% === DSP settings ===
dsp_options = struct();
dsp_options.mode = "run_id";
dsp_options.recipe = @mpi_recipe_dev;
dsp_options.append_to_db = false;
dsp_options.start_occurence = 1;
dsp_options.max_occurences = 10;
dsp_options.debug_plots = false;

dsp_options.database_type = "mysql";

dsp_options.dataBase = "labor";
dsp_options.storage_path = "W:\labdata\ECOC Silas\ecoc_2025";

dsp_options.server = "192.168.178.192";
dsp_options.port = 3306;
dsp_options.user = "silas";
dsp_options.password = "silas";

db = DBHandler("dataBase", [dsp_options.dataBase],...
    "type", dsp_options.database_type,...
    "server", dsp_options.server,...
    "user", dsp_options.user, "password", dsp_options.password);

%%
fp = QueryFilter();
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', 'db_mode',                   'EQUALS', '"no_db"');
% fp.where('Runs', 'is_mpi',                    'EQUALS', 1);
fp.where('Runs', 'pam_level',                 'EQUALS', 4);
% fp.where('Runs', 'v_bias',                    'EQUALS', 2.65);

[dataTable,~] = db.queryDB(fp, db.getTableFieldNames('Runs'));

% [~, uniqueSirRows] = unique(dataTable.sir, "stable");
% dataTable = dataTable(uniqueSirRows, :);

[~, sortIdx] = sort(dataTable.sir, 'descend');
dataTable = dataTable(sortIdx, :);
run_ids = dataTable.run_id;

if isempty(run_ids)
    error("run_minimal_recipe:MissingRunIds", ...
        "Set run_ids to one or more known run IDs before running this example.");
end

%% === Warehouse setup ===
dsp_options.userParameters = struct();
% dsp_options.userParameters.smoothing_length = [0,linspace(100,1000,10),2500,5000,10000];
wh = DataStorage(dsp_options.userParameters);


%%
n_realizations = (dsp_options.max_occurences - dsp_options.start_occurence + 1);
n_userparams = prod(wh.dim);
n_run_ids = numel(run_ids);
parallel_jobs = n_userparams * n_run_ids;
queried_jobs = n_realizations * n_userparams * n_run_ids;

fprintf("-> [ %d run_id(s) × %d userParam combination(s) = %d parallel job(s) ] × %d realizations = %d total jobs \n", ...
    n_run_ids, n_userparams, parallel_jobs, n_realizations, queried_jobs);

%% === Run ===
% submitJobs returns the raw per-job results and the filled Warehouse. For a
% single run_id and remove_dc = [0, 1], results is a 2-by-1 cell array.
[results, wh] = submitJobs(run_ids, dsp_options, processingMode.serial, ...
    "wh", wh, ...
    "waitbar", true);

%% Analyze

storageNames = fieldnames(wh.sto);
x_vars = run_ids;%dsp_options.userParameters.smoothing_length;
x_vars = reshape(x_vars,1,[]);

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};
    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);
end