Charles R Harris wrote:
> I won't comment on the code itself. Tell us what you want to do and I
> bet we can speed it up.
>
Here's a bit of sample code. It's actually very useful for my purposes.
Are there good ways to express these ideas in numpy as opposed to
using all of the nested loop
>
> I won't comment on the code itself.
Appreciate it :). Again, I'm just pointing out an example! I would
guess that I'm not the only person using numpy in this sort of
unsophisticated manner!
Tell us what you want to do and I
> bet we can speed it up.
>
> Chuck
I'll probably do that in
On 3/1/07, Robert Kern <[EMAIL PROTECTED]> wrote:
Charles R Harris wrote:
> Looks like function call overhead has gone way up or the cost of
> returning a float vs an array has gone way up. The loop overhead is
> about .01 and not significant. So something is definitely wrong here.
> Time to go
Charles R Harris wrote:
> Looks like function call overhead has gone way up or the cost of
> returning a float vs an array has gone way up. The loop overhead is
> about .01 and not significant. So something is definitely wrong here.
> Time to go look in trac ;)
It might have been when Travis put
On 3/1/07, Mark P. Miller <[EMAIL PROTECTED]> wrote:
OK...here goes. This code is going to look goofy, so please bear in
mind that it is only an abstraction of what my real code does (which
happens to provide interesting and meaning insights!).
I've attached saved versions of my interactive py
OK...here goes. This code is going to look goofy, so please bear in
mind that it is only an abstraction of what my real code does (which
happens to provide interesting and meaning insights!).
I've attached saved versions of my interactive python sessions that
document the phenomenon. Again,
On 3/1/07, Mark P. Miller <[EMAIL PROTECTED]> wrote:
One more note (this perhaps may need a separate topic):
I've been using the Enthought python edition that contains python 2.4.3
and numpy 0.9.9.2706. After Robert Kern pointed out that I should try
numpy 1.0.1, I went ahead and installed it
Mark P. Miller wrote:
> Now however, I'm seeing perhaps a more serious problem: The test
> program that I'm working with went from taking ~80 seconds to run to
> taking over 10 minutes to run. I've rolled back to my old numpy version
> and confirmed that the old version was much faster. I a
One more note (this perhaps may need a separate topic):
I've been using the Enthought python edition that contains python 2.4.3
and numpy 0.9.9.2706. After Robert Kern pointed out that I should try
numpy 1.0.1, I went ahead and installed it (downloaded a few hours ago:
"numpy-1.0.1.win32-py2.4
Mark P. Miller wrote:
>>Ops, this seems a bug with your numpy version:
>>
>>In [46]:array1 = numpy.zeros((10,10),int)
>>In [47]:array1.itemset((5,5),9)
>>In [48]:array1
>>Out[48]:
>>array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
>> [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
>> [0, 0, 0, 0, 0, 0, 0, 0, 0,
Francesc Altet wrote:
> Ops, this seems a bug with your numpy version:
yup, it's a bug here too:
>>> numpy.__version__
'1.0.1'
this is the dmg for python2.5 on pythonmac.org/packages
--
Christopher Barker, Ph.D.
Oceanographer
Emergency Response Division
NOAA/NOS/OR&R(206) 526-6
>>
>
> Ops, this seems a bug with your numpy version:
>
> In [46]:array1 = numpy.zeros((10,10),int)
> In [47]:array1.itemset((5,5),9)
> In [48]:array1
> Out[48]:
> array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
>[0,
El dj 01 de 03 del 2007 a les 12:47 -0700, en/na Mark P. Miller va
escriure:
> > try with
> >
> array1.itemset((5,5),9)
>
>
> Yep...tried that. But I don't get it!
>
> >>> import numpy
> >>> array1 = numpy.zeros((10,10),int)
> >>> array1
> array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
>
> try with
>
array1.itemset((5,5),9)
Yep...tried that. But I don't get it!
>>> import numpy
>>> array1 = numpy.zeros((10,10),int)
>>> array1
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0
El dj 01 de 03 del 2007 a les 12:03 -0700, en/na Mark P. Miller va
escriure:
> Sorry to pester, but is this the intended behavior of itemset?
>
> >>> array1=numpy.zeros((10,10),int)
> >>> array1
> array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
> [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
> [0, 0, 0
Sorry to pester, but is this the intended behavior of itemset?
>>> array1=numpy.zeros((10,10),int)
>>> array1
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0,
Mark P. Miller wrote:
> Travis: Can you clarify this for me. The book doesn't have much detail
> here and seems to differ from your notation (which gives me errors).
> numpy 0.9.9.2706
This is the problem. You will need to upgrade to 1.0.1.
--
Robert Kern
"I have come to believe that the wh
Travis: Can you clarify this for me. The book doesn't have much detail
here and seems to differ from your notation (which gives me errors).
>> Getting single indices like this is a bit slower for NumPy then for
>> lists because of all the possibilities that must be distinguished for
>> array
>> ##imports
>> import numpy as NP
>>from numpy.random import randint
>> #numpy array code
>> array1 = NP.zeros((50,50), int)
>>
>> def random1():
>> c = array1(randint(10), randint(10))
>>
>>
> Is this a bug? You can't "call" an array. Did you mean,
> array1[randint(10), randint(10)]?
On 3/1/07, Mark P. Miller <[EMAIL PROTECTED]> wrote:
I've been using Numpy arrays for some work recently. Just for fun, I
compared some "representative" code using Numpy arrays and an object
comprised of nested lists to represent my arrays. To my surprise, the
array of nested lists outperforme
Mark P. Miller wrote:
>I've been using Numpy arrays for some work recently. Just for fun, I
>compared some "representative" code using Numpy arrays and an object
>comprised of nested lists to represent my arrays. To my surprise, the
>array of nested lists outperformed Numpy in this particular
Mark P. Miller wrote:
>I've been using Numpy arrays for some work recently. Just for fun, I
>compared some "representative" code using Numpy arrays and an object
>comprised of nested lists to represent my arrays. To my surprise, the
>array of nested lists outperformed Numpy in this particular
Interesting...
I also tried the following and got similar results (using a 1,000 x
1,000 arrays). The time required to initialize the nested list array
was much higher (but nonetheless small in the context of the overall
time that my programs will run). But array element access is always
fas
On Mar 1, 2007, at 11:03 AM, Mark P. Miller wrote:
> I've been using Numpy arrays for some work recently. Just for fun, I
> compared some "representative" code using Numpy arrays and an object
> comprised of nested lists to represent my arrays. To my surprise, the
> array of nested lists outper
I've been using Numpy arrays for some work recently. Just for fun, I
compared some "representative" code using Numpy arrays and an object
comprised of nested lists to represent my arrays. To my surprise, the
array of nested lists outperformed Numpy in this particular application
(in my actual
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