dpnp.log1p

dpnp.log1p(x, out=None, where=True, order='K', dtype=None, subok=True, **kwargs)

Computes an approximation of log(1+x) element-wise.

For full documentation refer to numpy.log1p.

Parameters:

x ({dpnp.ndarray, usm_ndarray}) – Input array, expected to have numeric data type.
out ({None, dpnp.ndarray}, optional) – Output array to populate. Array must have the correct shape and the expected data type.
order ({"C", "F", "A", "K"}, optional) – Memory layout of the newly output array, if parameter out is None. Default: “K”.

Returns:

out – An array containing the element-wise log(1+x) values. The data type of the returned array is determined by the Type Promotion Rules.

Return type:

dpnp.ndarray

Limitations

Parameters where and subok are supported with their default values. Keyword argument kwargs is currently unsupported. Otherwise NotImplementedError exception will be raised.

See also

dpnp.expm1: exp(x) - 1, the inverse of dpnp.log1p.
dpnp.log: Natural logarithm, element-wise.
dpnp.log10: Return the base 10 logarithm of the input array, element-wise.
dpnp.log2: Return the base 2 logarithm of the input array, element-wise.

Examples

>>> import dpnp as np
>>> x = np.arange(3.)
>>> np.log1p(x)
array([0.0, 0.69314718, 1.09861229])

>>> np.log1p(array(1e-99))
array(1e-99)

>>> np.log(array(1 + 1e-99))
array(0.0)