Elementwise

ivy.abs(x, /, *, out=None)[source]

Calculates 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 this particular case,

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.

Parameters

x (Union[float, 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 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 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.NativeArray input:

>>> x = ivy.native_array([0, -0, -2.6, -1, 1, 3.6])
>>> y = ivy.abs(x)
>>> print(y)
ivy.array([ 0., 0., 2.6, 1., 1., 3.6])

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]

Calculates 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 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([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.NativeArray input:

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

Calculates 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 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, 2.5, 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.NativeArray input:

>>> x = ivy.array([1., 2., 10.])
>>> y = ivy.acosh(x)
>>> print(y)
ivy.array([0.  , 1.32, 2.99])

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]

Calculates 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 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 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.asin(x, /, *, out=None)[source]

Calculates 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 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

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.NativeArray input:

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

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])}

Instance Method Examples

Using ivy.Array instance method:

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

Using ivy.Container instance method:

>>> x = ivy.Container(a=ivy.array([0., 0.1, 0.2]),b=ivy.array([0.3, 0.4, 0.5]))
>>> y = x.asin()
>>> 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]

Calculates 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 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([-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.NativeArray input:

>>> x = ivy.native_array([-0, -2.6, 1, 3.6])
>>> y = ivy.asinh(x)
>>> print(y)
ivy.array([ 0. , -1.68 , 0.881, 1.99 ])

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]

Calculates 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 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([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.NativeArray input:

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

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]

Calculates 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 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, -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)
>>> x.atan2(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])
>>> x.atan2(y)
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])
>>> x.atan2(y)
ivy.array([0.  , 0.  , 0.  , 3.14, 0.  , 0.  , 0.  , 3.14])
>>> y.atan2(x)
ivy.array([ 1.57,  0.  ,  0.  , -1.57,  1.57,  0.  ,  0.  , -1.57])

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

With ivy.NativeArray input:

>>> x = ivy.native_array([0, -0, -2.6, -1, 1, 3.6])
>>> y = ivy.native_array([-1.1, 2.5, -2.0, 1.0, 5.0, -2.5])
>>> z = ivy.atan2(x, y)
>>> print(z)
ivy.array([ 3.14 ,  0.   , -2.23 , -0.785,  0.197,  2.18 ])

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])
>>> x.atan2(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 = x.atan2(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]

Returns 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 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([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.NativeArray input:

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

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]

Computes 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 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

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.NativeArray inputs:

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

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

>>> x = ivy.array([[6, 5], [3, 7]])
>>> y = ivy.native_array([[2, 11], [9, 13]])
>>> z = ivy.bitwise_and(x, y)
>>> print(z)
ivy.array([[2, 1],[1, 5]])

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]) – 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 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

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

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 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

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

Computes 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 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:

>>> 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 ivy.NativeArray input:

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

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]

Computes 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 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.

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 ivy.NativeArray input:

>>> a = ivy.native_array([0, 1, 3, 67, 91])
>>> b = ivy.native_array([4, 7, 90, 89, 98])
>>> y = ivy.bitwise_xor(a, b)
>>> print(y)
ivy.array([ 4, 6, 89, 26, 57])

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]))
>>> b = ivy.array([90])
>>> y = ivy.Container(a = ivy.array([12]))
>>> b = ivy.array([78])
>>> 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])
}

Operator Examples

With ivy.Array instances:

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

With ivy.Container instances:

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

With mix of ivy.Array and ivy.Container instances:

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

ivy.ceil(x, /, *, out=None)[source]

Rounds 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 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([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.NativeArray input:

>>> x = ivy.native_array([0, -0, -2.5, -1, 2, 3.5])
>>> y = ivy.ceil(x)
>>> print(y)
ivy.array([ 0.,  0., -2., -1.,  2.,  4.])

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]

Calculates 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 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([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]

Calculates 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 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.

Functional 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.])}

Instance Method Examples

Using ivy.Array instance method:

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

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

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

Converts 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 ]])

With ivy.NativeArray input:

>>> x=ivy.native_array([-0,20.1,-50.5,-ivy.nan])
>>> y=ivy.deg2rad(x)
>>> print(y)
ivy.array([ 0.   ,  0.351, -0.881,    nan])

>>> 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]

Calculates 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 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 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 ivy.NativeArray inputs:

>>> x1 = ivy.native_array([2., 7., 9.])
>>> x2 = ivy.native_array([2., 2., 2.])
>>> y = ivy.divide(x1, x2)
>>> print(y)
ivy.array([1., 3.5, 4.5])

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]

Computes 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 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 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 ivy.NativeArray inputs:

>>> x1 = ivy.native_array([2.5, 7.3, 9.375])
>>> x2 = ivy.native_array([2.5, 2.9, 9.375])
>>> y = ivy.equal(x1, x2)
>>> print(y)
ivy.array([True, False,  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]

Computes 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.

ivy.exp(x, /, *, out=None)[source]

Calculates 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]) – 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.

Examples

>>> x = ivy.array([1., 2., 3.])
>>> y = ivy.exp(x)
>>> print(y)
ivy.array([2.72,7.39,20.1])

ivy.expm1(x, /, *, out=None)[source]

Calculates 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 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 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.NativeArray inputs:

>>> x = ivy.native_array([[1], [5], [-ivy.inf]])
>>> ivy.expm1(x)
ivy.array([[  1.72],
   [147.  ],
   [ -1.  ]])

With ivy.Array instance method:

>>> x = ivy.array([20])
>>> x.expm1()
ivy.array([4.85e+08])

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])
}

With ivy.Container instance method:

>>> x = ivy.Container(a=ivy.array([10, 13]))
>>> x.expm1(x)
{
    a: ivy.array([22000., 442000.])
}

With ivy.Container static method:

>>> x = ivy.Container(a=ivy.array([1]))
>>> ivy.Container.static_expm1(x)
{
    a: ivy.array([1.72])
}

ivy.floor(x, /, *, out=None)[source]

Rounds 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 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.

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.NativeArray input:

>>> x = ivy.native_array([0, -0, -1.5, -1, 1, 2.5])
>>> y = ivy.floor(x)
>>> print(y)
ivy.array([ 0.,  0., -2., -1.,  1.,  2.])

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]

Rounds 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 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 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 ivy.NativeArray inputs:

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

Computes 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 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.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 ivy.NativeArray input:

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

Computes 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 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

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 ivy.NativeArray input:

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

Instance Method Examples

Using ivy.Array instance method:

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

Using ivy.Container instance method:

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

Operator Examples

With ivy.Array instances:

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

With ivy.Container instances:

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

With mix of ivy.Array and ivy.Container instances:

>>> 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 = 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]])
}

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

Tests 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.

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.

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.NativeArray input:

>>> x = ivy.native_array([0, -0, ivy.nan , -1, ivy.inf])
>>> y = ivy.isfinite(x)
>>> print(y)
ivy.array([ True,  True, False,  True, 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])
}

With ivy.Array instance method:

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

With ivy.Container instance method:

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

ivy.isinf(x, /, *, out=None)[source]

Tests 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.
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 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 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])
}

Instance Method Examples

With ivy.Array inputs:

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

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

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

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

>>> x = ivy.zeros((3, 3))
>>> x.isinf()
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]))
>>> x.isinf()
{
    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]))
>>> z = ivy.Container.static_isinf(x)
>>> print(z)
{
    a: ivy.array([False, True, False]),
    b: ivy.array([True, False, False])
}

ivy.isnan(x, /, *, out=None)[source]

Tests 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 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 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])
}

Instance Method Examples

With ivy.Array inputs:

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

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

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

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

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

>>> x = ivy.zeros((3, 3))
>>> x.isnan()
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]))
>>> x.isnan()
{
    a: ivy.array([False, True, False]),
    b: ivy.array([True, 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.Container.static_isnan(x)
>>> print(z)
{
    a: ivy.array([False, True, False]),
    b: ivy.array([True, False, False])
}

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

Tests 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.NativeArray inputs: >>> x = ivy.native_array([[1], [5+6.9j], [-5.5]]) >>> z = ivy.isreal(x) >>> print(z) ivy.array([[True], [False], [True]])

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

Computes 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 ivy.NativeArray input:

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

Computes 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.

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 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.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]

Calculates 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.NativeArray input:

>>> x = ivy.native_array([5.78, float('-inf')])
>>> y = ivy.log(x)
>>> print(y)
ivy.array([1.75, 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]

Calculates 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 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([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.NativeArray input:

>>> x = ivy.native_array([5.78, float('-inf')])
>>> y = ivy.log10(x)
>>> print(y)
ivy.array([0.762, 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]

Calculates 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 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 ,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 ]])

>>> x = ivy.array([1e-9] , dtype = ivy.float32)
>>> y = x.log1p()
>>> print(y)
ivy.array([1.e-09])

With ivy.NativeArray input:

>>> x = ivy.native_array([10., 20.])
>>> y = ivy.log1p(x)
>>> print(y)
ivy.array([2.4 , 3.04])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0., 1., 2.]), b=ivy.array([3., 4., 5.1]))
>>> y = ivy.Container.static_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]

Calculates 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 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.

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

Calculates 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 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([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.logical_and(x1, x2, /, *, out=None)[source]

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

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 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([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])
}

With ivy.NativeArray input:

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

Computes 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 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

Functional 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])

With ivy.NativeArray input:

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

>>> 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])
}

Instance Method Examples

With ivy.Array input:

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

>>> x=ivy.array([2,0,3,9])
>>> x.logical_not()
ivy.array([False,  True, False, False])

With ivy.Container input:

>>> x=ivy.Container(a=ivy.array([1,0,0,1]), b=ivy.array([3,1,7,0]))
>>> x.logical_not()
{
    a: ivy.array([False, True, True, False]),
    b: ivy.array([False, False, False, True])
}

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

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

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

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.

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 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([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.NativeArray input:

>>> x = ivy.native_array([True, False, False])
>>> y = ivy.native_array([2, True, False])
>>> z = ivy.logical_or(x, y)
>>> print(z)
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])
}

Using ivy.Array instance method:

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

Using ivy.Container instance method:

>>> x = ivy.Container(a=ivy.array([False,True,True]), b=ivy.array([3.14, 2.718, 1.618]))
>>> y = ivy.Container(a=ivy.array([0, 5.2, 0.8]), b=ivy.array([0.2, 0, 0.9]))
>>> z = x.logical_or(y)
>>> print(z)
{
    a: ivy.array([False, True, True]),
    b: ivy.array([True, True, True])
}

With ivy.Container static method:

>>> 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.Container.static_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]

Computes 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 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

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]

Returns 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]

Returns 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]

Calculates the product 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 either +infinity or -infinity and x2_i is either +0 or -0, the result is NaN.
If x1_i is either +0 or -0 and x2_i is either +infinity or -infinity, the result is NaN.
If x1_i and x2_i have the same mathematical sign, the result has a positive mathematical sign, unless the result is NaN. If the result is NaN, the “sign” of NaN is implementation-defined.
If x1_i and x2_i have different mathematical signs, the result has a negative mathematical sign, unless the result is NaN. If the result is NaN, the “sign” of NaN is implementation-defined.
If x1_i is either +infinity or -infinity and x2_i is either +infinity or -infinity, the result is a signed infinity with the mathematical sign determined by the rule already stated above.
If x1_i is either +infinity or -infinity and x2_i is a nonzero finite number, the result is a signed infinity with the mathematical sign determined by the rule already stated above.
If x1_i is a nonzero finite number and x2_i is either +infinity or -infinity, the result is a signed infinity with the mathematical sign determined by the rule already stated above.

In the remaining cases, where neither infinity nor NaN is involved, the product must be computed and rounded to the nearest representable value according to IEEE 754-2019 and a supported rounding mode. If the magnitude is too large to represent, the result is an infinity of appropriate mathematical sign. If the magnitude is too small to represent, the result is a zero of appropriate mathematical sign.

Note

Floating-point multiplication is not always associative due to finite precision.

Parameters

x1 (Union[Array, NativeArray]) – first input array. Should have a numeric data type.
x2 (Union[Array, NativeArray]) – second input array. Should have a numeric data type. Must be compatible with x1 The condition for compatibility is Broadcasting : x1.shape!=x2.shape . The arrays must be boradcastble to get a common shape for the output.

out: optional output array, for writing the array result to. It must have a shape that the inputs broadcast to.

This function conforms to the Array API Standard. This docstring is an extension of the docstring 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.

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.])

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

Computes the numerical negative of each element x_i (i.e., y_i = -x_i) of the input array 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 – an array containing the evaluated result for each element in x

This function conforms to the Array API Standard. This docstring is an extension of the docstring 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

Functional 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.NativeArray input:

>>> x = ivy.native_array([-1.1,-1,0,1,1.1])
>>> y = ivy.negative(x)
>>> print(y)
ivy.array([1.1,1.,-0.,-1.,-1.1])

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.])}

Instance Method Examples

Using ivy.Array instance method:

>>> x = ivy.array([-1.1,-1,0,-0,1,1.1])
>>> y = x.negative()
>>> print(y)
ivy.array([1.1,1.,-0.,-0.,-1.,-1.1])

Using ivy.Container instance method:

>>> x = ivy.Container(a=ivy.array([1,2,3]),
...                   b=ivy.array([-4.4,5,-6.6]))
>>> y = x.negative()
>>> print(y)
{a:ivy.array([-1,-2,-3]),b:ivy.array([4.4,-5.,6.6])}

Operator Examples

Using ivy.Array instance method:

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

Using ivy.Container instance method:

>>> x = ivy.Container(a=ivy.array([1,2,3]),
...                   b=ivy.array([-4.4,5,-6.6]))
>>> y = -x
>>> print(y)
{a:ivy.array([-1,-2,-3]),b:ivy.array([4.4,-5.,6.6])}

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

Computes 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 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.

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])
}

Instance Method Examples

Using ivy.Array instance method:

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

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

Using ivy.Container instance method:

>>> 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 = x1.not_equal(x2, out=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, 3, 3.0]),
...                    b=ivy.array([1.0, 4.0, 5.0]))
>>> y = x1.not_equal(x2, out=x2)
>>> print(y)
{
    a: ivy.array([False, True, False]),
    b: ivy.array([False, False, False])
}

Operator Examples

With ivy.Array instances:

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

With ivy.Container instances:

>>> 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 = (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, 3, 3.0]),
...                    b=ivy.array([1.0, 4.0, 5.0]))
>>> y = (x1 != x2)
>>> print(y)
{
    a: ivy.array([False, True, False]),
    b: ivy.array([False, False, False])
}

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

>>> 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 = (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 = (x1 != x2)
>>> print(y)
{
    a: ivy.array([False, False, False]),
    b: ivy.array([False, False, False])
}

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

Returns 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 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

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.NativeArray input:

>>> x = ivy.native_array([-1.1, -1, 0, 1, 1.1])
>>> y = ivy.positive(x)
>>> print(y)
ivy.array([-1.1, -1.,  0.,  1.,  1.1])

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]

Calculates 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 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

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

Converts 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.]])

With ivy.NativeArray input:

>>> x=ivy.native_array([-0,20.1,-50.5,-ivy.nan])
>>> y=ivy.rad2deg(x)
>>> print(y)
ivy.array([    0.,  1150., -2890.,    nan])

>>> 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.reciprocal(x, /, *, out=None)[source]

Returns 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.

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

Returns 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 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 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 ivy.NativeArray inputs:

>>> x1 = ivy.native_array([2., 4., 7.])
>>> x2 = ivy.native_array([3., 2., 5.])
>>> y = ivy.remainder(x1, x2)
>>> print(y)
ivy.array([2., 0., 2.])

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, /, *, out=None)[source]

Rounds 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.
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. 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. This docstring is an extension of the docstring 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.

Functional 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.NativeArray input:

>>> x = ivy.native_array([20.2, 30.5, -5.81])
>>> y = ivy.round(x)
>>> print(y)
ivy.array([20.,30.,-6.])

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]

Returns 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 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([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.NativeArray input:

>>> x = ivy.native_array([8.95, -124.6, -0.001, 0, 1.5, 7.1])
>>> y = ivy.sign(x)
>>> print(y)
ivy.array([ 1., -1., -1.,  0.,  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]

Calculates 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 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([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.NativeArray input:

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

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]

Calculates 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 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, 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.NativeArray input:

>>> x = ivy.native_array([2, 4, 7])
>>> y = ivy.sinh(x)
>>> print(y)
    ivy.array([3.63, 27.3, 548.])

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]

Calculates 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 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

Functional 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.NativeArray input:

>>> x = ivy.native_array([-50., 1000., 34.])
>>> y = ivy.sqrt(x)
>>> print(y)
ivy.array([nan, 31.6, 5.83])

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]])
}

Instance Method Examples

Using ivy.Array instance method:

>>> x = ivy.array([[1., 2.],  [3., 4.]])
>>> y = x.sqrt()
>>> print(y)
ivy.array([[1.  , 1.41],
           [1.73, 2.  ]])

Using ivy.Container instance method:

>>> x = ivy.Container(a=ivy.array([0., 100., 27.]), b=ivy.native_array([93., 54., 25.]))
>>> y = x.sqrt()
>>> print(y)
{
    a: ivy.array([0., 10., 5.2]),
    b: ivy.array([9.64, 7.35, 5.])
}

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 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.

Functional 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.NativeArray input:

>>> a = ivy.native_array([1, 2, 3])
>>> b = ivy.square(a)
>>> print(b)
ivy.array([1, 4, 9])

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])}

Instance Method Examples

With ivy.Array instance method:

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

With ivy.Container instance method:

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

Operator Examples

With ivy.Array input:

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

With ivy.Container input:

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

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

Calculates 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 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

>>> 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]

Calculates 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 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([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]

Calculates 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 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([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.NativeArray input:

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

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.trunc(x, /, *, out=None)[source]

Rounds 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 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, 0.54, 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.NativeArray input:

>>> x = ivy.native_array([0.34, -6., 0.09, 25.4])
>>> y = ivy.trunc(x)
>>> print(y)
ivy.array([ 0., -6.,  0., 25.])

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]

Performs elementwise 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!