Seems so.
numpy/fft/__init__.py
when installed with conda contains a thin optional wrapper around
mklfft, e.g. this here:
https://docs.continuum.io/accelerate/mkl_fft
It is part of the accelerate package from continuum and thus not free.
Cheers!
Lion
On 01/06/16 09:44, Gregor Thalhammer w
On 30/05/16 10:07, Joseph Martinot-Lagarde wrote:
> Marten van Kerkwijk gmail.com> writes:
>
>> I did a few simple timing tests (see comment in PR), which suggests it is
> hardly worth having the cache. Indeed, if one really worries about speed,
> one should probably use pyFFTW (scipy.fft is a
> You can backport the pure Python version of lru_cache for Python 2 (or
> vendor the backport done here:
> https://pypi.python.org/pypi/backports.functools_lru_cache/).
> The advantage is that lru_cache is C-accelerated in Python 3.5 and
> upwards...
That's a pretty big back-port. The speed also
Hi all,
I was told to take this to the mailing list. Relevant pull request:
https://github.com/numpy/numpy/pull/7686
NumPy's FFT implementation caches some form of execution plan for each
encountered input data length. This is currently implemented as a simple
dictionary which can grow without b
