Yeah, 10% of improvement by using multi-cores is an expected figure for memory
bound problems. This is something people must know: if their computations are
memory bound (and this is much more common that one may initially think), then
they should not expect significant speed-ups on their
Hi! Sorry for the cross-post, but my own investigation has led me to
suspect that mine is actually a numpy problem, not a matplotlib problem.
I'm getting the following traceback from a call to matplotlib.imshow:
Traceback (most recent call last):
File
On Fri, Mar 05, 2010 at 09:53:02AM +0100, Francesc Alted wrote:
Yeah, 10% of improvement by using multi-cores is an expected figure for
memory bound problems. This is something people must know: if their
computations are memory bound (and this is much more common that one
may initially
On Mar 5, 2010, at 4:38 AM, David Goldsmith wrote:
Hi! Sorry for the cross-post, but my own investigation has led me to suspect
that mine is actually a numpy problem, not a matplotlib problem. I'm getting
the following traceback from a call to matplotlib.imshow:
...
Based on examination
On 03/05/2010 11:51 AM, Pierre GM wrote:
On Mar 5, 2010, at 4:38 AM, David Goldsmith wrote:
Hi! Sorry for the cross-post, but my own investigation has led me to
suspect that mine is actually a numpy problem, not a matplotlib problem.
I'm getting the following traceback from a call to
Gael,
On Fri, Mar 05, 2010 at 10:51:12AM +0100, Gael Varoquaux wrote:
On Fri, Mar 05, 2010 at 09:53:02AM +0100, Francesc Alted wrote:
Yeah, 10% of improvement by using multi-cores is an expected figure for
memory bound problems. This is something people must know: if their
computations
On Fri, Mar 05, 2010 at 08:14:51AM -0500, Francesc Alted wrote:
FWIW, I observe very good speedups on my problems (pretty much linear in
the number of CPUs), and I have data parallel problems on fairly large
data (~100Mo a piece, doesn't fit in cache), with no synchronisation at
all
Hi,
I've just started playing with numpy and have noticed that when printing
a structured array that the output is not nicely formatted. Is there a
way to make the formatting look the same as it does for an unstructured
array?
Here an example of what I mean:
data = [ (1, 2), (3, 4.1) ]
dtype =
On Mon, Feb 15, 2010 at 9:24 PM, Bruce Southey bsout...@gmail.com wrote:
On Mon, Feb 15, 2010 at 8:35 PM, Pierre GM pgmdevl...@gmail.com wrote:
On Feb 15, 2010, at 8:51 PM, David Carmean wrote:
On Sun, Feb 14, 2010 at 03:22:04PM -0500, Pierre GM wrote:
I'm sorry, I can't follow you. Can you
A Friday 05 March 2010 14:46:00 Gael Varoquaux escrigué:
On Fri, Mar 05, 2010 at 08:14:51AM -0500, Francesc Alted wrote:
FWIW, I observe very good speedups on my problems (pretty much linear
in the number of CPUs), and I have data parallel problems on fairly
large data (~100Mo a piece,
Is there a good way in NumPy to convert from a bit string to a boolean
array?
For example, if I have a 2-byte string s='\xfd\x32', I want to get a
16-length boolean array out of it.
Here's what I came up with:
A = fromstring(s, dtype=uint8)
out = empty(A.size * 8, dtype=bool)
for bit in
On Fri, Mar 5, 2010 at 2:51 AM, Pierre GM pgmdevl...@gmail.com wrote:
On Mar 5, 2010, at 4:38 AM, David Goldsmith wrote:
Hi! Sorry for the cross-post, but my own investigation has led me to
suspect that mine is actually a numpy problem, not a matplotlib problem.
I'm getting the following
On Fri, Mar 5, 2010 at 11:11, Dan Lenski dlen...@gmail.com wrote:
Is there a good way in NumPy to convert from a bit string to a boolean
array?
For example, if I have a 2-byte string s='\xfd\x32', I want to get a
16-length boolean array out of it.
Here's what I came up with:
A =
On Fri, Mar 5, 2010 at 9:22 AM, David Goldsmith d.l.goldsm...@gmail.comwrote:
On Fri, Mar 5, 2010 at 2:51 AM, Pierre GM pgmdevl...@gmail.com wrote:
On Mar 5, 2010, at 4:38 AM, David Goldsmith wrote:
Hi! Sorry for the cross-post, but my own investigation has led me to
suspect that mine is
On Fri, Mar 5, 2010 at 9:43 AM, David Goldsmith d.l.goldsm...@gmail.comwrote:
On Fri, Mar 5, 2010 at 9:22 AM, David Goldsmith
d.l.goldsm...@gmail.comwrote:
On Fri, Mar 5, 2010 at 2:51 AM, Pierre GM pgmdevl...@gmail.com wrote:
On Mar 5, 2010, at 4:38 AM, David Goldsmith wrote:
Hi! Sorry
Is there a good way in NumPy to convert from a bit string to a boolean
array?
For example, if I have a 2-byte string s='\xfd\x32', I want to get a
16-length boolean array out of it.
numpy.unpackbits(numpy.fromstring('\xfd\x32', dtype=numpy.uint8))
Francesc,
Yeah, 10% of improvement by using multi-cores is an expected figure for
memory
bound problems. This is something people must know: if their computations
are
memory bound (and this is much more common that one may initially think),
then
they should not expect significant speed-ups
Do you have doublets in the v_array?
In case not, then you owe me a donut.
See attachment.
Friedrich
P.S.: You misunderstood too, the line you wanted to change was in
context to detect back-facing triangles, and there one vertex is
sufficient.
shading.py
Description: Binary data
On Fri, Mar 5, 2010 at 8:00 AM, Bruce Schultz bruce.schu...@gmail.comwrote:
Hi,
I've just started playing with numpy and have noticed that when printing a
structured array that the output is not nicely formatted. Is there a way to
make the formatting look the same as it does for an
Cool--this works perfectly now :-)
Unfortunately, it's actually slower :P Most of the slowest part is in the
removing doubles section.
Some of the costliest calls:
#takes 0.04 seconds
inner = np.inner(ns, v1s - some_point)
#0.0840001106262
sum_1 = sum.reshape((len(sum), 1)).repeat(len(sum),
On Fri, Mar 5, 2010 at 1:22 PM, Patrick Marsh patrickmars...@gmail.com wrote:
I've run the Numpy superpack installer for Python 2.6 built with MinGW
through the dependency walker. Unfortunately, outside of checking for some
extremely obviously things, I'm in way over my head in interpreting
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