Fast lookup of bulky "table"

[Dino]

On 1/16/2023 1:18 PM, Edmondo Giovannozzi wrote:
> 
> As a comparison with numpy. Given the following lines:
> 
> import numpy as np
> a = np.random.randn(400,100_000)
> ia = np.argsort(a[0,:])
> a_elem = a[56, ia[0]]
> 
> I have just taken an element randomly in a numeric table of 400x100000 elements
> To find it with numpy:
> 
> %timeit isel = a == a_elem
> 35.5 ms ± 2.79 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
> 
> And
> %timeit a[isel]
> 9.18 ms ± 371 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
> 
> As data are not ordered it is searching it one by one but at C level.
> Of course it depends on a lot of thing...

thank you for this. It's probably my lack of experience with Numpy, 
but... can you explain what is going on here in more detail?

Thank you

Dino