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 do a
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 still
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 about that
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;
the
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
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
