auswertungsfiles und algos for ECOC 2025 rush...
This commit is contained in:
Silas Oettinghaus
@@ -1,67 +1,129 @@
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local = 1;
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if local
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databasePath = 'C:\Users\sioe\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\';
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else
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databasePath = '\\ntserver.tf.uni-kiel.de\scratch\sioe\ECOC_2025\';
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end
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database_name = 'ecoc2025_loops.db';
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database = DBHandler("pathToDB", [databasePath, database_name]);
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figure();
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plotBoundaries = 1;
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plotRealizations = 1;
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cols = linspecer(3); % Ensure color count matches
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% cols = cols(8,:);
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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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database = DBHandler("type",'mysql');
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filterParams = database.tables;
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%filterParams.Runs.loop_id = 209;
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filterParams.Configurations = struct( ...
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'symbolrate', 112e9, ... %[224,336,360,390,420,448]
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'fiber_length', 0, ...
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'db_mode', '"no_db"', ...
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'interference_attenuation', [], ...
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'interference_path_length', 10, ...
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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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'wavelength', 1310, ...
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'precomp_amp', [], ...
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'signal_attenuation', [], ...
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'v_awg', 0.95, ...
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'v_bias', 2.5 ...
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'v_awg', [], ...
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'v_bias', [] ...
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);
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% filterParams.EqualizerParameters.diff_precode = int32(db_mode.db_encoded);
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% filterParams.EqualizerParameters.equalizer_structure = int32(equalizer_structure.vnle);
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% if 1
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% % filterParams.EqualizerParameters.dc_buffer_len = 1;
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% filterParams.EqualizerParameters.ffe_buffer_len = 1;
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% filterParams.EqualizerParameters.smoothing_buffer_len = 4096;
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% filterParams.EqualizerParameters.smoothing_buffer_update = 224;
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% filterParams.EqualizerParameters.DCmu = 0;
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% end
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selectedFields = {'Configurations.run_id' 'Runs.loop_id' 'Runs.date_of_run' 'Runs.rx_raw_path' 'Configurations.bitrate' 'Configurations.v_bias' 'Configurations.v_awg' 'Configurations.precomp_amp' 'Configurations.symbolrate' 'Configurations.pam_level'...
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'Configurations.db_mode' 'Configurations.rop_attenuation' 'Configurations.is_mpi' 'Configurations.interference_attenuation' 'Configurations.interference_path_length' 'Configurations.signal_attenuation' ...
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'EqualizerParameters.equalizer_structure' 'EqualizerParameters.diff_precode' 'EqualizerParameters.eq_id' 'Measurements.power_pd_in' ...
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'Measurements.power_mpi_interference' 'Measurements.power_mpi_signal' 'Results.BER' 'Results.BER_precoded' 'Results.SNR' 'Results.GMI' 'Results.Alpha' 'Results.date_of_processing'};
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'EqualizerParameters.equalizer_structure' 'EqualizerParameters.diff_precode' 'EqualizerParameters.eq_id' 'EqualizerParameters.dc_buffer_len' 'EqualizerParameters.ffe_buffer_len' 'EqualizerParameters.smoothing_buffer_len' 'EqualizerParameters.smoothing_buffer_update' 'EqualizerParameters.DCmu' 'Measurements.power_pd_in' ...
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'Measurements.power_mpi_interference' 'Measurements.power_mpi_signal' 'Results.BER' 'Results.BER_precoded' 'Results.EVM' 'Results.SNR' 'Results.GMI' 'Results.Alpha' 'Results.date_of_processing'};
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[dataTable,sql_query] = database.queryDB(filterParams, selectedFields);
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dataTable.SIR = round(-6 - dataTable.power_mpi_interference);
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dataTable = cleanUpTable(dataTable);
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% [dataTable_raw,sql_query] = database.queryDB(filterParams, selectedFields);
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%%
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dataTable_clean = dataTable_raw;
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dataTable_clean.SIR = round(-7.5 - dataTable_clean.power_mpi_interference);
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dataTable_clean.NGMI = dataTable_clean.GMI ./ log2(dataTable_clean.pam_level);
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dataTable_clean = cleanUpTable(dataTable_clean);
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%%
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dataTable = dataTable_clean;
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figure(26);
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plotBoundaries = 1;
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plotRealizations = 0;
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cols = linspecer(8); % Ensure color count matches
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% ideal DC tracking
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if 0
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dataTable = dataTable(dataTable.dc_buffer_len == 1, :);
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dataTable = dataTable(dataTable.ffe_buffer_len == 1, :);
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dataTable = dataTable(dataTable.smoothing_buffer_len == 0, :);
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dataTable = dataTable(dataTable.smoothing_buffer_update == 0, :);
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dataTable = dataTable(dataTable.DCmu == 0.005, :);
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cols = cols(1:1+1,:);
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method = 'ideal dc tracking';
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% slow DC tracking
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elseif 1
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dataTable = dataTable(dataTable.dc_buffer_len == 224, :);
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dataTable = dataTable(dataTable.ffe_buffer_len == 1, :);
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dataTable = dataTable(dataTable.smoothing_buffer_len == 0, :);
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dataTable = dataTable(dataTable.smoothing_buffer_update == 0, :);
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dataTable = dataTable(dataTable.DCmu == 0.005, :);
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cols = cols(2:2+1,:);
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method = 'parallelized dc tracking';
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% slow DC smoothing
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elseif 0
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dataTable = dataTable(dataTable.dc_buffer_len == 1, :);
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dataTable = dataTable(dataTable.ffe_buffer_len == 1, :);
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dataTable = dataTable(dataTable.smoothing_buffer_len == 4096, :);
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dataTable = dataTable(dataTable.smoothing_buffer_update == 224, :);
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dataTable = dataTable(dataTable.DCmu == 0, :);
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cols = cols(3:3+1,:);
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method = 'dc smoothing';
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elseif 0
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% No compensation method
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dataTable = dataTable(dataTable.dc_buffer_len == 1, :);
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dataTable = dataTable(dataTable.ffe_buffer_len == 1, :);
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dataTable = dataTable(dataTable.smoothing_buffer_len == 0, :);
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dataTable = dataTable(dataTable.smoothing_buffer_update == 0, :);
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dataTable = dataTable(dataTable.DCmu == 0, :);
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cols = cols(4:4+1,:);
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method = 'ffe only';
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end
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dataTable(dataTable.eq_id==0,:) = [];
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dataTable(dataTable.equalizer_structure~=1,:) = [];
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% dataTable.interference_path_length(dataTable.interference_path_length < 20, :) = 20;
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dataTable = dataTable(dataTable.interference_path_length == 1000, :);
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% dataTable(dataTable.interference_path_length ~= 50, :) = [];
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% dataTable = dataTable(dataTable.interference_path_length < 51, :);
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% dataTable(dataTable.loop_id<200,:) = [];
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% Filter by time
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filter_by_time = 0;
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if filter_by_time
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startTime = datetime('2025-04-14 13:00:00', 'InputFormat', 'yyyy-MM-dd HH:mm:ss');
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stopTime = datetime('2025-04-14 19:30:00', 'InputFormat', 'yyyy-MM-dd HH:mm:ss');
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startTime = datetime('2025-04-20 18:00:00', 'InputFormat', 'yyyy-MM-dd HH:mm:ss');
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stopTime = datetime('2025-04-30 19:30:00', 'InputFormat', 'yyyy-MM-dd HH:mm:ss');
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dataTable.date_of_run = datetime(dataTable.date_of_run, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
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dataTable.date_of_processing = datetime(dataTable.date_of_processing, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
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dataTable = dataTable(dataTable.date_of_processing > startTime, :);
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dataTable = dataTable(dataTable.date_of_processing < stopTime, :);
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end
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% Group by smth
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y_var = 'BER';
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x_var = 'SIR';
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loop_var = 'eq_id';
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fixedVars = {'eq_id',x_var};
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loop_var = 'interference_path_length';
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fixedVars = {'equalizer_structure','interference_path_length',x_var};
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[dataTable, outliersTable] = removeGroupOutliers(dataTable, fixedVars, y_var);
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@@ -69,14 +131,13 @@ dataTableGrpd_mean = groupIt(fixedVars, dataTable, @mean);
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dataTableGrpd_min = groupIt(fixedVars, dataTable, @min);
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dataTableGrpd_max = groupIt(fixedVars, dataTable, @max);
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% Create a new figure
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mkr = '.';
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hold on
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unique_loop_var = unique(dataTable.(loop_var));
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for i = 1%:numel(unique_loop_var)
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for i = 1:numel(unique_loop_var)
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% Prepare filtered data for this loop variable
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loopValue = unique_loop_var(i);
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@@ -98,32 +159,43 @@ for i = 1%:numel(unique_loop_var)
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y_bounds = [y_lower, y_upper];
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% Display name (optional)
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idx = find(dataTable.(loop_var) == loopValue, 1, 'first');
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dispname = equalizer_structure(dataTable.equalizer_structure(idx));
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dispname = [char(dispname),'; ',num2str(unique(dataTable.interference_path_length)),' m'];
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try
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idx = find(dataTable.(loop_var) == loopValue, 1, 'first');
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% dispname = char(equalizer_structure(dataTable.equalizer_structure(idx)));
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dispname = [method];
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dispname = [dispname, '/ ',num2str(unique_loop_var(i)) ,' m'];
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% dispname = [dispname,'; ',num2str(unique(dataTable.interference_path_length)),' m'];
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dispname = [dispname, '/ PAM ', num2str(filterParams.Configurations.pam_level)];
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dispname = [dispname, '/ ', num2str(filterParams.Configurations.symbolrate.*1e-9),' GBd'];
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end
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if plotBoundaries
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% Plot bounded line
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[hl, hp] = boundedline(x_values, y_mean, y_bounds, ...
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'alpha', 'transparency', 0.2, ...
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'alpha', 'transparency', 0.1, ...
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'cmap', cols(i,:), ...
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'nan', 'fill', ...
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'orientation', 'vert');
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% Style the main line: thinnest, dotted, no marker
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% % Style the main line: thinnest, dotted, no marker
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set(hl, 'LineWidth', 1, 'LineStyle', '-', 'Marker', 'none', ...
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'Color', cols(i,:), 'DisplayName', string(dispname));
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plt = errorbar(x_values,y_mean,y_lower,y_upper,'LineWidth', 0.1, 'LineStyle', 'none', 'Marker', 'none','Color', cols(i,:), 'DisplayName', string(dispname),'HandleVisibility','off');
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% Hide patch (shaded area) from legend
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set(hp, 'HandleVisibility', 'off','LineStyle',':','LineWidth',0.5,'Marker','none');
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% Add invisible scatter for DataTips
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plt = scatter(x_values, y_mean, ...
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'Marker', 'o', 'MarkerEdgeColor', 'none', 'MarkerFaceColor', 'none', ...
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'HandleVisibility', 'off', 'PickableParts', 'all');
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% % Add invisible scatter for DataTips
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% plt = scatter(x_values, y_mean, ...
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% 'Marker', 'o', 'MarkerEdgeColor', 'none', 'MarkerFaceColor', 'none', ...
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% 'HandleVisibility', 'off', 'PickableParts', 'all');
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else
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plt= plot(x_values,y_mean,'LineWidth', 1, 'LineStyle', '-', 'Marker', 'none', ...
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'Color', cols(i,:), 'DisplayName', string(dispname));
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'Color', cols(i,:), 'DisplayName', string(dispname));
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% plt= errorbar(x_values,y_mean,y_lower,y_upper,'LineWidth', 1, 'LineStyle', '-', 'Marker', 'none','Color', cols(i,:), 'DisplayName', string(dispname));
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end
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% Add data tips to the invisible scatter
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pair_one = {'Run ID', dataTableGrpd_mean.run_id(loopFiltGrpd, :)};
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@@ -146,7 +218,7 @@ for i = 1%:numel(unique_loop_var)
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y_single = double(dataTable.(y_var)(loopFiltSingle, :));
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sc = scatter(x_single, y_single, 'Marker', mkr, 'MarkerEdgeColor', cols(i, :), ...
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'LineWidth', 0.5, 'HandleVisibility', 'on', 'DisplayName', string(dispname));
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'LineWidth', 0.5, 'HandleVisibility', 'off', 'DisplayName', string(dispname));
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pair_one = {'Run ID', dataTable.run_id(loopFiltSingle, :)};
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pair_two = {'Rate', dataTable.bitrate(loopFiltSingle, :) * 1e-9};
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@@ -161,12 +233,14 @@ xlabel(x_var);
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ylabel(y_var);
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title([x_var, ' vs. ', y_var]);
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if y_var == 'BER'
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if string(y_var) == "BER"
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yline(4e-4, 'LineWidth', 1, 'LineStyle', '--', 'HandleVisibility', 'off');
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yline(3.8e-3, 'LineWidth', 1, 'LineStyle', '--', 'HandleVisibility', 'off');
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yline(2e-2, 'LineWidth', 1, 'LineStyle', '--', 'HandleVisibility', 'off');
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ylim([1e-5, 0.1]);
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end
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xlim([15,50]);
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xlim([13,35]);
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% Enable grid and beautify
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grid on;
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@@ -243,7 +317,6 @@ 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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@@ -283,7 +356,6 @@ end
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end
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function cleanedTable = cleanUpTable(inputTable)
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% cleanUpTable Cleans a MATLAB table where numbers and NaNs are stored as strings or structs.
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%
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@@ -335,7 +407,7 @@ for i = 1:numel(varNames)
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else
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% Try convert to datetime
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try
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cleanedTable.(varNames{i}) = datetime(col, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
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cleanedTable.(varNames{i}) = datetime(col, 'InputFormat', 'yyyy-MM-dd HH:mm:ss');
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catch
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% Leave as string
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end
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@@ -391,8 +463,9 @@ for groupIdx = 1:height(groupKeys)
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continue;
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end
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% Detect outliers
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outlierMask = isoutlier(y_values, 'quartiles');
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% Detect outliers in log space
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y_log = log10(y_values);
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outlierMask = isoutlier(y_log, 'quartiles'); % or 'median', 'grubbs', etc.
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% If any outliers found, collect their data
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if any(outlierMask)
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