105 lines
3.6 KiB
Matlab
105 lines
3.6 KiB
Matlab
% === DSP settings ===
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dsp_options = struct();
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dsp_options.mode = "run_id";
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dsp_options.recipe = @mpi_recipe_dev;
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dsp_options.append_to_db = false;
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dsp_options.start_occurence = 2;
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dsp_options.max_occurences = 10;
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dsp_options.debug_plots = false;
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dsp_options.database_type = "mysql";
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dsp_options.dataBase = "labor";
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dsp_options.storage_path = "W:\labdata\ECOC Silas\ecoc_2025";
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dsp_options.server = "192.168.178.192";
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dsp_options.port = 3306;
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dsp_options.user = "silas";
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dsp_options.password = "silas";
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db = DBHandler("dataBase", [dsp_options.dataBase],...
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"type", dsp_options.database_type,...
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"server", dsp_options.server,...
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"user", dsp_options.user, "password", dsp_options.password);
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%%
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fp = QueryFilter();
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fp.where('Runs','run_id','GREATER_EQUAL',3153);
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fp.where('Runs', 'symbolrate', 'EQUALS', 112e9);
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fp.where('Runs', 'fiber_length', 'EQUALS', 0);
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fp.where('Runs', 'interference_path_length', 'EQUALS', 1000);
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fp.where('Runs', 'sir', 'LESS_EQUAL', 30);
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fp.where('Runs', 'db_mode', 'EQUALS', '"no_db"');
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% fp.where('Runs', 'is_mpi', 'EQUALS', 1);
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fp.where('Runs', 'pam_level', 'EQUALS', 4);
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% fp.where('Runs', 'v_bias', 'EQUALS', 2.65);
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[dataTable,~] = db.queryDB(fp, db.getTableFieldNames('Runs'));
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% [~, uniqueSirRows] = unique(dataTable.sir, "stable");
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% dataTable = dataTable(uniqueSirRows, :);
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[~, sortIdx] = sort(dataTable.sir, 'descend');
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dataTable = dataTable(sortIdx, :);
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run_ids = dataTable.run_id;
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if isempty(run_ids)
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error("run_minimal_recipe:MissingRunIds", ...
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"Set run_ids to one or more known run IDs before running this example.");
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end
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%% === Warehouse setup ===
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dsp_options.userParameters = struct();
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% dsp_options.userParameters.smoothing_length = [0,linspace(100,1000,10),2500,5000,10000];
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wh = DataStorage(dsp_options.userParameters);
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%%
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n_realizations = (dsp_options.max_occurences - dsp_options.start_occurence + 1);
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n_userparams = prod(wh.dim);
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n_run_ids = numel(run_ids);
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parallel_jobs = n_userparams * n_run_ids;
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queried_jobs = n_realizations * n_userparams * n_run_ids;
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fprintf("-> [ %d run_id(s) × %d userParam combination(s) = %d parallel job(s) ] × %d realizations = %d total jobs \n", ...
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n_run_ids, n_userparams, parallel_jobs, n_realizations, queried_jobs);
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%% === Run ===
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% submitJobs returns the raw per-job results and the filled Warehouse. For a
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% single run_id and remove_dc = [0, 1], results is a 2-by-1 cell array.
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[results, wh] = submitJobs(run_ids, dsp_options, processingMode.serial, ...
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"wh", wh, ...
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"waitbar", true);
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%% Analyze
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storageNames = fieldnames(wh.sto);
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x_vars = run_ids;%dsp_options.userParameters.smoothing_length;
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x_vars = reshape(x_vars,1,[]);
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figure(2026);hold on
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for i = 1:numel(run_ids)
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disp(run_ids(i))
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result = wh.getStoValue(storageNames{1}, x_vars);
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% Each cell in result is a cell array (packageCell) containing multiple packages.
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% Extract BERs from all packages and, for example, average them per x_var.
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ber_all = cellfun(@(packageCell) cellfun(@(pkg) pkg.metrics.BER, packageCell), result, 'UniformOutput', false);
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% Convert to numeric matrix (rows = packages, cols = x_vars) by padding if needed
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maxPackages = max(cellfun(@numel, ber_all));
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ber_mat = nan(maxPackages, numel(ber_all));
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for k = 1:numel(ber_all)
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ber_mat(1:numel(ber_all{k}), k) = ber_all{k};
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end
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% Choose aggregation: mean across packages (ignore NaNs)
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ber = mean(ber_mat, 1, 'omitnan');
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x = dataTable.sir;
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y = ber;
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plot(x,ber);
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scatter(x,ber_mat)
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beautifyBERplot("logscale",true,"setcolors",false,"setmarkers",true);
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end
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