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<div>>> Id rather have us discuss how to facilitate the
integration of as many possible fft libraries with numpy behind a
maximally uniform interface, rather than having us debate which
fft library is 'best'.</div>
<div><br>
I agree with the above.<br>
<br>
</div>
> I would agree if it were not already there, but removing it
(like Blas/Lapack) is out of the question for backward compatibility
reason. Too much code depends on it.<br>
<br>
And I definitely agree with that too.<br>
<br>
I think that numpy.fft should be left there in its current state
(although perhaps as deprecated). Now scipy.fft should have a good
generic algorithm as default, and easily allow for different
implementations to be accessed through the same interface.<br>
<br>
Pierre-André<br>
<br>
<div class="moz-cite-prefix">On 10/29/2014 03:33 AM, David
Cournapeau wrote:<br>
</div>
<blockquote
cite="mid:%3CCAGY4rcVjjkaVkicJOvU9q-2nHDFsUGJTV2OKgXypxkuUmmAqOg@mail.gmail.com%3E"
type="cite">
<div dir="ltr"><br>
<div class="gmail_extra"><br>
<div class="gmail_quote">On Wed, Oct 29, 2014 at 9:48 AM,
Eelco Hoogendoorn <span dir="ltr"><<a
moz-do-not-send="true"
href="mailto:hoogendoorn.eelco@gmail.com"
target="_blank">hoogendoorn.eelco@gmail.com</a>></span>
wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0
.8ex;border-left:1px #ccc solid;padding-left:1ex">
<div dir="ltr">
<div>My point isn't about speed; its about the scope of
numpy. typing np.fft.fft isn't more or less convenient
than using some other symbol from the scientific
python stack.</div>
<div><br>
</div>
<div>Numerical algorithms should be part of the stack,
for sure; but should they be part of numpy? I think
its cleaner to have them in a separate package. Id
rather have us discuss how to facilitate the
integration of as many possible fft libraries with
numpy behind a maximally uniform interface, rather
than having us debate which fft library is 'best'.</div>
</div>
</blockquote>
<div><br>
</div>
<div>I would agree if it were not already there, but
removing it (like Blas/Lapack) is out of the question for
backward compatibility reason. Too much code depends on
it.<br>
<br>
</div>
<div>David<br>
<br>
</div>
<blockquote class="gmail_quote" style="margin:0 0 0
.8ex;border-left:1px #ccc solid;padding-left:1ex">
<div class="HOEnZb">
<div class="h5">
<div class="gmail_extra"><br>
<div class="gmail_quote">On Tue, Oct 28, 2014 at
6:21 PM, Sturla Molden <span dir="ltr"><<a
moz-do-not-send="true"
href="mailto:sturla.molden@gmail.com"
target="_blank">sturla.molden@gmail.com</a>></span>
wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0
0 .8ex;border-left:1px #ccc
solid;padding-left:1ex"><span>Eelco Hoogendoorn
<<a moz-do-not-send="true"
href="mailto:hoogendoorn.eelco@gmail.com"
target="_blank">hoogendoorn.eelco@gmail.com</a>>
wrote:<br>
<br>
> Perhaps the 'batteries included'
philosophy made sense in the early days of<br>
> numpy; but given that there are several
fft libraries with their own pros<br>
> and cons, and that most numpy projects
will use none of them at all, why<br>
> should numpy bundle any of them?<br>
<br>
</span>Because sometimes we just need to compute
a DFT, just like we sometimes<br>
need to compute a sine or an exponential. It
does that job perfectly well.<br>
It is not always about speed. Just typing
np.fft.fft(x) is convinient.<br>
<div>
<div><br>
Sturla<br>
<br>
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</blockquote>
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