2008/10/15 Charles R Harris <[EMAIL PROTECTED]>:
> numpy.linalg has qr and cholesky factorizations, but LU factorization is
> only available in scipy. That doesn't seem quite right. I think is would
> make sense to include the LU factorization in numpy among the basic linalg
> operations, and proba
Hi All,
numpy.linalg has qr and cholesky factorizations, but LU factorization is
only available in scipy. That doesn't seem quite right. I think is would
make sense to include the LU factorization in numpy among the basic linalg
operations, and probably LU_solve also. Thoughts?
Chuck
frank wang wrote:
> Hi,
>
> I have a large ndarray that I want to dump to a file. I know that I can
> use a for loop to write one data at a time. Since Python is a very
> powerfully language, I want to find a way that will dump the data fast
> and clean. The data can be in floating point or in
Robert Kern wrote:
>> from numpy import *
>> for i in range(1000):
>> a = random.randn(512**2)
>> b = a.argsort(kind='quick')
>
> Can you try upgrading to numpy 1.2.0? On my machine with numpy 1.2.0
> on OS X, the memory usage is stable.
>
I tried the code fragment on two platforms and the m
Hi,
I have a large ndarray that I want to dump to a file. I know that I can use a
for loop to write one data at a time. Since Python is a very powerfully
language, I want to find a way that will dump the data fast and clean. The data
can be in floating point or integer.
Thanks
Frank
_
On Tue, Oct 14, 2008 at 17:02, emil <[EMAIL PROTECTED]> wrote:
> Hi,
> I'm having a problem with my python code, using numpy, chewing up too
> much memory.
> In the following, I boiled down my program to the simplest example that
> has the problem:
>
> from numpy import *
> for i in range(1000):
Hi,
I'm having a problem with my python code, using numpy, chewing up too
much memory.
In the following, I boiled down my program to the simplest example that
has the problem:
from numpy import *
for i in range(1000):
a = random.randn(512**2)
b = a.argsort(kind='quick')
This loop takes a
On Tue, Oct 14, 2008 at 7:51 PM, T J <[EMAIL PROTECTED]> wrote:
> On Tue, Oct 14, 2008 at 1:02 AM, Sebastian Haase <[EMAIL PROTECTED]> wrote:
>> b) I don't want to use Python / numpy API code in the C functions I'm
>> wrapping - so I limit myself to "input" arrays! Since array memory
>> does not di
>> It seems like I should use:
>>
>> (int* ARGOUT_ARRAY1, int DIM1)
>>
>> and then provide a python function which reshapes the 1D array?
Oh, another option is to use:
( DATA_TYPE* INPLACE_FARRAY2, int DIM1, int DIM2 )
and create the array in python first. This could be done with a little
wr
T J wrote:
> I'm new to using SWIG and my reading of numpy_swig.pdf tells me that
> the following typemap does not exist:
>
> (int* ARGOUT_ARRAY2, int DIM1, int DIM2)
are you referring to this statement in the docs?
"""
Note that we support DATA_TYPE* argout typemaps in 1D, but not 2D or 3D.
On Tue, Oct 14, 2008 at 1:02 AM, Sebastian Haase <[EMAIL PROTECTED]> wrote:
> b) I don't want to use Python / numpy API code in the C functions I'm
> wrapping - so I limit myself to "input" arrays! Since array memory
> does not distinguish between input or output (assuming there is no
> copying nee
On Tue, Oct 14, 2008 at 11:12, <[EMAIL PROTECTED]> wrote:
> Do I need ATLAS to install NumPy?
No.
> Apologies if this is in the archives somewhere, but I can't find it, and I
> can't figure it out from http://www.scipy.org/Installing_SciPy/Linux.
> Clearly you need some kind of BLAS/ LAPACK, but
Do I need ATLAS to install NumPy?
Apologies if this is in the archives somewhere, but I can't find it, and I
can't figure it out from http://www.scipy.org/Installing_SciPy/Linux. Clearly
you need some kind of BLAS/ LAPACK, but it's not clear if ATLAS required, or is
just one option of many.
David Huard wrote:
...
> I've edited the docstring at
> http://sd-2116.dedibox.fr/pydocweb/doc/numpy.lib.shape_base.apply_along_axis/
>
> Feel free to improve on it.
Thanks! That is better.
___
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Hi Scott
2008/10/14 Scott Askey <[EMAIL PROTECTED]>:
> Where is a good place to look for python funtions similar to Matlab's
> ss , tf, ss2tf as use for transforming a linear time invariant(LTI) model
> into a state space model.
Look under scipy.signal. These utilities need a lot of work, so if
Ryan Krauss has been working on something, although I have not had a
chance to try it.
http://www.siue.edu/~rkrauss/python_intro.html
Scott Askey wrote:
> Where is a good place to look for python funtions similar to Matlab's
> ss , tf, ss2tf as use for transforming a linear time invariant(LTI) mo
Where is a good place to look for python funtions similar to Matlab's
ss , tf, ss2tf as use for transforming a linear time invariant(LTI) model into
a state space model.
V/R
Scott
http://www.mathworks.com/access/helpdesk/help/toolbox/control/
__
The numpy poly function work to solve most of my problem. As soon as I
realized the poly in numpy base and matlab perform the same funtion I was ble
to efficintly transform my state space model (A B C D ) into polynomial
transfer funtions.
H=num/den num=(poly(A-dot(B,C))-poly(A)) den =
On Tue, Oct 14, 2008 at 6:40 AM, T J <[EMAIL PROTECTED]> wrote:
> Hi,
>
> I'm new to using SWIG and my reading of numpy_swig.pdf tells me that
> the following typemap does not exist:
>
> (int* ARGOUT_ARRAY2, int DIM1, int DIM2)
>
> What is the recommended way to output a 2D array? It seems like I
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