Hi Chris
Do you have a Solaris machine that we can use as a client for the
buildbot (this can be a desktop machine)? I didn't see this problem
earlier, since all the other platforms built without problems.
I also noticed that not all platforms execute the same number of
tests, which is worrisome
Hello,
I deleted any old (2+ years since modified) branches and tags.
Nothing is actually deleted so if you need to access the old code
simply use the relevant revision number with svn checkout, svn switch,
or svn list.
It is also very easy to restore if you are planning to continue
working on so
Hi,
The latest version of numpy has a unit test failure on big endian machines.
==
FAIL: test_record_array (numpy.core.tests.test_multiarray.test_putmask)
--
Tra
Hi list,
When I do large array manipulations, I get out-of-memory errors.
For instance if the array size is 5000 by 6000, the following codes use
nearly 1G of RAM.
Then my PC displays a Python error box. The try/except won't even catch
it if the error happens in "astype" instead of "array1* arra
> Jens Jørgen Mortensen wrote:
>> I would like all these arrays to be contiguous:
>>
> import numpy as npy
> npy.__version__
>> '1.0.4.dev3967'
> x = npy.arange(4)
> y = x[npy.newaxis, :]
> z = x.reshape((1, 4))
> for a in [x, y, z]:
>> ... print a.shape, a.strides, a.fl
A Tuesday 21 August 2007, Mark.Miller escrigué:
> A slightly related question on this topic...
>
> Is there a good loopless way to identify all of the unique rows in an
> array? Something like numpy.unique() is ideal, but capable of
> extracting unique subarrays along an axis.
You can always do a
On Tue, Aug 21, 2007 at 02:14:00PM -0700, Timothy Hochberg wrote:
>I suppose someone should fix that someday. However, I still think
>vectorize is an attractive nuisance in the sense that someone has a
>function that they want to apply to an array and they get sucked into
>throwing
