151 lines
6.1 KiB
Matlab
151 lines
6.1 KiB
Matlab
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basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
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database = DBHandler("pathToDB",[basePath,'silas_labor.db']);
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filterParams = database.tables;
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filterParams.Configurations = struct( ...
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'bitrate', [], ... %[224,336,360,390,420,448]
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'db_mode', int32(db_mode.no_db), ...
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'fiber_length', 1, ...
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'interference_attenuation', [], ...
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'interference_path_length', [], ...
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'is_mpi', 1, ...
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'pam_level', 4, ...
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'rop_attenuation', 0, ...
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'wavelength', 1310 ...
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);
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% filterParams.EqualizerParameters.diff_precode = int32(db_mode.no_db);
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filterParams.EqualizerParameters.equalizer_structure = int32(equalizer_structure.vnle);
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% filterParams.EqualizerParameters.DCmu = 0.005;
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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'};
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[dataTable,sql_query] = database.queryDB(filterParams, selectedFields);
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fixedVars = {'run_id','eq_id','bitrate'};
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dataTableGrpd = groupIt(fixedVars,dataTable);
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% Create a new figure
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figure(3);
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hold on
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unique_rates = unique(dataTable.bitrate);
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cols = linspecer(8);
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for i = 1:numel(unique_rates)
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% Plot BER vs. interference_attenuation
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% plot(dataTableGrpd.power_mpi_signal(dataTableGrpd.bitrate==unique_rates(i),:)-dataTableGrpd.power_mpi_interference(dataTableGrpd.bitrate==unique_rates(i),:), dataTableGrpd.BER(dataTableGrpd.bitrate==unique_rates(i),:), '-', 'LineWidth', 0.5,'Color',cols(i,:));
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if filterParams.Configurations.is_mpi
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sir = dataTable.power_mpi_signal(dataTable.bitrate==unique_rates(i),:)-dataTable.power_mpi_interference(dataTable.bitrate==unique_rates(i),:);
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ber = dataTable.BER(dataTable.bitrate==unique_rates(i),:);
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sc=scatter(dataTable.power_mpi_signal(dataTable.bitrate==unique_rates(i),:)-dataTable.power_mpi_interference(dataTable.bitrate==unique_rates(i),:), dataTable.BER(dataTable.bitrate==unique_rates(i),:), 'LineWidth', 0.5,'Marker','.','MarkerEdgeColor',cols(i+1,:));
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pair_one = {'Run ID', dataTable.run_id(dataTable.bitrate==unique_rates(i),:)};
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pair_two = {'Rate', dataTable.bitrate(dataTable.bitrate==unique_rates(i),:)};
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addDatatips(sc, pair_one, pair_two);
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else
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sc=scatter(62*ones(size( dataTableGrpd.BER(dataTableGrpd.bitrate==unique_rates(i),:))), dataTableGrpd.BER(dataTableGrpd.bitrate==unique_rates(i),:), 'LineWidth', 0.5,'Marker','o','MarkerEdgeColor',cols(i,:),'MarkerFaceColor',cols(i,:));
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pair_one = {'Run ID', dataTableGrpd.run_id(dataTableGrpd.bitrate==unique_rates(i),:)};
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pair_two = {'Rate', dataTableGrpd.bitrate(dataTableGrpd.bitrate==unique_rates(i),:)};
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addDatatips(sc, pair_one, pair_two);
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end
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end
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% Label the axes and add a title
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xlabel('SIR in dB');
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ylabel('BER');
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title('BER vs. Signal to Interference Ratio');
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yline(3.8e-3,'LineWidth',1,'LineStyle','--','HandleVisibility','off');
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% Enable grid for better readability
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grid on;
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beautifyBERplot;
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ylim([1e-4 0.5]);
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function resultTable = groupIt(fixedVars,dataTable)
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% Group by run_id and eq_id (adjust grouping keys as needed)
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[G, groupKeys] = findgroups(dataTable(:, fixedVars));
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% Preallocate a cell array for aggregated data.
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varNames = dataTable.Properties.VariableNames;
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nVars = numel(varNames);
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aggData = cell(height(groupKeys), nVars);
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groupCount = zeros(height(groupKeys), 1); % To store the size of each group
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% Loop over each group.
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for i = 1:height(groupKeys)
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idx = (G == i); % Logical index for group i
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groupCount(i) = sum(idx); % Count number of rows in this group
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% For each variable in the table:
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for j = 1:nVars
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colData = dataTable.(varNames{j});
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if isnumeric(colData)
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% For numeric data, compute the mean.
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aggData{i, j} = min(colData(idx));
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else
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% For non-numeric data, take the first entry.
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if iscell(colData)
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aggData{i, j} = colData{find(idx, 1)};
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else
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aggData{i, j} = colData(find(idx, 1));
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end
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end
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end
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end
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% Convert the aggregated cell array into a table.
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resultTable = cell2table(aggData, 'VariableNames', varNames);
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% Append the group count as a new column.
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resultTable.nRows = groupCount;
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end
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function addDatatips(sc, varargin)
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% addDatatips Adds custom data tip rows to a scatter plot.
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%
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% addDatatips(sc, pair1, pair2, ...) adds one or more custom rows to the
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% data tip display of the scatter plot identified by sc.
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%
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% Each pair should be provided as a 1x2 cell array: {label, value}.
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% The value can be a scalar or a vector. If a vector is provided, its length
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% must match the number of scatter plot points.
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%
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% Example:
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% sc = scatter(x, y, 'LineWidth', 1.5, 'Marker', 'o');
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% pair_one = {'Attenuation', attenuationVector};
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% addDatatips(sc, pair_one);
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numPoints = numel(sc.XData);
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for k = 1:length(varargin)
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pair = varargin{k};
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if ~iscell(pair) || numel(pair) ~= 2
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error('Each pair must be a 1x2 cell array: {label, value}.');
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end
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label = pair{1};
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value = pair{2};
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% If value is a vector, ensure its length is either 1 or equal to the number of scatter points.
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if isvector(value) && numel(value) ~= 1 && numel(value) ~= numPoints
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error('The vector for "%s" must be a scalar or have %d elements matching the scatter data points.', label, numPoints);
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end
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% Create a new data tip row using the provided label and vector.
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newRow = dataTipTextRow(label, value);
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sc.DataTipTemplate.DataTipRows(end+1) = newRow;
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end
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end
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