moving_average v3.1 (Mar 2008)

Smooths a matrix (with/without NaN's) via recursive moving average method and eliminates data gaps.

MOVING_AVERAGE(X,F) smooths the vector data X with a boxcar window of size 2F+1, i.e., by means of averaging each element with the F elements at his right and the F elements at his left. The extreme elements are also averaged but with less data, obviously. Leaving the edges intact. The method is really fast.

MOVING_AVERAGE2(X,M,N) smooths the matrix X with a boxcar window of size (2M+1)x(2N+1), i.e., by means of averaging each element with its surrounding elements that fits in the mentioned box centered on it. This one is also really fast. The elements at the edges are averaged too, but the corners are left intact.
NANMOVING_AVERAGE(X,F) or NANMOVING_AVERAGE(X,F,1) accept NaN's elements in the vector X; the latter interpolates also those NaN's elements surrounded by numeric elements.

NANMOVING_AVERAGE2(X,M,N) or NANMOVING_AVERAGE2(X,M,N,1) accept elements NaN's in the matrix X; the latter interpolates also those NaN elements surrounded by numeric elements.

[ New simple GAP filling ]:
SMOOTH_MAVERAGE(X,M,N,IND) this one smooths only the X(IND) elements. ignoring NaNs. This can be used to elimante GAPS on your data.

Each M-files has an example (see the screenshot).

Check below to see the CHANGES on v3.1.

Note: Looking the 2 dimensional code from MOVING_AVERAGE2.M (and RUNMEAN for some hints) somebody can easily make an N-dimensional MA. Would you?