Ever wish you could easily perform some statistical calculations with a sliding window, but find convolution and filtering a bit daunting for this application? Then a set of new functions in Release R2016a might just be for you!
These functions complement the cum* functions, some of which have been in MATLAB for a long time, e.g., cumsum, and some of which are relatively new, e.g., * cummax.
Find the sliding maxima
Of course you could always find a local maximum in MATLAB by using a for-loop and working your way down the data, but paying careful attention to the ends. Or, with judicious use of indexing and reshaping, you could get the answer in a more vectorized fashion. Now you can just get it!
ndata = 100;
data = 0.5*rand(ndata,1)+0.5;
npts = 5;
datamvmax = movmax(data, npts);
axis([0 100 0.4 1.1])
So much easier now!
Computing moving results is much easier now. And with several options that will also make your computing life better - e.g., choice on handling NaN values, choice of direction, allowing a window that may not be symmetric with respect to lagging and leading values, and how to handle the endpoints. Will these new functions simplify your MATLAB code?