# 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
"""
import dpctl.tensor as dpt
import numpy
import dpnp
from dpnp.dpnp_algo import *
from dpnp.dpnp_array import dpnp_array
from dpnp.dpnp_utils import *
from .dpnp_algo.dpnp_elementwise_common import (
check_nd_call_func,
dpnp_equal,
dpnp_greater,
dpnp_greater_equal,
dpnp_isfinite,
dpnp_isinf,
dpnp_isnan,
dpnp_less,
dpnp_less_equal,
dpnp_logical_and,
dpnp_logical_not,
dpnp_logical_or,
dpnp_logical_xor,
dpnp_not_equal,
)
__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.
For full documentation refer to :obj:`numpy.allclose`.
Returns
-------
out : dpnp.ndarray
A boolean 0-dim array. If its value is ``True``,
two arrays are element-wise equal within a tolerance.
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(
"An argument `rtol` must be a scalar, but got {}".format(
type(rtol)
)
)
elif not dpnp.isscalar(atol):
raise TypeError(
"An argument `atol` must be a scalar, but got {}".format(
type(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
)
[docs]
def equal(
x1,
x2,
/,
out=None,
*,
order="K",
where=True,
dtype=None,
subok=True,
**kwargs,
):
"""
Return the truth value of (x1 == x2) element-wise.
For full documentation refer to :obj:`numpy.equal`.
Returns
-------
out : dpnp.ndarray
Output array of bool type, element-wise comparison of `x1` and `x2`.
Limitations
-----------
Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray`
or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time.
Parameters `where`, `dtype` and `subok` are supported with their default values.
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.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])
"""
return check_nd_call_func(
numpy.equal,
dpnp_equal,
x1,
x2,
out=out,
where=where,
order=order,
dtype=dtype,
subok=subok,
**kwargs,
)
[docs]
def greater(
x1,
x2,
/,
out=None,
*,
order="K",
where=True,
dtype=None,
subok=True,
**kwargs,
):
"""
Return the truth value of (x1 > x2) element-wise.
For full documentation refer to :obj:`numpy.greater`.
Returns
-------
out : dpnp.ndarray
Output array of bool type, element-wise comparison of `x1` and `x2`.
Limitations
-----------
Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray`
or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time.
Parameters `where`, `dtype` and `subok` are supported with their default values.
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.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])
"""
return check_nd_call_func(
numpy.greater,
dpnp_greater,
x1,
x2,
out=out,
where=where,
order=order,
dtype=dtype,
subok=subok,
**kwargs,
)
[docs]
def greater_equal(
x1,
x2,
/,
out=None,
*,
order="K",
where=True,
dtype=None,
subok=True,
**kwargs,
):
"""
Return the truth value of (x1 >= x2) element-wise.
For full documentation refer to :obj:`numpy.greater_equal`.
Returns
-------
out : dpnp.ndarray
Output array of bool type, element-wise comparison of `x1` and `x2`.
Limitations
-----------
Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray`
or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time.
Parameters `where`, `dtype` and `subok` are supported with their default values.
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.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])
"""
return check_nd_call_func(
numpy.greater_equal,
dpnp_greater_equal,
x1,
x2,
out=out,
where=where,
order=order,
dtype=dtype,
subok=subok,
**kwargs,
)
[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
)
[docs]
def isfinite(
x, /, out=None, *, where=True, order="K", dtype=None, subok=True, **kwargs
):
"""
Test element-wise for finiteness (not infinity or not Not a Number).
For full documentation refer to :obj:`numpy.isfinite`.
Limitations
-----------
Parameters `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`.
Parameters `where`, `dtype` and `subok` are supported with their default values.
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.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])
"""
return check_nd_call_func(
numpy.isfinite,
dpnp_isfinite,
x,
out=out,
where=where,
order=order,
dtype=dtype,
subok=subok,
**kwargs,
)
[docs]
def isinf(
x, /, out=None, *, where=True, order="K", dtype=None, subok=True, **kwargs
):
"""
Test element-wise for positive or negative infinity.
For full documentation refer to :obj:`numpy.isinf`.
Limitations
-----------
Parameters `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`.
Parameters `where`, `dtype` and `subok` are supported with their default values.
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.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])
"""
return check_nd_call_func(
numpy.isinf,
dpnp_isinf,
x,
out=out,
where=where,
order=order,
dtype=dtype,
subok=subok,
**kwargs,
)
[docs]
def isnan(
x, /, out=None, *, where=True, order="K", dtype=None, subok=True, **kwargs
):
"""
Test element-wise for NaN and return result as a boolean array.
For full documentation refer to :obj:`numpy.isnan`.
Limitations
-----------
Parameters `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`.
Parameters `where`, `dtype` and `subok` are supported with their default values.
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.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])
"""
return check_nd_call_func(
numpy.isnan,
dpnp_isnan,
x,
out=out,
where=where,
order=order,
dtype=dtype,
subok=subok,
**kwargs,
)
[docs]
def less(
x1,
x2,
/,
out=None,
*,
order="K",
where=True,
dtype=None,
subok=True,
**kwargs,
):
"""
Return the truth value of (x1 < x2) element-wise.
For full documentation refer to :obj:`numpy.less`.
Returns
-------
out : dpnp.ndarray
Output array of bool type, element-wise comparison of `x1` and `x2`.
Limitations
-----------
Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray`
or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time.
Parameters `where`, `dtype` and `subok` are supported with their default values.
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.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])
"""
return check_nd_call_func(
numpy.less,
dpnp_less,
x1,
x2,
out=out,
where=where,
order=order,
dtype=dtype,
subok=subok,
**kwargs,
)
[docs]
def less_equal(
x1,
x2,
/,
out=None,
*,
order="K",
where=True,
dtype=None,
subok=True,
**kwargs,
):
"""
Return the truth value of (x1 <= x2) element-wise.
For full documentation refer to :obj:`numpy.less_equal`.
Returns
-------
out : dpnp.ndarray
Output array of bool type, element-wise comparison of `x1` and `x2`.
Limitations
-----------
Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray`
or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time.
Parameters `where`, `dtype` and `subok` are supported with their default values.
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.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])
"""
return check_nd_call_func(
numpy.less_equal,
dpnp_less_equal,
x1,
x2,
out=out,
where=where,
order=order,
dtype=dtype,
subok=subok,
**kwargs,
)
[docs]
def logical_and(
x1,
x2,
/,
out=None,
*,
order="K",
where=True,
dtype=None,
subok=True,
**kwargs,
):
"""
Compute the truth value of x1 AND x2 element-wise.
For full documentation refer to :obj:`numpy.logical_and`.
Returns
-------
out : dpnp.ndarray
Boolean result of the logical AND operation applied to the elements
of `x1` and `x2`; the shape is determined by broadcasting.
Limitations
-----------
Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray`
or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time.
Parameters `where`, `dtype` and `subok` are supported with their default values.
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.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])
"""
return check_nd_call_func(
numpy.logical_and,
dpnp_logical_and,
x1,
x2,
out=out,
where=where,
order=order,
dtype=dtype,
subok=subok,
**kwargs,
)
[docs]
def logical_not(
x,
/,
out=None,
*,
order="K",
where=True,
dtype=None,
subok=True,
**kwargs,
):
"""
Compute the truth value of NOT x element-wise.
For full documentation refer to :obj:`numpy.logical_not`.
Returns
-------
out : dpnp.ndarray
Boolean result with the same shape as `x` of the NOT operation
on elements of `x`.
Limitations
-----------
Parameters `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`.
Parameters `where`, `dtype` and `subok` are supported with their default values.
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.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])
"""
return check_nd_call_func(
numpy.logical_not,
dpnp_logical_not,
x,
out=out,
where=where,
order=order,
dtype=dtype,
subok=subok,
**kwargs,
)
[docs]
def logical_or(
x1,
x2,
/,
out=None,
*,
order="K",
where=True,
dtype=None,
subok=True,
**kwargs,
):
"""
Compute the truth value of x1 OR x2 element-wise.
For full documentation refer to :obj:`numpy.logical_or`.
Returns
-------
out : dpnp.ndarray
Boolean result of the logical OR operation applied to the elements
of `x1` and `x2`; the shape is determined by broadcasting.
Limitations
-----------
Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray`
or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time.
Parameters `where`, `dtype` and `subok` are supported with their default values.
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.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])
"""
return check_nd_call_func(
numpy.logical_or,
dpnp_logical_or,
x1,
x2,
out=out,
where=where,
order=order,
dtype=dtype,
subok=subok,
**kwargs,
)
[docs]
def logical_xor(
x1,
x2,
/,
out=None,
*,
order="K",
where=True,
dtype=None,
subok=True,
**kwargs,
):
"""
Compute the truth value of x1 XOR x2 element-wise.
For full documentation refer to :obj:`numpy.logical_xor`.
Returns
-------
out : dpnp.ndarray
Boolean result of the logical XOR operation applied to the elements
of `x1` and `x2`; the shape is determined by broadcasting.
Limitations
-----------
Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray`
or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time.
Parameters `where`, `dtype` and `subok` are supported with their default values.
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.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]])
"""
return check_nd_call_func(
numpy.logical_xor,
dpnp_logical_xor,
x1,
x2,
out=out,
where=where,
order=order,
dtype=dtype,
subok=subok,
**kwargs,
)
[docs]
def not_equal(
x1,
x2,
/,
out=None,
*,
order="K",
where=True,
dtype=None,
subok=True,
**kwargs,
):
"""
Return the truth value of (x1 != x2) element-wise.
For full documentation refer to :obj:`numpy.not_equal`.
Returns
-------
out : dpnp.ndarray
Output array of bool type, element-wise comparison of `x1` and `x2`.
Limitations
-----------
Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray`
or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time.
Parameters `where`, `dtype` and `subok` are supported with their default values.
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.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])
"""
return check_nd_call_func(
numpy.not_equal,
dpnp_not_equal,
x1,
x2,
out=out,
where=where,
order=order,
dtype=dtype,
subok=subok,
**kwargs,
)