nums.numpy.log

nums.numpy.log(x, out=None, where=True, **kwargs)[source]

Natural logarithm, element-wise.

This docstring was copied from numpy.log.

Some inconsistencies with the NumS version may exist.

The natural logarithm log is the inverse of the exponential function, so that log(exp(x)) = x. The natural logarithm is logarithm in base e.

Parameters

  • x (BlockArray) – Input value.

  • out (BlockArray, None, or optional) – A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

  • where (BlockArray, optional) – This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.

  • **kwargs – For other keyword-only arguments, see the ufunc docs.

Returns

y – The natural logarithm of x, element-wise.

Return type

BlockArray

See also

log10, log2, log1p

Notes

Logarithm is a multivalued function: for each x there is an infinite number of z such that exp(z) = x. The convention is to return the z whose imaginary part lies in [-pi, pi].

For real-valued input data types, log always returns real output. For each value that cannot be expressed as a real number or infinity, it yields nan and sets the invalid floating point error flag.

For complex-valued input, log is a complex analytical function that has a branch cut [-inf, 0] and is continuous from above on it. log handles the floating-point negative zero as an infinitesimal negative number, conforming to the C99 standard.

References

1

M. Abramowitz and I.A. Stegun, “Handbook of Mathematical Functions”, 10th printing, 1964, pp. 67. http://www.math.sfu.ca/~cbm/aands/

2

Wikipedia, “Logarithm”. https://en.wikipedia.org/wiki/Logarithm

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> nps.log(nps.array([1, nps.e, nps.e**2, 0])).get()  
array([  0.,   1.,   2., -Inf])