[Numpy-discussion] Is there a pure numpy recipe for this?

[Slaunger]

jseabold wrote
> IIUC,
> 
> [~/]
> [1]: np.logical_and([True, False, True], [False, False, True])
> [1]: array([False, False,  True], dtype=bool)
> 
> You can avoid looping over k since they're all the same length
> 
> [~/]
> [3]: np.logical_and([[True, False],[False, True],[False, True]],
> [[False, False], [False, True], [True, True]])
> [3]:
> array([[False, False],
>        [False,  True],
>        [False,  True]], dtype=bool)
> 
> [~/]
> [4]: np.sum(np.logical_and([[True, False],[False, True],[False,
> True]], [[False, False], [False, True], [True, True]]), axis=0)
> [4]: array([0, 2])

Well, yes, if you work with the pure f_k and g_k that is true, but this
two-dimensional array will have 4*10^14 elements and will exhaust my memory.

That is why I have found a more efficient method for finding only the much
fewer changes_at elements for each k, and these arrays have unequal length,
and has to be considered for eack k (which is tolerable as long as I avoid a
further inner loop for each k in explicit Python).

I could implement this in C and get it done sufficiently efficient. I just
like to make a point in demonstrating this is also doable in finite time in
Python/numpy.



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