Good evening fellow Numpy-ers!
I sent an email a while ago about a employment opportunity at Argonne National
Laboratory but I think I sent it from the wrong address so it bounced.
We are seeking to hire some one to work on a python based toolkit for working
with scanning weather radar. The mai
On Mon, Sep 24, 2012 at 3:49 PM, Nathaniel Smith wrote:
> On Mon, Sep 24, 2012 at 10:47 PM, Charles R Harris
> wrote:
>>
>>
>> On Mon, Sep 24, 2012 at 2:25 PM, Frédéric Bastien wrote:
>>>
>>> Hi,
>>>
>>> I tested this new beta on Theano and discovered an interface change
>>> that was not there i
25.09.2012 00:55, Frédéric Bastien kirjoitti:
> On Mon, Sep 24, 2012 at 5:47 PM, Charles R Harris
[clip]
>> I think this problem has been brought up on the list. It is interesting that
>> it turned up after the first beta. Could you do a bisection to discover
>> which commit is responsible?
>
> I'
On Mon, Sep 24, 2012 at 10:47 PM, Charles R Harris
wrote:
>
>
> On Mon, Sep 24, 2012 at 2:25 PM, Frédéric Bastien wrote:
>>
>> Hi,
>>
>> I tested this new beta on Theano and discovered an interface change
>> that was not there in the beta 1.
>>
>> New behavior:
>> numpy.ndindex().next()
>> (0,)
>
On Mon, Sep 24, 2012 at 5:47 PM, Charles R Harris
wrote:
>
>
> On Mon, Sep 24, 2012 at 2:25 PM, Frédéric Bastien wrote:
>>
>> Hi,
>>
>> I tested this new beta on Theano and discovered an interface change
>> that was not there in the beta 1.
>>
>> New behavior:
>> numpy.ndindex().next()
>> (0,)
>>
On Mon, Sep 24, 2012 at 2:25 PM, Frédéric Bastien wrote:
> Hi,
>
> I tested this new beta on Theano and discovered an interface change
> that was not there in the beta 1.
>
> New behavior:
> numpy.ndindex().next()
> (0,)
>
> Old behavior:
> numpy.ndindex().next()
> ()
>
> This break some Theano c
Hi,
I tested this new beta on Theano and discovered an interface change
that was not there in the beta 1.
New behavior:
numpy.ndindex().next()
(0,)
Old behavior:
numpy.ndindex().next()
()
This break some Theano code that look like this:
import numpy
shape=()
out_shape=[12]
random_state=numpy.r
On Mon, Sep 24, 2012 at 07:59:11PM +0100, Nathaniel Smith wrote:
> > which means I probably forgot a DECREF while doing the
> > PyArray_Diagonal changes...
> Yep: https://github.com/numpy/numpy/pull/457
Awesome. I can confirm that this fixes the problem. Script below to check.
You are my hero!
On Mon, Sep 24, 2012 at 7:45 PM, Nathaniel Smith wrote:
> On Mon, Sep 24, 2012 at 7:19 PM, Gael Varoquaux
> wrote:
>> Hi Fred,
>>
>> On Mon, Sep 24, 2012 at 02:17:16PM -0400, Frédéric Bastien wrote:
>>> with numpy '1.6.1', I have no problem.
>>
>>> With numpy 1.7.0b2, I can reproduce the problem.
On Mon, Sep 24, 2012 at 7:19 PM, Gael Varoquaux
wrote:
> Hi Fred,
>
> On Mon, Sep 24, 2012 at 02:17:16PM -0400, Frédéric Bastien wrote:
>> with numpy '1.6.1', I have no problem.
>
>> With numpy 1.7.0b2, I can reproduce the problem.
>
> OK, thanks. I think that I'll start a bisect to figure out whe
Hi Fred,
On Mon, Sep 24, 2012 at 02:17:16PM -0400, Frédéric Bastien wrote:
> with numpy '1.6.1', I have no problem.
> With numpy 1.7.0b2, I can reproduce the problem.
OK, thanks. I think that I'll start a bisect to figure out when it crept
in.
Gael
__
Hi,
with numpy '1.6.1', I have no problem.
With numpy 1.7.0b2, I can reproduce the problem.
HTH
Fred
On Mon, Sep 24, 2012 at 1:04 PM, Gael Varoquaux
wrote:
> Hi list,
>
> I think that I am hit a memory leak with numpy master. The following code
> enables to reproduce it:
>
>
On Mon, Sep 24, 2012 at 7:09 PM, Chris Barker wrote:
> On Sat, Sep 22, 2012 at 1:00 PM, Sebastian Haase wrote:
>> Oh,
>> is this actually documented - I knew that np.array would (by default)
>> only create copies as need ... but I never knew it would - if all fits
>> - even just return the origin
On Sat, Sep 22, 2012 at 1:00 PM, Sebastian Haase wrote:
> Oh,
> is this actually documented - I knew that np.array would (by default)
> only create copies as need ... but I never knew it would - if all fits
> - even just return the original Python-object...
was that a typo? is is "asarray" that r
Hi list,
I think that I am hit a memory leak with numpy master. The following code
enables to reproduce it:
import numpy as np
n = 100
m = np.eye(n)
for i in range(3):
#np.linalg.slogdet(m)
t, result_t =
Thanks Sebastian. Casting it to an array would certainly help.
Another oddity of zero-ranked scalars is that they look iterable, but in
fact are not. Because all they do is generate an error.
>>> a = np.array(22)
Test if iterable:
>>> hasattr(a, __iter__)
True
Or:
>>> import collections
>>> i
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