# cython: language_level=3
# distutils: language = c++
# -*- coding: utf-8 -*-
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"""
Interface of the Logic part of the DPNP
Notes
-----
This module is a face or public interface file for the library
it contains:
- Interface functions
- documentation for the functions
- The functions parameters check
"""
# pylint: disable=protected-access
# pylint: disable=c-extension-no-member
# pylint: disable=duplicate-code
# pylint: disable=no-name-in-module
import dpctl.tensor as dpt
import dpctl.tensor._tensor_elementwise_impl as ti
import numpy
import dpnp
from dpnp.dpnp_algo import dpnp_allclose
from dpnp.dpnp_algo.dpnp_elementwise_common import DPNPBinaryFunc, DPNPUnaryFunc
from dpnp.dpnp_array import dpnp_array
from dpnp.dpnp_utils import call_origin
__all__ = [
"all",
"allclose",
"any",
"equal",
"greater",
"greater_equal",
"isclose",
"isfinite",
"isinf",
"isnan",
"less",
"less_equal",
"logical_and",
"logical_not",
"logical_or",
"logical_xor",
"not_equal",
]
[docs]
def all(x, /, axis=None, out=None, keepdims=False, *, where=True):
"""
Test whether all array elements along a given axis evaluate to True.
For full documentation refer to :obj:`numpy.all`.
Returns
-------
out : dpnp.ndarray
An array with a data type of `bool`
containing the results of the logical AND reduction.
Limitations
-----------
Parameters `x` is supported either as :class:`dpnp.ndarray`
or :class:`dpctl.tensor.usm_ndarray`.
Parameters `out` and `where` are supported with default value.
Input array data types are limited by supported DPNP :ref:`Data types`.
Otherwise the function will be executed sequentially on CPU.
See Also
--------
:obj:`dpnp.ndarray.all` : equivalent method
:obj:`dpnp.any` : Test whether any element along a given axis evaluates
to True.
Notes
-----
Not a Number (NaN), positive infinity and negative infinity
evaluate to `True` because these are not equal to zero.
Examples
--------
>>> import dpnp as np
>>> x = np.array([[True, False], [True, True]])
>>> np.all(x)
array(False)
>>> np.all(x, axis=0)
array([ True, False])
>>> x2 = np.array([-1, 4, 5])
>>> np.all(x2)
array(True)
>>> x3 = np.array([1.0, np.nan])
>>> np.all(x3)
array(True)
"""
if dpnp.is_supported_array_type(x):
if out is not None:
pass
elif where is not True:
pass
else:
dpt_array = dpnp.get_usm_ndarray(x)
return dpnp_array._create_from_usm_ndarray(
dpt.all(dpt_array, axis=axis, keepdims=keepdims)
)
return call_origin(
numpy.all, x, axis=axis, out=out, keepdims=keepdims, where=where
)
[docs]
def allclose(a, b, rtol=1.0e-5, atol=1.0e-8, **kwargs):
"""
Returns ``True`` if two arrays are element-wise equal within a tolerance.
The tolerance values are positive, typically very small numbers. The
relative difference (`rtol` * abs(`b`)) and the absolute difference `atol`
are added together to compare against the absolute difference between `a`
and `b`.
If either array contains one or more ``NaNs``, ``False`` is returned.
``Infs`` are treated as equal if they are in the same place and of the same
sign in both arrays.
For full documentation refer to :obj:`numpy.allclose`.
Returns
-------
out : dpnp.ndarray
A 0-dim array with ``True`` value if the two arrays are equal within
the given tolerance; with ``False`` otherwise.
Limitations
-----------
Parameters `a` and `b` are supported either as :class:`dpnp.ndarray`,
:class:`dpctl.tensor.usm_ndarray` or scalars, but both `a` and `b`
can not be scalars at the same time.
Keyword argument `kwargs` is currently unsupported.
Otherwise the functions will be executed sequentially on CPU.
Parameters `rtol` and `atol` are supported as scalars. Otherwise
``TypeError`` exception will be raised.
Input array data types are limited by supported integer and
floating DPNP :ref:`Data types`.
See Also
--------
:obj:`dpnp.isclose` : Test whether two arrays are element-wise equal.
:obj:`dpnp.all` : Test whether all elements evaluate to True.
:obj:`dpnp.any` : Test whether any element evaluates to True.
:obj:`dpnp.equal` : Return (x1 == x2) element-wise.
Examples
--------
>>> import dpnp as np
>>> a = np.array([1e10, 1e-7])
>>> b = np.array([1.00001e10, 1e-8])
>>> np.allclose(a, b)
array([False])
>>> a = np.array([1.0, np.nan])
>>> b = np.array([1.0, np.nan])
>>> np.allclose(a, b)
array([False])
>>> a = np.array([1.0, np.inf])
>>> b = np.array([1.0, np.inf])
>>> np.allclose(a, b)
array([ True])
"""
if dpnp.isscalar(a) and dpnp.isscalar(b):
# at least one of inputs has to be an array
pass
elif not (
dpnp.is_supported_array_or_scalar(a)
and dpnp.is_supported_array_or_scalar(b)
):
pass
elif kwargs:
pass
else:
if not dpnp.isscalar(rtol):
raise TypeError(
f"An argument `rtol` must be a scalar, but got {rtol}"
)
if not dpnp.isscalar(atol):
raise TypeError(
f"An argument `atol` must be a scalar, but got {atol}"
)
if dpnp.isscalar(a):
a = dpnp.full_like(b, fill_value=a)
elif dpnp.isscalar(b):
b = dpnp.full_like(a, fill_value=b)
elif a.shape != b.shape:
a, b = dpt.broadcast_arrays(a.get_array(), b.get_array())
a_desc = dpnp.get_dpnp_descriptor(a, copy_when_nondefault_queue=False)
b_desc = dpnp.get_dpnp_descriptor(b, copy_when_nondefault_queue=False)
if a_desc and b_desc:
return dpnp_allclose(a_desc, b_desc, rtol, atol).get_pyobj()
return call_origin(numpy.allclose, a, b, rtol=rtol, atol=atol, **kwargs)
[docs]
def any(x, /, axis=None, out=None, keepdims=False, *, where=True):
"""
Test whether any array element along a given axis evaluates to True.
For full documentation refer to :obj:`numpy.any`.
Returns
-------
out : dpnp.ndarray
An array with a data type of `bool`
containing the results of the logical OR reduction.
Limitations
-----------
Parameters `x` is supported either as :class:`dpnp.ndarray`
or :class:`dpctl.tensor.usm_ndarray`.
Parameters `out` and `where` are supported with default value.
Input array data types are limited by supported DPNP :ref:`Data types`.
Otherwise the function will be executed sequentially on CPU.
See Also
--------
:obj:`dpnp.ndarray.any` : equivalent method
:obj:`dpnp.all` : Test whether all elements along a given axis evaluate
to True.
Notes
-----
Not a Number (NaN), positive infinity and negative infinity evaluate
to `True` because these are not equal to zero.
Examples
--------
>>> import dpnp as np
>>> x = np.array([[True, False], [True, True]])
>>> np.any(x)
array(True)
>>> np.any(x, axis=0)
array([ True, True])
>>> x2 = np.array([0, 0, 0])
>>> np.any(x2)
array(False)
>>> x3 = np.array([1.0, np.nan])
>>> np.any(x3)
array(True)
"""
if dpnp.is_supported_array_type(x):
if out is not None:
pass
elif where is not True:
pass
else:
dpt_array = dpnp.get_usm_ndarray(x)
return dpnp_array._create_from_usm_ndarray(
dpt.any(dpt_array, axis=axis, keepdims=keepdims)
)
return call_origin(
numpy.any, x, axis=axis, out=out, keepdims=keepdims, where=where
)
_EQUAL_DOCSTRING = """
Calculates equality test results for each element `x1_i` of the input array `x1`
with the respective element `x2_i` of the input array `x2`.
For full documentation refer to :obj:`numpy.equal`.
Parameters
----------
x1 : {dpnp.ndarray, usm_ndarray}
First input array, expected to have numeric data type.
x2 : {dpnp.ndarray, usm_ndarray}
Second input array, also expected to have numeric data type.
out : {None, dpnp.ndarray}, optional
Output array to populate.
Array have the correct shape and the expected data type.
order : {"C", "F", "A", "K"}, optional
Memory layout of the newly output array, if parameter `out` is ``None``.
Default: "K".
Returns
-------
out : dpnp.ndarray
An array containing the result of element-wise equality comparison.
The returned array has a data type of `bool`.
Limitations
-----------
Parameters `where` and `subok` are supported with their default values.
Otherwise ``NotImplementedError`` exception will be raised.
See Also
--------
:obj:`dpnp.not_equal` : Return (x1 != x2) element-wise.
:obj:`dpnp.greater_equal` : Return the truth value of (x1 >= x2) element-wise.
:obj:`dpnp.less_equal` : Return the truth value of (x1 =< x2) element-wise.
:obj:`dpnp.greater` : Return the truth value of (x1 > x2) element-wise.
:obj:`dpnp.less` : Return the truth value of (x1 < x2) element-wise.
Examples
--------
>>> import dpnp as np
>>> x1 = np.array([0, 1, 3])
>>> x2 = np.arange(3)
>>> np.equal(x1, x2)
array([ True, True, False])
What is compared are values, not types. So an int (1) and an array of
length one can evaluate as True:
>>> np.equal(1, np.ones(1))
array([ True])
The ``==`` operator can be used as a shorthand for ``equal`` on
:class:`dpnp.ndarray`.
>>> a = np.array([2, 4, 6])
>>> b = np.array([2, 4, 2])
>>> a == b
array([ True, True, False])
"""
equal = DPNPBinaryFunc(
"equal",
ti._equal_result_type,
ti._equal,
_EQUAL_DOCSTRING,
)
_GREATER_DOCSTRING = """
Computes the greater-than test results for each element `x1_i` of
the input array `x1` with the respective element `x2_i` of the input array `x2`.
For full documentation refer to :obj:`numpy.greater`.
Parameters
----------
x1 : {dpnp.ndarray, usm_ndarray}
First input array, expected to have numeric data type.
x2 : {dpnp.ndarray, usm_ndarray}
Second input array, also expected to have numeric data type.
out : {None, dpnp.ndarray}, optional
Output array to populate.
Array must have the correct shape and the expected data type.
order : {"C", "F", "A", "K"}, optional
Memory layout of the newly output array, if parameter `out` is ``None``.
Default: "K".
Returns
-------
out : dpnp.ndarray
An array containing the result of element-wise greater-than comparison.
The returned array has a data type of `bool`.
Limitations
-----------
Parameters `where` and `subok` are supported with their default values.
Otherwise ``NotImplementedError`` exception will be raised.
See Also
--------
:obj:`dpnp.greater_equal` : Return the truth value of (x1 >= x2) element-wise.
:obj:`dpnp.less` : Return the truth value of (x1 < x2) element-wise.
:obj:`dpnp.less_equal` : Return the truth value of (x1 =< x2) element-wise.
:obj:`dpnp.equal` : Return (x1 == x2) element-wise.
:obj:`dpnp.not_equal` : Return (x1 != x2) element-wise.
Examples
--------
>>> import dpnp as np
>>> x1 = np.array([4, 2])
>>> x2 = np.array([2, 2])
>>> np.greater(x1, x2)
array([ True, False])
The ``>`` operator can be used as a shorthand for ``greater`` on
:class:`dpnp.ndarray`.
>>> a = np.array([4, 2])
>>> b = np.array([2, 2])
>>> a > b
array([ True, False])
"""
greater = DPNPBinaryFunc(
"greater",
ti._greater_result_type,
ti._greater,
_GREATER_DOCSTRING,
)
_GREATER_EQUAL_DOCSTRING = """
Computes the greater-than or equal-to test results for each element `x1_i` of
the input array `x1` with the respective element `x2_i` of the input array `x2`.
For full documentation refer to :obj:`numpy.greater_equal`.
Parameters
----------
x1 : {dpnp.ndarray, usm_ndarray}
First input array, expected to have numeric data type.
x2 : {dpnp.ndarray, usm_ndarray}
Second input array, also expected to have numeric data type.
out : {None, dpnp.ndarray}, optional
Output array to populate.
Array must have the correct shape and the expected data type.
order : {"C", "F", "A", "K"}, optional
Memory layout of the newly output array, if parameter `out` is ``None``.
Default: "K".
Returns
-------
out : dpnp.ndarray
An array containing the result of element-wise greater-than or equal-to
comparison.
The returned array has a data type of `bool`.
Limitations
-----------
Parameters `where` and `subok` are supported with their default values.
Otherwise ``NotImplementedError`` exception will be raised.
See Also
--------
:obj:`dpnp.greater` : Return the truth value of (x1 > x2) element-wise.
:obj:`dpnp.less` : Return the truth value of (x1 < x2) element-wise.
:obj:`dpnp.less_equal` : Return the truth value of (x1 =< x2) element-wise.
:obj:`dpnp.equal` : Return (x1 == x2) element-wise.
:obj:`dpnp.not_equal` : Return (x1 != x2) element-wise.
Examples
--------
>>> import dpnp as np
>>> x1 = np.array([4, 2, 1])
>>> x2 = np.array([2, 2, 2])
>>> np.greater_equal(x1, x2)
array([ True, True, False])
The ``>=`` operator can be used as a shorthand for ``greater_equal`` on
:class:`dpnp.ndarray`.
>>> a = np.array([4, 2, 1])
>>> b = np.array([2, 2, 2])
>>> a >= b
array([ True, True, False])
"""
greater_equal = DPNPBinaryFunc(
"greater",
ti._greater_equal_result_type,
ti._greater_equal,
_GREATER_EQUAL_DOCSTRING,
)
[docs]
def isclose(x1, x2, rtol=1e-05, atol=1e-08, equal_nan=False):
"""
Returns a boolean array where two arrays are element-wise equal within
a tolerance.
For full documentation refer to :obj:`numpy.isclose`.
Limitations
-----------
`x2` is supported to be integer if `x1` is :class:`dpnp.ndarray` or
at least either `x1` or `x2` should be as :class:`dpnp.ndarray`.
Otherwise the function will be executed sequentially on CPU.
Input array data types are limited by supported DPNP :ref:`Data types`.
See Also
--------
:obj:`dpnp.allclose` : Returns True if two arrays are element-wise equal
within a tolerance.
Examples
--------
>>> import dpnp as np
>>> x1 = np.array([1e10,1e-7])
>>> x2 = np.array([1.00001e10,1e-8])
>>> out = np.isclose(x1, x2)
>>> [i for i in out]
[True, False]
"""
# x1_desc = dpnp.get_dpnp_descriptor(x1)
# x2_desc = dpnp.get_dpnp_descriptor(x2)
# if x1_desc and x2_desc:
# result_obj = dpnp_isclose(
# x1_desc, x2_desc, rtol, atol, equal_nan
# ).get_pyobj()
# return result_obj
return call_origin(
numpy.isclose, x1, x2, rtol=rtol, atol=atol, equal_nan=equal_nan
)
_ISFINITE_DOCSTRING = """
Test if each element of input array is a finite number.
For full documentation refer to :obj:`numpy.isfinite`.
Parameters
----------
x : {dpnp.ndarray, usm_ndarray}
Input array, expected to have numeric data type.
out : {None, dpnp.ndarray}, optional
Output array to populate.
Array must have the correct shape and the expected data type.
order : {"C", "F", "A", "K"}, optional
Memory layout of the newly output array, if parameter `out` is ``None``.
Default: "K".
Returns
-------
out : dpnp.ndarray
An array which is True where `x` is not positive infinity,
negative infinity, or ``NaN``, False otherwise.
The data type of the returned array is `bool`.
Limitations
-----------
Parameters `where` and `subok` are supported with their default values.
Otherwise ``NotImplementedError`` exception will be raised.
See Also
--------
:obj:`dpnp.isinf` : Test element-wise for positive or negative infinity.
:obj:`dpnp.isneginf` : Test element-wise for negative infinity,
return result as bool array.
:obj:`dpnp.isposinf` : Test element-wise for positive infinity,
return result as bool array.
:obj:`dpnp.isnan` : Test element-wise for NaN and
return result as a boolean array.
Notes
-----
Not a Number, positive infinity and negative infinity are considered
to be non-finite.
Examples
--------
>>> import dpnp as np
>>> x = np.array([-np.inf, 0., np.inf])
>>> np.isfinite(x)
array([False, True, False])
"""
isfinite = DPNPUnaryFunc(
"isfinite",
ti._isfinite_result_type,
ti._isfinite,
_ISFINITE_DOCSTRING,
)
_ISINF_DOCSTRING = """
Test if each element of input array is an infinity.
For full documentation refer to :obj:`numpy.isinf`.
Parameters
----------
x : {dpnp.ndarray, usm_ndarray}
Input array, expected to have numeric data type.
out : {None, dpnp.ndarray}, optional
Output array to populate.
Array must have the correct shape and the expected data type.
order : {"C", "F", "A", "K"}, optional
Memory layout of the newly output array, if parameter `out` is ``None``.
Default: "K".
Returns
-------
out : dpnp.ndarray
An array which is True where `x` is positive or negative infinity,
False otherwise. The data type of the returned array is `bool`.
Limitations
-----------
Parameters `where` and `subok` are supported with their default values.
Otherwise ``NotImplementedError`` exception will be raised.
See Also
--------
:obj:`dpnp.isneginf` : Test element-wise for negative infinity,
return result as bool array.
:obj:`dpnp.isposinf` : Test element-wise for positive infinity,
return result as bool array.
:obj:`dpnp.isnan` : Test element-wise for NaN and
return result as a boolean array.
:obj:`dpnp.isfinite` : Test element-wise for finiteness.
Examples
--------
>>> import dpnp as np
>>> x = np.array([-np.inf, 0., np.inf])
>>> np.isinf(x)
array([ True, False, True])
"""
isinf = DPNPUnaryFunc(
"isinf",
ti._isinf_result_type,
ti._isinf,
_ISINF_DOCSTRING,
)
_ISNAN_DOCSTRING = """
Test if each element of an input array is a NaN.
For full documentation refer to :obj:`numpy.isnan`.
Parameters
----------
x : {dpnp.ndarray, usm_ndarray}
Input array, expected to have numeric data type.
out : {None, dpnp.ndarray}, optional
Output array to populate.
Array must have the correct shape and the expected data type.
order : {"C", "F", "A", "K"}, optional
Memory layout of the newly output array, if parameter `out` is ``None``.
Default: "K".
Returns
-------
out : dpnp.ndarray
An array which is True where `x` is ``NaN``, False otherwise.
The data type of the returned array is `bool`.
Limitations
-----------
Parameters `where` and `subok` are supported with their default values.
Otherwise ``NotImplementedError`` exception will be raised.
See Also
--------
:obj:`dpnp.isinf` : Test element-wise for positive or negative infinity.
:obj:`dpnp.isneginf` : Test element-wise for negative infinity,
return result as bool array.
:obj:`dpnp.isposinf` : Test element-wise for positive infinity,
return result as bool array.
:obj:`dpnp.isfinite` : Test element-wise for finiteness.
:obj:`dpnp.isnat` : Test element-wise for NaT (not a time)
and return result as a boolean array.
Examples
--------
>>> import dpnp as np
>>> x = np.array([np.inf, 0., np.nan])
>>> np.isnan(x)
array([False, False, True])
"""
isnan = DPNPUnaryFunc(
"isnan",
ti._isnan_result_type,
ti._isnan,
_ISNAN_DOCSTRING,
)
_LESS_DOCSTRING = """
Computes the less-than test results for each element `x1_i` of
the input array `x1` with the respective element `x2_i` of the input array `x2`.
For full documentation refer to :obj:`numpy.less`.
Parameters
----------
x1 : {dpnp.ndarray, usm_ndarray}
First input array, expected to have numeric data type.
x2 : {dpnp.ndarray, usm_ndarray}
Second input array, also expected to have numeric data type.
out : {None, dpnp.ndarray}, optional
Output array to populate.
Array must have the correct shape and the expected data type.
order : {"C", "F", "A", "K"}, optional
Memory layout of the newly output array, if parameter `out` is ``None``.
Default: "K".
Returns
-------
out : dpnp.ndarray
An array containing the result of element-wise less-than comparison.
The returned array has a data type of `bool`.
Limitations
-----------
Parameters `where` and `subok` are supported with their default values.
Otherwise ``NotImplementedError`` exception will be raised.
See Also
--------
:obj:`dpnp.greater` : Return the truth value of (x1 > x2) element-wise.
:obj:`dpnp.less_equal` : Return the truth value of (x1 =< x2) element-wise.
:obj:`dpnp.greater_equal` : Return the truth value of (x1 >= x2) element-wise.
:obj:`dpnp.equal` : Return (x1 == x2) element-wise.
:obj:`dpnp.not_equal` : Return (x1 != x2) element-wise.
Examples
--------
>>> import dpnp as np
>>> x1 = np.array([1, 2])
>>> x2 = np.array([2, 2])
>>> np.less(x1, x2)
array([ True, False])
The ``<`` operator can be used as a shorthand for ``less`` on
:class:`dpnp.ndarray`.
>>> a = np.array([1, 2])
>>> b = np.array([2, 2])
>>> a < b
array([ True, False])
"""
less = DPNPBinaryFunc(
"less",
ti._less_result_type,
ti._less,
_LESS_DOCSTRING,
)
_LESS_EQUAL_DOCSTRING = """
Computes the less-than or equal-to test results for each element `x1_i` of
the input array `x1` with the respective element `x2_i` of the input array `x2`.
For full documentation refer to :obj:`numpy.less_equal`.
Parameters
----------
x1 : {dpnp.ndarray, usm_ndarray}
First input array, expected to have numeric data type.
x2 : {dpnp.ndarray, usm_ndarray}
Second input array, also expected to have numeric data type.
out : {None, dpnp.ndarray}, optional
Output array to populate.
Array must have the correct shape and the expected data type.
order : {"C", "F", "A", "K"}, optional
Memory layout of the newly output array, if parameter `out` is ``None``.
Default: "K".
Returns
-------
out : dpnp.ndarray
An array containing the result of element-wise less-than or equal-to
comparison. The returned array has a data type of `bool`.
Limitations
-----------
Parameters `where` and `subok` are supported with their default values.
Otherwise ``NotImplementedError`` exception will be raised.
See Also
--------
:obj:`dpnp.greater` : Return the truth value of (x1 > x2) element-wise.
:obj:`dpnp.less` : Return the truth value of (x1 < x2) element-wise.
:obj:`dpnp.greater_equal` : Return the truth value of (x1 >= x2) element-wise.
:obj:`dpnp.equal` : Return (x1 == x2) element-wise.
:obj:`dpnp.not_equal` : Return (x1 != x2) element-wise.
Examples
--------
>>> import dpnp as np
>>> x1 = np.array([4, 2, 1])
>>> x2 = np.array([2, 2, 2]
>>> np.less_equal(x1, x2)
array([False, True, True])
The ``<=`` operator can be used as a shorthand for ``less_equal`` on
:class:`dpnp.ndarray`.
>>> a = np.array([4, 2, 1])
>>> b = np.array([2, 2, 2])
>>> a <= b
array([False, True, True])
"""
less_equal = DPNPBinaryFunc(
"less_equal",
ti._less_equal_result_type,
ti._less_equal,
_LESS_EQUAL_DOCSTRING,
)
_LOGICAL_AND_DOCSTRING = """
Computes the logical AND for each element `x1_i` of the input array `x1` with
the respective element `x2_i` of the input array `x2`.
For full documentation refer to :obj:`numpy.logical_and`.
Parameters
----------
x1 : {dpnp.ndarray, usm_ndarray}
First input array.
x2 : {dpnp.ndarray, usm_ndarray}
Second input array.
out : {None, dpnp.ndarray}, optional
Output array to populate.
Array must have the correct shape and the expected data type.
order : {"C", "F", "A", "K"}, optional
Memory layout of the newly output array, if parameter `out` is ``None``.
Default: "K".
Returns
-------
out : dpnp.ndarray
An array containing the element-wise logical AND results.
Limitations
-----------
Parameters `where` and `subok` are supported with their default values.
Otherwise ``NotImplementedError`` exception will be raised.
See Also
--------
:obj:`dpnp.logical_or` : Compute the truth value of x1 OR x2 element-wise.
:obj:`dpnp.logical_not` : Compute the truth value of NOT x element-wise.
:obj:`dpnp.logical_xor` : Compute the truth value of x1 XOR x2, element-wise.
:obj:`dpnp.bitwise_and` : Compute the bit-wise AND of two arrays element-wise.
Examples
--------
>>> import dpnp as np
>>> x1 = np.array([True, False])
>>> x2 = np.array([False, False])
>>> np.logical_and(x1, x2)
array([False, False])
>>> x = np.arange(5)
>>> np.logical_and(x > 1, x < 4)
array([False, False, True, True, False])
The ``&`` operator can be used as a shorthand for ``logical_and`` on
boolean :class:`dpnp.ndarray`.
>>> a = np.array([True, False])
>>> b = np.array([False, False])
>>> a & b
array([False, False])
"""
logical_and = DPNPBinaryFunc(
"logical_and",
ti._logical_and_result_type,
ti._logical_and,
_LOGICAL_AND_DOCSTRING,
)
_LOGICAL_NOT_DOCSTRING = """
Computes the logical NOT for each element `x_i` of input array `x`.
For full documentation refer to :obj:`numpy.logical_not`.
Parameters
----------
x : {dpnp.ndarray, usm_ndarray}
Input array.
out : {None, dpnp.ndarray}, optional
Output array to populate.
Array must have the correct shape and the expected data type.
order : {"C", "F", "A", "K"}, optional
Memory layout of the newly output array, if parameter `out` is ``None``.
Default: "K".
Returns
-------
out : dpnp.ndarray
An array containing the element-wise logical NOT results.
Limitations
-----------
Parameters `where` and `subok` are supported with their default values.
Otherwise ``NotImplementedError`` exception will be raised.
See Also
--------
:obj:`dpnp.logical_and` : Compute the truth value of x1 AND x2 element-wise.
:obj:`dpnp.logical_or` : Compute the truth value of x1 OR x2 element-wise.
:obj:`dpnp.logical_xor` : Compute the truth value of x1 XOR x2, element-wise.
Examples
--------
>>> import dpnp as np
>>> x = np.array([True, False, 0, 1])
>>> np.logical_not(x)
array([False, True, True, False])
>>> x = np.arange(5)
>>> np.logical_not(x < 3)
array([False, False, False, True, True])
"""
logical_not = DPNPUnaryFunc(
"logical_not",
ti._logical_not_result_type,
ti._logical_not,
_LOGICAL_NOT_DOCSTRING,
)
_LOGICAL_OR_DOCSTRING = """
Computes the logical OR for each element `x1_i` of the input array `x1`
with the respective element `x2_i` of the input array `x2`.
For full documentation refer to :obj:`numpy.logical_or`.
Parameters
----------
x1 : {dpnp.ndarray, usm_ndarray}
First input array.
x2 : {dpnp.ndarray, usm_ndarray}
Second input array.
out : {None, dpnp.ndarray}, optional
Output array to populate.
Array must have the correct shape and the expected data type.
order : {"C", "F", "A", "K"}, optional
Memory layout of the newly output array, if parameter `out` is ``None``.
Default: "K".
Returns
-------
out : dpnp.ndarray
An array containing the element-wise logical OR results.
Limitations
-----------
Parameters `where` and `subok` are supported with their default values.
Otherwise ``NotImplementedError`` exception will be raised.
See Also
--------
:obj:`dpnp.logical_and` : Compute the truth value of x1 AND x2 element-wise.
:obj:`dpnp.logical_not` : Compute the truth value of NOT x element-wise.
:obj:`dpnp.logical_xor` : Compute the truth value of x1 XOR x2, element-wise.
:obj:`dpnp.bitwise_or` : Compute the bit-wise OR of two arrays element-wise.
Examples
--------
>>> import dpnp as np
>>> x1 = np.array([True, False])
>>> x2 = np.array([False, False])
>>> np.logical_or(x1, x2)
array([ True, False])
>>> x = np.arange(5)
>>> np.logical_or(x < 1, x > 3)
array([ True, False, False, False, True])
The ``|`` operator can be used as a shorthand for ``logical_or`` on
boolean :class:`dpnp.ndarray`.
>>> a = np.array([True, False])
>>> b = np.array([False, False])
>>> a | b
array([ True, False])
"""
logical_or = DPNPBinaryFunc(
"logical_or",
ti._logical_or_result_type,
ti._logical_or,
_LOGICAL_OR_DOCSTRING,
)
_LOGICAL_XOR_DOCSTRING = """
Computes the logical XOR for each element `x1_i` of the input array `x1`
with the respective element `x2_i` of the input array `x2`.
For full documentation refer to :obj:`numpy.logical_xor`.
Parameters
----------
x1 : {dpnp.ndarray, usm_ndarray}
First input array.
x2 : {dpnp.ndarray, usm_ndarray}
Second input array.
out : {None, dpnp.ndarray}, optional
Output array to populate.
Array must have the correct shape and the expected data type.
order : {"C", "F", "A", "K"}, optional
Memory layout of the newly output array, if parameter `out` is ``None``.
Default: "K".
Returns
-------
out : dpnp.ndarray
An array containing the element-wise logical XOR results.
Limitations
-----------
Parameters `where` and `subok` are supported with their default values.
Otherwise ``NotImplementedError`` exception will be raised.
See Also
--------
:obj:`dpnp.logical_and` : Compute the truth value of x1 AND x2 element-wise.
:obj:`dpnp.logical_or` : Compute the truth value of x1 OR x2 element-wise.
:obj:`dpnp.logical_not` : Compute the truth value of NOT x element-wise.
:obj:`dpnp.bitwise_xor` : Compute the bit-wise XOR of two arrays element-wise.
Examples
--------
>>> import dpnp as np
>>> x1 = np.array([True, True, False, False])
>>> x2 = np.array([True, False, True, False])
>>> np.logical_xor(x1, x2)
array([False, True, True, False])
>>> x = np.arange(5)
>>> np.logical_xor(x < 1, x > 3)
array([ True, False, False, False, True])
Simple example showing support of broadcasting
>>> np.logical_xor(0, np.eye(2))
array([[ True, False],
[False, True]])
"""
logical_xor = DPNPBinaryFunc(
"logical_xor",
ti._logical_xor_result_type,
ti._logical_xor,
_LOGICAL_XOR_DOCSTRING,
)
_NOT_EQUAL_DOCSTRING = """
Calculates inequality test results for each element `x1_i` of the
input array `x1` with the respective element `x2_i` of the input array `x2`.
For full documentation refer to :obj:`numpy.not_equal`.
Parameters
----------
x1 : {dpnp.ndarray, usm_ndarray}
First input array, expected to have numeric data type.
x2 : {dpnp.ndarray, usm_ndarray}
Second input array, also expected to have numeric data type.
out : {None, dpnp.ndarray}, optional
Output array to populate.
Array must have the correct shape and the expected data type.
order : {"C", "F", "A", "K"}, optional
Memory layout of the newly output array, if parameter `out` is ``None``.
Default: "K".
Returns
-------
out : dpnp.ndarray
An array containing the result of element-wise inequality comparison.
The returned array has a data type of `bool`.
Limitations
-----------
Parameters `where` and `subok` are supported with their default values.
Otherwise ``NotImplementedError`` exception will be raised.
See Also
--------
:obj:`dpnp.equal` : Return (x1 == x2) element-wise.
:obj:`dpnp.greater` : Return the truth value of (x1 > x2) element-wise.
:obj:`dpnp.greater_equal` : Return the truth value of (x1 >= x2) element-wise.
:obj:`dpnp.less` : Return the truth value of (x1 < x2) element-wise.
:obj:`dpnp.less_equal` : Return the truth value of (x1 =< x2) element-wise.
Examples
--------
>>> import dpnp as np
>>> x1 = np.array([1., 2.])
>>> x2 = np.arange(1., 3.)
>>> np.not_equal(x1, x2)
array([False, False])
The ``!=`` operator can be used as a shorthand for ``not_equal`` on
:class:`dpnp.ndarray`.
>>> a = np.array([1., 2.])
>>> b = np.array([1., 3.])
>>> a != b
array([False, True])
"""
not_equal = DPNPBinaryFunc(
"not_equal",
ti._not_equal_result_type,
ti._not_equal,
_NOT_EQUAL_DOCSTRING,
)