Remove outliers until there are none left

Neeta Dsouza answered . 2024-12-20 19:37:47

directory_name=uigetdir('','Ordner mit Messungen auswählen');
[nur_file_name,pfad]=uigetfile({'*.csv','csv-files (*.csv)';'*.*','all Files'},...
    'Die csv-Files der Proben oeffnen (probe_001.csv=',[directory_name '/'], 'Multiselect', 'on');
nur_file_name=cellstr(nur_file_name);
nur_file_name=sort(nur_file_name);
filename=strcat(pfad,nur_file_name);
anzahl_files=size(filename,2);
for xy=1:anzahl_files
    fid_in=fopen(char(filename(xy)),'r');
    
    filename_s = matlab.lang.makeValidName(nur_file_name);
    filename_s=string(filename_s);
    filename_s = erase(filename_s,"_csv");
    filename_s = erase(filename_s,"LiqQuant_");
    filename_c=cellstr(filename_s);
    for c=1:anzahl_files
        filename_f{c}=extractBefore(filename_c{c},11);
    end
    filename_s=string(filename_f);
    
    
    %----------------Import elements and intensity--------------------
    
    clear element_RL
    clear intens_RL
    
    tmpImport = importdata(filename{xy},',');
    element_RL = tmpImport.colheaders;
    element_RL(:,[1 6 8 10 12 14 16 17 19 21 23 26 27 29 30 32 33 36 38 43 45 48 57 59 61 64 67 69 94 97 99 102 106 223 298 303 304 305])=[];
    element_RL=string(element_RL);
    [anzahl_zeile,anzahl_elemente]=size(element_RL);
    
    intens_RL=tmpImport.data;
    intens_RL(:,[1 6 8 10 12 14 16 17 19 21 23 26 27 29 30 32 33 36 38 43 45 48 57 59 61 64 67 69 94 97 99 102 106 223 298 303 304 305])=[];
    [anzahl_runs,anzahl_elemente]=size(intens_RL);
    
        %---------------remove outliers----------------
        
        figure(1)
        clim = [-5 7];
        subplot(211),imagesc(log10(abs(intens_RL)),clim);colormap('jet');colorbar("vert")
        title('before thresholding');
        c = 1; % init c above 0
        
        while c>0
            threshold = mean(intens_RL,1,'omitnan')+3*std(intens_RL,1,'omitnan');
           ind = intens_RL>(ones(anzahl_runs,1)*threshold);
    %         ind = intens_RL>threshold; % works too
            b = find(ind);
            c = numel(b)       % will display in the command window how many outliers are removed at each iteration
            intens_RL(ind) = NaN;
        end
        subplot(212),imagesc(log10(abs(intens_RL)),clim);colormap('jet');colorbar("vert")
        title('after thresholding');
        
        
    end

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