[Numpy-discussion] New test failures

>>> numpy.__version__ '1.4.0.dev7270' Python 2.5.1 on 64-bit box == FAIL: test_umath.TestComplexFunctions.test_precisions_consistent -- Traceback (most recent c

[Numpy-discussion] DumbArray, LoadArray, Numeric -- numpy

Hi all, how can I import arrays in numpy which are stored by DumpArray in the old Numeric package ? Nils ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Test failures on FreeBSD buildbot

On Mon, Jul 27, 2009 at 10:48 PM, David Cournapeau < da...@ar.media.kyoto-u.ac.jp> wrote: > Charles R Harris wrote: > > > > I'd just look at the difference and see if it exceeded some fraction > > of the expected value. There is the problem of zero, which could be > > handled in the usual way as d

Re: [Numpy-discussion] Test failures on FreeBSD buildbot

Charles R Harris wrote: > > I'd just look at the difference and see if it exceeded some fraction > of the expected value. There is the problem of zero, which could be > handled in the usual way as diff < abserr + relerr. I think abserr > would need to be a new keyword with a default value. Since th

Re: [Numpy-discussion] Test failures on FreeBSD buildbot

On Mon, Jul 27, 2009 at 10:16 PM, David Cournapeau < da...@ar.media.kyoto-u.ac.jp> wrote: > Charles R Harris wrote: > > > > > > On Mon, Jul 27, 2009 at 9:37 PM, David Cournapeau > > wrote: > > > > On Tue, Jul 28, 2009 at 8:44 AM, Charles R > > Harris >

Re: [Numpy-discussion] Test failures on FreeBSD buildbot

Charles R Harris wrote: > > > On Mon, Jul 27, 2009 at 9:37 PM, David Cournapeau > wrote: > > On Tue, Jul 28, 2009 at 8:44 AM, Charles R > Harris > wrote: > > > ERROR: test_nan_items (test_utils.TestApproxEqual) > > >

Re: [Numpy-discussion] Test failures on FreeBSD buildbot

On Mon, Jul 27, 2009 at 9:37 PM, David Cournapeau wrote: > On Tue, Jul 28, 2009 at 8:44 AM, Charles R > Harris wrote: > > > ERROR: test_nan_items (test_utils.TestApproxEqual) > > -- > > Traceback (most recent call last): > > Fil

[Numpy-discussion] datetime Parsing Functions

I'm a Summer of Code student working on the datetime implementation. The past few weeks I've been writing code to parse between dates and longs with a frequency. I'd like some feedback on my code before it gets integrated into the NumPy datetime branch: Clone URL: git://github.com/martyfuhry/npy_d

Re: [Numpy-discussion] Test failures on FreeBSD buildbot

On Tue, Jul 28, 2009 at 8:44 AM, Charles R Harris wrote: > ERROR: test_nan_items (test_utils.TestApproxEqual) > -- > Traceback (most recent call last): > File > "/tmp/numpy-buildbot/b12/numpy-install/lib/python2.5/site-packages/

Re: [Numpy-discussion] String manipulation summary

Chris Colbert wrote: > what machine spec are you using? Dual 2Ghz PPC OS-X 10.4. Python2.5, numpy 1.3.0rc1 (hmm -- I should upgrade that!) > Using your last function line2array5 WITH float conversion, i get the > following timing on a mobile quad core extreme: > > In [24]: a = np.arange(100).a

Re: [Numpy-discussion] String manipulation summary

what machine spec are you using? Using your last function line2array5 WITH float conversion, i get the following timing on a mobile quad core extreme: In [24]: a = np.arange(100).astype(str).tostring() In [25]: a Out[25]: '012345678911223344556677

[Numpy-discussion] Test failures on FreeBSD buildbot

== ERROR: test_nan_array (test_utils.TestApproxEqual) -- Traceback (most recent call last): File "/tmp/numpy-buildbot/b12/numpy-install/lib/python2.5/site-packa

[Numpy-discussion] String manipulation summary

Hi all, When I first saws this problem: reading in a fixed-width text file as numbers, it struck me that you really should be able to do it, and do it well, with numpy by slicing character arrays. I got carried away, and worked out a number of ways to do it. Lastly was a method inspired by a

[Numpy-discussion] Installed C libraries and using core math library

Hi, I have recently integrated my work on numpy.distutils to build so called "installable" C libraries, that is pure C libraries which can be installed and reused by 3rd parties. The general documentation is in the distutils section: http://docs.scipy.org/doc/numpy/reference/distutils.html#build

Re: [Numpy-discussion] Not enough storage for memmap on 32 bit WinXP for accumulated file size above approx. 1 GB

Kim Hansen wrote: > Yes, I see the problem in getting the same kind of reuse of objects > using simple indexing. For my specific case, I will just allocate a > new array as containing a copy of every 100th element and return this > array. It will basically give me the same result as the original >

Re: [Numpy-discussion] Why does assert_array_almost_equal sometimes raise ValueError instead of AssertionError ?

On Mon, Jul 27, 2009 at 1:00 AM, David Cournapeau < da...@ar.media.kyoto-u.ac.jp> wrote: > Hi, > >In some cases, some of the testing functions assert_array_* raise a > ValueError instead of AssertionError: > > >>> np.testing.assert_array_almost_equal(np.array([1, 2, np.nan]), > np.array([1, 2,

Re: [Numpy-discussion] Not enough storage for memmap on 32 bit WinXP for accumulated file size above approx. 1 GB

2009/7/27 Sebastian Haase : > Is PyTables any option for you ? > > -- > Sebastian Haase > That may indeed be something for me! I had heard the name before but I never realized exactly what it was. However, i have just seen their first tutorial video, and it seems like a very, very useful package a

Re: [Numpy-discussion] Not enough storage for memmap on 32 bit WinXP for accumulated file size above approx. 1 GB

> > You could think about using some kind of virtualisation - this is > exactly the sort of situation where I find it really useful. > > You can run a 64 bit host OS, then have 32 bit XP as a 'guest' in > VMware or Virtualbox or some other virtualisation software. With > recent CPU's there is very

Re: [Numpy-discussion] Not enough storage for memmap on 32 bit WinXP for accumulated file size above approx. 1 GB

Kim Hansen wrote: > The machine is new and shiny with loads of processing power and many > TB of HDD storage. I am however bound to 32 bits Win XP OS as there > are some other costum made third-party and very expensive applications > running on that machine (which generate the large files I analyze

Re: [Numpy-discussion] Not enough storage for memmap on 32 bit WinXP for accumulated file size above approx. 1 GB

On Mon, Jul 27, 2009 at 11:37 AM, Kim Hansen wrote: > The machine is new and shiny with loads of processing power and many > TB of HDD storage. I am however bound to 32 bits Win XP OS as there > are some other costum made third-party and very expensive applications > running on that machine (which

Re: [Numpy-discussion] Not enough storage for memmap on 32 bit WinXP for accumulated file size above approx. 1 GB

Is PyTables any option for you ? -- Sebastian Haase On Mon, Jul 27, 2009 at 12:37 PM, Kim Hansen wrote: >> >> I think it would be quite complicated. One fundamental "limitation" of >> numpy is that it views a contiguous chunk of memory. You can't have one >> numpy array which is the union of two

Re: [Numpy-discussion] Not enough storage for memmap on 32 bit WinXP for accumulated file size above approx. 1 GB

> > I think it would be quite complicated. One fundamental "limitation" of > numpy is that it views a contiguous chunk of memory. You can't have one > numpy array which is the union of two memory blocks with a hole in > between, so if you slice every 1000 items, the underlying memory of the > array

Re: [Numpy-discussion] Not enough storage for memmap on 32 bit WinXP for accumulated file size above approx. 1 GB

Kim Hansen wrote: > >From my (admittedly ignorant) point of view it seems like an > implementation detail for me, that there is a problem with some > intermediate memory address space. > Yes, it is an implementation detail, but as is 32 vs 64 bits :) > My typical use case would be to access an

Re: [Numpy-discussion] Not enough storage for memmap on 32 bit WinXP for accumulated file size above approx. 1 GB

2009/7/24 David Cournapeau : > > Well, the questions has popped up a few times already, so I guess this > is not so obvious :) 32 bits architecture fundamentally means that a > pointer is 32 bits, so you can only address 2^32 different memory > locations. The 2Gb instead of 4Gb is a consequence on

[Numpy-discussion] Why does assert_array_almost_equal sometimes raise ValueError instead of AssertionError ?

Hi, In some cases, some of the testing functions assert_array_* raise a ValueError instead of AssertionError: >>> np.testing.assert_array_almost_equal(np.array([1, 2, np.nan]), np.array([1, 2, 3])) # raises ValueError >>> np.testing.assert_array_almost_equal(np.array([1, 2, np.inf]), np.array