Dear All,
I'm working on a piece of optimisation code where it turns out to be
mathematically convenient to have a matrix where a few pre-chosen elements must
be computed at evaluation time for the dot product (i.e. matrix multiplication)
of a matrix with a vector.
As I see the problem, there
A Wednesday 17 March 2010 22:56:20 Charles R Harris escrigué:
On Wed, Mar 17, 2010 at 8:42 AM, Alan G Isaac ais...@american.edu wrote:
On 3/17/2010 10:16 AM, josef.p...@gmail.com wrote:
numpy.resize(a, new_shape)
Return a new array with the specified shape.
If the new array is
On Wed, Mar 17, 2010 at 10:56 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Wed, Mar 17, 2010 at 8:42 AM, Alan G Isaac ais...@american.edu wrote:
On 3/17/2010 10:16 AM, josef.p...@gmail.com wrote:
numpy.resize(a, new_shape)
Return a new array with the specified shape.
If
On Thu, Mar 18, 2010 at 09:56:21AM +0100, Sebastian Haase wrote:
How would people feel about unifying the function vs. the method behavior ?
One could add an addition option like
`repeat` or `fillZero`.
You mean fill_zero...
Sorry, my linter went off. :)
Gaël
On Wed, Mar 17, 2010 at 10:16 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Wed, Mar 17, 2010 at 7:39 PM, Darren Dale dsdal...@gmail.com wrote:
On Wed, Mar 17, 2010 at 8:22 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Wed, Mar 17, 2010 at 5:26 PM, Darren Dale
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Hash: SHA1
Hello,
I work on a 64bit machine with 64bits enable fedora on it.
I just discovered that numpy.int on the python part are 64bits ints, while
npy_int in the C api are 32bits ints.
I can live with it, but it seems to be different on 32bit machines,
Hi,
Konrad Hinsen has just told me that my article Why Modern CPUs Are Starving
and What Can Be Done About It, which has just released on the March/April
issue of Computing in Science and Engineering, also made into this month's
free-access selection on IEEE's ComputingNow portal:
On Mar 17, 2010, at 3:18 PM, josef.p...@gmail.com wrote:
On Wed, Mar 17, 2010 at 3:12 PM, Christopher Barker
chris.bar...@noaa.gov wrote:
One of the things I liked about MATLAB was that NaNs were well handled
almost all the time. Given all the limitations of NaN, having a masked
array is a
On 18 March 2010 09:57, Francesc Alted fal...@pytables.org wrote:
Hi,
Konrad Hinsen has just told me that my article Why Modern CPUs Are Starving
and What Can Be Done About It, which has just released on the March/April
issue of Computing in Science and Engineering, also made into this
-BEGIN PGP SIGNED MESSAGE-
Hash: SHA1
Dag Sverre Seljebotn skrev:
Martin Raspaud wrote:
Hello,
I work on a 64bit machine with 64bits enable fedora on it.
I just discovered that numpy.int on the python part are 64bits ints, while
npy_int in the C api are 32bits ints.
On Thu, Mar 18, 2010 at 08:33, Martin Raspaud martin.rasp...@smhi.se wrote:
-BEGIN PGP SIGNED MESSAGE-
Hash: SHA1
Hello,
I work on a 64bit machine with 64bits enable fedora on it.
I just discovered that numpy.int on the python part are 64bits ints, while
npy_int in the C api are
On Mar 17, 2010, at 5:43 PM, Charles R Harris wrote:
On Wed, Mar 17, 2010 at 3:13 PM, Darren Dale dsdal...@gmail.com wrote:
On Wed, Mar 17, 2010 at 4:48 PM, Pierre GM pgmdevl...@gmail.com wrote:
On Mar 17, 2010, at 8:19 AM, Darren Dale wrote:
I started thinking about a third method
On 03/17/2010 04:20 PM, Pierre GM wrote:
On Mar 17, 2010, at 11:09 AM, Bruce Southey wrote:
On 03/17/2010 01:07 AM, Pierre GM wrote:
All,
As you're probably aware, the current test suite for numpy.ma raises some
nagging warnings such as invalid value in These warnings are
On Mar 17, 2010, at 9:16 PM, Charles R Harris wrote:
On Wed, Mar 17, 2010 at 7:39 PM, Darren Dale dsdal...@gmail.com wrote:
On Wed, Mar 17, 2010 at 8:22 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
What bothers me here is the opposing desire to separate ufuncs from their
On Mar 18, 2010, at 11:03 AM, Bruce Southey wrote:
On 03/17/2010 04:20 PM, Pierre GM wrote:
On Mar 17, 2010, at 11:09 AM, Bruce Southey wrote:
On 03/17/2010 01:07 AM, Pierre GM wrote:
All,
As you're probably aware, the current test suite for numpy.ma raises some
nagging warnings such as
http://carshowcolombia.com/John.html
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NumPy-Discussion@scipy.org
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Hello,
Please tolerate my impatience for being the first announcing the new
discussion platform :) and my cross-posting over the lists.
The new site is beating at ask.scipy.org . David Warde-Farley is moving the
questions from the old-site at advice.mechanicalkern.com (announced at
SciPy09 by
A Thursday 18 March 2010 16:26:09 Anne Archibald escrigué:
Speak for your own CPUs :).
But seriously, congratulations on the wide publication of the article;
it's an important issue we often don't think enough about. I'm just a
little snarky because this exact issue came up for us recently -
Hi,
Can I get someone to look at: http://projects.scipy.org/numpy/ticket/1435
Basically, numpy.gradient() uses numpy.zeros() to create an output
array. This breaks the use of any ndarray subclasses, like masked
arrays, since the function will only return
ndarrays. I've attached a patch that
josef.p...@gmail.com wrote:
On Wed, Mar 17, 2010 at 3:12 PM, Christopher Barker
Given all the limitations of NaN, having a masked
array is a better way to go, but I'd love it if they were just there,
and therefore EVERY numpy function and package built on numpy would
handle them gracefully.
On Thu, Mar 18, 2010 at 12:12:10PM -0700, Christopher Barker wrote:
sure -- that's kind of my point -- if EVERY numpy array were
(potentially) masked, then folks would write code to deal with them
appropriately.
That's pretty much saying: I have a complicated problem and I want every
one
On Thu, Mar 18, 2010 at 3:19 PM, Gael Varoquaux
gael.varoqu...@normalesup.org wrote:
On Thu, Mar 18, 2010 at 12:12:10PM -0700, Christopher Barker wrote:
sure -- that's kind of my point -- if EVERY numpy array were
(potentially) masked, then folks would write code to deal with them
Gael Varoquaux wrote:
On Thu, Mar 18, 2010 at 12:12:10PM -0700, Christopher Barker wrote:
sure -- that's kind of my point -- if EVERY numpy array were
(potentially) masked, then folks would write code to deal with them
appropriately.
That's pretty much saying: I have a complicated problem
On Thu, Mar 18, 2010 at 2:46 PM, Christopher Barker
chris.bar...@noaa.gov wrote:
Gael Varoquaux wrote:
On Thu, Mar 18, 2010 at 12:12:10PM -0700, Christopher Barker wrote:
sure -- that's kind of my point -- if EVERY numpy array were
(potentially) masked, then folks would write code to deal with
On Thu, Mar 18, 2010 at 3:46 PM, Christopher Barker
chris.bar...@noaa.gov wrote:
Gael Varoquaux wrote:
On Thu, Mar 18, 2010 at 12:12:10PM -0700, Christopher Barker wrote:
sure -- that's kind of my point -- if EVERY numpy array were
(potentially) masked, then folks would write code to deal with
Ryan May wrote:
On Thu, Mar 18, 2010 at 2:46 PM, Christopher Barker
chris.bar...@noaa.gov wrote:
Gael Varoquaux wrote:
On Thu, Mar 18, 2010 at 12:12:10PM -0700, Christopher Barker wrote:
sure -- that's kind of my point -- if EVERY numpy array were
(potentially) masked, then folks would write
Hi all,
Is there a fast numpy way to find the peak boundaries in a (looong, millions of
points) smoothed signal? I've found some approaches, like this:
z = data[1:-1]
l = data[:-2]
r = data[2:]
f = np.greater(z, l)
f *= np.greater(z, r)
boundaries = np.nonzero(f)
but it is too sensitive... it
2010/3/18 Frank Horowitz fr...@horow.net:
I'm working on a piece of optimisation code where it turns out to be
mathematically convenient to have a matrix where a few pre-chosen elements
must be computed at evaluation time for the dot product (i.e. matrix
multiplication) of a matrix with a
On Thu, Mar 18, 2010 at 5:12 PM, Eric Firing efir...@hawaii.edu wrote:
Ryan May wrote:
On Thu, Mar 18, 2010 at 2:46 PM, Christopher Barker
chris.bar...@noaa.gov wrote:
Gael Varoquaux wrote:
On Thu, Mar 18, 2010 at 12:12:10PM -0700, Christopher Barker wrote:
sure -- that's kind of my point --
On Thu, Mar 18, 2010 at 5:19 PM, Davide Cittaro
davide.citt...@ifom-ieo-campus.it wrote:
Hi all,
Is there a fast numpy way to find the peak boundaries in a (looong, millions
of points) smoothed signal? I've found some approaches, like this:
z = data[1:-1]
l = data[:-2]
r = data[2:]
f =
josef.p...@gmail.com wrote:
I'm facing this at the moment: not a big deal, but I'm using histogram2d
on some data. I just realized that it may have some NaNs in it, and I
have no idea how those are being handled.
histogram2d handles neither masked arrays nor arrays with nans
correctly,
I
On Thu, Mar 18, 2010 at 7:26 PM, Christopher Barker
chris.bar...@noaa.gov wrote:
josef.p...@gmail.com wrote:
I'm facing this at the moment: not a big deal, but I'm using histogram2d
on some data. I just realized that it may have some NaNs in it, and I
have no idea how those are being handled.
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