On Tue, Mar 6, 2012 at 4:45 PM, Chris Barker chris.bar...@noaa.gov wrote:
On Thu, Mar 1, 2012 at 10:58 PM, Jay Bourque jayv...@gmail.com wrote:
1. Loading text files using loadtxt/genfromtxt need a significant
performance boost (I think at least an order of magnitude increase in
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). And
in
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:
On Tue, Mar 6, 2012 at 1:44 PM, Robert Kern robert.k...@gmail.com wrote:
On Tue, Mar 6, 2012 at 18:25, Travis Oliphant tra...@continuum.io 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
On Wed, Mar 7, 2012 at 9:45 AM, Pierre Haessig pierre.haes...@crans.orgwrote:
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
On Wed, Mar 7, 2012 at 4:35 PM, Pierre Haessig pierre.haes...@crans.org 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
On Wed, Mar 7, 2012 at 9:35 AM, Pierre Haessig pierre.haes...@crans.orgwrote:
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
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
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
On Wed, Mar 7, 2012 at 11:21 AM, Lluís xscr...@gmx.net 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
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
On Wed, Mar 7, 2012 at 12:35 PM, Skipper Seabold jsseab...@gmail.com 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
On Wed, Mar 7, 2012 at 5:17 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Wed, Mar 7, 2012 at 9:35 AM, Pierre Haessig pierre.haes...@crans.org
Coming back to Travis proposition bit-pattern approaches to missing
data (*at least* for float64 and int32) need to be implemented., I
On Wed, Mar 7, 2012 at 12:26 PM, Nathaniel Smith n...@pobox.com wrote:
On Wed, Mar 7, 2012 at 5:17 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Wed, Mar 7, 2012 at 9:35 AM, Pierre Haessig pierre.haes...@crans.org
Coming back to Travis proposition bit-pattern approaches to
On Wed, Mar 7, 2012 at 1:26 PM, Nathaniel Smith n...@pobox.com wrote:
On Wed, Mar 7, 2012 at 5:17 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Wed, Mar 7, 2012 at 9:35 AM, Pierre Haessig pierre.haes...@crans.org
Coming back to Travis proposition bit-pattern approaches to
Hi,
On Wed, Mar 7, 2012 at 11:37 AM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Wed, Mar 7, 2012 at 12:26 PM, Nathaniel Smith n...@pobox.com wrote:
On Wed, Mar 7, 2012 at 5:17 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Wed, Mar 7, 2012 at 9:35 AM, Pierre Haessig
On 03/07/2012 09:26 AM, Nathaniel Smith wrote:
On Wed, Mar 7, 2012 at 5:17 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Wed, Mar 7, 2012 at 9:35 AM, Pierre Haessigpierre.haes...@crans.org
Coming back to Travis proposition bit-pattern approaches to missing
data (*at least* for
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:
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
On Wed, Mar 7, 2012 at 1:05 PM, Neal Becker ndbeck...@gmail.com 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
Charles R Harris wrote:
On Wed, Mar 7, 2012 at 1:05 PM, Neal Becker ndbeck...@gmail.com 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
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 I
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 numpy
On Wednesday, March 7, 2012, Neal Becker ndbeck...@gmail.com wrote:
Charles R Harris wrote:
On Wed, Mar 7, 2012 at 1:05 PM, Neal Becker ndbeck...@gmail.com 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
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.
On Wed, Mar 7, 2012 at 1:54 PM, Charles R Harris
charlesr.har...@gmail.com 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:
On Wed, Mar 7, 2012 at 7:37 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Wed, Mar 7, 2012 at 12:26 PM, Nathaniel Smith n...@pobox.com wrote:
When it comes to missing data, bitpatterns can do everything that
masks can do, are no more complicated to implement, and have better
On Wed, Mar 7, 2012 at 7:39 PM, Benjamin Root ben.r...@ou.edu wrote:
On Wed, Mar 7, 2012 at 1:26 PM, Nathaniel Smith n...@pobox.com 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
On Wed, Mar 7, 2012 at 8:05 PM, Neal Becker ndbeck...@gmail.com 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
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:
pre
import numpy as np
Adata = np.zeros((2,), dtype=np.uint8)
Bdata = np.zeros((2,), dtype=np.int16)
Bzero =
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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
christoph.go...@mpq.mpg.dewrote:
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Hi,
I have been struggeling for quite some time now.
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 from
On Wednesday, March 7, 2012, Nathaniel Smith n...@pobox.com wrote:
On Wed, Mar 7, 2012 at 8:05 PM, Neal Becker ndbeck...@gmail.com 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.
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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