python - numpy: boolean indexing and memory usage -
consider following numpy code:
a[start:end] = b[mask] here:
a,b2d arrays same number of columns;start,endscalars;mask1d 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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