Final MPI Calculations here
This commit is contained in:
Silas Oettinghaus
@@ -0,0 +1,254 @@
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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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99
projects/Diss/MPI_revisit/db/appendMpiReductionDspOutput.m
Normal file
99
projects/Diss/MPI_revisit/db/appendMpiReductionDspOutput.m
Normal file
@@ -0,0 +1,99 @@
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function [summary, rows] = appendMpiReductionDspOutput(db, run_id, occurrence_idx, dspOutput, dsp_options, options)
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%appendMpiReductionDspOutput Store one DSP occurrence in MpiReductionResults.
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arguments
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db
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run_id
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occurrence_idx
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dspOutput struct
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dsp_options struct = struct()
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options.study_name string = "mpi_reduction_v1"
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options.dry_run (1,1) logical = false
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options.verbose (1,1) logical = false
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end
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summary = emptySummary();
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rows = {};
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packageNames = fieldnames(dspOutput);
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if isempty(packageNames)
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return
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end
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block_update = resolveBlockUpdate(dspOutput, dsp_options);
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wh = DataStorage(struct( ...
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"run_id", double(run_id), ...
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"block_update", double(block_update)));
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hasPackages = false;
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for packageIdx = 1:numel(packageNames)
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packageName = packageNames{packageIdx};
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package = dspOutput.(packageName);
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if isempty(package)
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continue
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end
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if iscell(package)
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packageCell = package;
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else
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packageCell = {package};
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end
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wh.addStorage(packageName);
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wh.addValueToStorageByLinIdx(packageCell, packageName, 1);
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hasPackages = true;
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end
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if ~hasPackages
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return
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end
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dsp_options_local = dsp_options;
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dsp_options_local.start_occurence = occurrence_idx;
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dsp_options_local.userParameters.block_update = block_update;
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[summary, rows] = appendMpiReductionWarehouse(db, wh, run_id, dsp_options_local, ...
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"study_name", options.study_name, ...
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"dry_run", options.dry_run, ...
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"verbose", options.verbose);
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end
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function summary = emptySummary()
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summary = struct( ...
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"rows_considered", 0, ...
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"rows_inserted", 0, ...
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"rows_skipped_duplicate", 0, ...
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"rows_skipped_existing_key", 0, ...
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"rows_skipped_empty", 0);
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end
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function block_update = resolveBlockUpdate(dspOutput, dsp_options)
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if isfield(dsp_options, "userParameters") && ...
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isfield(dsp_options.userParameters, "block_update") && ...
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isscalar(dsp_options.userParameters.block_update)
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block_update = dsp_options.userParameters.block_update;
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return
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end
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packageNames = fieldnames(dspOutput);
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for packageIdx = 1:numel(packageNames)
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package = dspOutput.(packageNames{packageIdx});
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if isempty(package)
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continue
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end
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if iscell(package)
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package = package{1};
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end
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if isstruct(package) && ...
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isfield(package, "mpi_reduction_config") && ...
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isfield(package.mpi_reduction_config, "params") && ...
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isfield(package.mpi_reduction_config.params, "block_update")
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block_update = package.mpi_reduction_config.params.block_update;
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return
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end
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end
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error("appendMpiReductionDspOutput:MissingBlockUpdate", ...
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"Could not resolve scalar block_update from dsp_options or package metadata.");
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end
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307
projects/Diss/MPI_revisit/db/appendMpiReductionWarehouse.m
Normal file
307
projects/Diss/MPI_revisit/db/appendMpiReductionWarehouse.m
Normal file
@@ -0,0 +1,307 @@
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function [summary, rows] = appendMpiReductionWarehouse(db, wh, run_ids, dsp_options, options)
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%appendMpiReductionWarehouse Store MPI reduction warehouse entries in MySQL.
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arguments
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db
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wh
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run_ids
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dsp_options struct
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options.study_name string = "mpi_reduction_v1"
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options.dry_run (1,1) logical = false
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options.verbose (1,1) logical = true
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end
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ensureTableVisible(db);
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storageNames = fieldnames(wh.sto);
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summary = struct( ...
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"rows_considered", 0, ...
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"rows_inserted", 0, ...
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"rows_skipped_duplicate", 0, ...
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"rows_skipped_existing_key", 0, ...
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"rows_skipped_empty", 0);
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rows = {};
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for storageIdx = 1:numel(storageNames)
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storageName = storageNames{storageIdx};
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storageArray = wh.sto.(storageName);
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for linIdx = 1:numel(storageArray)
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packageCell = storageArray{linIdx};
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if isempty(packageCell)
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summary.rows_skipped_empty = summary.rows_skipped_empty + 1;
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continue
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end
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phys = physicalCoordinates(wh, linIdx);
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run_id = resolveRunId(phys, run_ids);
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block_update = resolveBlockUpdate(phys, dsp_options);
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packageCell = normalizePackageCell(packageCell);
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for packageIdx = 1:numel(packageCell)
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package = packageCell{packageIdx};
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if isempty(package) || ~isstruct(package) || ~isfield(package, "metrics")
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summary.rows_skipped_empty = summary.rows_skipped_empty + 1;
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continue
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end
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occurrence_idx = startOccurrence(dsp_options) + packageIdx - 1;
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row = buildRow(db, package, storageName, run_id, occurrence_idx, ...
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block_update, options.study_name);
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summary.rows_considered = summary.rows_considered + 1;
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rows{end+1,1} = row; %#ok<AGROW>
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if options.dry_run
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continue
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end
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if resultHashExists(db, row.result_hash)
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summary.rows_skipped_duplicate = summary.rows_skipped_duplicate + 1;
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if options.verbose
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fprintf("MPI reduction DB: skipped duplicate %s, run_id=%d, occurrence=%d, block_update=%d\n", ...
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row.storage_name, row.run_id, row.occurrence_idx, row.block_update);
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end
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continue
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end
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if uniqueResultKeyExists(db, row)
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summary.rows_skipped_existing_key = summary.rows_skipped_existing_key + 1;
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if options.verbose
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fprintf("MPI reduction DB: skipped existing key %s, run_id=%d, occurrence=%d, block_update=%d\n", ...
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row.storage_name, row.run_id, row.occurrence_idx, row.block_update);
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end
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continue
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end
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db.appendToTable("MpiReductionResults", row);
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summary.rows_inserted = summary.rows_inserted + 1;
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end
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end
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end
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if options.verbose
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fprintf("MPI reduction DB: considered %d rows, inserted %d, duplicates %d, existing keys %d, empty %d\n", ...
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summary.rows_considered, summary.rows_inserted, ...
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summary.rows_skipped_duplicate, summary.rows_skipped_existing_key, ...
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summary.rows_skipped_empty);
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end
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end
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function ensureTableVisible(db)
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if ~isfield(db.tables, "MpiReductionResults")
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db.refresh();
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end
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if ~isfield(db.tables, "MpiReductionResults")
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error("appendMpiReductionWarehouse:MissingTable", ...
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"Table MpiReductionResults is not visible to DBHandler. Create the table and call db.refresh().");
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end
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end
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function phys = physicalCoordinates(wh, linIdx)
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phys = struct();
|
||||
[physValues, physNames] = wh.getPhysIndicesByLinIndex(linIdx);
|
||||
|
||||
for physIdx = 1:numel(physNames)
|
||||
name = char(physNames{physIdx});
|
||||
phys.(name) = physValues{physIdx};
|
||||
end
|
||||
end
|
||||
|
||||
function run_id = resolveRunId(phys, run_ids)
|
||||
if isfield(phys, "run_id")
|
||||
run_id = phys.run_id;
|
||||
return
|
||||
end
|
||||
|
||||
if isscalar(run_ids)
|
||||
run_id = run_ids;
|
||||
return
|
||||
end
|
||||
|
||||
error("appendMpiReductionWarehouse:MissingRunId", ...
|
||||
"Warehouse has no run_id axis, but run_ids is not scalar.");
|
||||
end
|
||||
|
||||
function block_update = resolveBlockUpdate(phys, dsp_options)
|
||||
if isfield(phys, "block_update")
|
||||
block_update = phys.block_update;
|
||||
return
|
||||
end
|
||||
|
||||
if isfield(dsp_options, "userParameters") && ...
|
||||
isfield(dsp_options.userParameters, "block_update") && ...
|
||||
isscalar(dsp_options.userParameters.block_update)
|
||||
block_update = dsp_options.userParameters.block_update;
|
||||
return
|
||||
end
|
||||
|
||||
error("appendMpiReductionWarehouse:MissingBlockUpdate", ...
|
||||
"Could not resolve block_update from warehouse coordinates or scalar userParameters.");
|
||||
end
|
||||
|
||||
function packageCell = normalizePackageCell(packageCell)
|
||||
if ~iscell(packageCell)
|
||||
packageCell = {packageCell};
|
||||
end
|
||||
end
|
||||
|
||||
function occurrence = startOccurrence(dsp_options)
|
||||
occurrence = 1;
|
||||
if isfield(dsp_options, "start_occurence")
|
||||
occurrence = dsp_options.start_occurence;
|
||||
end
|
||||
end
|
||||
|
||||
function row = buildRow(db, package, storageName, run_id, occurrence_idx, block_update, study_name)
|
||||
config = fieldOr(package, "mpi_reduction_config", fallbackConfig(storageName, block_update));
|
||||
params = fieldOr(config, "params", struct());
|
||||
if isempty(params)
|
||||
params = struct();
|
||||
end
|
||||
|
||||
metrics = package.metrics;
|
||||
if isobject(metrics) && ismethod(metrics, "toStruct")
|
||||
metrics = metrics.toStruct();
|
||||
end
|
||||
|
||||
param_json = jsonencode(params);
|
||||
if strlength(string(param_json)) == 0
|
||||
param_json = "{}";
|
||||
end
|
||||
|
||||
row = struct();
|
||||
row.run_id = double(run_id);
|
||||
row.occurrence_idx = double(occurrence_idx);
|
||||
row.study_name = char(study_name);
|
||||
row.storage_name = char(fieldOr(config, "storage_name", storageName));
|
||||
row.algorithm = char(fieldOr(config, "algorithm", "unknown"));
|
||||
row.algorithm_variant = char(fieldOr(config, "algorithm_variant", ""));
|
||||
row.eq_class = char(fieldOr(config, "eq_class", "unknown"));
|
||||
row.block_update = double(block_update);
|
||||
|
||||
row.sps = scalarParam(params, "sps");
|
||||
row.eq_order = scalarParam(params, "order");
|
||||
row.len_tr = scalarParam(params, "len_tr");
|
||||
row.epochs_tr = scalarParam(params, "epochs_tr");
|
||||
row.mu_tr = scalarParam(params, "mu_tr");
|
||||
row.dd_mode = logicalParam(params, "dd_mode");
|
||||
row.epochs_dd = scalarParam(params, "epochs_dd");
|
||||
row.mu_dd = scalarParam(params, "mu_dd");
|
||||
|
||||
row.numBits = metricScalar(metrics, "numBits", 0);
|
||||
row.numBitErr = metricScalar(metrics, "numBitErr", 0);
|
||||
row.BER = metricScalar(metrics, "BER", NaN);
|
||||
row.numBitErr_precoded = metricScalar(metrics, "numBitErr_precoded", NaN);
|
||||
row.BER_precoded = metricScalar(metrics, "BER_precoded", NaN);
|
||||
row.SNR = metricScalar(metrics, "SNR", NaN);
|
||||
row.SNR_level = metricJson(metrics, "SNR_level");
|
||||
row.GMI = metricScalar(metrics, "GMI", NaN);
|
||||
row.AIR = metricScalar(metrics, "AIR", NaN);
|
||||
row.EVM = metricScalar(metrics, "EVM", NaN);
|
||||
row.EVM_level = metricJson(metrics, "EVM_level");
|
||||
row.STD = metricScalar(metrics, "STD", NaN);
|
||||
row.STD_level = metricJson(metrics, "STD_level");
|
||||
row.STDrx = metricScalar(metrics, "STDrx", NaN);
|
||||
row.STDrx_level = metricJson(metrics, "STDrx_level");
|
||||
row.Alpha = firstFiniteScalar(fieldOr(metrics, "Alpha", NaN));
|
||||
|
||||
row.param_json = char(param_json);
|
||||
row.param_hash = db.calcHash(params);
|
||||
row.result_hash = db.calcHash(row);
|
||||
end
|
||||
|
||||
function config = fallbackConfig(storageName, block_update)
|
||||
params = struct("block_update", block_update);
|
||||
config = struct( ...
|
||||
"storage_name", storageName, ...
|
||||
"algorithm", "unknown", ...
|
||||
"algorithm_variant", "", ...
|
||||
"eq_class", "unknown", ...
|
||||
"params", params);
|
||||
end
|
||||
|
||||
function value = fieldOr(s, fieldName, defaultValue)
|
||||
fieldName = char(fieldName);
|
||||
if isstruct(s) && isfield(s, fieldName) && ~isempty(s.(fieldName))
|
||||
value = s.(fieldName);
|
||||
else
|
||||
value = defaultValue;
|
||||
end
|
||||
end
|
||||
|
||||
function value = scalarParam(params, fieldName)
|
||||
value = firstFiniteScalar(fieldOr(params, fieldName, NaN));
|
||||
end
|
||||
|
||||
function value = logicalParam(params, fieldName)
|
||||
value = firstFiniteScalar(fieldOr(params, fieldName, false));
|
||||
if isnan(value)
|
||||
value = false;
|
||||
else
|
||||
value = logical(value);
|
||||
end
|
||||
end
|
||||
|
||||
function value = metricScalar(metrics, fieldName, defaultValue)
|
||||
value = firstFiniteScalar(fieldOr(metrics, fieldName, defaultValue));
|
||||
end
|
||||
|
||||
function value = metricJson(metrics, fieldName)
|
||||
metricValue = fieldOr(metrics, fieldName, []);
|
||||
value = char(jsonencode(metricValue));
|
||||
end
|
||||
|
||||
function value = firstFiniteScalar(valueIn)
|
||||
if islogical(valueIn)
|
||||
valueIn = double(valueIn);
|
||||
end
|
||||
|
||||
if isempty(valueIn)
|
||||
value = NaN;
|
||||
return
|
||||
end
|
||||
|
||||
if isnumeric(valueIn)
|
||||
finiteValues = valueIn(isfinite(valueIn));
|
||||
if isempty(finiteValues)
|
||||
value = NaN;
|
||||
else
|
||||
value = double(finiteValues(1));
|
||||
end
|
||||
return
|
||||
end
|
||||
|
||||
value = str2double(string(valueIn));
|
||||
if isnan(value)
|
||||
value = NaN;
|
||||
end
|
||||
end
|
||||
|
||||
function exists = resultHashExists(db, resultHash)
|
||||
query = sprintf("SELECT mpi_result_id FROM MpiReductionResults WHERE result_hash = '%s' LIMIT 1", ...
|
||||
char(resultHash));
|
||||
existing = db.fetch(query);
|
||||
exists = ~isempty(existing);
|
||||
end
|
||||
|
||||
function exists = uniqueResultKeyExists(db, row)
|
||||
query = sprintf( ...
|
||||
"SELECT mpi_result_id FROM MpiReductionResults " + ...
|
||||
"WHERE run_id = %d " + ...
|
||||
"AND occurrence_idx = %d " + ...
|
||||
"AND study_name = '%s' " + ...
|
||||
"AND storage_name = '%s' " + ...
|
||||
"AND block_update = %d " + ...
|
||||
"AND param_hash = '%s' LIMIT 1", ...
|
||||
row.run_id, row.occurrence_idx, ...
|
||||
sqlString(row.study_name), sqlString(row.storage_name), ...
|
||||
row.block_update, sqlString(row.param_hash));
|
||||
existing = db.fetch(query);
|
||||
exists = ~isempty(existing);
|
||||
end
|
||||
|
||||
function value = sqlString(value)
|
||||
value = strrep(char(value), '''', '''''');
|
||||
end
|
||||
@@ -7,6 +7,12 @@ dsp_options.start_occurence = 1;
|
||||
dsp_options.max_occurences = 15;
|
||||
dsp_options.debug_plots = false;
|
||||
|
||||
write_mpi_reduction_db = 1;
|
||||
stream_mpi_reduction_db = 1;
|
||||
mpi_reduction_study_name = "block_update_sweep";
|
||||
mpi_reduction_writer_path = fullfile(fileparts(mfilename('fullpath')),"db");
|
||||
addpath(mpi_reduction_writer_path);
|
||||
|
||||
dsp_options.database_type = "mysql";
|
||||
|
||||
dsp_options.dataBase = "labor";
|
||||
@@ -16,6 +22,9 @@ dsp_options.server = "192.168.178.192";
|
||||
dsp_options.port = 3306;
|
||||
dsp_options.user = "silas";
|
||||
dsp_options.password = "silas";
|
||||
dsp_options.append_mpi_reduction_db = write_mpi_reduction_db && stream_mpi_reduction_db;
|
||||
dsp_options.mpi_reduction_study_name = mpi_reduction_study_name;
|
||||
dsp_options.mpi_reduction_writer_path = mpi_reduction_writer_path;
|
||||
|
||||
db = DBHandler("dataBase", [dsp_options.dataBase],...
|
||||
"type", dsp_options.database_type,...
|
||||
@@ -45,7 +54,6 @@ for i = 1
|
||||
|
||||
[dataTable,~] = db.queryDB(fp, db.getTableFieldNames('Runs'));
|
||||
|
||||
|
||||
[~, sortIdx] = sort(dataTable.sir, 'descend');
|
||||
dataTable = dataTable(sortIdx, :);
|
||||
|
||||
@@ -55,11 +63,10 @@ for i = 1
|
||||
|
||||
%% === Warehouse setup ===
|
||||
dsp_options.userParameters = struct();
|
||||
dsp_options.userParameters.block_update = 1;%[1,2,4,8,16,32,64,112,224,448,512,1024,2048,2048*2,2048*4];%%logspace(-3.8,-1,22);%[linspace(2,4096,22)];
|
||||
dsp_options.userParameters.block_update = [1,2,4,8,16,32,64,112,224,448,448*2,1024,2048,4096,8192,16384];%%logspace(-3.8,-1,22);%[linspace(2,4096,22)];
|
||||
% dsp_options.userParameters.block_update = linspace(1,224,22);
|
||||
wh = DataStorage(dsp_options.userParameters);
|
||||
|
||||
|
||||
%%
|
||||
n_realizations = (dsp_options.max_occurences - dsp_options.start_occurence + 1);
|
||||
n_userparams = prod(wh.dim);
|
||||
@@ -76,6 +83,14 @@ for i = 1
|
||||
[results, wh] = submitJobs(run_ids, dsp_options, processingMode.parallel, ...
|
||||
"wh", wh, ...
|
||||
"waitbar", true);
|
||||
|
||||
%%
|
||||
|
||||
if write_mpi_reduction_db && ~stream_mpi_reduction_db
|
||||
db.refresh();
|
||||
appendMpiReductionWarehouse(db, wh, run_ids, dsp_options, ...
|
||||
"study_name", mpi_reduction_study_name);
|
||||
end
|
||||
|
||||
% savepath = fullfile("C:","Users","Silas","Documents","MATLAB","imdd_simulation","projects","Diss","MPI_revisit","algorithms",sprintf("pam_%d_results.mat",M));
|
||||
% save(wh,savepath)
|
||||
|
||||
Reference in New Issue
Block a user