On Fri, Mar 5, 2010 at 1:22 PM, Patrick Marsh 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 the
> output (althoug
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), ax
On Fri, Mar 5, 2010 at 8:00 AM, Bruce Schultz wrote:
> 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?
>
> Her
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
_
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 spee
> 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))
___
On Fri, Mar 5, 2010 at 9:43 AM, David Goldsmith wrote:
> On Fri, Mar 5, 2010 at 9:22 AM, David Goldsmith
> wrote:
>
>> On Fri, Mar 5, 2010 at 2:51 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
On Fri, Mar 5, 2010 at 9:22 AM, David Goldsmith wrote:
> On Fri, Mar 5, 2010 at 2:51 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 matp
On Fri, Mar 5, 2010 at 11:11, Dan Lenski 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 = fromstring(s, dtyp
On Fri, Mar 5, 2010 at 2:51 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
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 range(0,
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
On Mon, Feb 15, 2010 at 9:24 PM, Bruce Southey wrote:
> On Mon, Feb 15, 2010 at 8:35 PM, Pierre GM 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 post a simpler self-co
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 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
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
> > computati
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
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 examinati
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 thi
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
"C:\Users\Fermat\Documents\Fractals\Python\S
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 paral
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