On Mon, Sep 22, 2008 at 09:40:42AM +0200, Stéfan van der Walt wrote:
2008/9/19 David M. Kaplan [EMAIL PROTECTED]:
My 2 cents - I personally think the version that always returns a list
will ultimately be more transparent and cause fewer problems than the
newer version. In either case, the
David Cournapeau david at ar.media.kyoto-u.ac.jp writes:
Still, it is indeed really slow for your case; when I fixed nanmean and
co, I did not know much about numpy, I just wanted them to give the
right answer :) I think this can be made faster, specially for your case
(where the axis along
Hi all,
Now that Travis' book is freely available, it is great to see the
NumPy section of
http://www.scipy.org/Documentation has been updated. However, could
someone update the main numpy webpage (numpy.scipy.org) too?
Quoting http://numpy.scipy.org/
Much of the documentation for Numeric and
Peter Saffrey wrote:
I've found that if I just cut nans from the list and use regular numpy median,
it is quicker - 10 times slower than list median, rather than 35 times slower.
Could you just wire nanmedian to do it this way?
Unfortunately, we can't, because we would loose generality: we
David Cournapeau david at ar.media.kyoto-u.ac.jp writes:
Unfortunately, we can't, because we would loose generality: we need to
compute median on any axis, not only the last one. The proper solution
would be to have a sort/max/min/etc... which knows about nan in numpy,
which is what Chuck and
The SciPy conference proceedings are finally available online:
http://conference.scipy.org/proceedings/SciPy2008 .
I hope you enjoy them. I find it great to have this set of excellent
articles talking about works done with, or for, Python in science. For
me, it is a reference to remember what was
I have a an array of indices into a larger array where some condition
is satisfied. I want to create a larger set of indices which *mark*
all the indicies following the condition over some Nmark length
window. In code:
import numpy as np
N = 1000
Nmark = 20
ind =
Le lundi 22 septembre 2008 à 09:41 -0500, John Hunter a écrit :
I have a an array of indices into a larger array where some condition
is satisfied. I want to create a larger set of indices which *mark*
all the indicies following the condition over some Nmark length
window.
A =
On Mon, Sep 22, 2008 at 09:41, John Hunter [EMAIL PROTECTED] wrote:
I have a an array of indices into a larger array where some condition
is satisfied. I want to create a larger set of indices which *mark*
all the indicies following the condition over some Nmark length
window. In code:
On Mon, Sep 22, 2008 at 10:13 AM, Robert Kern [EMAIL PROTECTED] wrote:
marked[ind + np.arange(Nmark)] = True
That triggers a broadcasting error:
Traceback (most recent call last):
File /home/titan/johnh/test.py, line 13, in ?
marked3[ind + np.arange(Nmark)] = True
ValueError: shape
On Mon, Sep 22, 2008 at 03:53, Gael Varoquaux
[EMAIL PROTECTED] wrote:
On Mon, Sep 22, 2008 at 09:40:42AM +0200, Stéfan van der Walt wrote:
2008/9/19 David M. Kaplan [EMAIL PROTECTED]:
My 2 cents - I personally think the version that always returns a list
will ultimately be more transparent
On Mon, Sep 22, 2008 at 10:22, Robert Kern [EMAIL PROTECTED] wrote:
ind2mark = np.asarray((ind[:,np.newaxis] + np.arange(Nmark).flat).clip(0, N-1)
marked[ind2mark] = True
Missing parenthesis:
ind2mark = np.asarray((ind[:,np.newaxis] + np.arange(Nmark)).flat).clip(0, N-1)
--
Robert Kern
I
On Mon, Sep 22, 2008 at 10:23 AM, Robert Kern [EMAIL PROTECTED] wrote:
On Mon, Sep 22, 2008 at 10:22, Robert Kern [EMAIL PROTECTED] wrote:
ind2mark = np.asarray((ind[:,np.newaxis] + np.arange(Nmark).flat).clip(0,
N-1)
marked[ind2mark] = True
Missing parenthesis:
ind2mark =
Hi, All,
I am struggling to make the loadtxt works. In my file, I have several colunms
of data, say I have two. When I use the following command to load the data,
fid=loadtxt('filename.csv',comments='',dtype='|S4',converters={0:lambda
s:int(s,16)})
It will load an array has two columns.
Greetings,
We've recently posted the second beta release of the Enthought Python
Distribution (EPD) for our upcoming general release of version 4.0.300
with Python 2.5. You may download the beta from here:
http://www.enthought.com/products/epdbeta.php
Please feel free to test it out and
On Tuesday 23 September 2008 00:06:14 Tony Yu wrote:
BTW, is the tuple argument you use with `view` documented anywhere; I
haven't seen it before and a quick search doesn't give any results.
http://www.scipy.org/RecordArrays
+
Travis O.'s numpy book, the chapter on dtype (pp. 137-138)
Note
Michael Abshoff wrote:
This is python 2.5.2 build with gcc 4.2.4, numpy itself is build with
-O0, i.e. this is unlikely to be a compiler bug IMHO. This bug has
been present in 1.0.4, 1.1.0 and it seems unfixed in 1.2.rc1. The numpy
1.1 test suite passed with that install, I did not run the
David Cournapeau wrote:
Michael Abshoff wrote:
Hi David,
This is python 2.5.2 build with gcc 4.2.4, numpy itself is build with
-O0, i.e. this is unlikely to be a compiler bug IMHO. This bug has
been present in 1.0.4, 1.1.0 and it seems unfixed in 1.2.rc1. The numpy
1.1 test suite passed
Michael Abshoff wrote:
Sorry for not being precise: Both python and numpy have been build with
OPT=-DNDEBUG -g -O0 -fwrapv -Wall -Wstrict-prototypes
Hm, strange. I don't know why you can't get any debug info, then.
i.e. -O0 instead of -O3. I am using ATLAS and netlib.org Lapack, so
I
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