[Numpy-discussion] numpy.lib functions as gufuncs

Hi, Inspired by the great rewrite of numpy.linalg in 1.8, I've spent the last couple of days coding a couple of the functions in numpy.lib as gufuncs, namely np.interp and np.bincount. I want to do something along the same lines to np.digitize, but haven't started on it just yet. I'm currently kee

Re: [Numpy-discussion] Caution about using intrisincs, and other 'advanced' optimizations

On Mon, Nov 11, 2013 at 7:17 PM, David Cournapeau wrote: > Hi there, > > I have noticed more and more subtle and hard to track serious bugs in > numpy and scipy, due to the use of advanced optimization features (flags, > or gcc intrinsics). > > I am wondering whether those are worth it: they compi

[Numpy-discussion] Caution about using intrisincs, and other 'advanced' optimizations

Hi there, I have noticed more and more subtle and hard to track serious bugs in numpy and scipy, due to the use of advanced optimization features (flags, or gcc intrinsics). I am wondering whether those are worth it: they compile wrongly under quite a few configurations, and it is not always obvi

Re: [Numpy-discussion] mingw-w64 and openblas test

On Mon, Nov 11, 2013 at 1:32 PM, Carl Kleffner wrote: > Hi David, > > i used my customized mingw-w64 toolkit mentioned in this thread to > circumvent several problems with the mixed compiler enviroment. It is a > fully statically gcc build. Hence the compiled executables and dlls never > depend o

Re: [Numpy-discussion] Can I use numpy-MKL for commercial purposes without an Intel MKL license?

On Mon, Nov 11, 2013 at 12:52 PM, Brunick, Gerard:(Constellation) < gerard.brun...@constellation.com> wrote: > I am looking for a 64-bit, Windows version of Numpy and I have found a > couple pre-built versions that are linked against the Intel MKL library. I > would like to use this library for

[Numpy-discussion] Can I use numpy-MKL for commercial purposes without an Intel MKL license?

I am looking for a 64-bit, Windows version of Numpy and I have found a couple pre-built versions that are linked against the Intel MKL library. I would like to use this library for in-house commercial use. In particular, I do not intend to redistribute my code or link against the Intel MKL lib

[Numpy-discussion] Error in docstring np.random.pareto

I think the docstring of np.random.pareto (Version 1.8.0) is erroneous. In a nutshell np.random.pareto draws samples from a Lomax distribution with shape a and location m'=1. To convert those samples to a classical Pareto distribution with shape a and location m you have to add 1 and multiply by

Re: [Numpy-discussion] mingw-w64 and openblas test

Hi David, i used my customized mingw-w64 toolkit mentioned in this thread to circumvent several problems with the mixed compiler enviroment. It is a fully statically gcc build. Hence the compiled executables and dlls never depend on mingw dlls even without usage of -static -static-libgcc ... compi

Re: [Numpy-discussion] mingw-w64 and openblas test

On Mon, Nov 11, 2013 at 10:28 AM, Carl Kleffner wrote: > done > > all logs in https://gist.github.com/anonymous/7411039 > Thanks. Looking at the log, it does not look like you are statically linking the mingw runtimes, though. I would expect numpy not to work if you don't have mingw dlls in your

Re: [Numpy-discussion] mingw-w64 and openblas test

done all logs in https://gist.github.com/anonymous/7411039 Regards Carl 2013/11/11 David Cournapeau > Carl, > > It looks like the google drive contains the binary and not the actual log > ? For the log, it is more convenient to put it on gist.github.com, > > thanks for the work, > David > > >

Re: [Numpy-discussion] mingw-w64 and openblas test

Carl, It looks like the google drive contains the binary and not the actual log ? For the log, it is more convenient to put it on gist.github.com, thanks for the work, David On Mon, Nov 11, 2013 at 9:51 AM, Carl Kleffner wrote: > Hi David, > > I made a new build with the numpy-1.8.0 code base

Re: [Numpy-discussion] mingw-w64 and openblas test

Hi David, I made a new build with the numpy-1.8.0 code base. binaries and logs are included in the following archive: 2013-11-11_i686-numpy-1.8.0-py27-openblastest.7z https://drive.google.com/file/d/0B4DmELLTwYmlajBzZF