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
I have 2 PCs with 2 different installs:
ActivePython 2.4.3 Build 12 with numpy version '1.0b1'
and
Enthought 2.4.3 (1.0.0 #69) with numpy version '0.9.7.2476'
The attached runs Ok on numpy v1.0, but on Enthought's, print a1[0]
gives:
IndexError: 0-d arrays can't be indexed.
It seems that
I just installed 1.0.3.1 on top of Enthought's and asarray() works.
But...
Although the creation of an array from an address via a Dummy class
is kosher in one process (as in the previous attachment), it fails
across processes - the array is created, but gives a Python has
generated errors
Thanks all:
At 10:00 AM 10/10/2007, Robert Kern wrote:
Something like the following should suffice (untested, though
I've done similar things with ctypes before):
I tested, successfully:
nFromAddress.py
def fromaddress(address, dtype, shape, strides=None):
Create a numpy array from an
On 10/9/07, Sebastian Haase replied:
Did you find that locks
or semaphores were needed?
Maybe that's why it crashed ;-) !? But for simple use it seems
fine.
I just did some code (below) that does read/write to the array AFAP,
and there is no crash, or any other issue (Win2000, py2.4,
Is anyone sharing arrays between processes on Windows?
I tried compiling the posh sources (once, so far) with the new MS
toolkit and failed...
What other solutions are in use?
Have a second process create an array view from an address would
suffice for this particular purpose. I could pass the
While investigating ctypes and numpy for sharing, I saw that the
example on
http://www.scipy.org/Cookbook/Ctypes#head-7def99d882618b52956c6334e08e085e297cb0c6
does not quite work. However, with numpy.version.version=='1.0b1',
ActivePython 2.4.3 Build 12:
import numpy as N
from ctypes import *
Hi Nadev,
A long time ago I translated a free code of chirp z transform (zoom
fft)
into python.
Thanks, I'll try it out.
I did, however read before on the differences:
From Numerix http://www.numerix-dsp.com/zoomfft.html:
One common question is : Is the zoom FFT the same as the chirp
We'd like to do what most call a zoom FFT; we only are interested
in the frequencies of say, 6kHZ to 9kHz with a given N, and so the
computations from DC to 6kHz are wasted CPU time.
Can this be done without additional numpy pre-filtering computations?
If explicit filtering is needed to
