Many changes in DBHandler
new general processing structure just before splitting off the projects folder from this repo
This commit is contained in:
Silas Oettinghaus
@@ -16,7 +16,7 @@ filterParams.Configurations = struct( ...
|
||||
'wavelength', 1310, ...
|
||||
'precomp_amp', [], ...
|
||||
'signal_attenuation', [], ...
|
||||
'v_awg', [], ...
|
||||
'v_awg', [], ...
|
||||
'v_bias', [] ...
|
||||
);
|
||||
|
||||
@@ -25,7 +25,7 @@ filterParams.Configurations = struct( ...
|
||||
% % filterParams.EqualizerParameters.dc_buffer_len = 1;
|
||||
% filterParams.EqualizerParameters.ffe_buffer_len = 1;
|
||||
% filterParams.EqualizerParameters.smoothing_buffer_len = 4096;
|
||||
% filterParams.EqualizerParameters.smoothing_buffer_update = 224;
|
||||
% filterParams.EqualizerParameters.smoothing_buffer_update = 224;
|
||||
% filterParams.EqualizerParameters.DCmu = 0;
|
||||
% end
|
||||
|
||||
@@ -34,220 +34,291 @@ selectedFields = {'Configurations.run_id' 'Runs.loop_id' 'Runs.date_of_run' 'Run
|
||||
'EqualizerParameters.equalizer_structure' 'EqualizerParameters.diff_precode' 'EqualizerParameters.eq_id' 'EqualizerParameters.dc_buffer_len' 'EqualizerParameters.ffe_buffer_len' 'EqualizerParameters.smoothing_buffer_len' 'EqualizerParameters.smoothing_buffer_update' 'EqualizerParameters.DCmu' 'Measurements.power_pd_in' ...
|
||||
'Measurements.power_mpi_interference' 'Measurements.power_mpi_signal' 'Results.BER' 'Results.BER_precoded' 'Results.EVM' 'Results.SNR' 'Results.GMI' 'Results.Alpha' 'Results.date_of_processing'};
|
||||
|
||||
% [dataTable_raw,sql_query] = database.queryDB(filterParams, selectedFields);
|
||||
[dataTable_raw,sql_query] = database.queryDB(filterParams, selectedFields);
|
||||
|
||||
%%
|
||||
|
||||
dataTable_clean = dataTable_raw;
|
||||
dataTable_clean.SIR = -7 - round(dataTable_clean.power_mpi_interference);
|
||||
dataTable_clean.NGMI = dataTable_clean.GMI ./ log2(dataTable_clean.pam_level);
|
||||
dataTable_clean = cleanUpTable(dataTable_clean);
|
||||
|
||||
dataTable_clean(dataTable_clean.BER>0.2,:) = [];
|
||||
|
||||
%%
|
||||
|
||||
|
||||
|
||||
|
||||
dataTable_clean = dataTable_raw;
|
||||
dataTable_clean.SIR = round(-7.5 - dataTable_clean.power_mpi_interference);
|
||||
dataTable_clean.NGMI = dataTable_clean.GMI ./ log2(dataTable_clean.pam_level);
|
||||
dataTable_clean = cleanUpTable(dataTable_clean);
|
||||
|
||||
%%
|
||||
|
||||
dataTable = dataTable_clean;
|
||||
|
||||
figure(26);
|
||||
plotBoundaries = 1;
|
||||
plotRealizations = 0;
|
||||
cols = linspecer(8); % Ensure color count matches
|
||||
figure()
|
||||
tiledlayout(1, 4, 'TileSpacing', 'compact', 'Padding', 'compact');
|
||||
y_here = 0;
|
||||
figcnt = 0;
|
||||
for int_len = [0,50,300,1000]
|
||||
figcnt = figcnt+1;
|
||||
% figure(int_len+1);
|
||||
nexttile;
|
||||
hold on
|
||||
mode = 4;
|
||||
|
||||
% ideal DC tracking
|
||||
if 0
|
||||
dataTable = dataTable(dataTable.dc_buffer_len == 1, :);
|
||||
dataTable = dataTable(dataTable.ffe_buffer_len == 1, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_len == 0, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_update == 0, :);
|
||||
dataTable = dataTable(dataTable.DCmu == 0.005, :);
|
||||
cols = cols(1:1+1,:);
|
||||
method = 'ideal dc tracking';
|
||||
% slow DC tracking
|
||||
elseif 1
|
||||
|
||||
dataTable = dataTable(dataTable.dc_buffer_len == 224, :);
|
||||
dataTable = dataTable(dataTable.ffe_buffer_len == 1, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_len == 0, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_update == 0, :);
|
||||
dataTable = dataTable(dataTable.DCmu == 0.005, :);
|
||||
cols = cols(2:2+1,:);
|
||||
method = 'parallelized dc tracking';
|
||||
% slow DC smoothing
|
||||
elseif 0
|
||||
for mode = [1,4]
|
||||
|
||||
hold on;
|
||||
dataTable = dataTable_clean;
|
||||
|
||||
dataTable = dataTable(dataTable.dc_buffer_len == 1, :);
|
||||
dataTable = dataTable(dataTable.ffe_buffer_len == 1, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_len == 4096, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_update == 224, :);
|
||||
dataTable = dataTable(dataTable.DCmu == 0, :);
|
||||
cols = cols(3:3+1,:);
|
||||
method = 'dc smoothing';
|
||||
elseif 0
|
||||
% No compensation method
|
||||
dataTable = dataTable(dataTable.dc_buffer_len == 1, :);
|
||||
dataTable = dataTable(dataTable.ffe_buffer_len == 1, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_len == 0, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_update == 0, :);
|
||||
dataTable = dataTable(dataTable.DCmu == 0, :);
|
||||
cols = cols(4:4+1,:);
|
||||
method = 'ffe only';
|
||||
end
|
||||
|
||||
dataTable(dataTable.eq_id==0,:) = [];
|
||||
dataTable(dataTable.equalizer_structure~=1,:) = [];
|
||||
plotBoundaries = 1;
|
||||
plotRealizations = 1;
|
||||
cols = linspecer(8); % Ensure color count matches
|
||||
|
||||
% dataTable.interference_path_length(dataTable.interference_path_length < 20, :) = 20;
|
||||
if mode == 1
|
||||
% No compensation method
|
||||
dataTable = dataTable(dataTable.dc_buffer_len == 1, :);
|
||||
dataTable = dataTable(dataTable.ffe_buffer_len == 1, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_len == 0, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_update == 0, :);
|
||||
dataTable = dataTable(dataTable.DCmu == 0, :);
|
||||
cols = cols(1:1+1,:);
|
||||
method = 'ffe only';
|
||||
|
||||
dataTable = dataTable(dataTable.interference_path_length == 1000, :);
|
||||
% slow DC smoothing
|
||||
elseif mode == 2
|
||||
|
||||
% dataTable(dataTable.interference_path_length ~= 50, :) = [];
|
||||
% dataTable = dataTable(dataTable.interference_path_length < 51, :);
|
||||
dataTable = dataTable(dataTable.dc_buffer_len == 1, :);
|
||||
dataTable = dataTable(dataTable.ffe_buffer_len == 1, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_len == 4096, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_update == 224, :);
|
||||
dataTable = dataTable(dataTable.DCmu == 0, :);
|
||||
|
||||
cols = cols(4:4+1,:);
|
||||
method = 'dc smoothing';
|
||||
|
||||
% dataTable(dataTable.loop_id<200,:) = [];
|
||||
|
||||
% Filter by time
|
||||
filter_by_time = 0;
|
||||
if filter_by_time
|
||||
startTime = datetime('2025-04-20 18:00:00', 'InputFormat', 'yyyy-MM-dd HH:mm:ss');
|
||||
stopTime = datetime('2025-04-30 19:30:00', 'InputFormat', 'yyyy-MM-dd HH:mm:ss');
|
||||
dataTable.date_of_run = datetime(dataTable.date_of_run, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
|
||||
dataTable.date_of_processing = datetime(dataTable.date_of_processing, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
|
||||
dataTable = dataTable(dataTable.date_of_processing > startTime, :);
|
||||
dataTable = dataTable(dataTable.date_of_processing < stopTime, :);
|
||||
end
|
||||
% slow DC tracking
|
||||
elseif mode == 3
|
||||
|
||||
% Group by smth
|
||||
y_var = 'BER';
|
||||
x_var = 'SIR';
|
||||
dataTable = dataTable(dataTable.dc_buffer_len == 224, :);
|
||||
dataTable = dataTable(dataTable.ffe_buffer_len == 1, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_len == 0, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_update == 0, :);
|
||||
dataTable = dataTable(dataTable.DCmu == 0.005, :);
|
||||
|
||||
cols = cols(3:3+1,:);
|
||||
method = 'parallelized dc tracking';
|
||||
|
||||
loop_var = 'interference_path_length';
|
||||
fixedVars = {'equalizer_structure','interference_path_length',x_var};
|
||||
elseif mode == 4
|
||||
|
||||
[dataTable, outliersTable] = removeGroupOutliers(dataTable, fixedVars, y_var);
|
||||
% ideal DC tracking
|
||||
dataTable = dataTable(dataTable.dc_buffer_len == 1, :);
|
||||
dataTable = dataTable(dataTable.ffe_buffer_len == 1, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_len == 0, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_update == 0, :);
|
||||
dataTable = dataTable(dataTable.DCmu == 0.005, :);
|
||||
|
||||
cols = cols(2:2+1,:);
|
||||
method = 'ideal dc tracking';
|
||||
end
|
||||
|
||||
dataTableGrpd_mean = groupIt(fixedVars, dataTable, @mean);
|
||||
dataTableGrpd_min = groupIt(fixedVars, dataTable, @min);
|
||||
dataTableGrpd_max = groupIt(fixedVars, dataTable, @max);
|
||||
dataTable(dataTable.eq_id==0,:) = [];
|
||||
dataTable(dataTable.equalizer_structure~=1,:) = [];
|
||||
|
||||
% Create a new figure
|
||||
mkr = '.';
|
||||
hold on
|
||||
% Modify values in 'interference_path_length' where the condition is met
|
||||
dataTable.interference_path_length(dataTable.interference_path_length < 101 & dataTable.interference_path_length > 1) = 50;
|
||||
|
||||
unique_loop_var = unique(dataTable.(loop_var));
|
||||
dataTable.interference_path_length(dataTable.interference_path_length == 1) = 0;
|
||||
dataTable = dataTable(dataTable.interference_path_length == int_len, :);
|
||||
|
||||
for i = 1:numel(unique_loop_var)
|
||||
|
||||
% Prepare filtered data for this loop variable
|
||||
loopValue = unique_loop_var(i);
|
||||
dataTable(dataTable.run_id == 3866, :) = [];
|
||||
dataTable(dataTable.run_id == 3865, :) = [];
|
||||
dataTable(dataTable.run_id == 3796, :) = [];
|
||||
dataTable(dataTable.run_id == 3797, :) = [];
|
||||
dataTable(dataTable.run_id == 3798, :) = [];
|
||||
dataTable(dataTable.run_id == 4002, :) = [];
|
||||
dataTable(dataTable.run_id == 4200, :) = [];
|
||||
dataTable(dataTable.run_id == 4199, :) = [];
|
||||
|
||||
loopFiltGrpd = dataTableGrpd_mean.(loop_var) == loopValue;
|
||||
if ~any(loopFiltGrpd)
|
||||
continue; % Skip if no data for this loop var
|
||||
end
|
||||
% 0
|
||||
% 1
|
||||
% 10
|
||||
% 15
|
||||
% 20
|
||||
% 50
|
||||
% 100
|
||||
% 300
|
||||
% 1000
|
||||
|
||||
% Extract values
|
||||
x_values = dataTableGrpd_mean.(x_var)(loopFiltGrpd, :);
|
||||
y_mean = dataTableGrpd_mean.(y_var)(loopFiltGrpd, :);
|
||||
y_min = dataTableGrpd_min.(y_var)(loopFiltGrpd, :);
|
||||
y_max = dataTableGrpd_max.(y_var)(loopFiltGrpd, :);
|
||||
% dataTable(dataTable.interference_path_length ~= 50, :) = [];
|
||||
% dataTable = dataTable(dataTable.interference_path_length < 51, :);
|
||||
|
||||
% Compute bounds: distance from mean
|
||||
y_lower = y_mean - y_min;
|
||||
y_upper = y_max - y_mean;
|
||||
y_bounds = [y_lower, y_upper];
|
||||
% dataTable(dataTable.loop_id<200,:) = [];
|
||||
|
||||
% Display name (optional)
|
||||
try
|
||||
idx = find(dataTable.(loop_var) == loopValue, 1, 'first');
|
||||
% dispname = char(equalizer_structure(dataTable.equalizer_structure(idx)));
|
||||
dispname = [method];
|
||||
dispname = [dispname, '/ ',num2str(unique_loop_var(i)) ,' m'];
|
||||
% dispname = [dispname,'; ',num2str(unique(dataTable.interference_path_length)),' m'];
|
||||
dispname = [dispname, '/ PAM ', num2str(filterParams.Configurations.pam_level)];
|
||||
dispname = [dispname, '/ ', num2str(filterParams.Configurations.symbolrate.*1e-9),' GBd'];
|
||||
end
|
||||
% Filter by time
|
||||
filter_by_time = 0;
|
||||
if filter_by_time
|
||||
startTime = datetime('2025-04-20 18:00:00', 'InputFormat', 'yyyy-MM-dd HH:mm:ss');
|
||||
stopTime = datetime('2025-04-30 19:30:00', 'InputFormat', 'yyyy-MM-dd HH:mm:ss');
|
||||
dataTable.date_of_run = datetime(dataTable.date_of_run, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
|
||||
dataTable.date_of_processing = datetime(dataTable.date_of_processing, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
|
||||
dataTable = dataTable(dataTable.date_of_processing > startTime, :);
|
||||
dataTable = dataTable(dataTable.date_of_processing < stopTime, :);
|
||||
end
|
||||
|
||||
if plotBoundaries
|
||||
% Plot bounded line
|
||||
[hl, hp] = boundedline(x_values, y_mean, y_bounds, ...
|
||||
'alpha', 'transparency', 0.1, ...
|
||||
'cmap', cols(i,:), ...
|
||||
'nan', 'fill', ...
|
||||
'orientation', 'vert');
|
||||
|
||||
% % Style the main line: thinnest, dotted, no marker
|
||||
set(hl, 'LineWidth', 1, 'LineStyle', '-', 'Marker', 'none', ...
|
||||
'Color', cols(i,:), 'DisplayName', string(dispname));
|
||||
% Group by smth
|
||||
y_var = 'BER';
|
||||
x_var = 'SIR';
|
||||
|
||||
plt = errorbar(x_values,y_mean,y_lower,y_upper,'LineWidth', 0.1, 'LineStyle', 'none', 'Marker', 'none','Color', cols(i,:), 'DisplayName', string(dispname),'HandleVisibility','off');
|
||||
|
||||
% Hide patch (shaded area) from legend
|
||||
set(hp, 'HandleVisibility', 'off','LineStyle',':','LineWidth',0.5,'Marker','none');
|
||||
|
||||
% % Add invisible scatter for DataTips
|
||||
% plt = scatter(x_values, y_mean, ...
|
||||
% 'Marker', 'o', 'MarkerEdgeColor', 'none', 'MarkerFaceColor', 'none', ...
|
||||
% 'HandleVisibility', 'off', 'PickableParts', 'all');
|
||||
else
|
||||
loop_var = 'interference_path_length';
|
||||
fixedVars = {'equalizer_structure','interference_path_length',x_var};
|
||||
|
||||
plt= plot(x_values,y_mean,'LineWidth', 1, 'LineStyle', '-', 'Marker', 'none', ...
|
||||
'Color', cols(i,:), 'DisplayName', string(dispname));
|
||||
% plt= errorbar(x_values,y_mean,y_lower,y_upper,'LineWidth', 1, 'LineStyle', '-', 'Marker', 'none','Color', cols(i,:), 'DisplayName', string(dispname));
|
||||
[dataTable, outliersTable] = removeGroupOutliers(dataTable, fixedVars, y_var);
|
||||
|
||||
dataTableGrpd_mean = groupIt(fixedVars, dataTable, @mean);
|
||||
dataTableGrpd_min = groupIt(fixedVars, dataTable, @min);
|
||||
dataTableGrpd_max = groupIt(fixedVars, dataTable, @max);
|
||||
|
||||
% Create a new figure
|
||||
|
||||
hold on
|
||||
|
||||
unique_loop_var = unique(dataTable.(loop_var));
|
||||
|
||||
for i = 1:numel(unique_loop_var)
|
||||
|
||||
% Prepare filtered data for this loop variable
|
||||
loopValue = unique_loop_var(i);
|
||||
|
||||
loopFiltGrpd = dataTableGrpd_mean.(loop_var) == loopValue;
|
||||
if ~any(loopFiltGrpd)
|
||||
continue; % Skip if no data for this loop var
|
||||
end
|
||||
|
||||
% Extract values
|
||||
x_values = dataTableGrpd_mean.(x_var)(loopFiltGrpd, :);
|
||||
y_mean = dataTableGrpd_mean.(y_var)(loopFiltGrpd, :);
|
||||
y_min = dataTableGrpd_min.(y_var)(loopFiltGrpd, :);
|
||||
y_max = dataTableGrpd_max.(y_var)(loopFiltGrpd, :);
|
||||
|
||||
% Compute bounds: distance from mean
|
||||
y_lower = y_mean - y_min;
|
||||
y_upper = y_max - y_mean;
|
||||
y_bounds = [y_lower, y_upper];
|
||||
|
||||
% Display name (optional)
|
||||
try
|
||||
idx = find(dataTable.(loop_var) == loopValue, 1, 'first');
|
||||
% dispname = char(equalizer_structure(dataTable.equalizer_structure(idx)));
|
||||
dispname = [method];
|
||||
dispname = [dispname, '/ ',num2str(unique_loop_var(i)) ,' m'];
|
||||
% dispname = [dispname,'; ',num2str(unique(dataTable.interference_path_length)),' m'];
|
||||
% dispname = [dispname, '/ PAM ', num2str(filterParams.Configurations.pam_level)];
|
||||
% dispname = [dispname, '/ ', num2str(filterParams.Configurations.symbolrate.*1e-9),' GBd'];
|
||||
end
|
||||
|
||||
if plotBoundaries
|
||||
% Plot bounded line
|
||||
[hl, hp] = boundedline(x_values, y_mean, y_bounds, ...
|
||||
'alpha', 'transparency', 0.1, ...
|
||||
'cmap', cols(i,:), ...
|
||||
'nan', 'fill', ...
|
||||
'orientation', 'vert');
|
||||
|
||||
% % Style the main line: thinnest, dotted, no marker
|
||||
set(hl, 'LineWidth', 0.5, 'LineStyle', ':', 'Marker', 'none', ...
|
||||
'Color', cols(i,:), 'DisplayName', string(dispname));
|
||||
|
||||
plt = errorbar(x_values,y_mean,y_lower,y_upper,'LineWidth', 0.9, 'LineStyle', 'none', 'Marker', 'none','Color', cols(i,:), 'DisplayName', string(dispname),'HandleVisibility','off');
|
||||
|
||||
% Hide patch (shaded area) from legend
|
||||
set(hp, 'HandleVisibility', 'off','LineStyle',':','LineWidth',0.5,'Marker','none');
|
||||
|
||||
% Fit a 4th-order polynomial to log10(BER)
|
||||
p = polyfit(x_values, log10(y_mean), 3); % 4 is fitting order, adjust as needed
|
||||
|
||||
% Evaluate the fitted polynomial
|
||||
x_fit = linspace(min(x_values), max(x_values), 300); % Fine points
|
||||
y_fit_log = polyval(p, x_fit); % Still in log10 domain
|
||||
y_fit = 10.^y_fit_log; % Back to BER domain
|
||||
|
||||
|
||||
|
||||
plot(x_fit,y_fit,'LineWidth', 1, 'LineStyle', '-', 'Marker', 'none', ...
|
||||
'Color', cols(i,:), 'DisplayName', string(dispname),'HandleVisibility','off');
|
||||
|
||||
% % Add invisible scatter for DataTips
|
||||
% plt = scatter(x_values, y_mean, ...
|
||||
% 'Marker', 'o', 'MarkerEdgeColor', 'none', 'MarkerFaceColor', 'none', ...
|
||||
% 'HandleVisibility', 'off', 'PickableParts', 'all');
|
||||
else
|
||||
|
||||
plt= plot(x_values,y_mean,'LineWidth', 1, 'LineStyle', '-', 'Marker', 'none', ...
|
||||
'Color', cols(i,:), 'DisplayName', string(dispname));
|
||||
% plt= errorbar(x_values,y_mean,y_lower,y_upper,'LineWidth', 1, 'LineStyle', '-', 'Marker', 'none','Color', cols(i,:), 'DisplayName', string(dispname));
|
||||
|
||||
end
|
||||
% Add data tips to the invisible scatter
|
||||
pair_one = {'Run ID', dataTableGrpd_mean.run_id(loopFiltGrpd, :)};
|
||||
pair_two = {'Rate', dataTableGrpd_mean.bitrate(loopFiltGrpd, :) * 1e-9};
|
||||
pair_three = {'PD in', round(dataTableGrpd_mean.power_pd_in(loopFiltGrpd, :), 2)};
|
||||
addDatatips(plt, pair_one, pair_two, pair_three);
|
||||
|
||||
|
||||
|
||||
% Optionally: outline bounds for better visibility (optional)
|
||||
% hnew = outlinebounds(hl, hp);
|
||||
|
||||
% Tick marks (x-axis)
|
||||
|
||||
|
||||
% Optional: scatter realizations
|
||||
if plotRealizations
|
||||
loopFiltSingle = dataTable.(loop_var) == loopValue;
|
||||
x_single = double(dataTable.(x_var)(loopFiltSingle, :));
|
||||
y_single = double(dataTable.(y_var)(loopFiltSingle, :));
|
||||
|
||||
mkr = '.';
|
||||
sc = scatter(x_single+(mode*0.1)-0.2, y_single,15, 'Marker', mkr, 'MarkerEdgeColor', cols(i, :), ...
|
||||
'LineWidth', 0.5, 'HandleVisibility', 'off', 'DisplayName', string(dispname));
|
||||
|
||||
pair_one = {'Run ID', dataTable.run_id(loopFiltSingle, :)};
|
||||
pair_two = {'Rate', dataTable.bitrate(loopFiltSingle, :) * 1e-9};
|
||||
pair_three = {'PD in', round(dataTable.power_pd_in(loopFiltSingle, :), 2)};
|
||||
addDatatips(sc, pair_one, pair_two, pair_three);
|
||||
end
|
||||
end
|
||||
|
||||
% Label axes and title
|
||||
legend('Interpreter', 'latex');
|
||||
xlabel(x_var);
|
||||
if ~y_here
|
||||
ylabel(y_var);
|
||||
yticklabels = [];
|
||||
|
||||
y_here = 1;
|
||||
end
|
||||
if int_len ~= 0
|
||||
set(gca, 'YTick', []);
|
||||
end
|
||||
% title([x_var, ' vs. ', y_var]);
|
||||
|
||||
if string(y_var) == "BER"
|
||||
yline(2.2e-4, 'LineWidth', 1, 'LineStyle', '--', 'HandleVisibility', 'off');
|
||||
yline(3.8e-3, 'LineWidth', 1, 'LineStyle', '--', 'HandleVisibility', 'off');
|
||||
yline(2e-2, 'LineWidth', 1, 'LineStyle', '--', 'HandleVisibility', 'off');
|
||||
ylim([1e-5, 0.1]);
|
||||
end
|
||||
|
||||
xlim([15,35]);
|
||||
ylim([9e-5 0.1 ]);
|
||||
xticks([13:2:35]);
|
||||
|
||||
% Enable grid and beautify
|
||||
grid on;
|
||||
beautifyBERplot;
|
||||
|
||||
end
|
||||
% Add data tips to the invisible scatter
|
||||
pair_one = {'Run ID', dataTableGrpd_mean.run_id(loopFiltGrpd, :)};
|
||||
pair_two = {'Rate', dataTableGrpd_mean.bitrate(loopFiltGrpd, :) * 1e-9};
|
||||
pair_three = {'PD in', round(dataTableGrpd_mean.power_pd_in(loopFiltGrpd, :), 2)};
|
||||
addDatatips(plt, pair_one, pair_two, pair_three);
|
||||
|
||||
|
||||
|
||||
% Optionally: outline bounds for better visibility (optional)
|
||||
% hnew = outlinebounds(hl, hp);
|
||||
|
||||
% Tick marks (x-axis)
|
||||
xticks(round(unique(x_values),2));
|
||||
|
||||
% Optional: scatter realizations
|
||||
if plotRealizations
|
||||
loopFiltSingle = dataTable.(loop_var) == loopValue;
|
||||
x_single = double(dataTable.(x_var)(loopFiltSingle, :));
|
||||
y_single = double(dataTable.(y_var)(loopFiltSingle, :));
|
||||
|
||||
sc = scatter(x_single, y_single, 'Marker', mkr, 'MarkerEdgeColor', cols(i, :), ...
|
||||
'LineWidth', 0.5, 'HandleVisibility', 'off', 'DisplayName', string(dispname));
|
||||
|
||||
pair_one = {'Run ID', dataTable.run_id(loopFiltSingle, :)};
|
||||
pair_two = {'Rate', dataTable.bitrate(loopFiltSingle, :) * 1e-9};
|
||||
pair_three = {'PD in', round(dataTable.power_pd_in(loopFiltSingle, :), 2)};
|
||||
addDatatips(sc, pair_one, pair_two, pair_three);
|
||||
end
|
||||
end
|
||||
|
||||
% Label axes and title
|
||||
legend('Interpreter', 'latex');
|
||||
xlabel(x_var);
|
||||
ylabel(y_var);
|
||||
title([x_var, ' vs. ', y_var]);
|
||||
|
||||
if string(y_var) == "BER"
|
||||
yline(4e-4, 'LineWidth', 1, 'LineStyle', '--', 'HandleVisibility', 'off');
|
||||
yline(3.8e-3, 'LineWidth', 1, 'LineStyle', '--', 'HandleVisibility', 'off');
|
||||
yline(2e-2, 'LineWidth', 1, 'LineStyle', '--', 'HandleVisibility', 'off');
|
||||
ylim([1e-5, 0.1]);
|
||||
end
|
||||
|
||||
xlim([13,35]);
|
||||
|
||||
% Enable grid and beautify
|
||||
grid on;
|
||||
beautifyBERplot;
|
||||
|
||||
|
||||
|
||||
|
||||
function resultTable = groupIt(fixedVars, dataTable, aggregationFunction)
|
||||
% groupIt Groups data in a table based on fixedVars and applies aggregationFunction to numeric data.
|
||||
@@ -463,9 +534,9 @@ for groupIdx = 1:height(groupKeys)
|
||||
continue;
|
||||
end
|
||||
|
||||
% Detect outliers in log space
|
||||
% Detect outliers in log space
|
||||
y_log = log10(y_values);
|
||||
outlierMask = isoutlier(y_log, 'quartiles'); % or 'median', 'grubbs', etc.
|
||||
outlierMask = isoutlier(y_log, 'quartiles',1); % or 'median', 'grubbs', etc.
|
||||
|
||||
% If any outliers found, collect their data
|
||||
if any(outlierMask)
|
||||
|
||||
Reference in New Issue
Block a user