[Numpy-discussion] Choosing between NumPy and SciPy functions

[josef.pktd at gmail.com]

On Mon, Oct 27, 2014 at 11:31 PM, <josef.pktd at gmail.com> wrote:

>
>
> On Mon, Oct 27, 2014 at 10:50 PM, Sturla Molden <sturla.molden at gmail.com>
> wrote:
>
>> <josef.pktd at 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
>>
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>>
>
>
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