Many changes for 400G DSP
Minimal Example ...
This commit is contained in:
sioe
170
projects/HighSpeedExperiment_2024/auswertung/ber_vs_bitrate.m
Normal file
170
projects/HighSpeedExperiment_2024/auswertung/ber_vs_bitrate.m
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basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
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useGui = 0;
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pamlvls = [4,6,8];
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wlengths = [1302];
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figoffset = 0;
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figure(30)
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tiledlayout(1, 3, 'TileSpacing', 'compact', 'Padding', 'compact');
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for p = 1:numel(pamlvls)
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nexttile;
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for w = 1:numel(wlengths)
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sgtitle(['Lambda: ',num2str(wlengths),' nm'])
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hold on
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pamlvl = pamlvls(p);
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wlength = wlengths(w);
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db = DBHandler("pathToDB",[basePath,'silas_labor.db']);
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if useGui
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filterParams = db.promptFilterParameters();
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selectedFields = db.promptSelectFields();
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else
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filterParams = db.tables;
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filterParams.Configurations = struct( ...
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'bitrate', [], ...
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'db_mode', [], ...
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'fiber_length', 10, ...
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'interference_attenuation', [], ...
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'interference_path_length', [], ...
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'is_mpi', 0, ...
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'pam_level', pamlvl, ...
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'precomp_amp', [], ...
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'rop_attenuation', 0, ...
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'symbolrate', [], ...
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'v_awg', [], ...
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'v_bias', [], ...
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'wavelength', wlength ...
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);
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selectedFields = {'Runs.run_id','BERs.ber_id','Equalizer.eq_id','Equalizer.eq_type','BERs.ber','BERs.occurrence',...
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'Configurations.db_mode','Configurations.pam_level','Configurations.bitrate','Configurations.symbolrate','Configurations.fiber_length','Configurations.wavelength','Configurations.precomp_amp',...
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'Measurements.power_rop','Measurements.power_laser','Measurements.power_pd_in'};
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end
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% Get data table from DB
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[dataTable,~] = db.queryDB(filterParams, selectedFields);
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% Grouping variables: 'bitrate' and 'eq_type'
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groupVars = {'bitrate', 'eq_type'};
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% Calculate mean BER for each combination of 'bitrate' and 'eq_type'
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groupedData = groupsummary(dataTable, groupVars, {'mean', 'min', @(x) meanExcludingOutliers(x)}, {'ber', 'power_rop','power_pd_in'});
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% Collect the run_id values for each group
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groupedRunIDs = varfun(@(x) {unique(x)}, dataTable, 'GroupingVariables', groupVars, 'InputVariables', 'run_id');
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groupedRunIDs.Properties.VariableNames(end) = {'GroupedRunIDs'};
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% Join the grouped data with the grouped run_id list
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avgdBerTable = join(groupedData, groupedRunIDs, 'Keys', groupVars);
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% Define the fields that you want to use for filtering
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filterFields = {'eq_type'};
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% Loop over each field you want to filter by
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for f = 1:numel(filterFields)
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currentField = filterFields{f};
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% Determine unique values for the current field
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uniqueValues = unique(avgdBerTable.(currentField));
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% Loop over each unique value for the current field
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for i = 1:numel(uniqueValues)
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currentValue = uniqueValues(i);
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% Filter joinedData for the current value
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if isnumeric(currentValue)
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filteredData = avgdBerTable(avgdBerTable.(currentField) == currentValue, :);
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else
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filteredData = avgdBerTable(strcmp(avgdBerTable.(currentField), currentValue), :);
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end
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cols = linspecer(8);
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lst = ["-",":","--"];
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a=plot(filteredData.bitrate.*1e-9,filteredData.min_ber,...
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'Color',cols(i,:),'MarkerSize',4,'LineWidth',1,'LineStyle',lst(w),...
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'Marker','o','MarkerFaceColor','auto','MarkerEdgeColor',cols(i,:),...
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'DisplayName',[char(currentValue),'; ',num2str(wlength),' nm' ]);
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a.DataTipTemplate.DataTipRows(1).Label = 'Bitrate';
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a.DataTipTemplate.DataTipRows(1).Format = ['%.1f',' Gbit/s'];
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a.DataTipTemplate.DataTipRows(2).Label = 'BER';
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a.DataTipTemplate.DataTipRows(2).Format ='%.1e';
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a.DataTipTemplate.DataTipRows(3).Label = 'P_{out}';
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a.DataTipTemplate.DataTipRows(3).Value = filteredData.mean_power_rop;
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a.DataTipTemplate.DataTipRows(3).Format = ['%.2f',' dBm'];
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a.DataTipTemplate.DataTipRows(4).Label = 'Run ID';
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a.DataTipTemplate.DataTipRows(4).Value = filteredData.GroupedRunIDs;
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a.DataTipTemplate.DataTipRows(4).Format = ['%d',' '];
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a.DataTipTemplate.FontSize = 9;
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a.DataTipTemplate.FontName = 'arial';
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%
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% a=scatter(filteredData.bitrate.*1e-9,filteredData.min_ber,5,'Marker','diamond',...
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% 'Color',cols(i,:),'LineWidth',1,...
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% 'MarkerFaceColor','auto','MarkerEdgeColor',cols(i,:),...
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% 'DisplayName',currentValue);
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%
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% a.DataTipTemplate.DataTipRows(1).Label = 'Bitrate';
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% a.DataTipTemplate.DataTipRows(1).Format = ['%.1f',' Gbit/s'];
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%
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% a.DataTipTemplate.DataTipRows(2).Label = 'BER';
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% a.DataTipTemplate.DataTipRows(2).Format ='%.1e';
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%
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% a.DataTipTemplate.DataTipRows(3).Label = 'P_{out}';
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% a.DataTipTemplate.DataTipRows(3).Value = filteredData.mean_power_rop;
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% a.DataTipTemplate.DataTipRows(3).Format = ['%.2f',' dBm'];
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%
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% a.DataTipTemplate.DataTipRows(4).Label = 'Run ID';
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% a.DataTipTemplate.DataTipRows(4).Value = filteredData.GroupedRunIDs;
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% a.DataTipTemplate.DataTipRows(4).Format = ['%d',' '];
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%
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%
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% a.DataTipTemplate.FontSize = 9;
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% a.DataTipTemplate.FontName = 'arial';
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end
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end
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% Continue with the rest of your plot settings
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title(sprintf('%d km | %d nm | PAM %d',unique(filterParams.Configurations.fiber_length),wlength,pamlvl));
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yline(2e-2, 'DisplayName', '20% O-FEC', 'LineStyle', '--', 'HandleVisibility', 'off','LineWidth',1);
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yline(3.8e-3, 'DisplayName', 'HD-FEC', 'LineStyle', '--', 'HandleVisibility', 'off','LineWidth',1);
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xlabel('Bit Rate in Gbps');
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ylabel('Bit Error Rate');
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xlim([300, 480])
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ylim([8e-4,0.5])
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set(gca, 'yscale', 'log');
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set(gca, 'Box', 'on');
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grid on;
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grid minor;
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end
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end
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legend('Interpreter', 'none');
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set(gcf, 'Units', 'pixels', 'Position', 1.0e+03 * [0.2483 0.7303 1.2093 0.3980]);
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% Custom function using rmoutliers to calculate mean after removing outliers
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function meanWithoutOutliers = meanExcludingOutliers(x)
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% Remove outliers using rmoutliers with default method (based on median)
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xWithoutOutliers = rmoutliers(x);
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% Calculate the mean of the non-outliers
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if isempty(xWithoutOutliers)
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% Handle the case where all values are outliers
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meanWithoutOutliers = NaN;
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else
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meanWithoutOutliers = mean(xWithoutOutliers);
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end
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end
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