On 2015/08/28 10:36 AM, Sebastian Berg wrote: > If you don't mind the extra dependency or licensing and this is an issue > for you, you can try pyfftw (there are likely other similar projects) > which wraps fftw and does not have this problem as far as I know. It > exposes a numpy-like interface.
Sort of; that interface returns a function, not the result. fftw is still an fft algorithm, so it is still subject to a huge difference in run time depending on how the input array can be factored. Furthermore, it gets its speed by figuring out how to optimize a calculation for a given size of input array. That initial optimization can be very slow. The overall speed gain is realized only when one saves the result of that optimization, and applies it to many calculations on arrays of the same size. Eric > > - sebastian > > > On Fr, 2015-08-28 at 19:13 +0000, Joseph Codadeen wrote: >> Great, thanks Stefan and everyone. >> >>> From: [email protected] >>> To: [email protected] >>> Date: Fri, 28 Aug 2015 12:03:52 -0700 >>> Subject: Re: [Numpy-discussion] Numpy FFT.FFT slow with certain >> samples >>> >>> >>> On 2015-08-28 11:51:47, Joseph Codadeen <[email protected]> >>> wrote: >>>> my_1_minute_noise_with_gaps_truncated - Array len is >>>> 2646070my_1_minute_noise_with_gaps - Array len is 2649674 >>> >>> In [6]: from sympy import factorint In [7]: >>> max(factorint(2646070)) Out[7]: 367 In [8]: >>> max(factorint(2649674)) Out[8]: 1324837 >>> >>> Those numbers give you some indication of how long the FFT will >>> take to compute. >>> >>> Stéfan >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> [email protected] >>> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> _______________________________________________ >> NumPy-Discussion mailing list >> [email protected] >> http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
