Dear Val,
I agree that more detail is needed. Sorry for that it was late yesterday.
I am running Python 2.6.1, numpy development branch
(numpy-2.0.0.dev_20101104-py2.6-macosx-10.6-universal.egg). maybe I should
switch to release?
I compile with your setup.py using 'python setup.py build_ext -
On Wednesday, March 7, 2012, Nathaniel Smith wrote:
> On Wed, Mar 7, 2012 at 8:05 PM, Neal Becker wrote:
>> I'm wondering what is the use for the ignored data feature?
>>
>> I can use:
>>
>> A[valid_A_indexes] = whatever
>>
>> to process only the 'non-ignored' portions of A. So at least some
sim
FWIW, this crashes on Windows with numpy 1.6.1 but not numpy 1.7-git
debug build.
Christoph Gohlke
On 3/7/2012 5:36 PM, Val Kalatsky wrote:
>
> Tried it on my Ubuntu 10.10 box, no problem:
>
> 1) Saved as spampub.c
> 2) Compiled with (setup.py attached): python setup.py build_ext -i
> 3) Tested
Tried it on my Ubuntu 10.10 box, no problem:
1) Saved as spampub.c
2) Compiled with (setup.py attached): python setup.py build_ext -i
3) Tested from ipython:
In [1]: import spampub
In [2]: ua=spampub.UnitArray([0,1,2,3.0],'liter')
In [3]: ua
Out[3]: UnitArray([ 0., 1., 2., 3.])
In [4]: ua.unit
Seeing the backtrace would be helpful.
Can you do whatever leads to the segfault
from python run from gdb?
Val
On Wed, Mar 7, 2012 at 7:04 PM, Christoph Gohle
wrote:
> -BEGIN PGP SIGNED MESSAGE-
> Hash: SHA1
>
> Hi,
>
> I have been struggeling for quite some time now. Desperate as I am, n
-BEGIN PGP SIGNED MESSAGE-
Hash: SHA1
Hi,
I have been struggeling for quite some time now. Desperate as I am, now I need
help.
I was trying to subclass ndarrays in a c extension (see code below) and do
constantly get segfaults. I have been checking my INCREF and DECREF stuff up
and
Hi,
I noticed a casting change running the test suite on our image reader,
nibabel:
https://github.com/nipy/nibabel/blob/master/nibabel/tests/test_casting.py
For this script:
import numpy as np
Adata = np.zeros((2,), dtype=np.uint8)
Bdata = np.zeros((2,), dtype=np.int16)
Bzero = np.int16(0)
B
On Wed, Mar 7, 2012 at 8:05 PM, Neal Becker wrote:
> I'm wondering what is the use for the ignored data feature?
>
> I can use:
>
> A[valid_A_indexes] = whatever
>
> to process only the 'non-ignored' portions of A. So at least some simple
> cases
> of ignored data are already supported without i
On Wed, Mar 7, 2012 at 7:39 PM, Benjamin Root wrote:
> On Wed, Mar 7, 2012 at 1:26 PM, Nathaniel Smith wrote:
>> When it comes to "missing data", bitpatterns can do everything that
>> masks can do, are no more complicated to implement, and have better
>> performance characteristics.
>>
>
> Not tr
On Wed, Mar 7, 2012 at 7:37 PM, Charles R Harris
wrote:
>
>
> On Wed, Mar 7, 2012 at 12:26 PM, Nathaniel Smith wrote:
>> When it comes to "missing data", bitpatterns can do everything that
>> masks can do, are no more complicated to implement, and have better
>> performance characteristics.
>>
>
On Wed, Mar 7, 2012 at 1:54 PM, Charles R Harris
wrote:
> Hi All,
>
> Many here have probably received the message from github about push/pull
> access being blocked until you have auditied your ssh keys. To generate a
> key fingerprint on fedora, I did the following:
>
> $charris@f16 ~$ ssh-keyge
Hi Charles,
Le 07/03/2012 18:00, Charles R Harris a écrit :
>
> That's a good idea, I'll take care of it. Note the caveat about the
> coefficients going in the opposite direction.
Great ! In the mean time I changed a bit the root polynomials reference
to emphasize the new Polynomial class.
http://
On Wednesday, March 7, 2012, Neal Becker wrote:
> Charles R Harris wrote:
>
>> On Wed, Mar 7, 2012 at 1:05 PM, Neal Becker wrote:
>>
>>> I'm wondering what is the use for the ignored data feature?
>>>
>>> I can use:
>>>
>>> A[valid_A_indexes] = whatever
>>>
>>> to process only the 'non-ignored' p
On 03/07/2012 11:15 AM, Pierre Haessig wrote:
> Hi,
> Le 07/03/2012 20:57, Eric Firing a écrit :
>> In other words, good low-level support for numpy.ma functionality?
> Coming back to *existing* ma support, I was just wondering whether it
> was now possible to "np.save" a masked array.
> (I'm using
Hi,
Le 07/03/2012 20:57, Eric Firing a écrit :
> In other words, good low-level support for numpy.ma functionality?
Coming back to *existing* ma support, I was just wondering whether it
was now possible to "np.save" a masked array.
(I'm using numpy 1.5)
In the end, this is the most annoying problem
Charles R Harris wrote:
> On Wed, Mar 7, 2012 at 1:05 PM, Neal Becker wrote:
>
>> I'm wondering what is the use for the ignored data feature?
>>
>> I can use:
>>
>> A[valid_A_indexes] = whatever
>>
>> to process only the 'non-ignored' portions of A. So at least some simple
>> cases
>> of ignore
On Wed, Mar 7, 2012 at 1:05 PM, Neal Becker wrote:
> I'm wondering what is the use for the ignored data feature?
>
> I can use:
>
> A[valid_A_indexes] = whatever
>
> to process only the 'non-ignored' portions of A. So at least some simple
> cases
> of ignored data are already supported without i
I'm wondering what is the use for the ignored data feature?
I can use:
A[valid_A_indexes] = whatever
to process only the 'non-ignored' portions of A. So at least some simple cases
of ignored data are already supported without introducing a new type.
OTOH:
w = A[valid_A_indexes]
will copy A'
On 06/03/2012 20:57, Sturla Molden wrote:
On 05.03.2012 14:26, "V. Armando Solé" wrote:
In 2009 there was a thread in this mailing list concerning the access to
BLAS from C extension modules.
If I have properly understood the thread:
http://mail.scipy.org/pipermail/numpy-discussion/2009-Novem
On 03/07/2012 09:26 AM, Nathaniel Smith wrote:
> On Wed, Mar 7, 2012 at 5:17 PM, Charles R Harris
> wrote:
>> On Wed, Mar 7, 2012 at 9:35 AM, Pierre Haessig
>>> Coming back to Travis proposition "bit-pattern approaches to missing
>>> data (*at least* for float64 and int32) need to be implemented.
Hi,
On Wed, Mar 7, 2012 at 11:37 AM, Charles R Harris
wrote:
>
>
> On Wed, Mar 7, 2012 at 12:26 PM, Nathaniel Smith wrote:
>>
>> On Wed, Mar 7, 2012 at 5:17 PM, Charles R Harris
>> wrote:
>> > On Wed, Mar 7, 2012 at 9:35 AM, Pierre Haessig
>> >
>> >> Coming back to Travis proposition "bit-patt
On Wed, Mar 7, 2012 at 1:26 PM, Nathaniel Smith wrote:
> On Wed, Mar 7, 2012 at 5:17 PM, Charles R Harris
> wrote:
> > On Wed, Mar 7, 2012 at 9:35 AM, Pierre Haessig >
> >> Coming back to Travis proposition "bit-pattern approaches to missing
> >> data (*at least* for float64 and int32) need to
On Wed, Mar 7, 2012 at 12:26 PM, Nathaniel Smith wrote:
> On Wed, Mar 7, 2012 at 5:17 PM, Charles R Harris
> wrote:
> > On Wed, Mar 7, 2012 at 9:35 AM, Pierre Haessig >
> >> Coming back to Travis proposition "bit-pattern approaches to missing
> >> data (*at least* for float64 and int32) need to
On Wed, Mar 7, 2012 at 5:17 PM, Charles R Harris
wrote:
> On Wed, Mar 7, 2012 at 9:35 AM, Pierre Haessig
>> Coming back to Travis proposition "bit-pattern approaches to missing
>> data (*at least* for float64 and int32) need to be implemented.", I
>> wonder what is the amount of extra work to go
On Wed, Mar 7, 2012 at 12:35 PM, Skipper Seabold wrote:
> Is there a way to use numpy.distuils to programmatically check for a C
> compiler at build time in a platform independent way?
Wading through the numpy/distutils code some more. Would something as
simple as this work all the time? Seems to
Hi All,
Many here have probably received the message from github about push/pull
access being blocked until you have auditied your ssh keys. To generate a
key fingerprint on fedora, I did the following:
$charris@f16 ~$ ssh-keygen -l -f .ssh/id_dsa.pub
I don't how this looks for those of you usin
On Wed, Mar 7, 2012 at 11:21 AM, Lluís wrote:
> Charles R Harris writes:
> [...]
> > One inconvenience I have run into with the current API is that is should
> be
> > easier to clear the mask from an "ignored" value without taking a new
> view or
> > assigning known data.
>
> AFAIR, the inability
Hi everyone,
I am proposing to add the the two following functions to
numpy/lib/twodim_base.py:
sum_angle() computes the sum of a 2-d array along an angled axis
sum_polar() computes the sum of a 2-d array along radial lines or
along azimuthal circles
https://github.com/numpy/numpy/pull/230
Comme
Charles R Harris writes:
[...]
> One inconvenience I have run into with the current API is that is should be
> easier to clear the mask from an "ignored" value without taking a new view or
> assigning known data.
AFAIR, the inability to directly access a "mask" attribute was intentional to
make bi
Is there a way to use numpy.distuils to programmatically check for a C
compiler at build time in a platform independent way?
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On Wed, Mar 7, 2012 at 9:35 AM, Pierre Haessig wrote:
> Hi,
>
> Thanks you very much for your lights !
>
> Le 06/03/2012 21:59, Nathaniel Smith a écrit :
> > Right -- R has a very impoverished type system as compared to numpy.
> > There's basically four types: "numeric" (meaning double precision
>
On Wed, Mar 7, 2012 at 4:35 PM, Pierre Haessig wrote:
> Hi,
>
> Thanks you very much for your lights !
>
> Le 06/03/2012 21:59, Nathaniel Smith a écrit :
>> Right -- R has a very impoverished type system as compared to numpy.
>> There's basically four types: "numeric" (meaning double precision
>>
On Wed, Mar 7, 2012 at 9:45 AM, Pierre Haessig wrote:
> Hi,
> Le 06/03/2012 22:19, Charles R Harris a écrit :
> > Use polynomial.Polynomial and you won't have this problem.
> I was not familiar with the "poly1d vs. Polynomial" choice.
>
> Now, I found in the doc some more or less explicit guidelin
On Tue, Mar 6, 2012 at 1:44 PM, Robert Kern wrote:
> On Tue, Mar 6, 2012 at 18:25, Travis Oliphant wrote:
> > Why do we want to return a single string char instead of an int?
>
> I suspect just to ensure that any provided value fits in the range
> 0..255. But that's easily done explicitly.
>
Th
Hi,
Le 06/03/2012 22:19, Charles R Harris a écrit :
> Use polynomial.Polynomial and you won't have this problem.
I was not familiar with the "poly1d vs. Polynomial" choice.
Now, I found in the doc some more or less explicit guidelines in:
http://docs.scipy.org/doc/numpy/reference/routines.polynomi
Hi,
Thanks you very much for your lights !
Le 06/03/2012 21:59, Nathaniel Smith a écrit :
> Right -- R has a very impoverished type system as compared to numpy.
> There's basically four types: "numeric" (meaning double precision
> float), "integer", "logical" (boolean), and "character" (string).
On Tue, Mar 6, 2012 at 4:45 PM, Chris Barker wrote:
> On Thu, Mar 1, 2012 at 10:58 PM, Jay Bourque wrote:
>
> > 1. Loading text files using loadtxt/genfromtxt need a significant
> > performance boost (I think at least an order of magnitude increase in
> > performance is very doable based on what
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