MPI FFE with A1 and A2 algorithms

projects/Diss/MPI_revisit/investigate_mpi_algorithms.m

@@ -4,7 +4,7 @@ dsp_options.mode = "run_id";
 dsp_options.recipe = @mpi_recipe_dev;
 dsp_options.append_to_db = false;
 dsp_options.start_occurence = 2;
-dsp_options.max_occurences = 10;
+dsp_options.max_occurences = 1;
 dsp_options.debug_plots = false;
 
 dsp_options.database_type = "mysql";
@@ -28,7 +28,7 @@ fp.where('Runs','run_id','GREATER_EQUAL',3153);
 fp.where('Runs', 'symbolrate',                'EQUALS', 112e9);
 fp.where('Runs', 'fiber_length',              'EQUALS', 0);
 fp.where('Runs', 'interference_path_length',  'EQUALS', 1000);
-fp.where('Runs', 'sir',                       'LESS_EQUAL', 30);
+fp.where('Runs', 'sir',                       'LESS_EQUAL', 20);
 fp.where('Runs', 'db_mode',                   'EQUALS', '"no_db"');
 % fp.where('Runs', 'is_mpi',                    'EQUALS', 1);
 fp.where('Runs', 'pam_level',                 'EQUALS', 4);
@@ -41,6 +41,7 @@ fp.where('Runs', 'pam_level',                 'EQUALS', 4);
 
 [~, sortIdx] = sort(dataTable.sir, 'descend');
 dataTable = dataTable(sortIdx, :);
+dataTable = dataTable(1, :);
 run_ids = dataTable.run_id;
 
 if isempty(run_ids)
@@ -74,31 +75,43 @@ fprintf("-> [ %d run_id(s) × %d userParam combination(s) = %d parallel job(s) ]
 %% Analyze
 
 storageNames = fieldnames(wh.sto);
-x_vars = run_ids;%dsp_options.userParameters.smoothing_length;
-x_vars = reshape(x_vars,1,[]);
+result = wh.sto.(storageNames{1});
+result = result(:).';
 
-figure(2026);hold on
-for i = 1:numel(run_ids)
-    disp(run_ids(i))
-    result = wh.getStoValue(storageNames{1}, x_vars);
-    % Each cell in result is a cell array (packageCell) containing multiple packages.
-    % Extract BERs from all packages and, for example, average them per x_var.
-    ber_all = cellfun(@(packageCell) cellfun(@(pkg) pkg.metrics.BER, packageCell), result, 'UniformOutput', false);
-    % Convert to numeric matrix (rows = packages, cols = x_vars) by padding if needed
-    maxPackages = max(cellfun(@numel, ber_all));
-    ber_mat = nan(maxPackages, numel(ber_all));
-    for k = 1:numel(ber_all)
-        ber_mat(1:numel(ber_all{k}), k) = ber_all{k};
+% Each stored entry is a package cell containing one or more occurrences.
+ber_all = cell(1,numel(result));
+for result_idx = 1:numel(result)
+    packageCell = result{result_idx};
+    if isempty(packageCell)
+        ber_all{result_idx} = NaN;
+        continue
     end
-    % Choose aggregation: mean across packages (ignore NaNs)
-    ber = mean(ber_mat, 1, 'omitnan');
-    
-    
-    x = dataTable.sir;
-    y = ber;
-    
-    plot(x,ber);
-    scatter(x,ber_mat)
-    beautifyBERplot("logscale",true,"setcolors",false,"setmarkers",true);
+
+    ber_values = nan(1,numel(packageCell));
+    for package_idx = 1:numel(packageCell)
+        if isstruct(packageCell{package_idx}) && isfield(packageCell{package_idx},"metrics")
+            ber_values(package_idx) = packageCell{package_idx}.metrics.BER;
+        end
+    end
+    ber_all{result_idx} = ber_values;
 end
 
+maxPackages = max(cellfun(@numel,ber_all));
+ber_mat = nan(maxPackages,numel(ber_all));
+for k = 1:numel(ber_all)
+    ber_mat(1:numel(ber_all{k}),k) = ber_all{k};
+end
+ber = mean(ber_mat,1,"omitnan");
+
+x = dataTable.sir(:).';
+if numel(x) ~= numel(ber)
+    x = 1:numel(ber);
+end
+
+figure(2026); clf; hold on
+plot(x,ber);
+for k = 1:numel(x)
+    scatter(repmat(x(k),maxPackages,1),ber_mat(:,k))
+end
+beautifyBERplot("logscale",true,"setcolors",false,"setmarkers",true);
+