basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
useGui = 0;
pamlvls = [4, 6, 8];
wlengths = [1310];
db = DBHandler("pathToDB", [basePath, 'silas_labor.db']);

for w = 1:numel(wlengths)
    for p = 1:numel(pamlvls)
        % Load data from database
        joinedData = loadDataFromDB(db, pamlvls(p), wlengths(w), useGui);
        
        % Plot filtered data
        figure(pamlvls(p));
        hold on;
        plotFilteredData(joinedData, wlengths(w));
        % Continue with the rest of your plot settings
        finalizePlot();
    end
end


% Custom function to load data from database
function joinedData = loadDataFromDB(db, pamlvl, wlength, useGui)
    if useGui
        filterParams = db.promptFilterParameters();
        selectedFields = db.promptSelectFields();
    else
        filterParams = db.tables;
        filterParams.Configurations = struct( ...
            'bitrate', [], 'db_mode', [], 'fiber_length', 1, ...
            'interference_attenuation', [], 'interference_path_length', [], ...
            'is_mpi', 0, 'pam_level', pamlvl, 'precomp_amp', [], ...
            'rop_attenuation', 0, 'symbolrate', [], 'v_awg', [], 'v_bias', [], ...
            'wavelength', wlength ...
        );

        selectedFields = {'Runs.run_id', 'BERs.ber_id', 'Equalizer.eq_id', 'Equalizer.eq_type', 'BERs.ber', 'BERs.occurrence', ...
            'Configurations.db_mode', 'Configurations.pam_level', 'Configurations.bitrate', 'Configurations.symbolrate', ...
            'Configurations.fiber_length', 'Configurations.wavelength', 'Configurations.precomp_amp', ...
            'Measurements.power_rop', 'Measurements.power_laser', 'Measurements.power_pd_in'};
    end

    % Get data table from DB
    [dataTable, ~] = db.queryDB(filterParams, selectedFields);

    % Extract unique rows for each run_id
    uniqueConfigFields = {'run_id', 'pam_level', 'bitrate', 'symbolrate', 'fiber_length', 'wavelength', 'precomp_amp', 'db_mode'};
    [~, uniqueIdx] = unique(dataTable.run_id);
    configDetails = dataTable(uniqueIdx, uniqueConfigFields);

    % Calculate the mean BER for each combination of 'run_id' and 'eq_type'
    groupedData = groupsummary(dataTable, {'run_id', 'eq_type'}, {'mean', 'min', @(x) meanExcludingOutliers(x)}, {'ber', 'power_rop', 'power_pd_in'});

    % Join groupedData with configDetails on the run_id field
    joinedData = join(groupedData, configDetails, 'Keys', 'run_id');
end

% Custom function to plot filtered data
function plotFilteredData(joinedData, wlength)
    filterFields = {'eq_type'};
    cols = linspecer(8);
    lst = ["-", ":", "--"];

    % Loop over each field you want to filter by
    for f = 1:numel(filterFields)
        currentField = filterFields{f};
        uniqueValues = unique(joinedData.(currentField));

        for i = 1:numel(uniqueValues)
            currentValue = uniqueValues(i);

            % Filter joinedData for the current value
            if isnumeric(currentValue)
                filteredData = joinedData(joinedData.(currentField) == currentValue, :);
            else
                filteredData = joinedData(strcmp(joinedData.(currentField), currentValue), :);
            end

            % Group and average BERs of several run ids => repeated measurements in lab!
            groupVars = {'bitrate'};
            groupedDataWithMeans = groupsummary(filteredData, groupVars, {'mean', 'min'}, {'mean_ber', 'min_ber', 'fun1_ber', 'mean_power_rop', 'mean_power_pd_in'});
            [~, uniqueIdx] = unique(filteredData.bitrate);
            constantFields = filteredData(uniqueIdx, {'bitrate', 'GroupCount', 'pam_level', 'symbolrate', 'fiber_length', 'wavelength', 'precomp_amp', 'db_mode'});
            groupedRunIDs = varfun(@(x) {unique(x)}, filteredData, 'GroupingVariables', groupVars, 'InputVariables', 'run_id');
            groupedRunIDs.Properties.VariableNames(end) = {'GroupedRunIDs'};
            groupedDataWithMeans = join(groupedDataWithMeans, constantFields, 'Keys', 'bitrate');
            groupedDataWithMeans = join(groupedDataWithMeans, groupedRunIDs, 'Keys', 'bitrate');
            filteredData = groupedDataWithMeans;

            % Plotting
            a = plot(filteredData.bitrate .* 1e-9, filteredData.mean_fun1_ber, ...
                'Color', cols(i, :), 'MarkerSize', 4, 'LineWidth', 1, 'LineStyle', lst(mod(wlength - 1, numel(lst)) + 1), ...
                'Marker', 'o', 'MarkerFaceColor', 'auto', 'MarkerEdgeColor', cols(i, :), ...
                'DisplayName', [char(currentValue), '; ', num2str(wlength), ' nm']);
            
            a.DataTipTemplate.DataTipRows(1).Label = 'Bitrate';
            a.DataTipTemplate.DataTipRows(1).Format = ['%.1f', ' Gbit/s'];
            a.DataTipTemplate.DataTipRows(2).Label = 'BER';
            a.DataTipTemplate.DataTipRows(2).Format = '%.1e';
            a.DataTipTemplate.DataTipRows(3).Label = 'P_{out}';
            a.DataTipTemplate.DataTipRows(3).Value = filteredData.mean_mean_power_rop;
            a.DataTipTemplate.DataTipRows(3).Format = ['%.2f', ' dBm'];
            a.DataTipTemplate.DataTipRows(4).Label = 'Baudr';
            a.DataTipTemplate.DataTipRows(4).Value = filteredData.bitrate .* 1e-9;
            a.DataTipTemplate.DataTipRows(4).Format = ['%.1f', ' GBd'];
            a.DataTipTemplate.DataTipRows(5).Label = 'Run ID';
            a.DataTipTemplate.DataTipRows(5).Value = filteredData.GroupedRunIDs;
            a.DataTipTemplate.FontSize = 9;
            a.DataTipTemplate.FontName = 'arial';
        end
    end
end

% Custom function to finalize the plot settings
function finalizePlot()
    yline(2e-2, 'DisplayName', '20% O-FEC', 'LineStyle', '--', 'HandleVisibility', 'off');
    yline(3.8e-3, 'DisplayName', 'HD-FEC', 'LineStyle', '--', 'HandleVisibility', 'off');
    xlabel('Bit Rate in GBps');
    ylabel('Bit Error Rate (BER)');
    xlim([300, 480]);
    set(gca, 'yscale', 'log');
    set(gca, 'Box', 'on');
    grid on;
    grid minor;
    legend('Interpreter', 'none');
end

% Custom function using rmoutliers to calculate mean after removing outliers
function meanWithoutOutliers = meanExcludingOutliers(x)
    [xWithoutOutliers,outlierpos] = rmoutliers(x);
    if isempty(xWithoutOutliers)
        meanWithoutOutliers = NaN;
    else
        meanWithoutOutliers = mean(xWithoutOutliers);
    end

end