[Numpy-discussion] numpy.correlate with phase offset 1D data series
Tue Mar 4 12:47:39 CST 2008
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)
R1= (Ffft_im1 * Ffft_im2.conjugate())
R2= (abs(Ffft_im1) * abs(Ffft_im2))
R = R1 / R2
flat_IR = numpy.ravel(numpy.transpose(IR)).real
phase_offset = (I % len(Im1))
At 09:29 AM 3/4/2008, Anne Archibald wrote:
> * What do you want to happen at the endpoints? Without padding, only a
> small interval (the difference in lengths plus one) is valid.
> Zero-padding works, but guarantees a fall-off at the ends. Circular
> correlation is easy to implement but not appropriate most of the time.
How much should I be concerned?, since the only desired information
from this is the scalar best-fit phase value, presumably the argmax()
of the xcorr.
In current operation, imagine a tone pattern/template of n samples
which we want to align to streaming data; the desired result (at
least in my current FFT code) is the sample number of recent ADC data
where the zero'th sample of the pattern best aligns. Since it is a
repeating pattern, we know that it will always align somewhere in the
latest n samples.
> * Do you care about sub-sample alignment? How much accuracy do you
> really need?
Integer alignment is sufficient, due both to electronic noise, and
> The other common application is to have a template (that presumably
> falls to zero at its endpoint) and to want to compute a running
> correlation against a stream of data. This too can be done both ways,
> depending on the size of the template; all that is needed is to think
> carefully about overlaps.
This is very much what the application is, although the template does
not terminate at zero. It does terminate at a value near the zero'th
value however, and I assumed the FFTs would be well-behaved.
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