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MPI measurements

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Silas Labor Zizou
2025-04-11 15:27:32 +02:00
parent 1e35c24a8c
commit 00f1c557c0
9 changed files with 1560 additions and 0 deletions
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projects/ECOC_2025/b2b_measurement.m
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projects/ECOC_2025/dsp_run_id.m Normal file
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function [output] = dsp_run_id(run_id,options)
arguments
run_id
options.append_to_db = 0;
options.max_occurences = 4;
options.parameters = struct();
options.database_path
options.database_name
options.storage_path
end
database = DBHandler("pathToDB",[options.database_path,options.database_name]);
filterParams = database.tables;
filterParams.Configurations = struct('run_id', run_id);
selectedFields = {'Runs.run_id','Runs.tx_bits_path','Runs.tx_signal_path','Runs.tx_symbols_path','Runs.rx_sync_path','Runs.rx_raw_path',...
'Configurations.db_mode','Configurations.pam_level','Configurations.bitrate','Configurations.symbolrate','Configurations.fiber_length','Configurations.wavelength','Configurations.precomp_amp','Measurements.power_rop','Configurations.v_bias',...
'Configurations.interference_attenuation'};
[dataTable,sql_query] = database.queryDB(filterParams, selectedFields);
[~, uniqueIdx] = unique(dataTable.run_id); % Get unique run_id indices
dataTable = dataTable(uniqueIdx,:); % Extract unique configurations for each run_id
fsym = dataTable.symbolrate;
M = double(dataTable.pam_level);
duob_mode = db_mode.(strrep(char(dataTable.db_mode),'"',''));
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
len_tr = 4096*2;
vnle_order1 = 50;
vnle_order2 = 5;
vnle_order3 = 5;
dfe_order = [0 0 0];
pf_ncoeffs = 1;
mu_ffe1 = 0.0001;
mu_ffe2 = 0.0008;
mu_ffe3 = 0.001;
mu_dfe = 0.0004;
mu_dc = 0.00;
mu_ffe = [mu_ffe1 mu_ffe2 mu_ffe3];
vnle_order=[vnle_order1,vnle_order2,vnle_order3];
% Overwrite default parameters if given in options.parameters
paramStruct = options.parameters;
if ~isempty(paramStruct)
paramNames = fieldnames(paramStruct);
for i = 1:numel(paramNames)
thisName = paramNames{i};
thisValue = paramStruct.(thisName);
eval([thisName ' = thisValue;']);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
eq_ = EQ("Ne",vnle_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
mlse_ = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
mlse_db_ = MLSE_viterbi("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels);
eq_post = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",2001,"sps",1,"decide",0);
output = struct();
vnle_pf_package = {};
vnle_dfe_package = {};
dbtgt_package = {};
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Tx_signal = load([options.storage_path, char(dataTable.tx_signal_path)]);
Tx_signal = Tx_signal.Digi_sig;
Tx_bits = load([options.storage_path, char(dataTable.tx_bits_path)]);
Tx_bits = Tx_bits.Bits;
Symbols_mapped = PAMmapper(M,0).map(Tx_bits);
Symbols_mapped.fs = dataTable.symbolrate;
Symbols = load([options.storage_path, char(dataTable.tx_symbols_path)]);
Symbols = Symbols.Symbols;
found_sync = 0;
try
Scpe_load = load([options.storage_path, char(dataTable.rx_sync_path)]);
Scpe_cell = Scpe_load.S;
[~,~,found_sync] = Scpe_cell{2}.tsynch("reference",Symbols,"fs_ref",dataTable.symbolrate,"debug_plots",0);
end
if ~found_sync
Scpe_sig_raw = load([options.storage_path, char(dataTable.rx_raw_path(1))]);
Scpe_sig_raw = Scpe_sig_raw.Scpe_sig_raw;
Scpe_sig_resampled = Scpe_sig_raw.resample("fs_in",Scpe_sig_raw.fs,"fs_out",2*fsym);
[~,Scpe_cell,found_sync] =Scpe_sig_resampled.tsynch("reference",Symbols,"fs_ref",dataTable.symbolrate,"debug_plots",1);
end
if ~found_sync
if length(Symbols_mapped.signal) == sum(Symbols_mapped.signal == Symbols.signal)
warning('Could not synchronize the received signal with the stored symbols!')
else
[~,Scpe_cell,found_sync] =Scpe_sig_raw.tsynch("reference",Symbols_mapped,"fs_ref",dataTable.symbolrate,"debug_plots",0);
end
if ~found_sync
warning('Could not synchronize the received signal with the stored symbols!')
end
end
record_realizations = min(options.max_occurences,length(Scpe_cell));
for occ = 1:record_realizations
Scpe_sig = Scpe_cell{occ};
%%%%%% Sample to 2x fsym %%%%%%
Scpe_sig = Scpe_sig.resample("fs_out",2*fsym);
%%%%%% Sync Rx signal with reference %%%%%%
[Scpe_sig,~] = Scpe_sig.tsynch("reference",Symbols,"fs_ref",fsym,"debug_plots",0);
Scpe_sig = Filter('filtdegree',4,"f_cutoff",Symbols.fs.*0.6,"fs",Scpe_sig.fs,"filterType",filtertypes.gaussian,"active",true).process(Scpe_sig);
Scpe_sig = Scpe_sig - mean(Scpe_sig.signal);
if duob_mode ~= db_mode.db_encoded
% %%%%% VNLE + DFE %%%%
if 0
eq_vnle_dfe = EQ("Ne",vnle_order,"Nb",[0,0,0],"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
eq_post = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",2001,"sps",1,"decide",0);
[result] = vnle(eq_vnle_dfe,M,Scpe_sig,Symbols,Tx_bits,"precode_mode",duob_mode,"showAnalysis",1,"postFFE",[]);
vnle_dfe_package{occ} = result;
end
%%%%% VNLE + PF + MLSE %%%%
if 1
[result] = vnle_postfilter_mlse(eq_,pf_,mlse_,M,Scpe_sig,Symbols,Tx_bits,"precode_mode",duob_mode,'showAnalysis',1,"postFFE",[],"eth_style_symbol_mapping",0);
vnle_pf_package{occ} = result;
if options.append_to_db
database.addProcessingResult(run_id,result.resultsMLSE, result.equalizerConfigMLSE);
database.addProcessingResult(run_id,result.resultsVNLE, result.equalizerConfigVNLE);
end
end
%%%%% Duobinary Targeting %%%%
if 1
[result] = duobinary_target(eq_, mlse_db_, M, Scpe_sig, Symbols, Tx_bits, "precode_mode", duob_mode,'showAnalysis',0,"postFFE",[]);
dbtgt_package{occ} = result;
if options.append_to_db
database.addProcessingResult(run_id, result.resultsDBtgt, result.equalizerConfigDBtgt);
end
end
% fprintf("BER VNLE: %.2e | %.2e; BER MLSE: %.2e | %.2e; BER DB tgt: %.2e | %.2e \n",vnle_pf_package{occ}.resultsVNLE.BER,vnle_pf_package{occ}.resultsVNLE.BER_precoded ,vnle_pf_package{occ}.resultsMLSE.BER,vnle_pf_package{occ}.resultsMLSE.BER_precoded,dbtgt_package{occ}.resultsDBtgt.BER,dbtgt_package{occ}.resultsDBtgt.BER_precoded)
% % fprintf("BER VNLE: %.2e | %.2e; BER MLSE: %.2e | %.2e \n",vnle_pf_package{occ}.resultsVNLE.BER,vnle_pf_package{occ}.resultsVNLE.BER_precoded ,vnle_pf_package{occ}.resultsMLSE.BER,vnle_pf_package{occ}.resultsMLSE.BER_precoded);
occ = 1; % or whatever your loop index is
% Extract VNLE results for readability
vnle = vnle_pf_package{occ}.resultsVNLE;
mlse = vnle_pf_package{occ}.resultsMLSE;
dbtgt = dbtgt_package{occ}.resultsDBtgt;
% Print header
fprintf("==== EQUALIZATION RUN-ID %d | PAM-%d | %.2f GBd ====\n\n", run_id, M, Symbols.fs.*1e-9);
% VNLE Results
fprintf(">> VNLE Results:\n");
fprintf(" BER %.2e\n", vnle.BER);
fprintf(" BER (pre-code) %.2e\n", vnle.BER_precoded);
fprintf(" SNR: %.2f dB\n", vnle.SNR);
fprintf(" GMI: %.4f\n", vnle.GMI);
fprintf(" Linerate: %.2f Gbps\n", Symbols.fs .* floor(log2(M)*10)/10 .*1e-9);
fprintf(" AIR: %.2f Gbps\n", vnle.AIR.*1e-9);
fprintf("\n");
% MLSE Results
fprintf(">> MLSE Results:\n");
fprintf(" BER : %.2e\n", mlse.BER);
fprintf(" BER (pre-code): %.2e\n", mlse.BER_precoded);
fprintf(" Channel Alpha : %.2f\n", mlse.Alpha);
fprintf("\n");
% DB Target Results
fprintf(">> DB Target Results:\n");
fprintf(" BER: %.2e\n", dbtgt.BER);
fprintf(" BER (pre-code): %.2e\n", dbtgt.BER_precoded);
fprintf("\n");
else
%%%%%% %db signaling => db encoded %%%%%
if 1
mlse_db_enc = MLSE_viterbi("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels);
eq_db_enc = EQ("Ne",vnle_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
[result] = duobinary_signaling(eq_db_enc, mlse_db_enc,M, Scpe_sig ,Symbols, Tx_bits);
dbenc_package{occ} = result;
if options.append_to_db
database.addProcessingResult(run_id, result.resultsDBsignaling, result.equalizerConfigDBsignaling);
end
end
fprintf("BER DB: %.2e \n",dbenc_package{occ}.resultsDBsignaling.BER);
end
% autoArrangeFigures;
disp('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ')
fprintf('\n')
end
output.dataTable = dataTable;
output.vnle_pf_package = vnle_pf_package;
output.dbtgt_package = dbtgt_package;
end

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projects/ECOC_2025/dsp_standalone.m Normal file
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basePath = 'C:\Users\sioe\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\';
database_name='ecoc2025.db';
savePath = 'Z:\2025\ECOC Silas\ecoc_2025\';
num_occ = 1;
run_par = false;
run_id = 562;
[out, ~] = submit_dsp(run_id, basePath, database_name, savePath,"parallel",run_par,'max_occurences',num_occ);

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projects/ECOC_2025/measure_run.m Normal file
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function measure_run(options)
arguments
options.parallel (1,1) logical = false
options.max_occurences = 1;
options.config = [];
options.only_record = 0;
end
savePath = 'Z:\2025\ECOC Silas\ecoc_2025\';
current_folder = 'precomp_optimization_friday';
fullFolderPath = fullfile(savePath, current_folder);
if ~exist(fullFolderPath, 'dir')
mkdir(fullFolderPath);
end
basePath = 'C:\Users\sioe\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\';
database_name='ecoc2025.db';
db = DBHandler("pathToDB",[basePath,database_name]);
precomp_path = "C:\Users\sioe\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\";
precomp_fn = "precomp_1km_1mm_cable_70ghz_pd_shf_t850_2p8_bias_shot2";
precomp_mode = 2; %0=do nothing ; 1= measure; 2=precomp active
run_lab_automation = 1;
%% Configuration
conf = db.tables.Configurations;
referenceFields = fieldnames(conf);
conf.unique_elab_id = "20250408-842b0a3078172b48dd032795226fbe683190afc4";
conf.symbolrate = 112e9;
conf.bitrate = conf.symbolrate .* floor(log2(6)*10)/10;
conf.pam_level = 4;
conf.db_mode = db_mode.no_db;
conf.pulsef_alpha = 0.3;
conf.v_bias = 2.8;
conf.v_awg = 0.8;
conf.precomp_amp = -64;
conf.wavelength = 1310;
conf.laser_power = 10;
conf.fiber_length = 0;
conf.pd_in_desired = 7;
conf.is_mpi = 1;
conf.signal_attenuation = 0;
conf.interference_path_length = 0.001; %m
conf.interference_attenuation = 40;
conf.pam_source = [];
% === Optional Override of Conf ===
if ~isempty(options.config)
paramStruct = options.config;
paramNames = fieldnames(paramStruct);
for i = 1:numel(paramNames)
thisName = paramNames{i};
thisValue = paramStruct.(thisName);
if isfield(conf, thisName)
conf.(thisName) = thisValue;
else
warning('Unknown parameter: %s', thisName);
end
end
end
assert(isequal(fieldnames(conf), referenceFields), 'Fieldnames do not match the reference structure!');
currentTime = datetime('now', 'Format', 'yyyyMMdd_HHmmss');
timeStr = char(currentTime);
filename = sprintf('%s_PAM_%d_R_%d',timeStr,conf.pam_level,conf.symbolrate.*1e-9);
%% Lab Automation
if run_lab_automation
% DC Source
dcs = DC_supply("active",[1,1],"voltage",[conf.v_bias, 5]);
dcs.set("voltage",[conf.v_bias, 5]);
[v_bias_meas,i_bias_meas]=dcs.readVals();
% Laser
laser = Exfo_laser("mainframe_channel",1,"safety_mode",0,"connection_id",'10','lab_interface','gpib');
laser.setWavelength(conf.wavelength);
laser.setPower(conf.laser_power);
laser.enableLaser();
% Optical Attenuator
voa = OptAtten("active",[0,2,1,1],"value",[0,conf.pd_in_desired,conf.signal_attenuation,conf.interference_attenuation],"wavelength",[conf.wavelength,conf.wavelength,conf.wavelength,conf.wavelength]);
voa.set('value',[0,conf.pd_in_desired,conf.signal_attenuation,conf.interference_attenuation]);
% PDFA
pdfa = Thor_PDFA("safety_mode",0,"serialport_number",'COM15');
% Construct AWG and Scope Modules %%%%%%
fdac = 256e9;
fadc = 160e9;
Scp = ScopeKeysight("model","DSAZ634A",'autoscale',1,"fadc","GSa_160","channel",[0,0,1,0],"recordLen",2000000,"removeDC",1,"extRef",1);
Awg = AwgKeysight("model","M8199A_ILV","fdac",fdac,"scaletodac",[1,1],"skews",[0,0],"voltages",[0,conf.v_awg]);
A2S = Awg2Scope(Awg,Scp,[0,0,3,0],"waitUntilClick",0);
end
%% Signal generation and transmission
%%%%% Symbol Generation %%%%%%
Pform = Pulseformer("fsym",conf.symbolrate,"fdac",8*conf.symbolrate,"pulse","rc","pulselength",16,"alpha",conf.pulsef_alpha);
Pamsource = PAMsource(...
"fsym",conf.symbolrate,"M",conf.pam_level,"order",18,"useprbs",1,...
"fs_out",fdac,...
"applyclipping",0,...
"clipfactor",4,...
"applypulseform",1,...
"pulseformer",Pform,...
"randkey",1,...
"duobinary_mode", conf.db_mode);
conf.pam_source = Pamsource;
[Digi_sig,Symbols,Bits] = Pamsource.process();
%%%%% Precompensation Routine %%%%%%
if precomp_mode == 1 % measure channel
precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',fdac);
Digi_sig = precomp_est.buildOFDM();
elseif precomp_mode == 2 % apply precomp
precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',Digi_sig.fs);
Digi_sig = precomp_est.precomp(Digi_sig,'maxampdb',conf.precomp_amp,'loadPath',precomp_path,'fileName',precomp_fn);
end
Digi_sig.spectrum("displayname",'Tx Spectrum','fignum',10,'normalizeTo0dB',0);
%%%%% Resample to DAC rate %%%%%%
Digi_sig = Digi_sig.resample("fs_out",Awg.fdac);
[~,~,Scpe_sig_raw,~] = A2S.process("signal2",Digi_sig);
% Awg.upload("signal2",Digi_sig);
% Scpe_sig_raw = Scp.read("channel",[0,0,1,0]);
% Scpe_sig_raw = Scpe_sig_raw{[0,0,1,0]==1};
%%%%% Precompensation Routine %%%%%%
if precomp_mode == 1
% Scpe_sig_resampled = Scpe_sig_raw.resample("fs_in",fadc,"fs_out",2*fsym);
precomp_est.estimate(Scpe_sig_raw,"save",true,"savePath",precomp_path,"fileName",precomp_fn);
precomp_est.plot();
return;
end
Scpe_sig_raw.spectrum("displayname",'Rx Spectrum','fignum',10,'normalizeTo0dB',0);
Scpe_sig_raw.plot("clear",1,"displayname",'Rx Signal','fignum',11);
%% %%%%% Save Routine %%%%%%%%%%%%%%%%%%%%%%%%%
tx_bits_path=[filesep,current_folder,filesep,filename,'_bits'];
save([savePath,tx_bits_path],"Bits");
tx_symbols_path=[filesep,current_folder,filesep,filename,'_symbols'];
save([savePath,tx_symbols_path],"Symbols");
tx_signal_path=[filesep,current_folder,filesep,filename,'_signal'];
save([savePath,tx_signal_path],"Digi_sig");
rx_raw_path = [filesep,current_folder,filesep,filename,'_rx_signal_raw'];
save([savePath,rx_raw_path],"Scpe_sig_raw");
% Table 1: Append to Runs
newRun = db.tables.Runs;
newRun.run_id = NaN;
newRun.date_of_run = datetime(currentTime, 'InputFormat', 'yyyyMMdd_HHmmss');
newRun.tx_bits_path = tx_bits_path;
newRun.tx_symbols_path = tx_symbols_path;
newRun.rx_sync_path = [];
newRun.rx_raw_path = rx_raw_path;
newRun.filename = filename;
newRun.tx_signal_path = tx_signal_path;
run_id = db.appendToTable('Runs', newRun);
% Table 2: Append to Configurations
conf.configuration_id = NaN;
conf.run_id = run_id;
db.appendToTable('Configurations', conf);
% Table 3: Append to Measurements
meas = db.tables.Measurements;
meas.measurement_id = NaN; % Auto-increment, leave empty
meas.run_id = run_id;
meas.power_laser = laser.cur_power;
meas.power_rop = [];
meas.power_pd_in = voa.power_state(2);
meas.power_mpi_interference = voa.power_state(4);
meas.power_mpi_signal = voa.power_state(3);
meas.voa_class = voa;
meas.pdfa_class = pdfa;
meas.laser_class = laser;
assert(isequal(fieldnames(meas), fieldnames(db.tables.Measurements)), 'Fieldnames of "meas" do not match the reference structure!');
db.appendToTable('Measurements', meas);
%% %%%%% DSP Routine %%%%%%%%%%%%%%%%%%%%%%%%%
[out, ~] = submit_dsp(run_id, basePath, database_name, savePath,"parallel",options.parallel,'max_occurences',options.max_occurences);
end

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projects/ECOC_2025/measure_run_script.m Normal file
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savePath = 'Z:\2025\ECOC Silas\ecoc_2025\';
current_folder = 'precomp_optimization_friday';
fullFolderPath = fullfile(savePath, current_folder);
if ~exist(fullFolderPath, 'dir')
mkdir(fullFolderPath);
end
basePath = 'C:\Users\sioe\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\';
database_name='ecoc2025.db';
db = DBHandler("pathToDB",[basePath,database_name]);
precomp_path = "C:\Users\sioe\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\";
precomp_fn = "precomp_1km_1mm_cable_70ghz_pd_shf_t850_2p8_bias_shot2";
precomp_mode = 2; %0=do nothing ; 1= measure; 2=precomp active
run_lab_automation = 1;
%% Configuration
conf = confRequest;
referenceFields = fieldnames(db.tables.Configurations);
assert(isequal(fieldnames(conf), referenceFields), 'Fieldnames do not match the reference structure!');
currentTime = datetime('now', 'Format', 'yyyyMMdd_HHmmss');
timeStr = char(currentTime);
filename = sprintf('%s_PAM_%d_R_%d',timeStr,conf.pam_level,conf.symbolrate.*1e-9);
%% Lab Automation
if run_lab_automation
% DC Source
if confPrev.v_bias ~= conf.v_bias
dcs = DC_supply("active",[1,1],"voltage",[conf.v_bias, 5]);
dcs.set("voltage",[conf.v_bias, 5]);
[v_bias_meas,i_bias_meas]=dcs.readVals();
end
% Laser
if confPrev.laser_power ~= conf.laser_power
laser = Exfo_laser("mainframe_channel",1,"safety_mode",0,"connection_id",'10','lab_interface','gpib');
laser.setWavelength(conf.wavelength);
laser.setPower(conf.laser_power);
laser.enableLaser();
end
% Optical Attenuator
voa = OptAtten("active",[0,2,1,1],"value",[0,conf.pd_in_desired,conf.signal_attenuation,conf.interference_attenuation],"wavelength",[conf.wavelength,conf.wavelength,conf.wavelength,conf.wavelength]);
voa.set('value',[0,conf.pd_in_desired,conf.signal_attenuation,conf.interference_attenuation]);
% PDFA
pdfa = Thor_PDFA("safety_mode",0,"serialport_number",'COM15');
% Construct AWG and Scope Modules %%%%%%
fdac = 256e9;
fadc = 160e9;
Scp = ScopeKeysight("model","DSAZ634A",'autoscale',1,"fadc","GSa_160","channel",[0,0,1,0],"recordLen",5000000,"removeDC",1,"extRef",1);
Awg = AwgKeysight("model","M8199A_ILV","fdac",fdac,"scaletodac",[1,1],"skews",[0,0],"voltages",[0,conf.v_awg]);
A2S = Awg2Scope(Awg,Scp,[0,0,3,0],"waitUntilClick",0);
end
%% Signal generation and transmission
%%%%% Symbol Generation %%%%%%
Pform = Pulseformer("fsym",conf.symbolrate,"fdac",8*conf.symbolrate,"pulse","rc","pulselength",16,"alpha",conf.pulsef_alpha);
Pamsource = PAMsource(...
"fsym",conf.symbolrate,"M",conf.pam_level,"order",19,"useprbs",1,...
"fs_out",fdac,...
"applyclipping",0,...
"clipfactor",4,...
"applypulseform",1,...
"pulseformer",Pform,...
"randkey",1,...
"duobinary_mode", conf.db_mode);
conf.pam_source = Pamsource;
upload_required = ~isequal(Pamsource, confPrev.pam_source);
confPrev = conf;
[Digi_sig,Symbols,Bits] = Pamsource.process();
%%%%% Precompensation Routine %%%%%%
if precomp_mode == 1 % measure channel
precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',fdac);
Digi_sig = precomp_est.buildOFDM();
upload_required = 1;
elseif precomp_mode == 2 % apply precomp
precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',Digi_sig.fs);
Digi_sig = precomp_est.precomp(Digi_sig,'maxampdb',conf.precomp_amp,'loadPath',precomp_path,'fileName',precomp_fn);
end
Digi_sig.spectrum("displayname",'Tx Spectrum','fignum',10,'normalizeTo0dB',0);
%%%%% Resample to DAC rate %%%%%%
Digi_sig = Digi_sig.resample("fs_out",Awg.fdac);
% [~,~,Scpe_sig_raw,~] = A2S.process("signal2",Digi_sig);
if upload_required || confPrev.v_awg ~= conf.v_awg
Awg.upload("signal2",Digi_sig);
end
Scpe_sig_raw = Scp.read("channel",[0,0,1,0]);
Scpe_sig_raw = Scpe_sig_raw{[0,0,1,0]==1};
%%%%% Precompensation Routine %%%%%%
if precomp_mode == 1
% Scpe_sig_resampled = Scpe_sig_raw.resample("fs_in",fadc,"fs_out",2*fsym);
precomp_est.estimate(Scpe_sig_raw,"save",true,"savePath",precomp_path,"fileName",precomp_fn);
precomp_est.plot();
return;
end
Scpe_sig_raw.spectrum("displayname",'Rx Spectrum','fignum',10,'normalizeTo0dB',0);
Scpe_sig_raw.plot("clear",1,"displayname",'Rx Signal','fignum',11);
%% %%%%% Save Routine %%%%%%%%%%%%%%%%%%%%%%%%%
tx_bits_path=[filesep,current_folder,filesep,filename,'_bits'];
save([savePath,tx_bits_path],"Bits");
tx_symbols_path=[filesep,current_folder,filesep,filename,'_symbols'];
save([savePath,tx_symbols_path],"Symbols");
tx_signal_path=[filesep,current_folder,filesep,filename,'_signal'];
save([savePath,tx_signal_path],"Digi_sig");
rx_raw_path = [filesep,current_folder,filesep,filename,'_rx_signal_raw'];
save([savePath,rx_raw_path],"Scpe_sig_raw");
% Table 1: Append to Runs
newRun = db.tables.Runs;
newRun.run_id = NaN;
newRun.date_of_run = datetime(currentTime, 'InputFormat', 'yyyyMMdd_HHmmss');
newRun.tx_bits_path = tx_bits_path;
newRun.tx_symbols_path = tx_symbols_path;
newRun.rx_sync_path = [];
newRun.rx_raw_path = rx_raw_path;
newRun.filename = filename;
newRun.tx_signal_path = tx_signal_path;
run_id = db.appendToTable('Runs', newRun);
% Table 2: Append to Configurations
conf.configuration_id = NaN;
conf.run_id = run_id;
db.appendToTable('Configurations', conf);
% Table 3: Append to Measurements
meas = db.tables.Measurements;
meas.measurement_id = NaN; % Auto-increment, leave empty
meas.run_id = run_id;
meas.power_laser = laser.cur_power;
meas.power_rop = [];
meas.power_pd_in = voa.power_state(2);
meas.power_mpi_interference = voa.power_state(4);
meas.power_mpi_signal = voa.power_state(3);
meas.voa_class = voa;
meas.pdfa_class = pdfa;
meas.laser_class = laser;
assert(isequal(fieldnames(meas), fieldnames(db.tables.Measurements)), 'Fieldnames of "meas" do not match the reference structure!');
db.appendToTable('Measurements', meas);
%% %%%%% DSP Routine %%%%%%%%%%%%%%%%%%%%%%%%%
[out, ~] = submit_dsp(run_id, basePath, database_name, savePath,"parallel",parallel_dsp,'max_occurences',max_occurences);

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basePath = 'C:\Users\sioe\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\';
database_name = 'ecoc2025.db';
database = DBHandler("pathToDB", [basePath, database_name]);
filterParams = database.tables;
filterParams.Configurations = struct( ...
'symbolrate', 112e9, ... %[224,336,360,390,420,448]
'fiber_length', 0, ...
'db_mode', '"no_db"', ...
'interference_attenuation', [], ...
'interference_path_length', [], ...
'is_mpi', 1, ...
'pam_level', 4, ...
'wavelength', 1310, ...
'precomp_amp', -64, ...
'signal_attenuation', '0', ...
'v_awg', [], ...
'v_bias', [] ...
);
% filterParams.EqualizerParameters.diff_precode = int32(db_mode.db_encoded);
% filterParams.EqualizerParameters.equalizer_structure = int32(equalizer_structure.vnle);
selectedFields = {'Configurations.run_id' 'Runs.date_of_run' 'Runs.rx_raw_path' 'Configurations.bitrate' 'Configurations.v_bias' 'Configurations.v_awg' 'Configurations.precomp_amp' 'Configurations.symbolrate' 'Configurations.pam_level'...
'Configurations.db_mode' 'Configurations.rop_attenuation' 'Configurations.is_mpi' 'Configurations.interference_attenuation' 'Configurations.signal_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);
dataTable = cleanUpTable(dataTable);
% Filter by time
startTime = datetime('2025-04-11 13:00:00', 'InputFormat', 'yyyy-MM-dd HH:mm:ss');
stopTime = datetime('2025-04-11 14:00:00', '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, :);
% Group by smth
y_var = 'SNR';
x_var = 'v_bias';
loop_var = 'v_awg';
fixedVars = {'eq_id',loop_var};
dataTableGrpd = groupIt(fixedVars,dataTable);
plotRealizations = 1;
% Create a new figure
mkr = 'x';
figure(10);
hold on
unique_loop_var = unique(dataTable.(loop_var));
cols = linspecer(8);
for i = 1:numel(unique_loop_var)
idx = find(dataTable.(loop_var)== unique_loop_var(i), 1, 'first');
% dispname = equalizer_structure(dataTable.equalizer_structure(idx));
dispname = num2str(unique_loop_var(i));
% Plot SCATTERS: timestamp vs. interference_attenuation
loop_filt_2 = dataTable.(loop_var)==unique_loop_var(i);
if plotRealizations
y_values = dataTable.(y_var)(loop_filt_2,:);
x_values = dataTable.(x_var)(loop_filt_2,:);
y_values = double(y_values);
x_values = double(x_values);
sc = scatter(x_values, y_values, 'LineWidth', 0.5,'Marker',mkr,'MarkerEdgeColor',cols(i,:),'HandleVisibility','on','DisplayName',string(dispname));
pair_one = {'Run ID', dataTable.run_id(loop_filt_2,:)};
pair_two = {'Rate', dataTable.bitrate(loop_filt_2,:).*1e-9};
pair_three = {'PD in', round(dataTable.power_pd_in(loop_filt_2,:),2)};
addDatatips(sc, pair_one, pair_two,pair_three);
xticks(unique(x_values));
end
end
% Label the axes and add a title
legend('Interpreter','latex');
xlabel(x_var);
ylabel(y_var);
title([x_var,' vs. ',y_var]);
if y_var == 'BER'
yline(3.8e-3,'LineWidth',1,'LineStyle','--','HandleVisibility','off');
ylim([1e-4 0.5]);
end
% 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} = 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
function cleanedTable = cleanUpTable(inputTable)
% cleanUpTable Cleans a MATLAB table where numbers and NaNs are stored as strings or structs.
%
% cleanedTable = cleanUpTable(inputTable)
%
% This function goes through all columns of the input table:
% - Converts strings of numbers to numeric values
% - Converts string 'NaN' and struct NaNs to real NaN
% - Converts date strings to datetime (if possible)
%
% Input:
% inputTable - MATLAB table with mixed types
%
% Output:
% cleanedTable - Cleaned MATLAB table with proper numeric types
cleanedTable = inputTable;
varNames = cleanedTable.Properties.VariableNames;
for i = 1:numel(varNames)
col = cleanedTable.(varNames{i});
% Case 1: If it's a cell array (likely mixed strings/struct)
if iscell(col)
% Convert struct 'NaN' entries to string 'NaN'
col = cellfun(@(x) convertStructToString(x), col, 'UniformOutput', false);
% Try to convert string numbers to actual numbers
numericCol = str2double(col);
if all(isnan(numericCol) == strcmpi(col, 'NaN') | cellfun(@isempty, col))
% If conversion is successful (NaNs correspond to 'NaN' strings), use it
cleanedTable.(varNames{i}) = numericCol;
else
% Else, try to convert to datetime
try
cleanedTable.(varNames{i}) = datetime(col, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
catch
% If it fails, leave as cell array of strings
cleanedTable.(varNames{i}) = string(col);
end
end
% Case 2: If it's already a string array
elseif isstring(col)
numericCol = str2double(col);
if all(isnan(numericCol) == strcmpi(col, "NaN"))
cleanedTable.(varNames{i}) = numericCol;
else
% Try convert to datetime
try
cleanedTable.(varNames{i}) = datetime(col, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
catch
% Leave as string
end
end
% Case 3: If it's already numeric, keep as is
elseif isnumeric(col)
continue;
% Case 4: If it's datetime, keep as is
elseif isdatetime(col)
continue;
else
% Catch-all for unexpected types, convert to string
cleanedTable.(varNames{i}) = string(col);
end
end
end
function out = convertStructToString(x)
% Helper function to convert struct NaN to string 'NaN'
if isstruct(x)
out = "NaN";
elseif isstring(x) || ischar(x)
out = string(x);
else
out = x;
end
end

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basePath = 'C:\Users\sioe\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\';
database_name = 'ecoc2025.db';
database = DBHandler("pathToDB", [basePath, database_name]);
filterParams = database.tables;
filterParams.Configurations = struct( ...
'symbolrate', 112e9, ... %[224,336,360,390,420,448]
'fiber_length', 0, ...
'db_mode', '"no_db"', ...
'interference_attenuation', [], ...
'interference_path_length', [], ...
'is_mpi', 0, ...
'pam_level', 4, ...
'wavelength', 1310, ...
'precomp_amp', [], ...
'signal_attenuation', [], ...
'v_awg', 0.8, ...
'v_bias', 2.8 ...
);
% filterParams.EqualizerParameters.diff_precode = int32(db_mode.db_encoded);
% filterParams.EqualizerParameters.equalizer_structure = int32(equalizer_structure.vnle);
selectedFields = {'Configurations.run_id' 'Runs.date_of_run' 'Runs.rx_raw_path' 'Configurations.bitrate' 'Configurations.v_bias' 'Configurations.v_awg' 'Configurations.precomp_amp' 'Configurations.symbolrate' 'Configurations.pam_level'...
'Configurations.db_mode' 'Configurations.rop_attenuation' 'Configurations.is_mpi' 'Configurations.interference_attenuation' 'Configurations.signal_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);
dataTable = cleanUpTable(dataTable);
% Filter by time
startTime = datetime('2025-04-11 13:00:00', 'InputFormat', 'yyyy-MM-dd HH:mm:ss');
stopTime = datetime('2025-04-11 14:00:00', '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, :);
% Group by smth
y_var = 'BER';
x_var = 'interference_attenuation';
loop_var = 'eq_id';
fixedVars = {'eq_id',loop_var};
dataTableGrpd = groupIt(fixedVars,dataTable);
plotRealizations = 1;
% Create a new figure
mkr = '*';
figure(10);
hold on
unique_loop_var = unique(dataTable.(loop_var));
cols = linspecer(8);
for i = 1:numel(unique_loop_var)
idx = find(dataTable.(loop_var)== unique_loop_var(i), 1, 'first');
dispname = equalizer_structure(dataTable.equalizer_structure(idx));
% dispname = num2str(unique_loop_var(i));
% Plot SCATTERS: timestamp vs. interference_attenuation
loop_filt_2 = dataTable.(loop_var)==unique_loop_var(i);
if plotRealizations
y_values = dataTable.(y_var)(loop_filt_2,:);
x_values = dataTable.(x_var)(loop_filt_2,:);
y_values = double(y_values);
x_values = double(x_values);
sc = scatter(x_values, y_values, 'LineWidth', 0.5,'Marker',mkr,'MarkerEdgeColor',cols(i,:),'HandleVisibility','on','DisplayName',string(dispname));
pair_one = {'Run ID', dataTable.run_id(loop_filt_2,:)};
pair_two = {'Rate', dataTable.bitrate(loop_filt_2,:).*1e-9};
pair_three = {'PD in', round(dataTable.power_pd_in(loop_filt_2,:),2)};
addDatatips(sc, pair_one, pair_two,pair_three);
xticks(unique(x_values));
end
end
% Label the axes and add a title
legend('Interpreter','latex');
xlabel(x_var);
ylabel(y_var);
title([x_var,' vs. ',y_var]);
if y_var == 'BER'
yline(3.8e-3,'LineWidth',1,'LineStyle','--','HandleVisibility','off');
ylim([1e-5 0.1]);
end
% 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} = 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
function cleanedTable = cleanUpTable(inputTable)
% cleanUpTable Cleans a MATLAB table where numbers and NaNs are stored as strings or structs.
%
% cleanedTable = cleanUpTable(inputTable)
%
% This function goes through all columns of the input table:
% - Converts strings of numbers to numeric values
% - Converts string 'NaN' and struct NaNs to real NaN
% - Converts date strings to datetime (if possible)
%
% Input:
% inputTable - MATLAB table with mixed types
%
% Output:
% cleanedTable - Cleaned MATLAB table with proper numeric types
cleanedTable = inputTable;
varNames = cleanedTable.Properties.VariableNames;
for i = 1:numel(varNames)
col = cleanedTable.(varNames{i});
% Case 1: If it's a cell array (likely mixed strings/struct)
if iscell(col)
% Convert struct 'NaN' entries to string 'NaN'
col = cellfun(@(x) convertStructToString(x), col, 'UniformOutput', false);
% Try to convert string numbers to actual numbers
numericCol = str2double(col);
if all(isnan(numericCol) == strcmpi(col, 'NaN') | cellfun(@isempty, col))
% If conversion is successful (NaNs correspond to 'NaN' strings), use it
cleanedTable.(varNames{i}) = numericCol;
else
% Else, try to convert to datetime
try
cleanedTable.(varNames{i}) = datetime(col, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
catch
% If it fails, leave as cell array of strings
cleanedTable.(varNames{i}) = string(col);
end
end
% Case 2: If it's already a string array
elseif isstring(col)
numericCol = str2double(col);
if all(isnan(numericCol) == strcmpi(col, "NaN"))
cleanedTable.(varNames{i}) = numericCol;
else
% Try convert to datetime
try
cleanedTable.(varNames{i}) = datetime(col, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
catch
% Leave as string
end
end
% Case 3: If it's already numeric, keep as is
elseif isnumeric(col)
continue;
% Case 4: If it's datetime, keep as is
elseif isdatetime(col)
continue;
else
% Catch-all for unexpected types, convert to string
cleanedTable.(varNames{i}) = string(col);
end
end
end
function out = convertStructToString(x)
% Helper function to convert struct NaN to string 'NaN'
if isstruct(x)
out = "NaN";
elseif isstring(x) || ischar(x)
out = string(x);
else
out = x;
end
end

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basePath = 'C:\Users\sioe\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\';
database_name = 'ecoc2025.db';
database = DBHandler("pathToDB", [basePath, database_name]);
filterParams = database.tables;
filterParams.Configurations = struct( ...
'symbolrate', 112e9, ... %[224,336,360,390,420,448]
'fiber_length', 0, ...
'db_mode', '"no_db"', ...
'interference_attenuation', [], ...
'interference_path_length', 0.06, ...
'is_mpi', 1, ...
'pam_level', 4, ...
'wavelength', 1310, ...
'precomp_amp', [], ...
'signal_attenuation', [], ...
'v_awg', 0.9, ...
'v_bias', 2.8 ...
);
% filterParams.EqualizerParameters.diff_precode = int32(db_mode.db_encoded);
% filterParams.EqualizerParameters.equalizer_structure = int32(equalizer_structure.vnle);
selectedFields = {'Configurations.run_id' 'Runs.date_of_run' 'Runs.rx_raw_path' 'Configurations.bitrate' 'Configurations.v_bias' 'Configurations.v_awg' 'Configurations.precomp_amp' 'Configurations.symbolrate' 'Configurations.pam_level'...
'Configurations.db_mode' 'Configurations.rop_attenuation' 'Configurations.is_mpi' 'Configurations.interference_attenuation' 'Configurations.signal_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);
dataTable.SIR = dataTable.power_mpi_signal - dataTable.power_mpi_interference;
dataTable = cleanUpTable(dataTable);
% Filter by time
startTime = datetime('2025-04-11 14:40:00', 'InputFormat', 'yyyy-MM-dd HH:mm:ss');
stopTime = datetime('2025-04-11 15:30:00', '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, :);
% Group by smth
y_var = 'BER';
x_var = 'SIR';
loop_var = 'eq_id';
fixedVars = {'eq_id',x_var};
% dataTableGrpd_mean = groupIt(fixedVars,dataTable);
dataTableGrpd_mean = groupIt(fixedVars, dataTable, @mean);
dataTableGrpd_min = groupIt(fixedVars, dataTable, @min);
dataTableGrpd_max = groupIt(fixedVars, dataTable, @max);
plotRealizations = 1;
% Create a new figure
mkr = '*';
figure();
hold on
unique_loop_var = unique(dataTable.(loop_var));
cols = linspecer(numel(unique_loop_var)); % Ensure color count matches
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)
idx = find(dataTable.(loop_var) == loopValue, 1, 'first');
dispname = equalizer_structure(dataTable.equalizer_structure(idx));
% Plot bounded line
[hl, hp] = boundedline(x_values, y_mean, y_bounds, ...
'alpha', 'transparency', 0.2, ...
'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));
% Hide patch (shaded area) from legend
set(hp, 'HandleVisibility', 'off','LineStyle',':','LineWidth',0.5,'Marker','none');
% Add invisible scatter for DataTips
sc_fake = scatter(x_values, y_mean, ...
'Marker', 'o', 'MarkerEdgeColor', 'none', 'MarkerFaceColor', 'none', ...
'HandleVisibility', 'off', 'PickableParts', 'all');
% 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(sc_fake, 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', 'on', '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 y_var == 'BER'
yline(3.8e-3, 'LineWidth', 1, 'LineStyle', '--', 'HandleVisibility', 'off');
ylim([1e-5, 0.1]);
end
% 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.
%
% resultTable = groupIt(fixedVars, dataTable, aggregationFunction)
%
% Inputs:
% fixedVars - Cell array of variable names to group by
% dataTable - Input MATLAB table
% aggregationFunction - Function handle (e.g., @mean, @min, @max)
%
% Output:
% resultTable - Grouped and aggregated table
% Group data
[G, groupKeys] = findgroups(dataTable(:, fixedVars));
% Prepare aggregation
varNames = dataTable.Properties.VariableNames;
nVars = numel(varNames);
aggData = cell(height(groupKeys), nVars);
groupCount = zeros(height(groupKeys), 1); % Store number of rows in each group
% Loop over groups
for i = 1:height(groupKeys)
idx = (G == i); % Logical index for group i
groupCount(i) = sum(idx); % Count rows in group
% Loop over each variable
for j = 1:nVars
colData = dataTable.(varNames{j});
if isnumeric(colData)
% Numeric: apply aggregation function (skip empty groups safely)
if any(idx)
aggData{i, j} = aggregationFunction(colData(idx));
else
aggData{i, j} = NaN;
end
else
% Non-numeric: take first non-empty value
if iscell(colData)
nonEmptyIdx = find(idx & ~cellfun(@isempty, colData), 1);
if ~isempty(nonEmptyIdx)
aggData{i, j} = colData{nonEmptyIdx};
else
aggData{i, j} = [];
end
else
nonEmptyIdx = find(idx, 1);
if ~isempty(nonEmptyIdx)
aggData{i, j} = colData(nonEmptyIdx);
else
aggData{i, j} = [];
end
end
end
end
end
% Convert aggregated data to table
resultTable = cell2table(aggData, 'VariableNames', varNames);
% Add group size as 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
function cleanedTable = cleanUpTable(inputTable)
% cleanUpTable Cleans a MATLAB table where numbers and NaNs are stored as strings or structs.
%
% cleanedTable = cleanUpTable(inputTable)
%
% This function goes through all columns of the input table:
% - Converts strings of numbers to numeric values
% - Converts string 'NaN' and struct NaNs to real NaN
% - Converts date strings to datetime (if possible)
%
% Input:
% inputTable - MATLAB table with mixed types
%
% Output:
% cleanedTable - Cleaned MATLAB table with proper numeric types
cleanedTable = inputTable;
varNames = cleanedTable.Properties.VariableNames;
for i = 1:numel(varNames)
col = cleanedTable.(varNames{i});
% Case 1: If it's a cell array (likely mixed strings/struct)
if iscell(col)
% Convert struct 'NaN' entries to string 'NaN'
col = cellfun(@(x) convertStructToString(x), col, 'UniformOutput', false);
% Try to convert string numbers to actual numbers
numericCol = str2double(col);
if all(isnan(numericCol) == strcmpi(col, 'NaN') | cellfun(@isempty, col))
% If conversion is successful (NaNs correspond to 'NaN' strings), use it
cleanedTable.(varNames{i}) = numericCol;
else
% Else, try to convert to datetime
try
cleanedTable.(varNames{i}) = datetime(col, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
catch
% If it fails, leave as cell array of strings
cleanedTable.(varNames{i}) = string(col);
end
end
% Case 2: If it's already a string array
elseif isstring(col)
numericCol = str2double(col);
if all(isnan(numericCol) == strcmpi(col, "NaN"))
cleanedTable.(varNames{i}) = numericCol;
else
% Try convert to datetime
try
cleanedTable.(varNames{i}) = datetime(col, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
catch
% Leave as string
end
end
% Case 3: If it's already numeric, keep as is
elseif isnumeric(col)
continue;
% Case 4: If it's datetime, keep as is
elseif isdatetime(col)
continue;
else
% Catch-all for unexpected types, convert to string
cleanedTable.(varNames{i}) = string(col);
end
end
end
function out = convertStructToString(x)
% Helper function to convert struct NaN to string 'NaN'
if isstruct(x)
out = "NaN";
elseif isstring(x) || ischar(x)
out = string(x);
else
out = x;
end
end

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function [output, future] = submit_dsp(run_id, basePath, database_name, savePath, options)
% runDSPInBackground Runs or submits DSP processing based on 'parallel' flag.
%
% [output, future] = runDSPInBackground(..., options)
% - output: result from dsp_run_id (only immediately available in serial mode)
% - future: FevalFuture object if run in parallel, otherwise []
arguments
run_id
basePath
database_name
savePath
options.parallel (1,1) logical = true
options.max_occurences = 1;
end
if options.parallel
% Check if a pool exists
pool = gcp('nocreate');
if isempty(pool)
parpool;
end
% Submit the DSP function asynchronously
future = parfeval( ...
@dsp_run_id, 1, ... % One output
run_id, ...
"database_path", basePath, ...
"database_name", database_name, ...
'storage_path', savePath, ...
'append_to_db', 1, ...
'max_occurences', options.max_occurences ...
);
output = [];
fprintf('DSP task for run_id %d submitted to the pool.\n', run_id);
else
% Run synchronously (debug mode), capture output
fprintf('Running DSP task for run_id %d in main thread (debug mode).\n', run_id);
output = dsp_run_id( ...
run_id, ...
"database_path", basePath, ...
"database_name", database_name, ...
'storage_path', savePath, ...
'append_to_db', 1, ...
'max_occurences', options.max_occurences ...
);
future = []; % No future since it's synchronous
end
end