basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
useGui = 0;
pamlvls = [4,6,8];
wlengths = [1302];
figoffset = 0;

figure(30)
tiledlayout(1, 3, 'TileSpacing', 'compact', 'Padding', 'compact');
       
for p = 1:numel(pamlvls)
    nexttile;

    for w = 1:numel(wlengths)
        sgtitle(['Lambda: ',num2str(wlengths),' nm'])

        hold on
        pamlvl = pamlvls(p);
        wlength = wlengths(w);
        db = DBHandler("pathToDB",[basePath,'silas_labor.db']);
        if useGui
            filterParams    = db.promptFilterParameters();
            selectedFields  = db.promptSelectFields();
        else
            filterParams = db.tables;
            filterParams.Configurations = struct( ...
                'bitrate',          [], ...
                'db_mode',          [], ...
                'fiber_length',     10, ...
                '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);

        % Grouping variables: 'bitrate' and 'eq_type'
        groupVars = {'bitrate', 'eq_type'};

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

        % Collect the run_id values for each group
        groupedRunIDs = varfun(@(x) {unique(x)}, dataTable, 'GroupingVariables', groupVars, 'InputVariables', 'run_id');
        groupedRunIDs.Properties.VariableNames(end) = {'GroupedRunIDs'};

        % Join the grouped data with the grouped run_id list
        avgdBerTable = join(groupedData, groupedRunIDs, 'Keys', groupVars);

        % Define the fields that you want to use for filtering
        filterFields = {'eq_type'};

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

            % Determine unique values for the current field
            uniqueValues = unique(avgdBerTable.(currentField));

            % Loop over each unique value for the current field
            for i = 1:numel(uniqueValues)
                currentValue = uniqueValues(i);

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

                cols = linspecer(8);

                lst = ["-",":","--"];
                a=plot(filteredData.bitrate.*1e-9,filteredData.min_ber,...
                    'Color',cols(i,:),'MarkerSize',4,'LineWidth',1,'LineStyle',lst(w),...
                    '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_power_rop;
                a.DataTipTemplate.DataTipRows(3).Format = ['%.2f',' dBm'];

                a.DataTipTemplate.DataTipRows(4).Label = 'Run ID';
                a.DataTipTemplate.DataTipRows(4).Value = filteredData.GroupedRunIDs;
                a.DataTipTemplate.DataTipRows(4).Format = ['%d',' '];


                a.DataTipTemplate.FontSize = 9;
                a.DataTipTemplate.FontName = 'arial';
                %
                % a=scatter(filteredData.bitrate.*1e-9,filteredData.min_ber,5,'Marker','diamond',...
                %     'Color',cols(i,:),'LineWidth',1,...
                %     'MarkerFaceColor','auto','MarkerEdgeColor',cols(i,:),...
                %     'DisplayName',currentValue);
                % 
                % 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_power_rop;
                % a.DataTipTemplate.DataTipRows(3).Format = ['%.2f',' dBm'];
                % 
                % a.DataTipTemplate.DataTipRows(4).Label = 'Run ID';
                % a.DataTipTemplate.DataTipRows(4).Value = filteredData.GroupedRunIDs;
                % a.DataTipTemplate.DataTipRows(4).Format = ['%d',' '];
                % 
                % 
                % a.DataTipTemplate.FontSize = 9;
                % a.DataTipTemplate.FontName = 'arial';




            end
        end



        % Continue with the rest of your plot settings
        title(sprintf('%d km | %d nm | PAM %d',unique(filterParams.Configurations.fiber_length),wlength,pamlvl));
        yline(2e-2, 'DisplayName', '20% O-FEC', 'LineStyle', '--', 'HandleVisibility', 'off','LineWidth',1);
        yline(3.8e-3, 'DisplayName', 'HD-FEC', 'LineStyle', '--', 'HandleVisibility', 'off','LineWidth',1);
        xlabel('Bit Rate in Gbps');
        ylabel('Bit Error Rate');
        xlim([300, 480])
        ylim([8e-4,0.5])
        set(gca, 'yscale', 'log');
        set(gca, 'Box', 'on');
        grid on;
        grid minor;


    end
end
legend('Interpreter', 'none');
set(gcf, 'Units', 'pixels', 'Position', 1.0e+03 * [0.2483    0.7303    1.2093    0.3980]); 

% Custom function using rmoutliers to calculate mean after removing outliers
function meanWithoutOutliers = meanExcludingOutliers(x)
% Remove outliers using rmoutliers with default method (based on median)
xWithoutOutliers = rmoutliers(x);

% Calculate the mean of the non-outliers
if isempty(xWithoutOutliers)
    % Handle the case where all values are outliers
    meanWithoutOutliers = NaN;
else
    meanWithoutOutliers = mean(xWithoutOutliers);
end
end