On Thu, Sep 27, 2012 at 6:08 PM, Sergio Pascual wrote:
> Hello, I'm trying to understand how to work with nditer to do a
> reduction, in my case converting a 3d array into a 2d array.
>
> I followed the help here
> http://docs.scipy.org/doc/numpy/reference/arrays.nditer.html and
> managed to creat
f views problem but also fixes the problem
> sklearn was having with base pointing to an unexpected mmap object.
>
> --
> Travis Oliphant
> (on a mobile)
> 512-826-7480
>
>
> On Sep 30, 2012, at 3:50 PM, Han Genuit wrote:
>
>> On Sun, Sep 30, 2012 at 10:35 PM,
r, but it does fix a real issue.
>
> --
> Travis Oliphant
> (on a mobile)
> 512-826-7480
>
>
> On Sep 30, 2012, at 3:30 PM, Han Genuit wrote:
>
>> On Sun, Sep 30, 2012 at 9:59 PM, Travis Oliphant wrote:
>>> Hey all,
>>>
>>> In a github-discuss
On Sun, Sep 30, 2012 at 9:59 PM, Travis Oliphant wrote:
> Hey all,
>
> In a github-discussion with Gael and Nathaniel, we came up with a proposal
> for .base that we should put before this list.Traditionally, .base has
> always pointed to None for arrays that owned their own memory and to th
[snip]
> Hello,
>
> I ran some compatibility tests on Windows, using
> numpy-MKL-1.7.x.dev.win-amd64-py2.7 with packages built against
> numpy-MKL-1.6.2.
>
> There are new test failures in scipy, bottleneck, pymc, and mvpa2 of the
> following types:
>
> IndexError: too many indices
> ValueError: n
vis
>
> On Sep 15, 2012, at 3:14 PM, Han Genuit wrote:
>
>> Yeah, that merge was fast. :-)
>>
>> Regards,
>> Han
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Yeah, that merge was fast. :-)
Regards,
Han
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Okay, sent in a pull request: https://github.com/numpy/numpy/pull/443.
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I think there is something wrong with the implementation.. I would
expect each incoming array in PyArray_ConcatenateFlattenedArrays to be
flattened and the sizes of all of them added into a one-dimensional
shape. Now the shape is two-dimensional, which does not make sense to
me. Also the requiremen
>> Is the difference between prod and sum intentional? I would expect
>> that numpy.prod would also work on a generator, just like numpy.sum.
>
>
>
> Whatever the correct result may be, I would expect them to have the same
> behavior with respect to a generator argument.
>
I found out that np.sum
Hi,
Maybe try something like this?
>>> args = np.array([4,8])
>>> np.prod(args > 0)
1
>>> np.sum(args > 0)
2
Cheers,
Han
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To be honest, you have been slandering a lot, also in previous
discussions, to get what you wanted. This is not a healthy way of
discussion, nor does it help in any way.
There have been many people willing to listen and agree with you on
points; and this is exactly what discussion is all about, bu
On Sun, Oct 30, 2011 at 12:47 AM, Eric Firing wrote:
> On 10/29/2011 12:02 PM, Olivier Delalleau wrote:
>
>>
>> I haven't been following the discussion closely, but wouldn't it be instead:
>> a.mask[0:2] = True?
>
> That would be consistent with numpy.ma and the opposite of Mark's
> implementation
Hi, instead of putting up a pull request that reverts all the 25000
lines of code than have been written to support an NA mask, why won't
you set up a pull request that uses the current code base to implement
your own ideas on how it should work?
___
NumP
Yes, to further iterate on that, you can also create multiple masked
views with each its own mask properties. It would be ambiguous to mix
a bit-pattern NA together with standard NA's in the same mask, but you
can make different specialized masked views on the same data.
Also, I like the short and
There is a way to assign whole masks in the current implementation:
>>> a = np.arange(9, maskna=True).reshape((3,3))
>>> a
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
>>> mask = np.array([[False, False, True],
[False, True, False],
There is also:
Missing/accumulating data
http://mail.scipy.org/pipermail/numpy-discussion/2011-July/057406.html
An NA compromise idea -- many-NA
http://mail.scipy.org/pipermail/numpy-discussion/2011-July/057408.html
NEPaNEP lessons - was: alterNEP
http://mail.scipy.org/pipermail/numpy-discussion
Well, if I may have a say, I think that an open source project is
especially open when users as developers can contribute to the code
base and can participate in discussions on how to improve the existing
designs and ideas. I do not think a project is open when it crumbles
down into politics.. I ha
> Still, it shouldn't segfault, and it's worth figuring out why it does.
> gdb has been mostly unenlightening for me since gdb won't let me
> navigate the traceback.
You could try faulthandler, it prints the (python) traceback after a crash:
http://pypi.python.org/pypi/faulthandler/
__
2011/9/27 Olivier Delalleau
> 2011/9/27 Zbigniew Jędrzejewski-Szmek
>
>> On 09/22/2011 12:09 PM, Pauli Virtanen wrote:
>> > Thu, 22 Sep 2011 08:12:12 +0200, Han Genuit wrote:
>> > [clip]
>> >> I also noticed that it does strange things when using a
2011/9/20 Stéfan van der Walt
> On Tue, Sep 20, 2011 at 12:43 AM, Robert Kern
> wrote:
> > If the array is short in a dimension, it gets implicitly continued
> > with Falses. You can see this in one dimension:
>
> [...]
>
> > I honestly don't know if this is documented or tested anywhere or even
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