Thanks that was the problem!
You never stop to learn =)
Original-Nachricht
> Datum: Mon, 17 Jan 2011 18:22:17 +0100
> Von: Francesc Alted
> An: Discussion of Numerical Python
> Betreff: Re: [Numpy-discussion] Strange behaviour with for loops + numpy
> arrays
> A Monday
Scientific Python Tools not only for Scientists and Engineers
=
This is the title of my three-hour tutorial at PyCon US:
http://us.pycon.org/2011/schedule/sessions/164/
It is a compressed version of my much longer course about:
* NumPy
On Mon, Jan 17, 2011 at 12:18 PM, Bruce Southey wrote:
> On 01/17/2011 10:32 AM, josef.p...@gmail.com wrote:
>> On Mon, Jan 17, 2011 at 11:28 AM, wrote:
>>> On Sat, Jan 15, 2011 at 3:27 PM, wrote:
After upgrading to numpy 1.5.1 I got caught by some depreciated
features. Given the depre
I resolved the problem by commenting out two lines in my setup.py
#"optimize":1,
#"bundle_files": 2,
The defmatrix lib was inside \lib\library.zip. However, the program.exe could
not find it.
Cheers
--- On Sun, 1/16/11, zb wrote:
> From: zb
>
I just took a look at
http://www.katjaas.nl/chirpZ/chirpZ2.html
I'm VERY interested in the zoom. Does the code
https://github.com/cournape/numpy/tree/bluestein
implement the zoom feature?
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A Monday 17 January 2011 17:02:43 Stefan Reiterer escrigué:
> Hi all!
>
> I made some "performance" tests with numpy to compare numpy on one
> cpu with mpi on 4 processesors, and something appears quite strange
> to me:
>
> I have the following code:
>
> N = 2**10*4
> K = 16000
>
> x = numpy.ra
On 01/17/2011 10:32 AM, josef.p...@gmail.com wrote:
> On Mon, Jan 17, 2011 at 11:28 AM, wrote:
>> On Sat, Jan 15, 2011 at 3:27 PM, wrote:
>>> After upgrading to numpy 1.5.1 I got caught by some depreciated
>>> features. Given the depreciation policy of numpy, if we want to
>>> support more than t
On Mon, Jan 17, 2011 at 11:28 AM, wrote:
> On Sat, Jan 15, 2011 at 3:27 PM, wrote:
>> After upgrading to numpy 1.5.1 I got caught by some depreciated
>> features. Given the depreciation policy of numpy, if we want to
>> support more than two versions of numpy, then we need some conditional
>> e
On Sat, Jan 15, 2011 at 3:27 PM, wrote:
> After upgrading to numpy 1.5.1 I got caught by some depreciated
> features. Given the depreciation policy of numpy, if we want to
> support more than two versions of numpy, then we need some conditional
> execution.
>
> Does anyone have any compatibility
We are glad to announce release 3.0 of the Modular toolkit for Data
Processing (MDP).
MDP is a Python library of widely used data processing algorithms
that can be combined according to a pipeline analogy to build more
complex data processing software. The base of available algorithms
includes si
Hi all!
I made some "performance" tests with numpy to compare numpy on one cpu with mpi
on 4 processesors, and something appears quite strange to me:
I have the following code:
N = 2**10*4
K = 16000
x = numpy.random.randn(N).astype(numpy.float32)
x *= 10**10
print "x:", x
t1 = time.time()
#do
On Mon, Jan 17, 2011 at 06:35, Tom Holderness
wrote:
> Hi,
>
> How do I find the maximum possible array size for a given data type on a
> given architecture?
> For example if I do the following on a 32-bit Windows machine:
>
> matrix = np.zeros((8873,9400),np.dtype('f8'))
>
> I get,
> Traceback (
Hi all,
Are there plans to provide official 64bit Windows installers for NumPy?
I'm aware that Christoph Gohlke had been able to do this, since
he offers unofficial plain builds and MKL builds for NumPy here:
http://www.lfd.uci.edu/~gohlke/pythonlibs/
Regards,
Peter
Hi,
How do I find the maximum possible array size for a given data type on a given
architecture?
For example if I do the following on a 32-bit Windows machine:
matrix = np.zeros((8873,9400),np.dtype('f8'))
I get,
Traceback (most recent call last):
File "", line 1, in
matrix = np.zeros(
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