I recently started getting into a numerical method in an attempt to smooth out data from linear response detectors in an instrumentation experiment.
I think the Savitzky-Golay is a great analytical method for this sort of digital data form. Wiki link.
From wiki "
the purpose of smoothing the data, that is, to increase the
signal-to-noise ratio without greatly distorting the signal. This is
achieved, in a process known as convolution, by fitting successive
sub-sets of adjacent data points with a low-degree polynomial by the
method of linear least squares.
" Which is good for the reactor data because we know how the curve
should react along constant reactor power.
Another method for smoothing is simply a moving average. (S.G. uses a
method of triangular moving average to some degree by the way) There are
various
forms of moving average, but the idea is to take a window of points in
your
dataset, compute an average of the points, then shift the window over by
one point and repeat.
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