Final MPI Calculations here
This commit is contained in:
Silas Oettinghaus
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%% BER over SIR from MpiReductionResults
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% 1) gather data from the database
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% 2) clean data and remove per-curve outliers
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% 3) plot BER over SIR grouped by algorithm
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clear; clc;
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%% 1) Gather data
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studyName = "pam4_112_greater_3153";
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pathLengthToPlot = 0;
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selectedBlockUpdate = 1; % set [] to pool all block_update values
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selectedPamLevels = []; % set [] to use all PAM levels in the query result
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selectedAlgorithms = []; % set [] to use all algorithms in the query result
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useBoundedLines = true;
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usePolyfit = true;
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polyfitOrderMax = 4;
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maxBerForPlot = 0.1;
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db = DBHandler( ...
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"dataBase", "labor", ...
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"type", "mysql", ...
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"server", "192.168.178.192", ...
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"user", "silas", ...
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"password", "silas");
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db.refresh();
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fp = QueryFilter();
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fp.where('Runs', 'interference_path_length', 'EQUALS', pathLengthToPlot);
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fp.where('Runs', 'fiber_length', 'EQUALS', 0);
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fp.where('Runs', 'db_mode', 'EQUALS', '"no_db"');
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fp.where('MpiReductionResults', 'study_name', 'EQUALS', char(studyName));
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if ~isempty(selectedBlockUpdate)
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fp.where('MpiReductionResults', 'block_update', 'EQUALS', selectedBlockUpdate);
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end
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selectedFields = { ...
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'Runs.run_id', ...
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'Runs.sir', ...
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'Runs.pam_level', ...
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'Runs.symbolrate', ...
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'Runs.interference_path_length', ...
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'Runs.power_mpi_interference', ...
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'MpiReductionResults.occurrence_idx', ...
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'MpiReductionResults.storage_name', ...
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'MpiReductionResults.algorithm', ...
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'MpiReductionResults.algorithm_variant', ...
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'MpiReductionResults.eq_class', ...
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'MpiReductionResults.block_update', ...
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'MpiReductionResults.BER', ...
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'MpiReductionResults.BER_precoded', ...
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'MpiReductionResults.SNR', ...
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'MpiReductionResults.GMI', ...
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'MpiReductionResults.AIR'};
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selectedFields = selectedFields(:);
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[rawData, query] = db.queryDB(fp, selectedFields);
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disp(query);
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fprintf("Fetched %d MPI reduction rows.\n", height(rawData));
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%% 2) Clean data
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data = rawData;
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numericFields = ["run_id", "sir", "pam_level", "symbolrate", ...
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"interference_path_length", "occurrence_idx", "block_update", ...
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"power_mpi_interference", "BER", "BER_precoded", "SNR", "GMI", "AIR"];
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for fieldIdx = 1:numel(numericFields)
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fieldName = numericFields(fieldIdx);
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if ismember(fieldName, string(data.Properties.VariableNames))
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data.(char(fieldName)) = numericColumn(data.(char(fieldName)));
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end
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end
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stringFields = ["storage_name", "algorithm", "algorithm_variant", "eq_class"];
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for fieldIdx = 1:numel(stringFields)
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fieldName = stringFields(fieldIdx);
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if ismember(fieldName, string(data.Properties.VariableNames))
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data.(char(fieldName)) = stringColumn(data.(char(fieldName)));
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end
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end
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data = data(isfinite(data.BER) & data.BER > 0 & data.BER < maxBerForPlot, :);
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data.sir_exact = -7 - data.power_mpi_interference;
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if isempty(data)
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warning("PLOT_mpi_reduction_db_ber_vs_sir:NoRows", ...
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"No rows remain after initial BER/path/study/block filtering.");
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return
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end
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if isempty(selectedPamLevels)
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selectedPamLevels = unique(data.pam_level(isfinite(data.pam_level))).';
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else
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data = data(ismember(data.pam_level, selectedPamLevels), :);
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end
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if isempty(selectedAlgorithms)
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selectedAlgorithms = unique(data.algorithm, "stable").';
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else
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selectedAlgorithms = string(selectedAlgorithms);
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data = data(ismember(data.algorithm, selectedAlgorithms), :);
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end
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if isempty(data)
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warning("PLOT_mpi_reduction_db_ber_vs_sir:NoSelectedRows", ...
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"No rows remain after selectedPamLevels/selectedAlgorithms filtering.");
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return
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end
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data.clean_keep = true(height(data), 1);
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[groupId, groupPam, groupAlgorithm, groupSir] = findgroups(data.pam_level, data.algorithm, data.sir);
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for groupIdx = 1:max(groupId)
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rowMask = groupId == groupIdx;
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berValues = data.BER(rowMask);
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if nnz(rowMask) > 3
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data.clean_keep(rowMask) = ~isoutlier(berValues);
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end
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end
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cleanData = data(data.clean_keep, :);
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summaryTable = groupsummary(cleanData, ["pam_level", "algorithm", "sir"], ...
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{"mean", "min", "max"}, "BER");
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sirExactTable = groupsummary(cleanData, ["pam_level", "algorithm", "sir"], ...
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"median", "sir_exact");
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summaryTable.sir_exact = sirExactTable.median_sir_exact;
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summaryTable = sortrows(summaryTable, ["pam_level", "algorithm", "sir"]);
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fprintf("Cleaned to %d rows across %d PAM/algorithm/SIR groups.\n", ...
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height(cleanData), height(summaryTable));
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disp(groupcounts(cleanData, ["pam_level", "algorithm"]));
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%% 3) Plot data
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if exist("linspecer", "file")
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algColors = linspecer(numel(selectedAlgorithms));
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else
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algColors = lines(numel(selectedAlgorithms));
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end
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figure(); clf;
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tiledlayout(numel(selectedPamLevels), 1, "TileSpacing", "compact");
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for pamIdx = 1:numel(selectedPamLevels)
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pamLevel = selectedPamLevels(pamIdx);
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nexttile; hold on;
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for algIdx = 1:numel(selectedAlgorithms)
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algorithmName = selectedAlgorithms(algIdx);
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algColor = algColors(algIdx, :);
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rawMask = cleanData.pam_level == pamLevel & cleanData.algorithm == algorithmName;
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curveMask = summaryTable.pam_level == pamLevel & summaryTable.algorithm == algorithmName;
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if ~any(curveMask)
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continue
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end
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scatter(cleanData.sir_exact(rawMask), cleanData.BER(rawMask), ...
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30, ...
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"Marker", ".", ...
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"MarkerEdgeColor", algColor, ...
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"HandleVisibility", "off");
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sirValues = summaryTable.sir_exact(curveMask).';
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meanBer = summaryTable.mean_BER(curveMask).';
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minBer = summaryTable.min_BER(curveMask).';
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maxBer = summaryTable.max_BER(curveMask).';
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valid = isfinite(sirValues) & isfinite(meanBer) & meanBer > 0;
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if useBoundedLines && exist("boundedline", "file") && any(valid)
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yLower = max(meanBer - minBer, 0);
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yUpper = max(maxBer - meanBer, 0);
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yBounds = [yLower(:), yUpper(:)];
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[hl, hp] = boundedline(sirValues(valid).', meanBer(valid).', yBounds(valid, :), ...
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'alpha', 'transparency', 0.08, ...
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'cmap', algColor, ...
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'nan', 'fill', ...
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'orientation', 'vert');
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set(hl, "LineStyle", "none", "Marker", "none", "HandleVisibility", "off");
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set(hp, "LineStyle", "none", "HandleVisibility", "off");
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end
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plot(sirValues(valid), meanBer(valid), ...
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"LineStyle", "none", ...
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"Marker", "o", ...
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"MarkerSize", 5, ...
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"LineWidth", 1.2, ...
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"Color", algColor, ...
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"MarkerFaceColor", algColor, ...
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"DisplayName", char(algorithmName));
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if usePolyfit
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fitMask = valid & meanBer > 0;
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if nnz(fitMask) >= 2
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fitOrder = min(polyfitOrderMax, nnz(fitMask) - 1);
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fitCoeff = polyfit(sirValues(fitMask), log10(meanBer(fitMask)), fitOrder);
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xFit = linspace(min(sirValues(fitMask)), max(sirValues(fitMask)), 300);
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yFit = 10 .^ polyval(fitCoeff, xFit);
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plot(xFit, yFit, ...
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"LineStyle", "--", ...
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"LineWidth", 1.1, ...
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"Color", algColor, ...
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"HandleVisibility", "off");
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end
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end
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end
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yline(2.2e-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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title(sprintf("PAM %.0f, path %.0f m, block update %s", ...
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pamLevel, pathLengthToPlot, blockUpdateLabel(selectedBlockUpdate)));
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xlabel("SIR (dB)");
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ylabel("BER");
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set(gca, "YScale", "log");
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ylim([9e-5, maxBerForPlot]);
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grid on;
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box on;
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legend("Location", "best", "Interpreter", "none");
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end
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if exist("beautifyBERplot", "file")
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beautifyBERplot("logscale", true, "setcolors", false, "setmarkers", false);
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end
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%% Local helpers
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function values = numericColumn(values)
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if iscell(values)
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values = string(values);
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end
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if isstring(values) || ischar(values)
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values = str2double(values);
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end
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values = double(values);
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end
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function values = stringColumn(values)
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if iscell(values)
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values = string(values);
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elseif ischar(values)
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values = string(values);
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end
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values = strip(string(values));
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end
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function label = blockUpdateLabel(selectedBlockUpdate)
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if isempty(selectedBlockUpdate)
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label = "pooled";
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else
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label = string(selectedBlockUpdate);
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end
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end
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