Changes from mwork PC.

PDP 2025

MPI analysis

new focus on database and SQL

projects/HighSpeedExperiment_2024/auswertung MPI/plot_from_database.m

@@ -0,0 +1,91 @@
+
+
+basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
+database = DBHandler("pathToDB",[basePath,'silas_labor.db']);
+
+filterParams = database.tables;
+filterParams.Configurations = struct( ...
+    'bitrate',          336e9, ...
+    'db_mode',          0, ...
+    'fiber_length',     1, ...
+    'interference_attenuation', [], ...
+    'interference_path_length', [], ...
+    'is_mpi',           1, ...
+    'pam_level',        4, ...
+    'rop_attenuation',  0 ...
+    );
+
+filterParams.EqualizerParameters.diff_precode = int32(db_mode.no_db);
+filterParams.EqualizerParameters.equalizer_structure = int32(equalizer_structure.vnle);
+
+selectedFields = {'Configurations.run_id' 'Runs.rx_raw_path'	'Configurations.bitrate'	'Configurations.symbolrate'	'Configurations.pam_level'	'Configurations.db_mode'	'Configurations.rop_attenuation'	'Configurations.is_mpi'	'Configurations.interference_attenuation'	'EqualizerParameters.equalizer_structure'	'EqualizerParameters.diff_precode' 'EqualizerParameters.eq_id'	'Measurements.power_pd_in'	'Measurements.power_mpi_interference'	'Measurements.power_mpi_signal'	'Results.BER'	'Results.SNR'	'Results.GMI'	'Results.Alpha'};
+
+[dataTable,sql_query] = database.queryDB(filterParams, selectedFields);
+
+fixedVars = {'run_id','eq_id','bitrate'};
+resultTable = groupIt(fixedVars,dataTable);
+
+
+
+% Create a new figure
+figure(1);
+hold on
+unique_rates = unique(resultTable.bitrate);
+for i = 1:numel(unique_rates)
+    % Plot BER vs. interference_attenuation
+    plot(resultTable.power_mpi_signal(resultTable.bitrate==unique_rates(i),:)-resultTable.power_mpi_interference(resultTable.bitrate==unique_rates(i),:), resultTable.BER(resultTable.bitrate==unique_rates(i),:), 'o-', 'LineWidth', 1.5);
+end
+
+% Label the axes and add a title
+xlabel('Interference Attenuation');
+ylabel('BER');
+title('BER vs. Interference Attenuation');
+
+% Enable grid for better readability
+grid on;
+
+beautifyBERplot;
+
+
+
+
+function resultTable = groupIt(fixedVars,dataTable)
+
+    % Group by run_id and eq_id (adjust grouping keys as needed)
+    
+    [G, groupKeys] = findgroups(dataTable(:, fixedVars));
+    
+    % Preallocate a cell array for aggregated data.
+    varNames = dataTable.Properties.VariableNames;
+    nVars = numel(varNames);
+    aggData = cell(height(groupKeys), nVars);
+    groupCount = zeros(height(groupKeys), 1);  % To store the size of each group
+    
+    % Loop over each group.
+    for i = 1:height(groupKeys)
+        idx = (G == i); % Logical index for group i
+        groupCount(i) = sum(idx);  % Count number of rows in this group
+        % For each variable in the table:
+        for j = 1:nVars
+            colData = dataTable.(varNames{j});
+            if isnumeric(colData)
+                % For numeric data, compute the mean.
+                aggData{i, j} = mean(colData(idx));
+            else
+                % For non-numeric data, take the first entry.
+                if iscell(colData)
+                    aggData{i, j} = colData{find(idx, 1)};
+                else
+                    aggData{i, j} = colData(find(idx, 1));
+                end
+            end
+        end
+    end
+    
+    % Convert the aggregated cell array into a table.
+    resultTable = cell2table(aggData, 'VariableNames', varNames);
+    
+    % Append the group count as a new column.
+    resultTable.nRows = groupCount;
+
+end