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
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).
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
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
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 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 9:35 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
>
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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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
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
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 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 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
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 12:26 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 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
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 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.
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
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 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
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
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
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
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
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 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
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 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 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
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
-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
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
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
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
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
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 -
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