In discussing smoothing filters,Numerical Recipes p. 772 says... irregularly sampled data, where the values $f_i$ are not uniformly spaced ...  one can simply pretend that the data points are equally spaced ...  a rough criterion:  If the change in $f$ across the full width of the $N$ point window is  less than $\sqrt{N/2}$ times the measurement noise on a single point,  then the cheap method can be used.Where does this come from ?It looks odd:for $f(x) = \text{sin} ( 2 \pi f x )$ on [$-N \dots N]$,change in $f = 2 < \sqrt{N/2}\ \sigma$ would require bigger $N$ for smaller $\sigma$ . Or have I misunderstood ?Also, does this work for smoothing data on non-uniformly spaced 2d squares, 3d cubes ... ?Added: https://gist.github.com/denis-bz/5957279 is a short Python programthat tries to elucidate this, for quantization noise(sample at integers +- random-uniform( -1/2, 1/2 )).
Error in treating non-uniformly spaced data as uniform
data analysis;signal processing