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log2(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])

Notes

versionadded

Logarithm is a multivalued function: for each x there is an infinite number of :None:None:`z` such that :None:None:`2**z = x`. The convention is to return the :None:None:`z` whose imaginary part lies in :None:None:`[-pi, pi]`.

For real-valued input data types, log2 always returns real output. For each value that cannot be expressed as a real number or infinity, it yields nan and sets the :None:None:`invalid` floating point error flag.

For complex-valued input, log2 is a complex analytical function that has a branch cut :None:None:`[-inf, 0]` and is continuous from above on it. log2 handles the floating-point negative zero as an infinitesimal negative number, conforming to the C99 standard.

Parameters

x : array_like: Input values.
out : ndarray, None, or tuple of ndarray and None, 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 : array_like, optional: This condition is broadcast over the input. At locations where the condition is True, the :None:None:`out` array will be set to the ufunc result. Elsewhere, the :None:None:`out` array will retain its original value. Note that if an uninitialized :None:None:`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 <ufuncs.kwargs> .

Returns

y : ndarray: Base-2 logarithm of x. This is a scalar if x is a scalar.

Base-2 logarithm of x.

Examples

This example is valid syntax, but we were not able to check execution

>>> x = np.array([0, 1, 2, 2**4])
... np.log2(x)
array([-Inf,   0.,   1.,   4.])

This example is valid syntax, but we were not able to check execution

>>> xi = np.array([0+1.j, 1, 2+0.j, 4.j])
... np.log2(xi)
array([ 0.+2.26618007j,  0.+0.j        ,  1.+0.j        ,  2.+2.26618007j])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.ma.core.log2 numpy.ma.core.log

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