Everything installed without problem on Intel Pentium M on my notebook
recognized as SSE2 capable.
Installer found Python 2.5. immediately, which I just installed and
all my windows environment settings are still setup for python 2.4
Thanks,
Josef
numpy.test()
Numpy is installed in
Testing the windows installers
On an 8 years old Windows 2000 with Intel processor (which ?) no sse,
installed numpy-1/1/0rc1-nosse.exe
* with Python 2.4.3 (no ctypes)
- numpy.test(): 1001 test OK, no errors or failures
- numpy.test(): 1271 tests, errors=12, failures=1
no crash,
joep wrote:
Testing the windows installers
On an 8 years old Windows 2000 with Intel processor (which ?) no sse,
installed numpy-1/1/0rc1-nosse.exe
* with Python 2.4.3 (no ctypes)
- numpy.test(): 1001 test OK, no errors or failures
typo: missed with option all=True
Hi,
I was just looking around at the new numpy documentation and got a
xhtml parsing error on the page (with Firefox):
http://mentat.za.net/numpy/refguide/random.xhtml#index-29351
The offending line contains
$X pprox prod_{i=1}^{k}{x^{lpha_i-1}_i}$
in the docstring of the dirichlet
On May 22, 11:11 am, joep [EMAIL PROTECTED] wrote:
Hi,
When looking for this, I found that the Dirichlet distribution is
missing from the new Docstring
Wiki,http://sd-2116.dedibox.fr/doc/Docstrings/numpy/random
Actually, a search on the wiki finds dirichlet in
http://sd-2116.dedibox.fr
On May 22, 1:30 pm, Pauli Virtanen [EMAIL PROTECTED] wrote:
to, 2008-05-22 kello 09:51 -0700, joep kirjoitti:
It is not intentional. And for the majority of cases this does not
happen, and I can fix this for numpy.random.mtrand. Thanks for
reporting.
I was looking some more
The Dirichlet distribution is missing in __all__ in
http://projects.scipy.org/scipy/numpy/browser/trunk/numpy/random/info.py
As a consequence numpy.lookfor does not find Dirichlet
numpy.lookfor('dirichlet')
Search results for 'dirichlet'
--
import numpy.random
all ok on python 2.4 WindowsXP sse2
scipy test results are the same as with numpy 1.1.0
C:\Josef\work-oth\sort\pypipython
Python 2.4.3 (#69, Mar 29 2006, 17:35:34) [MSC v.1310 32 bit (Intel)]
on win32
Type help, copyright, credits or license for more information.
import numpy
Hi,
I was trying to use lookfor on python 2.4 and got an exception with
np.lookfor because of a missing method in pkgutil that was added in
python 2.5.
see the following session
np.lookfor('range')
Traceback (most recent call last):
File pyshell#55, line 1, in ?
np.lookfor('range')
File
I just fell over this:
np.min(np.inf,1000)
1.#INF
min(np.inf,1000)
1000
np.max(-np.inf,1000)
-1.#INF
max(-np.inf,1000)
1000
Is this known behavior?
I finally realized, that I have to do this (the doc string says array
like)
np.min([np.inf,1000])
1000.0
np.max([-np.inf,1000])
1000.0
revision
Josef
On Sep 23, 4:55 pm, Jarrod Millman [EMAIL PROTECTED] wrote:
On Tue, Sep 23, 2008 at 1:35 PM, Robert Kern [EMAIL PROTECTED] wrote:
On Tue, Sep 23, 2008 at 15:18, joep [EMAIL PROTECTED] wrote:
Hi,
I was trying to use lookfor on python 2.4 and got an exception with
np.lookfor
On Sep 23, 6:48 pm, joep [EMAIL PROTECTED] wrote:
A possible solution would be in
http://bazaar.launchpad.net/~pauli-virtanen/scipy/pydocweb/revision/386
It seems to be possible to be used in the same way when iter_modules
is not available.
The usage in ``_lookfor_generate_cache`` in lib
I have a question about the use of vectorize in new.instancemethod:
Using vectorize with *args requires adjustment to the number of
arguments, nin = nargs. This works when it is used with a function.
However, I don't manage to set nin when using vectorize with a method
created with
On Sep 26, 11:35 am, joep [EMAIL PROTECTED] wrote:
I have a question about the use of vectorize in new.instancemethod:
# case D: class with vectorize with nin adjustment - broken
# nin is not correctly used by vectorize
class D(object):
def __init__(self):
# define
should
have good and bad reversed, i.e.
{{{
806 if (bad good) Z = m - Z;
}}}
Can you verify this? I never tried to build numpy from source.
Josef
On Sep 25, 4:18 pm, joep [EMAIL PROTECTED] wrote:
In my fuzz testing of scipy stats, I get sometimes a test failure. I
think there is something
27, 2:12 pm, joep [EMAIL PROTECTED] wrote:
random numbers generated by numpy.random.logseries do not converge to
theoretical distribution:
for probability paramater pr = 0.8, the random number generator
converges to a
frequency for k=1 at 39.8 %, while the theoretical probability mass
Why does converting nan to an integer not throw an exception (as with
inf), instead numpy silently replaces nan by zero?
inti = np.array([0,1,2])
inti.dtype
dtype('int32')
inti[1] = np.inf
Traceback (most recent call last):
File pyshell#34, line 1, in ?
inti[1] = np.inf
OverflowError:
On Oct 20, 11:02 am, Andrea Gavana [EMAIL PROTECTED] wrote:
Hi All,
On Mon, Oct 20, 2008 at 12:48 PM, Alan G Isaac wrote:
On 10/20/2008 5:20 AM Andrea Gavana apparently wrote:
this is probably a very silly question, but combinatorial math is
not exactly my strength and I am not
On Oct 20, 11:30 pm, David Cournapeau [EMAIL PROTECTED]
wrote:
Ravi wrote:
Hi all,
Is anyone aware of a bridge between octave numpy? As I port stuff from
Matlab to numpy, I noticed that most of my Matlab code has workarounds that
allow the code to be used from octave. My current
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