Hi Robert,
2008/11/18 Robert Young [EMAIL PROTECTED]:
Is there a method in NumPy that reduces a matrix to it's reduced row echelon
form? I'm brand new to both NumPy and linear algebra, and I'm not quite sure
where to look.
I use the Sympy package. It is small, easy to install, runs on pure
What's the best way to convert a pair of real arrays representing real and imag
parts to a complex array?
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2008/11/19 Robert Kern [EMAIL PROTECTED]:
On Wed, Nov 19, 2008 at 01:31, David Warde-Farley [EMAIL PROTECTED] wrote:
On 18-Nov-08, at 3:06 PM, Robert Kern wrote:
I like to discourage this use of where(). For some reason, back in
Numeric's days, where() got stuck with two functionalities.
If if look at help(np.linalg.solve) I am
told what it does but not how it does it.
If I look at
http://www.scipy.org/doc/numpy_api_docs/numpy.linalg.linalg.html#solve
there is even less info. I'll guess the
algorithm is Gaussian elimination, but how
would I use the documentation to confirm this?
If you use iPython and use numpy.linalg.solve??, you can see the
source code of the file numpy/linalg/linalg.py that corresponds to the
solve(a,b) function, not just the docstring:
def solve(a, b):
Solve the equation ``a x = b`` for ``x``.
Parameters
--
a :
On Wed, Nov 19, 2008 at 8:14 AM, Alan G Isaac [EMAIL PROTECTED] wrote:
If if look at help(np.linalg.solve) I am
told what it does but not how it does it.
If I look at
http://www.scipy.org/doc/numpy_api_docs/numpy.linalg.linalg.html#solve
there is even less info. I'll guess the
algorithm is
Thanks Charles and Josh, but my question
about the documentation goal remains.
Here is how this came up. I mentioned
to a class that I have using NumPy that
solving Ax=b with an inverse is
computationally wasteful and also has
accuracy problems, and I recommend
using `solve` instead. So the
On Wed, Nov 19, 2008 at 07:15, Neal Becker [EMAIL PROTECTED] wrote:
What's the best way to convert a pair of real arrays representing real and
imag parts to a complex array?
What do you mean by best? Easiest to type? Easiest to read? Fastest?
Memory efficient?
Ideally, they'd all be the same,
Le mercredi 19 novembre 2008 à 14:27 -0500, Alan G Isaac a écrit :
So my question is not just what is the algorithm
but also, what is the documentation goal?
Concerning the algorithm (only):
in Joshua answer, you have might have seen that solve is a wrapper to
lapack routines *gesv (z* or d*
A library for matlab io, perhaps this could be useful?
http://sourceforge.net/projects/matio
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On Wed, Nov 19, 2008 at 02:27:11PM -0500, Alan G Isaac wrote:
So my question is not just what is the algorithm
but also, what is the documentation goal?
That's a good question. I feel the documentation should be as precise as
possible, and thus answer this question. Currently it doesn't, but
Hi,
Can someone please explain what happens here:
In testfile.py:
x = 5
def arbFunc():
print x
del x
print Done with Test
arbFunc()
Then run the file with python testfile.py
As opposed to
x = 5
print x
del x
print Done with Test
Which of course works fine. This question is of
On Wed, Nov 19, 2008 at 17:30, Frank Lagor [EMAIL PROTECTED] wrote:
Hi,
Can someone please explain what happens here:
In testfile.py:
x = 5
def arbFunc():
print x
You get an UnboundLocalError here.
del x
During the compilation process, Python sees this statement and assumes
that
Hello,
Can I request that mod be added to numpy.ma ?
Thx,
C
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Thank you very much Robert.
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On Wed, Nov 19, 2008 at 3:20 PM, Fabrice Silva [EMAIL PROTECTED]wrote:
Le mercredi 19 novembre 2008 à 14:27 -0500, Alan G Isaac a écrit :
So my question is not just what is the algorithm
but also, what is the documentation goal?
Concerning the algorithm (only):
in Joshua answer, you have
I have only used pickle a little and I did not see this in the docstring:
Is there anyway to unpickle in reverse order?
It appears the pickling works like a queue.
I execute:
pickle.dump(obj1,file)
pickle.dump(obj2,file)
Then when I go to retrieve:
pickle.load(file) returns obj1
On Wed, Nov 19, 2008 at 22:36, Frank Lagor [EMAIL PROTECTED] wrote:
I have only used pickle a little and I did not see this in the docstring:
Is there anyway to unpickle in reverse order?
No.
This, and your previous question, are mostly off-topic for
numpy-discussion. You may want to ask such
2008/11/20 Charles R Harris [EMAIL PROTECTED]:
On Wed, Nov 19, 2008 at 3:20 PM, Fabrice Silva [EMAIL PROTECTED]
wrote:
Le mercredi 19 novembre 2008 à 14:27 -0500, Alan G Isaac a écrit :
So my question is not just what is the algorithm
but also, what is the documentation goal?
Concerning
On Wed, Nov 19, 2008 at 10:58 PM, Scott Sinclair scott.sinclair.za@
gmail.com wrote:
2008/11/20 Charles R Harris [EMAIL PROTECTED]:
On Wed, Nov 19, 2008 at 3:20 PM, Fabrice Silva [EMAIL PROTECTED]
wrote:
Le mercredi 19 novembre 2008 à 14:27 -0500, Alan G Isaac a écrit :
So my
Hello,
Sorry about this bug report I know how much us programmers like
these kind of bugs :-/.
In testing the latest svn version of numpy (6083), I get a memory
corruption error:
test_csingle (test_linalg.TestSolve) ... ok
test_double (test_linalg.TestSolve) ... ok
test_empty
On Wed, 2008-11-19 at 23:23 -0800, Hoyt Koepke wrote:
Hello,
Sorry about this bug report I know how much us programmers like
these kind of bugs :-/.
In testing the latest svn version of numpy (6083), I get a memory
corruption error:
test_csingle (test_linalg.TestSolve) ... ok
Sorry;
I also added '-fPIC' to the compile flags, though it may work without
having it there. I just got errors related to not having it and so
threw it at everything...
--Hoyt
On Wed, Nov 19, 2008 at 11:41 PM, Hoyt Koepke [EMAIL PROTECTED] wrote:
Attached.
I had a bunch of issues getting
On Wed, 2008-11-19 at 23:41 -0800, Hoyt Koepke wrote:
Attached.
I had a bunch of issues getting things to install with lapack and
ATLAS.
Which ones ?
In the end I had to specify the following environment
variables (+ appropriate command line options to ATLAS lapack) to
get it to work.
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