Hi,
Implementing moving average, moving std and other functions working
over rolling windows using python for loops are slow. This is a
effective stride trick I learned from Keith Goodman's
Bottleneck code but generalized into arrays of
any dimension. This trick allows the loop to be performed in
On Fri, Dec 31, 2010 at 7:44 PM, Gideon wrote:
> I noticed that 1.5.1 was released, and sourceforge is suggesting I use
> the package numpy-1.5.1-py2.6-python.org-macosx10.3.dmg. However, I
> have an OS X 10.6 machine.
>
> Can/should I use this binary?
>
> Should I just compile from source?
I su
I noticed that 1.5.1 was released, and sourceforge is suggesting I use
the package numpy-1.5.1-py2.6-python.org-macosx10.3.dmg. However, I
have an OS X 10.6 machine.
Can/should I use this binary?
Should I just compile from source?
___
NumPy-Discussion
On Fri, Dec 31, 2010 at 8:21 AM, Lev Givon wrote:
> Received from Erik Rigtorp on Fri, Dec 31, 2010 at 08:52:53AM EST:
>> Hi,
>>
>> I just send a pull request for some faster NaN functions,
>> https://github.com/rigtorp/numpy.
>>
>> I implemented the following generalized ufuncs: nansum(), nancums
Received from Erik Rigtorp on Fri, Dec 31, 2010 at 08:52:53AM EST:
> Hi,
>
> I just send a pull request for some faster NaN functions,
> https://github.com/rigtorp/numpy.
>
> I implemented the following generalized ufuncs: nansum(), nancumsum(),
> nanmean(), nanstd() and for fun mean() and std().
On Fri, Dec 31, 2010 at 02:13, Paul Ivanov wrote:
> Erik Rigtorp, on 2010-12-30 21:30, wrote:
>> Hi,
>>
>> I was trying to parallelize some algorithms and needed a writable
>> array shared between processes. It turned out to be quite simple and
>> gave a nice speed up almost linear in number of c
Hi,
I just send a pull request for some faster NaN functions,
https://github.com/rigtorp/numpy.
I implemented the following generalized ufuncs: nansum(), nancumsum(),
nanmean(), nanstd() and for fun mean() and std(). It turns out that
the generalized ufunc mean() and std() is faster than the curr
