FM / lib

/opt/imh-python/lib/python3.9/site-packages/numpy/lib/

/opt/imh-python/lib/python3.9/site-packages/numpy/lib

Name	Size	Mode	Actions
tests/	-	0755	rm
__pycache__/	-	0755	rm
arraypad.py	31803	0644	edit dl rm
arraypad.pyi	1728	0644	edit dl rm
arraysetops.py	33655	0644	edit dl rm
arraysetops.pyi	8372	0644	edit dl rm
arrayterator.py	7063	0644	edit dl rm
arrayterator.pyi	1537	0644	edit dl rm
format.py	34769	0644	edit dl rm
format.pyi	748	0644	edit dl rm
function_base.py	189103	0644	edit dl rm
function_base.pyi	16585	0644	edit dl rm
histograms.py	37778	0644	edit dl rm
histograms.pyi	995	0644	edit dl rm
index_tricks.py	31346	0644	edit dl rm
index_tricks.pyi	4251	0644	edit dl rm
mixins.py	7071	0644	edit dl rm
mixins.pyi	3117	0644	edit dl rm
nanfunctions.py	65775	0644	edit dl rm
nanfunctions.pyi	606	0644	edit dl rm
npyio.py	97316	0644	edit dl rm
npyio.pyi	9728	0644	edit dl rm
polynomial.py	44133	0644	edit dl rm
polynomial.pyi	6958	0644	edit dl rm
recfunctions.py	59423	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	38947	0644	edit dl rm
shape_base.pyi	5341	0644	edit dl rm
stride_tricks.py	17911	0644	edit dl rm
stride_tricks.pyi	1747	0644	edit dl rm
twodim_base.py	32947	0644	edit dl rm
twodim_base.pyi	5370	0644	edit dl rm
type_check.py	19954	0644	edit dl rm
type_check.pyi	5571	0644	edit dl rm
ufunclike.py	6325	0644	edit dl rm
ufunclike.pyi	1293	0644	edit dl rm
user_array.py	7721	0644	edit dl rm
utils.py	37804	0644	edit dl rm
utils.pyi	2360	0644	edit dl rm
_datasource.py	22631	0644	edit dl rm
_iotools.py	30868	0644	edit dl rm
_version.py	4855	0644	edit dl rm
_version.pyi	633	0644	edit dl rm
__init__.py	2713	0644	edit dl rm
__init__.pyi	5596	0644	edit dl rm

Edit: /opt/imh-python/lib/python3.9/site-packages/numpy/lib/ufunclike.py (6325B)

"""
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
import warnings
import functools

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

@array_function_dispatch(_dispatcher, verify=False, module='numpy')
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')
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')
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)

cmd: