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I am trying to create a code section that will take a 1D array and create a moving average array.  Sorry if this is a bad description.  I want to take x elements of the input array, average them, and put that average in the first element of a new array.  Then take the next x elements, average them, and put them as the second element of the new array.  I want this done until the array is empty.
 
I have two possible ways to do it, but neither are running as fast as I wanted them to.  I want to see if anyone knows of a faster way to conduct this averaging.

 

Thanks

Joe

post-41745-0-29380400-1404254943_thumb.j

post-41745-0-20619800-1404254944_thumb.j

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That's not quite a moving average, rather a down-sampling - i.e. Filtered Array is shorter than Input Data.  Your first solution is pretty good - just speed it up with a Parallel For Loop.  An alternative is to reshape into a 2D array.  Both these come out roughly the same speed, about 5x faster on my machine than your solutions above.

 

post-3889-0-82171900-1404257804.png

 

If you want a true Moving Average (where the result is the same length as the original) I think this suggestion from the NI forums using an FIR filter is nice and simple, although you might look carefully at the first Num values if that's important.

 

post-3889-0-43491200-1404258457.png

Edited by GregSands
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You have to be careful with the FIR filter the way you're using it because it applies a shift to your data by the amount of "Num of Averages".

To fix that, you have to do some padding at both ends of your input data. In this example I just repeat the first and last value:

post-28303-0-67116300-1404288460_thumb.p

 

If you run it through a graph it becomes obvious:

post-28303-0-07739400-1404288605_thumb.p

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Hadn't thought of using a FIR filter.  :thumbup1:  I bench marked a mean method and your FIR method. The FIR method was ~8-9x faster on my system than the mean with standard for loop, and the FIR was ~2-3x faster with the mean method for loop set for parallelism.

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I don't have LabVIEW installed on this machine and I may be confusing LabVIEW and MATLAB primatives, but I've seen a pretty fast approach that relies on a cumulative summation.  Textually, the algorithm is:

B=CumulativeSum(A)

SlidingAverage(i)=(B(i+windowsize)-B(i))/windowsize

Adjust indices as necessary to center your window in the right place and be careful with the edge conditions.

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  • 2 weeks later...

Thanks for your help.  I was able to parallelize the for loop with the mean in it and it helped speed it up.  I didn't try the FIR approach.  I'm still waiting on verification that the averaging will work for the customer.

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