Hi Stéfan,

upd: 

indeed, rfft2 has equal memory usage with our fft2d in terms of reals. thanks, 
Stefan. 

to this moment, i believe the results are following:

> scipy time outperformance on rectangular signals with sides of power-of-two. 
> equal memory usage with rfft2

in my eyes, it's worth trying putting our algorithm and scipy multithreading 
together, considering previous results, I believe it'll show major performance 
improvements. in case it does, i still think it's worthy trying putting the 
Cooley-Tukey operation in work in terms of cases of the mentioned signals. like 
i suggest we try testing our code as a part of numpy/scipy, tbh, i really lost 
the track of whether this thread is about numpy or scipy embedding. 

i believe if we could place the butterfly algorithm into scipy and add a 
'checking if' for the size of the matrix, we would rather win some performance 
than lose, i think any advantage in performance of the algorithm is important, 
considering the balance of memory usage and time is still observed. i suppose 
in terms of algorithm performance one step for a man is a leap for mankind in 
terms of other projects.

please lmk if you share this opinion and we should try testing. 

regards

Alexander
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