Simplify large for loop running average.

I'm looking to perform a running average of a height dataset based on conditional time criteria.

First I'm trying to find the overall range of indices that I would perform the running average on:

    indi = find( time >= time( 1, 1 ) + hintv );
    inde = find( time <= time( end, 1 ) - hintv );

A running average would then be calculated for each cell of time over a 25 hour period (12 hours on either side).

    for cc = indi( 1, 1 ):1:inde( end, 1 );

      fX( cc, 1 ) = nanmean( height( time >= ( time( cc, 1 ) - hintv ) & ...
        time <= ( time( cc, 1 ) + hintv ), 1 ) );
      fts( cc, 1 ) = time( cc, 1 );

    end

Is there a way of achieving this without having to index each selection of heights within a for loop and then average them?

The problem is that the size of the the time and height datasets for which I would like to get averages are very large, 57000 cells. So a for loop takes way too long.

Answer 1

[Ken Atwell]

I've used the filter function perform moving average calculations -- seven day data smoothing to remove the effects of weekends in my case. You can find the code toward the end of this blog post.

Hope this helps.