On Mon, Oct 27, 2014 at 11:31 PM, <josef.p...@gmail.com> wrote: > > > On Mon, Oct 27, 2014 at 10:50 PM, Sturla Molden <sturla.mol...@gmail.com> > wrote: > >> <josef.p...@gmail.com> wrote: >> >> > For fft I use mostly scipy, IIRC. (scipy's fft imports numpy's fft, >> > partially?) >> >> No. SciPy uses the Fortran library FFTPACK (wrapped with f2py) and NumPy >> uses a smaller C library called fftpack_lite. Algorithmically they are are >> similar, but fftpack_lite has fewer features (e.g. no DCT). scipy.fftpack >> does not import numpy.fft. Neither of these libraries are very "fast", but >> usually they are "fast enough" for practical purposes. If we really need a >> kick-ass fast FFT we need to go to libraries like FFTW, Intel MKL or >> Apple's Accelerate Framework, or even use tools like CUDA or OpenCL to run >> the FFT on the GPU. But using such tools takes more coding (and reading >> API >> specifications) than the convinience of just using the FFTs already in >> NumPy or SciPy. So if you count in your own time as well, it might not be >> that FFTW or MKL are the "faster" FFTs. >> > > > Ok, I didn't remember correctly. > > I didn't use much fft recently, I never used DCT. My favorite "fft > function" is fftconvolve. > > https://github.com/scipy/scipy/blob/e758c482efb8829685dcf494bdf71eeca3dd77f0/scipy/signal/signaltools.py#L13 > doesn't seem to mind mixing numpy and scipy (quick github search) > > > It's sometimes useful to have simplified functions that are "good enough" > where we don't have to figure out all the extras that the docstring of the > fancy version is mentioning. >
I take this back (even if it's true), because IMO the defaults should work, and I have a tendency to pile on options in my code that are intended for experts. Josef > > > Josef > > > >> >> Sturla >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> > >
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