Re: [Numpy-discussion] numpy vendor repo

On Thu, Apr 30, 2015 at 9:32 PM, Ralf Gommers wrote: > > > On Mon, Apr 27, 2015 at 5:20 PM, Ralf Gommers > wrote: > >> >> >> >> On Mon, Apr 27, 2015 at 5:04 PM, Peter Cock >> wrote: >> >>> On Mon, Apr 27, 2015 at 1:04 PM, Ralf Gommers >>> wrote: >>> > >>> > Done in the master branch of https:/

Re: [Numpy-discussion] numpy vendor repo

On Mon, Apr 27, 2015 at 5:20 PM, Ralf Gommers wrote: > > > > On Mon, Apr 27, 2015 at 5:04 PM, Peter Cock > wrote: > >> On Mon, Apr 27, 2015 at 1:04 PM, Ralf Gommers >> wrote: >> > >> > Done in the master branch of https://github.com/rgommers/vendor. I >> think >> > that "numpy-vendor" is a bett

Re: [Numpy-discussion] code snippet: assert all close or large

Sorry, hit the wrong key just an example that I think is not covered by numpy.testing assert absolute tolerance for `inf`: "assert x and y are allclose or x is large if y is inf" On Thu, Apr 30, 2015 at 2:24 PM, wrote: > > > def assert_allclose_large(x, y, rtol=1e-6, atol=0, ltol=1e30): "

[Numpy-discussion] code snippet: assert all close or large

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Re: [Numpy-discussion] performance of numpy.array()

I have had good luck with Continuum's Miniconda Python distributions on Linux. http://conda.pydata.org/miniconda.html The `conda` command makes it very easy to create specific testing environments for Python 2 and 3 with many different packages. Everything is precompiled, so you won't have to worry

Re: [Numpy-discussion] performance of numpy.array()

I have seen a big improvement in performance with numpy 1.9.2 with python 2.7.8, numpy.array takes 5 s instead of 300s. On the other side, I have also tried numpy 1.9.2 and 1.9.0 with python 3.4 and the results are terrible: numpy.array takes 20s, but the other routines are slowed down, for examp