How do I put my values in certain bins and retrieve the values from the highest count bin

I have been trying to do this for a while and failed, am hopping to get some guidance. So I have a 3D variable called Zo, its 3601 by 7201 by 100, and all the values in it range from 0 to 1. What I would like to do is sort my values while ignoring values of Zo that are exactly zero. For example, if I am investigating Zo(1,1,:), I would like to sort the 100 values in Zo(1,1,:) that are not zeros into 10 bins that each has 0.1 width. And after, I would like to retain the values of Zo(1,1,:) that fall under the bin with the highest count. The final result should be a new variable called N that has the non-zero values of the bin with the highest count, and another variable called M that has the correspodning index of the values picked out from Zo(1,1,:). I hope I explained it well and I appreciate any guidance.

[Bruno Luong]

The title says SORT then you describe BINNING. What do you do exactly?

But whatever you do, replace 0 by NaN probably does the trick.

[Ali Almakhmari]

Binning is what I want (I edited the title)

[Bruno Luong]

But again in your description

"I would like to sort the 100 values in Zo(1,1,:) that are not zeros into 10 bins"

Still your decription still mix both, sort is rearange the data, binning means find where is the bin the data falls into. Please be precise.

a = rand(1,20)

a = 1×20

0.2156 0.7017 0.9893 0.2969 0.5716 0.4926 0.1556 0.7251 0.1279 0.5596 0.2909 0.0361 0.7691 0.8392 0.7579 0.2132 0.4142 0.5608 0.5313 0.0022

sort(a)

ans = 1×20

0.0022 0.0361 0.1279 0.1556 0.2132 0.2156 0.2909 0.2969 0.4142 0.4926 0.5313 0.5596 0.5608 0.5716 0.7017 0.7251 0.7579 0.7691 0.8392 0.9893

binloc = discretize(a, 0:0.1:1)

binloc = 1×20

3 8 10 3 6 5 2 8 2 6 3 1 8 9 8 3 5 6 6 1

Answer 1

[Bruno Luong]

Adapt this to your need

% Example data
A=rand(2,3,20);
A(randi(numel(A),1,10))=0;

[m,n,p] = size(A);
edges = 0:0.1:1;
A(A==0)=NaN;
B = discretize(A,edges);
[I,J] = ndgrid(1:m,1:n);
I = repmat(I,1,1,p);
J = repmat(J,1,1,p);
IJK = [I(:) J(:) B(:)];
IJK = IJK(B >= 1,:);
q = length(edges)-1;

bincount = accumarray(IJK, 1, [m,n,q]);
[highestcount, binnum] = max(bincount,[],3)

highestcount = 2×3

3 4 4 4 5 4

binnum = 2×3

7 6 9 3 7 6

ilin = find(B==binnum);
[i,j,k] = ind2sub(size(B), ilin);
slide = accumarray([i(:) j(:)], k(:), [m,n], @(k) {k.'});
Avalue = accumarray([i(:) j(:)], A(ilin(:)), [m,n], @(v) {v.'});
[r,c] = ndgrid(1:m,1:n);
r = r(:); c = c(:); slide = slide(:); Avalue=Avalue(:); binnum = binnum(:);
T = table(r, c, binnum, slide, Avalue)

T = 6×5 table
    r    c    binnum         slide                         Avalue                
    _    _    ______    _______________    ______________________________________

    1    1      7       {[      3 4 8]}    {[              0.6469 0.6739 0.6261]}
    2    1      3       {[ 9 10 13 15]}    {[       0.2052 0.2092 0.2407 0.2721]}
    1    2      6       {[   3 7 8 15]}    {[       0.5394 0.5322 0.5644 0.5406]}
    2    2      7       {[1 3 7 10 12]}    {[0.6275 0.6546 0.6259 0.6836 0.6720]}
    1    3      9       {[15 16 17 20]}    {[       0.8654 0.8613 0.8931 0.8730]}
    2    3      6       {[ 9 14 16 19]}    {[       0.5944 0.5440 0.5698 0.5119]}