% Diesen PLot habe ich in der Diss als Tabelle

% Create a compact 3-column by 4-row MPI time-signal figure table in LaTeX.
% 
% Use these plots from 04_Experimental_Evaluation/tikz/mpi/:
% - columns: SIR = 20 dB, 30 dB, 50 dB
% - rows: path length = 1 m, 20 m, 300 m, 1000 m
% - file pattern: mpi_timesignal_<pathlen>m_<sir>_db.tikz
% 
% Use a tabular layout with thin black table gridlines:
% - \arrayrulewidth = 0.2pt
% - \arrayrulecolor{black}
% - vertical and horizontal rules around all cells
% - rotated row labels for 1 m, 20 m, 300 m, 1000 m
% - center the rotated row labels vertically against the plot cells
% - use a dedicated left-column macro so the SIR 20 dB plots are slightly wider:
% - left column fwidth = 0.255\textwidth
% - inner columns fwidth = 0.240\textwidth
% - all fheight = 0.102\textheight
% 
% For the TikZ plot files used in the table:
% - leftmost/SIR 20 dB plots keep the y-label:
% ylabel={Norm. Amplitude}
% - SIR 30 dB and 50 dB plots have no y-label:
% ylabel={}
% - SIR 30 dB and 50 dB plots hide y ticks:
% ytick=\empty
% - axis labels and tick labels use scriptsize:
% every axis/.append style={font=\scriptsize}
% xlabel style={font=\color{white!15!black}\scriptsize}
% ylabel style={font=\color{white!15!black}\scriptsize}
% - sigma annotation node boxes use tiny:
% \node[font=\tiny, ...]
%     - node boxes:
%     inner sep=0.5pt
%     at (axis cs:10,...)
%     sigma labels use normal math subscripts, e.g. $\sigma^2_2$, not $\sigma^2\_2$
% - all plotted line widths and grid style widths were normalized as needed:
% addplot line width=1.0pt
% - grid should be visually off:
% remove xmajorgrids and ymajorgrids
% keep style definitions available:
% grid style={line width=0.4pt, solid, color=black!20}
% minor grid style={line width=0.2pt, solid, color=black!10}
% 
% db = DBHandler("type","mysql","dataBase",'labor');
% savePath = 'W:\labdata\ECOC Silas\ecoc_2025\';


for sirval = [20,30,50]
    for pathlen = [1,20,300,1000]
    
        fp = QueryFilter();
        % fp.where('Runs', 'run_id','EQUALS', 987);
        M = 4;
        fp.where('Runs', 'pam_level','EQUALS', M);     
        % fp.where('Runs', 'symbolrate','NOT_EQUAL', 112e9);
        fp.where('Runs', 'fiber_length','EQUALS', 0);   
        fp.where('Runs', 'is_mpi','EQUALS', 1); 
        
        fp.where('Runs', 'interference_path_length','EQUALS', pathlen);      
        % fp.where('Runs', 'loop_id','GREATER_THAN', 11);  
    
        fp.where('Runs', 'sir','GREATER_EQUAL',sirval);  
        fp.where('Runs','run_id','GREATER_EQUAL',3153);
        
        % Sort results by SIR value ascending and reorder corresponding rows
        [dataTable,sql_query] = db.queryDB(fp, db.getTableFieldNames('Runs'));
        [~, idx] = sort(dataTable.sir, 'ascend', 'MissingPlacement', 'last');
        dataTable = dataTable(idx, :);
        
        dataTable = dataTable(1,:);
        dataTable_ = dataTable;
        [~, uniqueIdx] = unique(dataTable.run_id);  % Get unique run_id indices
        
        % dataTable.SIR = -7 - round(dataTable.power_mpi_interference);
        
        fsym = dataTable.symbolrate;
        M = double(dataTable_.pam_level);
        duob_mode = db_mode.(strrep(char(dataTable_.db_mode),'"',''));
        
        Tx_signal = load([savePath, char(dataTable_.tx_signal_path),'.mat']);
        Tx_signal = Tx_signal.Digi_sig;
        
        Tx_bits     = load([savePath, char(dataTable_.tx_bits_path)]);
        Tx_bits     = Tx_bits.Bits;
        Symbols_mapped = PAMmapper(M,0).map(Tx_bits);
        Symbols_mapped.fs = dataTable_.symbolrate;
        
        Symbols  = load([savePath, char(dataTable_.tx_symbols_path)]);
        Symbols  = Symbols.Symbols;
        
        Scpe_sig_raw = load([savePath, char(dataTable_.rx_raw_path(1))]);
        Scpe_sig_raw = Scpe_sig_raw.Scpe_sig_raw;
        Scpe_sig_raw = Scpe_sig_raw.normalize("mode","rms");
        % Scpe_sig_raw.plot("displayname",['Scope Signal (Run ID: ',num2str(dataTable_.run_id)],"fignum",dataTable_.run_id,"clear",0);
        % 
        % Scpe_sig_raw.spectrum("normalizeTo0dB",1);
        
        
        disp(num2str(dataTable_.interference_path_length));
        
        % Scpe_sig_raw.eye(dataTable.symbolrate,dataTable.pam_level);
        
        [separated_levels_sig, avg_sig, plotInfo] = showLevelScatter(Scpe_sig_raw, Symbols, ...
            "fsym", fsym, ...
            "syncFs", 2*fsym, ...
            "fignum", sirval+pathlen, ...
            "normalize", true, ...
            "debug_plots", false, ...
            "showPlot", true, ...
            "showStdAnnotations", true, ...
            "yLimits", [-2.5 2.5], ...
            "xLimits",[0 25],...
            "scatterAlpha", 0.25, ...
            "scatterSize", 1, ...
            "avgLineMaxPoints", 500, ...
            "avgLineSmoothWindow", 5);
            
        title(sprintf("%d m, %d db", pathlen,sirval));
        
        exportDir = "C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/04_Experimental_Evaluation/tikz/mpi";
        exportName = string(sprintf("mpi_timesignal_v2_%dm_%d_db", pathlen,sirval));
        pngFile = fullfile(exportDir, exportName + "-1.png");
        tikzFile = fullfile(exportDir, exportName + ".tikz");
        pngTikzPath = "04_Experimental_Evaluation/tikz/mpi/" + exportName + "-1.png";
        
        figure(sirval + pathlen);
        exportLevelScatterTikz(gca, pngFile, tikzFile, pngTikzPath, ...
            "resolutionDpi", 150, ...
            "generatedBy", "plot_mpi_timesignal.m");
    
    end
end
%%



% std_levels = std(separated_levels_sig,0,2,'omitnan');
% mean_levels = mean(separated_levels_sig,2,'omitnan');
% std_tot = std(Scpe_sig_raw.signal,0,1,'omitnan');
% 
% var_levels = std_levels.^2;
% p = polyfit(mean_levels, var_levels, 1);
% 
% var_slope  = p(1);
% var_offset = p(2);c