On Tue, Feb 12, 2008 at 09:09:11PM +, Matthew Brett wrote:
Suggestion 1:
def median(a, axis=0, out=None)
[...]
Suggestion 2:
def median(a, axis=0, scratch_input=False)
No reason not to combine the two. It's a pretty straightforward
modification to do the sorting in place,
A Tuesday 12 February 2008, Charles R Harris escrigué:
On Feb 12, 2008 9:07 AM, Francesc Altet [EMAIL PROTECTED] wrote:
* The newqsort performs the best on all the platforms we have
checked (ranging from a 5% of improvement on Opteron/SuSe, up to
3.8x with some Pentium4/Ubuntu systems).
A Tuesday 12 February 2008, Bruce Southey escrigué:
Hi,
I have a Opteron 248 (2.66GHz) that with gcc 4.1.0 (SUSE10.1?) that
gives C qsort with C style compare: 0.65
C qsort with Python style compare: 0.64
NumPy newqsort: 0.36
That's very intersting. In a similar configuration,
On Feb 14, 2008 12:14 AM, Lou Pecora [EMAIL PROTECTED] wrote:
--- David Cournapeau [EMAIL PROTECTED]
wrote:
Oh, I may have misunderstood what you are trying to
do then. You just
want to call a shared library from another shared
library ? This is
possible on any platform supporting
On Feb 11, 2008 9:24 PM, David Cournapeau [EMAIL PROTECTED]
wrote:
On Mon, 2008-02-11 at 20:42 -0700, Charles R Harris wrote:
On Feb 11, 2008 7:10 PM, David Cournapeau [EMAIL PROTECTED] wrote:
On Feb 11, 2008 5:40 PM, Charles R Harris
[EMAIL PROTECTED] wrote:
--- David Cournapeau [EMAIL PROTECTED] wrote:
But the real question is : if you are concerned with
code bload, why
using static lib at all ? Why not using shared
library, which is
exactly designed to solve what you are trying to do
?
cheers,
David
Yes, a good question. Two reasons I
--- David Cournapeau [EMAIL PROTECTED]
wrote:
Oh, I may have misunderstood what you are trying to
do then. You just
want to call a shared library from another shared
library ? This is
possible on any platform supporting shared library
(including but not
limited to mac os x, windows,
Hi,
I added gcc 4.2 from the openSUSE 10.1 repository so I now have both
the 4.1.2 and 4.2.1 compilers installed. But still have glibc-2.4-31.1
installed. I see your result with 4.2.1 but not with 4.1.2 so I think
that there could be a difference in the compiler flags. I don't know
enough about
On Feb 13, 2008 9:19 AM, Bruce Southey [EMAIL PROTECTED] wrote:
Hi,
I added gcc 4.2 from the openSUSE 10.1 repository so I now have both
the 4.1.2 and 4.2.1 compilers installed. But still have glibc-2.4-31.1
installed. I see your result with 4.2.1 but not with 4.1.2 so I think
that there
On Feb 13, 2008 10:56 AM, Francesc Altet [EMAIL PROTECTED] wrote:
A Wednesday 13 February 2008, Charles R Harris escrigué:
OK,
The new quicksorts are in svn. Francesc, can you check them out?
Looks good here. However, you seem to keep using your own copy_string()
instead of plain
A Wednesday 13 February 2008, Charles R Harris escrigué:
OK,
The new quicksorts are in svn. Francesc, can you check them out?
Looks good here. However, you seem to keep using your own copy_string()
instead of plain memcpy(). In previous benchmarks, I've seen that
copy_string() is faster
On Feb 13, 2008 12:37 PM, Francesc Altet [EMAIL PROTECTED] wrote:
A Wednesday 13 February 2008, Francesc Altet escrigué:
A Wednesday 13 February 2008, Bruce Southey escrigué:
Hi,
I added gcc 4.2 from the openSUSE 10.1 repository so I now have
both the 4.1.2 and 4.2.1 compilers
Ah,
To answer my own question:
Suggestion 1:
Wrap the .sort method call in a tiny python wrapper of the form:
def sort(self, axis=-1, kind='quicksort', order=None):
if axis=None:
_c_sort(self.ravel(), axis, kind, order)
else:
_c_sort(self, axis, kind, order)
I guess
Hi,
Is it possible, in fact, to do an inplace sort on an array with
axis=None (ie flat sort)?
Should the sort method have its docstring changed to reflect the fact
that axis=None is not valid?
Sorry to press on, but it would be good to resolve this somehow.
Is there some reason not to:
On Feb 13, 2008 1:52 PM, Matthew Brett [EMAIL PROTECTED] wrote:
Ah,
To answer my own question:
Suggestion 1:
Wrap the .sort method call in a tiny python wrapper of the form:
def sort(self, axis=-1, kind='quicksort', order=None):
if axis=None:
_c_sort(self.ravel(), axis,
Robert Kern wrote:
That's just what asfarray is designed to do. If you don't give it a dtype, it
uses float64.
For the record, it upcasts float32 arrays also.
So why does it exist at all? Is is just syntactic sugar for:
asarray(a, dtype=float64)
Which kind of seems to be not worth it.
If,
Alan G Isaac wrote:
On Tue, 12 Feb 2008, dmitrey apparently wrote:
from numpy import *
a = array((1.0, 2.0), float128)
b=asfarray(a)
type(a[0])
#type 'numpy.float128'
type(b[0])
#type 'numpy.float64'
__version__
'1.0.5.dev4767'
Dmitrey noted an unexpected down cast (above).
Is
On Wednesday 13 February 2008 02:37:37 pm Francesc Altet wrote:
So, I'd say that the guilty is the gcc 4.2.1, 64-bit (or at very
least, AMD Opteron architecture) and that newqsort performs really
well in general (provided that the compiler can find the best path
for optimizing its code).
Hi Stefan,
I believe these come from your latest commit.
File /usr/lib/python2.5/site-packages/numpy/lib/tests/test_ufunclike.py,
line 25, in test_ufunclike
Failed example:
nx.sign(a)
Expected:
array([ 1., -1., 0., 0., 1., -1.])
Got:
array([ 1., -1., -1., 0., 1., -1.])
Hi all,
I'm looking for some advice on how to implement a 'unit checking proxy array'.
Background: I'm writing a package to run simulations which make extensive use of
linear algebra, for which I'm using numpy. However - it is important to my
package that quantities can have dimesions, so I've
Travis,
I notice that you used PyDataMem_NEW, PyDimMem_NEW, and friends to allocate
memory in the sort routines. Is there a good reason to use these rather than
malloc?
Chuck
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Charles R Harris wrote:
Travis,
I notice that you used PyDataMem_NEW, PyDimMem_NEW, and friends to
allocate memory in the sort routines. Is there a good reason to use
these rather than malloc?
Only to allow for the possibility of different allocation routines.
There is an option to use
Thanks for that. The docs suggest library dl is Unix only. Does that
mean this solution will not work on Windows? Windows is on my
implementation roadmap but I'm not quite there yet to test it.
I guess I am now thinking maybe I can assemble (using f2py) an
(aggregated) shared library on the
On 13/02/2008, Dan Goodman
[EMAIL PROTECTED] wrote:
Background: I'm writing a package to run simulations which make extensive use
of
linear algebra, for which I'm using numpy. However - it is important to my
package that quantities can have dimesions, so I've written a class Quantity
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