[Numpy-discussion] numpy on windows 64 bit
Hi, I am having some problems with win64 with all my tests failing. I installed amd64 Python from Python.org and numpy and scipy from http://www.lfd.uci.edu/~gohlke/pythonlibs/ I noticed that on windows sys.maxint is the 32bit value (2147483647 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy on windows 64 bit
Hi Robin, On Mon, Jul 5, 2010 at 5:24 PM, Robin robi...@gmail.com wrote: Hi, I am having some problems with win64 with all my tests failing. Short of saying what those failures are, we can't help you, I installed amd64 Python from Python.org and numpy and scipy from http://www.lfd.uci.edu/~gohlke/pythonlibs/ I noticed that on windows sys.maxint is the 32bit value (2147483647 This is not surprising: sys.maxint gives you the max value of a long, which is 32 bits even on 64 bits on windows. David ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy on windows 64 bit
On Mon, Jul 5, 2010 at 12:09 PM, David Cournapeau courn...@gmail.com wrote: Short of saying what those failures are, we can't help you, Thanks for reply... Somehow my message got truncated - I had written more detail about the errors! I noticed that on windows sys.maxint is the 32bit value (2147483647 This is not surprising: sys.maxint gives you the max value of a long, which is 32 bits even on 64 bits on windows. I just got to figuring this out... But it makes some problems. The main one I'm having is that I assume because of this problem array shapes are longs instead of ints (ie x.shape[0] is a long). This breaks np.random.permutation(x.shape[1]) which I use all over the place (I opened a ticket for this, #1535). Something I asked in the previous mail that got lost is what is the best cross platform way of doing this? np.random.permutation(int(x.shape[1]))? Actually that and the problems with scipy.sparse (spsolve doesn't work) cover all of the errors I'm seeing... (I detailed those in a seperate mail to the scipy list). Cheers Robin ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] debian benchmarks
Sturla Molden sturla at molden.no writes: It is also the kind of tasks where NumPy would help. It would be nice to get NumPy into the shootout. At least for the sake of advertising http://shootout.alioth.debian.org/u32/program.php?test=spectralnormlang=pythonid=2 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] subtle behavior when subtracting sub-arrays
Hi I stumbled upon some numpy behavior which I was not aware of. Say I have an array of shape (2,2,3) and want to subtract the sub-array a[...,0] of shape (2,2) from each a[...,i], i=0,1,2 . ## ok ## In [1]: a=arange(2*2*3).reshape(2,2,3) # Copy the array to be subtracted. In [2]: a0=a[...,0].copy() # Trivial approach. That works. In [3]: for k in range(a.shape[-1]): ...: a[...,k] -= a0 ...: ...: # OK In [4]: a Out[4]: array([[[0, 1, 2], [0, 1, 2]], [[0, 1, 2], [0, 1, 2]]]) In [5]: a=arange(2*2*3).reshape(2,2,3) # The same, with broadcasting. In [6]: a=a-a[...,0][...,None] # OK In [7]: a Out[7]: array([[[0, 1, 2], [0, 1, 2]], [[0, 1, 2], [0, 1, 2]]]) ## not ok ## In [8]: a=arange(2*2*3).reshape(2,2,3) In [9]: a-=a[...,0][...,None] # NOT OK In [10]: a Out[10]: array([[[ 0, 1, 2], [ 0, 4, 5]], [[ 0, 7, 8], [ 0, 10, 11]]]) In [11]: a=arange(2*2*3).reshape(2,2,3) # NOT OK, same as above In [12]: for k in range(a.shape[-1]): ...: a[...,k] -= a[...,0] ...: ...: In [14]: a Out[14]: array([[[ 0, 1, 2], [ 0, 4, 5]], [[ 0, 7, 8], [ 0, 10, 11]]]) To sum up, I find it a bit subtle that a = a - a[...,0][...,None] works as expected, while a -= a[...,0][...,None] does not. I guess the reason is that in the latter case (and the corresponding loop), a[...,0] itself is changed during the loop, while in the former case, numpy makes a copy of a[...,0] ? Is this intended? This is with numpy 1.3.0. best, Steve ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] subtle behavior when subtracting sub-arrays
Mon, 05 Jul 2010 16:03:56 +0200, Steve Schmerler wrote: [clip] To sum up, I find it a bit subtle that a = a - a[...,0][...,None] works as expected, while a -= a[...,0][...,None] does not. I guess the reason is that in the latter case (and the corresponding loop), a[...,0] itself is changed during the loop, while in the former case, numpy makes a copy of a[...,0] ? Correct. Is this intended? Not really. It's a feature we're planning to get rid of eventually, once a way to do it without sacrificing performance in safe cases is implemented. -- Pauli Virtanen ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] ANN: scipy 0.8.0 release candidate 1
On 7/5/2010 11:13 AM, Ralf Gommers wrote: The failure is yet another case of test precision set slightly too high. Thought we had got them all... Not sure about the matlab thing. Which version of Windows are you running? Vista 64bit (with 32 bit Python 2.6). Alan ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] BOF notes: Fernando's proposal: NumPy ndarray with named axes
Fernando Perez proposed a NumPy enhancement, an ndarray with named axes, prototyped as DataArray by him, Mike Trumpis, Jonathan Taylor, Matthew Brett, Kilian Koepsell and Stefan van der Walt. At SciPy 2010 on July 1, Fernando convened a BOF (Birds of a Feather) discussion of this proposal. The notes from this BOF can be found at: http://projects.scipy.org/numpy/wiki/NdarrayWithNamedAxes (linked from the Plans section of http://projects.scipy.org/numpy ) HELP NEEDED: Fernando does not have the resources to drive the project beyond this prototype, which already does what he needs. If this is to go anywhere, it needs people to do the work. Please step forward. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
