xarray.ufuncs.bitwise_count

Contents

xarray.ufuncs.bitwise_count#

xarray.ufuncs.bitwise_count = <xarray.ufuncs._unary_ufunc object>#

xarray specific variant of numpy.bitwise_count(). Handles xarray objects by dispatching to the appropriate function for the underlying array type.

Documentation from numpy:

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

Parameters
  • x (array_like, unsigned int) – Input array.

  • 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 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 (ndarray) – 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

>>> 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)