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([1,
2009/7/24 David Cournapeau da...@ar.media.kyoto-u.ac.jp:
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
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 and
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 still
Is PyTables any option for you ?
--
Sebastian Haase
On Mon, Jul 27, 2009 at 12:37 PM, Kim Hansenslaun...@gmail.com 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
On Mon, Jul 27, 2009 at 11:37 AM, Kim Hansenslaun...@gmail.com 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
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),
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 little
2009/7/27 Sebastian Haase seb.ha...@gmail.com:
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
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, 3])) #
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
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:
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
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]:
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 =
On Tue, Jul 28, 2009 at 8:44 AM, Charles R
Harrischarlesr.har...@gmail.com wrote:
ERROR: test_nan_items (test_utils.TestApproxEqual)
--
Traceback (most recent call last):
File
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:
On Mon, Jul 27, 2009 at 9:37 PM, David Cournapeau courn...@gmail.comwrote:
On Tue, Jul 28, 2009 at 8:44 AM, Charles R
Harrischarlesr.har...@gmail.com wrote:
ERROR: test_nan_items (test_utils.TestApproxEqual)
--
Traceback
Charles R Harris wrote:
On Mon, Jul 27, 2009 at 9:37 PM, David Cournapeau courn...@gmail.com
mailto:courn...@gmail.com wrote:
On Tue, Jul 28, 2009 at 8:44 AM, Charles R
Harrischarlesr.har...@gmail.com
mailto:charlesr.har...@gmail.com wrote:
ERROR: test_nan_items
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 the
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 diff
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