a good way to do it.
They could download the Example List once and save it in a HTML file on their
local disk...
Joris
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NumPy can do.
So I would like to add a categorized overview of the NumPy functions.
Cheers,
Joris
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[im]: Sorry if this is an obvious question, but what is the easiest way to
multiply matrices in numpy? Suppose I want to do A=B*C*D. The ' * ' operator
apparently does element wise multiplication, as does the 'multiply' ufunc.
[im] All I could find was the numeric function 'matrix_multipl
On Friday 27 October 2006 19:06, Francesc Altet wrote:
[FA]: for example:
[FA]:
[FA]: In [67]: dtype([('f1', int16)])
[FA]: Out[67]: dtype([('f1', 'http://www.kuleuven.be/cwis/email_disclaimer.htm
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On Friday 27 October 2006 19:01, Francesc Altet wrote:
[FA]: A Divendres 27 Octubre 2006 17:58, Joris De Ridder va escriure:
[FA]: > Hi,
[FA]: >
[FA]: > Is the following behaviour of astype() intentional in NumPy 1.0?
[FA]: >
[FA]: > >>> x
Hi,
The following did once work in NumPy:
>>> dtype([int16])
>>> dtype([[int16]])
>>> dtype([uint,int32])
>>> dtype(['f8','S10'])
but now they all generate a "TypeError: data type not understood". Why?
I could not find the answer in help(dtype), the RecordArrays tutorial on
scipy.org, the NumPy
Hi,
Is the following behaviour of astype() intentional in NumPy 1.0?
>>> x = array([1,2,3])
>>> x.astype(None)
array([ 1., 2., 3.])
That is, the int32 is converted to float64.
J.
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Forgot the link to the NEL: http://www.scipy.org/Numpy_Example_List
J.
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Get stuff don
Hi,
[SU]: Now, my problem is to construct a degree matrix D which is a 3 * 3
matrix with diagonal elements 4,7,7
You might have a look at the Numpy Example List, at the function diag().
Ciao,
Joris
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without having seen how you
installed it.
I see that you tried
>>> from numarray import *
Perhaps a stupid question, but you did import numpy with
>>> from numpy import *
didn't you?
Cheers,
Joris
Disclaimer: http://www
or k in range(1000):
...A *= zeros(100, complex)
(I replaced 'complex' with 'Complex' in Numarray). Can anyone confirm this?
Ciao,
Joris
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#x27;t find it right away...
Anybody experienced something similar, or other pointers?
Ciao,
Joris
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Get stuff done quickly with pre-integrated technol
pointers?
Cheers,
Joris
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Get stuff done quickly with pre-integrated technology to make
pack.a is 305k
(expected >4000k)
which he shouldn't use at all. Strangely enough, renaming libcombinedlapack.a
to liblapack.a
and adapting the site.cfg file accordingly still gives the same message.
Any pointers?
Joris
I've taken care of many examples of this function in the Numpy
Example List. But perhaps I'm misunderstanding your comment...
Joris
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Take Sur
would you accept concrete suggestions or 'help' to improve
that page?
Cheers,
Joris
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Using Tomcat but need to do more? Need to support web services, security?
Get stuff done quickly with pre-integrated technology to make
y.org/documentation/weave for more info,
but there is nothing anymore.
Thanks in advance,
Joris
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Using Tomcat but need to do more? Need to support web services, security?
Get stuff done quickly with pre-integrated technology to make your job e
nd NumPy? Would NumPy be more difficult to install
if the SciPy algorithm for inv() was incorporated?
Joris
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Using Tomcat but need to do more? Need to support web services, security?
Get stuff done quickly with pre-integrated technolo
On Tuesday 27 June 2006 09:37, Nils Wagner wrote:
[NW]: Please can you add a docstring to numpy.linalg.pinv.
In case it might help, I added an example to the Numpy Example
List (http://www.scipy.org/Numpy_Example_List) which illustrates
the use of pinv().
J.
Disclaimer: http://www.kuleu
On Friday 23 June 2006 16:34, Sven Schreiber wrote:
[SS]: Keith Goodman schrieb:
[SS]: > How do I make a NxN diagonal matrix with a Nx1 column vector x along
[SS]: > the diagonal?
[SS]: >
[SS]:
[SS]: >>> help(n.diag)
[SS]: Help on function diag in module numpy.lib.twodim_bas
[DC]: My only concern with this is numpy is positioned for a wider audience:
[DC]: everybody who needs arrays, and the extra speed that numpy gives, but
[DC]: doesn't need what scipy gives. So merging the two could lead to
[DC]: confusion on what provides what, and what you need to do which.
/_dotblas.c routine? Any other
numpy functions that are affected?
Joris
On Friday 02 June 2006 16:16, George Nurser wrote:
[GN]: Yes, using numpy.dot I get 250ms, numpy.matrixmultiply 11.8s.
[GN]:
[GN]: while a sans-BLAS Numeric.matrixmultiply takes 12s.
[GN]:
[GN]: The first 100
On Friday 02 June 2006 14:58, Eric Jonas wrote:
[EJ]: Hello! I've been using numeric for a while, and the recent list traffic
[EJ]: prompted me to finally migrate all my old code. On a whim, we were
[EJ]: benchmarking numpy vs numeric and have been lead to the conclusion that
[EJ]: nu
numpy.org will hopefully lead to a larger SciPy
community and this in turn hopefully leads to user-friendly installation
procedures.
Cheers,
Joris
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