%% ============================================================
%  LOAD DATA
% ============================================================
database_type = 'mysql';
db = DBHandler("dataBase", "labor_highspeed", "type", database_type);

M = 4;  % PAM level for this analysis

fp = QueryFilter();
fp.where('Runs', 'pam_level',      'EQUALS', M);
fp.where('Runs', 'fiber_length',   'EQUALS', 10);
fp.where('Runs', 'bitrate',        'EQUALS', 360e9);
fp.where('Runs', 'is_mpi',         'EQUALS', 0);

fields = [
    db.getTableFieldNames('power_state_info');
    db.getTableFieldNames('dashboard_ungrouped_alltime')
];

[dataTable, ~] = db.queryDB(fp, fields);


%% ============================================================
%  COMMON CONFIGURATION FOR ALL SUBPLOTS
% ============================================================
%% ============================================================
%  DEFINE DSP ALGORITHMS FOR THE 4 SUBPLOTS
% ============================================================
curves = struct;

curves(1).name  = 'VNLE';
curves(1).eq    = equalizer_structure.vnle;
curves(1).pre   = 0;
curves(1).color = clr.Paired.red;

curves(2).name  = 'PF + MLSE';
curves(2).eq    = equalizer_structure.vnle_pf_mlse;
curves(2).pre   = 0;
curves(2).color = clr.Paired.green;

curves(3).name  = 'DB-target + MLSE';
curves(3).eq    = equalizer_structure.vnle_db_mlse;
curves(3).pre   = 0;
curves(3).color = clr.Paired.blue;

curves(4).name  = 'ML-based MLSE';
curves(4).eq    = equalizer_structure.ml_mlse;
if M == 4
curves(4).pre   = 0;
else
curves(4).pre   = 1;
end
curves(4).color = clr.Paired.purple;


%% ============================================================
%  ANALYSIS ENGINE — NO PLOTTING
% ============================================================
results = struct;

for k = 1:numel(curves)

    %% ---- BASE CONFIG ----
    cfg = struct;
    cfg.x_axis   = 'wavelength';
    cfg.y_axis   = 'BER';
    cfg.agg      = 'min';
    cfg.outlier  = 'none';
    % cfg.group_by = {'wavelength'};
    cfg.show_raw = false;

    cfg.filters = struct( ...
        'pam_level',    M, ...
        'is_mpi',       0, ...
        'bitrate',      360e9, ...
        'fiber_length', 10, ...
        'equalizer_structure', curves(k).eq, ...
        'pre_emph',     curves(k).pre);

    %% ---- GET BER ----
    cfg.y_axis = 'BER';
    A = analyze_measurements_gpt(dataTable, cfg);

    results(k).wavelength = A.group{1}.x;

    if curves(k).eq == equalizer_structure.vnle_db_mlse || ...
       curves(k).eq == equalizer_structure.ml_mlse
        % DB and ML-based need precoded BER
        results(k).ber = A.group{1}.y_precoded;
    else
        results(k).ber = A.group{1}.y;
    end

end

%% ============================================================
%  1×4 TILED BER-vs-WAVELENGTH FIGURE
% ============================================================
fig=figure(901); 
tiledlayout(1,4,'TileSpacing','compact','Padding','compact');

lw = 1.8;    % line width
ms = 6;      % marker size

for k = 1:numel(curves)
    nexttile; hold on;

    plot(results(k).wavelength, results(k).ber, ...
        '-o', ...
        'Color', curves(k).color, ...
        'MarkerFaceColor', curves(k).color, ...
        'MarkerSize', ms, ...
        'LineWidth', lw);

    set(gca,'YScale','log');
    grid on;
    xlabel('wavelength');
    ylabel('BER');
    title(curves(k).name);
    ylim([1e-4, 0.1])
    beautifyBERplot();
end
pos = 1e3.*[0.1070    0.5497    1.4113    0.3253];
set(fig, 'Position', pos);