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POInterpolaton algorithm to correct a slight clock drift
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  1. POInterpolaton algorithm to correct a slight clock drift
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    copied!<p>I have some sampled (univariate) data - but the clock driving the sampling process is inaccurate - resulting in a random slip of (less than) 1 sample every 30. A more accurate clock at approximately 1/30 of the frequency provides reliable samples for the same data ... allowing me to establish a good estimate of the clock drift.</p> <p>I am looking to interpolate the sampled data to correct for this so that I 'fit' the high frequency data to the low-frequency. I need to do this 'real time' - with no more than the latency of a few low-frequency samples.</p> <p>I recognise that there is a wide range of interpolation algorithms - and, among those I've considered, a spline based approach looks most promising for this data.</p> <p>I'm working in Python - and have found the scipy.interpolate package - though I could see no obvious way to use it to 'stretch' n samples to correct a small timing error. Am I overlooking something?</p> <p>I am interested in pointers to either a suitable published algorithm, or - ideally - a Python library function to achieve this sort of transform. Is this supported by SciPy (or anything else)?</p> <p>UPDATE...</p> <p>I'm beginning to realise that what, at first, seemed a trivial problem isn't as straightforward as I first thought. I am no-longer convinced that naive use of splines will suffice. I've also realised that my problem can be better described without reference to 'clock drift'... like this:</p> <p>A single random variable is sampled at two different frequencies - one low and one high, with no common divisor - e.g. 5hz and 144hz. If we assume sample 0 is identical at both sample rates, sample 1 @5hz falls between samples 28 amd 29. I want to construct a new series - at 720hz, say - that fits all the known data points "as smoothly as possible".</p> <p>I had hoped to find an 'out of the box' solution.</p>
 

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