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projects/Diss/MPI_revisit/investigate_database.m

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+
+
+dsp_options.database_type = "mysql";
+dsp_options.dataBase = "labor";
+dsp_options.server = "192.168.178.192";
+dsp_options.port = 3306;
+dsp_options.user = "silas";
+dsp_options.password = "silas";
+
+db = DBHandler("dataBase", dsp_options.dataBase, "type", dsp_options.database_type, ...
+    "server", dsp_options.server, "port", dsp_options.port, ...
+    "user", dsp_options.user, "password", dsp_options.password);
+
+fp = QueryFilter();
+% fp.where('Runs', 'symbolrate','EQUALS', 112e9);
+[dataTable,~] = db.queryDB(fp, db.getTableFieldNames('Runs'));
+
+%% TOTAL RUNS
+
+totalRuns = height(dataTable);
+
+%% Difference between "opti" and not opti (before and after run_id = 2661)
+
+fp = QueryFilter();
+fp.where('Runs','run_id','LESS_THAN',3153);
+fp.where('Runs', 'db_mode','EQUALS', '"no_db"');
+% fp.where('Runs', 'interference_path_length','EQUALS', 300);
+[dataTable_1,~] = db.queryDB(fp, db.getTableFieldNames('Runs'));
+
+fp = QueryFilter();
+fp.where('Runs','run_id','GREATER_EQUAL',3153);
+% fp.where('Runs', 'interference_path_length','EQUALS', 300);
+[dataTable_2,~] = db.queryDB(fp, db.getTableFieldNames('Runs'));
+
+%% prepare x/y and plotting without forcing datetime priority for x
+% simplified: assume columns exist and no datetime special cases
+xCol = 'symbolrate';
+yCol = 'v_awg';
+
+% get x and y (fallback to NaN vectors of appropriate length)
+if ismember(xCol, dataTable_1.Properties.VariableNames)
+    x1 = dataTable_1.(xCol);
+else
+    x1 = NaN(height(dataTable_1),1);
+end
+if ismember(xCol, dataTable_2.Properties.VariableNames)
+    x2 = dataTable_2.(xCol);
+else
+    x2 = NaN(height(dataTable_2),1);
+end
+
+if ismember(yCol, dataTable_1.Properties.VariableNames)
+    y1 = dataTable_1.(yCol);
+else
+    y1 = NaN(height(dataTable_1),1);
+end
+if ismember(yCol, dataTable_2.Properties.VariableNames)
+    y2 = dataTable_2.(yCol);
+else
+    y2 = NaN(height(dataTable_2),1);
+end
+
+% convert text to numeric where needed
+toNum = @(v) double(string(v));
+if iscell(x1) || isstring(x1) || ischar(x1), x1 = toNum(x1); end
+if iscell(x2) || isstring(x2) || ischar(x2), x2 = toNum(x2); end
+if iscell(y1) || isstring(y1) || ischar(y1), y1 = toNum(y1); end
+if iscell(y2) || isstring(y2) || ischar(y2), y2 = toNum(y2); end
+
+% simple scatter plots side-by-side
+figure;
+subplot(1,2,1);
+scatter(x1, y1, 36, 'b', 'filled');
+xlabel(xCol); ylabel(yCol); title('dataTable\_1'); grid on;
+
+subplot(1,2,2);
+scatter(x2, y2, 36, 'r', 'filled');
+xlabel(xCol); ylabel(yCol); title('dataTable\_2'); grid on;
+
+linkaxes(findall(gcf,'Type','axes'),'y');
+
+%% Count interference path lengths by PAM and symbolrate
+
+pamCol = 'pam_level';
+iplCol = 'interference_path_length';
+rateCol = 'symbolrate';
+pamLevels = [2, 4, 6, 8];
+
+% Current analysis uses only the post-opti/post-threshold runs.
+% Use [dataTable_1; dataTable_2] here if the pre-threshold runs should be included.
+allData = dataTable_2;
+
+requiredCols = {pamCol, iplCol, rateCol};
+missingCols = setdiff(requiredCols, allData.Properties.VariableNames);
+if ~isempty(missingCols)
+    error('Missing required column(s): %s', strjoin(missingCols, ', '));
+end
+
+pam = double(string(allData.(pamCol)));
+ipl = double(string(allData.(iplCol)));
+symbolrate = double(string(allData.(rateCol)));
+
+validRows = ~isnan(pam) & ~isnan(ipl) & ~isnan(symbolrate);
+iplValues = unique(ipl(validRows));
+symbolrateValues = unique(symbolrate(validRows));
+
+if isempty(iplValues) || isempty(symbolrateValues)
+    error('No valid rows found for PAM/interference_path_length/symbolrate plotting.');
+end
+
+iplCategories = categorical(string(iplValues), string(iplValues), string(iplValues));
+symbolrateLabels = compose('%.0f GBd', symbolrateValues*1e-9);
+
+figure('Units', 'normalized', 'Position', [0.05 0.1 0.9 0.7]);
+tiledlayout(1, 5, 'TileSpacing', 'compact', 'Padding', 'compact');
+
+for k = 1:numel(pamLevels)
+    ax = nexttile;
+    pamMask = validRows & pam == pamLevels(k);
+    counts = zeros(numel(iplValues), numel(symbolrateValues));
+    for rateIdx = 1:numel(symbolrateValues)
+        for iplIdx = 1:numel(iplValues)
+            counts(iplIdx, rateIdx) = sum(pamMask ...
+                & ipl == iplValues(iplIdx) ...
+                & symbolrate == symbolrateValues(rateIdx));
+        end
+    end
+
+    bar(ax, iplCategories, counts, 'stacked');
+    xlabel(ax, 'interference\_path\_length');
+    ylabel(ax, 'count');
+    title(ax, sprintf('PAM %d', pamLevels(k)));
+    xtickangle(ax, 45);
+    grid(ax, 'on');
+end
+
+axAll = nexttile;
+countsAll = zeros(numel(iplValues), numel(symbolrateValues));
+for rateIdx = 1:numel(symbolrateValues)
+    for iplIdx = 1:numel(iplValues)
+        countsAll(iplIdx, rateIdx) = sum(validRows ...
+            & ipl == iplValues(iplIdx) ...
+            & symbolrate == symbolrateValues(rateIdx));
+    end
+end
+
+bar(axAll, iplCategories, countsAll, 'stacked');
+xlabel(axAll, 'interference\_path\_length');
+ylabel(axAll, 'count');
+title(axAll, 'All PAMs');
+xtickangle(axAll, 45);
+grid(axAll, 'on');
+
+lgd = legend(axAll, symbolrateLabels, 'Location', 'eastoutside');
+title(lgd, 'symbolrate');