# log#

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

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

Special cases

For floating-point operands,

• If `x_i` is `NaN`, the result is `NaN`.

• If `x_i` is less than `0`, the result is `NaN`.

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

• If `x_i` is `1`, the result is `+0`.

• If `x_i` is `+infinity`, the result is `+infinity`.

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

• If `a` is `-0` and `b` is `+0`, the result is `-infinity + πj`.

• If `a` is `+0` and `b` is `+0`, the result is `-infinity + 0j`.

• If `a` is a finite number and `b` is `+infinity`, the result is `+infinity + πj/2`.

• If `a` is a finite number and `b` is `NaN`, the result is `NaN + NaN j`.

• If `a` is `-infinity` and `b` is a positive (i.e., greater than `0`) finite number, the result is `+infinity + πj`.

• If `a` is `+infinity` and `b` is a positive (i.e., greater than `0`) finite number, the result is `+infinity + 0j`.

• If `a` is `-infinity` and `b` is `+infinity`, the result is `+infinity + 3πj/4`.

• If `a` is `+infinity` and `b` is `+infinity`, the result is `+infinity + πj/4`.

• If `a` is either `+infinity` or `-infinity` and `b` is `NaN`, the result is `+infinity + NaN j`.

• If `a` is `NaN` and `b` is a finite number, the result is `NaN + NaN j`.

• If `a` is `NaN` and `b` is `+infinity`, the result is `+infinity + NaN j`.

• If `a` is `NaN` and `b` is `NaN`, the result is `NaN + NaN j`.

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 evaluated natural logarithm for each element in `x`. The returned array must have a floating-point data type determined by type-promotion.

Examples

With `ivy.Array` input:

```>>> x = ivy.array([4.0, 1, -0.0, -5.0])
>>> y = ivy.log(x)
>>> print(y)
ivy.array([1.39, 0., -inf, nan])
```
```>>> x = ivy.array([[float('nan'), 1, 5.0, float('+inf')],
...                [+0, -1.0, -5, float('-inf')]])
>>> y = ivy.log(x)
>>> print(y)
ivy.array([[nan, 0., 1.61, inf],
[-inf, nan, nan, nan]])
```

With `ivy.Container` input:

```>>> x = ivy.Container(a=ivy.array([0.0, float('nan')]),
...                   b=ivy.array([-0., -3.9, float('+inf')]),
...                   c=ivy.array([7.9, 1.1, 1.]))
>>> y = ivy.log(x)
>>> print(y)
{
a: ivy.array([-inf, nan]),
b: ivy.array([-inf, nan, inf]),
c: ivy.array([2.07, 0.0953, 0.])
}
```
Array.log(self, *, out=None)[source]#

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

Parameters:
• self (`Array`) – input array. Should have a real-valued 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 evaluated result for each element in `self`. The returned array must have a real-valued floating-point data type determined by type-promotion.

Examples

Using `ivy.Array` instance method:

```>>> x = ivy.array([4.0, 1, -0.0, -5.0])
>>> y = x.log()
>>> print(y)
ivy.array([1.39, 0., -inf, nan])
```
```>>> x = ivy.array([float('nan'), -5.0, -0.0, 1.0, 5.0, float('+inf')])
>>> y = x.log()
>>> print(y)
ivy.array([nan, nan, -inf, 0., 1.61, inf])
```
```>>> x = ivy.array([[float('nan'), 1, 5.0, float('+inf')],
...                [+0, -1.0, -5, float('-inf')]])
>>> y = x.log()
>>> print(y)
ivy.array([[nan, 0., 1.61, inf],
[-inf, nan, nan, nan]])
```
Container.log(self, *, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)[source]#

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

Parameters:
• self (`Container`) – input container. Should have a real-valued 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 log for each element in `self`. The returned array must have a real-valued floating-point data type determined by type-promotion.

Examples

Using `ivy.Container` instance method:

```>>> 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 = x.log()
>>> print(y)
{
a: ivy.array([-inf, nan]),
b: ivy.array([-inf, nan, inf]),
c: ivy.array([2.07, 0.0953, 0.])
}
```