python - numpy: boolean indexing and memory usage -

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consider following numpy code:

a[start:end] = b[mask]

here:

  • a , b 2d arrays same number of columns;
  • start , end scalars;
  • mask 1d boolean array;
  • (end - start) == sum(mask).

in principle, above operation can carried out using o(1) temporary storage, copying elements of b directly a.

is happens in practice, or numpy construct temporary array b[mask]? if latter, there way avoid rewriting statement?

using boolean arrays index fancy indexing, numpy needs make copy. write cython extension deal it, if getting memory problems.


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