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/opt/cloudlinux/venv/lib/python3.11/site-packages/numpy/lib/

/opt/cloudlinux/venv/lib/python3.11/site-packages/numpy/lib

Name	Size	Mode	Actions
tests/	-	0755	rm
__pycache__/	-	0755	rm
arraypad.py	31362	0644	edit dl rm
arraypad.pyi	1728	0644	edit dl rm
arraysetops.py	33655	0644	edit dl rm
arraysetops.pyi	8337	0644	edit dl rm
arrayterator.py	7063	0644	edit dl rm
arrayterator.pyi	1537	0644	edit dl rm
format.py	34211	0644	edit dl rm
format.pyi	748	0644	edit dl rm
function_base.py	185141	0644	edit dl rm
function_base.pyi	16585	0644	edit dl rm
histograms.py	37698	0644	edit dl rm
histograms.pyi	995	0644	edit dl rm
index_tricks.py	30944	0644	edit dl rm
index_tricks.pyi	4241	0644	edit dl rm
mixins.py	7052	0644	edit dl rm
mixins.pyi	3117	0644	edit dl rm
nanfunctions.py	65556	0644	edit dl rm
nanfunctions.pyi	606	0644	edit dl rm
npyio.py	97461	0644	edit dl rm
npyio.pyi	9616	0644	edit dl rm
polynomial.py	44143	0644	edit dl rm
polynomial.pyi	6958	0644	edit dl rm
recfunctions.py	56247	0644	edit dl rm
scimath.py	15037	0644	edit dl rm
scimath.pyi	2883	0644	edit dl rm
setup.py	405	0644	edit dl rm
shape_base.py	39267	0644	edit dl rm
shape_base.pyi	5184	0644	edit dl rm
stride_tricks.py	17911	0644	edit dl rm
stride_tricks.pyi	1747	0644	edit dl rm
twodim_base.py	31381	0644	edit dl rm
twodim_base.pyi	5370	0644	edit dl rm
type_check.py	19931	0644	edit dl rm
type_check.pyi	5571	0644	edit dl rm
ufunclike.py	8031	0644	edit dl rm
ufunclike.pyi	1293	0644	edit dl rm
user_array.py	7721	0644	edit dl rm
utils.py	36147	0644	edit dl rm
utils.pyi	2360	0644	edit dl rm
_datasource.py	22643	0644	edit dl rm
_iotools.py	30873	0644	edit dl rm
_version.py	4855	0644	edit dl rm
_version.pyi	633	0644	edit dl rm
__init__.py	2253	0644	edit dl rm
__init__.pyi	5596	0644	edit dl rm

Edit: /opt/cloudlinux/venv/lib/python3.11/site-packages/numpy/lib/ufunclike.py (8031B)

"""
Module of functions that are like ufuncs in acting on arrays and optionally
storing results in an output array.

"""
__all__ = ['fix', 'isneginf', 'isposinf']

import numpy.core.numeric as nx
from numpy.core.overrides import (
    array_function_dispatch, ARRAY_FUNCTION_ENABLED,
)
import warnings
import functools

def _deprecate_out_named_y(f):
    """
    Allow the out argument to be passed as the name `y` (deprecated)

In future, this decorator should be removed.
    """
    @functools.wraps(f)
    def func(x, out=None, **kwargs):
        if 'y' in kwargs:
            if 'out' in kwargs:
                raise TypeError(
                    "{} got multiple values for argument 'out'/'y'"
                    .format(f.__name__)
                )
            out = kwargs.pop('y')
            # NumPy 1.13.0, 2017-04-26
            warnings.warn(
                "The name of the out argument to {} has changed from `y` to "
                "`out`, to match other ufuncs.".format(f.__name__),
                DeprecationWarning, stacklevel=3)
        return f(x, out=out, **kwargs)

return func

def _fix_out_named_y(f):
    """
    Allow the out argument to be passed as the name `y` (deprecated)

This decorator should only be used if _deprecate_out_named_y is used on
    a corresponding dispatcher function.
    """
    @functools.wraps(f)
    def func(x, out=None, **kwargs):
        if 'y' in kwargs:
            # we already did error checking in _deprecate_out_named_y
            out = kwargs.pop('y')
        return f(x, out=out, **kwargs)

return func

def _fix_and_maybe_deprecate_out_named_y(f):
    """
    Use the appropriate decorator, depending upon if dispatching is being used.
    """
    if ARRAY_FUNCTION_ENABLED:
        return _fix_out_named_y(f)
    else:
        return _deprecate_out_named_y(f)

@_deprecate_out_named_y
def _dispatcher(x, out=None):
    return (x, out)

@array_function_dispatch(_dispatcher, verify=False, module='numpy')
@_fix_and_maybe_deprecate_out_named_y
def fix(x, out=None):
    """
    Round to nearest integer towards zero.

Round an array of floats element-wise to nearest integer towards zero.
    The rounded values are returned as floats.

Parameters
    ----------
    x : array_like
        An array of floats to be rounded
    out : ndarray, optional
        A location into which the result is stored. If provided, it must have
        a shape that the input broadcasts to. If not provided or None, a
        freshly-allocated array is returned.

Returns
    -------
    out : ndarray of floats
        A float array with the same dimensions as the input.
        If second argument is not supplied then a float array is returned
        with the rounded values.

If a second argument is supplied the result is stored there.
        The return value `out` is then a reference to that array.

See Also
    --------
    rint, trunc, floor, ceil
    around : Round to given number of decimals

Examples
    --------
    >>> np.fix(3.14)
    3.0
    >>> np.fix(3)
    3.0
    >>> np.fix([2.1, 2.9, -2.1, -2.9])
    array([ 2.,  2., -2., -2.])

"""
    # promote back to an array if flattened
    res = nx.asanyarray(nx.ceil(x, out=out))
    res = nx.floor(x, out=res, where=nx.greater_equal(x, 0))

# when no out argument is passed and no subclasses are involved, flatten
    # scalars
    if out is None and type(res) is nx.ndarray:
        res = res[()]
    return res

@array_function_dispatch(_dispatcher, verify=False, module='numpy')
@_fix_and_maybe_deprecate_out_named_y
def isposinf(x, out=None):
    """
    Test element-wise for positive infinity, return result as bool array.

Parameters
    ----------
    x : array_like
        The input array.
    out : array_like, optional
        A location into which the result is stored. If provided, it must have a
        shape that the input broadcasts to. If not provided or None, a
        freshly-allocated boolean array is returned.

Returns
    -------
    out : ndarray
        A boolean array with the same dimensions as the input.
        If second argument is not supplied then a boolean array is returned
        with values True where the corresponding element of the input is
        positive infinity and values False where the element of the input is
        not positive infinity.

If a second argument is supplied the result is stored there. If the
        type of that array is a numeric type the result is represented as zeros
        and ones, if the type is boolean then as False and True.
        The return value `out` is then a reference to that array.

See Also
    --------
    isinf, isneginf, isfinite, isnan

Notes
    -----
    NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
    (IEEE 754).

Errors result if the second argument is also supplied when x is a scalar
    input, if first and second arguments have different shapes, or if the
    first argument has complex values

Examples
    --------
    >>> np.isposinf(np.PINF)
    True
    >>> np.isposinf(np.inf)
    True
    >>> np.isposinf(np.NINF)
    False
    >>> np.isposinf([-np.inf, 0., np.inf])
    array([False, False,  True])

>>> x = np.array([-np.inf, 0., np.inf])
    >>> y = np.array([2, 2, 2])
    >>> np.isposinf(x, y)
    array([0, 0, 1])
    >>> y
    array([0, 0, 1])

"""
    is_inf = nx.isinf(x)
    try:
        signbit = ~nx.signbit(x)
    except TypeError as e:
        dtype = nx.asanyarray(x).dtype
        raise TypeError(f'This operation is not supported for {dtype} values '
                        'because it would be ambiguous.') from e
    else:
        return nx.logical_and(is_inf, signbit, out)

@array_function_dispatch(_dispatcher, verify=False, module='numpy')
@_fix_and_maybe_deprecate_out_named_y
def isneginf(x, out=None):
    """
    Test element-wise for negative infinity, return result as bool array.

Returns
    -------
    out : ndarray
        A boolean array with the same dimensions as the input.
        If second argument is not supplied then a numpy boolean array is
        returned with values True where the corresponding element of the
        input is negative infinity and values False where the element of
        the input is not negative infinity.

If a second argument is supplied the result is stored there. If the
        type of that array is a numeric type the result is represented as
        zeros and ones, if the type is boolean then as False and True. The
        return value `out` is then a reference to that array.

See Also
    --------
    isinf, isposinf, isnan, isfinite

Notes
    -----
    NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
    (IEEE 754).

Errors result if the second argument is also supplied when x is a scalar
    input, if first and second arguments have different shapes, or if the
    first argument has complex values.

Examples
    --------
    >>> np.isneginf(np.NINF)
    True
    >>> np.isneginf(np.inf)
    False
    >>> np.isneginf(np.PINF)
    False
    >>> np.isneginf([-np.inf, 0., np.inf])
    array([ True, False, False])

>>> x = np.array([-np.inf, 0., np.inf])
    >>> y = np.array([2, 2, 2])
    >>> np.isneginf(x, y)
    array([1, 0, 0])
    >>> y
    array([1, 0, 0])

"""
    is_inf = nx.isinf(x)
    try:
        signbit = nx.signbit(x)
    except TypeError as e:
        dtype = nx.asanyarray(x).dtype
        raise TypeError(f'This operation is not supported for {dtype} values '
                        'because it would be ambiguous.') from e
    else:
        return nx.logical_and(is_inf, signbit, out)

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