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Many changes towards simulation of JLT and once again the evaluation of the Highspeed data from Lab experiments 2024

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Silas Oettinghaus
2025-07-09 10:50:53 +02:00
parent 9ce23c4a10
commit 2cff29f239
35 changed files with 1874 additions and 549 deletions
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projects/HighSpeedExperiment_2024/Auswertung_JLT/run_dsp_from_db.m Normal file
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% === SETTINGS ===
dsp_options.append_to_db = 1;
dsp_options.max_occurences = 1;
experiment = "highspeed_2024";
dsp_options.mode = "load_run_id"; % 'simulate' & 'load_files'
dsp_options.load_file_path = struct();
if dsp_options.mode == "load_run_id"
if experiment == "highspeed_2024"
dsp_options.database_type = 'mysql';
dsp_options.dataBase = 'labor_highspeed';%'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\silas_labor_newdsp_newstructure.db';
dsp_options.storage_path = 'Z:\2024\sioe_labor\';
db = DBHandler("dataBase", [dsp_options.dataBase], "type", dsp_options.database_type);
elseif experiment == "mpi_ecoc_2025"
dsp_options.database_type = 'mysql';
dsp_options.dataBase = 'labor';
dsp_options.storage_path = 'Z:\2025\ECOC Silas\ecoc_2025\';
db = DBHandler("dataBase", [dsp_options.dataBase], "type", dsp_options.database_type);
end
elseif dsp_options.mode == "load_files"
dsp_options.load_file_path.tx_bits_path = "Z:\2025\ECOC Silas\ecoc_2025\mpi_opti_1000m_pam2 4 6 8\20250417_091513_PAM_4_R_112_bits.mat"';
dsp_options.load_file_path.tx_symbols_path = "Z:\2025\ECOC Silas\ecoc_2025\mpi_opti_1000m_pam2 4 6 8\20250417_091513_PAM_4_R_112_symbols.mat"';
dsp_options.load_file_path.rx_raw_path = "Z:\2025\ECOC Silas\ecoc_2025\mpi_opti_1000m_pam2 4 6 8\20250417_091525_PAM_4_R_112_rec01_rx_signal_raw.mat"';
elseif dsp_options.mode == "simulate"
error('Not yet implemented')
end
% === Get Run ID's ===
fp = QueryFilter();
% fp.where('Runs', 'run_id','EQUALS', 987);
% fp.where('Runs', 'pam_level','EQUALS', 4);
fp.where('Runs', 'bitrate','LESS_THAN', 310e9);
fp.where('Runs', 'fiber_length','EQUALS', 1);
fp.where('Runs', 'is_mpi','EQUALS', 0);
% fp.where('Runs', 'interference_path_length','EQUALS', 1000);
% fp.where('Runs', 'loop_id','GREATER_THAN', 11);
% fp.where('Runs', 'sir','EQUALS',18);
fp.where('Runs', 'wavelength','EQUALS', 1310);
% fp.where('Runs', 'db_mode','EQUALS', 1);
fp.where('Runs', 'rop_attenuation','EQUALS', 0);
% fp.where('Runs', 'power_pd_in','GREATER_THAN', 7);
[dataTable,~] = db.queryDB(fp, db.getTableFieldNames('Runs'));
% Keep only the rows corresponding to the first occurrence of each 'sir' value
% [~, unique_indices] = unique(dataTable.sir, 'first');
% dataTable = dataTable(unique_indices, :);
% dataTable = dataTable(1,:);
% === Set LOOPS & Initialize DataStorage ===
dsp_options.parameters = struct();
% dsp_options.parameters.pf_ncoeffs = [1,2];%[0,logspace(-4,0,10)];
wh = DataStorage(dsp_options.parameters);
wh.addStorage("ffe_package");
wh.addStorage("mlse_package");
wh.addStorage("vnle_package");
wh.addStorage("dbtgt_package");
wh.addStorage("dbenc_package");
% === RUN IT ===
[results,wh] = submitJobs(dataTable.run_id(:), dsp_options, "serial", 'wh', wh, 'waitbar', true);
% wh.getStoValue('ffe_package',0.005);
% wh.getStoValue('mlse_package',0.005);
% [dataTable,~] = db.queryDB(fp, [db.getTableFieldNames('Runs');db.getTableFieldNames('Results');db.getTableFieldNames('Equalizer')]);
%
% dataTable = cleanUpTable(dataTable);
% wh_analyze = wh_adap;
% % wh_analyze = wh_dcremoval_old2;
% % wh_analyze = wh_dcremoval_old2;
%
% res = cell(wh_analyze.parameter.mu_dc.length,wh_analyze.parameter.dc_buffer_len.length);
% ber_mean = zeros(wh_analyze.parameter.mu_dc.length,wh_analyze.parameter.dc_buffer_len.length);
% for m = 1:wh_analyze.parameter.mu_dc.length
% for b = 1:wh_analyze.parameter.dc_buffer_len.length
% res{m,b}=wh_analyze.getStoValue("ffe_package",wh_analyze.parameter.mu_dc.values(m),wh_analyze.parameter.dc_buffer_len.values(b));
% try
% cells = res{m,b}{1};
% idx = cellfun(@(c) ~isempty(c), cells);
% ber = cellfun(@(c) c.metrics.BER, cells(idx));
% ber_mean(m,b) = mean(ber);
% catch
% ber_mean(m,b) = NaN;
% end
% end
% end
%
% figure()
% hold on
% ber_fix = cellfun(@(c) c.ffe_package{1}.metrics.BER, results_fix);
% ber_adap_m2 = cellfun(@(c) c.ffe_package{1}.metrics.BER, results_adap);
% ber_adap_method1 = cellfun(@(c) c.ffe_package{1}.metrics.BER, results);
% plot(dataTable.sir,ber_fix,'LineWidth',1,'DisplayName',sprintf('DCt; fix mu = 0.5; p=1024'),'Marker','.','MarkerSize',10);
% plot(dataTable.sir,ber_adap_method1,'LineWidth',1,'DisplayName',sprintf('DCt; adap mu 1; p=1024'),'Marker','.','MarkerSize',10);
% plot(dataTable.sir,ber_adap_m2,'LineWidth',1,'DisplayName',sprintf('DCt; adap mu 2; p=1024'),'Marker','.','MarkerSize',10);
% xlabel('BER');
% xlabel('SIR');
% yline([4.85e-3,2e-2],'HandleVisibility', 'off','LineWidth',1,'LineStyle','--');
% % ylim([9e-4, 0.5]);
% set(gca, 'YScale', 'log'); % BER is usually plotted log-scale
% legend('show', 'Location', 'best');
% grid on;
%
%
%
%
% figure; clf
%
% % Create meshgrid for contourf
% [X, Y] = meshgrid(dsp_options.parameters.mu_dc, dsp_options.parameters.dc_buffer_len);
%
% % Create contour plot
% contourf(X, Y, ber_mean', 20); % 20 contour levels, adjust as needed
%
% % Set axes to logarithmic scale
% set(gca, 'XScale', 'log', 'YScale', 'log');
%
% colormap("parula");
% c = colorbar;
% c.Label.String = 'BER';
%
% xlabel('\mu_{dc}');
% ylabel('dc\_buffer\_len');
% title('BER Optimization over \mu_{dc} and dc\_buffer\_len');
%
% % Make plot prettier
% grid on
% set(gca, 'Layer', 'top'); % Put grid lines on top of contours
%
%
% % === Look at it ===
% y_var = 'BER_precoded';
% x_var = 'bitrate';
% fixedVars = {'equalizer_structure', x_var};
%
% [dataTableClean, outliersTable] = removeGroupOutliers(dataTable, fixedVars, y_var);
%
% % --- Group and aggregate ---
% dataTableGrpd_mean = groupIt(fixedVars, dataTableClean, @mean);
% dataTableGrpd_min = groupIt(fixedVars, dataTableClean, @min);
% dataTableGrpd_max = groupIt(fixedVars, dataTableClean, @max);
%
% % Choose a color map
% cols = linspecer(numel(unique(dataTableGrpd_mean.equalizer_structure)));
%
% figure;
% hold on;
%
% % Get unique equalizer structures for grouping
% unique_eq = unique(dataTableGrpd_mean.equalizer_structure);
%
% for i = 1:numel(unique_eq)
% eq_val = unique_eq(i);
%
% % Filter grouped data for this equalizer structure
% filt = dataTableGrpd_mean.equalizer_structure == eq_val;
%
% x = dataTableGrpd_mean.(x_var)(filt);
% y_mean = dataTableGrpd_mean.(y_var)(filt);
% y_min = dataTableGrpd_min.(y_var)(filt);
% y_max = dataTableGrpd_max.(y_var)(filt);
%
% % Bounds for boundedline (distance from mean)
% y_lower = y_mean - y_min;
% y_upper = y_max - y_mean;
% y_bounds = [y_lower, y_upper];
%
% % --- Bounded line (mean ± min/max) ---
% if exist('boundedline', 'file')
% [hl, hp] = boundedline(x, y_mean, y_bounds, ...
% 'alpha', 'transparency', 0.1, ...
% 'cmap', cols(i,:), ...
% 'nan', 'fill', ...
% 'orientation', 'vert');
% set(hl, 'LineWidth', 1.2, 'DisplayName', sprintf('Eq %s', eq_val));
% set(hp, 'HandleVisibility', 'off');
% else
% % If boundedline is not available, use errorbar
% errorbar(x, y_mean, y_lower, y_upper, ...
% 'o-', 'Color', cols(i,:), 'LineWidth', 1.2, ...
% 'DisplayName', sprintf('Eq %d', eq_val),'HandleVisibility', 'off');
% end
%
% % --- Normal line (mean only) ---
% plot(x, y_mean, '-', 'Color', cols(i,:), 'LineWidth', 1.5, ...
% 'DisplayName', sprintf('Mean Eq %s', eq_val),'HandleVisibility', 'off');
%
% % --- Scatter plot for individual points (from original data) ---
% % Filter original data for this group
% orig_filt = dataTableClean.equalizer_structure == eq_val;
% x_scatter = dataTableClean.(x_var)(orig_filt);
% y_scatter = dataTableClean.(y_var)(orig_filt);
%
% scatter(x_scatter, y_scatter, 10,cols(i,:), 'filled', ...
% 'MarkerFaceAlpha', 0.5, 'DisplayName', sprintf('Scatter Eq %s', eq_val),'HandleVisibility', 'off');
% end
%
% yline([2.2e-4,4.85e-3,2e-2],'HandleVisibility', 'off','LineWidth',1,'LineStyle','--');
% set(gca, 'YScale', 'log'); % BER is usually plotted log-scale
% xlabel(x_var, 'Interpreter', 'none');
% ylabel(y_var, 'Interpreter', 'none');
% legend('show', 'Location', 'best');
% grid on;
% title(sprintf('%s vs. %s', y_var, x_var), 'Interpreter', 'none');
% hold off;