Thanks all, my client actually wants the output at a minimum time.
On Thu, Aug 29, 2013 at 11:30 PM, Ralf Gommers wrote:
>
> if you have a reasonably large amount of data (say O(100)),
>
> I need to deal with nearly 2**19 or 2**20 arrays of length about 250 each.
On Thu, Aug 29, 2013 at 11:30 P
On Aug 29, 2013 4:11 PM, "Jonathan T. Niehof" wrote:
>
> On 08/29/2013 01:48 PM, Ralf Gommers wrote:
>
> > Thanks. I had read that quite differently, and I'm sure I'm not the only
> > one. Some context would have helped
>
> My apologies--that was a rather obtuse reference.
>
Just for future r
Ralf,
Could you please elaborate on the matrix weaknesses?
Is there any work planned to eliminate the peculiarities?
Regards,
Colin W.
Subject: Re: [Numpy-discussion] Relative speed
To: Discussion of Numerical
Anyone know what _PyADt is? It turns up in ndarraytypes.h
#define PyDataType_ISBOOL(obj) PyTypeNum_ISBOOL(_PyADt(obj))
and only there. It's not in the build directory, google yields nothing. I
suspect it is an historical artifact turned bug and should be replaced by
((PyArray_Descr*)(obj))->type_
Thanks a lot!!
José Luis
De: Brett Olsen
Para: Discussion of Numerical Python
Enviado: lunes, 26 de agosto de 2013 14:08
Asunto: Re: [Numpy-discussion] Stick (line segments) percolation algorithm -
graph theory?
I can see a couple opportunities for impr
> And as you pointed out,
> most of the time for non-trivial datasets the numpy operations will be
> faster. (I'm daunted by the notion of trying to do linear algebra on
> lists of tuples, assuming that's the relevant set of operations given
> the comparison to the matrix class.)
Note the impo
On 08/29/2013 01:48 PM, Ralf Gommers wrote:
> Thanks. I had read that quite differently, and I'm sure I'm not the only
> one. Some context would have helped
My apologies--that was a rather obtuse reference.
In my oddly-wired brain it struck me as a fairly similar,
suboptimally-posed questio
On 8/29/2013 3:48 PM, Ralf Gommers wrote:
> Some context would have helped.
http://www.youtube.com/watch?v=y2R3FvS4xr4
fwiw,
Alan
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On Thu, Aug 29, 2013 at 6:10 PM, Eric Moore wrote:
>
> > African or European?
> >
> >
> > Why on earth would you ask that?
> >
> >
>
> Its a Monty Python and the Holy Grail reference.
Thanks. I had read that quite differently, and I'm sure I'm not the only
one. Some context would have helpe
> African or European?
>
>
> Why on earth would you ask that?
>
>
Its a Monty Python and the Holy Grail reference.
Eric
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On Thu, Aug 29, 2013 at 3:41 PM, Jonathan T. Niehof wrote:
> On 08/29/2013 09:33 AM, Anubhab Baksi wrote:
> > Hi,
> > I need to know about the relative speed (i.e., which one is faster) of
> > the followings:
> > 1. list and numpy array, tuples and numpy array
> > 2. list of tuples and numpy matri
In the Clawpack projects (specifically the Riemann solvers) we compile
against LAPACK and the BLAS using f2py via the `--link-lapack_opt` flag.
This does cause some problems in terms of portability though, Aron Ahmadia
might be able to shed some light on this as he has looked into it most
recently
On 08/29/2013 09:33 AM, Anubhab Baksi wrote:
> Hi,
> I need to know about the relative speed (i.e., which one is faster) of
> the followings:
> 1. list and numpy array, tuples and numpy array
> 2. list of tuples and numpy matrix (first one is rectangular)
> 3. random.randint() and numpy.random.rand
On Thu, Aug 29, 2013 at 9:39 AM, Benjamin Root wrote:
>
>
> On Thu, Aug 29, 2013 at 8:04 AM, Robert Kern wrote:
>
>> On Thu, Aug 29, 2013 at 12:00 PM, Martin Luethi
>> wrote:
>> >
>> > Dear all,
>> >
>> > After some surprise, I noticed an inconsistency while adding array
>> > slices:
>> >
>> > >
Hi,
I need to know about the relative speed (i.e., which one is faster) of the
followings:
1. list and numpy array, tuples and numpy array
2. list of tuples and numpy matrix (first one is rectangular)
3. random.randint() and numpy.random.random_integers()
Thank you.
___
On Thu, Aug 29, 2013 at 8:04 AM, Robert Kern wrote:
> On Thu, Aug 29, 2013 at 12:00 PM, Martin Luethi
> wrote:
> >
> > Dear all,
> >
> > After some surprise, I noticed an inconsistency while adding array
> > slices:
> >
> > > a = np.arange(5)
> > > a[1:] = a[1:] + a[:-1]
> > > a
> > array([0, 1,
On Thu, Aug 29, 2013 at 12:00 PM, Martin Luethi
wrote:
>
> Dear all,
>
> After some surprise, I noticed an inconsistency while adding array
> slices:
>
> > a = np.arange(5)
> > a[1:] = a[1:] + a[:-1]
> > a
> array([0, 1, 3, 5, 7])
>
> versus inplace
>
> > a = np.arange(5)
> > a[1:] += a[:-1]
> > a
Dear all,
After some surprise, I noticed an inconsistency while adding array
slices:
> a = np.arange(5)
> a[1:] = a[1:] + a[:-1]
> a
array([0, 1, 3, 5, 7])
versus inplace
> a = np.arange(5)
> a[1:] += a[:-1]
> a
array([ 0, 1, 3, 6, 10])
My suspicition is that the second variant does not cre
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