Interpolaton algorithm to correct a slight clock drift
Bsed on your updated question, if the data is smooth with time, just place all the samples in a time trace, and interpolate on the sparse grid (time).
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.
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.
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.
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?
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)?
UPDATE...
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:
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".
I had hoped to find an 'out of the box' solution.