Hi all,
Absolutely gorgeous, I confirm the 1.6x speed-up over the weave
version, i.e. a 25x speed-up over the existing version.
It would be good if the redefinition of the range function could be
changed in the numpy modules, before it goes into subversion, to
avoid the need for Rick's line
lran
Looks to me like Rick's version is simpler and faster.It looks like it
offers a speed-up of about 1.6 on my machine over the weave version. I
believe this is because the sorting approach results in quite a few less
compares than the algorithm I used.
Very cool. I vote that his version go int
On Dec 12, 2006, at 10:27 PM, Cameron Walsh wrote:
> I'm trying to generate histograms of extremely large datasets. I've
> tried a few methods, listed below, all with their own shortcomings.
> Mailing-list archive and google searches have not revealed any
> solutions.
The numpy.histogram functio
Glad to here it worked for you.
see ya,
eric
Cameron Walsh wrote:
> Thanks very much, Eric. That line fixed it for me, although I'm still
> not sure why it broke with the last line.
>
> Your weave_histogram works a charm and is around 16 times faster than
> any of the other options I've tried.
Thanks very much, Eric. That line fixed it for me, although I'm still
not sure why it broke with the last line.
Your weave_histogram works a charm and is around 16 times faster than
any of the other options I've tried. On my laptop it took 30 seconds
to generate a histogram from 500 million numb
Hmmm.
? Not sure. ?
Change that line to this instead which should work as well.
code = array_converter.declaration_code(self, templatize, inline)
Both work for me.
eric
Cameron Walsh wrote:
> On 13/12/06, Cameron Walsh <[EMAIL PROTECTED]> wrote:
>
>> On 13/12/06, eric jones <[EMAI
Is this code from linalg.lstsq for the complex case correct?
lapack_routine = lapack_lite.zgelsd
lwork = 1
rwork = zeros((lwork,), real_t)
work = zeros((lwork,),t)
results = lapack_routine(m, n, n_rhs, a, m, bstar, ldb, s, rcond,
On 13/12/06, Cameron Walsh <[EMAIL PROTECTED]> wrote:
> On 13/12/06, eric jones <[EMAIL PROTECTED]> wrote 290 lines of
> awesome code and a fantastic explanation:
>
> > Hey Cameron,
> >
> > I wrote a simple weave based histogram function that should work for
> > your problem. It should work for an
> Generate a column-permutation tuple and use fancy indexing:
Works like a charm, thanks a lot !
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On Wednesday 13 December 2006 15:29, A. M. Archibald wrote:
> Generate an axis-permutation tuple and use transpose:
Ah OK. It took me a little while to get it running: instead of
s=list(A.shape)
in your example, one should read
s=range(A.ndim)
But it does the trick, thanks a lot!
And now, dou
Thanks for everyone's help and thoughts. I agree that this behavior is
buggy. I submitted a bug report to the python project at sourceforge,
with a link to this thread. Hopefully the report will be helpful.
tim
Charles R Harris wrote:
>
>
> On 12/12/06, *Travis Oliphant* <[EMAIL PROTECTED]
On 13/12/06, Pierre GM <[EMAIL PROTECTED]> wrote:
> All,
> I have a ND array whose axes I want to reorganize, so that axis "i" is at the
> end while the others stay in their relative position. What's the easiest ?
Generate an axis-permutation tuple and use transpose:
s = list(A.shape)
s.remove(i)
All,
I have a ND array whose axes I want to reorganize, so that axis "i" is at the
end while the others stay in their relative position. What's the easiest ?
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Hi,
I'm a bit confused about which cases the .byteswap() method in NumPy
would return a copy or a view. From the docstrings:
"""
a.byteswap(False) -> View or copy. Swap the bytes in the array.
Swap the bytes in the array. Return the byteswapped array. If the
first argument is TRUE, byteswap in
On 13/12/06, eric jones <[EMAIL PROTECTED]> wrote 290 lines of
awesome code and a fantastic explanation:
> Hey Cameron,
>
> I wrote a simple weave based histogram function that should work for
> your problem. It should work for any array input data type. The needed
> files (and a few tests and e
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