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Topic: Averaging algorithm
Replies: 3   Last Post: Aug 30, 2013 10:58 AM

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Gordon Sande

Posts: 137
Registered: 5/13/10
Re: Averaging algorithm
Posted: Aug 30, 2013 10:12 AM
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On 2013-08-30 05:12:26 +0000, Default User said:

> Posted to both sci.math.num-analysis and sci.math.symbolic based on a
> suggestion in sci.math. I will follow both groups if you'd like to
> remove the crosspost.
> I'm working on bit of code that does some data processing. I'd like
> some input on developing the algorithm. This will effectively condense
> a large set of data values to a smaller one.
> The original data is the result of sampling a waveform. The processed
> data should still represent that waveform, of course.
> Input: 10,000 integer values.
> Output: 1023 integer values.
> There's nothing that can be done to change the input or output
> requirements.
> My current method is a bit brute force, using a moving window that
> sometimes averages 10 raw values, sometimes 9, to derive the results.
> I'm not overly happy with that, so if anyone has some more elegant or
> efficient suggestions I'd be quite interested.
> I had looked at data compression algorithms. I think those are really
> overkill for what I need, plus some problems. My situation is not a
> round-trip one. That is, it's not the case that I need to compress,
> store or transmit, and then uncompress later.
> The 1023 data points are the final format for the receiving device. So
> I have to end up with that number of data points representing the
> entire sampled waveform. The instrument can only provide certain
> numbers of points.
> It would be convenient if I could get the raw data as 1023 points, but
> that's not an option. In the range we want to work in, I can get 1000
> or 10,000. So we go up a range and average the results.
> Brian

What else do you know about the data? You have suggested one extreme of
a form of low pass filtering. How about just 1023 sets of average of 9
and discard the rest to avoid the changing 9 or 10 averaging. Explaining
why this is a good or bad idea may help understand the data. Another
extreme is the average the 9 batches of 1023 adjacent data points and
toss the rest. Or maybe treat the rest as a partial batch of 1023 and
then figure out how to combine it. Is there any reason for 1023 beyond
some sort of caprice?

Is a data point more like it immediate neighbour or does it have more in
common with the data point a month later or something else or 1023
points later?

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