Thank you for the input!
It sounds like Fourier methods will be fastest, by design, for sample
counts of hundreds to thousands.
I currently do steps like:
Im1 = get_stream_array_data()
Im2 = load_template_array_data(fh2)
##note: len(im1)==len(im2)
Ffft_im1=fftpack.rfft(Im1)
At 03:28 PM 3/3/2008, Ann wrote:
Sounds familiar. If you have a good signal-to-noise ratio, you can get
subpixel accuracy by oversampling the irfft, or better but slower, by
using numerical optimization to refine the peak you found with argmax.
the S/N here is poor, and high data rates work
I'm trying to figure out what numpy.correlate does, and, what are
people using to calculate the phase shift of 1D signals?
(I coded on routine that uses rfft, conjugate, ratio, irfft, and
argmax based on a paper by Hongjie Xie An IDL/ENVI implementation
of the FFT Based Algorithm for
On Mon, Mar 3, 2008 at 12:57 PM, Ray Schumacher [EMAIL PROTECTED]
wrote:
I'm trying to figure out what numpy.correlate does, and, what are people
using to calculate the phase shift of 1D signals?
(I coded on routine that uses rfft, conjugate, ratio, irfft, and argmax
based on a paper by
At 01:24 PM 3/3/2008, you wrote:
If you use 'same' or 'full' you'll end of with different
amounts of offset. I imagine that this is due to the way the data is padded.
The offset should be deterministic based on the mode and the size of the
data, so it should be straightforward to compensate
On 03/03/2008, Ray Schumacher [EMAIL PROTECTED] wrote:
I'm trying to figure out what numpy.correlate does, and, what are people
using to calculate the phase shift of 1D signals?
I use a hand-rolled Fourier-domain cross-correlation, but then, I'm
using a Fourier-domain representation of my
On Mon, Mar 3, 2008 at 2:45 PM, Ray Schumacher [EMAIL PROTECTED]
wrote:
At 01:24 PM 3/3/2008, you wrote:
If you use 'same' or 'full' you'll end of with different
amounts of offset. I imagine that this is due to the way the data is
padded.
The offset should be deterministic based on the
On 03/03/2008, Ray Schumacher [EMAIL PROTECTED] wrote:
Xie's 2D algorithm reduced to 1D works nicely for computing the
relative phase, but is it the fastest way? It might be, since some
correlation algorithms use FFTs as well. What does _correlateND use, in
scipy?
Which way will be the
