
>>> np.geomspace(1, 1000, num=4)
array([ 1., 10., 100., 1000.])
>>> np.geomspace(1, 1000, num=3, endpoint=False)
array([ 1., 10., 100.])
>>> np.geomspace(1, 1000, num=4, endpoint=False)
array([ 1. , 5.62341325, 31.6227766 , 177.827941 ])
>>> np.geomspace(1, 256, num=9)
array([ 1., 2., 4., 8., 16., 32., 64., 128., 256.])
Note that the above may not produce exact integers:
>>> np.geomspace(1, 256, num=9, dtype=int)
array([ 1, 2, 4, 7, 16, 32, 63, 127, 256])
>>> np.around(np.geomspace(1, 256, num=9)).astype(int)
array([ 1, 2, 4, 8, 16, 32, 64, 128, 256])
Negative, decreasing, and complex inputs are allowed:
>>> np.geomspace(1000, 1, num=4)
array([1000., 100., 10., 1.])
>>> np.geomspace(-1000, -1, num=4)
array([-1000., -100., -10., -1.])
>>> np.geomspace(1j, 1000j, num=4) # Straight line
array([0. +1.j, 0. +10.j, 0. +100.j, 0.+1000.j])
>>> np.geomspace(-1+0j, 1+0j, num=5) # Circle
array([-1.00000000e+00+1.22464680e-16j, -7.07106781e-01+7.07106781e-01j,
6.12323400e-17+1.00000000e+00j, 7.07106781e-01+7.07106781e-01j,
1.00000000e+00+0.00000000e+00j])
Graphical illustration of `endpoint` parameter:
>>> import matplotlib.pyplot as plt
>>> N = 10
>>> y = np.zeros(N)
>>> plt.semilogx(np.geomspace(1, 1000, N, endpoint=True), y + 1, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.semilogx(np.geomspace(1, 1000, N, endpoint=False), y + 2, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.axis([0.5, 2000, 0, 3])
[0.5, 2000, 0, 3]
>>> plt.grid(True, color='0.7', linestyle='-', which='both', axis='both')
>>> plt.show()
"""
start = asanyarray(start)
stop = asanyarray(stop)
if _nx.any(start == 0) or _nx.any(stop == 0):
raise ValueError('Geometric sequence cannot include zero')
dt = result_type(start, stop, float(num), _nx.zeros((), dtype))
if dtype is None:
dtype = dt
else:
# complex to dtype('complex128'), for instance
dtype = _nx.dtype(dtype)
# Promote both arguments to the same dtype in case, for instance, one is
# complex and another is negative and log would produce NaN otherwise.
# Copy since we may change things in-place further down.
start = start.astype(dt, copy=True)
stop = stop.astype(dt, copy=True)
# Allow negative real values and ensure a consistent result for complex
# (including avoiding negligible real or imaginary parts in output) by
# rotating start to positive real, calculating, then undoing rotation.
out_sign = _nx.sign(start)
start /= out_sign
stop = stop / out_sign
log_start = _nx.log10(start)
log_stop = _nx.log10(stop)
result = logspace(log_start, log_stop, num=num,
endpoint=endpoint, base=10.0, dtype=dt)
# Make sure the endpoints match the start and stop arguments. This is
# necessary because np.exp(np.log(x)) is not necessarily equal to x.
if num > 0:
result[0] = start
if num > 1 and endpoint:
result[-1] = stop
result *= out_sign
if axis != 0:
result = _nx.moveaxis(result, 0, axis)
return result.astype(dtype, copy=False)
def _needs_add_docstring(obj):
"""
Returns true if the only way to set the docstring of `obj` from python is
via add_docstring.
This function errs on the side of being overly conservative.
"""
Py_TPFLAGS_HEAPTYPE = 1 << 9
if isinstance(obj, (types.FunctionType, types.MethodType, property)):
return False
if isinstance(obj, type) and obj.__flags__ & Py_TPFLAGS_HEAPTYPE:
return False
return True
def _add_docstring(obj, doc, warn_on_python):
if warn_on_python and not _needs_add_docstring(obj):
warnings.warn(
f"add_newdoc was used on a pure-python object {obj}. "
"Prefer to attach it directly to the source.",
UserWarning,
stacklevel=3)
try:
add_docstring(obj, doc)
except Exception:
pass
def add_newdoc(place, obj, doc, warn_on_python=True):
"""
Add documentation to an existing object, typically one defined in C
The purpose is to allow easier editing of the docstrings without requiring
a re-compile. This exists primarily for internal use within numpy itself.
Parameters
----------
place : str
The absolute name of the module to import from
obj : str or None
The name of the object to add documentation to, typically a class or
function name.
doc : {str, Tuple[str, str], List[Tuple[str, str]]}
If a string, the documentation to apply to `obj`
If a tuple, then the first element is interpreted as an attribute
of `obj` and the second as the docstring to apply -
``(method, docstring)``
If a list, then each element of the list should be a tuple of length
two - ``[(method1, docstring1), (method2, docstring2), ...]``
warn_on_python : bool
If True, the default, emit `UserWarning` if this is used to attach
documentation to a pure-python object.
Notes
-----
This routine never raises an error if the docstring can't be written, but
will raise an error if the object being documented does not exist.
This routine cannot modify read-only docstrings, as appear
in new-style classes or built-in functions. Because this
routine never raises an error the caller must check manually
that the docstrings were changed.
Since this function grabs the ``char *`` from a c-level str object and puts
it into the ``tp_doc`` slot of the type of `obj`, it violates a number of
C-API best-practices, by:
- modifying a `PyTypeObject` after calling `PyType_Ready`
- calling `Py_INCREF` on the str and losing the reference, so the str
will never be released
If possible it should be avoided.
"""
new = getattr(__import__(place, globals(), {}, [obj]), obj)
if isinstance(doc, str):
if "${ARRAY_FUNCTION_LIKE}" in doc:
doc = overrides.get_array_function_like_doc(new, doc)
_add_docstring(new, doc.strip(), warn_on_python)
elif isinstance(doc, tuple):
attr, docstring = doc
_add_docstring(getattr(new, attr), docstring.strip(), warn_on_python)
elif isinstance(doc, list):
for attr, docstring in doc:
_add_docstring(
getattr(new, attr), docstring.strip(), warn_on_python
)

278
venv/lib/python3.13/site-packages/numpy/_core/function_base.pyi
View file

@ -1,278 +0,0 @@
from typing import Literal as L
from typing import SupportsIndex, TypeAlias, TypeVar, overload
from _typeshed import Incomplete
import numpy as np
from numpy._typing import (
DTypeLike,
NDArray,
_ArrayLikeComplex_co,
_ArrayLikeFloat_co,
_DTypeLike,
)
from numpy._typing._array_like import _DualArrayLike
__all__ = ["geomspace", "linspace", "logspace"]
_ScalarT = TypeVar("_ScalarT", bound=np.generic)
_ToArrayFloat64: TypeAlias = _DualArrayLike[np.dtype[np.float64 | np.integer | np.bool], float]
@overload
def linspace(
start: _ToArrayFloat64,
stop: _ToArrayFloat64,
num: SupportsIndex = 50,
endpoint: bool = True,
retstep: L[False] = False,
dtype: None = None,
axis: SupportsIndex = 0,
*,
device: L["cpu"] | None = None,
) -> NDArray[np.float64]: ...
@overload
def linspace(
start: _ArrayLikeFloat_co,
stop: _ArrayLikeFloat_co,
num: SupportsIndex = 50,
endpoint: bool = True,
retstep: L[False] = False,
dtype: None = None,
axis: SupportsIndex = 0,
*,
device: L["cpu"] | None = None,
) -> NDArray[np.floating]: ...
@overload
def linspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = 50,
endpoint: bool = True,
retstep: L[False] = False,
dtype: None = None,
axis: SupportsIndex = 0,
*,
device: L["cpu"] | None = None,
) -> NDArray[np.complexfloating]: ...
@overload
def linspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex,
endpoint: bool,
retstep: L[False],
dtype: _DTypeLike[_ScalarT],
axis: SupportsIndex = 0,
*,
device: L["cpu"] | None = None,
) -> NDArray[_ScalarT]: ...
@overload
def linspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = 50,
endpoint: bool = True,
retstep: L[False] = False,
*,
dtype: _DTypeLike[_ScalarT],
axis: SupportsIndex = 0,
device: L["cpu"] | None = None,
) -> NDArray[_ScalarT]: ...
@overload
def linspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = 50,
endpoint: bool = True,
retstep: L[False] = False,
dtype: DTypeLike | None = None,
axis: SupportsIndex = 0,
*,
device: L["cpu"] | None = None,
) -> NDArray[Incomplete]: ...
@overload
def linspace(
start: _ToArrayFloat64,
stop: _ToArrayFloat64,
num: SupportsIndex = 50,
endpoint: bool = True,
*,
retstep: L[True],
dtype: None = None,
axis: SupportsIndex = 0,
device: L["cpu"] | None = None,
) -> tuple[NDArray[np.float64], np.float64]: ...
@overload
def linspace(
start: _ArrayLikeFloat_co,
stop: _ArrayLikeFloat_co,
num: SupportsIndex = 50,
endpoint: bool = True,
*,
retstep: L[True],
dtype: None = None,
axis: SupportsIndex = 0,
device: L["cpu"] | None = None,
) -> tuple[NDArray[np.floating], np.floating]: ...
@overload
def linspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = 50,
endpoint: bool = True,
*,
retstep: L[True],
dtype: None = None,
axis: SupportsIndex = 0,
device: L["cpu"] | None = None,
) -> tuple[NDArray[np.complexfloating], np.complexfloating]: ...
@overload
def linspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = 50,
endpoint: bool = True,
*,
retstep: L[True],
dtype: _DTypeLike[_ScalarT],
axis: SupportsIndex = 0,
device: L["cpu"] | None = None,
) -> tuple[NDArray[_ScalarT], _ScalarT]: ...
@overload
def linspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = 50,
endpoint: bool = True,
*,
retstep: L[True],
dtype: DTypeLike | None = None,
axis: SupportsIndex = 0,
device: L["cpu"] | None = None,
) -> tuple[NDArray[Incomplete], Incomplete]: ...
@overload
def logspace(
start: _ToArrayFloat64,
stop: _ToArrayFloat64,
num: SupportsIndex = 50,
endpoint: bool = True,
base: _ToArrayFloat64 = 10.0,
dtype: None = None,
axis: SupportsIndex = 0,
) -> NDArray[np.float64]: ...
@overload
def logspace(
start: _ArrayLikeFloat_co,
stop: _ArrayLikeFloat_co,
num: SupportsIndex = 50,
endpoint: bool = True,
base: _ArrayLikeFloat_co = 10.0,
dtype: None = None,
axis: SupportsIndex = 0,
) -> NDArray[np.floating]: ...
@overload
def logspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = 50,
endpoint: bool = True,
base: _ArrayLikeComplex_co = 10.0,
dtype: None = None,
axis: SupportsIndex = 0,
) -> NDArray[np.complexfloating]: ...
@overload
def logspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex,
endpoint: bool,
base: _ArrayLikeComplex_co,
dtype: _DTypeLike[_ScalarT],
axis: SupportsIndex = 0,
) -> NDArray[_ScalarT]: ...
@overload
def logspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = 50,
endpoint: bool = True,
base: _ArrayLikeComplex_co = 10.0,
*,
dtype: _DTypeLike[_ScalarT],
axis: SupportsIndex = 0,
) -> NDArray[_ScalarT]: ...
@overload
def logspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = 50,
endpoint: bool = True,
base: _ArrayLikeComplex_co = 10.0,
dtype: DTypeLike | None = None,
axis: SupportsIndex = 0,
) -> NDArray[Incomplete]: ...
@overload
def geomspace(
start: _ToArrayFloat64,
stop: _ToArrayFloat64,
num: SupportsIndex = 50,
endpoint: bool = True,
dtype: None = None,
axis: SupportsIndex = 0,
) -> NDArray[np.float64]: ...
@overload
def geomspace(
start: _ArrayLikeFloat_co,
stop: _ArrayLikeFloat_co,
num: SupportsIndex = 50,
endpoint: bool = True,
dtype: None = None,
axis: SupportsIndex = 0,
) -> NDArray[np.floating]: ...
@overload
def geomspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = 50,
endpoint: bool = True,
dtype: None = None,
axis: SupportsIndex = 0,
) -> NDArray[np.complexfloating]: ...
@overload
def geomspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex,
endpoint: bool,
dtype: _DTypeLike[_ScalarT],
axis: SupportsIndex = 0,
) -> NDArray[_ScalarT]: ...
@overload
def geomspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = 50,
endpoint: bool = True,
*,
dtype: _DTypeLike[_ScalarT],
axis: SupportsIndex = 0,
) -> NDArray[_ScalarT]: ...
@overload
def geomspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = 50,
endpoint: bool = True,
dtype: DTypeLike | None = None,
axis: SupportsIndex = 0,
) -> NDArray[Incomplete]: ...
def add_newdoc(
place: str,
obj: str,
doc: str | tuple[str, str] | list[tuple[str, str]],
warn_on_python: bool = True,
) -> None: ...

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@ -1,748 +0,0 @@
"""Machine limits for Float32 and Float64 and (long double) if available...
"""
__all__ = ['finfo', 'iinfo']
import types
import warnings
from numpy._utils import set_module
from . import numeric
from . import numerictypes as ntypes
from ._machar import MachAr
from .numeric import array, inf, nan
from .umath import exp2, isnan, log10, nextafter
def _fr0(a):
"""fix rank-0 --> rank-1"""
if a.ndim == 0:
a = a.copy()
a.shape = (1,)
return a
def _fr1(a):
"""fix rank > 0 --> rank-0"""
if a.size == 1:
a = a.copy()
a.shape = ()
return a
class MachArLike:
""" Object to simulate MachAr instance """
def __init__(self, ftype, *, eps, epsneg, huge, tiny,
ibeta, smallest_subnormal=None, **kwargs):
self.params = _MACHAR_PARAMS[ftype]
self.ftype = ftype
self.title = self.params['title']
# Parameter types same as for discovered MachAr object.
if not smallest_subnormal:
self._smallest_subnormal = nextafter(
self.ftype(0), self.ftype(1), dtype=self.ftype)
else:
self._smallest_subnormal = smallest_subnormal
self.epsilon = self.eps = self._float_to_float(eps)
self.epsneg = self._float_to_float(epsneg)
self.xmax = self.huge = self._float_to_float(huge)
self.xmin = self._float_to_float(tiny)
self.smallest_normal = self.tiny = self._float_to_float(tiny)
self.ibeta = self.params['itype'](ibeta)
self.__dict__.update(kwargs)
self.precision = int(-log10(self.eps))
self.resolution = self._float_to_float(
self._float_conv(10) ** (-self.precision))
self._str_eps = self._float_to_str(self.eps)
self._str_epsneg = self._float_to_str(self.epsneg)
self._str_xmin = self._float_to_str(self.xmin)
self._str_xmax = self._float_to_str(self.xmax)
self._str_resolution = self._float_to_str(self.resolution)
self._str_smallest_normal = self._float_to_str(self.xmin)
@property
def smallest_subnormal(self):
"""Return the value for the smallest subnormal.
Returns
-------
smallest_subnormal : float
value for the smallest subnormal.
Warns
-----
UserWarning
If the calculated value for the smallest subnormal is zero.
"""
# Check that the calculated value is not zero, in case it raises a
# warning.
value = self._smallest_subnormal
if self.ftype(0) == value:
warnings.warn(
f'The value of the smallest subnormal for {self.ftype} type is zero.',
UserWarning, stacklevel=2)
return self._float_to_float(value)
@property
def _str_smallest_subnormal(self):
"""Return the string representation of the smallest subnormal."""
return self._float_to_str(self.smallest_subnormal)
def _float_to_float(self, value):
"""Converts float to float.
Parameters
----------
value : float
value to be converted.
"""
return _fr1(self._float_conv(value))
def _float_conv(self, value):
"""Converts float to conv.
Parameters
----------
value : float
value to be converted.
"""
return array([value], self.ftype)
def _float_to_str(self, value):
"""Converts float to str.
Parameters
----------
value : float
value to be converted.
"""
return self.params['fmt'] % array(_fr0(value)[0], self.ftype)
_convert_to_float = {
ntypes.csingle: ntypes.single,
ntypes.complex128: ntypes.float64,
ntypes.clongdouble: ntypes.longdouble
}
# Parameters for creating MachAr / MachAr-like objects
_title_fmt = 'numpy {} precision floating point number'
_MACHAR_PARAMS = {
ntypes.double: {
'itype': ntypes.int64,
'fmt': '%24.16e',
'title': _title_fmt.format('double')},
ntypes.single: {
'itype': ntypes.int32,
'fmt': '%15.7e',
'title': _title_fmt.format('single')},
ntypes.longdouble: {
'itype': ntypes.longlong,
'fmt': '%s',
'title': _title_fmt.format('long double')},
ntypes.half: {
'itype': ntypes.int16,
'fmt': '%12.5e',
'title': _title_fmt.format('half')}}
# Key to identify the floating point type. Key is result of
#
# ftype = np.longdouble # or float64, float32, etc.
# v = (ftype(-1.0) / ftype(10.0))
# v.view(v.dtype.newbyteorder('<')).tobytes()
#
# Uses division to work around deficiencies in strtold on some platforms.
# See:
# https://perl5.git.perl.org/perl.git/blob/3118d7d684b56cbeb702af874f4326683c45f045:/Configure
_KNOWN_TYPES = {}
def _register_type(machar, bytepat):
_KNOWN_TYPES[bytepat] = machar
_float_ma = {}
def _register_known_types():
# Known parameters for float16
# See docstring of MachAr class for description of parameters.
f16 = ntypes.float16
float16_ma = MachArLike(f16,
machep=-10,
negep=-11,
minexp=-14,
maxexp=16,
it=10,
iexp=5,
ibeta=2,
irnd=5,
ngrd=0,
eps=exp2(f16(-10)),
epsneg=exp2(f16(-11)),
huge=f16(65504),
tiny=f16(2 ** -14))
_register_type(float16_ma, b'f\xae')
_float_ma[16] = float16_ma
# Known parameters for float32
f32 = ntypes.float32
float32_ma = MachArLike(f32,
machep=-23,
negep=-24,
minexp=-126,
maxexp=128,
it=23,
iexp=8,
ibeta=2,
irnd=5,
ngrd=0,
eps=exp2(f32(-23)),
epsneg=exp2(f32(-24)),
huge=f32((1 - 2 ** -24) * 2**128),
tiny=exp2(f32(-126)))
_register_type(float32_ma, b'\xcd\xcc\xcc\xbd')
_float_ma[32] = float32_ma
# Known parameters for float64
f64 = ntypes.float64
epsneg_f64 = 2.0 ** -53.0
tiny_f64 = 2.0 ** -1022.0
float64_ma = MachArLike(f64,
machep=-52,
negep=-53,
minexp=-1022,
maxexp=1024,
it=52,
iexp=11,
ibeta=2,
irnd=5,
ngrd=0,
eps=2.0 ** -52.0,
epsneg=epsneg_f64,
huge=(1.0 - epsneg_f64) / tiny_f64 * f64(4),
tiny=tiny_f64)
_register_type(float64_ma, b'\x9a\x99\x99\x99\x99\x99\xb9\xbf')
_float_ma[64] = float64_ma
# Known parameters for IEEE 754 128-bit binary float
ld = ntypes.longdouble
epsneg_f128 = exp2(ld(-113))
tiny_f128 = exp2(ld(-16382))
# Ignore runtime error when this is not f128
with numeric.errstate(all='ignore'):
huge_f128 = (ld(1) - epsneg_f128) / tiny_f128 * ld(4)
float128_ma = MachArLike(ld,
machep=-112,
negep=-113,
minexp=-16382,
maxexp=16384,
it=112,
iexp=15,
ibeta=2,
irnd=5,
ngrd=0,
eps=exp2(ld(-112)),
epsneg=epsneg_f128,
huge=huge_f128,
tiny=tiny_f128)
# IEEE 754 128-bit binary float
_register_type(float128_ma,
b'\x9a\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\xfb\xbf')
_float_ma[128] = float128_ma
# Known parameters for float80 (Intel 80-bit extended precision)
epsneg_f80 = exp2(ld(-64))
tiny_f80 = exp2(ld(-16382))
# Ignore runtime error when this is not f80
with numeric.errstate(all='ignore'):
huge_f80 = (ld(1) - epsneg_f80) / tiny_f80 * ld(4)
float80_ma = MachArLike(ld,
machep=-63,
negep=-64,
minexp=-16382,
maxexp=16384,
it=63,
iexp=15,
ibeta=2,
irnd=5,
ngrd=0,
eps=exp2(ld(-63)),
epsneg=epsneg_f80,
huge=huge_f80,
tiny=tiny_f80)
# float80, first 10 bytes containing actual storage
_register_type(float80_ma, b'\xcd\xcc\xcc\xcc\xcc\xcc\xcc\xcc\xfb\xbf')
_float_ma[80] = float80_ma
# Guessed / known parameters for double double; see:
# https://en.wikipedia.org/wiki/Quadruple-precision_floating-point_format#Double-double_arithmetic
# These numbers have the same exponent range as float64, but extended
# number of digits in the significand.
huge_dd = nextafter(ld(inf), ld(0), dtype=ld)
# As the smallest_normal in double double is so hard to calculate we set
# it to NaN.
smallest_normal_dd = nan
# Leave the same value for the smallest subnormal as double
smallest_subnormal_dd = ld(nextafter(0., 1.))
float_dd_ma = MachArLike(ld,
machep=-105,
negep=-106,
minexp=-1022,
maxexp=1024,
it=105,
iexp=11,
ibeta=2,
irnd=5,
ngrd=0,
eps=exp2(ld(-105)),
epsneg=exp2(ld(-106)),
huge=huge_dd,
tiny=smallest_normal_dd,
smallest_subnormal=smallest_subnormal_dd)
# double double; low, high order (e.g. PPC 64)
_register_type(float_dd_ma,
b'\x9a\x99\x99\x99\x99\x99Y<\x9a\x99\x99\x99\x99\x99\xb9\xbf')
# double double; high, low order (e.g. PPC 64 le)
_register_type(float_dd_ma,
b'\x9a\x99\x99\x99\x99\x99\xb9\xbf\x9a\x99\x99\x99\x99\x99Y<')
_float_ma['dd'] = float_dd_ma
def _get_machar(ftype):
""" Get MachAr instance or MachAr-like instance
Get parameters for floating point type, by first trying signatures of
various known floating point types, then, if none match, attempting to
identify parameters by analysis.
Parameters
----------
ftype : class
Numpy floating point type class (e.g. ``np.float64``)
Returns
-------
ma_like : instance of :class:`MachAr` or :class:`MachArLike`
Object giving floating point parameters for `ftype`.
Warns
-----
UserWarning
If the binary signature of the float type is not in the dictionary of
known float types.
"""
params = _MACHAR_PARAMS.get(ftype)
if params is None:
raise ValueError(repr(ftype))
# Detect known / suspected types
# ftype(-1.0) / ftype(10.0) is better than ftype('-0.1') because stold
# may be deficient
key = (ftype(-1.0) / ftype(10.))
key = key.view(key.dtype.newbyteorder("<")).tobytes()
ma_like = None
if ftype == ntypes.longdouble:
# Could be 80 bit == 10 byte extended precision, where last bytes can
# be random garbage.
# Comparing first 10 bytes to pattern first to avoid branching on the
# random garbage.
ma_like = _KNOWN_TYPES.get(key[:10])
if ma_like is None:
# see if the full key is known.
ma_like = _KNOWN_TYPES.get(key)
if ma_like is None and len(key) == 16:
# machine limits could be f80 masquerading as np.float128,
# find all keys with length 16 and make new dict, but make the keys
# only 10 bytes long, the last bytes can be random garbage
_kt = {k[:10]: v for k, v in _KNOWN_TYPES.items() if len(k) == 16}
ma_like = _kt.get(key[:10])
if ma_like is not None:
return ma_like
# Fall back to parameter discovery
warnings.warn(
f'Signature {key} for {ftype} does not match any known type: '
'falling back to type probe function.\n'
'This warnings indicates broken support for the dtype!',
UserWarning, stacklevel=2)
return _discovered_machar(ftype)
def _discovered_machar(ftype):
""" Create MachAr instance with found information on float types
TODO: MachAr should be retired completely ideally. We currently only
ever use it system with broken longdouble (valgrind, WSL).
"""
params = _MACHAR_PARAMS[ftype]
return MachAr(lambda v: array([v], ftype),
lambda v: _fr0(v.astype(params['itype']))[0],
lambda v: array(_fr0(v)[0], ftype),
lambda v: params['fmt'] % array(_fr0(v)[0], ftype),
params['title'])
@set_module('numpy')
class finfo:
"""
finfo(dtype)
Machine limits for floating point types.
Attributes
----------
bits : int
The number of bits occupied by the type.
dtype : dtype
Returns the dtype for which `finfo` returns information. For complex
input, the returned dtype is the associated ``float*`` dtype for its
real and complex components.
eps : float
The difference between 1.0 and the next smallest representable float
larger than 1.0. For example, for 64-bit binary floats in the IEEE-754
standard, ``eps = 2**-52``, approximately 2.22e-16.
epsneg : float
The difference between 1.0 and the next smallest representable float
less than 1.0. For example, for 64-bit binary floats in the IEEE-754
standard, ``epsneg = 2**-53``, approximately 1.11e-16.
iexp : int
The number of bits in the exponent portion of the floating point
representation.
machep : int
The exponent that yields `eps`.
max : floating point number of the appropriate type
The largest representable number.
maxexp : int
The smallest positive power of the base (2) that causes overflow.
min : floating point number of the appropriate type
The smallest representable number, typically ``-max``.
minexp : int
The most negative power of the base (2) consistent with there
being no leading 0's in the mantissa.
negep : int
The exponent that yields `epsneg`.
nexp : int
The number of bits in the exponent including its sign and bias.
nmant : int
The number of bits in the mantissa.
precision : int
The approximate number of decimal digits to which this kind of
float is precise.
resolution : floating point number of the appropriate type
The approximate decimal resolution of this type, i.e.,
``10**-precision``.
tiny : float
An alias for `smallest_normal`, kept for backwards compatibility.
smallest_normal : float
The smallest positive floating point number with 1 as leading bit in
the mantissa following IEEE-754 (see Notes).
smallest_subnormal : float
The smallest positive floating point number with 0 as leading bit in
the mantissa following IEEE-754.
Parameters
----------
dtype : float, dtype, or instance
Kind of floating point or complex floating point
data-type about which to get information.
See Also
--------
iinfo : The equivalent for integer data types.
spacing : The distance between a value and the nearest adjacent number
nextafter : The next floating point value after x1 towards x2
Notes
-----
For developers of NumPy: do not instantiate this at the module level.
The initial calculation of these parameters is expensive and negatively
impacts import times. These objects are cached, so calling ``finfo()``
repeatedly inside your functions is not a problem.
Note that ``smallest_normal`` is not actually the smallest positive
representable value in a NumPy floating point type. As in the IEEE-754
standard [1]_, NumPy floating point types make use of subnormal numbers to
fill the gap between 0 and ``smallest_normal``. However, subnormal numbers
may have significantly reduced precision [2]_.
This function can also be used for complex data types as well. If used,
the output will be the same as the corresponding real float type
(e.g. numpy.finfo(numpy.csingle) is the same as numpy.finfo(numpy.single)).
However, the output is true for the real and imaginary components.
References
----------
.. [1] IEEE Standard for Floating-Point Arithmetic, IEEE Std 754-2008,
pp.1-70, 2008, https://doi.org/10.1109/IEEESTD.2008.4610935
.. [2] Wikipedia, "Denormal Numbers",
https://en.wikipedia.org/wiki/Denormal_number
Examples
--------
>>> import numpy as np
>>> np.finfo(np.float64).dtype
dtype('float64')
>>> np.finfo(np.complex64).dtype
dtype('float32')
"""
_finfo_cache = {}
__class_getitem__ = classmethod(types.GenericAlias)
def __new__(cls, dtype):
try:
obj = cls._finfo_cache.get(dtype) # most common path
if obj is not None:
return obj
except TypeError:
pass
if dtype is None:
# Deprecated in NumPy 1.25, 2023-01-16
warnings.warn(
"finfo() dtype cannot be None. This behavior will "
"raise an error in the future. (Deprecated in NumPy 1.25)",
DeprecationWarning,
stacklevel=2
)
try:
dtype = numeric.dtype(dtype)
except TypeError:
# In case a float instance was given
dtype = numeric.dtype(type(dtype))
obj = cls._finfo_cache.get(dtype)
if obj is not None:
return obj
dtypes = [dtype]
newdtype = ntypes.obj2sctype(dtype)
if newdtype is not dtype:
dtypes.append(newdtype)
dtype = newdtype
if not issubclass(dtype, numeric.inexact):
raise ValueError(f"data type {dtype!r} not inexact")
obj = cls._finfo_cache.get(dtype)
if obj is not None:
return obj
if not issubclass(dtype, numeric.floating):
newdtype = _convert_to_float[dtype]
if newdtype is not dtype:
# dtype changed, for example from complex128 to float64
dtypes.append(newdtype)
dtype = newdtype
obj = cls._finfo_cache.get(dtype, None)
if obj is not None:
# the original dtype was not in the cache, but the new
# dtype is in the cache. we add the original dtypes to
# the cache and return the result
for dt in dtypes:
cls._finfo_cache[dt] = obj
return obj
obj = object.__new__(cls)._init(dtype)
for dt in dtypes:
cls._finfo_cache[dt] = obj
return obj
def _init(self, dtype):
self.dtype = numeric.dtype(dtype)
machar = _get_machar(dtype)
for word in ['precision', 'iexp',
'maxexp', 'minexp', 'negep',
'machep']:
setattr(self, word, getattr(machar, word))
for word in ['resolution', 'epsneg', 'smallest_subnormal']:
setattr(self, word, getattr(machar, word).flat[0])
self.bits = self.dtype.itemsize * 8
self.max = machar.huge.flat[0]
self.min = -self.max
self.eps = machar.eps.flat[0]
self.nexp = machar.iexp
self.nmant = machar.it
self._machar = machar
self._str_tiny = machar._str_xmin.strip()
self._str_max = machar._str_xmax.strip()
self._str_epsneg = machar._str_epsneg.strip()
self._str_eps = machar._str_eps.strip()
self._str_resolution = machar._str_resolution.strip()
self._str_smallest_normal = machar._str_smallest_normal.strip()
self._str_smallest_subnormal = machar._str_smallest_subnormal.strip()
return self
def __str__(self):
fmt = (
'Machine parameters for %(dtype)s\n'
'---------------------------------------------------------------\n'
'precision = %(precision)3s resolution = %(_str_resolution)s\n'
'machep = %(machep)6s eps = %(_str_eps)s\n'
'negep = %(negep)6s epsneg = %(_str_epsneg)s\n'
'minexp = %(minexp)6s tiny = %(_str_tiny)s\n'
'maxexp = %(maxexp)6s max = %(_str_max)s\n'
'nexp = %(nexp)6s min = -max\n'
'smallest_normal = %(_str_smallest_normal)s '
'smallest_subnormal = %(_str_smallest_subnormal)s\n'
'---------------------------------------------------------------\n'
)
return fmt % self.__dict__
def __repr__(self):
c = self.__class__.__name__
d = self.__dict__.copy()
d['klass'] = c
return (("%(klass)s(resolution=%(resolution)s, min=-%(_str_max)s,"
" max=%(_str_max)s, dtype=%(dtype)s)") % d)
@property
def smallest_normal(self):
"""Return the value for the smallest normal.
Returns
-------
smallest_normal : float
Value for the smallest normal.
Warns
-----
UserWarning
If the calculated value for the smallest normal is requested for
double-double.
"""
# This check is necessary because the value for smallest_normal is
# platform dependent for longdouble types.
if isnan(self._machar.smallest_normal.flat[0]):
warnings.warn(
'The value of smallest normal is undefined for double double',
UserWarning, stacklevel=2)
return self._machar.smallest_normal.flat[0]
@property
def tiny(self):
"""Return the value for tiny, alias of smallest_normal.
Returns
-------
tiny : float
Value for the smallest normal, alias of smallest_normal.
Warns
-----
UserWarning
If the calculated value for the smallest normal is requested for
double-double.
"""
return self.smallest_normal
@set_module('numpy')
class iinfo:
"""
iinfo(type)
Machine limits for integer types.
Attributes
----------
bits : int
The number of bits occupied by the type.
dtype : dtype
Returns the dtype for which `iinfo` returns information.
min : int
The smallest integer expressible by the type.
max : int
The largest integer expressible by the type.
Parameters
----------
int_type : integer type, dtype, or instance
The kind of integer data type to get information about.
See Also
--------
finfo : The equivalent for floating point data types.
Examples
--------
With types:
>>> import numpy as np
>>> ii16 = np.iinfo(np.int16)
>>> ii16.min
-32768
>>> ii16.max
32767
>>> ii32 = np.iinfo(np.int32)
>>> ii32.min
-2147483648
>>> ii32.max
2147483647
With instances:
>>> ii32 = np.iinfo(np.int32(10))
>>> ii32.min
-2147483648
>>> ii32.max
2147483647
"""
_min_vals = {}
_max_vals = {}
__class_getitem__ = classmethod(types.GenericAlias)
def __init__(self, int_type):
try:
self.dtype = numeric.dtype(int_type)
except TypeError:
self.dtype = numeric.dtype(type(int_type))
self.kind = self.dtype.kind
self.bits = self.dtype.itemsize * 8
self.key = "%s%d" % (self.kind, self.bits)
if self.kind not in 'iu':
raise ValueError(f"Invalid integer data type {self.kind!r}.")
@property
def min(self):
"""Minimum value of given dtype."""
if self.kind == 'u':
return 0
else:
try:
val = iinfo._min_vals[self.key]
except KeyError:
val = int(-(1 << (self.bits - 1)))
iinfo._min_vals[self.key] = val
return val
@property
def max(self):
"""Maximum value of given dtype."""
try:
val = iinfo._max_vals[self.key]
except KeyError:
if self.kind == 'u':
val = int((1 << self.bits) - 1)
else:
val = int((1 << (self.bits - 1)) - 1)
iinfo._max_vals[self.key] = val
return val
def __str__(self):
"""String representation."""
fmt = (
'Machine parameters for %(dtype)s\n'
'---------------------------------------------------------------\n'
'min = %(min)s\n'
'max = %(max)s\n'
'---------------------------------------------------------------\n'
)
return fmt % {'dtype': self.dtype, 'min': self.min, 'max': self.max}
def __repr__(self):
return "%s(min=%s, max=%s, dtype=%s)" % (self.__class__.__name__,
self.min, self.max, self.dtype)

3
venv/lib/python3.13/site-packages/numpy/_core/getlimits.pyi
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@ -1,3 +0,0 @@
from numpy import finfo, iinfo
__all__ = ["finfo", "iinfo"]

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