2008/5/16 Anne Archibald [EMAIL PROTECTED]:
How frequently does numpy receive patches that warrant review? The
zillion little doc fixes don't, even moderate-sized patches from
experienced developers probably don't warrant review.
Those moderately-sized patches are the ones that need review,
Hello,
I believe that we have now addressed everything that was holding up
the 1.1.0 release, so I will be tagging the 1.1.0rc1 in about 12
hours. Please be extremely conservative and careful about any commits
you make to the trunk until we officially release 1.1.0 (now may be a
good time to
Jarrod Millman wrote:
Hello,
I believe that we have now addressed everything that was holding up
the 1.1.0 release, so I will be tagging the 1.1.0rc1 in about 12
hours. Please be extremely conservative and careful about any commits
you make to the trunk until we officially release 1.1.0
Dear NumPy and SciPy users,
we are proud to announce release 2.3 of the Modular toolkit for Data Processing
(MDP): a Python data processing framework. The base of readily available
algorithms includes Principal Component Analysis (PCA and NIPALS), four flavors
of Independent Component Analysis
Hi,
I hope you're the right person to ask about this - sorry if not.
I have just noticed that our (neuroimaging.scipy.org) wiki link no longer works:
http://projects.scipy.org/neuroimaging/ni/wiki
gives a 502 proxy error:
Proxy Error
The proxy server received an invalid response from an
On May 16, 2008, at 5:35 AM, Matthew Brett wrote:
Hi,
I hope you're the right person to ask about this - sorry if not.
I have just noticed that our (neuroimaging.scipy.org) wiki link no
longer works:
http://projects.scipy.org/neuroimaging/ni/wiki
gives a 502 proxy error:
Proxy Error
The
Hi,
can someone comment on these timing numbers ?
http://narray.rubyforge.org/bench.html.en
Is the current numpy faster ?
Cheers,
Sebastian Haase
On Sat, May 3, 2008 at 2:07 AM, Travis E. Oliphant
[EMAIL PROTECTED] wrote:
http://narray.rubyforge.org/matrix-e.html
It seems they've
Sebastian Haase wrote:
Hi,
can someone comment on these timing numbers ?
http://narray.rubyforge.org/bench.html.en
Is the current numpy faster ?
It is hard to know without getting the same machine or having the
benchmark sources. But except for add, all other operations rely on
2008/5/16 David Cournapeau [EMAIL PROTECTED]:
Sebastian Haase wrote:
Hi,
can someone comment on these timing numbers ?
http://narray.rubyforge.org/bench.html.en
Is the current numpy faster ?
It is hard to know without getting the same machine or having the
benchmark sources. But except
On Sat, May 17, 2008 at 12:00 AM, Anne Archibald
[EMAIL PROTECTED] wrote:
There are four benchmarks: add, multiply, dot, and solve. dot and
solve use BLAS, and for them numpy ruby and octave are comparable. Add
and multiply are much slower in numpy, but they are implemented in
numpy itself.
Hi guys,
Just a quick note. I've been playing with NumPy again, looking at
corner cases of function evaluation. I noticed this:
In [66]: numpy.sign(numpy.nan)
Out[66]: 0.0
IMO, the output should be NaN, not zero.
If you agree, then I'll be happy to file a bug in the NumPy tracker.
Or if
I have a sparse matrix 416x52. I tried to factorize this matrix using svd
from numpy. But it didn't produce a result and looked like it is in an
infinite loop.
I tried a similar operation using random numbers in the matrix. Even this is
in an infinite loop.
Did anyone else face a similar problem?
Hi,
I tried using Matlab with the same matrix and its eig() function. It can
diagonalize the matrix with a correct result, which is not the case for
linalg.eigh().
Strange.
Matthieu
2008/4/17 Matthieu Brucher [EMAIL PROTECTED]:
Hi,
Ive implemented the classical MultiDimensional Scaling for
la, 2008-05-17 kello 00:39 +0900, David Cournapeau kirjoitti:
On Sat, May 17, 2008 at 12:00 AM, Anne Archibald
[EMAIL PROTECTED] wrote:
There are four benchmarks: add, multiply, dot, and solve. dot and
solve use BLAS, and for them numpy ruby and octave are comparable. Add
and multiply
Nripun Sredar wrote:
I have a sparse matrix 416x52. I tried to factorize this matrix using
svd from numpy. But it didn't produce a result and looked like it is
in an infinite loop.
I tried a similar operation using random numbers in the matrix. Even
this is in an infinite loop.
Did anyone
On Fri, May 16, 2008 at 11:23 AM, Stuart Brorson [EMAIL PROTECTED] wrote:
Hi guys,
Just a quick note. I've been playing with NumPy again, looking at
corner cases of function evaluation. I noticed this:
In [66]: numpy.sign(numpy.nan)
Out[66]: 0.0
IMO, the output should be NaN, not zero.
On Fri, May 16, 2008 at 11:47 AM, Stuart Brorson [EMAIL PROTECTED] wrote:
Hi --
Sorry to be a pest with corner cases, but I found another one.
In this case, if you try to take the arccos of numpy.inf in the
context of a complex array, you get a bogus return (IMO). Like this:
In [147]: R =
On Fri, May 16, 2008 at 1:37 PM, Robert Kern [EMAIL PROTECTED] wrote:
On Fri, May 16, 2008 at 11:47 AM, Stuart Brorson [EMAIL PROTECTED] wrote:
Hi --
Sorry to be a pest with corner cases, but I found another one.
In this case, if you try to take the arccos of numpy.inf in the
context of a
On Fri, May 16, 2008 at 11:23 AM, Robert Kern [EMAIL PROTECTED] wrote:
On Fri, May 16, 2008 at 11:23 AM, Stuart Brorson [EMAIL PROTECTED] wrote:
In [66]: numpy.sign(numpy.nan)
Out[66]: 0.0
IMO, the output should be NaN, not zero.
You're probably right. I would like to see what other systems
On Fri, May 16, 2008 at 2:27 PM, Keith Goodman [EMAIL PROTECTED] wrote:
On Fri, May 16, 2008 at 11:23 AM, Robert Kern [EMAIL PROTECTED] wrote:
On Fri, May 16, 2008 at 11:23 AM, Stuart Brorson [EMAIL PROTECTED] wrote:
In [66]: numpy.sign(numpy.nan)
Out[66]: 0.0
IMO, the output should be NaN,
2008/5/16 Stuart Brorson [EMAIL PROTECTED]:
Hi --
Sorry to be a pest with corner cases, but I found another one.
In this case, if you try to take the arccos of numpy.inf in the
context of a complex array, you get a bogus return (IMO). Like this:
In [147]: R = numpy.array([1, numpy.inf])
Hello,
I have a custom array, which contains custom objects (I give a
stripped down example below), and I want to loop over all of the
elements of the array and call a method of the object. I can do it
like:
a=MyArray((5,5),MyObject,10)
for obj in a.flat:
obj.update()
2008/5/16 Brian Blais [EMAIL PROTECTED]:
I have a custom array, which contains custom objects (I give a stripped down
example below), and I want to loop over all of the elements of the array and
call a method of the object. I can do it like:
a=MyArray((5,5),MyObject,10)
for obj in
On Fri, May 16, 2008 at 12:38 AM, Pearu Peterson [EMAIL PROTECTED] wrote:
I am working with the ticket 752 at the moment and I would probably
not want to commit my work to 1.1.0 at this time, so I shall commit
when trunk is open as 1.1.1.
That sounds reasonable.
My question regarding
NUMPY/SCIPY DOCUMENTATION MARATHON 2008
As we all know, the state of the numpy and scipy reference
documentation (aka the docstrings) is best described as incomplete.
Most functions have docstrings shorter than 5 lines, whereas our
competitors IDL and Matlab usually have a concise and
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