150 lines
5.7 KiB
Matlab
150 lines
5.7 KiB
Matlab
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% Diesen PLot habe ich in der Diss als Tabelle
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% Create a compact 3-column by 4-row MPI time-signal figure table in LaTeX.
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%
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% Use these plots from 04_Experimental_Evaluation/tikz/mpi/:
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% - columns: SIR = 20 dB, 30 dB, 50 dB
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% - rows: path length = 1 m, 20 m, 300 m, 1000 m
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% - file pattern: mpi_timesignal_<pathlen>m_<sir>_db.tikz
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%
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% Use a tabular layout with thin black table gridlines:
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% - \arrayrulewidth = 0.2pt
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% - \arrayrulecolor{black}
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% - vertical and horizontal rules around all cells
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% - rotated row labels for 1 m, 20 m, 300 m, 1000 m
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% - center the rotated row labels vertically against the plot cells
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% - use a dedicated left-column macro so the SIR 20 dB plots are slightly wider:
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% - left column fwidth = 0.255\textwidth
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% - inner columns fwidth = 0.240\textwidth
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% - all fheight = 0.102\textheight
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%
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% For the TikZ plot files used in the table:
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% - leftmost/SIR 20 dB plots keep the y-label:
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% ylabel={Norm. Amplitude}
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% - SIR 30 dB and 50 dB plots have no y-label:
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% ylabel={}
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% - SIR 30 dB and 50 dB plots hide y ticks:
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% ytick=\empty
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% - axis labels and tick labels use scriptsize:
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% every axis/.append style={font=\scriptsize}
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% xlabel style={font=\color{white!15!black}\scriptsize}
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% ylabel style={font=\color{white!15!black}\scriptsize}
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% - sigma annotation node boxes use tiny:
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% \node[font=\tiny, ...]
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% - node boxes:
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% inner sep=0.5pt
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% at (axis cs:10,...)
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% sigma labels use normal math subscripts, e.g. $\sigma^2_2$, not $\sigma^2\_2$
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% - all plotted line widths and grid style widths were normalized as needed:
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% addplot line width=1.0pt
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% - grid should be visually off:
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% remove xmajorgrids and ymajorgrids
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% keep style definitions available:
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% grid style={line width=0.4pt, solid, color=black!20}
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% minor grid style={line width=0.2pt, solid, color=black!10}
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%
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% db = DBHandler("type","mysql","dataBase",'labor');
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% savePath = 'W:\labdata\ECOC Silas\ecoc_2025\';
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for sirval = [20,30,50]
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for pathlen = [1,20,300,1000]
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fp = QueryFilter();
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% fp.where('Runs', 'run_id','EQUALS', 987);
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M = 4;
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fp.where('Runs', 'pam_level','EQUALS', M);
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% fp.where('Runs', 'symbolrate','NOT_EQUAL', 112e9);
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fp.where('Runs', 'fiber_length','EQUALS', 0);
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fp.where('Runs', 'is_mpi','EQUALS', 1);
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fp.where('Runs', 'interference_path_length','EQUALS', pathlen);
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% fp.where('Runs', 'loop_id','GREATER_THAN', 11);
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fp.where('Runs', 'sir','GREATER_EQUAL',sirval);
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fp.where('Runs','run_id','GREATER_EQUAL',3153);
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% Sort results by SIR value ascending and reorder corresponding rows
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[dataTable,sql_query] = db.queryDB(fp, db.getTableFieldNames('Runs'));
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[~, idx] = sort(dataTable.sir, 'ascend', 'MissingPlacement', 'last');
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dataTable = dataTable(idx, :);
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dataTable = dataTable(1,:);
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dataTable_ = dataTable;
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[~, uniqueIdx] = unique(dataTable.run_id); % Get unique run_id indices
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% dataTable.SIR = -7 - round(dataTable.power_mpi_interference);
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fsym = dataTable.symbolrate;
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M = double(dataTable_.pam_level);
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duob_mode = db_mode.(strrep(char(dataTable_.db_mode),'"',''));
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Tx_signal = load([savePath, char(dataTable_.tx_signal_path),'.mat']);
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Tx_signal = Tx_signal.Digi_sig;
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Tx_bits = load([savePath, char(dataTable_.tx_bits_path)]);
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Tx_bits = Tx_bits.Bits;
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Symbols_mapped = PAMmapper(M,0).map(Tx_bits);
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Symbols_mapped.fs = dataTable_.symbolrate;
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Symbols = load([savePath, char(dataTable_.tx_symbols_path)]);
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Symbols = Symbols.Symbols;
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Scpe_sig_raw = load([savePath, char(dataTable_.rx_raw_path(1))]);
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Scpe_sig_raw = Scpe_sig_raw.Scpe_sig_raw;
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Scpe_sig_raw = Scpe_sig_raw.normalize("mode","rms");
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% Scpe_sig_raw.plot("displayname",['Scope Signal (Run ID: ',num2str(dataTable_.run_id)],"fignum",dataTable_.run_id,"clear",0);
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%
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% Scpe_sig_raw.spectrum("normalizeTo0dB",1);
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disp(num2str(dataTable_.interference_path_length));
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% Scpe_sig_raw.eye(dataTable.symbolrate,dataTable.pam_level);
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[separated_levels_sig, avg_sig, plotInfo] = showLevelScatter(Scpe_sig_raw, Symbols, ...
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"fsym", fsym, ...
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"syncFs", 2*fsym, ...
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"fignum", sirval+pathlen, ...
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"normalize", true, ...
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"debug_plots", false, ...
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"showPlot", true, ...
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"showStdAnnotations", true, ...
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"yLimits", [-2.5 2.5], ...
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"xLimits",[0 25],...
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"scatterAlpha", 0.25, ...
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"scatterSize", 1, ...
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"avgLineMaxPoints", 500, ...
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"avgLineSmoothWindow", 5);
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title(sprintf("%d m, %d db", pathlen,sirval));
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exportDir = "C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/04_Experimental_Evaluation/tikz/mpi";
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exportName = string(sprintf("mpi_timesignal_v2_%dm_%d_db", pathlen,sirval));
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pngFile = fullfile(exportDir, exportName + "-1.png");
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tikzFile = fullfile(exportDir, exportName + ".tikz");
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pngTikzPath = "04_Experimental_Evaluation/tikz/mpi/" + exportName + "-1.png";
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figure(sirval + pathlen);
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exportLevelScatterTikz(gca, pngFile, tikzFile, pngTikzPath, ...
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"resolutionDpi", 150, ...
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"generatedBy", "plot_mpi_timesignal.m");
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end
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end
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%%
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% std_levels = std(separated_levels_sig,0,2,'omitnan');
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% mean_levels = mean(separated_levels_sig,2,'omitnan');
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% std_tot = std(Scpe_sig_raw.signal,0,1,'omitnan');
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%
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% var_levels = std_levels.^2;
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% p = polyfit(mean_levels, var_levels, 1);
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%
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% var_slope = p(1);
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% var_offset = p(2);c
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