Comments and Ratings

It works pretty good. I made a small modification to read files without geographical information since I work with hyperspectral image of mushrooms.

It'll be also useful if you return the parameter 'I' in the same manner that the sort function does. 'I' should be an array or cell array in the case that 'SORTBYFIELDS' was not used.

Cory,
Please give some more details of your error *BEFORE* trashing the function with a 1-star rating so that I can fix it. You say a that the function is failing in returning the correct month with a 1-day time series. If I create such as time series with:
>t = datenum('jan-0-2008')+(1:365);
and then sample this time series about once a month:
>> ts = t(1:30:end);
>> datestr(ts)
ans =
01-Jan-2008
31-Jan-2008
01-Mar-2008
31-Mar-2008
30-Apr-2008
30-May-2008
29-Jun-2008
29-Jul-2008
28-Aug-2008
27-Sep-2008
27-Oct-2008
26-Nov-2008
26-Dec-2008

and then get the decimal month using datepart:
>> datepart(ts,'month')
ans =
  Columns 1 through 9
    1.0000 1.9677 3.0000 3.9677 4.9667 5.9355 6.9333 7.9032 8.8710
  Columns 10 through 13
    9.8667 10.8387 11.8333 12.8065
Looks good to me ... sure you're using it correctly??????

If the map/image coordinates in the map info in the header are not the upper left corner (1.5,1.5), the program will not create the correct X Y vectors. I've uploaded a fix for this.

My thanks to the author for his continued modifications. Along the way, he has considerably improved this code. With those enhancements, perhaps it is now time to revise my own rating.

I did like the help in fillnans. There is no error checking, but relatively little to check. ;-) It has an H1 line, although part of it spills over into a second line. The code is quite simple.

I did some testing to compare fillnans with my own inpaint_nans tool. The first test has a moderately sparse sampling, with fully 98% of it a NaN.

[X,Y] = meshgrid(0:.02:2);
Z = sin((X+Y)*3).*cos((X-Y)*5);
B = rand(size(X))>.02;
Z(B) = NaN;

tic,Zip = inpaint_nans(Z);toc
% Elapsed time is 8.266630 seconds.

tic,Zf = fillnans(Z);toc
% Elapsed time is 59.740188 seconds.

figure
surf(Z0)
title 'Original surface'

figure
surf(Zip)
title 'Inpaint_nans surface'

figure
surf(Zf)
title 'Fillnans surface'

There is a significant difference in speed between the two, as you should see. You should also note the spiky-ness of the fillnans result. This is a characteristic of an inverse distance interpolation. Changing the value of p will have significant influence on your results. For example:

Zf = fillnans(Z,6);
surf(Zf)
title 'Fillnans surface, p == 6'

This was a bit less spiky.

Lets see what happens for more densely populated arrays.
This time I'll make 40% of it NaNs.

[X,Y] = meshgrid(0:.02:2);
Z = sin((X+Y)*3).*cos((X-Y)*5);
B = rand(size(X))>.6;
Z(B) = NaN;

tic,Zip = inpaint_nans(Z);toc
% Elapsed time is 0.821078 seconds.

tic,Zf = fillnans(Z);toc
% Elapsed time is 47.494882 seconds.

figure
surf(Zip)
title 'Inpaint_nans surface'

figure
surf(Zf)
title 'Fillnans surface'

Inpaint_nans was very much faster here, and nicely smoother. How accurate is fillnans? For the problem above, where Z0 is a well behaved, smooth function, inpaint_nans replicates its surface well, better by almost a factor of 200.

std(Z0(:) - Zip(:))
ans =
    0.0007134

std(Z0(:) - Zf(:))
ans =
      0.13365

Can the speed of fillnans be improved? Sadly, the use of waitbar is itself a real time eater.

>> tic,Zf = fillnans(Z);toc
Elapsed time is 47.233541 seconds.

Next, I modified the code to call waitbar only 100 times over the entire main loop. This just took an extra rem call in an if statement. The waitbar is still updated often enough to see it move, but not too often that it was a time hog.

>> tic,Zf = fillnans(Z);toc
Elapsed time is 18.823048 seconds.

It turns out that the calls to waitbar actually wasted roughly 60% of the entire time running. I'll bet the author makes this enhancement quickly.

One behavior of fillnans that is useful is based on its being a convex combination of the data. So at any location, the predicted value must be bounded by the min and max of the data itself. (Method 4 of inpaint_nans should have a similar behavior, for those who must minimize extrapolation
at all costs.)

Another property of fillnans that may be more useful is based on its underlying algorithm. While fillnans is moderately slow, the time required for its solution is
O(K*L), where K is the number of NaN elements to be interpolated, and L is the number of non-NaN elements. Since inpaint_nans is based on the solution of a sparse system of linear equations, that system may grow huge, making the solution of a truly huge problem impossible in the RAM available. However, fillnans may still succeed eventually, as long as you have the RAM to make a copy of the original array.

So for those who may read my review, your own rating of this tool will be a function of the problems that you pose to it. If your problems are simply too large for inpaint_nans, and you are not too worried about a lesser accuracy, then fillnans may be worth a 5 rating for you. For those of you who have smaller problems, who need more accuracy/smoothness, then your rating will be lower. I've chosen to rate it as a 4.

This error comes from a call to a non-standard toolbox function. I've uploaded a corrected version. (Make sure the revision is noted on this page before re-uploading).

>> Af = fillnans(A);
??? Undefined function or method 'vsum' for input arguments of type 'double'.

Error in ==> fillnans at 34
    D = vsum(rn(k)-r,cn(k)-c);

ok,thanks