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

filterParams = database.tables;
filterParams.Configurations = struct( ...
    'bitrate',          336e9, ...
    'db_mode',          0, ...
    'fiber_length',     1, ...
    'interference_attenuation', [], ...
    'interference_path_length', [], ...
    'is_mpi',           1, ...
    'pam_level',        4, ...
    'rop_attenuation',  0 ...
    );

filterParams.EqualizerParameters.diff_precode = int32(db_mode.no_db);
filterParams.EqualizerParameters.equalizer_structure = int32(equalizer_structure.vnle);

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.SNR'	'Results.GMI'	'Results.Alpha'};

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

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



% Create a new figure
figure(1);
hold on
unique_rates = unique(resultTable.bitrate);
for i = 1:numel(unique_rates)
    % Plot BER vs. interference_attenuation
    plot(resultTable.power_mpi_signal(resultTable.bitrate==unique_rates(i),:)-resultTable.power_mpi_interference(resultTable.bitrate==unique_rates(i),:), resultTable.BER(resultTable.bitrate==unique_rates(i),:), 'o-', 'LineWidth', 1.5);
end

% Label the axes and add a title
xlabel('Interference Attenuation');
ylabel('BER');
title('BER vs. Interference Attenuation');

% Enable grid for better readability
grid on;

beautifyBERplot;




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} = mean(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