Replacing values closest to Nan with a value, with

Question

I am new to Matlab and I have a matrix:

M =[NaN NaN NaN 2010 5454;
    NaN NaN 2009 3000 5000
    NaN 2011 3256 5454 6000
    2009 4000 5666 6545 5555
    5000 5666 6000 7000 8000];

I want to replace values closest to Nan with a value of 2010. I know how to do it manually and one by one. Is there any to create a loop to find these values and replace them? The result should look like this:

M =[NaN NaN NaN 2010 5454;
    NaN NaN 2010 3000 5000
    NaN 2010 3256 5454 6000
    2010 4000 5666 6545 5555
    5000 5666 6000 7000 8000];

Thank you in advance.

Answer 1

It is possible without defining any explicit loops. Below are the steps and sample code.

• Use the find function to determine which elements are NaN.
• Then, offset those indices by 1 both in the positive and negative direction to find positions of neighboring elements.
• Finally replace all such locations with required value, after deleting those positions that are outside the array.

Sample code

% Row and column indices of NaN in array `a`
[x, y] = find(isnan(a));

% All 4-neighbor elements around each NaN
r = [x-1 x+1 x x];
c = [y y y-1 y+1];

% Delete those values that are outside the array bounds
% (For NaNs in the edges)
outInd = r < 1 | r > size(a, 1) | c < 1 | c > size(a, 2);
r(outInd) = [];
c(outInd) = [];

% Replace all these neighbors with required value
a(sub2ind(size(a), r, c)) = 2010;

Answer 2

Thanks to @crazyGamer for improving the answer with explanations and clearer variable names.

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You can use 2D-convolution to detect entries that are close to a NaN; select non-NaN's among those entries, and write the desired value there.

Closeness is defined by means of a neighbourhood binary mask. This usually has 4 neighbours (up, down, left, right) or 8 (including diagonals).

The code is generalized to use either mask as per choice.

Solution

% Data:
M = [ NaN NaN NaN 2010 5454;
      NaN NaN 2009 3000 5000;
      NaN 2011 3256 5454 6000;
      2009 4000 5666 6545 5555;
      5000 5666 6000 7000 8000 ];

neighbourhood = [0 1 0; 1 0 1; 0 1 0];
% or [1 1 1; 1 0 1; 1 1 1] for 8-neighbours

new_value = 2010;

% Computations:
nanInds = isnan(M);
nanIndsWithNeighs = conv2(nanInds, neighbourhood, 'same')~=0;
neighInds = ~nanInds & nanIndsWithNeighs; % logical AND

M(neighInds) = new_value;