Elementwise#

ivy.abs(x, /, *, where=True, out=None)[source]#

Calculate the absolute value for each element x_i of the input array x (i.e., the element-wise result has the same magnitude as the respective element in x but has positive sign).

Note

For signed integer data types, the absolute value of the minimum representable integer is implementation-dependent.

Special Cases

For real-valued floating-point operands,

If x_i is NaN, the result is NaN.
If x_i is -0, the result is +0.
If x_i is -infinity, the result is +infinity.

For complex floating-point operands, let a = real(x_i) and b = imag(x_i). and

If a is either +infinity or -infinity and b is any value (including NaN), the result is +infinity.
If a is any value (including NaN) and b is +infinity, the result is +infinity.
If a is either +0 or -0, the result is abs(b).
If b is +0 or -0, the result is abs(a).
If a is NaN and b is a finite number, the result is NaN.
If a is a finite number and b is NaN, the result is NaN.
If a is Na``N and ``b is NaN, the result is NaN.

Parameters:

x (Union[float, Array, NativeArray]) – input array. Should have a numeric data type
where (Optional[Array]) – optional boolean mask (default: True)
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the absolute value of each element in x. The returned array must have the same data type as x.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Both the description and the type hints above assumes an array input for simplicity, but this function is nestable, and therefore also accepts ivy.Container instances in place of any of the arguments

Examples

With ivy.Array input:

>>> x = ivy.array([-1,0,-6])
>>> y = ivy.abs(x)
>>> print(y)
ivy.array([1, 0, 6])

>>> x = ivy.array([3.7, -7.7, 0, -2, -0])
>>> y = ivy.abs(x)
>>> print(y)
ivy.array([ 3.7, 7.7, 0., 2., 0.])

>>> x = ivy.array([[1.1, 2.2, 3.3], [-4.4, -5.5, -6.6]])
>>> ivy.abs(x, out=x)
>>> print(x)
ivy.array([[ 1.1,  2.2,  3.3],
           [4.4, 5.5, 6.6]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0., 2.6, -3.5]),b=ivy.array([4.5, -5.3, -0, -2.3]))
>>> y = ivy.abs(x)
>>> print(y)
{
    a: ivy.array([0., 2.6, 3.5]),
    b: ivy.array([4.5, 5.3, 0., 2.3])
}

ivy.acos(x, /, *, out=None)[source]#

Calculate an implementation-dependent approximation of the principal value of the inverse cosine, having domain [-1, +1] and codomain [+0, +π], for each element x_i of the input array x. Each element-wise result is expressed in radians.

Special cases

For floating-point operands,

If x_i is NaN, the result is NaN.
If x_i is greater than 1, the result is NaN.
If x_i is less than -1, the result is NaN.
If x_i is 1, the result is +0.

Parameters:

x (Union[Array, NativeArray]) – input array. Should have a floating-point data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the inverse cosine of each element in x. The returned array must have a floating-point data type determined by type-promotion.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([0., 1., -1.])
>>> y = ivy.acos(x)
>>> print(y)
ivy.array([1.57, 0.  , 3.14])

>>> x = ivy.array([1., 0., -1.])
>>> y = ivy.zeros(3)
>>> ivy.acos(x, out=y)
>>> print(y)
ivy.array([0.  , 1.57, 3.14])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0., -1, 1]), b=ivy.array([1., 0., -1]))
>>> y = ivy.acos(x)
>>> print(y)
{
    a: ivy.array([1.57, 3.14, 0.]),
    b: ivy.array([0., 1.57, 3.14])
}

ivy.acosh(x, /, *, out=None)[source]#

Calculate an implementation-dependent approximation to the inverse hyperbolic cosine, having domain [+1, +infinity] and codomain [+0, +infinity], for each element x_i of the input array x.

Special cases

For floating-point operands,

If x_i is NaN, the result is NaN.
If x_i is less than 1, the result is NaN.
If x_i is 1, the result is +0.
If x_i is +infinity, the result is +infinity.

Parameters:

x (Union[Array, NativeArray]) – input array whose elements each represent the area of a hyperbolic sector. Should have a floating-point data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the inverse hyperbolic cosine of each element in x. The returned array must have a floating-point data type determined by type-promotion.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([1, 2.5, 10])
>>> y = ivy.acosh(x)
>>> print(y)
ivy.array([0.  , 1.57, 2.99])

>>> x = ivy.array([1., 2., 6.])
>>> y = ivy.zeros(3)
>>> ivy.acosh(x, out=y)
>>> print(y)
ivy.array([0.  , 1.32, 2.48])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([1, 2, 10]), b=ivy.array([1., 10, 6]))
>>> y = ivy.acosh(x)
>>> print(y)
{
    a: ivy.array([0., 1.32, 2.99]),
    b: ivy.array([0., 2.99, 2.48])
}

ivy.add(x1, x2, /, *, alpha=None, out=None)[source]#

Calculate the sum for each element x1_i of the input array x1 with the respective element x2_i of the input array x2.

Special cases

For floating-point operands,

If either x1_i or x2_i is NaN, the result is NaN.
If x1_i is +infinity and x2_i is -infinity, the result is NaN.
If x1_i is -infinity and x2_i is +infinity, the result is NaN.
If x1_i is +infinity and x2_i is +infinity, the result is +infinity.
If x1_i is -infinity and x2_i is -infinity, the result is -infinity.
If x1_i is +infinity and x2_i is a finite number, the result is +infinity.
If x1_i is -infinity and x2_i is a finite number, the result is -infinity.
If x1_i is a finite number and x2_i is +infinity, the result is +infinity.
If x1_i is a finite number and x2_i is -infinity, the result is -infinity.
If x1_i is -0 and x2_i is -0, the result is -0.
If x1_i is -0 and x2_i is +0, the result is +0.
If x1_i is +0 and x2_i is -0, the result is +0.
If x1_i is +0 and x2_i is +0, the result is +0.
If x1_i is either +0 or -0 and x2_i is a nonzero finite number, the result is x2_i.
If x1_i is a nonzero finite number and x2_i is either +0 or -0, the result is x1_i.
If x1_i is a nonzero finite number and x2_i is -x1_i, the result is +0.
In the remaining cases, when neither infinity, +0, -0, nor a NaN is involved, and the operands have the same mathematical sign or have different magnitudes, the sum must be computed and rounded to the nearest representable value according to IEEE 754-2019 and a supported round mode. If the magnitude is too large to represent, the operation overflows and the result is an infinity of appropriate mathematical sign.

Note

Floating-point addition is a commutative operation, but not always associative.

Parameters:

x1 (Union[float, Array, NativeArray]) – first input array. Should have a numeric data type.
x2 (Union[float, Array, NativeArray]) – second input array. Must be compatible with x1 (see broadcasting). Should have a numeric data type.
alpha (Optional[Union[int, float]]) – optional scalar multiplier for x2. (default: None)
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise sums. The returned array must have a data type determined by type-promotion.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array inputs:

>>> x = ivy.array([1, 2, 3])
>>> y = ivy.array([4, 5, 6])
>>> z = ivy.add(x, y)
>>> print(z)
ivy.array([5, 7, 9])

>>> x = ivy.array([1, 2, 3])
>>> y = ivy.array([4, 5, 6])
>>> z = ivy.add(x, y, alpha=2)
>>> print(z)
ivy.array([9, 12, 15])

>>> x = ivy.array([[1.1, 2.3, -3.6]])
>>> y = ivy.array([[4.8], [5.2], [6.1]])
>>> z = ivy.zeros((3, 3))
>>> ivy.add(x, y, out=z)
>>> print(z)
ivy.array([[5.9, 7.1, 1.2],
           [6.3, 7.5, 1.6],
           [7.2, 8.4, 2.5]])

>>> x = ivy.array([[[1.1], [3.2], [-6.3]]])
>>> y = ivy.array([[8.4], [2.5], [1.6]])
>>> ivy.add(x, y, out=x)
>>> print(x)
ivy.array([[[9.5],
            [5.7],
            [-4.7]]])

ivy.angle(z, /, *, deg=False, out=None)[source]#

Calculate Element-wise the angle for an array of complex numbers(x+yj).

Parameters:

z (Union[Array, NativeArray]) – Array-like input.
deg (bool) – optional bool. (default: False)
out (Optional[Array]) – optional output array, for writing the result to. (default: None)

Return type:

Array

Returns:

ret – Returns an array of angles for each complex number in the input. If deg is False(default), angle is calculated in radian and if deg is True, then angle is calculated in degrees.

Examples

>>> ivy.set_backend('tensorflow')
>>> z = ivy.array([-1 + 1j, -2 + 2j, 3 - 3j])
>>> z
ivy.array([-1.+1.j, -2.+2.j,  3.-3.j])
>>> ivy.angle(z)
ivy.array([ 2.35619449,  2.35619449, -0.78539816])
>>> ivy.set_backend('numpy')
>>> ivy.angle(z,deg=True)
ivy.array([135., 135., -45.])

ivy.asin(x, /, *, out=None)[source]#

Calculate an implementation-dependent approximation of the principal value of the inverse sine, having domain [-1, +1] and codomain [-π/2, +π/2] for each element x_i of the input array x. Each element-wise result is expressed in radians.

Special cases

For floating-point operands,

If x_i is NaN, the result is NaN.
If x_i is greater than 1, the result is NaN.
If x_i is less than -1, the result is NaN.
If x_i is +0, the result is +0.
If x_i is -0, the result is -0.

Parameters:

x (Union[Array, NativeArray]) – input array. Should have a floating-point data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the inverse sine of each element in x. The returned array must have a floating-point data type determined by type-promotion.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Functional Examples

With ivy.Array input:

>>> x = ivy.array([-2.4, -0, +0, 3.2, float('nan')])
>>> y = ivy.asin(x)
>>> print(y)
ivy.array([nan,  0.,  0., nan, nan])

>>> x = ivy.array([-1, -0.5, 0.6, 1])
>>> y = ivy.zeros(4)
>>> ivy.asin(x, out=y)
>>> print(y)
ivy.array([-1.57,-0.524,0.644,1.57])

>>> x = ivy.array([[0.1, 0.2, 0.3],[-0.4, -0.5, -0.6]])
>>> ivy.asin(x, out=x)
>>> print(x)
ivy.array([[0.1,0.201,0.305],[-0.412,-0.524,-0.644]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0., 0.1, 0.2]),
...                   b=ivy.array([0.3, 0.4, 0.5]))
>>> y = ivy.asin(x)
>>> print(y)
{a:ivy.array([0.,0.1,0.201]),b:ivy.array([0.305,0.412,0.524])}

ivy.asinh(x, /, *, out=None)[source]#

Calculate an implementation-dependent approximation to the inverse hyperbolic sine, having domain [-infinity, +infinity] and codomain [-infinity, +infinity], for each element x_i in the input array x.

Special cases

For floating-point operands,

If x_i is NaN, the result is NaN.
If x_i is +0, the result is +0.
If x_i is -0, the result is -0.
If x_i is +infinity, the result is +infinity.
If x_i is -infinity, the result is -infinity.

Parameters:

x (Union[Array, NativeArray]) – input array whose elements each represent the area of a hyperbolic sector. Should have a floating-point data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the inverse hyperbolic sine of each element in x. The returned array must have a floating-point data type determined by type-promotion.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([-3.5, -0, +0, 1.3, float('nan')])
>>> y = ivy.asinh(x)
>>> print(y)
ivy.array([-1.97, 0., 0., 1.08, nan])

>>> x = ivy.array([-2, -0.75, 0.9, 1])
>>> y = ivy.zeros(4)
>>> ivy.asinh(x, out=y)
>>> print(y)
ivy.array([-1.44, -0.693, 0.809, 0.881])

>>> x = ivy.array([[0.2, 0.4, 0.6],[-0.8, -1, -2]])
>>> ivy.asinh(x, out=x)
>>> print(x)
ivy.array([[ 0.199, 0.39, 0.569],
           [-0.733, -0.881, -1.44]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0., 1, 2]),
...                   b=ivy.array([4.2, -5.3, -0, -2.3]))
>>> y = ivy.asinh(x)
>>> print(y)
{
    a: ivy.array([0., 0.881, 1.44]),
    b: ivy.array([2.14, -2.37, 0., -1.57])
}

ivy.atan(x, /, *, out=None)[source]#

Calculate an implementation-dependent approximation of the principal value of the inverse tangent, having domain [-infinity, +infinity] and codomain [-π/2, +π/2], for each element x_i of the input array x. Each element-wise result is expressed in radians.

Special cases

For floating-point operands,

If x_i is NaN, the result is NaN.
If x_i is +0, the result is +0.
If x_i is -0, the result is -0.
If x_i is +infinity, the result is an implementation-dependent approximation to +π/2.
If x_i is -infinity, the result is an implementation-dependent approximation to -π/2.

Parameters:

x (Union[Array, NativeArray]) – input array. Should have a floating-point data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the inverse tangent of each element in x. The returned array must have a floating-point data type determined by type-promotion.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([0., 1., 2.])
>>> y = ivy.atan(x)
>>> print(y)
ivy.array([0.   , 0.785, 1.11 ])

>>> x = ivy.array([4., 0., -6.])
>>> y = ivy.zeros(3)
>>> ivy.atan(x, out=y)
>>> print(y)
ivy.array([ 1.33,  0.  , -1.41])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0., -1, 1]), b=ivy.array([1., 0., -6]))
>>> y = ivy.atan(x)
>>> print(y)
{
    a: ivy.array([0., -0.785, 0.785]),
    b: ivy.array([0.785, 0., -1.41])
}

ivy.atan2(x1, x2, /, *, out=None)[source]#

Calculate an implementation-dependent approximation of the inverse tangent of the quotient x1/x2, having domain [-infinity, +infinity] x. [-infinity, +infinity] (where the x notation denotes the set of ordered pairs of elements (x1_i, x2_i)) and codomain [-π, +π], for each pair of elements (x1_i, x2_i) of the input arrays x1 and x2, respectively. Each element-wise result is expressed in radians. The mathematical signs of x1_i and x2_i determine the quadrant of each element-wise result. The quadrant (i.e., branch) is chosen such that each element-wise result is the signed angle in radians between the ray ending at the origin and passing through the point (1,0) and the ray ending at the origin and passing through the point (x2_i, x1_i).

Special cases

For floating-point operands,

If either x1_i or x2_i is NaN, the result is NaN.
If x1_i is greater than 0 and x2_i is +0, the result is an approximation to +π/2.
If x1_i is greater than 0 and x2_i is -0, the result is an approximation to +π/2.
If x1_i is +0 and x2_i is greater than 0, the result is +0.
If x1_i is +0 and x2_i is +0, the result is +0.
If x1_i is +0 and x2_i is -0, the result is an approximation to +π.
If x1_i is +0 and x2_i is less than 0, the result is an approximation to +π.
If x1_i is -0 and x2_i is greater than 0, the result is -0.
If x1_i is -0 and x2_i is +0, the result is -0.
If x1_i is -0 and x2_i is -0, the result is an approximation to -π.
If x1_i is -0 and x2_i is less than 0, the result is an approximation to -π.
If x1_i is less than 0 and x2_i is +0, the result is an approximation to -π/2.
If x1_i is less than 0 and x2_i is -0, the result is an approximation to -π/2.
If x1_i is greater than 0, x1_i is a finite number, and x2_i is +infinity, the result is +0.
If x1_i is greater than 0, x1_i is a finite number, and x2_i is -infinity, the result is an approximation to +π.
If x1_i is less than 0, x1_i is a finite number, and x2_i is +infinity, the result is -0.
If x1_i is less than 0, x1_i is a finite number, and x2_i is -infinity, the result is an approximation to -π.
If x1_i is +infinity and x2_i is finite, the result is an approximation to +π/2.
If x1_i is -infinity and x2_i is finite, the result is an approximation to -π/2.
If x1_i is +infinity and x2_i is +infinity, the result is an approximation to +π/4.
If x1_i is +infinity and x2_i is -infinity, the result is an approximation to +3π/4.
If x1_i is -infinity and x2_i is +infinity, the result is an approximation to -π/4.
If x1_i is -infinity and x2_i is -infinity, the result is an approximation to -3π/4.

Parameters:

x1 (Union[Array, NativeArray]) – input array corresponding to the y-coordinates. Should have a floating-point data type.
x2 (Union[Array, NativeArray]) – input array corresponding to the x-coordinates. Must be compatible with x1. Should have a floating-point data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the inverse tangent of the quotient x1/x2. The returned array must have a floating-point data type.

This method conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([1.0, -1.0, -2.0])
>>> y = ivy.array([2.0, 0.0, 3.0])
>>> z = ivy.atan2(x, y)
>>> print(z)
ivy.array([ 0.464, -1.57 , -0.588])

>>> x = ivy.array([1.0, 2.0])
>>> y = ivy.array([-2.0, 3.0])
>>> z = ivy.zeros(2)
>>> ivy.atan2(x, y, out=z)
>>> print(z)
ivy.array([2.68 , 0.588])

>>> nan = float("nan")
>>> x = ivy.array([nan, 1.0, 1.0, -1.0, -1.0])
>>> y = ivy.array([1.0, +0, -0, +0, -0])
>>> z = ivy.atan2(x, y)
>>> print(z)
ivy.array([  nan,  1.57,  1.57, -1.57, -1.57])

>>> x = ivy.array([+0, +0, +0, +0, -0, -0, -0, -0])
>>> y = ivy.array([1.0, +0, -0, -1.0, 1.0, +0, -0, -1.0])
>>> z = ivy.atan2(x, y)
>>> print(z)
ivy.array([0.  , 0.  , 0.  , 3.14, 0.  , 0.  , 0.  , 3.14])

>>> inf = float("infinity")
>>> x = ivy.array([inf, -inf, inf, inf, -inf, -inf])
>>> y = ivy.array([1.0, 1.0, inf, -inf, inf, -inf])
>>> z = ivy.atan2(x, y)
>>> print(z)
ivy.array([ 1.57 , -1.57 ,  0.785,  2.36 , -0.785, -2.36 ])

>>> x = ivy.array([2.5, -1.75, 3.2, 0, -1.0])
>>> y = ivy.array([-3.5, 2, 0, 0, 5])
>>> z = ivy.atan2(x, y)
>>> print(z)
ivy.array([ 2.52 , -0.719,  1.57 ,  0.   , -0.197])

>>> x = ivy.array([[1.1, 2.2, 3.3], [-4.4, -5.5, -6.6]])
>>> y = ivy.atan2(x, x)
>>> print(y)
ivy.array([[ 0.785,  0.785,  0.785],
    [-2.36 , -2.36 , -2.36 ]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0., 2.6, -3.5]),
...                   b=ivy.array([4.5, -5.3, -0]))
>>> y = ivy.array([3.0, 2.0, 1.0])
>>> z = ivy.atan2(x, y)
{
    a: ivy.array([0., 0.915, -1.29]),
    b: ivy.array([0.983, -1.21, 0.])
}

>>> x = ivy.Container(a=ivy.array([0., 2.6, -3.5]),
...                   b=ivy.array([4.5, -5.3, -0, -2.3]))
>>> y = ivy.Container(a=ivy.array([-2.5, 1.75, 3.5]),
...                   b=ivy.array([2.45, 6.35, 0, 1.5]))
>>> z = ivy.atan2(x, y)
>>> print(z)
{
    a: ivy.array([3.14, 0.978, -0.785]),
    b: ivy.array([1.07, -0.696, 0., -0.993])
}

ivy.atanh(x, /, *, out=None)[source]#

Return a new array with the inverse hyperbolic tangent of the elements of x.

Parameters:

x (Union[Array, NativeArray]) – input array whose elements each represent the area of a hyperbolic sector. Should have a floating-point data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the inverse hyperbolic tangent of each element in x. The returned array must have a floating-point data type determined by Type Promotion Rules.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([0, -0.5])
>>> y = ivy.atanh(x)
>>> print(y)
ivy.array([ 0.   , -0.549])

>>> x = ivy.array([0.5, -0.5, 0.])
>>> y = ivy.zeros(3)
>>> ivy.atanh(x, out=y)
>>> print(y)
ivy.array([ 0.549, -0.549,  0.   ])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0., -0.5]), b=ivy.array([ 0., 0.5]))
>>> y = ivy.atanh(x)
>>> print(y)
{
    a: ivy.array([0., -0.549]),
    b: ivy.array([0., 0.549])
}

ivy.bitwise_and(x1, x2, /, *, out=None)[source]#

Compute the bitwise AND of the underlying binary representation of each element x1_i of the input array x1 with the respective element x2_i of the input array x2.

Parameters:

x1 (Union[int, bool, Array, NativeArray]) – first input array. Should have an integer or boolean data type.
x2 (Union[int, bool, Array, NativeArray]) – second input array. Must be compatible with x1 (see broadcasting). Should have an integer or boolean data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise results. The returned array must have a data type determined by type-promotion.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Functional Examples

With ivy.Array inputs:

>>> x = ivy.array([2, 3, 7])
>>> y = ivy.array([7, 1, 15])
>>> z = ivy.bitwise_and(x, y)
>>> print(z)
ivy.array([2, 1, 7])

>>> x = ivy.array([[True], [False]])
>>> y = ivy.array([[True], [True]])
>>> ivy.bitwise_and(x, y, out=x)
>>> print(x)
ivy.array([[ True],[False]])

>>> x = ivy.array([1])
>>> y = ivy.array([3])
>>> ivy.bitwise_and(x, y, out=y)
>>> print(y)
ivy.array([1])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([1, 2, 3]), b=ivy.array([4, 5, 6]))
>>> y = ivy.Container(a=ivy.array([7, 8, 9]), b=ivy.array([10, 11, 11]))
>>> z = ivy.bitwise_and(x, y)
>>> print(z)
{
    a: ivy.array([1, 0, 1]),
    b: ivy.array([0, 1, 2])
}

With a mix of ivy.Array and ivy.Container inputs:

>>> x = ivy.array([True, True])
>>> y = ivy.Container(a=ivy.array([True, False]), b=ivy.array([False, True]))
>>> z = ivy.bitwise_and(x, y)
>>> print(z)
{
    a: ivy.array([True, False]),
    b: ivy.array([False, True])
}

ivy.bitwise_invert(x, /, *, out=None)[source]#

Inverts (flips) each bit for each element x_i of the input array x.

Parameters:

x (Union[int, bool, Array, NativeArray, Container]) – input array. Should have an integer or boolean data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise results. The returned array must have the same data type as x.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([1, 6, 9])
>>> y = ivy.bitwise_invert(x)
>>> print(y)
ivy.array([-2, -7, -10])

With ivy.Container input:

>>> x = ivy.Container(a=[False, True, False], b=[True, True, False])
>>> y = ivy.bitwise_invert(x)
>>> print(y)
{
    a: ivy.array([True, False, True]),
    b: ivy.array([False, False, True])
}

With int input:

>>> x = -8
>>> y = ivy.bitwise_invert(x)
>>> print(y)
ivy.array(7)

With bool input:

>>> x = False
>>> y = ivy.bitwise_invert(x)
>>> print(y)
ivy.array(True)

ivy.bitwise_left_shift(x1, x2, /, *, out=None)[source]#

Shifts the bits of each element x1_i of the input array x1 to the left by appending x2_i (i.e., the respective element in the input array x2) zeros to the right of x1_i.

Parameters:

x1 (Union[int, Array, NativeArray]) – first input array. Should have an integer data type.
x2 (Union[int, Array, NativeArray]) – second input array. Must be compatible with x1 (see broadcasting). Should have an integer data type. Each element must be greater than or equal to 0.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise results. The returned array must have a data type determined by type-promotion.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

ivy.bitwise_or(x1, x2, /, *, out=None)[source]#

Compute the bitwise OR of the underlying binary representation of each element x1_i of the input array x1 with the respective element x2_i of the input array x2.

Parameters:

x1 (Union[int, bool, Array, NativeArray]) – first input array. Should have an integer or boolean data type.
x2 (Union[int, bool, Array, NativeArray]) – second input array. Must be compatible with x1 (see broadcasting). Should have an integer or boolean data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise results. The returned array must have a data type determined by type-promotion.

Examples

With ivy.Array inputs:

>>> x = ivy.array([1, 2, 3])
>>> y = ivy.array([4, 5, 6])
>>> z = ivy.bitwise_or(x, y)
>>> print(z)
ivy.array([5, 7, 7])

>>> x = ivy.array([[[1], [2], [3], [4]]])
>>> y = ivy.array([[[4], [5], [6], [7]]])
>>> ivy.bitwise_or(x, y, out=x)
>>> print(x)
ivy.array([[[5],
            [7],
            [7],
            [7]]])

>>> x = ivy.array([[[1], [2], [3], [4]]])
>>> y = ivy.array([4, 5, 6, 7])
>>> z = ivy.bitwise_or(x, y)
>>> print(z)
ivy.array([[[5, 5, 7, 7],
            [6, 7, 6, 7],
            [7, 7, 7, 7],
            [4, 5, 6, 7]]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([1, 2, 3]),b=ivy.array([2, 3, 4]))
>>> y = ivy.Container(a=ivy.array([4, 5, 6]),b=ivy.array([5, 6, 7]))
>>> z = ivy.bitwise_or(x, y)
>>> print(z)
{
    a: ivy.array([5, 7, 7]),
    b: ivy.array([7, 7, 7])
}

With a mix of ivy.Array and ivy.Container inputs:

>>> x = ivy.array([1, 2, 3])
>>> y = ivy.Container(a=ivy.array([4, 5, 6]),b=ivy.array([5, 6, 7]))
>>> z = ivy.bitwise_or(x, y)
>>> print(z)
{
    a: ivy.array([5,7,7]),
    b: ivy.array([5,6,7])
}

ivy.bitwise_right_shift(x1, x2, /, *, out=None)[source]#

Shifts the bits of each element x1_i of the input array x1 to the right according to the respective element x2_i of the input array x2.

Note

This operation must be an arithmetic shift (i.e., sign-propagating) and thus equivalent to floor division by a power of two.

Parameters:

x1 (Union[int, Array, NativeArray]) – first input array. Should have an integer data type.
x2 (Union[int, Array, NativeArray]) – second input array. Must be compatible with x1 (see broadcasting). Should have an integer data type. Each element must be greater than or equal to 0.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise results. The returned array must have a data type determined by type-promotion.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> a = ivy.array([2, 9, 16, 31])
>>> b = ivy.array([0, 1, 2, 3])
>>> y = ivy.bitwise_right_shift(a, b)
>>> print(y)
ivy.array([2, 4, 4, 3])

>>> a = ivy.array([[32, 40, 55], [16, 33, 170]])
>>> b = ivy.array([5, 2, 1])
>>> y = ivy.zeros((2, 3))
>>> ivy.bitwise_right_shift(a, b, out=y)
>>> print(y)
ivy.array([[ 1., 10., 27.],
           [ 0.,  8., 85.]])

>>> a = ivy.array([[10, 64],[43, 87],[5, 37]])
>>> b = ivy.array([1, 3])
>>> ivy.bitwise_right_shift(a, b, out=a)
>>> print(a)
ivy.array([[ 5,  8],
           [21, 10],
           [ 2,  4]])

With a mix of ivy.Array and ivy.NativeArray inputs:

>>> a = ivy.array([[10, 64],[43, 87],[5, 37]])
>>> b = ivy.native_array([1, 3])
>>> y = ivy.bitwise_right_shift(a, b)
>>> print(y)
ivy.array([[ 5,  8],[21, 10],[ 2,  4]])

With one ivy.Container input:

>>> a = ivy.Container(a = ivy.array([100, 200]),
...                   b = ivy.array([125, 243]))
>>> b = ivy.array([3, 6])
>>> y = ivy.bitwise_right_shift(a, b)
>>> print(y)
{
    a: ivy.array([12, 3]),
    b: ivy.array([15, 3])
}

With multiple ivy.Container inputs:

>>> a = ivy.Container(a = ivy.array([10, 25, 42]),
...                   b = ivy.array([64, 65]),
...                   c = ivy.array([200, 225, 255]))
>>> b = ivy.Container(a = ivy.array([0, 1, 2]),
...                   b = ivy.array([6]),
...                   c = ivy.array([4, 5, 6]))
>>> y = ivy.bitwise_right_shift(a, b)
>>> print(y)
{
    a: ivy.array([10, 12, 10]),
    b: ivy.array([1, 1]),
    c: ivy.array([12, 7, 3])
}

ivy.bitwise_xor(x1, x2, /, *, out=None)[source]#

Compute the bitwise XOR of the underlying binary representation of each element x1_i of the input array x1 with the respective element x2_i of the input array x2.

Special cases

This function does not take floating point operands

Parameters:

x1 (Union[int, bool, Array, NativeArray]) – first input array. Should have an integer or boolean data type.
x2 (Union[int, bool, Array, NativeArray]) – second input array. Must be compatible with x1 (see broadcasting). Should have an integer or boolean data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise results. The returned array must have a data type determined by type-promotion.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Both the description and the type hints above assume an array input for simplicity, but this function is nestable, and therefore also accepts ivy.Container instances in place of any of the arguments.

Functional Examples

With ivy.Array input:

>>> a = ivy.array([1, 2, 3])
>>> b = ivy.array([3, 2, 1])
>>> y = ivy.bitwise_xor(a, b)
>>> print(y)
ivy.array([2, 0, 2])

>>> a = ivy.array([78, 91, 23])
>>> b = ivy.array([66, 77, 88])
>>> ivy.bitwise_xor(a, b, out=y)
>>> print(y)
ivy.array([12, 22, 79])

>>> a = ivy.array([1, 2, 3])
>>> b = ivy.array([3, 2, 1])
>>> ivy.bitwise_xor(a, b, out = a)
>>> print(a)
ivy.array([2, 0, 2])

With a mix of ivy.Array and ivy.NativeArray inputs:

>>> a = ivy.array([0, 1, 3, 67, 91])
>>> a = ivy.native_array([4, 7, 90, 89, 98])
>>> y = ivy.bitwise_xor(a, b)
>>> print(y)
ivy.array([0,0,0,0,0])

With ivy.Container input:

>>> x = ivy.Container(a = ivy.array([89]))
>>> y = ivy.Container(a = ivy.array([12]))
>>> z = ivy.bitwise_xor(x, y)
>>> print(z)
{
    a:ivy.array([85])
}

With a mix of ivy.Array and ivy.Container inputs:

>>> x = ivy.Container(a = ivy.array([-67, 21]))
>>> b = ivy.array([78, 34])
>>> y = ivy.array([12, 13])
>>> z = ivy.bitwise_xor(x, y)
>>> print(z)
{
    a: ivy.array([-79, 24])
}

ivy.ceil(x, /, *, out=None)[source]#

Round each element x_i of the input array x to the smallest (i.e., closest to -infinity) integer-valued number that is not less than x_i.

Special cases

If x_i is already integer-valued, the result is x_i.

For floating-point operands,

If x_i is +infinity, the result is +infinity.
If x_i is -infinity, the result is -infinity.
If x_i is +0, the result is +0.
If x_i is -0, the result is -0.
If x_i is NaN, the result is NaN.

Parameters:

x (Union[Array, NativeArray]) – input array. Should have a numeric data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the rounded result for each element in x. The returned array must have the same data type as x.

This method conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([0.1, 0, -0.1])
>>> y = ivy.ceil(x)
>>> print(y)
ivy.array([1., 0., -0.])

>>> x = ivy.array([2.5, -3.5, 0, -3, -0])
>>> y = ivy.ones(5)
>>> ivy.ceil(x, out=y)
>>> print(y)
ivy.array([ 3., -3.,  0., -3.,  0.])

>>> x = ivy.array([[3.3, 4.4, 5.5], [-6.6, -7.7, -8.8]])
>>> ivy.ceil(x, out=x)
>>> print(x)
ivy.array([[ 4.,  5.,  6.],
           [-6., -7., -8.]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([2.5, 0.5, -1.4]),
...                   b=ivy.array([5.4, -3.2, -0, 5.2]))
>>> y = ivy.ceil(x)
>>> print(y)
{
    a: ivy.array([3., 1., -1.]),
    b: ivy.array([6., -3., 0., 6.])
}

ivy.cos(x, /, *, out=None)[source]#

Calculate an implementation-dependent approximation to the cosine, having domain (-infinity, +infinity) and codomain [-1, +1], for each element x_i of the input array x. Each element x_i is assumed to be expressed in radians.

Special cases

For floating-point operands,

If x_i is NaN, the result is NaN.
If x_i is +0, the result is 1.
If x_i is -0, the result is 1.
If x_i is +infinity, the result is NaN.
If x_i is -infinity, the result is NaN.

Parameters:

x (Union[Array, NativeArray]) – input array whose elements are each expressed in radians. Should have a floating-point data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the cosine of each element in x. The returned array must have a floating-point data type determined by type-promotion.

This method conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([0., 1., 2.])
>>> y = ivy.cos(x)
>>> print(y)
ivy.array([1., 0.54, -0.416])

>>> x = ivy.array([4., 0., -6.])
>>> y = ivy.zeros(3)
>>> ivy.cos(x, out=y)
>>> print(y)
ivy.array([-0.654, 1., 0.96])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0., -1, 1]), b=ivy.array([1., 0., -6]))
>>> y = ivy.cos(x)
>>> print(y)
{
    a: ivy.array([1., 0.54, 0.54]),
    b: ivy.array([0.54, 1., 0.96])
}

ivy.cosh(x, /, *, out=None)[source]#

Calculate an implementation-dependent approximation to the hyperbolic cosine, having domain [-infinity, +infinity] and codomain [-infinity, +infinity], for each element x_i in the input array x.

Special cases

For floating-point operands,

If x_i is NaN, the result is NaN.
If x_i is +0, the result is 1.
If x_i is -0, the result is 1.
If x_i is +infinity, the result is +infinity.
If x_i is -infinity, the result is +infinity.

Parameters:

x (Union[Array, NativeArray]) – input array whose elements each represent a hyperbolic angle. Should have a floating-point data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the hyperbolic cosine of each element in x. The returned array must have a floating-point data type determined by type-promotion.

This method conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([1, 2, 3, 4])
>>> y = ivy.cosh(x)
>>> print(y)
ivy.array([1.54,3.76,10.1,27.3])

>>> x = ivy.array([0.2, -1.7, -5.4, 1.1])
>>> y = ivy.zeros(4)
>>> ivy.cosh(x, out=y)
ivy.array([[1.67,4.57,13.6,12.3],[40.7,122.,368.,670.]])

>>> x = ivy.array([[1.1, 2.2, 3.3, 3.2],
...                [-4.4, -5.5, -6.6, -7.2]])
>>> y = ivy.cosh(x)
>>> print(y)
ivy.array([[1.67,4.57,13.6,12.3],[40.7,122.,368.,670.]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([1., 2., 3.]), b=ivy.array([6., 7., 8.]))
>>> y = ivy.cosh(x)
>>> print(y)
{
    a:ivy.array([1.54,3.76,10.1]),
    b:ivy.array([202.,548.,1490.])
}

ivy.deg2rad(x, /, *, out=None)[source]#

Convert the input from degrees to radians.

Parameters:

x (Union[Array, NativeArray]) – input array whose elements are each expressed in degrees.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array with each element in x converted from degrees to radians.

Examples

With ivy.Array input:

>>> x=ivy.array([0,90,180,270,360])
>>> y=ivy.deg2rad(x)
>>> print(y)
ivy.array([0.  , 1.57, 3.14, 4.71, 6.28])

>>> x=ivy.array([0,-1.5,-50,ivy.nan])
>>> y=ivy.zeros(5)
>>> ivy.deg2rad(x,out=y)
>>> print(y)
ivy.array([ 0.    , -0.0262, -0.873 ,     nan])

>>> x = ivy.array([[1.1, 2.2, 3.3],[-4.4, -5.5, -6.6]])
>>> ivy.deg2rad(x, out=x)
>>> print(x)
ivy.array([[ 0.0192,  0.0384,  0.0576],
    [-0.0768, -0.096 , -0.115 ]])

>>> x=ivy.native_array([-0,20.1,ivy.nan])
>>> y=ivy.zeros(3)
>>> ivy.deg2rad(x,out=y)
>>> print(y)
ivy.array([0.   , 0.351,   nan])

With ivy.Container input:

>>> x=ivy.Container(a=ivy.array([-0,20.1,-50.5,-ivy.nan]),
                    b=ivy.array([0,90,180,270,360]))
>>> y=ivy.deg2rad(x)
>>> print(y)
{
    a: ivy.array([0., 0.351, -0.881, nan]),
    b: ivy.array([0., 1.57, 3.14, 4.71, 6.28])
}

>>> x=ivy.Container(a=ivy.array([0,90,180,270,360]),
...                 b=ivy.native_array([0,-1.5,-50,ivy.nan]))
>>> y=ivy.deg2rad(x)
>>> print(y)
{
    a: ivy.array([0., 1.57, 3.14, 4.71, 6.28]),
    b: ivy.array([0., -0.0262, -0.873, nan])
}

ivy.divide(x1, x2, /, *, out=None)[source]#

Calculate the division for each element x1_i of the input array x1 with the respective element x2_i of the input array x2.

Parameters:

x1 (Union[float, Array, NativeArray]) – dividend input array. Should have a numeric data type.
x2 (Union[float, Array, NativeArray]) – divisor input array. Must be compatible with x1 (see Broadcasting). Should have a numeric data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise results. The returned array must have a floating-point data type determined by Type Promotion Rules.

This method conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array inputs:

>>> x1 = ivy.array([2., 7., 9.])
>>> x2 = ivy.array([3., 4., 0.6])
>>> y = ivy.divide(x1, x2)
>>> print(y)
ivy.array([0.667, 1.75, 15.])

With mixed ivy.Array and ivy.NativeArray inputs:

>>> x1 = ivy.array([5., 6., 9.])
>>> x2 = ivy.native_array([2., 2., 2.])
>>> y = ivy.divide(x1, x2)
>>> print(y)
ivy.array([2.5, 3., 4.5])

With ivy.Container inputs:

>>> x1 = ivy.Container(a=ivy.array([12., 3.5, 6.3]), b=ivy.array([3., 1., 0.9]))
>>> x2 = ivy.Container(a=ivy.array([1., 2.3, 3]), b=ivy.array([2.4, 3., 2.]))
>>> y = ivy.divide(x1, x2)
>>> print(y)
{
    a: ivy.array([12., 1.52, 2.1]),
    b: ivy.array([1.25, 0.333, 0.45])
}

With mixed ivy.Container and ivy.Array inputs:

>>> x1 = ivy.Container(a=ivy.array([12., 3.5, 6.3]), b=ivy.array([3., 1., 0.9]))
>>> x2 = ivy.array([4.3, 3., 5.])
>>> y = ivy.divide(x1, x2)
{
    a: ivy.array([2.79, 1.17, 1.26]),
    b: ivy.array([0.698, 0.333, 0.18])
}

ivy.equal(x1, x2, /, *, out=None)[source]#

Compute the truth value of x1_i == x2_i for each element x1_i of the input array x1 with the respective element x2_i of the input array x2.

Parameters:

x1 (Union[float, Array, NativeArray, Container]) – first input array. May have any data type.
x2 (Union[float, Array, NativeArray, Container]) – second input array. Must be compatible with x1 (with Broadcasting). May have any data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise results. The returned array must have a data type of bool.

This method conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array inputs:

>>> x1 = ivy.array([2., 7., 9.])
>>> x2 = ivy.array([1., 7., 9.])
>>> y = ivy.equal(x1, x2)
>>> print(y)
ivy.array([False, True, True])

With mixed ivy.Array and ivy.NativeArray inputs:

>>> x1 = ivy.array([5, 6, 9])
>>> x2 = ivy.native_array([2, 6, 2])
>>> y = ivy.equal(x1, x2)
>>> print(y)
ivy.array([False, True, False])

With ivy.Container inputs:

>>> x1 = ivy.Container(a=ivy.array([12, 3.5, 6.3]), b=ivy.array([3., 1., 0.9]))
>>> x2 = ivy.Container(a=ivy.array([12, 2.3, 3]), b=ivy.array([2.4, 3., 2.]))
>>> y = ivy.equal(x1, x2)
>>> print(y)
{
    a: ivy.array([True, False, False]),
    b: ivy.array([False, False, False])
}

With mixed ivy.Container and ivy.Array inputs:

>>> x1 = ivy.Container(a=ivy.array([12., 3.5, 6.3]), b=ivy.array([3., 1., 0.9]))
>>> x2 = ivy.array([3., 1., 0.9])
>>> y = ivy.equal(x1, x2)
>>> print(y)
{
    a: ivy.array([False, False, False]),
    b: ivy.array([True, True, True])
}

ivy.erf(x, /, *, out=None)[source]#

Compute the Gauss error function of x element-wise.

Parameters:

x (Union[Array, NativeArray]) – Value to compute exponential for.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – The Gauss error function of x.

Examples

>>> x = ivy.array([0, 0.3, 0.7, 1.0])
>>> ivy.erf(x)
ivy.array([0., 0.328, 0.677, 0.842])

ivy.exp(x, /, *, out=None)[source]#

Calculate an implementation-dependent approximation to the exponential function, having domain [-infinity, +infinity] and codomain [+0, +infinity], for each element x_i of the input array x (e raised to the power of x_i, where e is the base of the natural logarithm).

Special cases

For floating-point operands,

If x_i is NaN, the result is NaN.
If x_i is +0, the result is 1.
If x_i is -0, the result is 1.
If x_i is +infinity, the result is +infinity.
If x_i is -infinity, the result is +0.

Parameters:

x (Union[Array, NativeArray, Number]) – input array. Should have a floating-point data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the evaluated exponential function result for each element in x. The returned array must have a floating-point data type determined by type-promotion.
This method conforms to the
`Array API Standard <https (//data-apis.org/array-api/latest/>`_.)
This docstring is an extension of the
`docstring <https (//data-apis.org/array-api/latest/API_specification/generated/signatures.elementwise_functions.exp.html>`_ # noqa)
in the standard.
Both the description and the type hints above assumes an array input for simplicity,
but this function is nestable, and therefore also accepts ivy.Container
instances in place of any of the arguments.

Examples

With :class:Number:

>>> x = 3
>>> y = ivy.exp(x)
>>> print(y)
ivy.array(20.08553692)

With ivy.Array input:

>>> x = ivy.array([1., 2., 3.])
>>> y = ivy.exp(x)
>>> print(y)
ivy.array([ 2.71828175,  7.38905621, 20.08553696])

With nested inputs in ivy.Array:

>>> x = ivy.array([[-5.67], [ivy.nan], [0.567]])
>>> y = ivy.exp(x)
>>> print(y)
ivy.array([[0.00344786],
       [       nan],
       [1.76297021]])

With ivy.NativeArray input:

>>> x = ivy.native_array([0., 4., 2.])
>>> y = ivy.exp(x)
>>> print(y)
ivy.array([ 1.        , 54.59814835,  7.38905621])

With ivy.Container input:

>>> x = ivy.Container(a=3.1, b=ivy.array([3.2, 1.]))
>>> y = ivy.exp(x)
>>> print(y)
{
    a: ivy.array(22.197948),
    b: ivy.array([24.53253174, 2.71828175])
}

ivy.exp2(x, /, *, out=None)[source]#

Calculate 2**p for all p in the input array.

Parameters:

x (Union[Array, float, list, tuple]) – Array-like input.
out (Optional[Array]) – optional output array, for writing the result to. (default: None)

Return type:

Array

Returns:

ret – Element-wise 2 to the power x. This is a scalar if x is a scalar.

Examples

>>> x = ivy.array([1, 2, 3])
>>> ivy.exp2(x)
ivy.array([2.,    4.,   8.])
>>> x = [5, 6, 7]
>>> ivy.exp2(x)
ivy.array([32.,   64.,  128.])

ivy.expm1(x, /, *, out=None)[source]#

Calculate an implementation-dependent approximation to exp(x)-1, having domain [-infinity, +infinity] and codomain [-1, +infinity], for each element x_i of the input array x.

Note

The purpose of this function is to calculate exp(x)-1.0 more accurately when x is close to zero. Accordingly, conforming implementations should avoid implementing this function as simply exp(x)-1.0. See FDLIBM, or some other IEEE 754-2019 compliant mathematical library, for a potential reference implementation.

Special cases

For floating-point operands,

If x_i is NaN, the result is NaN.
If x_i is +0, the result is +0.
If x_i is -0, the result is -0.
If x_i is +infinity, the result is +infinity.
If x_i is -infinity, the result is -1.

Parameters:

x (Union[Array, NativeArray]) – input array. Should have a numeric data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the evaluated result for each element in x. The returned array must have a floating-point data type determined by type-promotion.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array inputs:

>>> x = ivy.array([[0, 5, float('-0'), ivy.nan]])
>>> ivy.expm1(x)
ivy.array([[  0., 147.,  -0.,  nan]])

>>> x = ivy.array([ivy.inf, 1, float('-inf')])
>>> y = ivy.zeros(3)
>>> ivy.expm1(x, out=y)
ivy.array([  inf,  1.72, -1.  ])

With ivy.Container inputs:

>>> x = ivy.Container(a=ivy.array([-1, 0,]),
...                   b=ivy.array([10, 1]))
>>> ivy.expm1(x)
{
    a: ivy.array([-0.632, 0.]),
    b: ivy.array([2.20e+04, 1.72e+00])
}

ivy.floor(x, /, *, out=None)[source]#

Round each element x_i of the input array x to the greatest (i.e., closest to +infinity) integer-valued number that is not greater than x_i.

Special cases

If x_i is already integer-valued, the result is x_i.

For floating-point operands,

If x_i is +infinity, the result is +infinity.
If x_i is -infinity, the result is -infinity.
If x_i is +0, the result is +0.
If x_i is -0, the result is -0.
If x_i is NaN, the result is NaN.

Parameters:

x (Union[Array, NativeArray]) – input array. Should have a numeric data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the rounded result for each element in x. The returned array must have the same data type as x.

This method conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Functional Examples

With ivy.Array input:

>>> x = ivy.array([2,3,4])
>>> y = ivy.floor(x)
>>> print(y)
ivy.array([2, 3, 4])

>>> x = ivy.array([1.5, -5.5, 0, -1, -0])
>>> y = ivy.zeros(5)
>>> ivy.floor(x, out=y)
>>> print(y)
ivy.array([ 1., -6.,  0., -1.,  0.])

>>> x = ivy.array([[1.1, 2.2, 3.3], [-4.4, -5.5, -6.6]])
>>> ivy.floor(x, out=x)
>>> print(x)
ivy.array([[ 1.,  2.,  3.],
           [-5., -6., -7.]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0., 1.5, -2.4]),
...                   b=ivy.array([3.4, -4.2, -0, -1.2]))
>>> y = ivy.floor(x)
>>> print(y)
{
    a: ivy.array([0., 1., -3.]),
    b: ivy.array([3., -5., 0., -2.])
}

ivy.floor_divide(x1, x2, /, *, out=None)[source]#

Round the result of dividing each element x1_i of the input array x1 by the respective element x2_i of the input array x2 to the greatest (i.e., closest to +infinity) integer-value number that is not greater than the division result.

Parameters:

x1 (Union[float, Array, NativeArray]) – first input array. Must have a numeric data type.
x2 (Union[float, Array, NativeArray]) – second input array. Must be compatible with x1 (with Broadcasting). Must have a numeric data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise results. The returned array must have a numeric data type.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array inputs:

>>> x1 = ivy.array([13., 7., 8.])
>>> x2 = ivy.array([3., 2., 7.])
>>> y = ivy.floor_divide(x1, x2)
>>> print(y)
ivy.array([4., 3., 1.])

With mixed ivy.Array and ivy.NativeArray inputs:

>>> x1 = ivy.array([3., 4., 5.])
>>> x2 = ivy.native_array([5., 2., 1.])
>>> y = ivy.floor_divide(x1, x2)
>>> print(y)
ivy.array([0., 2., 5.])

With ivy.Container inputs:

>>> x1 = ivy.Container(a=ivy.array([4., 5., 6.]), b=ivy.array([7., 8., 9.]))
>>> x2 = ivy.Container(a=ivy.array([5., 4., 2.5]), b=ivy.array([2.3, 3.7, 5]))
>>> y = ivy.floor_divide(x1, x2)
>>> print(y)
{
    a: ivy.array([0., 1., 2.]),
    b: ivy.array([3., 2., 1.])
}

With mixed ivy.Container and ivy.Array inputs:

>>> x1 = ivy.Container(a=ivy.array([4., 5., 6.]), b=ivy.array([7., 8., 9.]))
>>> x2 = ivy.array([2., 2., 2.])
>>> y = ivy.floor_divide(x1, x2)
>>> print(y)
{
    a: ivy.array([2., 2., 3.]),
    b: ivy.array([3., 4., 4.])
}

ivy.fmin(x1, x2, /, *, out=None)[source]#

Compute the element-wise minimums of two arrays. Differs from ivy.minimum in the case where one of the elements is NaN. ivy.minimum returns the NaN element while ivy.fmin returns the non-NaN element.

Parameters:

x1 (Union[Array, NativeArray]) – First input array.
x2 (Union[Array, NativeArray]) – Second input array.
out (Optional[Union[Array, NativeArray]]) – optional output array, for writing the result to. (default: None)

Return type:

Union[Array, NativeArray]

Returns:

ret – Array with element-wise minimums.

Examples

>>> x1 = ivy.array([2, 3, 4])
>>> x2 = ivy.array([1, 5, 2])
>>> ivy.fmin(x1, x2)
ivy.array([1, 3, 2])

>>> x1 = ivy.array([ivy.nan, 0, ivy.nan])
>>> x2 = ivy.array([0, ivy.nan, ivy.nan])
>>> ivy.fmin(x1, x2)
ivy.array([ 0.,  0., nan])

ivy.fmod(x1, x2, /, *, out=None)[source]#

Compute the element-wise remainder of divisions of two arrays.

Parameters:

x1 (Union[Array, NativeArray]) – First input array.
x2 (Union[Array, NativeArray]) – Second input array
out (Optional[Union[Array, NativeArray]]) – optional output array, for writing the result to. (default: None)

Return type:

Union[Array, NativeArray]

Returns:

ret – Array with element-wise remainder of divisions.

Examples

>>> x1 = ivy.array([2, 3, 4])
>>> x2 = ivy.array([1, 5, 2])
>>> ivy.fmod(x1, x2)
ivy.array([ 0,  3,  0])

>>> x1 = ivy.array([ivy.nan, 0, ivy.nan])
>>> x2 = ivy.array([0, ivy.nan, ivy.nan])
>>> ivy.fmod(x1, x2)
ivy.array([ nan,  nan,  nan])

ivy.gcd(x1, x2, /, *, out=None)[source]#

Return the greatest common divisor of |x1| and |x2|.

Parameters:

x1 (Union[Array, NativeArray, int, list, tuple]) – First array-like input.
x2 (Union[Array, NativeArray, int, list, tuple]) – Second array-input.
out (Optional[Array]) – optional output array, for writing the result to. (default: None)

Return type:

Array

Returns:

ret – Element-wise gcd of |x1| and |x2|.

Examples

>>> x1 = ivy.array([1, 2, 3])
>>> x2 = ivy.array([4, 5, 6])
>>> ivy.gcd(x1, x2)
ivy.array([1.,    1.,   3.])
>>> x1 = ivy.array([1, 2, 3])
>>> ivy.gcd(x1, 10)
ivy.array([1.,   2.,  1.])

ivy.greater(x1, x2, /, *, out=None)[source]#

Compute the truth value of x1_i < x2_i for each element x1_i of the input array x1 with the respective element x2_i of the input array x2.

Parameters:

x1 (Union[float, Array, NativeArray]) – Input array.
x2 (Union[float, Array, NativeArray]) – Input array.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise results. The returned array must have a data type of bool.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.greater(ivy.array([1,2,3]),ivy.array([2,2,2]))
>>> print(x)
ivy.array([False, False,  True])

>>> x = ivy.array([[[1.1], [3.2], [-6.3]]])
>>> y = ivy.array([[8.4], [2.5], [1.6]])
>>> ivy.greater(x, y, out=x)
>>> print(x)
ivy.array([[[False],[True],[False]]])

With a mix of ivy.Array and ivy.NativeArray inputs:

>>> x = ivy.array([1, 2, 3])
>>> y = ivy.native_array([4, 5, 0])
>>> z = ivy.greater(x, y)
>>> print(z)
ivy.array([False,False,True])

With a mix of ivy.Array and ivy.Container inputs:

>>> x = ivy.array([[5.1, 2.3, -3.6]])
>>> y = ivy.Container(a=ivy.array([[4.], [5.], [6.]]),
...                   b=ivy.array([[5.], [6.], [7.]]))
>>> z = ivy.greater(x, y)
>>> print(z)
{
    a: ivy.array([[True, False, False],
                  [True, False, False],
                  [False, False, False]]),
    b: ivy.array([[True, False, False],
                  [False, False, False],
                  [False, False, False]])
}

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([4, 5, 6]),
...                   b=ivy.array([2, 3, 4]))
>>> y = ivy.Container(a=ivy.array([1, 2, 3]),
...                   b=ivy.array([5, 6, 7]))
>>> z = ivy.greater(x, y)
>>> print(z)
{
    a: ivy.array([True, True, True]),
    b: ivy.array([False, False, False])
}

ivy.greater_equal(x1, x2, /, *, out=None)[source]#

Compute the truth value of x1_i >= x2_i for each element x1_i of the input array x1 with the respective element x2_i of the input array x2.

Parameters:

x1 (Union[float, Array, NativeArray]) – first input array. May have any data type.
x2 (Union[float, Array, NativeArray]) – second input array. Must be compatible with x1 (with Broadcasting). May have any data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise results. The returned array must have a data type of bool.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Functional Examples

With ivy.Array input:

>>> x = ivy.greater_equal(ivy.array([1,2,3]),ivy.array([2,2,2]))
>>> print(x)
ivy.array([False,True,True])

>>> x = ivy.array([[10.1, 2.3, -3.6]])
>>> y = ivy.array([[4.8], [5.2], [6.1]])
>>> shape = (3,3)
>>> fill_value = False
>>> z = ivy.full(shape, fill_value)
>>> ivy.greater_equal(x, y, out=z)
>>> print(z)
ivy.array([[True,False,False],[True,False,False],[True,False,False]])

>>> x = ivy.array([[[1.1], [3.2], [-6.3]]])
>>> y = ivy.array([[8.4], [2.5], [1.6]])
>>> ivy.greater_equal(x, y, out=x)
>>> print(x)
ivy.array([[[False],[True],[False]]])

With a mix of ivy.Array and ivy.NativeArray inputs:

>>> x = ivy.array([1, 2, 3])
>>> y = ivy.native_array([4, 5, 0])
>>> z = ivy.greater_equal(x, y)
>>> print(z)
ivy.array([False,False,True])

With a mix of ivy.Array and ivy.Container inputs:

>>> x = ivy.array([[5.1, 2.3, -3.6]])
>>> y = ivy.Container(a=ivy.array([[4.], [5.], [6.]]),b=ivy.array([[5.], [6.], [7.]]))
>>> z = ivy.greater_equal(x, y)
>>> print(z)
{
    a:ivy.array([[True,False,False],[True,False,False],[False,False,False]]),
    b:ivy.array([[True,False,False],[False,False,False],[False,False,False]])
}

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([4, 5, 6]),b=ivy.array([2, 3, 4]))
>>> y = ivy.Container(a=ivy.array([1, 2, 3]),b=ivy.array([5, 6, 7]))
>>> z = ivy.greater_equal(x, y)
>>> print(z)
{
    a:ivy.array([True,True,True]),
    b:ivy.array([False,False,False])
}

ivy.imag(val, /, *, out=None)[source]#

Return the Imaginary part of a complex numbers(x+yj).

Parameters:

val (Union[Array, NativeArray]) – Array-like input.
out (Optional[Array]) – optional output array, for writing the result to. (default: None)

Return type:

Array

Returns:

ret – Returns an array with the imaginary part of complex numbers.

Examples

>>> b = ivy.array(np.array([1+2j, 3+4j, 5+6j]))
>>> b
ivy.array([1.+2.j, 3.+4.j, 5.+6.j])
>>> ivy.imag(b)
ivy.array([2., 4., 6.])

ivy.isfinite(x, /, *, out=None)[source]#

Test each element x_i of the input array x to determine if finite (i.e., not NaN and not equal to positive or negative infinity).

Parameters:

x (Union[Array, NativeArray]) – input array. Should have a numeric data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing test results. An element out_i is True if x_i is finite and False otherwise. The returned array must have a data type of bool.

This method conforms to the `Array API Standard<https://data-apis.org/array-api/latest/>`_. This docstring is an extension of the docstring <https://data-apis.org/array-api/latest/API_specification/generated/signatures.elementwise_functions.isfinite.html> _ in the standard.

Functional Examples

With ivy.Array input:

>>> x = ivy.array([0, ivy.nan, -ivy.inf, float('inf')])
>>> y = ivy.isfinite(x)
>>> print(y)
ivy.array([ True, False, False, False])

>>> x = ivy.array([0, ivy.nan, -ivy.inf])
>>> y = ivy.zeros(3)
>>> ivy.isfinite(x, out=y)
>>> print(y)
ivy.array([ True, False, False])

>>> x = ivy.array([[9, float('-0')], [ivy.nan, ivy.inf]])
>>> ivy.isfinite(x, out=x)
>>> print(x)
ivy.array([[ True,  True],
    [False, False]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0., 999999999999]),
...                   b=ivy.array([float('-0'), ivy.nan]))
>>> y = ivy.isfinite(x)
>>> print(y)
{
    a: ivy.array([True, True]),
    b: ivy.array([True, False])
}

ivy.isinf(x, /, *, detect_positive=True, detect_negative=True, out=None)[source]#

Test each element x_i of the input array x to determine if equal to positive or negative infinity.

Parameters:

x (Union[Array, NativeArray]) – input array. Should have a numeric data type.
detect_positive (bool) – if True, positive infinity is detected. (default: True)
detect_negative (bool) – if True, negative infinity is detected. (default: True)
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing test results. An element out_i is True if x_i is either positive or negative infinity and False otherwise. The returned array must have a data type of bool.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array inputs:

>>> x = ivy.array([1, 2, 3])
>>> z = ivy.isinf(x)
>>> print(z)
ivy.array([False, False, False])

>>> x = ivy.array([[1.1, 2.3, -3.6]])
>>> z = ivy.isinf(x)
>>> print(z)
ivy.array([[False, False, False]])

>>> x = ivy.array([[[1.1], [float('inf')], [-6.3]]])
>>> z = ivy.isinf(x)
>>> print(z)
ivy.array([[[False],
        [True],
        [False]]])

>>> x = ivy.array([[-float('inf'), float('inf'), 0.0]])
>>> z = ivy.isinf(x)
>>> print(z)
ivy.array([[ True,  True, False]])

>>> x = ivy.zeros((3, 3))
>>> z = ivy.isinf(x)
>>> print(z)
ivy.array([[False, False, False],
   [False, False, False],
   [False, False, False]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([-1, -float('inf'), 1.23]),
...                   b=ivy.array([float('inf'), 3.3, -4.2]))
>>> z = ivy.isinf(x)
>>> print(z)
{
    a: ivy.array([False, True, False]),
    b: ivy.array([True, False, False])
}

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([-1, -float('inf'), 1.23]),
...                   b=ivy.array([float('inf'), 3.3, -4.2]))
>>> x.isinf()
{
    a: ivy.array([False, True, False]),
    b: ivy.array([True, False, False])
}

ivy.isnan(x, /, *, out=None)[source]#

Test each element x_i of the input array x to determine whether the element is NaN.

Parameters:

x (Union[Array, NativeArray]) – input array. Should have a numeric data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing test results. An element out_i is True if x_i is NaN and False otherwise. The returned array should have a data type of bool.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array inputs:

>>> x = ivy.array([1, 2, 3])
>>> z = ivy.isnan(x)
>>> print(z)
ivy.array([False, False, False])

>>> x = ivy.array([[1.1, 2.3, -3.6]])
>>> z = ivy.isnan(x)
>>> print(z)
ivy.array([[False, False, False]])

>>> x = ivy.array([[[1.1], [float('inf')], [-6.3]]])
>>> z = ivy.isnan(x)
>>> print(z)
ivy.array([[[False],
            [False],
            [False]]])

>>> x = ivy.array([[-float('nan'), float('nan'), 0.0]])
>>> z = ivy.isnan(x)
>>> print(z)
ivy.array([[ True,  True, False]])

>>> x = ivy.array([[-float('nan'), float('inf'), float('nan'), 0.0]])
>>> z = ivy.isnan(x)
>>> print(z)
ivy.array([[ True, False,  True, False]])

>>> x = ivy.zeros((3, 3))
>>> z = ivy.isnan(x)
>>> print(z)
ivy.array([[False, False, False],
   [False, False, False],
   [False, False, False]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([-1, -float('nan'), 1.23]),
...                   b=ivy.array([float('nan'), 3.3, -4.2]))
>>> z = ivy.isnan(x)
>>> print(z)
{
    a: ivy.array([False, True, False]),
    b: ivy.array([True, False, False])
}

ivy.isreal(x, /, *, out=None)[source]#

Test each element x_i of the input array x to determine whether the element is real number. Returns a bool array, where True if input element is real. If element has complex type with zero complex part, the return value for that element is True.

Parameters:

x (Union[Array, NativeArray]) – input array.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing test results. An element out_i is True if x_i is real number and False otherwise. The returned array should have a data type of bool.
The descriptions above assume an array input for simplicity, but
the method also accepts ivy.Container instances in place of
ivy.Array or ivy.NativeArray instances, as shown in the type hints
and also the examples below.

Examples

With ivy.Array inputs: >>> x = ivy.array([[[1.1], [float(‘inf’)], [-6.3]]]) >>> z = ivy.isreal(x) >>> print(z) ivy.array([[[True], [True], [True]]])

>>> x = ivy.array([1-0j, 3j, 7+5j])
>>> z = ivy.isreal(x)
>>> print(z)
ivy.array([ True, False, False])

With ivy.Container input: >>> x = ivy.Container(a=ivy.array([-6.7-7j, -np.inf, 1.23]), b=ivy.array([5j, 5-6j, 3])) >>> z = ivy.isreal(x) >>> print(z) {

a: ivy.array([False, True, True]), b: ivy.array([False, False, True])

}

ivy.lcm(x1, x2, /, *, out=None)[source]#

Compute the element-wise least common multiple (LCM) of x1 and x2.

Parameters:

x1 (Union[Array, NativeArray]) – first input array, must be integers
x2 (Union[Array, NativeArray]) – second input array, must be integers
out (Optional[Array]) – optional output array, for writing the result to. (default: None)

Return type:

Array

Returns:

ret – an array that includes the element-wise least common multiples of x1 and x2

Examples

With ivy.Array input:

>>> x1=ivy.array([2, 3, 4])
>>> x2=ivy.array([5, 8, 15])
>>> x1.lcm(x1, x2)
ivy.array([10, 21, 60])

ivy.less(x1, x2, /, *, out=None)[source]#

Compute the truth value of x1_i < x2_i for each element x1_i of the input array x1 with the respective element x2_i of the input array x2.

Parameters:

x1 (Union[float, Array, NativeArray]) – first input array. Should have a numeric data type.
x2 (Union[float, Array, NativeArray]) – second input array. Must be compatible with x1 (see ref:broadcasting). Should have a numeric data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise results. The returned array must have a data type of bool.

Examples

With ivy.Array input:

>>> x = ivy.less(ivy.array([1,2,3]),ivy.array([2,2,2]))
>>> print(x)
ivy.array([ True, False, False])

>>> x = ivy.array([[[1.1], [3.2], [-6.3]]])
>>> y = ivy.array([[8.4], [2.5], [1.6]])
>>> ivy.less(x, y, out=x)
>>> print(x)
ivy.array([[[True],[False],[True]]])

With a mix of ivy.Array and ivy.NativeArray inputs:

>>> x = ivy.array([1, 2, 3])
>>> y = ivy.native_array([4, 5, 0])
>>> z = ivy.less(x, y)
>>> print(z)
ivy.array([ True,  True, False])

With a mix of ivy.Array and ivy.Container inputs:

>>> x = ivy.array([[5.1, 2.3, -3.6]])
>>> y = ivy.Container(a=ivy.array([[4.], [5.], [6.]]),
...                   b=ivy.array([[5.], [6.], [7.]]))
>>> z = ivy.less(x, y)
>>> print(z)
{
    a: ivy.array([[False, True, True],
                  [False, True, True],
                  [True, True, True]]),
    b: ivy.array([[False, True, True],
                  [True, True, True],
                  [True, True, True]])
}

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([4, 5, 6]),b=ivy.array([2, 3, 4]))
>>> y = ivy.Container(a=ivy.array([1, 2, 3]),b=ivy.array([5, 6, 7]))
>>> z = ivy.less(x, y)
>>> print(z)
{
    a: ivy.array([False, False, False]),
    b: ivy.array([True, True, True])
}

ivy.less_equal(x1, x2, /, *, out=None)[source]#

Compute the truth value of x1_i <= x2_i for each element x1_i of the input array x1 with the respective element x2_i of the input array x2.

Parameters:

x1 (Union[Array, NativeArray]) – first input array. May have any data type.
x2 (Union[Array, NativeArray]) – second input array. Must be compatible with x1 (with Broadcasting). May have any data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise results. The returned array must have a data type of bool.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.less_equal(ivy.array([1,2,3]),ivy.array([2,2,2]))
>>> print(x)
ivy.array([True, True,  False])

>>> x = ivy.array([[10.1, 2.3, -3.6]])
>>> y = ivy.array([[4.8], [5.2], [6.1]])
>>> shape = (3,3)
>>> fill_value = False
>>> z = ivy.full(shape, fill_value)
>>> ivy.less_equal(x, y, out=z)
>>> print(z)
ivy.array([[False, True, True],
   [ False, True, True],
   [ False, True, True]])

>>> x = ivy.array([[[1.1], [3.2], [-6.3]]])
>>> y = ivy.array([[8.4], [2.5], [1.6]])
>>> ivy.less_equal(x, y, out=x)
>>> print(x)
ivy.array([[[True],[False],[True]]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([4, 5, 6]),b=ivy.array([2, 3, 4]))
>>> y = ivy.Container(a=ivy.array([1, 2, 3]),b=ivy.array([5, 6, 7]))
>>> z = ivy.less_equal(x, y)
>>> print(z)
{
    a: ivy.array([False, False, False]),
    b: ivy.array([True, True, True])
}

ivy.log(x, /, *, out=None)[source]#

Calculate an implementation-dependent approximation to the natural (base e) logarithm, having domain [0, +infinity] and codomain [-infinity, +infinity], for each element x_i of the input array x.

Special cases

For floating-point operands,

If x_i is NaN, the result is NaN.
If x_i is less than 0, the result is NaN.
If x_i is either +0 or -0, the result is -infinity.
If x_i is 1, the result is +0.
If x_i is +infinity, the result is +infinity.

Parameters:

x (Union[Array, NativeArray]) – input array. Should have a floating-point data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the evaluated natural logarithm for each element in x. The returned array must have a floating-point data type determined by type-promotion.

Examples

With ivy.Array input:

>>> x = ivy.array([4.0, 1, -0.0, -5.0])
>>> y = ivy.log(x)
>>> print(y)
ivy.array([1.39, 0., -inf, nan])

>>> x = ivy.array([[float('nan'), 1, 5.0, float('+inf')],
...                [+0, -1.0, -5, float('-inf')]])
>>> y = ivy.log(x)
>>> print(y)
ivy.array([[nan, 0., 1.61, inf],
           [-inf, nan, nan, nan]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0.0, float('nan')]),
...                   b=ivy.array([-0., -3.9, float('+inf')]),
...                   c=ivy.array([7.9, 1.1, 1.]))
>>> y = ivy.log(x)
>>> print(y)
{
    a: ivy.array([-inf, nan]),
    b: ivy.array([-inf, nan, inf]),
    c: ivy.array([2.07, 0.0953, 0.])
}

ivy.log10(x, /, *, out=None)[source]#

Calculate an implementation-dependent approximation to the base 10 logarithm, having domain [0, +infinity] and codomain [-infinity, +infinity], for each element x_i of the input array x.

Special cases

For floating-point operands,

If x_i is NaN, the result is NaN.
If x_i is less than 0, the result is NaN.
If x_i is either +0 or -0, the result is -infinity.
If x_i is 1, the result is +0.
If x_i is +infinity, the result is +infinity.

Parameters:

x (Union[Array, NativeArray]) – input array. Should have a floating-point data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the evaluated base 10 logarithm for each element in x. The returned array must have a floating-point data type determined by type-promotion.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([4.0, 1, -0.0, -5.0])
>>> y = ivy.log10(x)
>>> print(y)
ivy.array([0.602, 0., -inf, nan])

>>> x = ivy.array([[float('nan'), 1, 5.0, float('+inf')],
...                [+0, -1.0, -5, float('-inf')]])
>>> y = ivy.log10(x)
>>> print(y)
ivy.array([[nan, 0., 0.699, inf],
           [-inf, nan, nan, nan]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0.0, float('nan')]),
...                   b=ivy.array([-0., -3.9, float('+inf')]),
...                   c=ivy.array([7.9, 1.1, 1.]))
>>> y = ivy.log10(x)
>>> print(y)
{
    a: ivy.array([-inf, nan]),
    b: ivy.array([-inf, nan, inf]),
    c: ivy.array([0.898, 0.0414, 0.])
}

ivy.log1p(x, /, *, out=None)[source]#

Calculate an implementation-dependent approximation to log(1+x), where log refers to the natural (base e) logarithm. .. note:

The purpose of this function is to calculate ``log(1+x)`` more accurately when `x` is close to zero.
Accordingly, conforming implementations should avoid implementing this function as simply ``log(1+x)``.
See FDLIBM, or some other IEEE 754-2019 compliant mathematical library, for a potential reference implementation.

Special cases

For floating-point operands,

If x_i is NaN, the result is NaN.
If x_i is less than -1, the result is NaN.
If x_i is -1, the result is -infinity.
If x_i is -0, the result is -0.
If x_i is +0, the result is +0.
If x_i is +infinity, the result is +infinity.

Parameters:

x (Union[Array, NativeArray]) – input array.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the evaluated Natural logarithm of 1 + x for each element in x. The returned array must have a floating-point data type determined by type-promotion.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([1 , 2 ,3 ])
>>> y = ivy.log1p(x)
>>> print(y)
ivy.array([0.693, 1.1  , 1.39 ])

>>> x = ivy.array([0 , 1 ])
>>> y = ivy.zeros(2)
>>> ivy.log1p(x , out = y)
>>> print(y)
ivy.array([0.   , 0.693])

>>> x = ivy.array([[1.1, 2.2, 3.3],[4.4, 5.5, 6.6]])
>>> ivy.log1p(x, out = x)
>>> print(x)
ivy.array([[0.742, 1.16 , 1.46 ],[1.69 , 1.87 , 2.03 ]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0., 1., 2.]), b=ivy.array([3., 4., 5.1]))
>>> y = ivy.log1p(x)
>>> print(y)
{
    a: ivy.array([0., 0.693, 1.1]),
    b: ivy.array([1.39, 1.61, 1.81])
}

ivy.log2(x, /, *, out=None)[source]#

Calculate an implementation-dependent approximation to the base 2 logarithm, having domain [0, +infinity] and codomain [-infinity, +infinity], for each element x_i of the input array x.

Special cases

For floating-point operands,

If x_i is NaN, the result is NaN.
If x_i is less than 0, the result is NaN.
If x_i is either +0 or -0, the result is -infinity.
If x_i is 1, the result is +0.
If x_i is +infinity, the result is +infinity.

Parameters:

x (Union[Array, NativeArray]) – input array. Should have a floating-point data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the evaluated base 2 logarithm for each element in x. The returned array must have a floating-point data type determined by type-promotion.

This method conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

ivy.logaddexp(x1, x2, /, *, out=None)[source]#

Calculate the logarithm of the sum of exponentiations log(exp(x1) + exp(x2)) for each element x1_i of the input array x1 with the respective element x2_i of the input array x2.

Special cases

For floating-point operands,

If either x1_i or x2_i is NaN, the result is NaN.
If x1_i is +infinity and x2_i is not NaN, the result is +infinity.
If x1_i is not NaN and x2_i is +infinity, the result is +infinity.

Parameters:

x1 (Union[Array, NativeArray]) – first input array. Should have a floating-point data type.
x2 (Union[Array, NativeArray]) – second input array. Must be compatible with x1 (see broadcasting). Should have a floating-point data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise results. The returned array must have a floating-point data type determined by type-promotion.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([2., 5., 15.])
>>> y = ivy.array([3., 2., 4.])
>>> z = ivy.logaddexp(x, y)
>>> print(z)
ivy.array([ 3.31,  5.05, 15.  ])

>>> x = ivy.array([[[1.1], [3.2], [-6.3]]])
>>> y = ivy.array([[8.4], [2.5], [1.6]])
>>> ivy.logaddexp(x, y, out=x)
>>> print(x)
ivy.array([[[8.4], [3.6], [1.6]]])

With one ivy.Container input:

>>> x = ivy.array([[5.1, 2.3, -3.6]])
>>> y = ivy.Container(a=ivy.array([[4.], [5.], [6.]]),
...                   b=ivy.array([[5.], [6.], [7.]]))
>>> z = ivy.logaddexp(x, y)
>>> print(z)
{
a: ivy.array([[5.39, 4.17, 4.],
              [5.74, 5.07, 5.],
              [6.34, 6.02, 6.]]),
b: ivy.array([[5.74, 5.07, 5.],
              [6.34, 6.02, 6.],
              [7.14, 7.01, 7.]])
}

With multiple ivy.Container inputs:

>>> x = ivy.Container(a=ivy.array([4., 5., 6.]),b=ivy.array([2., 3., 4.]))
>>> y = ivy.Container(a=ivy.array([1., 2., 3.]),b=ivy.array([5., 6., 7.]))
>>> z = ivy.logaddexp(y,x)
>>> print(z)
{
    a: ivy.array([4.05, 5.05, 6.05]),
    b: ivy.array([5.05, 6.05, 7.05])
}

ivy.logaddexp2(x1, x2, /, *, out=None)[source]#

Calculate log2(2**x1 + 2**x2).

Parameters:

x1 (Union[Array, NativeArray, float, list, tuple]) – First array-like input.
x2 (Union[Array, NativeArray, float, list, tuple]) – Second array-input.
out (Optional[Array]) – optional output array, for writing the result to. (default: None)

Return type:

Array

Returns:

ret – Element-wise logaddexp2 of x1 and x2.

Examples

>>> x1 = ivy.array([1, 2, 3])
>>> x2 = ivy.array([4, 5, 6])
>>> ivy.logaddexp2(x1, x2)
ivy.array([4.169925, 5.169925, 6.169925])

ivy.logical_and(x1, x2, /, *, out=None)[source]#

Compute the logical AND for each element x1_i of the input array x1 with the respective element x2_i of the input array x2.

Parameters:

x1 (Union[Array, NativeArray]) – first input array. Should have a boolean data type.
x2 (Union[Array, NativeArray]) – second input array. Must be compatible with x1. Should have a boolean data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise results. The returned array must have a data type of bool.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([True, True, False])
>>> y = ivy.array([True, False, True])
>>> print(ivy.logical_and(x, y))
ivy.array([True,False,False])

>>> ivy.logical_and(x, y, out=y)
>>> print(y)
ivy.array([True,False,False])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([False, True, True]),
...                   b=ivy.array([True, False, False]))
>>> y = ivy.Container(a=ivy.array([True, True, False]),
...                   b=ivy.array([False, False, True]))
>>> print(ivy.logical_and(y, x))
{
    a: ivy.array([False, True, False]),
    b: ivy.array([False, False, False])
}

>>> ivy.logical_and(y, x, out=y)
>>> print(y)
{
    a: ivy.array([False, True, False]),
    b: ivy.array([False, False, False])
}

>>> x = ivy.Container(a=ivy.array([False, True, True]),
...                   b=ivy.array([True, False, False]))
>>> y = ivy.array([True, False, True])
>>> print(ivy.logical_and(y, x))
{
    a: ivy.array([False, False, True]),
    b: ivy.array([True, False, False])
}

>>> x = ivy.Container(a=ivy.array([False, True, True]),
...                   b=ivy.array([True, False, False]))
>>> y = ivy.array([True, False, True])
>>> ivy.logical_and(y, x, out=x)
>>> print(x)
{
    a: ivy.array([False, False, True]),
    b: ivy.array([True, False, False])
}

ivy.logical_not(x, /, *, out=None)[source]#

Compute the logical NOT for each element x_i of the input array x.

Note

While this specification recommends that this function only accept input arrays having a boolean data type, specification-compliant array libraries may choose to accept input arrays having numeric data types. If non-boolean data types are supported, zeros must be considered the equivalent of False, while non-zeros must be considered the equivalent of True.

Special cases

For this particular case,

If x_i is NaN, the result is False.
If x_i is -0, the result is True.
If x_i is -infinity, the result is False.
If x_i is +infinity, the result is False.

Parameters:

x (Union[Array, NativeArray]) – input array. Should have a boolean data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise results. The returned array must have a data type of bool.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x=ivy.array([1,0,1,1,0])
>>> y=ivy.logical_not(x)
>>> print(y)
ivy.array([False, True, False, False,  True])

>>> x=ivy.array([2,0,3,5])
>>> y=ivy.logical_not(x)
>>> print(y)
ivy.array([False, True, False, False])

>>> x=ivy.native_array([1,0,6,5])
>>> y=ivy.logical_not(x)
>>> print(y)
ivy.array([False, True, False, False])

With ivy.Container input:

>>> x=ivy.Container(a=ivy.array([1,0,1,1]), b=ivy.array([1,0,8,9]))
>>> y=ivy.logical_not(x)
>>> print(y)
{
    a: ivy.array([False, True, False, False]),
    b: ivy.array([False, True, False, False])
}

>>> x=ivy.Container(a=ivy.array([1,0,1,0]), b=ivy.native_array([5,2,0,3]))
>>> y=ivy.logical_not(x)
>>> print(y)
{
    a: ivy.array([False, True, False, True]),
    b: ivy.array([False, False, True, False])
}

ivy.logical_or(x1, x2, /, *, out=None)[source]#

Compute the logical OR for each element x1_i of the input array x1 with the respective element x2_i of the input array x2.

Note

Parameters:

x1 (Union[Array, NativeArray]) – first input array. Should have a boolean data type.
x2 (Union[Array, NativeArray]) – second input array. Must be compatible with x1 (see broadcasting). Should have a boolean data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise results. The returned array must have a data type of bool.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([True, False, True])
>>> y = ivy.array([True, True, False])
>>> print(ivy.logical_or(x, y))
ivy.array([ True,  True,  True])

>>> x = ivy.array([[False, False, True], [True, False, True]])
>>> y = ivy.array([[False, True, False], [True, True, False]])
>>> z = ivy.zeros_like(x)
>>> ivy.logical_or(x, y, out=z)
>>> print(z)
ivy.array([[False,  True,  True],
   [ True,  True,  True]])

>>> x = ivy.array([False, 3, 0])
>>> y = ivy.array([2, True, False])
>>> ivy.logical_or(x, y, out=x)
>>> print(x)
ivy.array([ True,  True, False])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([False, False, True]),
...                   b=ivy.array([True, False, True]))
>>> y = ivy.Container(a=ivy.array([False, True, False]),
...                   b=ivy.array([True, True, False]))
>>> z = ivy.logical_or(x, y)
>>> print(z)
{
    a: ivy.array([False, True, True]),
    b: ivy.array([True, True, True])
}

ivy.logical_xor(x1, x2, /, *, out=None)[source]#

Compute the bitwise XOR of the underlying binary representation of each element x1_i of the input array x1 with the respective element x2_i of the input array x2.

Parameters:

x1 (Union[Array, NativeArray]) – first input array. Should have an integer or boolean data type.
x2 (Union[Array, NativeArray]) – second input array. Must be compatible with x1 (see broadcasting). Should have an integer or boolean data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Return type:: Array
Returns:: ret – an array containing the element-wise results. The returned array must have a data type determined by type-promotion.

Examples

With ivy.Array inputs:

>>> x = ivy.array([1,0,1,1,0])
>>> y = ivy.array([1,0,1,1,0])
>>> z = ivy.logical_xor(x,y)
>>> print(z)
ivy.array([False, False, False, False, False])

>>> x = ivy.array([[[1], [2], [3], [4]]])
>>> y = ivy.array([[[4], [5], [6], [7]]])
>>> z = ivy.logical_xor(x,y)
>>> print(z)
ivy.array([[[False],
        [False],
        [False],
        [False]]])

>>> x = ivy.array([[[1], [2], [3], [4]]])
>>> y = ivy.array([4, 5, 6, 7])
>>> z = ivy.logical_xor(x,y)
>>> print(z)
ivy.array([[[False, False, False, False],
        [False, False, False, False],
        [False, False, False, False],
        [False, False, False, False]]])

With ivy.Container inputs:

>>> x = ivy.Container(a=ivy.array([1,0,0,1,0]), b=ivy.array([1,0,1,0,0]))
>>> y = ivy.Container(a=ivy.array([0,0,1,1,0]), b=ivy.array([1,0,1,1,0]))
>>> z = ivy.logical_xor(x,y)
>>> print(z)
{
a: ivy.array([True, False, True, False, False]),
b: ivy.array([False, False, False, True, False])
}

With a mix of ivy.Array and ivy.Container inputs:

>>> x = ivy.Container(a=ivy.array([1,0,0,1,0]), b=ivy.array([1,0,1,0,0]))
>>> y = ivy.array([0,0,1,1,0])
>>> z = ivy.logical_xor(x,y)
>>> print(z)
{
a: ivy.array([True, False, True, False, False]),
b: ivy.array([True, False, False, True, False])
}

ivy.maximum(x1, x2, /, *, use_where=True, out=None)[source]#

Return the max of x1 and x2 (i.e. x1 > x2 ? x1 : x2) element-wise.

Parameters:

x1 (Union[Array, NativeArray, Number]) – Input array containing elements to maximum threshold.
x2 (Union[Array, NativeArray, Number]) – Tensor containing maximum values, must be broadcastable to x1.
use_where (bool) – Whether to use where() to calculate the maximum. If False, the maximum (default: True) is calculated using the (x + y + |x - y|)/2 formula. Default is True.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – An array with the elements of x1, but clipped to not be lower than the x2 values.

Examples

With ivy.Array inputs: >>> x = ivy.array([7, 9, 5]) >>> y = ivy.array([9, 3, 2]) >>> z = ivy.maximum(x, y) >>> print(z) ivy.array([9, 9, 5])

>>> x = ivy.array([1, 5, 9, 8, 3, 7])
>>> y = ivy.array([[9], [3], [2]])
>>> z = ivy.zeros((3, 6))
>>> ivy.maximum(x, y, out=z)
>>> print(z)
ivy.array([[9.,9.,9.,9.,9.,9.],
           [3.,5.,9.,8.,3.,7.],
           [2.,5.,9.,8.,3.,7.]])

>>> x = ivy.array([[7, 3]])
>>> y = ivy.array([0, 7])
>>> ivy.maximum(x, y, out=x)
>>> print(x)
ivy.array([[7, 7]])

With one ivy.Container input:

>>> x = ivy.array([[1, 3], [2, 4], [3, 7]])
>>> y = ivy.Container(a=ivy.array([1, 0,]),
...                   b=ivy.array([-5, 9]))
>>> z = ivy.maximum(x, y)
>>> print(z)
{
    a: ivy.array([[1, 3],
                  [2, 4],
                  [3, 7]]),
    b: ivy.array([[1, 9],
                  [2, 9],
                  [3, 9]])
}

With multiple ivy.Container inputs:

>>> x = ivy.Container(a=ivy.array([1, 3, 1]),b=ivy.array([2, 8, 5]))
>>> y = ivy.Container(a=ivy.array([1, 5, 6]),b=ivy.array([5, 9, 7]))
>>> z = ivy.maximum(x, y)
>>> print(z)
{
    a: ivy.array([1, 5, 6]),
    b: ivy.array([5, 9, 7])
}

ivy.minimum(x1, x2, /, *, use_where=True, out=None)[source]#

Return the min of x1 and x2 (i.e. x1 < x2 ? x1 : x2) element-wise.

Parameters:

x1 (Union[Array, NativeArray]) – Input array containing elements to minimum threshold.
x2 (Union[Array, NativeArray]) – Tensor containing minimum values, must be broadcastable to x1.
use_where (bool) – Whether to use where() to calculate the minimum. If False, the minimum (default: True) is calculated using the (x + y - |x - y|)/2 formula. Default is True.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – An array with the elements of x1, but clipped to not exceed the x2 values.

Examples

With ivy.Array inputs:

>>> x = ivy.array([7, 9, 5])
>>> y = ivy.array([9, 3, 2])
>>> z = ivy.minimum(x, y)
>>> print(z)
ivy.array([7, 3, 2])

>>> x = ivy.array([1, 5, 9, 8, 3, 7])
>>> y = ivy.array([[9], [3], [2]])
>>> z = ivy.zeros((3, 6))
>>> ivy.minimum(x, y, out=z)
>>> print(z)
ivy.array([[1.,5.,9.,8.,3.,7.],
           [1.,3.,3.,3.,3.,3.],
           [1.,2.,2.,2.,2.,2.]])

>>> x = ivy.array([[7, 3]])
>>> y = ivy.array([0, 7])
>>> ivy.minimum(x, y, out=x)
>>> print(x)
ivy.array([[0, 3]])

With one ivy.Container input:

>>> x = ivy.array([[1, 3], [2, 4], [3, 7]])
>>> y = ivy.Container(a=ivy.array([1, 0,]),b=ivy.array([-5, 9]))
>>> z = ivy.minimum(x, y)
>>> print(z)
{
    a: ivy.array([[1, 0],
                  [1, 0],
                  [1, 0]]),
    b: ivy.array([[-5, 3],
                  [-5, 4],
                  [-5, 7]])
}

With multiple ivy.Container inputs:

>>> x = ivy.Container(a=ivy.array([1, 3, 1]),
...                   b=ivy.array([2, 8, 5]))
>>> y = ivy.Container(a=ivy.array([1, 5, 6]),
...                   b=ivy.array([5, 9, 7]))
>>> z = ivy.minimum(x, y)
>>> print(z)
{
    a: ivy.array([1, 3, 1]),
    b: ivy.array([2, 8, 5])
}

ivy.multiply(x1, x2, /, *, out=None)[source]#

Calculate the product for each element x1_i of the input array x1 with the respective element x2_i of the input array x2.

Parameters:

x1 (Union[float, Array, NativeArray]) – first input array. Should have a numeric data type.
x2 (Union[float, Array, NativeArray]) – second input array. Must be compatible with x1 (see :ref’broadcasting). Should have a numeric data type
out (Optional[Array]) – optional output array, for writing the array result to. (default: None) It must have a shape that the inputs broadcast to.
Standard (This function conforms to the `Array API) –
<https (`docstring) –
<https –
standard. (in the) –
simplicity (Both the description and the type hints above assumes an array input for) –

:param : :param but this function is nestable: :param and therefore also accepts ivy.Container: :param instances in place of any of the arguments.:

Return type:: Array
Returns:: ret – an array containing the element-wise products. The returned array must have a data type determined by Type Promotion Rules.

Examples

With ivy.Array inputs:

>>> x1 = ivy.array([3., 5., 7.])
>>> x2 = ivy.array([4., 6., 8.])
>>> y = ivy.multiply(x1, x2)
>>> print(y)
ivy.array([12., 30., 56.])

With ivy.NativeArray inputs:

>>> x1 = ivy.native_array([1., 3., 9.])
>>> x2 = ivy.native_array([4., 7.2, 1.])
>>> y = ivy.multiply(x1, x2)
>>> print(y)
ivy.array([ 4. , 21.6,  9. ])

With mixed ivy.Array and ivy.NativeArray inputs:

>>> x1 = ivy.array([8., 6., 7.])
>>> x2 = ivy.native_array([1., 2., 3.])
>>> y = ivy.multiply(x1, x2)
>>> print(y)
ivy.array([ 8., 12., 21.])

With ivy.Container inputs:

>>> x1 = ivy.Container(a=ivy.array([12.,4.,6.]), b=ivy.array([3.,1.,5.]))
>>> x2 = ivy.Container(a=ivy.array([1.,3.,4.]), b=ivy.array([3.,3.,2.]))
>>> y = ivy.multiply(x1, x2)
>>> print(y)
{
    a: ivy.array([12.,12.,24.]),
    b: ivy.array([9.,3.,10.])
}

With mixed ivy.Container and ivy.Array inputs:

>>> x1 = ivy.Container(a=ivy.array([3., 4., 5.]), b=ivy.array([2., 2., 1.]))
>>> x2 = ivy.array([1.,2.,3.])
>>> y = ivy.multiply(x1, x2)
>>> print(y)
{
    a: ivy.array([3.,8.,15.]),
    b: ivy.array([2.,4.,3.])
}

ivy.nan_to_num(x, /, *, copy=True, nan=0.0, posinf=None, neginf=None, out=None)[source]#

Replace NaN with zero and infinity with large finite numbers (default behaviour) or with the numbers defined by the user using the nan, posinf and/or neginf keywords.

Parameters:

x (Union[Array, NativeArray]) – Array input.
copy (bool) – Whether to create a copy of x (True) or to replace values in-place (False). (default: True) The in-place operation only occurs if casting to an array does not require a copy. Default is True.
nan (Union[float, int]) – Value to be used to fill NaN values. If no value is passed then NaN values (default: 0.0) will be replaced with 0.0.
posinf (Optional[Union[int, float]]) – Value to be used to fill positive infinity values. If no value is passed (default: None) then positive infinity values will be replaced with a very large number.
neginf (Optional[Union[int, float]]) – Value to be used to fill negative infinity values. (default: None) If no value is passed then negative infinity values will be replaced with a very small (or negative) number.
out (Optional[Array]) – optional output array, for writing the result to. (default: None)

Return type:

Array

Returns:

ret – Array with the non-finite values replaced. If copy is False, this may be x itself.

Examples

>>> x = ivy.array([1, 2, 3, nan])
>>> ivy.nan_to_num(x)
ivy.array([1.,    1.,   3.,   0.0])
>>> x = ivy.array([1, 2, 3, inf])
>>> ivy.nan_to_num(x, posinf=5e+100)
ivy.array([1.,   2.,   3.,   5e+100])

ivy.negative(x, /, *, out=None)[source]#

Return a new array with the negative value of each element in x.

Parameters:

x (Union[float, Array, NativeArray]) – Input array.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – A new array with the negative value of each element in x.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([0,1,1,2])
>>> y = ivy.negative(x)
>>> print(y)
ivy.array([ 0, -1, -1, -2])

>>> x = ivy.array([0,-1,-0.5,2,3])
>>> y = ivy.zeros(5)
>>> ivy.negative(x, out=y)
>>> print(y)
ivy.array([-0. ,  1. ,  0.5, -2. , -3. ])

>>> x = ivy.array([[1.1, 2.2, 3.3],
...                [-4.4, -5.5, -6.6]])
>>> ivy.negative(x,out=x)
>>> print(x)
ivy.array([[-1.1, -2.2, -3.3],
   [4.4, 5.5, 6.6]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0., 1., 2.]),
...                   b=ivy.array([3., 4., -5.]))
>>> y = ivy.negative(x)
>>> print(y)
{
    a: ivy.array([-0., -1., -2.]),
    b: ivy.array([-3., -4., 5.])
}

ivy.not_equal(x1, x2, /, *, out=None)[source]#

Compute the truth value of x1_i != x2_i for each element x1_i of the input array x1 with the respective element x2_i of the input array x2.

Parameters:

x1 (Union[float, Array, NativeArray, Container]) – first input array. Should have a numeric data type.
x2 (Union[float, Array, NativeArray, Container]) – second input array. Must be compatible with x1 (see ref:broadcasting). Should have a numeric data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise results. The returned array must have a data type of bool.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Functional Examples

With ivy.Array inputs:

>>> x1 = ivy.array([1, 0, 1, 1])
>>> x2 = ivy.array([1, 0, 0, -1])
>>> y = ivy.not_equal(x1, x2)
>>> print(y)
ivy.array([False, False, True, True])

>>> x1 = ivy.array([1, 0, 1, 0])
>>> x2 = ivy.array([0, 1, 0, 1])
>>> y = ivy.not_equal(x1, x2)
>>> print(y)
ivy.array([True, True, True, True])

>>> x1 = ivy.array([1, -1, 1, -1])
>>> x2 = ivy.array([0, -1, 1, 0])
>>> y = ivy.zeros(4)
>>> ivy.not_equal(x1, x2, out=y)
>>> print(y)
ivy.array([True, False, False, True])

>>> x1 = ivy.array([1, -1, 1, -1])
>>> x2 = ivy.array([0, -1, 1, 0])
>>> y = ivy.not_equal(x1, x2, out=x1)
>>> print(y)
ivy.array([True, False, False, True])

With a mix of ivy.Array and ivy.NativeArray inputs:

>>> x1 = ivy.native_array([1, 2])
>>> x2 = ivy.array([1, 2])
>>> y = ivy.not_equal(x1, x2)
>>> print(y)
ivy.array([False, False])

>>> x1 = ivy.native_array([1, -1])
>>> x2 = ivy.array([0, 1])
>>> y = ivy.not_equal(x1, x2)
>>> print(y)
ivy.array([True, True])

>>> x1 = ivy.native_array([1, -1, 1, -1])
>>> x2 = ivy.native_array([0, -1, 1, 0])
>>> y = ivy.zeros(4)
>>> ivy.not_equal(x1, x2, out=y)
>>> print(y)
ivy.array([True, False, False, True])

>>> x1 = ivy.native_array([1, 2, 3, 4])
>>> x2 = ivy.native_array([0, 2, 3, 4])
>>> y = ivy.zeros(4)
>>> ivy.not_equal(x1, x2, out=y)
>>> print(y)
ivy.array([True, False, False, False])

With ivy.Container input:

>>> x1 = ivy.Container(a=ivy.array([1, 0, 3]),
...                    b=ivy.array([1, 2, 3]),
...                    c=ivy.native_array([1, 2, 4]))
>>> x2 = ivy.Container(a=ivy.array([1, 2, 3]),
...                    b=ivy.array([1, 2, 3]),
...                    c=ivy.native_array([1, 2, 4]))
>>> y = ivy.not_equal(x1, x2)
>>> print(y)
{
    a: ivy.array([False, True, False]),
    b: ivy.array([False, False, False]),
    c: ivy.array([False, False, False])
}

>>> x1 = ivy.Container(a=ivy.native_array([0, 1, 0]),
...                    b=ivy.array([1, 2, 3]),
...                    c=ivy.native_array([1.0, 2.0, 4.0]))
>>> x2 = ivy.Container(a=ivy.array([1, 2, 3]),
...                    b=ivy.native_array([1.1, 2.1, 3.1]),
...                    c=ivy.native_array([1, 2, 4]))
>>> y = ivy.not_equal(x1, x2)
>>> print(y)
{
    a: ivy.array([True, True, True]),
    b: ivy.array([True, True, True]),
    c: ivy.array([False, False, False])
}

With a mix of ivy.Array and ivy.Container inputs:

>>> x1 = ivy.Container(a=ivy.array([1, 2, 3]),
...                    b=ivy.array([1, 3, 5]))
>>> x2 = ivy.Container(a=ivy.array([1, 2, 3]),
...                    b=ivy.array([1, 4, 5]))
>>> y = ivy.not_equal(x1, x2)
>>> print(y)
{
    a: ivy.array([False, False, False]),
    b: ivy.array([False, True, False])
}

>>> x1 = ivy.Container(a=ivy.array([1.0, 2.0, 3.0]),
...                    b=ivy.array([1, 4, 5]))
>>> x2 = ivy.Container(a=ivy.array([1, 2, 3.0]),
...                    b=ivy.array([1.0, 4.0, 5.0]))
>>> y = ivy.not_equal(x1, x2)
>>> print(y)
{
    a: ivy.array([False, False, False]),
    b: ivy.array([False, False, False])
}

ivy.positive(x, /, *, out=None)[source]#

Return a new array with the positive value of each element in x.

Parameters:

x (Union[float, Array, NativeArray]) – Input array.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – A new array with the positive value of each element in x.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Functional Examples

With ivy.Array input:

>>> x = ivy.array([2, 3 ,5, 7])
>>> y = ivy.positive(x)
>>> print(y)
ivy.array([2, 3, 5, 7])

>>> x = ivy.array([0, -1, -0.5, 2, 3])
>>> y = ivy.zeros(5)
>>> ivy.positive(x, out=y)
>>> print(y)
ivy.array([0., -1., -0.5,  2.,  3.])

>>> x = ivy.array([[1.1, 2.2, 3.3],
...                [-4.4, -5.5, -6.6]])
>>> ivy.positive(x,out=x)
>>> print(x)
ivy.array([[ 1.1,  2.2,  3.3],
   [-4.4, -5.5, -6.6]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0., 1., 2.]),
...                   b=ivy.array([3., 4., -5.]))
>>> y = ivy.positive(x)
>>> print(y)
{
a: ivy.array([0., 1., 2.]),
b: ivy.array([3., 4., -5.])
}

ivy.pow(x1, x2, /, *, out=None)[source]#

Calculate an implementation-dependent approximation of exponentiation by raising each element x1_i (the base) of the input array x1 to the power of x2_i (the exponent), where x2_i is the corresponding element of the input array x2.

Note

If both x1 and x2 have integer data types, the result of pow when x2_i is negative (i.e., less than zero) is unspecified and thus implementation-dependent. If x1 has an integer data type and x2 has a floating-point data type, behavior is implementation-dependent (type promotion between data type “kinds” (integer versus floating-point) is unspecified).

Special cases

For floating-point operands,

If x1_i is not equal to 1 and x2_i is NaN, the result is NaN.
If x2_i is +0, the result is 1, even if x1_i is NaN.
If x2_i is -0, the result is 1, even if x1_i is NaN.
If x1_i is NaN and x2_i is not equal to 0, the result is NaN.
If abs(x1_i) is greater than 1 and x2_i is +infinity, the result is +infinity.
If abs(x1_i) is greater than 1 and x2_i is -infinity, the result is +0.
If abs(x1_i) is 1 and x2_i is +infinity, the result is 1.
If abs(x1_i) is 1 and x2_i is -infinity, the result is 1.
If x1_i is 1 and x2_i is not NaN, the result is 1.
If abs(x1_i) is less than 1 and x2_i is +infinity, the result is +0.
If abs(x1_i) is less than 1 and x2_i is -infinity, the result is +infinity.
If x1_i is +infinity and x2_i is greater than 0, the result is +infinity.
If x1_i is +infinity and x2_i is less than 0, the result is +0.
If x1_i is -infinity, x2_i is greater than 0, and x2_i is an odd integer value, the result is -infinity.
If x1_i is -infinity, x2_i is greater than 0, and x2_i is not an odd integer value, the result is +infinity.
If x1_i is -infinity, x2_i is less than 0, and x2_i is an odd integer value, the result is -0.
If x1_i is -infinity, x2_i is less than 0, and x2_i is not an odd integer value, the result is +0.
If x1_i is +0 and x2_i is greater than 0, the result is +0.
If x1_i is +0 and x2_i is less than 0, the result is +infinity.
If x1_i is -0, x2_i is greater than 0, and x2_i is an odd integer value, the result is -0.
If x1_i is -0, x2_i is greater than 0, and x2_i is not an odd integer value, the result is +0.
If x1_i is -0, x2_i is less than 0, and x2_i is an odd integer value, the result is -infinity.
If x1_i is -0, x2_i is less than 0, and x2_i is not an odd integer value, the result is +infinity.
If x1_i is less than 0, x1_i is a finite number, x2_i is a finite number, and x2_i is not an integer value, the result is NaN.

Parameters:

x1 (Union[float, Array, NativeArray]) – first input array whose elements correspond to the exponentiation base. Should have a numeric data type.
x2 (Union[float, Array, NativeArray]) – second input array whose elements correspond to the exponentiation exponent. Must be compatible with x1 (see broadcasting). Should have a numeric data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise results. The returned array must have a data type determined by type-promotion.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([1, 2, 3])
>>> y = ivy.pow(x, 3)
>>> print(y)
ivy.array([1, 8, 27])

>>> x = ivy.array([1.5, -0.8, 0.3])
>>> y = ivy.zeros(3)
>>> ivy.pow(x, 2, out=y)
>>> print(y)
ivy.array([2.25, 0.64, 0.09])

>>> x = ivy.array([[1.2, 2, 3.1], [1, 2.5, 9]])
>>> ivy.pow(x, 2.3, out=x)
>>> print(x)
ivy.array([[  1.52095687,   4.92457771,  13.49372482],
       [  1.        ,   8.22738838, 156.5877228 ]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0, 1]), b=ivy.array([2, 3]))
>>> y = ivy.pow(x, 3)
>>> print(y)
{
    a:ivy.array([0,1]),
    b:ivy.array([8,27])
}

ivy.rad2deg(x, /, *, out=None)[source]#

Convert the input from radians to degrees.

Parameters:

x (Union[Array, NativeArray]) – input array whose elements are each expressed in radians.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array with each element in x converted from radians to degrees.

Examples

With ivy.Array input:

>>> x=ivy.array([0.,1.57,3.14,4.71,6.28])
>>> y=ivy.rad2deg(x)
>>> print(y)
ivy.array([  0.,  90., 180., 270., 360.])

>>> x=ivy.array([0.,-0.0262,-0.873,ivy.nan])
>>> y=ivy.zeros(5)
>>> ivy.rad2deg(x,out=y)
>>> print(y)
ivy.array([  0. ,  -1.5, -50. ,   nan])

>>> x = ivy.array([[1.1, 2.2, 3.3],[-4.4, -5.5, -6.6]])
>>> ivy.rad2deg(x, out=x)
>>> print(x)
ivy.array([[  63.,  126.,  189.],
    [-252., -315., -378.]])

>>> x=ivy.native_array([-0,20.1,ivy.nan])
>>> y=ivy.zeros(3)
>>> ivy.rad2deg(x,out=y)
>>> print(y)
ivy.array([   0., 1150.,   nan])

With ivy.Container input:

>>> x=ivy.Container(a=ivy.array([-0,20.1,-50.5,-ivy.nan]),
...                 b=ivy.array([0,1,2,3,4]))
>>> y=ivy.rad2deg(x)
>>> print(y)
{
    a: ivy.array([0., 1150., -2890., nan]),
    b: ivy.array([0., 57.3, 115., 172., 229.])
}

>>> x=ivy.Container(a=ivy.array([0,10,180,8.5,6]),
...                 b=ivy.native_array([0,-1.5,0.5,ivy.nan]))
>>> y=ivy.rad2deg(x)
>>> print(y)
{
    a: ivy.array([0., 573., 10300., 487., 344.]),
    b: ivy.array([0., -85.9, 28.6, nan])
}

ivy.real(x, /, *, out=None)[source]#

Test each element x_i of the input array x to take only real part from it. Returns a float array, where it only contains . If element has complex type with zero complex part, the return value will be that element, else it only returns real part.

Parameters:

x (Union[Array, NativeArray]) – input array.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing test results. An element out_i is real number if x_i contain real number part only and if it is real number with complex part also then it returns the real number part. The returned array should have a data type of float.
The descriptions above assume an array input for simplicity, but
the method also accepts ivy.Container instances
in place of (class:ivy.Array or ivy.NativeArray)
instances, as shown in the type hints and also the examples below.

Examples

With ivy.Array inputs: >>> x = ivy.array([[[1.1], [2], [-6.3]]]) >>> z = ivy.real(x) >>> print(z) ivy.array([[[1.1], [2.], [-6.3]]])

>>> x = ivy.array([4.2-0j, 3j, 7+5j])
>>> z = ivy.real(x)
>>> print(z)
ivy.array([4.2, 0., 7.])

With ivy.Container input: >>> x = ivy.Container(a=ivy.array([-6.7-7j, 0.314+0.355j, 1.23]), b=ivy.array([5j, 5.32-6.55j, 3.001])) >>> z = ivy.real(x) >>> print(z) {

a: ivy.array([-6.7, 0.314, 1.23]), b: ivy.array([0., 5.32, 3.001])

}

ivy.reciprocal(x, /, *, out=None)[source]#

Return a new array with the reciprocal of each element in x.

Parameters:

x (Union[float, Array, NativeArray]) – Input array.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – A new array with the positive value of each element in x.

Examples

>>> x = ivy.array([1, 2, 3])
>>> y = ivy.reciprocal(x)
>>> print(y)
ivy.array([1.        , 0.5       , 0.33333333])

ivy.remainder(x1, x2, /, *, modulus=True, out=None)[source]#

Return the remainder of division for each element x1_i of the input array x1 and the respective element x2_i of the input array x2.

Note

This function is equivalent to the Python modulus operator x1_i % x2_i. For input arrays which promote to an integer data type, the result of division by zero is unspecified and thus implementation-defined. In general, similar to Python’s % operator, this function is not recommended for floating-point operands as semantics do not follow IEEE 754. That this function is specified to accept floating-point operands is primarily for reasons of backward compatibility.

Special Cases

For floating-point operands,

If either x1_i or x2_i is NaN, the result is NaN.
If x1_i is either +infinity or -infinity and x2_i is either +infinity or -infinity, the result is NaN.
If x1_i is either +0 or -0 and x2_i is either +0 or -0, the result is NaN.
If x1_i is +0 and x2_i is greater than 0, the result is +0.
If x1_i is -0 and x2_i is greater than 0, the result is +0.
If x1_i is +0 and x2_i is less than 0, the result is -0.
If x1_i is -0 and x2_i is less than 0, the result is -0.
If x1_i is greater than 0 and x2_i is +0, the result is NaN.
If x1_i is greater than 0 and x2_i is -0, the result is NaN.
If x1_i is less than 0 and x2_i is +0, the result is NaN.
If x1_i is less than 0 and x2_i is -0, the result is NaN.
If x1_i is +infinity and x2_i is a positive (i.e., greater than 0) finite number, the result is NaN.
If x1_i is +infinity and x2_i is a negative (i.e., less than 0) finite number, the result is NaN.
If x1_i is -infinity and x2_i is a positive (i.e., greater than 0) finite number, the result is NaN.
If x1_i is -infinity and x2_i is a negative (i.e., less than 0) finite number, the result is NaN.
If x1_i is a positive (i.e., greater than 0) finite number and x2_i is +infinity, the result is x1_i. (note: this result matches Python behavior.)
If x1_i is a positive (i.e., greater than 0) finite number and x2_i is -infinity, the result is x2_i. (note: this result matches Python behavior.)
If x1_i is a negative (i.e., less than 0) finite number and x2_i is +infinity, the result is x2_i. (note: this results matches Python behavior.)
If x1_i is a negative (i.e., less than 0) finite number and x2_i is -infinity, the result is x1_i. (note: this result matches Python behavior.)
In the remaining cases, the result must match that of the Python % operator.

Parameters:

x1 (Union[float, Array, NativeArray]) – dividend input array. Should have a numeric data type.
x2 (Union[float, Array, NativeArray]) – divisor input array. Must be compatible with x1 (see ref:Broadcasting). Should have a numeric data type.
modulus (bool) – whether to compute the modulus instead of the remainder. Default is True. (default: True)
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise results. Each element-wise result must have the same sign as the respective element x2_i. The returned array must have a data type determined by Type Promotion Rules.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array inputs:

>>> x1 = ivy.array([2., 5., 15.])
>>> x2 = ivy.array([3., 2., 4.])
>>> y = ivy.remainder(x1, x2)
>>> print(y)
ivy.array([2., 1., 3.])

With mixed ivy.Array and ivy.NativeArray inputs:

>>> x1 = ivy.array([23., 1., 6.])
>>> x2 = ivy.native_array([11., 2., 4.])
>>> y = ivy.remainder(x1, x2)
>>> print(y)
ivy.array([1., 1., 2.])

With ivy.Container inputs:

>>> x1 = ivy.Container(a=ivy.array([2., 3., 5.]), b=ivy.array([2., 2., 4.]))
>>> x2 = ivy.Container(a=ivy.array([1., 3., 4.]), b=ivy.array([1., 3., 3.]))
>>> y = ivy.remainder(x1, x2)
>>> print(y)
{
    a: ivy.array([0., 0., 1.]),
    b: ivy.array([0., 2., 1.])
}

ivy.round(x, /, *, decimals=0, out=None)[source]#

Round each element x_i of the input array x to the nearest integer-valued number.

Special cases

If x_i is already an integer-valued, the result is x_i.

For floating-point operands,

If x_i is +infinity, the result is +infinity.
If x_i is -infinity, the result is -infinity.
If x_i is +0, the result is +0.
If x_i is -0, the result is -0.
If x_i is NaN, the result is NaN.
If two integers are equally close to x_i, the result is the even integer closest to x_i.

Parameters:

x (Union[Array, NativeArray]) – input array containing elements to round.
decimals (Optional[int]) – number of decimal places to round to. Default is 0. (default: 0)
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – An array of the same shape and type as x, with the elements rounded to integers.
Note (PyTorch supports an additional argument decimals for the)
`round function <https (//pytorch.org/docs/stable/generated/torch.round.html>`_.)
It has been deliberately omitted here due to the imprecise
nature of the argument in torch.round.
This function conforms to the `Array API Standard
<https (//data-apis.org/array-api/latest/>`_. This docstring is an extension of the)
`docstring <https (//data-apis.org/array-api/latest/API_specification/generated/signatures.elementwise_functions.round.html>`_ # noqa)
in the standard.
Both the description and the type hints above assumes an array input for simplicity,
but this function is nestable, and therefore also accepts ivy.Container
instances in place of any of the arguments.

Examples

With ivy.Array input:

>>> x = ivy.array([1.2, 2.4, 3.6])
>>> y = ivy.round(x)
>>> print(y)
ivy.array([1.,2.,4.])

>>> x = ivy.array([-0, 5, 4.5])
>>> y = ivy.round(x)
>>> print(y)
ivy.array([0.,5.,4.])

>>> x = ivy.array([1.5654, 2.034, 15.1, -5.0])
>>> y = ivy.zeros(4)
>>> ivy.round(x, out=y)
>>> print(y)
ivy.array([2.,2.,15.,-5.])

>>> x = ivy.array([[0, 5.433, -343.3, 1.5],
...                [-5.5, 44.2, 11.5, 12.01]])
>>> ivy.round(x, out=x)
>>> print(x)
ivy.array([[0.,5.,-343.,2.],[-6.,44.,12.,12.]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([4.20, 8.6, 6.90, 0.0]),
...                   b=ivy.array([-300.9, -527.3, 4.5]))
>>> y = ivy.round(x)
>>> print(y)
{
    a:ivy.array([4.,9.,7.,0.]),
    b:ivy.array([-301.,-527.,4.])
}

ivy.sign(x, /, *, out=None)[source]#

Return an indication of the sign of a number for each element x_i of the input array x.

Special cases

If x_i is less than 0, the result is -1.
If x_i is either -0 or +0, the result is 0.
If x_i is greater than 0, the result is +1.

Parameters:

x (Union[Array, NativeArray]) – input array. Should have a numeric data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the evaluated result for each element in x. The returned array must have the same data type as x.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([8.3, -0, 6.8, 0.07])
>>> y = ivy.sign(x)
>>> print(y)
ivy.array([1., 0., 1., 1.])

>>> x = ivy.array([[5.78, -4., -6.9, 0],
...                [-.4, 0.5, 8, -0.01]])
>>> y = ivy.sign(x)
>>> print(y)
ivy.array([[ 1., -1., -1.,  0.],
           [-1.,  1.,  1., -1.]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0., -0.]),
...                   b=ivy.array([1.46, 5.9, -0.0]),
...                   c=ivy.array([-8.23, -4.9, -2.6, 7.4]))
>>> y = ivy.sign(x)
>>> print(y)
{
    a: ivy.array([0., 0.]),
    b: ivy.array([1., 1., 0.]),
    c: ivy.array([-1., -1., -1., 1.])
}

ivy.sin(x, /, *, out=None)[source]#

Calculate an implementation-dependent approximation to the sine, having domain (-infinity, +infinity) and codomain [-1, +1], for each element x_i of the input array x. Each element x_i is assumed to be expressed in radians.

Special cases

For floating-point operands,

If x_i is NaN, the result is NaN.
If x_i is +0, the result is +0.
If x_i is -0, the result is -0.
If x_i is either +infinity or -infinity, the result is NaN.

Parameters:

x (Union[Array, NativeArray]) – input array whose elements are each expressed in radians. Should have a floating-point data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the sine of each element in x. The returned array must have a floating-point data type determined by type-promotion.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([0., 1., 2.])
>>> y = ivy.sin(x)
>>> print(y)
ivy.array([0., 0.841, 0.909])

>>> x = ivy.array([0., 1.2, -2.3, 3.6])
>>> y = ivy.zeros(4)
>>> ivy.sin(x, out=y)
>>> print(y)
ivy.array([0., 0.932, -0.746, -0.443])

>>> x = ivy.array([[1., 2., 3.], [-4., -5., -6.]])
>>> ivy.sin(x, out=x)
>>> print(x)
ivy.array([[0.841, 0.909, 0.141],
           [0.757, 0.959, 0.279]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0., 1., 2., 3.]),
...                   b=ivy.array([-4., -5., -6., -7.]))
>>> y = ivy.sin(x)
>>> print(y)
{
    a: ivy.array([0., 0.841, 0.909, 0.141]),
    b: ivy.array([0.757, 0.959, 0.279, -0.657])
}

ivy.sinh(x, /, *, out=None)[source]#

Calculate an implementation-dependent approximation to the hyperbolic sine, having domain [-infinity, +infinity] and codomain [-infinity, +infinity], for each element x_i of the input array x.

Special cases

For floating-point operands,

If x_i is NaN, the result is NaN.
If x_i is +0, the result is +0.
If x_i is -0, the result is -0.
If x_i is +infinity, the result is +infinity.
If x_i is -infinity, the result is -infinity.

Parameters:

x (Union[Array, NativeArray]) – input array whose elements each represent a hyperbolic angle. Should have a floating-point data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the hyperbolic sine of each element in x. The returned array must have a floating-point data type determined by type-promotion.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([1, 2, 3])
>>> y = ivy.sinh(x)
>>> print(y)
    ivy.array([1.18, 3.63, 10.])

>>> x = ivy.array([0.23, 3., -1.2])
>>> ivy.sinh(x, out=x)
>>> print(x)
    ivy.array([0.232, 10., -1.51])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0.23, -0.25, 1]), b=ivy.array([3, -4, 1.26]))
>>> y = ivy.sinh(x)
>>> print(y)
{
    a: ivy.array([0.232, -0.253, 1.18]),
    b: ivy.array([10., -27.3, 1.62])
}

ivy.sqrt(x, /, *, out=None)[source]#

Calculate the square root, having domain [0, +infinity] and codomain [0, +infinity], for each element x_i of the input array x. After rounding, each result must be indistinguishable from the infinitely precise result (as required by IEEE 754).

Special cases

For floating-point operands,

If x_i is NaN, the result is NaN.
If x_i is less than 0, the result is NaN.
If x_i is +0, the result is +0.
If x_i is -0, the result is -0.
If x_i is +infinity, the result is +infinity.

Parameters:

x (Union[Array, NativeArray]) – input array. Should have a floating-point data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the square root of each element in x. The returned array must have a floating-point data type determined by type-promotion.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([0, 4., 8.])
>>> y = ivy.sqrt(x)
>>> print(y)
ivy.array([0., 2., 2.83])

>>> x = ivy.array([1, 2., 4.])
>>> y = ivy.zeros(3)
>>> ivy.sqrt(x, out=y)
ivy.array([1., 1.41, 2.])

>>> X = ivy.array([40., 24., 100.])
>>> ivy.sqrt(x, out=x)
>>> ivy.array([6.32455532, 4.89897949, 10.])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([44., 56., 169.]), b=ivy.array([[49.,1.], [0,20.]]))
>>> y = ivy.sqrt(x)
>>> print(y)
{
    a: ivy.array([6.63, 7.48, 13.]),
    b: ivy.array([[7., 1.],
                  [0., 4.47]])
}

ivy.square(x, /, *, out=None)[source]#

Each element x_i of the input array x.

Parameters:

x (Union[Array, NativeArray]) – Input array. Should have a numeric data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the evaluated result for each element in x.

This method conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([1, 2, 3])
>>> y = ivy.square(x)
>>> print(y)
ivy.array([1, 4, 9])

>>> x = ivy.array([1.5, -0.8, 0.3])
>>> y = ivy.zeros(3)
>>> ivy.square(x, out=y)
>>> print(y)
ivy.array([2.25, 0.64, 0.09])

>>> x = ivy.array([[1.2, 2, 3.1], [-1, -2.5, -9]])
>>> ivy.square(x, out=x)
>>> print(x)
ivy.array([[1.44,4.,9.61],[1.,6.25,81.]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0, 1]), b=ivy.array([2, 3]))
>>> y = ivy.square(x)
>>> print(y)
{
    a:ivy.array([0,1]),
    b:ivy.array([4,9])
}

ivy.subtract(x1, x2, /, *, alpha=None, out=None)[source]#

Calculate the difference for each element x1_i of the input array x1 with the respective element x2_i of the input array x2.

Parameters:

x1 (Union[float, Array, NativeArray]) – first input array. Should have a numeric data type.
x2 (Union[float, Array, NativeArray]) – second input array. Must be compatible with x1 (see ref:broadcasting). Should have a numeric data type.
alpha (Optional[Union[int, float]]) – optional scalar multiplier for x2. (default: None)
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise differences.

This method conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

>>> x = ivy.array([3, 6, 3])
>>> y = ivy.array([2, 1, 6])
>>> z = ivy.subtract(x, y)
>>> print(z)
ivy.array([ 1,  5, -3])

>>> x = ivy.array([3, 6, 3])
>>> y = ivy.array([2, 1, 6])
>>> z = ivy.subtract(x, y, alpha=2)
>>> print(z)
ivy.array([-1,  4, -9])

ivy.tan(x, /, *, out=None)[source]#

Calculate an implementation-dependent approximation to the tangent, having domain (-infinity, +infinity) and codomain (-infinity, +infinity), for each element x_i of the input array x. Each element x_i is assumed to be expressed in radians.

Special cases

For floating-point operands,

If x_i is NaN, the result is NaN.
If x_i is +0, the result is +0.
If x_i is -0, the result is -0.
If x_i is either +infinity or -infinity, the result is NaN.

Parameters:

x (Union[Array, NativeArray]) – input array whose elements are expressed in radians. Should have a floating-point data type.
out (Optional[Array]) – optional output, for writing the result to. It must have a shape that the inputs (default: None) broadcast to.

Return type:

Array

Returns:

ret – an array containing the tangent of each element in x. The return must have a floating-point data type determined by type-promotion.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([0., 1., 2.])
>>> y = ivy.tan(x)
>>> print(y)
ivy.array([0., 1.56, -2.19])

>>> x = ivy.array([0.5, -0.7, 2.4])
>>> y = ivy.zeros(3)
>>> ivy.tan(x, out=y)
>>> print(y)
ivy.array([0.546, -0.842, -0.916])

>>> x = ivy.array([[1.1, 2.2, 3.3],
...                [-4.4, -5.5, -6.6]])
>>> ivy.tan(x, out=x)
>>> print(x)
ivy.array([[1.96, -1.37, 0.16],
    [-3.1, 0.996, -0.328]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0., 1., 2.]), b=ivy.array([3., 4., 5.]))
>>> y = ivy.tan(x)
>>> print(y)
{
    a: ivy.array([0., 1.56, -2.19]),
    b: ivy.array([-0.143, 1.16, -3.38])
}

ivy.tanh(x, /, *, out=None)[source]#

Calculate an implementation-dependent approximation to the hyperbolic tangent, having domain [-infinity, +infinity] and codomain [-1, +1], for each element x_i of the input array x.

Special cases

For floating-point operands,

If x_i is NaN, the result is NaN.
If x_i is +0, the result is +0.
If x_i is -0, the result is -0.
If x_i is +infinity, the result is +1.
If x_i is -infinity, the result is -1.

Parameters:

x (Union[Array, NativeArray]) – input array whose elements each represent a hyperbolic angle. Should have a real-valued floating-point data type.
out (Optional[Array]) – optional output, for writing the result to. It must have a shape that the inputs (default: None) broadcast to.

Return type:

Array

Returns:

ret – an array containing the hyperbolic tangent of each element in x. The returned array must have a real-valued floating-point data type determined by type-promotion.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([0., 1., 2.])
>>> y = ivy.tanh(x)
>>> print(y)
ivy.array([0., 0.762, 0.964])

>>> x = ivy.array([0.5, -0.7, 2.4])
>>> y = ivy.zeros(3)
>>> ivy.tanh(x, out=y)
>>> print(y)
ivy.array([0.462, -0.604, 0.984])

>>> x = ivy.array([[1.1, 2.2, 3.3],
...                [-4.4, -5.5, -6.6]])
>>> ivy.tanh(x, out=x)
>>> print(x)
ivy.array([[0.8, 0.976, 0.997],
          [-1., -1., -1.]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0., 1., 2.]),
...                   b=ivy.array([3., 4., 5.]))
>>> y = ivy.tanh(x)
>>> print(y)
{
    a: ivy.array([0., 0.762, 0.964]),
    b: ivy.array([0.995, 0.999, 1.])
}

ivy.trapz(y, /, *, x=None, dx=1.0, axis=-1, out=None)[source]#

Integrate along the given axis using the composite trapezoidal rule.

If x is provided, the integration happens in sequence along its elements - they are not sorted..

Parameters:

y (Array) – The array that should be integrated.
x (Optional[Array]) – The sample points corresponding to the input array values. (default: None) If x is None, the sample points are assumed to be evenly spaced dx apart. The default is None.
dx (float) – The spacing between sample points when x is None. The default is 1. (default: 1.0)
axis (int) – The axis along which to integrate. (default: -1)
out (Optional[Array]) – optional output array, for writing the result to. (default: None)

Return type:

Array

Returns:

ret – Definite integral of n-dimensional array as approximated along a single axis by the trapezoidal rule. If the input array is a 1-dimensional array, then the result is a float. If n is greater than 1, then the result is an n-1 dimensional array.

Examples

>>> y = ivy.array([1, 2, 3])
>>> ivy.trapz([1,2,3])
4.0
>>> y = ivy.array([1, 2, 3])
>>> ivy.trapz([1,2,3], x=[4, 6, 8])
8.0
>>> y = ivy.array([1, 2, 3])
>>> ivy.trapz([1,2,3], dx=2)
8.0

ivy.trunc(x, /, *, out=None)[source]#

Round each element x_i of the input array x to the integer-valued number that is closest to but no greater than x_i.

Special cases

If x_i is already an integer-valued, the result is x_i.

For floating-point operands,

If x_i is +infinity, the result is +infinity.
If x_i is -infinity, the result is -infinity.
If x_i is +0, the result is +0.
If x_i is -0, the result is -0.
If x_i is NaN, the result is NaN.

Parameters:

x (Union[Array, NativeArray]) – input array. Should have a numeric data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the rounded result for each element in x. The returned array must have the same data type as x.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Examples

With ivy.Array input:

>>> x = ivy.array([-1, 0.54, 3.67, -0.025])
>>> y = ivy.trunc(x)
>>> print(y)
ivy.array([-1.,  0.,  3., -0.])

>>> x = ivy.array([0.56, 7, -23.4, -0.0375])
>>> ivy.trunc(x, out=x)
>>> print(x)
ivy.array([  0.,   7., -23.,  -0.])

>>> x = ivy.array([[0.4, -8, 0.55], [0, 0.032, 2]])
>>> y = ivy.zeros([2,3])
>>> ivy.trunc(x, out=y)
>>> print(y)
ivy.array([[ 0., -8.,  0.],
       [ 0.,  0.,  2.]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([-0.25, 4, 1.3]), b=ivy.array([12, -3.5, 1.234]))
>>> y = ivy.trunc(x)
>>> print(y)
{
    a: ivy.array([-0., 4., 1.]),
    b: ivy.array([12., -3., 1.])
}

ivy.trunc_divide(x1, x2, /, *, out=None)[source]#

Perform element-wise integer division of the inputs rounding the results towards zero.

Parameters:

x1 (Union[float, Array, NativeArray]) – dividend input array. Should have a numeric data type.
x2 (Union[float, Array, NativeArray]) – divisor input array. Must be compatible with x1 (see Broadcasting). Should have a numeric data type.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise results. The returned array must have a floating-point data type determined by Type Promotion Rules.

Examples

With ivy.Array inputs:

>>> x1 = ivy.array([2., 7., 9.])
>>> x2 = ivy.array([3., -4., 0.6])
>>> y = ivy.trunc_divide(x1, x2)
>>> print(y)
ivy.array([ 0., -1., 14.])

This should have hopefully given you an overview of the elementwise submodule, if you have any questions, please feel free to reach out on our discord in the elementwise channel or in the elementwise forum!