At 10:57 PM 11/1/2007, Charles R Harris wrote:
> > An additional complication is that I pass the numpy (or Numeric)
> > array address to the ctypes library call so that the data is placed
> > directly into the array from the call. I use the if/else end wrap
> > logic to determine whether I need to
On 11/1/07, Ray S <[EMAIL PROTECTED]> wrote:
>
> At 09:00 AM 11/1/2007, you wrote:
> I saw that Numeric did also (I still use Numeric for smaller array
> speed) but much more slowly.
> I will try to repeat with a small demo and post.
>
> It turns out to be some aspect of mixing numpy and Numeric;
>
On 11/1/07, Ray S <[EMAIL PROTECTED]> wrote:
>
> At 09:00 AM 11/1/2007, Chuck wrote:
>
> In Python, collections.deque makes a pretty good circular buffer.
> Numpy will
> make an array out of it, which involves a copy, but it might be
> better than what you are doing now.
>
> hmmm, I'll think more a
At 09:00 AM 11/1/2007, you wrote:
I saw that Numeric did also (I still use Numeric for smaller array
speed) but much more slowly.
I will try to repeat with a small demo and post.
It turns out to be some aspect of mixing numpy and Numeric;
the attached *Stable.py files allocate memory that stays
At 09:00 AM 11/1/2007, Chuck wrote:
In Python, collections.deque makes a pretty good circular buffer.
Numpy will
make an array out of it, which involves a copy, but it might be
better than what you are doing now.
hmmm, I'll think more about that - and the copy is only at program
start, it seem
On 11/1/07, Ray Schumacher <[EMAIL PROTECTED]> wrote:
> At 11:55 PM 10/31/2007, Travis wrote:
> >Ray S wrote:
> > > I am using
> > > fftRes = abs(fft.rfft(data_array[end-2**15:end]))
> > >
> >At first glance, I would say that I don't expect memory to be growing
> >here, so it looks like a problem w
At 11:55 PM 10/31/2007, Travis wrote:
>Ray S wrote:
> > I am using
> > fftRes = abs(fft.rfft(data_array[end-2**15:end]))
> >
>At first glance, I would say that I don't expect memory to be growing
>here, so it looks like a problem with rfft that deserves looking into.
I saw that Numeric did also (I
On 31/10/2007, Ray S <[EMAIL PROTECTED]> wrote:
> I am using
> fftRes = abs(fft.rfft(data_array[end-2**15:end]))
> to do running analysis on streaming data. The N never changes.
> It sucks memory up at ~1MB/sec with 70kHz data rate and 290 ffts/sec.
> (Interestingly, Numeric FFT accumulates much sl
Ray S wrote:
> I am using
> fftRes = abs(fft.rfft(data_array[end-2**15:end]))
> to do running analysis on streaming data. The N never changes.
> It sucks memory up at ~1MB/sec with 70kHz data rate and 290 ffts/sec.
> (Interestingly, Numeric FFT accumulates much slower..)
> (Commenting out that one
I am using
fftRes = abs(fft.rfft(data_array[end-2**15:end]))
to do running analysis on streaming data. The N never changes.
It sucks memory up at ~1MB/sec with 70kHz data rate and 290 ffts/sec.
(Interestingly, Numeric FFT accumulates much slower..)
(Commenting out that one line stops memory growth.
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