Interpolaton algorithm to correct a slight clock drift
Before you can ask the programming question, it seems to me you need to investigate a more fundamental scientific one.
Before you can start picking out particular equations to fit badfastclock to goodslowclock, you should investigate the nature of the drift. Let both clocks run a while, and look at their points together. Is badfastclock bad because it drifts linearly away from real time? If so, a simple quadratic equation should fit badfastclock to goodslowclock, just as a quadratic equation describes the linear acceleration of a object in gravity; i.e., if badfastclock is accelerating linearly away from real time, you can deterministically shift badfastclock toward real time. However, if you find that badfastclock is bad because it is jumping around, then smooth curves -- even complex smooth curves like splines -- won't fit. You must understand the data before trying to manipulate it.
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.