basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
database = DBHandler("pathToDB",[basePath,'silas_labor.db'],"type",'sqlite');

filterParams = database.tables;
filterParams.Configurations = struct( ...
    'bitrate',          420e9, ... %[224,336,360,390,420,448]
    'db_mode',          int32(db_mode.no_db), ...
    'fiber_length',     10, ...
    'interference_attenuation', [], ...
    'interference_path_length', [], ...
    'is_mpi',           0, ...
    'pam_level',        4, ...
    'rop_attenuation',  0, ...
    'wavelength', 1310 ...
    );

% filterParams.EqualizerParameters.diff_precode = int32(db_mode.db_encoded);
% filterParams.EqualizerParameters.equalizer_structure = int32(equalizer_structure.vnle);
filterParams.EqualizerParameters.DCmu = 0.00;

selectedFields = {'Configurations.run_id' 'Runs.rx_raw_path'	'Configurations.bitrate'	'Configurations.symbolrate'	'Configurations.pam_level'...
    'Configurations.db_mode'	'Configurations.rop_attenuation'	'Configurations.is_mpi'	'Configurations.interference_attenuation'	...
    'EqualizerParameters.equalizer_structure'	'EqualizerParameters.diff_precode' 'EqualizerParameters.eq_id'	'Measurements.power_pd_in' ...
    'Measurements.power_mpi_interference'	'Measurements.power_mpi_signal'	'Results.BER' 'Results.BER_precoded'	'Results.SNR'	'Results.GMI'	'Results.Alpha' 'Results.date_of_processing'};

[dataTable,sql_query] = database.queryDB(filterParams, selectedFields);

fixedVars = {'eq_id','bitrate'};
dataTableGrpd = groupIt(fixedVars,dataTable);


plotRealizations = 1;

% Create a new figure
figure();
hold on
unique_eq = unique(dataTable.eq_id);
cols = linspecer(8);

for i = 1:numel(unique_eq)

    idx = find(dataTableGrpd.eq_id == unique_eq(i), 1, 'first');
    equalizer_ = equalizer_structure(dataTableGrpd.equalizer_structure(idx));

    % Plot SCATTERS: timestamp vs. interference_attenuation

    loop_filt = dataTable.eq_id==unique_eq(i);
    if plotRealizations
        switch equalizer_
            case equalizer_structure.vnle
                bers = dataTable.BER(loop_filt,:);
            case equalizer_structure.vnle_pf_mlse
                bers = dataTable.BER(loop_filt,:);
            case equalizer_structure.vnle_db_mlse
                bers = dataTable.BER_precoded(loop_filt,:);
            case equalizer_structure.db_encoded
                bers = dataTable.BER(loop_filt,:);
        end
        date_of_proc = datetime(dataTable.date_of_processing(loop_filt,:));
        
        sc = scatter(date_of_proc, bers, 'LineWidth', 0.5,'Marker','*','MarkerEdgeColor',cols(i,:),'HandleVisibility','off');
        pair_one = {'Run ID', dataTable.run_id(loop_filt,:)};
        pair_two = {'Rate', dataTable.bitrate(loop_filt,:)};
        addDatatips(sc, pair_one, pair_two);
        xticks(date_of_proc);
    end

end


% Label the axes and add a title
xlabel('Time of Processing');
ylabel('BER');
title('Line Rate vs. BER');
yline(3.8e-3,'LineWidth',1,'LineStyle','--','HandleVisibility','off');
% Enable grid for better readability
grid on;

beautifyBERplot;
ylim([1e-4 0.5]);



function resultTable = groupIt(fixedVars,dataTable)

    % Group by run_id and eq_id (adjust grouping keys as needed)
    
    [G, groupKeys] = findgroups(dataTable(:, fixedVars));
    
    % Preallocate a cell array for aggregated data.
    varNames = dataTable.Properties.VariableNames;
    nVars = numel(varNames);
    aggData = cell(height(groupKeys), nVars);
    groupCount = zeros(height(groupKeys), 1);  % To store the size of each group
    
    % Loop over each group.
    for i = 1:height(groupKeys)
        idx = (G == i); % Logical index for group i
        groupCount(i) = sum(idx);  % Count number of rows in this group
        % For each variable in the table:
        for j = 1:nVars
            colData = dataTable.(varNames{j});
            if isnumeric(colData)
                % For numeric data, compute the mean.
                aggData{i, j} = min(colData(idx));
            else
                % For non-numeric data, take the first entry.
                if iscell(colData)
                    aggData{i, j} = colData{find(idx, 1)};
                else
                    aggData{i, j} = colData(find(idx, 1));
                end
            end
        end
    end
    
    % Convert the aggregated cell array into a table.
    resultTable = cell2table(aggData, 'VariableNames', varNames);
    
    % Append the group count as a new column.
    resultTable.nRows = groupCount;

end


function addDatatips(sc, varargin)
% addDatatips Adds custom data tip rows to a scatter plot.
%
%   addDatatips(sc, pair1, pair2, ...) adds one or more custom rows to the 
%   data tip display of the scatter plot identified by sc.
%
%   Each pair should be provided as a 1x2 cell array: {label, value}.
%   The value can be a scalar or a vector. If a vector is provided, its length
%   must match the number of scatter plot points.
%
%   Example:
%       sc = scatter(x, y, 'LineWidth', 1.5, 'Marker', 'o');
%       pair_one = {'Attenuation', attenuationVector};
%       addDatatips(sc, pair_one);

    numPoints = numel(sc.XData);
    
    for k = 1:length(varargin)
        pair = varargin{k};
        
        if ~iscell(pair) || numel(pair) ~= 2
            error('Each pair must be a 1x2 cell array: {label, value}.');
        end
        
        label = pair{1};
        value = pair{2};
        
        % If value is a vector, ensure its length is either 1 or equal to the number of scatter points.
        if isvector(value) && numel(value) ~= 1 && numel(value) ~= numPoints
            error('The vector for "%s" must be a scalar or have %d elements matching the scatter data points.', label, numPoints);
        end
        
        % Create a new data tip row using the provided label and vector.
        newRow = dataTipTextRow(label, value);
        sc.DataTipTemplate.DataTipRows(end+1) = newRow;
    end
end