Matlab Saving the Output of a Function (preferably as a csv) - matlab

Question

I know this kind of thing has been asked before but nothing I have tried has helped.
I've borrowed a function from github for a project but it's in matlab and my project is in python so I want to save the output data as a csv so I can input it back into python.
I've literally only been using matlab for about 4 hrs so this could be a stupid question...
I've tried an array of different methods for saving as a csv but none of them have worked. They don't throw errors though, the files just don't show up.
This is my latest attempt:
clear;clc;
fileName = 'AlzScSK.csv';
csvData = importdata(fileName);
rawData = csvData.data;
dataMV = rawData;
scaledMV = dataMV
K = 6;
function [dataImputed dataImputedWeighted] = NSkNNDataHM(dataMV)
% Function to impute missing values in a dataset using NSkNN. scaledMV is
% the autoscale dataset with the missing values and K is the # of nearest
% neighbors to use to impute the data. filteredMV is the missing value
% dataset after filtering, but before autoscaling.
% NSkNNData_HM does not skip neighbors that have NaN values in the same
% location as the metabolite being imputed. Instead, it replaces these NaN
% values with the half minimum value of that metabolite.
numCol = size(scaledMV,2);
for col = 1:numCol
rowMV{col} = find(isnan(scaledMV(:,col))); % Finds the row # of every missing value in each column
end
counter = 1;
for targetCol = 1:numCol % i is the target sample
for neighborCol = 1:numCol % Calculate the Euclidean distances between the target sample (i) and the other samples (j)
MVRowsRemoved = scaledMV;
rowsToRemove = union(rowMV{targetCol},rowMV{neighborCol}); % Ignore NaNs when calculating distances
MVRowsRemoved(rowsToRemove,:) = []; % Remove rows in target sample that have missing values
numMetInCalc = size(MVRowsRemoved,1); % # of metabolites used in calculation of metabolites in order to weight distance
% Divide by numMetInCalc to avoid scenarios where distances
% calculated with only a few metabolites are weighted more heavily
% over distances that are close to the same distance, but used more
% metabolites in the calculation.
distance = pdist2(MVRowsRemoved(:,targetCol)',MVRowsRemoved(:,neighborCol)')/sqrt(numMetInCalc);
%distance = pdist2(MVRowsRemoved(:,targetCol)',MVRowsRemoved(:,neighborCol)');
distIdx(counter,:) = [targetCol distance neighborCol];
counter = counter+1;
end
end
% Remove rows that calculated the Euclidean distance between a sample and
% itself.
sameSample = find(distIdx(:,1)==distIdx(:,3));
distIdx(sameSample,:) = [];
distIdxSorted = sortrows(distIdx);
minValperRow = min(scaledMV,[],2); % Finds minimum of each metabolite
% Implement NSkNN with half minimum replacement
dataImputed = scaledMV;
dataImputedWeighted = scaledMV;
for targetCol = 1:numCol
numMV = size(rowMV{targetCol},1); % # of missing values in a column
firstNNIdx = (targetCol-1)*(numCol-1)+1; % Column index of the first nearest neighbor of target sample (i) in distIdxSorted
for MVidx = 1:numMV % For each missing value in the target sample...
tempDataMV = scaledMV;
NN = distIdxSorted(firstNNIdx:firstNNIdx+K-1,3); % Column #s of the k nearest neighbors
DistanceNN = distIdxSorted(firstNNIdx:firstNNIdx+K-1,2); % Distances of k nearest neighbors
idxNaNinCol = find(isnan(tempDataMV(rowMV{targetCol}(MVidx),NN))); % Finds missing values that are the same metabolite as the target metabolite to be imputed
if isempty(idxNaNinCol)~=1 % If NaN values found...
% If there are NaN values in the nearest neighbor
% metabolite that is the same as the target metabolite to
% be imputed, replace with half min value of the target
% metabolite
tempDataMV(rowMV{targetCol}(MVidx),NN(idxNaNinCol)) = (minValperRow(rowMV{targetCol}(MVidx)) - avgMV(rowMV{targetCol}(MVidx))/stddevMV(rowMV{targetCol}(MVidx)))/2;
end
% Imputed data is weighted by the inverse of the distance
WeightMultiplier = (1./DistanceNN')/sum(1./DistanceNN);
dataImputedWeighted(rowMV{targetCol}(MVidx),targetCol) = sum(tempDataMV(rowMV{targetCol}(MVidx),NN).*WeightMultiplier);
% Not weighted
dataImputed(rowMV{targetCol}(MVidx),targetCol) = mean(tempDataMV(rowMV{targetCol}(MVidx),NN));
writematrix(dataImputed,['M.csv'])
end
end
end
I'm not sure if I'm maybe referring to the wrong thing or if I've put the writematrix(dataImputed,['M.csv']) in the wrong place.
I've also tried these sorts of routes:
% Convert cell to a table and use first row as variable names
T = cell2table(c(2:end,:),'VariableNames',c(1,:))
% Write the table to a CSV file
writetable(T,'myDataFile.csv')
dlmwrite(filename,M)
csvwrite('filename.csv',variable2,0,2)
This is currently what my workspace looks like

Answer 1

Try this: writematrix(dataImputed,'M.csv','WriteMode','append')
If it's still not working, sharing the AlzScSK.csv with us would be helpful.