# sinc#

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

Calculate an implementation-dependent approximation of the principal value of the normalized sinc function, having domain ```(-infinity, +infinity)``` and codomain `[-0.217234, 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 either `+infinity` or `-infinity`, the result is `NaN`.

Parameters:
• x (`Union`[`Array`, `NativeArray`]) – input array. Should have a floating-point data type.

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

Return type:

`Array`

Returns:

ret – an array containing the normalized sinc function of 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([0.5, 1.5, 2.5, 3.5])
>>> y = x.sinc()
>>> print(y)
ivy.array([0.637,-0.212,0.127,-0.0909])
```
```>>> x = ivy.array([1.5, 0.5, -1.5])
>>> y = ivy.zeros(3)
>>> ivy.sinc(x, out=y)
>>> print(y)
ivy.array([-0.212,0.637,-0.212])
```

With `ivy.NativeArray` input:

```>>> x = ivy.array([0.5, 1.5, 2.5, 3.5])
>>> y = ivy.sinc(x)
>>> print(y)
ivy.array([0.637,-0.212,0.127,-0.0909])
```

With `ivy.Container` input:

```>>> x = ivy.Container(a=ivy.array([0.5, 1.5, 2.5]),
...                   b=ivy.array([3.5, 4.5, 5.5]))
>>> y = x.sinc()
>>> print(y)
{
a: ivy.array([0.637,-0.212,0.127]),
b: ivy.array([-0.0909,0.0707,-0.0579])
}
```
Array.sinc(self, *, out=None)[source]#

ivy.Array instance method variant of ivy.sinc. This method simply wraps the function, and so the docstring for ivy.sinc also applies to this method with minimal changes.

Parameters:
• self (`Array`) – input array whose elements are each expressed in radians. Should have a floating-point data type.

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

Return type:

`Array`

Returns:

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

Examples

```>>> x = ivy.array([0.5, 1.5, 2.5, 3.5])
>>> y = x.sinc()
>>> print(y)
ivy.array([0.637,-0.212,0.127,-0.0909])
```
Container.sinc(self, *, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)[source]#

ivy.Container instance method variant of ivy.sinc. This method simply wraps the function, and so the docstring for ivy.sinc also applies to this method with minimal changes.

Parameters:
• self (`Container`) – input container whose elements are each expressed in radians. Should have a floating-point data type.

• key_chains (`Optional`[`Union`[`List`[`str`], `Dict`[`str`, `str`], `Container`]], default: `None`) – The key-chains to apply or not apply the method to. Default is `None`.

• to_apply (`Union`[`bool`, `Container`], default: `True`) – If True, the method will be applied to key_chains, otherwise key_chains will be skipped. Default is `True`.

• prune_unapplied (`Union`[`bool`, `Container`], default: `False`) – Whether to prune key_chains for which the function was not applied. Default is `False`.

• map_sequences (`Union`[`bool`, `Container`], default: `False`) – Whether to also map method to sequences (lists, tuples). Default is `False`.

• out (`Optional`[`Container`], default: `None`) – optional output container, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

`Container`

Returns:

ret – a container containing the sinc of each element in `self`. The returned container must have a floating-point data type determined by type-promotion.

Examples

```>>> x = ivy.Container(a=ivy.array([0.5, 1.5, 2.5]),
...                   b=ivy.array([3.5, 4.5, 5.5]))
>>> y = x.sinc()
>>> print(y)
{
a: ivy.array([0.637,-0.212,0.127]),
b: ivy.array([-0.0909,0.0707,-0.0579])
}
```