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/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
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format.py	34769	0644	edit dl rm
format.pyi	748	0644	edit dl rm
function_base.py	189103	0644	edit dl rm
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histograms.py	37778	0644	edit dl rm
histograms.pyi	995	0644	edit dl rm
index_tricks.py	31346	0644	edit dl rm
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mixins.py	7071	0644	edit dl rm
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nanfunctions.py	65775	0644	edit dl rm
nanfunctions.pyi	606	0644	edit dl rm
npyio.py	97316	0644	edit dl rm
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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
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stride_tricks.py	17911	0644	edit dl rm
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twodim_base.py	32947	0644	edit dl rm
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type_check.py	19954	0644	edit dl rm
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ufunclike.py	6325	0644	edit dl rm
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user_array.py	7721	0644	edit dl rm
utils.py	37804	0644	edit dl rm
utils.pyi	2360	0644	edit dl rm
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_iotools.py	30868	0644	edit dl rm
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__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/scimath.py (15037B)

"""
Wrapper functions to more user-friendly calling of certain math functions
whose output data-type is different than the input data-type in certain
domains of the input.

For example, for functions like `log` with branch cuts, the versions in this
module provide the mathematically valid answers in the complex plane::

>>> import math
  >>> np.emath.log(-math.exp(1)) == (1+1j*math.pi)
  True

Similarly, `sqrt`, other base logarithms, `power` and trig functions are
correctly handled.  See their respective docstrings for specific examples.

Functions
---------

.. autosummary::
   :toctree: generated/

sqrt
   log
   log2
   logn
   log10
   power
   arccos
   arcsin
   arctanh

"""
import numpy.core.numeric as nx
import numpy.core.numerictypes as nt
from numpy.core.numeric import asarray, any
from numpy.core.overrides import array_function_dispatch
from numpy.lib.type_check import isreal

__all__ = [
    'sqrt', 'log', 'log2', 'logn', 'log10', 'power', 'arccos', 'arcsin',
    'arctanh'
    ]

_ln2 = nx.log(2.0)

def _tocomplex(arr):
    """Convert its input `arr` to a complex array.

The input is returned as a complex array of the smallest type that will fit
    the original data: types like single, byte, short, etc. become csingle,
    while others become cdouble.

A copy of the input is always made.

Parameters
    ----------
    arr : array

Returns
    -------
    array
        An array with the same input data as the input but in complex form.

Examples
    --------

First, consider an input of type short:

>>> a = np.array([1,2,3],np.short)

>>> ac = np.lib.scimath._tocomplex(a); ac
    array([1.+0.j, 2.+0.j, 3.+0.j], dtype=complex64)

>>> ac.dtype
    dtype('complex64')

If the input is of type double, the output is correspondingly of the
    complex double type as well:

>>> b = np.array([1,2,3],np.double)

>>> bc = np.lib.scimath._tocomplex(b); bc
    array([1.+0.j, 2.+0.j, 3.+0.j])

>>> bc.dtype
    dtype('complex128')

Note that even if the input was complex to begin with, a copy is still
    made, since the astype() method always copies:

>>> c = np.array([1,2,3],np.csingle)

>>> cc = np.lib.scimath._tocomplex(c); cc
    array([1.+0.j,  2.+0.j,  3.+0.j], dtype=complex64)

>>> c *= 2; c
    array([2.+0.j,  4.+0.j,  6.+0.j], dtype=complex64)

>>> cc
    array([1.+0.j,  2.+0.j,  3.+0.j], dtype=complex64)
    """
    if issubclass(arr.dtype.type, (nt.single, nt.byte, nt.short, nt.ubyte,
                                   nt.ushort, nt.csingle)):
        return arr.astype(nt.csingle)
    else:
        return arr.astype(nt.cdouble)

def _fix_real_lt_zero(x):
    """Convert `x` to complex if it has real, negative components.

Otherwise, output is just the array version of the input (via asarray).

Parameters
    ----------
    x : array_like

Returns
    -------
    array

Examples
    --------
    >>> np.lib.scimath._fix_real_lt_zero([1,2])
    array([1, 2])

>>> np.lib.scimath._fix_real_lt_zero([-1,2])
    array([-1.+0.j,  2.+0.j])

"""
    x = asarray(x)
    if any(isreal(x) & (x < 0)):
        x = _tocomplex(x)
    return x

def _fix_int_lt_zero(x):
    """Convert `x` to double if it has real, negative components.

Otherwise, output is just the array version of the input (via asarray).

Parameters
    ----------
    x : array_like

Returns
    -------
    array

Examples
    --------
    >>> np.lib.scimath._fix_int_lt_zero([1,2])
    array([1, 2])

>>> np.lib.scimath._fix_int_lt_zero([-1,2])
    array([-1.,  2.])
    """
    x = asarray(x)
    if any(isreal(x) & (x < 0)):
        x = x * 1.0
    return x

def _fix_real_abs_gt_1(x):
    """Convert `x` to complex if it has real components x_i with abs(x_i)>1.

Otherwise, output is just the array version of the input (via asarray).

Parameters
    ----------
    x : array_like

Returns
    -------
    array

Examples
    --------
    >>> np.lib.scimath._fix_real_abs_gt_1([0,1])
    array([0, 1])

>>> np.lib.scimath._fix_real_abs_gt_1([0,2])
    array([0.+0.j, 2.+0.j])
    """
    x = asarray(x)
    if any(isreal(x) & (abs(x) > 1)):
        x = _tocomplex(x)
    return x

def _unary_dispatcher(x):
    return (x,)

@array_function_dispatch(_unary_dispatcher)
def sqrt(x):
    """
    Compute the square root of x.

For negative input elements, a complex value is returned
    (unlike `numpy.sqrt` which returns NaN).

Parameters
    ----------
    x : array_like
       The input value(s).

Returns
    -------
    out : ndarray or scalar
       The square root of `x`. If `x` was a scalar, so is `out`,
       otherwise an array is returned.