Question

This post follows a previous question regarding the restructuring of a matrix:

re-formatting a matrix in matlab

An additional problem I face is demonstrated by the following example:

depth = [0:1:20]';
data = rand(1,length(depth))';
d = [depth,data];
d = [d;d(1:20,:);d];

Here I would like to alter this matrix so that each column represents a specific depth and each row represents time, so eventually I will have 3 rows (i.e. days) and 21 columns (i.e. measurement at each depth). However, we cannot reshape this because the number of measurements for a given day are not the same i.e. some are missing. This is known by:

dd = sortrows(d,1);
for i = 1:length(depth);
    e(i) = length(dd(dd(:,1)==depth(i),:));
end

From ‘e’ we find that the number of depth is different for different days. How could I insert a nan into the matrix so that each day has the same depth values? I could find the unique depths first by:

unique(d(:,1))
From this, if a depth (from unique) is missing for a given day I would like to insert the depth to the correct position and insert a nan into the respective location in the column of data. How can this be achieved?

Answer 1

You were thinking correctly that unique may come in handy here. You also need the third output argument, which maps the unique depths onto the positions in the original d vector. have a look at this code – comments explain what I do

% find unique depths and their mapping onto the d array
[depths, ~, j] = unique(d(:,1));

% find the start of every day of measurements
% the assumption here is that the depths for each day are in increasing order 
days_data = [1; diff(d(:,1))<0];

% count the number of days
ndays = sum(days_data);

% map every entry in d to the correct day
days_data = cumsum(days_data);

% construct the output array full of nans
dd = nan(numel(depths), ndays);

% assing the existing measurements using linear indices
% Where data does not exist, NaN will remain
dd(sub2ind(size(dd), j, days_data)) = d(:,2)

dd =

0.5115    0.5115    0.5115
0.8194    0.8194    0.8194
0.5803    0.5803    0.5803
0.9404    0.9404    0.9404
0.3269    0.3269    0.3269
0.8546    0.8546    0.8546
0.7854    0.7854    0.7854
0.8086    0.8086    0.8086
0.5485    0.5485    0.5485
0.0663    0.0663    0.0663
0.8422    0.8422    0.8422
0.7958    0.7958    0.7958
0.1347    0.1347    0.1347
0.8326    0.8326    0.8326
0.3549    0.3549    0.3549
0.9585    0.9585    0.9585
0.1125    0.1125    0.1125
0.8541    0.8541    0.8541
0.9872    0.9872    0.9872
0.2892    0.2892    0.2892
0.4692       NaN    0.4692

You may want to transpose the matrix.