numpy.bitwise_count

numpy.bitwise_count(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature]) = <ufunc 'bitwise_count'>

Computes the number of 1-bits in the absolute value of x. Analogous to the builtin int.bit_count or popcount in C++.

Parameters:

xarray_like, unsigned int

Input array.

outndarray, 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.

wherearray_like, 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:

yndarray

The corresponding number of 1-bits in the input. Returns uint8 for all integer types This is a scalar if x is a scalar.

References

[1]

https://graphics.stanford.edu/~seander/bithacks.html#CountBitsSetParallel

[2]

Wikipedia, “Hamming weight”, https://en.wikipedia.org/wiki/Hamming_weight

[3]

http://aggregate.ee.engr.uky.edu/MAGIC/#Population%20Count%20(Ones%20Count)

Examples

>>> import numpy as np
>>> np.bitwise_count(1023)
10
>>> a = np.array([2**i - 1 for i in range(16)])
>>> np.bitwise_count(a)
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15],
      dtype=uint8)