On 10/27/2016 04:52 PM, Todd wrote:
On Thu, Oct 27, 2016 at 10:43 AM, Julian Taylor <jtaylor.deb...@googlemail.com <mailto:jtaylor.deb...@googlemail.com>> wrote:On 10/27/2016 04:30 PM, Todd wrote: On Thu, Oct 27, 2016 at 4:25 AM, Ralf Gommers <ralf.gomm...@gmail.com <mailto:ralf.gomm...@gmail.com> <mailto:ralf.gomm...@gmail.com <mailto:ralf.gomm...@gmail.com>>> wrote: On Thu, Oct 27, 2016 at 10:25 AM, Pavlyk, Oleksandr <oleksandr.pav...@intel.com <mailto:oleksandr.pav...@intel.com> <mailto:oleksandr.pav...@intel.com <mailto:oleksandr.pav...@intel.com>>> wrote: Please see responses inline. *From:*NumPy-Discussion [mailto:numpy-discussion-boun...@scipy.org <mailto:numpy-discussion-boun...@scipy.org> <mailto:numpy-discussion-boun...@scipy.org <mailto:numpy-discussion-boun...@scipy.org>>] *On Behalf Of *Todd *Sent:* Wednesday, October 26, 2016 4:04 PM *To:* Discussion of Numerical Python <numpy-discussion@scipy.org <mailto:numpy-discussion@scipy.org> <mailto:numpy-discussion@scipy.org <mailto:numpy-discussion@scipy.org>>> *Subject:* Re: [Numpy-discussion] Intel random number package On Wed, Oct 26, 2016 at 4:30 PM, Pavlyk, Oleksandr <oleksandr.pav...@intel.com <mailto:oleksandr.pav...@intel.com> <mailto:oleksandr.pav...@intel.com <mailto:oleksandr.pav...@intel.com>>> wrote: Another point already raised by Nathaniel is that for numpy's randomness ideally should provide a way to override default algorithm for sampling from a particular distribution. For example RandomState object that implements PCG may rely on default acceptance-rejection algorithm for sampling from Gamma, while the RandomState object that provides interface to MKL might want to call into MKL directly. The approach that pyfftw uses at least for scipy, which may also work here, is that you can monkey-patch the scipy.fftpack module at runtime, replacing it with pyfftw's drop-in replacement. scipy then proceeds to use pyfftw instead of its built-in fftpack implementation. Might such an approach work here? Users can either use this alternative randomstate replacement directly, or they can replace numpy's with it at runtime and numpy will then proceed to use the alternative. The only reason that pyfftw uses monkeypatching is that the better approach is not possible due to license constraints with FFTW (it's GPL). Yes, that is exactly why I brought it up. Better approaches are also not possible with MKL due to license constraints. It is a very similar situation overall. Its not that similar, the better approach is certainly possible with FFTW, the GPL is compatible with numpys license. It is only a concern users of binary distributions. Nobody provided the code to use fftw yet, but it would certainly be accepted. Although it is technically compatible, it would make numpy effectively GPL. Suggestions for this have been explicitly rejected on these grounds [1] [1] https://github.com/numpy/numpy/issues/3485
Yes it would make numpy GPL, but that is not a concern for a lot of users. Users for who it is a problem can still use the non-GPL version. A more interesting debate is whether our binary wheels should then be GPL wheels by default or not. Probably not, but that is something that should be discussed when its an actual issue.
But to clarify what I said, it would be accepted if the value it provides is sufficient compared to the code maintenance it adds. Given that pyfftw already exists the value is probably relatively small, but personally I'd still be interested in code that allows switching the fft backend as that could also allow plugging e.g. gpu based implementations (though again this is already covered by other third party modules).
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