Add new minimal example (+ a ton of other, not so important, changes)

Classes/00_signals/Signal.m

@@ -347,8 +347,10 @@ classdef Signal
                 options.fignum = 2025
                 options.displayname = "";
                 options.color = [];
+                options.linestyle = '-';
                 options.normalizeToNyquist = 0;
                 options.addDCoffset = 0;
+                options.normalizeToDC = 0;
                 options.normalizeTo0dB = 0;
                 options.max_num_lines = []; % Leave empty or omit to disable line rotation
                 options.fft_length = [];
@@ -361,6 +363,8 @@ classdef Signal
                 options.fft_length = 2^(nextpow2(length(obj.signal))-9);
             end
 
+            
+
             if options.normalizeToNyquist == 0
                 [p_lin,f_Hz] = pwelch(obj.signal, hanning(options.fft_length), ...
                     options.fft_length/2, options.fft_length, ...
@@ -374,6 +378,8 @@ classdef Signal
                 % We'll keep f_rad for the x-axis in that mode.
             end
 
+            % p_lin = movmean(p_lin,4);
+
             if options.normalizeTo0dB
                 p_lin = p_lin ./ max(p_lin);
                 p_dbm = 10*log10(p_lin); % normalized to 0 dB
@@ -418,11 +424,18 @@ classdef Signal
 
             p_dbm = p_dbm+options.addDCoffset;
 
+            if options.normalizeToDC
+                [~,min_idx]=min(abs(f_GHz));
+                pow_at_dc = p_dbm(min_idx);
+                p_dbm = p_dbm-pow_at_dc;
+            end
+            % p_dbm = movmean(p_dbm,10);
+
             for s = 1:min(size(p_dbm))
                 if isempty(options.color)
                     plot(x_vec, p_dbm(:,s), 'DisplayName', options.displayname, 'LineWidth', 1);
                 else
-                    plot(x_vec, p_dbm(:,s), 'DisplayName', options.displayname, 'LineWidth', 1, 'Color', options.color);
+                    plot(x_vec, p_dbm(:,s), 'DisplayName', options.displayname, 'LineWidth', 1, 'Color', options.color,'LineStyle',options.linestyle);
                 end
             end
 
@@ -968,8 +981,8 @@ classdef Signal
                 maxA = max(sig(100:end-100))*1.3;
                 minA = min(sig(100:end-100))*1.3;
 
-                % maxA = 0.0025;
+                maxA = 0.12;
-                % minA = 0;
+                minA = -0.08;
 
                 difference= maxA-minA;
 

Classes/01_transmit/ChannelFreqResp.m

@@ -139,6 +139,8 @@ classdef ChannelFreqResp < handle
             fnew = linspace(0,fstarget/2,length(Target)/2+1);
             fnew = fnew(2:end-1);
 
+
+
             % Old frequency axis (should be much coarser)
             idx_old  = find((obj.faxis > 0) .* (obj.faxis < fstarget/2)); %positions of all Frequencies smaller than fs/2
             int_fold = obj.faxis(idx_old); %old frequencies from 0 to fs/2
@@ -149,6 +151,10 @@ classdef ChannelFreqResp < handle
             % interpolate the frequency response that had a coarse frequency resolution (e.g. 256 bins) to the current frequency resolution (e.g. 21843 bins)
             iH  = interp1(int_fold, real(H_inv(idx_old)) ,fnew, 'linear') + 1i*interp1(int_fold, imag(H_inv(idx_old)) ,fnew, 'linear');
 
+            if 0
+                figure(7);hold on;plot(fnew,20*log10(abs(iH)))
+            end
+
             % set all NaN values to the fist/ last non-NaN value
             nH    = find(~isnan(iH),1,'first');
             iH(1:nH)=iH(nH);
@@ -171,6 +177,8 @@ classdef ChannelFreqResp < handle
             % five frequencies -> should be the vaue at f=0=DC component?
             iH  = iH./mean(abs(iH)); %why 1:5??
 
+            dc = 20*log10(abs(mean(abs(iH(1:5)))));
+
             % set maximum amplification
             % set als values higher than hmax to hmax and keep the
             % phase information by multiplication with respective

Classes/04_DSP/Equalizer/ML_MLSE.m

@@ -164,8 +164,8 @@ classdef ML_MLSE < handle
         % EQUALIZE
         % ==============================================================
         function [y,y_ref] = equalize(obj,x,d,mu,epochs,N,training)
-            debug = 1;
+            debug = 0;
-            showPlots = 1;
+            showPlots = 0;
             y = zeros(N,1);
             nSymbols = ceil(N/obj.sps);
 

Classes/DataBaseHandler/Metricstruct.m

@@ -71,16 +71,22 @@ classdef Metricstruct
             end
         end
 
-        function print(obj)
+        function print(obj,options)
             % Print method to display key metrics in a formatted way
+            arguments
+                obj 
+                options.description = ''; 
+            end
+
 
             % Define the width for formatting
             nameWidth = 15;  % Width for parameter names
             valueWidth = 12; % Width for values
 
             % Print header
+            
             fprintf('\n%s\n', repmat('=', 1, nameWidth + valueWidth));
-            fprintf(' Results \n');
+            fprintf([char(options.description),' Results \n']);
             fprintf('%s\n', repmat('=', 1, nameWidth + valueWidth));
 
             % Function to format numbers with appropriate precision

Classes/Warehouse_class/functions/phase_predist_plots/fwm_plots/automate_JLT_plots.m

@@ -21,7 +21,7 @@ height = 200;
 plotJob.Position = [100 100 width 100+height];
 cols = cbrewer2("paired",12);
 plotJob.color = cols(1,:);
-plotJob.l = 2;
+plotJob.l = 10;
 plotJob.ch = 16;
 plotJob.d = 0;
 plotJob.sgm = 0;
@@ -30,7 +30,7 @@ plotJob.p_in = 3;
 plotJob.gamma = 0.0023;
 plotJob.pmd = 0.1;
 plotJob.channelspacing = 400e9;
-plotJob.randzdw = 0;
+plotJob.randzdw = 1;
 
 
 plotJob.plot_ber_curve = 0;
@@ -52,7 +52,7 @@ plotJob.yAxisLabel = 'BER';
 
  % createbercurves(wh,plotJob)
 
- P = [3,6];
+ P = [3];
  for i = 1:2
      plotJob.p_in = P(i); 
      createviolinplots(wh,plotJob);
@@ -234,8 +234,8 @@ Title = ["Co Pol.","Link Segmentation","Paired Pol. Interl.","Alternating Pol. I
 D = [0,3,0,0];
 Sgm = [0,1,0,0];
 
-colidx = [3];
+colidx = [4];
-Len = [10];
+Len = plotJob.l;
 
 fig = findall(groot, 'Type', 'figure', 'Name', plotJob.figName);
 if isvalid(fig)

Functions/EQ_structures/dsp_runid.m

@@ -95,11 +95,11 @@ try
     adaption= 1;
     use_dd_mode = 1;
 
-    use_ffe         = 1;
+    use_ffe         = 0;
-    use_dfe         = 1;
+    use_dfe         = 0;
-    use_vnle_mlse   = 1;
+    use_vnle_mlse   = 0;
-    use_dbtgt       = 1;
+    use_dbtgt       = 0;
-    use_dbenc       = 1;
+    use_dbenc       = 0;
     use_ml_mlse     = 1;
 
     addProcessingResultToDatabase = 0;

Functions/EQ_structures/duobinary_target.m

@@ -130,6 +130,10 @@ db_results.config.equalizer_structure = int32(equalizer_structure.vnle_db_mlse);
 db_results.config.comment = 'function: Duobinary tgt. (VNLE -> MLSE)';
 
 if options.showAnalysis
+
+    eq_signal.eye(eq_signal.fs,M,"fignum",249);
+
+
     eq_noise = eq_noise - mean(eq_noise.signal);
 
     rx_signal.spectrum("normalizeTo0dB",1,"fignum",250,"displayname","Rx Spectrum");

Functions/EQ_visuals/showEQNoisePSD.m

@@ -22,7 +22,6 @@ end
 
     % Ensure the figure is ready before calling spectrum
     eq_noise.spectrum("displayname", options.displayname, "fignum", fig.Number, "normalizeTo0dB", 1,"color",options.color);
-    title('EEN')
 
     if ~isnan(options.postfilter_taps)
         % Hold on to the figure for further plotting

Functions/EQ_visuals/showLevelHistogram.m

@@ -52,6 +52,8 @@ end
     legend
     grid on
 
+    % view([90 -90]);
+
 
 
 end

Functions/Job_Processing/preprocessSignal.m

@@ -16,13 +16,17 @@ Scpe_sig = Scpe_sig.resample("fs_out", 2*fsym);
 [Scpe_sig, ~] = Scpe_sig.tsynch("reference", Symbols, "fs_ref", fsym, "debug_plots", 0);
 
 % Apply Gaussian filter
-if 1 
+if 1
-Scpe_sig = Filter('filtdegree', 8, "f_cutoff", Symbols.fs.*0.52, ...
+    Scpe_sig = Filter('filtdegree', 8, "f_cutoff", Symbols.fs.*0.52, ...
-                 "fs", Scpe_sig.fs, "filterType", filtertypes.gaussian, ...
+        "fs", Scpe_sig.fs, "filterType", filtertypes.gaussian, ...
-                 "active", true).process(Scpe_sig);
+        "active", true).process(Scpe_sig);
+else
+    Scpe_sig = Filter('filtdegree', 4, "f_cutoff", Symbols.fs.*0.6, ...
+        "fs", Scpe_sig.fs, "filterType", filtertypes.gaussian, ...
+        "active", true).process(Scpe_sig);
 end
 
-% Remove DC offset
+%Remove DC offset
 Scpe_sig = Scpe_sig - mean(Scpe_sig.signal);
 
 end

Functions/Theory/dispersion_10km.m

@@ -2,11 +2,11 @@
 %  IM/DD Fading Notch – λ_null vs. Bandwidth (Fixed 10 km)
 % ============================================================
 
-
+clear; clc;
 
 %% Fiber and dispersion parameters
-lambda0 = 1315e-9;   % Zero-dispersion wavelength [m]
+lambda0 = 1310e-9;   % Zero-dispersion wavelength [m]
-S0      = 0.08;      % Dispersion slope at ZDW [ps/(nm²·km)]
+S0      = 0.09;       % Dispersion slope at ZDW [ps/(nm²·km)]
 L       = 10e3;      % Fiber length [m]
 c       = physconst('lightspeed');
 
@@ -17,74 +17,73 @@ f_GHz = f_targets / 1e9;
 %% Compute wavelength λ_null for each target f_null
 [lambda_vec, Dacc_vec] = lambda_for_first_null_full(f_targets, L, lambda0, S0);
 lambda_nm = lambda_vec * 1e9;   % Convert to nm
+Dacc = Dacc_vec;                % [ps/nm]
 
 %% ------------------------------------------------------------
 % Plot λ_null vs. f_null for 10 km fiber
 % ------------------------------------------------------------
-% figure('Color','w');
-% plot(lambda_nm,f_GHz,  'LineWidth', 2);
-% grid on; box on;
-
 cols = cbrewer2('Paired',10);
-figure('Color','w');hold on
+figure('Color','w'); hold on;
 
-plot(lambda_nm, f_GHz, 'LineWidth',2,'DisplayName',sprintf('%d km',L),'Color',cols(2,:));
+hLine = plot(lambda_nm, f_GHz, ...
+    'LineWidth', 2, ...
+    'DisplayName', sprintf('L = %.1f km', L/1000), ...
+    'Color', cols(2,:));
 
-yticks([56,75,90,112])
+xlabel('Wavelength  λ [nm]');
-
+ylabel('First fading notch  f_{null} [GHz]');
-f_GHz = [56,75,90,112] * 1e9;
+title('IM/DD Fading Notch Position vs. Wavelength');
-[lambda_vec, Dacc_vec] = lambda_for_first_null_full(f_GHz, L, lambda0, S0);
-lambda_nm = lambda_vec * 1e9;   % Convert to nm
-
-xticks(round(lambda_nm))
-
-xlabel('$\Delta \lambda$ from ZDW [nm]');
-ylabel('$F_{null}$  [GHz]');
 grid on; box on;
-lim=(lambda0.*1e9)-[8,40];
+
+lim = (lambda0.*1e9) - [8, 40];
 xlim([lim(2) lim(1)]);
-% ylim([40,130])
+yticks([56,75,90,112]);
+
+%% ------------------------------------------------------------
+% Custom DataTip Template
+% ------------------------------------------------------------
+% Add accumulated dispersion value to the DataTip
+hLine.DataTipTemplate.DataTipRows(1).Label = 'λ [nm]';
+hLine.DataTipTemplate.DataTipRows(2).Label = 'f_{null} [GHz]';
+
+% Create a new row for Dacc
+dRow = dataTipTextRow('D_{acc} [ps/nm]', Dacc);
+hLine.DataTipTemplate.DataTipRows(end+1) = dRow;
 
 %% ------------------------------------------------------------
 % Helper function: lambda_for_first_null_full
-% Stable, single-branch, clamped to O-band
 % ------------------------------------------------------------
 function [lambda_vec, Dacc_vec] = lambda_for_first_null_full(f_target, L, lambda0, S0)
-c = physconst('lightspeed');
+    c = physconst('lightspeed');
-S0_si = S0 * 1e3;   % ps/(nm²·km) -> s/(m³)
+    S0_si = S0 * 1e3;   % ps/(nm²·km) -> s/(m³)
 
-% Define O-band boundaries [m]
+    lambda_min = 1260e-9;
-lambda_min = 1260e-9;
+    lambda_max = 1360e-9;
-lambda_max = 1360e-9;
 
-f_target = f_target(:);
+    f_target = f_target(:);
-N = numel(f_target);
+    N = numel(f_target);
 
-lambda_vec = zeros(N,1);
+    lambda_vec = zeros(N,1);
-Dacc_vec   = zeros(N,1);
+    Dacc_vec   = zeros(N,1);
 
-for k = 1:N
+    for k = 1:N
-    RHS = c * 0.5 / (f_target(k)^2 * L);
+        RHS = c * 0.5 / (f_target(k)^2 * L);
 
-    % Normal-dispersion branch (λ < λ0)
+        fun = @(lambda) -(S0_si/4).*(lambda - (lambda0^4)./(lambda.^3)).*lambda.^2 - RHS;
-    fun = @(lambda) -(S0_si/4).*(lambda - (lambda0^4)./(lambda.^3)).*lambda.^2 - RHS;
 
-    % Solve within normal-dispersion range
+        try
-    try
+            lambda_sol = fzero(fun, [lambda_min, lambda0 * 0.999]);
-        lambda_sol = fzero(fun, [lambda_min, lambda0 * 0.999]);
+        catch
-    catch
+            lambda_sol = lambda_min;
-        lambda_sol = lambda_min;
+        end
+
+        lambda_sol = min(max(lambda_sol, lambda_min), lambda_max);
+        lambda_vec(k) = lambda_sol;
+
+        D_lambda = (S0_si/4) * (lambda_sol - (lambda0^4)/(lambda_sol^3)) / 1e-6; % ps/(nm·km)
+        Dacc_val = D_lambda * (L/1000);                                          % ps/nm
+        Dacc_val = min(max(Dacc_val, -100), 100);
+
+        Dacc_vec(k) = Dacc_val;
     end
-
-    % Clamp to O-band
-    lambda_sol = min(max(lambda_sol, lambda_min), lambda_max);
-    lambda_vec(k) = lambda_sol;
-
-    % Compute D(lambda) and accumulated dispersion
-    D_lambda = (S0_si/4) * (lambda_sol - (lambda0^4)/(lambda_sol^3)) / 1e-6; % ps/(nm·km)
-    Dacc_val = D_lambda * (L/1000);                                          % ps/nm
-    Dacc_val = min(max(Dacc_val, -100), 100);
-
-    Dacc_vec(k) = Dacc_val;
-end
 end

Functions/beautifyBERplot.m

@@ -11,6 +11,7 @@ function beautifyBERplot(options)
 
 arguments
     options.logscale (1,1) logical = 1
+    options.setmarkers (1,1) logical = 1;
     options.polyfit (1,1) logical = 0
     options.polyorder (1,1) double = 2
     options.fitmethod (1,1) string = "polyfit"  % choose fit type
@@ -23,13 +24,15 @@ num_markers = length(markers);
 
 % --- style all lines consistently
 for i = 1:length(lines)
-    lines(i).LineWidth = 1;
+    lines(i).LineWidth = 1.2;
     % lines(i).LineStyle = '-';
-    if string(lines(i).Marker) == "none"
+    if options.setmarkers == 1
-        lines(i).Marker = markers{mod(i-1, num_markers) + 1};
+        if string(lines(i).Marker) == "none"
+            lines(i).Marker = markers{mod(i-1, num_markers) + 1};
+        end
+        lines(i).MarkerSize = 4;
+        lines(i).MarkerFaceColor = lines(i).Color;
     end
-    lines(i).MarkerSize = 4;
-    lines(i).MarkerFaceColor = lines(i).Color;
 end
 
 % --- optional smoothing/fitting overlay

Libs/wesanderson_colors/WesPalette.m

@@ -0,0 +1,133 @@
+classdef WesPalette
+    % WESPALETTE  Wes Anderson color palettes with auto-completion
+    % Usage:
+    %   cmap = WesPalette.Zissou1.rgb()
+    %   cmap = WesPalette.Zissou1.rgb(3)
+
+    % https://github.com/karthik/wesanderson?tab=readme-ov-file
+
+    enumeration
+        BottleRocket1
+        BottleRocket2
+        Rushmore1
+        Rushmore
+        Royal1
+        Royal2
+        Zissou1
+        Zissou1Continuous
+        Darjeeling1
+        Darjeeling2
+        Chevalier1
+        FantasticFox1
+        Moonrise1
+        Moonrise2
+        Moonrise3
+        Cavalcanti1
+        GrandBudapest1
+        GrandBudapest2
+        IsleofDogs1
+        IsleofDogs2
+        FrenchDispatch
+        AsteroidCity1
+        AsteroidCity2
+        AsteroidCity3
+    end
+
+    methods
+        function cmap = rgb(obj, n)
+            % Return palette as Nx3 RGB colormap [0–1]
+
+            hex = obj.hex();
+
+            rgb = hex2rgb(hex);
+
+            if nargin == 2
+                if n > size(rgb,1)
+                    error('Requested %d colors, but only %d available.', ...
+                          n, size(rgb,1))
+                end
+                cmap = rgb(1:n,:);
+            else
+                cmap = rgb;
+            end
+        end
+    end
+
+    methods (Access = private)
+        function hex = hex(obj)
+            % Internal HEX storage
+
+            switch obj
+                case WesPalette.BottleRocket1
+                    hex = {'#A42820','#5F5647','#9B110E','#3F5151','#4E2A1E','#550307','#0C1707'};
+
+                case WesPalette.BottleRocket2
+                    hex = {'#FAD510','#CB2314','#273046','#354823','#1E1E1E'};
+
+                case {WesPalette.Rushmore1, WesPalette.Rushmore}
+                    hex = {'#E1BD6D','#EABE94','#0B775E','#35274A','#F2300F'};
+
+                case WesPalette.Royal1
+                    hex = {'#899DA4','#C93312','#FAEFD1','#DC863B'};
+
+                case WesPalette.Royal2
+                    hex = {'#9A8822','#F5CDB4','#F8AFA8','#FDDDA0','#74A089'};
+
+                case WesPalette.Zissou1
+                    hex = {'#3B9AB2','#78B7C5','#EBCC2A','#E1AF00','#F21A00'};
+
+                case WesPalette.Zissou1Continuous
+                    hex = {'#3A9AB2','#6FB2C1','#91BAB6','#A5C2A3','#BDC881', ...
+                           '#DCCB4E','#E3B710','#E79805','#EC7A05','#EF5703','#F11B00'};
+
+                case WesPalette.Darjeeling1
+                    hex = {'#FF0000','#00A08A','#F2AD00','#F98400','#5BBCD6'};
+
+                case WesPalette.Darjeeling2
+                    hex = {'#ECCBAE','#046C9A','#D69C4E','#ABDDDE','#000000'};
+
+                case WesPalette.Chevalier1
+                    hex = {'#446455','#FDD262','#D3DDDC','#C7B19C'};
+
+                case WesPalette.FantasticFox1
+                    hex = {'#DD8D29','#E2D200','#46ACC8','#E58601','#B40F20'};
+
+                case WesPalette.Moonrise1
+                    hex = {'#F3DF6C','#CEAB07','#D5D5D3','#24281A'};
+
+                case WesPalette.Moonrise2
+                    hex = {'#798E87','#C27D38','#CCC591','#29211F'};
+
+                case WesPalette.Moonrise3
+                    hex = {'#85D4E3','#F4B5BD','#9C964A','#CDC08C','#FAD77B'};
+
+                case WesPalette.Cavalcanti1
+                    hex = {'#D8B70A','#02401B','#A2A475','#81A88D','#972D15'};
+
+                case WesPalette.GrandBudapest1
+                    hex = {'#F1BB7B','#FD6467','#5B1A18','#D67236'};
+
+                case WesPalette.GrandBudapest2
+                    hex = {'#E6A0C4','#C6CDF7','#D8A499','#7294D4'};
+
+                case WesPalette.IsleofDogs1
+                    hex = {'#9986A5','#79402E','#CCBA72','#0F0D0E','#D9D0D3','#8D8680'};
+
+                case WesPalette.IsleofDogs2
+                    hex = {'#EAD3BF','#AA9486','#B6854D','#39312F','#1C1718'};
+
+                case WesPalette.FrenchDispatch
+                    hex = {'#90D4CC','#BD3027','#B0AFA2','#7FC0C6','#9D9C85'};
+
+                case WesPalette.AsteroidCity1
+                    hex = {'#0A9F9D','#CEB175','#E54E21','#6C8645','#C18748'};
+
+                case WesPalette.AsteroidCity2
+                    hex = {'#C52E19','#AC9765','#54D8B1','#B67C3B','#175149','#AF4E24'};
+
+                case WesPalette.AsteroidCity3
+                    hex = {'#FBA72A','#D3D4D8','#CB7A5C','#5785C1'};
+            end
+        end
+    end
+end

Libs/wesanderson_colors/hex2rgb.m

@@ -0,0 +1,20 @@
+function rgb = hex2rgb(hex)
+% HEX2RGB Convert HEX color codes to RGB [0–1]
+
+if ischar(hex)
+    hex = {hex};
+end
+
+n = numel(hex);
+rgb = zeros(n,3);
+
+for k = 1:n
+    h = hex{k};
+    h = strrep(h,'#','');
+    rgb(k,1) = hex2dec(h(1:2));
+    rgb(k,2) = hex2dec(h(3:4));
+    rgb(k,3) = hex2dec(h(5:6));
+end
+
+rgb = rgb / 255;
+end

Libs/wesanderson_colors/minimal_example_wespalette.m

@@ -0,0 +1,64 @@
+x = -10:2:25;   % Input power [dBm]
+
+y1 = 1e-5 * 10.^(0.12*x);             % Dispersion-only
+y2 = 1e0 ./ (1 + exp(-0.4*(x-12)));  % NLPN
+y3 = 1e-6 * 10.^(0.45*x);             % RP on gamma
+y4 = 1e-2 * 10.^(0.18*(x-8));         % RP on beta2
+
+cmap = WesPalette.AsteroidCity1.rgb(4);
+cmap = linspecer(4);
+figure1=figure(202998);clf;hold on
+lw = 0.8; ms = 4;
+plot(x,y1,'LineWidth',lw,'Color',cmap(1,:),'Marker','o','MarkerEdgeColor',cmap(1,:),'MarkerFaceColor',[1,1,1],'MarkerSize',ms); 
+plot(x,y2,'LineWidth',lw,'Color',cmap(2,:),'Marker','square','MarkerEdgeColor',cmap(2,:),'MarkerFaceColor',[1,1,1],'MarkerSize',ms);
+plot(x,y3,'LineWidth',lw,'Color',cmap(3,:),'Marker','o','MarkerEdgeColor',cmap(3,:),'MarkerFaceColor',[1,1,1],'MarkerSize',ms);
+plot(x,y4,'LineWidth',lw,'Color',cmap(4,:),'Marker','o','MarkerEdgeColor',cmap(4,:),'MarkerFaceColor',[1,1,1],'MarkerSize',ms);
+yline(3.8e-3)
+
+grid on
+xlabel('Input power [dBm]')
+ylabel('NSD ($\%$)')
+legend({'Dispersion','NLPN','RP','RP on $\beta_2$'}, ...
+       'Location','best')
+
+grid off
+set(gca,'MinorGridLineWidth',0.5);
+set(gca,'GridLineWidth',0.5,'GridLineStyle','--','GridColor',[0.9,0.9,0.9]);
+
+set(gca,'FontSize',12,'YScale','log');
+ylim([1e-6 1e3])
+xlim([-10 23])
+
+% % Create textarrow
+% annotation(figure1,'textarrow',[0.564444444444444 0.548148148148148],...
+%     [0.768523809523809 0.63047619047619],'String',{'(A)'});
+% 
+% % Create doublearrow
+% annotation(figure1,'doublearrow',[0.724444444444444 0.699259259259259],...
+%     [0.854238095238095 0.723809523809524]);
+% 
+% % Create line
+% annotation(figure1,'line',[0.700740740740741 0.699259259259259],...
+%     [0.554285714285714 0.405714285714286]);
+% 
+% % Create textbox
+% annotation(figure1,'textbox',...
+%     [0.578777777777778 0.194285714285714 0.104185185185185 0.0685714285714286],...
+%     'String',{'BOX'},...
+%     'FitBoxToText','off');
+
+% 
+fig_path = 'C:\Users\Silas\Documents\Dissertation\00_Examples\tikz\textfig.tikz';
+matlab2tikz(fig_path, ...
+    'width','\fwidth', ...
+    'height','\fheight', ...
+    'showInfo',false, ...
+    'extraAxisOptions',{ ...
+        'legend style={font=\footnotesize}', ...
+        'xlabel style={font=\color{white!15!black},font=\small},',...
+        'ylabel style={font=\color{white!15!black},font=\small},',...
+        'legend columns=1', ...
+        'every axis/.append style={font=\scriptsize}',...
+    'legend columns=2',...
+    'legend style={at={(0.02,0.98)},font=\footnotesize,draw=black!60,rounded corners=2pt,inner sep=1pt,fill=white,column sep=6pt,anchor= north west}',...
+    });

Libs/wesanderson_colors/wes_palettes.m

@@ -0,0 +1,79 @@
+function palettes = wes_palettes()
+% WES_PALETTES  Full Wes Anderson color palette collection for MATLAB
+% Colors are stored as HEX and converted to RGB on demand.
+
+palettes = struct();
+
+palettes.BottleRocket1 = { ...
+    '#A42820', '#5F5647', '#9B110E', '#3F5151', '#4E2A1E', '#550307', '#0C1707'};
+
+palettes.BottleRocket2 = { ...
+    '#FAD510', '#CB2314', '#273046', '#354823', '#1E1E1E'};
+
+palettes.Rushmore1 = { ...
+    '#E1BD6D', '#EABE94', '#0B775E', '#35274A', '#F2300F'};
+
+palettes.Rushmore = palettes.Rushmore1;
+
+palettes.Royal1 = { ...
+    '#899DA4', '#C93312', '#FAEFD1', '#DC863B'};
+
+palettes.Royal2 = { ...
+    '#9A8822', '#F5CDB4', '#F8AFA8', '#FDDDA0', '#74A089'};
+
+palettes.Zissou1 = { ...
+    '#3B9AB2', '#78B7C5', '#EBCC2A', '#E1AF00', '#F21A00'};
+
+palettes.Zissou1Continuous = { ...
+    '#3A9AB2', '#6FB2C1', '#91BAB6', '#A5C2A3', '#BDC881', ...
+    '#DCCB4E', '#E3B710', '#E79805', '#EC7A05', '#EF5703', '#F11B00'};
+
+palettes.Darjeeling1 = { ...
+    '#FF0000', '#00A08A', '#F2AD00', '#F98400', '#5BBCD6'};
+
+palettes.Darjeeling2 = { ...
+    '#ECCBAE', '#046C9A', '#D69C4E', '#ABDDDE', '#000000'};
+
+palettes.Chevalier1 = { ...
+    '#446455', '#FDD262', '#D3DDDC', '#C7B19C'};
+
+palettes.FantasticFox1 = { ...
+    '#DD8D29', '#E2D200', '#46ACC8', '#E58601', '#B40F20'};
+
+palettes.Moonrise1 = { ...
+    '#F3DF6C', '#CEAB07', '#D5D5D3', '#24281A'};
+
+palettes.Moonrise2 = { ...
+    '#798E87', '#C27D38', '#CCC591', '#29211F'};
+
+palettes.Moonrise3 = { ...
+    '#85D4E3', '#F4B5BD', '#9C964A', '#CDC08C', '#FAD77B'};
+
+palettes.Cavalcanti1 = { ...
+    '#D8B70A', '#02401B', '#A2A475', '#81A88D', '#972D15'};
+
+palettes.GrandBudapest1 = { ...
+    '#F1BB7B', '#FD6467', '#5B1A18', '#D67236'};
+
+palettes.GrandBudapest2 = { ...
+    '#E6A0C4', '#C6CDF7', '#D8A499', '#7294D4'};
+
+palettes.IsleofDogs1 = { ...
+    '#9986A5', '#79402E', '#CCBA72', '#0F0D0E', '#D9D0D3', '#8D8680'};
+
+palettes.IsleofDogs2 = { ...
+    '#EAD3BF', '#AA9486', '#B6854D', '#39312F', '#1C1718'};
+
+palettes.FrenchDispatch = { ...
+    '#90D4CC', '#BD3027', '#B0AFA2', '#7FC0C6', '#9D9C85'};
+
+palettes.AsteroidCity1 = { ...
+    '#0A9F9D', '#CEB175', '#E54E21', '#6C8645', '#C18748'};
+
+palettes.AsteroidCity2 = { ...
+    '#C52E19', '#AC9765', '#54D8B1', '#B67C3B', '#175149', '#AF4E24'};
+
+palettes.AsteroidCity3 = { ...
+    '#FBA72A', '#D3D4D8', '#CB7A5C', '#5785C1'};
+
+end

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/Copy_of_FIGURE_ROP.m

@@ -0,0 +1,146 @@
+%% ============================================================
+%  SETTINGS
+% ============================================================
+database_type = 'mysql';
+db = DBHandler("dataBase", "labor_highspeed", "type", database_type);
+
+fiberL  = 1;        % km
+wlen    = 1310;     % nm
+bit    = 300e9;    % example (adjust if needed)
+max_pd  = 7;        % ROP limit (same as before)
+
+PAM_list = [4 6 8];     % formats to compare
+
+% Colors for PAM formats
+colors = {clr.Paired.red, clr.Paired.green, clr.Paired.blue};
+
+% Best DSP selection:
+bestDSP = struct;
+bestDSP = struct;
+bestDSP.P4 = equalizer_structure.vnle_db_mlse;    % PAM-4
+bestDSP.P6 = equalizer_structure.vnle;    % PAM-6
+bestDSP.P8 = equalizer_structure.vnle;    % PAM-8
+
+
+
+%% ============================================================
+%  LOOP over PAM formats — extract data
+% ============================================================
+results = struct;
+
+for pi = 1:numel(PAM_list)
+    M = PAM_list(pi);
+    eq = bestDSP.(sprintf('P%d', M));
+
+    % ---- DB FILTER ----
+    fp = QueryFilter();
+    fp.where('Runs','pam_level','EQUALS',M);
+    fp.where('Runs','fiber_length','EQUALS',fiberL);
+    fp.where('Runs','wavelength','EQUALS',wlen);
+    fp.where('Runs','bitrate','EQUALS',bit);
+    fp.where('Runs','power_pd_in','LESS_THAN',max_pd);
+
+    fields = [
+        db.getTableFieldNames('power_state_info');
+        db.getTableFieldNames('dashboard_ungrouped_alltime')
+    ];
+    [T,~] = db.queryDB(fp, fields);
+
+    % ---- DSP OPTIONS ----
+    pre_emph = decide_preemph(M, eq);
+    precoded = decide_precoded(M, eq);
+
+    cfg = struct;
+    cfg.x_axis   = 'power_mzm';
+    cfg.y_axis   = 'BER';
+    cfg.agg      = 'min';
+    cfg.outlier  = 'none';
+    cfg.show_raw = false;
+
+    cfg.filters = struct( ...
+        'pam_level', M, ...
+        'fiber_length', fiberL, ...
+        'wavelength', wlen, ...
+        'bitrate', bit, ...
+        'is_mpi', 0, ...
+        'equalizer_structure', eq, ...
+        'pre_emph', pre_emph);
+
+    A = analyze_measurements_gpt(T, cfg);
+
+    results(pi).M   = M;
+    results(pi).x   = A.group{1}.x;
+    results(pi).color = colors{pi};
+
+    if precoded
+        results(pi).ber = A.group{1}.y_precoded;
+    else
+        results(pi).ber = A.group{1}.y;
+    end
+end
+
+
+%% ============================================================
+%  PLOT — all PAM formats in one ROP plot
+% ============================================================
+fig = figure(91);  hold on;
+
+lw = 2.2; ms = 7;
+
+for pi = 1:numel(results)
+    plot(results(pi).x, results(pi).ber, ...
+        '-o', ...
+        'LineWidth', lw, ...
+        'MarkerSize', ms, ...
+        'MarkerFaceColor', results(pi).color, ...
+        'Color', results(pi).color, ...
+        'DisplayName', sprintf('PAM-%d', results(pi).M));
+end
+
+set(gca,'YScale','log');
+grid minor;
+
+xlabel('ROP / Power (MZM) [dBm]');
+ylabel('BER');
+
+ylim([1e-4 2e-1]);
+
+legend('Location','best');
+title(sprintf('BER vs ROP — Best DSP (4,6,8) at %.0f GBd, λ=%d nm, %.0f km', ...
+    bit*1e-9, wlen, fiberL));
+
+beautifyBERplot();
+
+set(fig,'Position',1e3*[0.35 0.45 1.0 0.45]);
+
+%% ============================================================
+% DECISION LOGIC (INLINE FUNCTIONS)
+% ============================================================
+
+function pe = decide_preemph(M, eq)
+    % PRE-EMPH RULES:
+    switch M
+        case 4
+            if eq == equalizer_structure.vnle
+                pe = 1;     % PAM4: VNLE → pre-emph on
+            else
+                pe = 0;     % PAM4: all others → off
+            end
+        case {6,8}
+            pe = 1;         % PAM6/8: all → pre-emph on
+        otherwise
+            pe = 0;
+    end
+end
+
+
+function flag = decide_precoded(M, eq)
+    % PRE-CODE RULES:
+    if eq == equalizer_structure.vnle_db_mlse
+        flag = 1;                         % Always for DB-target
+    elseif eq == equalizer_structure.ml_mlse && M == 4
+        flag = 1;                         % PAM4: ML-based → precoded
+    else
+        flag = 0;
+    end
+end

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/Copy_of_FIGURE_WAVELENGTH.m

@@ -0,0 +1,272 @@
+%% ============================================================
+%  LOAD DATA FOR PAM = 4,6,8
+% ============================================================
+database_type = 'mysql';
+db = DBHandler("dataBase", "labor_highspeed", "type", database_type);
+
+pam_levels = [4, 6, 8];      % three tiles
+bitrate_set = 360e9;
+fiberL = 10;
+
+fields = [
+    db.getTableFieldNames('power_state_info');
+    db.getTableFieldNames('dashboard_ungrouped_alltime')
+];
+
+%% ============================================================
+%  DEFINE DSP SCHEMES
+% ============================================================
+curves = struct;
+
+curves(1).name  = 'VNLE';
+curves(1).eq    = equalizer_structure.vnle;
+curves(1).color = clr.Paired.red;
+
+curves(2).name  = 'PF + MLSE';
+curves(2).eq    = equalizer_structure.vnle_pf_mlse;
+curves(2).color = clr.Paired.green;
+
+curves(3).name  = 'DB-target + MLSE';
+curves(3).eq    = equalizer_structure.vnle_db_mlse;
+curves(3).color = clr.Paired.blue;
+
+curves(4).name  = 'ML-based MLSE';
+curves(4).eq    = equalizer_structure.ml_mlse;
+curves(4).color = clr.Paired.purple;
+
+%% ============================================================
+%  ANALYSIS — NO PLOTTING
+%  results(p, k) → p: PAM index, k: DSP index
+% ============================================================
+results = struct;
+
+for p = 1:length(pam_levels)
+    M = pam_levels(p);
+
+    % --- Load DB rows for this PAM ---
+    fp = QueryFilter();
+    fp.where('Runs','pam_level','EQUALS', M);
+    fp.where('Runs','fiber_length','EQUALS', fiberL);
+    fp.where('Runs','bitrate','EQUALS', bitrate_set);
+    fp.where('Runs','is_mpi','EQUALS', 0);
+
+    [dataTable, ~] = db.queryDB(fp, fields);
+
+    for k = 1:numel(curves)
+
+        %% =====================================================
+        % DECIDE PRE-EMPHASIS AND PRECoded BER
+        % ======================================================
+        pre_emph     = decide_preemph(M, curves(k).eq);
+        use_precoded = decide_precoded(M, curves(k).eq);
+
+        %% ---- base config ----
+        cfg = struct;
+        cfg.x_axis = 'wavelength';
+        cfg.y_axis = 'BER';
+        cfg.agg    = 'min';
+        cfg.outlier = 'none';
+        % cfg.group_by = {'wavelength'};
+        cfg.show_raw = false;
+
+        cfg.filters = struct( ...
+            'pam_level', M, ...
+            'is_mpi', 0, ...
+            'bitrate', bitrate_set, ...
+            'fiber_length', fiberL, ...
+            'equalizer_structure', curves(k).eq, ...
+            'pre_emph', pre_emph);
+
+        %% ---- Run analysis ----
+        A = analyze_measurements_gpt(dataTable, cfg);
+
+        results(p,k).wavelength = A.group{1}.x;
+
+        %% ---- store BER variant ----
+        if use_precoded
+            results(p,k).ber = A.group{1}.y_precoded;
+        else
+            results(p,k).ber = A.group{1}.y;
+        end
+
+    end
+end
+
+
+%% ============================================================
+%  PLOT — 1×3 (PAM-4, PAM-6, PAM-8)
+% ============================================================
+fig = figure(9110); clf;
+tiledlayout(1,3,'TileSpacing','compact','Padding','compact');
+
+lw = 1.8;
+ms = 6;
+
+for p = 1:length(pam_levels)
+    nexttile; hold on;
+
+    for k = 1:numel(curves)
+        plot(results(p,k).wavelength, results(p,k).ber, ...
+            '-o', ...
+            'Color', curves(k).color, ...
+            'MarkerFaceColor', curves(k).color, ...
+            'MarkerSize', ms, ...
+            'LineWidth', lw, ...
+            'DisplayName', curves(k).name);
+    end
+
+    set(gca,'YScale','log');
+    grid on;
+    if p == 1
+    ylabel('BER');
+    else
+ylabel('');
+    end
+    xlabel('wavelength');
+    
+    ylim([4e-4, 0.1]);
+
+    beautifyBERplot();
+
+    yline([2.2e-4 4.85e-3 2e-2], ...
+        'LineWidth',1.1, 'Color',[0.2 0.2 0.2], ...
+        'LineStyle',':','HandleVisibility','off');
+
+    
+    if p == 1
+
+        x1 = 1290;
+        x2 = 1297;
+        x3 = 1300;
+        x4 = 1323;
+        x5 = 1325;
+        x6 = 1330;
+
+    elseif p == 2
+
+        x1 = 1290;
+        x2 = 1295;
+        x3 = 1300;
+        x4 = 1323.5;
+        x5 = 1325;
+        x6 = 1330;
+
+    elseif p == 3
+
+        x1 = 1290;
+        x2 = 1292;
+        x3 = 1298;
+        x4 = 1323;
+        x5 = 1327.5;
+        x6 = 1330;
+    end
+
+    % --- Get current y-limits ---
+    yl = ylim;
+
+    % --- LEFT AREA BELOW KP4 FEC ---
+    patch([x1 x2 x2 x1], [yl(1) yl(1) yl(2) yl(2)], ...
+        clr.Set1.red, ...          % RGB = red
+        'FaceAlpha', 0.1, ... % transparency 0.1
+        'EdgeColor', 'none');  % no border
+
+    % --- RIGHT AREA BELOW KP4 FEC ---
+    patch([x2 x3 x3 x2], [yl(1) yl(1) yl(2) yl(2)], ...
+        clr.Set1.blue, ...          % RGB = red
+        'FaceAlpha', 0.10, ... % transparency 0.1
+        'EdgeColor', 'none');  % no border
+
+    % --- LEFT AREA BELOW O-FEC ---
+    patch([x4 x5 x5 x4], [yl(1) yl(1) yl(2) yl(2)], ...
+        clr.Set1.blue, ...          % RGB = red
+        'FaceAlpha', 0.10, ... % transparency 0.1
+        'EdgeColor', 'none');  % no border
+
+    % --- RIGHT AREA BELOW O-FEC ---
+    patch([x5 x6 x6 x5], [yl(1) yl(1) yl(2) yl(2)], ...
+        clr.Set1.red, ...          % RGB = red
+        'FaceAlpha', 0.10, ... % transparency 0.1
+        'EdgeColor', 'none');  % no border
+
+    uistack(findobj(gca,'Type','patch'),'bottom'); % send the patch behind curves
+
+
+%  ax = gca;
+% axpos = ax.Position;  % [x y w h] normalized
+% xl = xlim;
+% yl = ylim;
+% 
+% % Convert axis coords → normalized figure coords
+% toNorm = @(x,y) [ ...
+%     axpos(1) + (x - xl(1)) / (xl(2)-xl(1)) * axpos(3), ...
+%     axpos(2) + (y - yl(1)) / (yl(2)-yl(1)) * axpos(4) ...
+% ];
+% 
+% % Choose vertical placement (10% above bottom of axis)
+% y_arrow = yl(1) * (yl(2)/yl(1))^0.10;   % works with log-scale axes
+% 
+% % === Arrow 1:  x3 <-> x4  ======================================
+% p1 = toNorm(x3, y_arrow);
+% p2 = toNorm(x4, y_arrow);
+% 
+% annotation('doublearrow', ...
+%     [p1(1) p2(1)], [p1(2) p2(2)], ...
+%     'Color', [0 0 0], 'LineWidth', 1.4);
+% 
+% % === Arrow 2:  x2 <-> x5  ======================================
+% p3 = toNorm(x2, y_arrow);
+% p4 = toNorm(x5, y_arrow);
+% 
+% annotation('doublearrow', ...
+%     [p3(1) p4(1)], [p3(2) p4(2)], ...
+%     'Color', [0 0 0], 'LineWidth', 1.4);
+
+end
+
+pos = 1e3.*[2.7770    1.2017    1.4000    0.3200];
+set(fig, 'Position', pos);
+
+%% === EXPORT ===
+outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\wavelength_analysis.tikz';
+matlab2tikz(outfile, ...
+    'width','\fwidth', ...
+    'height','\fheight', ...
+    'showInfo',false, ...
+    'extraAxisOptions',{ ...
+        'legend style={font=\footnotesize}', ...
+        'legend columns=1' ...
+    });
+
+
+
+%% ============================================================
+% DECISION LOGIC (INLINE FUNCTIONS)
+% ============================================================
+
+function pe = decide_preemph(M, eq)
+    % PRE-EMPH RULES:
+    switch M
+        case 4
+            if eq == equalizer_structure.vnle
+                pe = 1;     % PAM4: VNLE → pre-emph on
+            else
+                pe = 0;     % PAM4: all others → off
+            end
+        case {6,8}
+            pe = 1;         % PAM6/8: all → pre-emph on
+        otherwise
+            pe = 0;
+    end
+end
+
+
+function flag = decide_precoded(M, eq)
+    % PRE-CODE RULES:
+    if eq == equalizer_structure.vnle_db_mlse
+        flag = 1;                         % Always for DB-target
+    elseif eq == equalizer_structure.ml_mlse && M == 4
+        flag = 1;                         % PAM4: ML-based → precoded
+    else
+        flag = 0;
+    end
+end

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/FIGURE_BER_3x4.m

@@ -0,0 +1,132 @@
+database_type   = 'mysql';
+dataBase        = 'labor_highspeed';
+db = DBHandler("dataBase", dataBase, "type", database_type);
+
+%% FILTER QUERY
+fp = QueryFilter();
+fp.where('Runs', 'fiber_length','EQUALS', 2);
+fp.where('Runs', 'wavelength','EQUALS', 1310);
+fp.where('Runs', 'rop_attenuation','EQUALS', 0);
+
+fields = db.getTableFieldNames('power_state_info');
+fields = [fields; db.getTableFieldNames('dashboard_ungrouped_alltime')];
+
+[dataTable,~] = db.queryDB(fp, fields);
+
+%% ---- CONFIG ----
+cfg = struct;
+cfg.x_axis = 'grossrate';
+cfg.y_axis = 'BER';
+cfg.y_scale = 'log';
+cfg.outlier = 'mad';
+cfg.show_raw = false;
+cfg.show_spread = 'none';
+cfg.agg = 'min';
+cfg.show_precoded = 1;
+cfg.fec_lines = [];
+
+cfg.plot = struct;
+cfg.plot.use_cbrewer2 = false;
+cfg.plot.lineWidth = 2.0;
+cfg.plot.errWidth  = 1.2;
+cfg.plot.scatterAlpha = 0.35;
+cfg.plot.legendLocation = 'best';
+cfg.plot.fecLineWidth = 2.4;
+cfg.plot.custom_colors_scatter = [];    % disabled
+
+%% ---- DSP DEFINITIONS ----
+DSP(1).name        = 'VNLE';
+DSP(1).eq          = equalizer_structure.vnle;
+DSP(1).color       = clr.Paired.red;
+DSP(1).lightcolor  = clr.Paired.lightred;
+
+DSP(2).name        = 'VNLE PF MLSE';
+DSP(2).eq          = equalizer_structure.vnle_pf_mlse;
+DSP(2).color       = clr.Paired.green;
+DSP(2).lightcolor  = clr.Paired.lightgreen;
+
+DSP(3).name        = 'VNLE DB MLSE';
+DSP(3).eq          = equalizer_structure.vnle_db_mlse;
+DSP(3).color       = clr.Paired.blue;
+DSP(3).lightcolor  = clr.Paired.lightblue;
+
+DSP(4).name        = 'ML MLSE';
+DSP(4).eq          = equalizer_structure.ml_mlse;
+DSP(4).color       = clr.Paired.purple;
+DSP(4).lightcolor  = clr.Paired.lightpurple;
+
+%% ---- GRID CONFIG ----
+rows = 3;          % PAM 4,6,8
+cols = 4;          % DSP schemes
+pam  = [4 6 8];
+
+cfg.figure_number = 46;
+fig = figure(cfg.figure_number); clf;
+
+t = tiledlayout(rows, cols, ...
+    'TileSpacing','compact', ...
+    'Padding','compact');
+
+cfg.group_by = {'equalizer_structure','pre_emph'};
+cfg.plot.use_cbrewer2 = false;
+
+%% ==== MAIN PLOT LOOP =====
+for r = 1:rows
+    Mlev = pam(r);
+
+    for c = 1:cols
+        ax = nexttile(t, (r-1)*cols + c);
+        cfg.ax = ax;
+
+        % ---- PRE-EMPH = 1 ----
+        cfg.filters = struct('is_mpi',0,'pam_level',Mlev, ...
+                             'equalizer_structure',DSP(c).eq, ...
+                             'pre_emph',1);
+        cfg.plot.custom_colors     = DSP(c).lightcolor;
+        cfg.plot.custom_linetypes  = {'-'};
+        [~, M1] = plot_measurements_gpt(dataTable, cfg);
+
+        % ---- PRE-EMPH = 0 ----
+        cfg.filters.pre_emph = 0;
+        cfg.plot.custom_colors     = DSP(c).color;
+        cfg.plot.custom_linetypes  = {'-'};
+        [~, M0] = plot_measurements_gpt(dataTable, cfg);
+
+        % Axis limits
+        if Mlev == 4
+            ylim([1e-5 0.3]);
+        elseif Mlev == 6
+            ylim([6e-4 0.1]);
+        elseif Mlev == 8
+            ylim([9e-4 0.1]);
+        end
+
+        % ---- FEC lines ----
+        yline([2.2e-4 4.85e-3 2e-2], ...
+            'LineWidth',1.1, 'Color',[0.2 0.2 0.2], ...
+            'LineStyle',':','HandleVisibility','off');
+
+        beautifyBERplot;
+
+        % ---- Remove redundant labels ----
+        if c > 1, ax.YLabel = []; end
+        if r < rows, ax.XLabel = []; end
+
+        grid(ax,'on'); box(ax,'on');
+    end
+end
+
+%% ---- FIXED FIGURE SIZE ----
+pos = 1e3.*[0.1070    0.5497    1.4113    0.6847];
+set(fig, 'Position', pos);
+
+% %% === EXPORT ===
+% outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\compare_pre_emphasis.tikz';
+% matlab2tikz(outfile, ...
+%     'width','\fwidth', ...
+%     'height','\fheight', ...
+%     'showInfo',false, ...
+%     'extraAxisOptions',{ ...
+%         'legend style={font=\footnotesize}', ...
+%         'legend columns=1' ...
+%     });

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/FIGURE_EYES.m

@@ -0,0 +1,257 @@
+dsp_options.storage_path    = 'Z:\2024\sioe_labor\';
+dsp_options.max_occurences = 1;
+database    = DBHandler("dataBase", 'labor_highspeed', "type",  'mysql' );
+
+rate = [300e9];
+cols = cbrewer2('BuPu',25);
+cols = [cols(end-10:2:end,:)];
+cols = cbrewer2('Set1',6);
+
+fignum = 200;
+fig=figure(fignum);clf;
+
+dbmode = 0;
+
+
+% 1 - PAM 4 with preemphasis
+fp = QueryFilter();
+M = 6;
+fp.where('Runs', 'pam_level','EQUALS', M);
+fp.where('Runs', 'bitrate','EQUALS', rate);%360,390
+fp.where('Runs', 'fiber_length','EQUALS', 2);
+fp.where('Runs', 'wavelength','EQUALS', 1310);
+fp.where('Runs', 'db_mode','EQUALS', dbmode);
+fp.where('Runs', 'rop_attenuation','EQUAL', 0);
+
+[dataTable,~] = db.queryDB(fp, database.getTableFieldNames('Runs'));
+
+dataTable   = queryRunid(dataTable.run_id, database);
+fsym        = dataTable.symbolrate;
+M           = double(dataTable.pam_level);
+duob_mode   = db_mode(strrep(dataTable.db_mode,'"',''));
+
+% Load and Sync signal data from DB
+[Tx_bits, Symbols, Scpe_cell, ~] = loadAndSyncSignalDataFromDb(dataTable, dsp_options);
+
+% Preprocess signal
+Scpe_sig = preprocessSignal(Scpe_cell{1}, Symbols, fsym);
+
+Scpe_sig.eye(fsym,M,"fignum",M*10);
+
+%% === EXPORT TO TIKZ ===
+% outfile = ['C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\eye_pam_',num2str(M),'.tikz'];
+% outfile = ['C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\vnle_optimization.tikz'];
+% matlab2tikz(outfile, ...
+%     'width','\fwidth', ...
+%     'height','\fheight', ...
+%     'showInfo',false, ...
+%     'extraAxisOptions',{ ...
+%         'legend style={font=\footnotesize}', ...
+%         'legend columns=1' ...
+%     } );
+
+%%
+
+if duob_mode == db_mode.no_db && M == 6 %only for PAM-6 and no duobinary precoding, otherwise leads to false sequence estimation
+    trellexlusion = 1;
+else
+    trellexlusion = 0;
+end
+mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels,'scale_mode',2,'trellis_exclusion',trellexlusion,'trellis_state_mode',3);
+len_tr      = 4096*2;
+
+ffe_order   = [50, 5, 5];
+dfe_order   = [0, 0, 0];
+pf_ncoeffs  = 1;
+mu_ffe      = [0.0001, 0.0008, 0.001];
+mu_dfe      = 0.0004;
+mu_dc       = 0.005;
+dc_buffer_len = 1;
+
+mu_tr = 0;
+mu_dd = 0.05;
+adaption= 1;
+use_dd_mode = 1;
+ffe_order   = [50, 5, 5];
+eq_    = EQ("Ne",ffe_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);
+
+dbt_results = duobinary_target(eq_, mlse_db_, M, Scpe_sig, Symbols, Tx_bits, ...
+    "precode_mode", duob_mode, ...
+    'showAnalysis', 1,...
+    "postFFE", []);
+
+%% === FINAL FIGURE SIZE ===
+
+% Existing figure numbers
+figEye   = 249;
+figConst = 341;
+
+% Find axes in the source figures
+srcAxEye   = findobj(figEye, 'Type', 'axes');
+srcAxConst = findobj(figConst, 'Type', 'axes');
+
+% Create new combined figure
+figCombined = figure;
+t = tiledlayout(figCombined, 1, 2);
+t.TileSpacing = 'compact';
+t.Padding = 'compact';
+
+% ------------------------------------------------------------
+% LEFT TILE: EYE DIAGRAM
+% ------------------------------------------------------------
+ax1 = nexttile(t, 1);
+hold(ax1, 'on')
+
+% Copy children (images, lines, patches, hist objects, etc.)
+copyobj(srcAxEye.Children, ax1);
+
+% Copy labels and title
+ax1.XLabel.String = srcAxEye.XLabel.String;
+ax1.YLabel.String = srcAxEye.YLabel.String;
+ax1.Title.String  = srcAxEye.Title.String;
+
+% Copy axis limits
+ax1.XLim = srcAxEye.XLim;
+ax1.YLim = srcAxEye.YLim;
+ax1.YDir = srcAxEye.YDir;
+
+% Copy ticks + labels EXACTLY (including remapped/scaled ones)
+ax1.XTick      = srcAxEye.XTick;
+ax1.XTickLabel = srcAxEye.XTickLabel;
+ax1.YTick      = srcAxEye.YTick;
+ax1.YTickLabel = srcAxEye.YTickLabel;
+
+% Copy colormap + clim (important for density eye)
+colormap(ax1, colormap(srcAxEye.Parent)); 
+ax1.CLim = srcAxEye.CLim;
+
+% Copy any style props that matter
+ax1.TickDir     = srcAxEye.TickDir;
+ax1.TickLength  = srcAxEye.TickLength;
+ax1.FontSize    = srcAxEye.FontSize;
+ax1.Box         = srcAxEye.Box;
+
+grid(ax1,'on');
+
+
+% ------------------------------------------------------------
+% RIGHT TILE: CONSTELLATION HISTOGRAM
+% ------------------------------------------------------------
+ax2 = nexttile(t, 2);
+hold(ax2, 'on')
+
+copyobj(srcAxConst.Children, ax2);
+
+% Copy labels and title
+ax2.XLabel.String = srcAxConst.XLabel.String;
+ax2.YLabel.String = srcAxConst.YLabel.String;
+ax2.Title.String  = srcAxConst.Title.String;
+
+% The histogram uses the same y-axis as the eye
+% Extract mapping from eye
+rawTicks      = ax1.YTick;
+rawLabelsCell = ax1.YTickLabel;
+trueVoltages  = str2double(rawLabelsCell);
+
+% Apply true voltages to the histogram axis
+ax2.XTick      = flip(trueVoltages);
+ax2.XTickLabel = flip(rawLabelsCell);
+
+% Set histogram y-limits to match the actual voltages
+ax2.XLim = [min(trueVoltages) max(trueVoltages)];
+
+% Ensure eye diagram prints the same (we *do not* touch ax1.YLim)
+ax1.XTickLabel = rawLabelsCell;
+
+
+% Copy colormap (your histogram uses same palette)
+colormap(ax2, colormap(srcAxConst.Parent));
+
+% Style properties
+ax2.TickDir     = srcAxConst.TickDir;
+ax2.TickLength  = srcAxConst.TickLength;
+ax2.FontSize    = srcAxConst.FontSize;
+ax2.Box         = srcAxConst.Box;
+
+grid(ax2,'on');
+
+% ============================================================
+% remove right y-axis completely
+% ============================================================
+ax2.XAxis.Visible = 'off';     % hides ticks + labels + axis line
+
+% BUT we still keep the YTick positions internally for alignment:
+% ax2.YTick = <values already set earlier> ;
+
+
+% ============================================================
+% minimize distance between the two plots
+% ============================================================
+t.TileSpacing = 'none';      % no space between tiles
+t.Padding     = 'none';      % no outer padding
+
+% Also reduce internal padding for each axis
+ax1.Position(3) = ax1.Position(3) + 0.02;   % widen eye a bit
+ax2.Position(1) = ax2.Position(1) - 0.02;   % pull histogram closer
+
+
+% Keep left axis grid visible
+ax2.YGrid = 'off';
+
+%
+%  =====================================================================
+% FINAL POLISHING: unified visual style
+% =======================================================================
+
+% --- unified font size ---
+FS = 12;
+set([ax1 ax2], 'FontSize', FS);
+
+% --- unified axis line width (outline stroke thickness) ---
+LW = 1.0;
+set([ax1 ax2], 'LineWidth', LW);
+
+% --- unified tick length ---
+TL = [.015 .015];
+set([ax1 ax2], 'TickLength', TL);
+
+% --- unified grid style ---
+set([ax1 ax2], 'XGrid', 'on', 'YGrid', 'on');
+set([ax1 ax2], 'GridLineStyle', '--');
+set([ax1 ax2], 'GridAlpha', 0.2);
+
+% --- remove right y-axis ticks and labels ---
+ax2.YAxis.Visible = 'off';
+
+% --- copy colormap + CLim from the eye to histogram (synchronize look) ---
+colormap(ax1, colormap(srcAxEye.Parent));
+colormap(ax2, colormap(srcAxEye.Parent));
+ax2.CLim = ax1.CLim;
+
+% --- minimal spacing between tiles ---
+t.TileSpacing = 'none';
+t.Padding     = 'none';
+
+
+% --- pull the panels together (touching boundary effect) ---
+pos1 = ax1.Position;
+pos2 = ax2.Position;
+
+% Shift histogram left until the outlines touch
+pos2(1) = pos1(1) + pos1(3) - 0.002;   % 0.002 = fine overlap control
+ax2.Position = pos2;
+
+% Expand histogram slightly, remove white band
+pos2 = ax2.Position;
+pos2(3) = pos2(3) + 0.01;
+ax2.Position = pos2;
+
+% Ensure the left plot stays correct after the move
+ax1.Position = pos1;
+
+% --- enforce same visible outline ---
+% For ax2, create a fake left spine (since YAxis is hidden)
+ax2.Box = 'on';   % keep outline but no ticks on the right
+ax1.Box = 'on';
+
+ax2.View = [90 -90];

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/FIGURE_NGMI.m

@@ -0,0 +1,221 @@
+%% ============================================================
+%     GRID: NGMI, AIR, HD-NetRate, SD-NetRate  (1 × 4)
+% ============================================================
+
+db = DBHandler("dataBase","labor_highspeed","type","mysql");
+
+%% --- Base DB Filters (shared across all curves)
+fp = QueryFilter();
+fp.where('Runs','fiber_length','EQUALS', 2);
+fp.where('Runs','wavelength','EQUALS', 1310);
+fp.where('Runs','rop_attenuation','EQUALS', 0);
+fp.where('Runs','is_mpi','EQUALS', 0);
+
+fields = db.getTableFieldNames('dashboard_ungrouped_alltime');
+[dataTable,~] = db.queryDB(fp, fields);
+
+
+%% === Curve Definitions =======================================
+curves = struct;
+
+% PAM-8 — VNLE PF MLSE — no_emph = 1 — RED
+curves(1).pam   = 8;
+curves(1).eq    = equalizer_structure.vnle_pf_mlse;
+curves(1).pre   = 0;
+curves(1).color = clr.Paired.red;
+curves(1).mkr = 'o';
+
+% PAM-6 — VNLE PF MLSE — no_emph = 1 — BLUE
+curves(2).pam   = 6;
+curves(2).eq    = equalizer_structure.vnle_pf_mlse;
+curves(2).pre   = 1;
+curves(2).color = clr.Paired.blue;
+curves(2).mkr = 'square';
+
+% PAM-4 — VNLE DB MLSE — pre_emph = 0 — GREEN
+curves(3).pam   = 4;
+curves(3).eq    = equalizer_structure.vnle_db_mlse;
+curves(3).pre   = 0;
+curves(3).color = clr.Paired.green;
+curves(3).mkr = 'diamond';
+
+%% === Prepare Analysis Config ==================================
+base = struct;
+base.group_by = {'equalizer_structure','pre_emph'};
+base.x_axis   = 'symbolrate';
+base.outlier  = 'none';
+base.show_raw = false;
+base.filters  = struct;  % will be filled per curve
+
+
+%% === Precompute All Curves ====================================
+results = struct;
+
+for k = 1:numel(curves)
+
+    % --- BER ---
+    cfg = base;
+    cfg.y_axis = 'BER';
+    cfg.agg    = 'min';
+    cfg.filters = struct('pam_level', curves(k).pam, ...
+                         'equalizer_structure', curves(k).eq, ...
+                         'pre_emph', curves(k).pre);
+
+    A = analyze_measurements_gpt(dataTable, cfg);
+    cfg.x_axis = 'grossrate';
+    B = analyze_measurements_gpt(dataTable, cfg);
+
+    results(k).baudr = A.group{1}.x;
+    results(k).gross = B.group{1}.x;
+    
+    if curves(k).pam == 4
+        results(k).ber   = A.group{1}.y_precoded;
+    else
+        results(k).ber   = A.group{1}.y;
+    end
+
+    % --- NGMI ---
+    cfg.y_axis = 'NGMI';
+    cfg.agg    = 'max';
+    A = analyze_measurements_gpt(dataTable, cfg);
+    results(k).ngmi = A.group{1}.y;
+
+
+
+    % --- AIR ---
+    cfg.y_axis = 'AIR';
+    cfg.agg    = 'max';
+    A = analyze_measurements_gpt(dataTable, cfg);
+    results(k).air = A.group{1}.y;
+        results(k).air = results(k).ngmi .* results(k).gross;
+
+    % --- Net Rates ---
+    tp = TransmissionPerformance;
+    results(k).ndr = tp.calculateNetRate(results(k).gross, ...
+                                         'NGMI', results(k).ngmi, ...
+                                         'BER',  results(k).ber);
+end
+
+
+%% ============================================================
+%                 FIGURE: 1 × 4 GRID
+% ============================================================
+fig = figure(71); clf;
+t = tiledlayout(1,4, 'TileSpacing','compact', 'Padding','compact');
+
+lw = 1.0;
+
+% === NGMI vs Grossrate ===
+ax = nexttile(t,1);
+hold on;
+for k = 1:3
+    plot(results(k).baudr, results(k).ngmi, ...
+        'LineWidth', lw, ...
+        'Color', curves(k).color, ...
+        'MarkerSize', 1, ...
+        'MarkerFaceColor', curves(k).color,...
+        'Marker',curves(k).mkr);
+end
+ylabel('NGMI');
+xlabel('Baud rate [GBd]');
+xlim([100 210]);
+xticks(100:15:225);
+ylim([0.9, 1]);
+grid minor; box on;
+beautifyBERplot("logscale",0,"setmarkers",0);
+
+
+% === AIR vs Grossrate ===
+ax = nexttile(t,2);
+hold on;
+for k = 1:3
+    plot(results(k).baudr, results(k).air, ...
+        '-', 'LineWidth', lw, ...
+        'Color', curves(k).color, ...
+        'MarkerSize', 2, ...
+        'MarkerFaceColor', curves(k).color,'Marker',curves(k).mkr);
+end
+ylabel('AIR [Gb/s]');
+xlabel('Baud rate [GBd]');
+ylim([280 430]);
+yticks(280:30:440)
+xlim([100 210]);
+xticks(100:15:225);
+grid minor; box on;
+beautifyBERplot("logscale",0,"setmarkers",0);
+yline(400,'LineStyle','--');
+
+% === SD-FEC Net Rate ===
+ax = nexttile(t,3);
+hold on;
+for k = 1:3
+    plot(results(k).baudr, results(k).ndr.SDHD.NetRate, ...
+        'LineWidth', lw, ...
+        'Color', curves(k).color, ...
+        'MarkerSize', 2, ...
+        'MarkerFaceColor', curves(k).color,...
+        'Marker',curves(k).mkr);
+end
+ylabel('NDR [Gb/s]');
+xlabel('Baud rate [GBd]');
+ylim([280 430]);
+yticks(280:30:440)
+xlim([100 210]);
+xticks(100:15:225);
+grid minor; box on;
+beautifyBERplot("logscale",0,"setmarkers",0);
+yline(400,'LineStyle','--');
+
+% === HD-FEC Net Rate ===
+ax = nexttile(t,4);
+hold on;
+for k = 1:3
+    % plot(results(k).baudr, results(k).ndr.STAIR.NetRate, ...
+    %     '-', 'LineWidth', lw, ...
+    %     'Color', curves(k).color, ...
+    %     'MarkerSize', 4,'Marker','+', ...
+    %     'MarkerFaceColor', curves(k).color);
+
+        plot(results(k).baudr, results(k).ndr.O_FEC.NetRate, ...
+        ':', 'LineWidth', lw, ...
+        'Color', curves(k).color, ...
+        'MarkerSize', 2,...
+        'MarkerFaceColor', curves(k).color,...
+        'Marker',curves(k).mkr);
+
+            plot(results(k).baudr, results(k).ndr.KP4_hamming.NetRate, ...
+        '--', 'LineWidth', lw, ...
+        'Color', curves(k).color, ...
+        'MarkerSize', 2,'Marker','diamond', ...
+        'MarkerFaceColor', curves(k).color,...
+        'Marker',curves(k).mkr);
+end
+
+yline(400,'LineStyle','--');
+ylabel('');
+xlabel('Baud rate [GBd]');
+ylim([280 430]);
+yticks(280:30:440)
+xlim([100 210]);
+xticks(100:15:225);
+grid minor; box on;
+beautifyBERplot("logscale",0,"setmarkers",0);
+
+% === FINAL FIGURE SIZE ===
+pos = 1e3.*[0.7950    1.1150    1.4113    0.1900];
+set(fig, 'Position', pos);
+
+% % % %% === EXPORT ===
+outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\compare_ndr_v3.tikz';
+matlab2tikz(outfile, ...
+    'width','\fwidth', ...
+    'height','\fheight', ...
+    'showInfo',false, ...
+    'extraAxisOptions',{ ...
+        'legend style={font=\footnotesize}', ...
+        'legend columns=1' ...
+         'every axis/.append style={font=\scriptsize}',...
+         'minor grid style={line width=0.2pt, solid, color=black!10}',...
+         'grid style={line width=0.4pt, solid, color=black!20}',...
+        'grid style={dashed}',...
+    });

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/FIGURE_NGMI_v2.m

@@ -0,0 +1,180 @@
+%% ============================================================
+%       GRID (1 × 4):
+%   1) NGMI overview (PAM4+PAM6+PAM8 superimposed)
+%   2) PAM-4 tile (AIR + SD-NDR + HD-NDR)
+%   3) PAM-6 tile
+%   4) PAM-8 tile
+% ============================================================
+
+db = DBHandler("dataBase","labor_highspeed","type","mysql");
+
+%% --- Base DB Filters (shared across all curves)
+fp = QueryFilter();
+fp.where('Runs','fiber_length','EQUALS', 2);
+fp.where('Runs','wavelength','EQUALS', 1310);
+fp.where('Runs','rop_attenuation','EQUALS', 0);
+fp.where('Runs','is_mpi','EQUALS', 0);
+
+fields = db.getTableFieldNames('dashboard_ungrouped_alltime');
+[dataTable,~] = db.queryDB(fp, fields);
+
+%% === CURVE DEFINITIONS =================================================
+curves = struct;
+
+curves(1).pam   = 8;
+curves(1).eq    = equalizer_structure.vnle_pf_mlse;
+curves(1).pre   = 0;
+curves(1).color = clr.Paired.red;
+
+curves(2).pam   = 6;
+curves(2).eq    = equalizer_structure.vnle_pf_mlse;
+curves(2).pre   = 1;
+curves(2).color = clr.Paired.blue;
+
+curves(3).pam   = 4;
+curves(3).eq    = equalizer_structure.vnle_db_mlse;
+curves(3).pre   = 0;
+curves(3).color = clr.Paired.green;
+
+% === ANALYSIS ENGINE (extract BER/NGMI/AIR/netrates) ===================
+base = struct;
+base.group_by = {'equalizer_structure','pre_emph'};
+base.x_axis   = 'grossrate';
+base.outlier  = 'none';
+base.show_raw = false;
+
+results = struct;
+
+for k = 1:numel(curves)
+
+    % ========== BER ==========
+    cfg = base;
+    cfg.y_axis = 'BER';
+    cfg.agg    = 'min';
+    
+    cfg.filters = struct('pam_level', curves(k).pam, ...
+                         'equalizer_structure', curves(k).eq, ...
+                         'pre_emph', curves(k).pre);
+    A = analyze_measurements_gpt(dataTable, cfg);
+
+    results(k).gross = A.group{1}.x;
+    if curves(k).pam == 4
+        results(k).ber   = A.group{1}.y_precoded;
+    else
+        results(k).ber   = A.group{1}.y;
+    end
+
+    % ========== NGMI ==========
+    cfg.y_axis = 'NGMI'; cfg.agg = 'max';
+    A = analyze_measurements_gpt(dataTable, cfg);
+    results(k).ngmi = A.group{1}.y;
+
+    % ========== AIR ==========
+    cfg.y_axis = 'AIR'; cfg.agg = 'max';
+    A = analyze_measurements_gpt(dataTable, cfg);
+    results(k).air = A.group{1}.y;
+
+    % ========== NET RATES ==========
+    tp = TransmissionPerformance;
+    results(k).ndr = tp.calculateNetRate(results(k).gross, ...
+                                         'NGMI', results(k).ngmi, ...
+                                         'BER',  results(k).ber);
+end
+
+
+% ============================================================
+%                      FIGURE
+% ============================================================
+fig = figure(3);
+t = tiledlayout(1,4,'TileSpacing','compact','Padding','compact');
+
+lw = 1.7;
+
+% =======================================================================
+% (1) NGMI OVERVIEW TILE   (all 3 curves)
+% =======================================================================
+ax = nexttile(t,1); hold on;
+
+for k = 1:3
+    plot(results(k).gross, results(k).ngmi, ...
+        '-o', 'Color', curves(k).color, ...
+        'LineWidth',lw,'MarkerSize',5, ...
+        'MarkerFaceColor',curves(k).color);
+end
+
+ylabel('NGMI');
+xlabel('Grossrate [Gb/s]');
+ylim([0.9 1]);   % your chosen limits
+xlim([300 480]);
+xticks(300:30:480)
+grid minor; box on;
+beautifyBERplot;
+
+% =======================================================================
+% (2–4) PAM-SPECIFIC TILES: AIR, SD-NDR, HD-NDR
+% =======================================================================
+
+pam_order = [4 6 8];     % left → right
+
+for ti = 1:3
+    pam_target = pam_order(ti);
+    ax = nexttile(t, 1+ti); hold on;
+
+    % find matching curve
+    for k = 1:3
+        if curves(k).pam ~= pam_target, continue; end
+
+        col = curves(k).color;
+
+        % AIR
+        plot(results(k).gross, results(k).air, ...
+            '-','Color',col,'LineWidth',lw,'Marker','*', ...
+            'MarkerSize',5,'MarkerFaceColor',col,'DisplayName','AIR');
+
+        % SD-based net rate
+        plot(results(k).gross, results(k).ndr.SDHD.NetRate, ...
+            '--','Color',col,'LineWidth',lw,'Marker','v', ...
+            'MarkerSize',5,'MarkerFaceColor',col,'DisplayName','SD+HD');
+
+        % HD-based net rate
+        plot(results(k).gross, results(k).ndr.STAIR.NetRate, ...
+            ':','Color',col,'LineWidth',lw,'Marker','x', ...
+            'MarkerSize',5,'MarkerFaceColor',col,'DisplayName','HD-FEC (Staircase)');
+
+        % HD-based net rate
+        plot(results(k).gross, results(k).ndr.O_FEC.NetRate, ...
+            'LineStyle','-.','Color',col,'LineWidth',lw,'Marker','+', ...
+            'MarkerSize',5,'MarkerFaceColor',col,'DisplayName','O-FEC');
+
+        % HD-based net rate
+        plot(results(k).gross, results(k).ndr.KP4_hamming.NetRate, ...
+            'LineStyle','-','Color',col,'LineWidth',lw,'Marker','x', ...
+            'MarkerSize',5,'MarkerFaceColor',col,'DisplayName','KP4+Hamming');
+    end
+
+    ylabel('NDR [Gb/s]');
+    xlabel('Grossrate [Gb/s]');
+    ylim([300 440]);   % your chosen limits
+    yticks(300:20:480)
+    xlim([300 480]); 
+    xticks(300:30:480)
+    grid minor; box on;
+    beautifyBERplot;
+    yline(400,'HandleVisibility','off');
+end
+
+% === FIX FIGURE SIZE FOR TIKZ ==========================================
+if 0
+pos = 1e3.*[0.3643    0.9943    1.4113    0.2120];
+set(fig,'Position',pos);
+
+% outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\compare_ndr.tikz';
+% matlab2tikz(outfile, ...
+%     'width','\fwidth', ...
+%     'height','\fheight', ...
+%     'showInfo',false, ...
+%     'extraAxisOptions',{ ...
+%         'legend style={font=\footnotesize}', ...
+%         'legend columns=1' ...
+%     });
+end

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/FIGURE_ROP.m

@@ -0,0 +1,153 @@
+%% ============================================================
+%  LOAD DATA (PAM-4, sweep over ROP)
+% ============================================================
+database_type = 'mysql';
+db = DBHandler("dataBase", "labor_highspeed", "type", database_type);
+
+pam_level = 4;
+fiberL = 1;                      % 1 km
+wlen = 1310;
+baudrate = 360e9;
+
+fp = QueryFilter();
+fp.where('Runs','pam_level','EQUALS', pam_level);
+fp.where('Runs','fiber_length','EQUALS', fiberL);
+fp.where('Runs','wavelength','EQUALS', wlen);
+fp.where('Runs','bitrate','EQUALS', baudrate);
+fp.where('Runs','power_pd_in','LESS_THAN', 7);
+
+fields = [
+    db.getTableFieldNames('power_state_info');
+    db.getTableFieldNames('dashboard_ungrouped_alltime')
+];
+
+[dataTable,~] = db.queryDB(fp, fields);
+
+
+%% ============================================================
+%  DSP SCHEMES (Best combinations only)
+% ============================================================
+curves = struct;
+
+curves(1).name  = 'VNLE';
+curves(1).eq    = equalizer_structure.vnle;
+curves(1).color = clr.Paired.red;
+
+curves(2).name  = 'PF + MLSE';
+curves(2).eq    = equalizer_structure.vnle_pf_mlse;
+curves(2).color = clr.Paired.green;
+
+curves(3).name  = 'DB-target + MLSE';
+curves(3).eq    = equalizer_structure.vnle_db_mlse;
+curves(3).color = clr.Paired.blue;
+
+curves(4).name  = 'ML-based MLSE';
+curves(4).eq    = equalizer_structure.ml_mlse;
+curves(4).color = clr.Paired.purple;
+
+
+
+
+%% ============================================================
+%  ANALYSIS ENGINE (No plotting)
+% ============================================================
+results = struct;
+
+for k = 1:numel(curves)
+
+    pre_emph   = decide_preemph(pam_level,curves(k).eq);
+    precoded   = decide_precoded(pam_level,curves(k).eq);
+
+    cfg = struct;
+    cfg.x_axis   = 'power_mzm';      % ROP axis
+    cfg.y_axis   = 'BER';
+    cfg.agg      = 'min';
+    cfg.outlier  = 'none';
+    cfg.show_raw = false;
+
+    cfg.filters = struct( ...
+        'pam_level', pam_level, ...
+        'fiber_length', fiberL, ...
+        'wavelength', wlen, ...
+        'bitrate', baudrate, ...
+        'is_mpi', 0, ...
+        'equalizer_structure', curves(k).eq, ...
+        'pre_emph', pre_emph);
+
+    A = analyze_measurements_gpt(dataTable, cfg);
+
+    results(k).x = A.group{1}.x;
+    if precoded
+        results(k).ber = A.group{1}.y_precoded;
+    else
+        results(k).ber = A.group{1}.y;
+    end
+end
+
+
+%% ============================================================
+%  PLOT — BER vs ROP (Single Axis)
+% ============================================================
+fig = figure(); clf; hold on;
+
+lw = 2.0;
+ms = 7;
+
+for k = 1:numel(curves)
+    plot(results(k).x, results(k).ber, ...
+        '-o', ...
+        'Color', curves(k).color, ...
+        'MarkerFaceColor', curves(k).color, ...
+        'MarkerSize', ms, ...
+        'LineWidth', lw, ...
+        'DisplayName', curves(k).name);
+end
+
+set(gca,'YScale','log');
+grid on;
+
+xlabel('ROP / Power (MZM) [dBm]');
+ylabel('BER');
+
+ylim([1e-4 2e-1]);
+
+title(sprintf('BER vs ROP — PAM-%d, %.0f km, %.0f GBd, %.0f nm', ...
+      pam_level, fiberL, baudrate*1e-9, wlen));
+
+legend('Location','best');
+beautifyBERplot();
+
+pos = 1e3.*[0.2 0.6 1.3 0.4];
+set(fig, 'Position', pos);
+
+%% ============================================================
+% DECISION LOGIC (INLINE FUNCTIONS)
+% ============================================================
+
+function pe = decide_preemph(M, eq)
+    % PRE-EMPH RULES:
+    switch M
+        case 4
+            if eq == equalizer_structure.vnle
+                pe = 1;     % PAM4: VNLE → pre-emph on
+            else
+                pe = 0;     % PAM4: all others → off
+            end
+        case {6,8}
+            pe = 1;         % PAM6/8: all → pre-emph on
+        otherwise
+            pe = 0;
+    end
+end
+
+
+function flag = decide_precoded(M, eq)
+    % PRE-CODE RULES:
+    if eq == equalizer_structure.vnle_db_mlse
+        flag = 1;                         % Always for DB-target
+    elseif eq == equalizer_structure.ml_mlse && M == 4
+        flag = 1;                         % PAM4: ML-based → precoded
+    else
+        flag = 0;
+    end
+end

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/FIGURE_Spectra.m

@@ -0,0 +1,182 @@
+dsp_options.storage_path    = 'Z:\2024\sioe_labor\';
+dsp_options.max_occurences = 1;
+database    = DBHandler("dataBase", 'labor_highspeed', "type",  'mysql' );
+
+rates = [300e9];
+cols = cbrewer2('BuPu',25);
+cols = [cols(end-10:2:end,:)];
+cols = cbrewer2('Set1',6);
+
+fignum = 200;
+fig=figure(fignum);clf;
+
+for dbmode = 0:1%length(rates)
+
+
+    if 0
+    rcalpha     = 0.05;
+    fsym = rates/2;
+    pulsef = 1;
+    Pform       = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"alpha",rcalpha);
+
+    Pamsource = PAMsource(...
+        "fsym",fsym,"M",4,"order",18,"useprbs",0,...
+        "fs_out",fdac,...
+        "applyclipping",0,"clipfactor",1.2,...
+        "applypulseform",pulsef,"pulseformer",Pform,...
+        "randkey",20,...
+        "db_precode",dbmode,"db_encode",0,...
+        "mrds_code",0,"mrds_blocklength",512);
+
+    [Digi_sig,Symbols,Bits] = Pamsource.process();
+
+    Digi_sig = Digi_sig.normalize("mode","rms");
+
+    %%% 1) PLOT FULL RESPONSE SIGNAL
+    Digi_sig.spectrum("displayname","Full Response","fignum",fignum+dbmode,"normalizeToNyquist",0,"normalizeTo0dB",0,"color",[0.2,0.2,0.2],"linestyle",'-','addDCoffset',0,'normalizeToDC',1);
+
+    
+    %%% 2) PLOT PREEMPH. TX SIGNAL
+    if dbmode == 0
+        maxamp = -37;
+        precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',Digi_sig.fs);
+
+        precomp_path = "C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\precomp";
+        precomp_fn = "lab_high_speed";
+        Digi_sig_pre = precomp_est.precomp(Digi_sig,'maxampdb',maxamp,'loadPath',precomp_path,'fileName',precomp_fn);
+
+        Digi_sig_pre = Digi_sig_pre.resample("fs_out",fdac);
+
+        Digi_sig_pre= Digi_sig_pre.normalize("mode","rms");
+
+        Digi_sig_pre.spectrum("displayname","Strong Precomp","fignum",fignum+dbmode,"normalizeToNyquist",0,"normalizeTo0dB",0,"color",[0,0,0],"linestyle",'-.','addDCoffset',0,'normalizeToDC',1);
+    end
+
+    end
+
+    % 1 - PAM 4 with preemphasis
+    fp = QueryFilter();
+    M = 4;
+    fp.where('Runs', 'pam_level','EQUALS', M);     
+    fp.where('Runs', 'bitrate','EQUALS', rates);%360,390
+    fp.where('Runs', 'fiber_length','EQUALS', 10);   
+    fp.where('Runs', 'wavelength','EQUALS', 1322.7);  %1327.4       
+    fp.where('Runs', 'db_mode','EQUALS', dbmode);              
+    fp.where('Runs', 'rop_attenuation','EQUAL', 0);      
+    
+    [dataTable,~] = database.queryDB(fp, database.getTableFieldNames('Runs'));
+    
+    dataTable   = queryRunid(dataTable.run_id, database);
+    fsym        = dataTable.symbolrate;
+    M           = double(dataTable.pam_level);
+    
+    % Load and Sync signal data from DB
+    [Tx_bits, Symbols, Scpe_cell, ~] = loadAndSyncSignalDataFromDb(dataTable, dsp_options);
+    
+    % Preprocess signal
+    Scpe_sig = preprocessSignal(Scpe_cell{1}, Symbols, fsym);
+    Scpe_sig = Scpe_cell{1};
+
+    %%% 3) PLOT DB Tgt. SIGNAL
+    if 1
+        DB_Symbols = Duobinary().encode(Symbols);
+        DB_Symbols.spectrum("fignum",fignum+dbmode,"normalizeTo0dB",1,"displayname",'DB-Response','addDCoffset',0,'color',clr.Set1.blue,'normalizeToNyquist',0,'linestyle','--');
+    end
+
+    %%% 4) Plot RX Signal
+    Scpe_sig.spectrum("fignum",fignum+dbmode,"normalizeTo0dB",1,"displayname",'Rx','addDCoffset',1,'color',[0,0,0],'normalizeToNyquist',0,'linestyle',':');
+Scpe_sig.eye(fsym,M,"fignum",47,"displayname",' Eye of AVG Signal');
+    % xline(Symbols.fs/2.*1e-9,'Color',cols(r,:),'HandleVisibility','off');
+
+    average_signals = 1;
+    if average_signals
+        Scpe_sig_avg = Scpe_sig;
+        scope_mean = zeros(size(Scpe_cell{1}.signal));
+        for n=1:numel(Scpe_cell)
+            scope_mean = scope_mean + Scpe_cell{n}.signal;
+        end
+        scope_mean = scope_mean ./ n;
+        Scpe_sig_avg.signal = scope_mean;
+
+        Scpe_sig_avg.spectrum("displayname","Scope PSD","fignum",20,"normalizeTo0dB",1);
+        Scpe_sig_avg.plot("displayname","Scope raw signal","fignum",27,"clear",1);
+        Scpe_sig_avg.eye(fsym,M,"fignum",48,"displayname",' Eye of AVG Signal');
+    end
+
+
+    fig = figure(fignum+dbmode);
+    if dbmode == 0
+        ylim([-22,12]);
+    else
+        ylim([-22,2]);
+    end
+    xlim([0,105]);
+    xticks(-100:20:100);
+    yticks(-20:10:10);
+
+    beautifyBERplot("logscale",0,"setmarkers",0)
+    pos = [100.3333  991.6667  358.0000  192.6667];
+    set(fig, 'Position', pos);
+
+    %%%%%%%%%%%%
+    drawnow;
+
+    % Do EQ and find alpha's
+    len_tr      = 4096*2;
+    
+    ffe_order   = [50, 5, 5];
+    dfe_order   = [0, 0, 0];
+    pf_ncoeffs  = 1;
+    mu_ffe      = [0.0001, 0.0008, 0.001];
+    mu_dfe      = 0.0004;
+    mu_dc       = 0.005;
+    
+    %%% FULL RESP TARGET
+    eq_    = EQ("Ne",ffe_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",0);
+    pf_1    = Postfilter("ncoeff",1,"useBurg",1);
+    
+    [eq_signal_sd, eq_noise] = eq_.process(Scpe_sig, Symbols);
+
+    % eq_noise.signal = eq_noise.signal - mean(eq_noise.signal);
+    % eq_noise = eq_noise.normalize("mode","rms");
+    
+    [mlse_sig_sd,whitened_noise] = pf_1.process(eq_signal_sd, eq_noise);
+
+    fig = figure(fignum+dbmode+10); hold on
+    
+    [h, w] = freqz(1, pf_1.coefficients, length(eq_noise), "whole", eq_noise.fs);
+    h = h / max(abs(h)); % Normalize the filter response
+    w_ = (w - eq_noise.fs / 2);
+   
+    %%% DB TARGET
+    db_ref_sequence = Duobinary().encode(Symbols);
+     eq_    = EQ("Ne",ffe_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",0);
+    [eq_signal, db_noise] = eq_.process(Scpe_sig,db_ref_sequence);
+    
+    % db_noise.signal = db_noise.signal - mean(db_noise.signal);
+    % db_noise = db_noise.normalize("mode","rms");
+    
+    %%% 1-3) Plot EQ Noise EEN
+    figure(fignum+dbmode+10)
+    eq_noise.spectrum("displayname", 'Noise', "fignum", fignum+dbmode+10, "normalizeTo0dB", 0,"color",clr.Set1.green,"normalizeToDC",0,"addDCoffset",0);
+    if dbmode == 1
+        offset = 27.7;
+    else
+        offset = 29.8;
+    end
+    plot(w_ * 1e-9, 20 * log10(fftshift(abs(h)))-offset, 'DisplayName', ['Burg Coeffs: ', num2str(round(pf_1.coefficients, 2)), ' '], 'LineWidth', 1,'Color',clr.Set1.green,'LineStyle','--');
+    db_noise.spectrum("displayname", 'DBt. Noise', "fignum", fignum+dbmode+10, "normalizeTo0dB", 0,"color",clr.Set1.blue,"normalizeToDC",0,"addDCoffset",0);
+
+    ylim([-54,-25]);
+    xlim([0,105]);
+    xticks(0:20:110);
+    yticks(-50:10:10);
+
+    beautifyBERplot("logscale",0,"setmarkers",0)
+    pos = [100.3333  991.6667  358.0000  192.6667];
+    set(fig, 'Position', pos);
+
+end
+
+
+% === FINAL FIGURE SIZE ===

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/FIGURE_WAVELENGTH.m

@@ -0,0 +1,124 @@
+%% ============================================================
+%  LOAD DATA
+% ============================================================
+database_type = 'mysql';
+db = DBHandler("dataBase", "labor_highspeed", "type", database_type);
+
+M = 4;  % PAM level for this analysis
+
+fp = QueryFilter();
+fp.where('Runs', 'pam_level',      'EQUALS', M);
+fp.where('Runs', 'fiber_length',   'EQUALS', 10);
+fp.where('Runs', 'bitrate',        'EQUALS', 360e9);
+fp.where('Runs', 'is_mpi',         'EQUALS', 0);
+
+fields = [
+    db.getTableFieldNames('power_state_info');
+    db.getTableFieldNames('dashboard_ungrouped_alltime')
+];
+
+[dataTable, ~] = db.queryDB(fp, fields);
+
+
+%% ============================================================
+%  COMMON CONFIGURATION FOR ALL SUBPLOTS
+% ============================================================
+%% ============================================================
+%  DEFINE DSP ALGORITHMS FOR THE 4 SUBPLOTS
+% ============================================================
+curves = struct;
+
+curves(1).name  = 'VNLE';
+curves(1).eq    = equalizer_structure.vnle;
+curves(1).pre   = 0;
+curves(1).color = clr.Paired.red;
+
+curves(2).name  = 'PF + MLSE';
+curves(2).eq    = equalizer_structure.vnle_pf_mlse;
+curves(2).pre   = 0;
+curves(2).color = clr.Paired.green;
+
+curves(3).name  = 'DB-target + MLSE';
+curves(3).eq    = equalizer_structure.vnle_db_mlse;
+curves(3).pre   = 0;
+curves(3).color = clr.Paired.blue;
+
+curves(4).name  = 'ML-based MLSE';
+curves(4).eq    = equalizer_structure.ml_mlse;
+if M == 4
+curves(4).pre   = 0;
+else
+curves(4).pre   = 1;
+end
+curves(4).color = clr.Paired.purple;
+
+
+%% ============================================================
+%  ANALYSIS ENGINE — NO PLOTTING
+% ============================================================
+results = struct;
+
+for k = 1:numel(curves)
+
+    %% ---- BASE CONFIG ----
+    cfg = struct;
+    cfg.x_axis   = 'wavelength';
+    cfg.y_axis   = 'BER';
+    cfg.agg      = 'min';
+    cfg.outlier  = 'none';
+    % cfg.group_by = {'wavelength'};
+    cfg.show_raw = false;
+
+    cfg.filters = struct( ...
+        'pam_level',    M, ...
+        'is_mpi',       0, ...
+        'bitrate',      360e9, ...
+        'fiber_length', 10, ...
+        'equalizer_structure', curves(k).eq, ...
+        'pre_emph',     curves(k).pre);
+
+    %% ---- GET BER ----
+    cfg.y_axis = 'BER';
+    A = analyze_measurements_gpt(dataTable, cfg);
+
+    results(k).wavelength = A.group{1}.x;
+
+    if curves(k).eq == equalizer_structure.vnle_db_mlse || ...
+       curves(k).eq == equalizer_structure.ml_mlse
+        % DB and ML-based need precoded BER
+        results(k).ber = A.group{1}.y_precoded;
+    else
+        results(k).ber = A.group{1}.y;
+    end
+
+end
+
+%% ============================================================
+%  1×4 TILED BER-vs-WAVELENGTH FIGURE
+% ============================================================
+fig=figure(901); 
+tiledlayout(1,4,'TileSpacing','compact','Padding','compact');
+
+lw = 1.8;    % line width
+ms = 6;      % marker size
+
+for k = 1:numel(curves)
+    nexttile; hold on;
+
+    plot(results(k).wavelength, results(k).ber, ...
+        '-o', ...
+        'Color', curves(k).color, ...
+        'MarkerFaceColor', curves(k).color, ...
+        'MarkerSize', ms, ...
+        'LineWidth', lw);
+
+    set(gca,'YScale','log');
+    grid on;
+    xlabel('wavelength');
+    ylabel('BER');
+    title(curves(k).name);
+    ylim([1e-4, 0.1])
+    beautifyBERplot();
+end
+pos = 1e3.*[0.1070    0.5497    1.4113    0.3253];
+set(fig, 'Position', pos);

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/FIGURE_WAVELENGTH_VS_BAUDRATE.m

@@ -0,0 +1,160 @@
+%% ============================================================
+%  PARAMETERS
+% ============================================================
+database_type = 'mysql';
+db = DBHandler("dataBase", "labor_highspeed", "type", database_type);
+
+pam_level = 4;                    % FIXED for this figure
+baudrates = [300e9 330e9 360e9 390e9];
+fiberL = 10;
+
+fields = [
+    db.getTableFieldNames('power_state_info');
+    db.getTableFieldNames('dashboard_ungrouped_alltime')
+];
+
+%% ============================================================
+%  DEFINE DSP SCHEMES
+% ============================================================
+curves = struct;
+
+curves(1).name  = 'VNLE';
+curves(1).eq    = equalizer_structure.vnle;
+curves(1).color = clr.Paired.red;
+
+curves(2).name  = 'PF + MLSE';
+curves(2).eq    = equalizer_structure.vnle_pf_mlse;
+curves(2).color = clr.Paired.green;
+
+curves(3).name  = 'DB-target + MLSE';
+curves(3).eq    = equalizer_structure.vnle_db_mlse;
+curves(3).color = clr.Paired.blue;
+
+curves(4).name  = 'ML-based MLSE';
+curves(4).eq    = equalizer_structure.ml_mlse;
+curves(4).color = clr.Paired.purple;
+
+
+%% ============================================================
+%  ANALYSIS — results(b, k):  b = baudrate index, k = DSP scheme index
+% ============================================================
+results = struct;
+
+for b = 1:length(baudrates)
+
+    Rb = baudrates(b);
+
+    % --- query matching runs ---
+    fp = QueryFilter();
+    fp.where('Runs','pam_level','EQUALS', pam_level);
+    fp.where('Runs','fiber_length','EQUALS', fiberL);
+    fp.where('Runs','bitrate','EQUALS', Rb);
+    fp.where('Runs','is_mpi','EQUALS', 0);
+
+    [dataTable, ~] = db.queryDB(fp, fields);
+
+    for k = 1:numel(curves)
+
+        %% ---- DECIDE PRE-EMPH & PRECoded RULES for PAM-4 ----
+        pre_emph = decide_preemph(pam_level, curves(k).eq);
+        use_precoded = decide_precoded(pam_level, curves(k).eq);
+
+        %% ---- SETUP ANALYSIS CONFIG ----
+        cfg = struct;
+        cfg.x_axis = 'wavelength';
+        cfg.y_axis = 'BER';
+        cfg.agg    = 'min';
+        cfg.outlier = 'none';
+        % cfg.group_by = {'wavelength'};
+        cfg.show_raw = false;
+
+        cfg.filters = struct( ...
+            'pam_level', pam_level, ...
+            'is_mpi', 0, ...
+            'bitrate', Rb, ...
+            'fiber_length', fiberL, ...
+            'equalizer_structure', curves(k).eq, ...
+            'pre_emph', pre_emph);
+
+        %% ---- RUN ANALYSIS ----
+        A = analyze_measurements_gpt(dataTable, cfg);
+
+        results(b,k).wavelength = A.group{1}.x;
+
+        if use_precoded
+            results(b,k).ber = A.group{1}.y_precoded;
+        else
+            results(b,k).ber = A.group{1}.y;
+        end
+    end
+end
+
+
+%% ============================================================
+%  PLOT — 1×4 (one tile per baudrate)
+% ============================================================
+fig = figure(); clf;
+tiledlayout(1,4,'TileSpacing','compact','Padding','compact');
+
+lw = 1.8;
+ms = 6;
+
+for b = 1:length(baudrates)
+    nexttile; hold on;
+
+    for k = 1:numel(curves)
+        plot(results(b,k).wavelength, results(b,k).ber, ...
+            '-o', ...
+            'Color', curves(k).color, ...
+            'MarkerFaceColor', curves(k).color, ...
+            'MarkerSize', ms, ...
+            'LineWidth', lw, ...
+            'DisplayName', curves(k).name);
+    end
+
+    set(gca,'YScale','log');
+    grid on;
+    xlabel('Wavelength [nm]');
+    ylabel('BER');
+    ylim([1e-4 0.1]);
+    title(sprintf('PAM-%d @ %.0f GBd',pam_level, baudrates(b)/1e9));
+    legend('Location','best');
+    beautifyBERplot();
+end
+
+% Optional figure size
+pos = 1e3.*[0.1 0.55 1.4 0.32];
+set(fig, 'Position', pos);
+
+
+%% ============================================================
+% DECISION LOGIC (INLINE FUNCTIONS)
+% ============================================================
+
+function pe = decide_preemph(M, eq)
+    % PRE-EMPH RULES:
+    switch M
+        case 4
+            if eq == equalizer_structure.vnle
+                pe = 1;     % PAM4: VNLE → pre-emph on
+            else
+                pe = 0;     % PAM4: all others → off
+            end
+        case {6,8}
+            pe = 1;         % PAM6/8: all → pre-emph on
+        otherwise
+            pe = 0;
+    end
+end
+
+
+function flag = decide_precoded(M, eq)
+    % PRE-CODE RULES:
+    if eq == equalizer_structure.vnle_db_mlse
+        flag = 1;                         % Always for DB-target
+    elseif eq == equalizer_structure.ml_mlse && M == 4
+        flag = 1;                         % PAM4: ML-based → precoded
+    else
+        flag = 0;
+    end
+end

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/FIGURE_introduction.m

@@ -0,0 +1,88 @@
+%% ============================================================
+%   PLOT
+% ============================================================
+figure; hold on;
+ms = 32;      % scatter size
+lw = 0.8;     % line width
+
+for k = 1:4   % PAM-2/4/6/8
+
+    M = pam_list(k);
+    idxPam = (Mvals == M);
+
+    % Extract for this PAM
+    x = baud(idxPam);
+    y = netrate(idxPam);
+    n = names(idxPam);
+
+    % Get color for this PAM format
+    col = colors(k,:);
+
+    % ----- LEGEND FLAG (only add one entry per PAM) -----
+    firstLegend = true;
+
+    % ---- PLOT ALL POINTS (marker based on publication) ----
+    for i = 1:sum(idxPam)
+
+        % marker selection by publication
+        pubIdx = find(pub_list == n(i), 1);
+        marker = markerlist{mod(pubIdx-1, nMarkers) + 1};
+
+        if firstLegend
+            h = scatter(x(i), y(i), ms, ...
+                'Marker', marker, ...
+                'MarkerEdgeColor', col, ...
+                'MarkerFaceColor', col, ...
+                'DisplayName', sprintf('PAM-%d', M));
+            firstLegend = false;
+        else
+            h = scatter(x(i), y(i), ms, ...
+                'Marker', marker, ...
+                'MarkerEdgeColor', col, ...
+                'MarkerFaceColor', col, ...
+                'HandleVisibility','off');
+        end
+
+        % ====== CUSTOM DATATIP CONTENT ======
+        dt = h.DataTipTemplate;
+        dt.DataTipRows(1).Label = 'Baud rate';
+        dt.DataTipRows(2).Label = 'Net rate';
+
+        % Add publication name
+        dt.DataTipRows(end+1) = dataTipTextRow('Publication', n(i));
+
+
+    end
+
+    % ---- Fit (PAM-specific) ----
+    valid = ~isnan(x) & ~isnan(y);
+    if sum(valid) >= 3
+        p = polyfit(x(valid), y(valid), 2);
+        xfit = linspace(min(x(valid)), max(x(valid)), 200);
+        yfit = polyval(p, xfit);
+
+        plot(xfit, yfit, ':', ...
+            'LineWidth', lw, ...
+            'Color', col, ...
+            'HandleVisibility', 'off');     % do NOT add to legend
+    end
+end
+
+grid on;
+xlabel('Baud rate [GBd]');
+ylabel('Net rate [Gb/s]');
+
+legend('Location','northwest');
+set(gca,'FontSize',11);
+
+
+%% === EXPORT ===
+outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\highspeedresults.tikz';
+matlab2tikz(outfile, ...
+    'width','\fwidth', ...
+    'height','\fheight', ...
+    'showInfo',false, ...
+    'extraAxisOptions',{ ...
+    'legend style={font=\footnotesize}', ...
+    'legend columns=1' ...
+    });

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/analyze_measurements_gpt.m

@@ -0,0 +1,334 @@
+function [M, cfg] = analyze_measurements_gpt(T, cfg)
+% ANALYZE_MEASUREMENTS_GPT
+% Filter, compute X/Y, group and aggregate measurements from table T.
+% No plotting here.
+%
+% Usage:
+%   [M, cfg] = analyze_measurements_gpt(dataTable, cfg);
+%
+% Typical result (single group):
+%   M.x           -> aggregated x-values (e.g., grossrate)
+%   M.y           -> aggregated y-values (e.g., BER or NGMI)
+%   M.y_precoded  -> aggregated precoded BER (if available)
+%
+% For multiple groups:
+%   M.group(g).x, M.group(g).y, M.group(g).label, ...
+
+%% ---- Defaults (non-plot) ----
+if nargin < 2, cfg = struct; end
+defaults = struct( ...
+    'x_axis'      , 'symbolrate', ...
+    'y_axis'      , 'BER', ...
+    'y_scale'     , 'auto', ...
+    'group_by'    , {{'equalizer_structure','pre_emph'}}, ...
+    'filters'     , struct, ...
+    'agg'         , 'mean', ...
+    'outlier'     , 'auto', ...
+    'mad_z'       , 4, ...
+    'pct_limits'  , [2.5 97.5], ...
+    'min_pts_x'   , 3, ...
+    'show_raw'    , true, ...
+    'show_precoded', [], ...
+    'show_spread' , 'none', ...
+    'fec_lines'   , [], ...
+    'plot'        , struct() ...  % plot settings handled in plot function
+);
+cfg = filldefaults(cfg, defaults);
+
+%% ---- Derived/prep columns ----
+if ~ismember('pre_emph', T.Properties.VariableNames)
+    if ~ismember('db_mode', T.Properties.VariableNames)
+        error('Missing column "db_mode" for pre_emph derivation.');
+    end
+    T.pre_emph = T.db_mode == 0;
+end
+
+if ~ismember(cfg.y_axis, T.Properties.VariableNames)
+    error('y_axis "%s" not found in table.', cfg.y_axis);
+end
+
+isBER = startsWith(cfg.y_axis, "BER", 'IgnoreCase', true);
+M.isBER = isBER;
+
+if strcmpi(cfg.y_scale,'auto')
+    cfg.y_scale = tern(isBER, 'log', 'linear');
+end
+if strcmpi(cfg.outlier,'auto')
+    cfg.outlier = tern(isBER, 'mad', 'none');
+end
+if isempty(cfg.show_precoded)
+    cfg.show_precoded = isBER && ismember('BER_precoded', T.Properties.VariableNames);
+end
+
+%% ---- Filters & core X/Y extraction ----
+T = applyFilters(T, cfg.filters);
+
+[x_raw, x_label] = computeX(T, cfg.x_axis);
+y_raw = T.(cfg.y_axis);
+
+validXY = isfinite(x_raw) & isfinite(y_raw);
+T      = T(validXY, :);
+x_raw  = x_raw(validXY);
+y_raw  = y_raw(validXY);
+
+% Degiga if needed
+if mean(abs(y_raw)) > 1e8
+    y_raw = y_raw .* 1e-9;
+end
+
+if cfg.show_precoded && ismember('BER_precoded', T.Properties.VariableNames)
+    y_raw_p = T.BER_precoded(validXY);
+else
+    y_raw_p = [];
+end
+
+%% ---- Grouping ----
+group_by = cfg.group_by;
+if ~all(ismember(group_by, T.Properties.VariableNames))
+    error('Some group_by columns are missing in table.');
+end
+[G, grpTbl] = findgroups(T(:, group_by));
+nG = max(G);
+
+%% ---- Aggregation per group ----
+M = struct;
+M.cfg      = cfg;
+M.x_label  = x_label;
+M.y_axis   = cfg.y_axis;
+M.x_axis   = cfg.x_axis;
+M.nGroups  = nG;
+
+% raw (filtered) data
+M.raw = struct;
+M.raw.x          = x_raw;
+M.raw.y          = y_raw;
+M.raw.y_precoded = y_raw_p;
+M.raw.T          = T;
+
+M.group = cell(nG,1);
+
+useLog = strcmpi(cfg.y_scale,'log');
+for gi = 1:nG
+    idx = (G == gi);
+    Ti  = T(idx,:);
+    xi  = x_raw(idx);
+    yi  = y_raw(idx);
+
+    [xu, ~, iu] = unique(xi);
+    yu   = nan(size(xu));
+    ylo  = nan(size(xu));
+    yhi  = nan(size(xu));
+
+    for k = 1:numel(xu)
+        bin = (iu==k);
+        yy  = yi(bin);
+        yy  = yy(isfinite(yy));
+        if isempty(yy), continue; end
+
+        km = outlierMask(yy, cfg, useLog);
+        if nnz(km) < cfg.min_pts_x, km = true(size(yy)); end
+        yy = yy(km);
+
+        switch lower(cfg.agg)
+            case 'median'
+                yu(k) = median(yy,'omitnan');
+            case 'mean'
+                yu(k) = mean(yy,'omitnan');
+            case 'min'
+                yu(k) = min(yy);
+            case 'max'
+                yu(k) = max(yy);
+            otherwise
+                error('Unknown agg mode "%s".', cfg.agg);
+        end
+
+        if strcmpi(cfg.show_spread,'iqr')
+            q = prctile(yy,[25 75]);
+            ylo(k) = max(yu(k)-q(1), eps);
+            yhi(k) = max(q(2)-yu(k), eps);
+        elseif strcmpi(cfg.show_spread,'minmax')
+            ylo(k) = min(yy);
+            yhi(k) = max(yy);
+        end
+    end
+
+    % sort by x
+    [xu, ord] = sort(xu);
+    yu  = yu(ord);
+    ylo = ylo(ord);
+    yhi = yhi(ord);
+
+    g = struct;
+    g.label         = buildLabel(grpTbl(gi,:), group_by);
+    g.idx           = find(G==gi);
+    g.T             = Ti;
+    g.x_raw         = xi;
+    g.y_raw         = yi;
+    g.x             = xu;
+    g.y             = yu;
+    g.y_lo          = ylo;
+    g.y_hi          = yhi;
+    g.y_precoded    = [];
+    g.y_precoded_lo = [];
+    g.y_precoded_hi = [];
+
+    % Precoded aggregation (if requested & available)
+    if cfg.show_precoded && ~isempty(y_raw_p) && strcmpi(cfg.y_axis,'BER')
+        ypi = y_raw_p(idx);
+        ypu = nan(size(xu));
+
+        for k = 1:numel(xu)
+            bin = (iu==k);
+            yy  = ypi(bin);
+            yy  = yy(isfinite(yy));
+            if isempty(yy), continue; end
+
+            km = outlierMask(yy, cfg, true);
+            if nnz(km) < cfg.min_pts_x, km = true(size(yy)); end
+            yy = yy(km);
+
+            switch lower(cfg.agg)
+                case 'median'
+                    ypu(k) = median(yy,'omitnan');
+                case 'mean'
+                    ypu(k) = mean(yy,'omitnan');
+                case 'min'
+                    ypu(k) = min(yy);
+                case 'max'
+                    ypu(k) = max(yy);
+            end
+        end
+
+        g.y_precoded = ypu(ord);
+    end
+
+    M.group{gi} = g;
+end
+
+% Convenience flatten for single-group case
+if nG == 1
+    g = M.group{1};
+    M.x          = g.x;
+    M.y          = g.y;
+    M.y_precoded = g.y_precoded;
+end
+
+end % ===== main =====
+
+
+%% ===================== Helpers =====================
+
+function cfg = filldefaults(cfg, defs)
+fn = fieldnames(defs);
+for i = 1:numel(fn)
+    f = fn{i};
+    if ~isfield(cfg, f) || isempty(cfg.(f))
+        cfg.(f) = defs.(f);
+    elseif isstruct(defs.(f)) && isstruct(cfg.(f))
+        cfg.(f) = filldefaults(cfg.(f), defs.(f)); % recursive
+    end
+end
+end
+
+function out = tern(cond, a, b)
+if cond
+    out = a;
+else
+    out = b;
+end
+end
+
+function T2 = applyFilters(T, filters)
+if isempty(filters), T2 = T; return; end
+keep = true(height(T),1);
+fns  = fieldnames(filters);
+for i = 1:numel(fns)
+    name = fns{i};
+    if ~ismember(name, T.Properties.VariableNames)
+        warning('Filter column "%s" not found. Ignored.', name);
+        continue
+    end
+    val = filters.(name);
+    col = T.(name);
+    if isa(val,'function_handle')
+        m = val(col);
+        if ~islogical(m) || ~isequal(size(m), size(col))
+            error('Filter for %s must return logical mask of same size.', name);
+        end
+        keep = keep & m;
+    else
+        keep = keep & ismember(col, val);
+    end
+end
+T2 = T(keep,:);
+end
+
+function [x, label] = computeX(T, whichX)
+switch lower(whichX)
+    case {'symbolrate','baudrate'}
+        x = T.symbolrate * 1e-9;
+        label = 'Symbol rate [GBd]';
+    case 'bitrate'
+        if ~ismember('pam_level', T.Properties.VariableNames)
+            error('bitrate requires "pam_level" column.');
+        end
+        bits = floor(log2(double(T.pam_level))*10)/10;
+        x = (T.symbolrate .* bits) * 1e-9;
+        label = 'Grossrate [Gb/s]';
+    case 'grossrate'
+        x = T.grossrate * 1e-9;
+        label = 'Grossrate [Gb/s]';
+    otherwise
+        if ~ismember(whichX, T.Properties.VariableNames)
+            error('x_axis "%s" not found in table.', whichX);
+        end
+        x = T.(whichX);
+        label = whichX;
+end
+x = double(x(:));
+end
+
+function keep = outlierMask(y, cfg, useLog)
+if isempty(y), keep = false(size(y)); return; end
+y = y(:);
+switch lower(cfg.outlier)
+    case 'none'
+        keep = true(size(y)); return
+    case 'mad'
+        z = tern(useLog, log10(y), y);
+        med = median(z,'omitnan');
+        madv = median(abs(z-med),'omitnan');
+        if ~(isfinite(madv) && madv>0)
+            keep = true(size(y)); return
+        end
+        sigma = 1.4826*madv;
+        zz = tern(useLog, log10(y), y);
+        keep = abs(zz - med) <= cfg.mad_z*sigma;
+    case 'pctl'
+        pr = prctile(y, cfg.pct_limits);
+        keep = (y >= pr(1)) & (y <= pr(2));
+    otherwise
+        error('Unknown outlier mode "%s".', cfg.outlier);
+end
+end
+
+function s = buildLabel(grpRow, group_by)
+parts = strings(1, numel(group_by));
+for i = 1:numel(group_by)
+    key = group_by{i};
+    val = grpRow.(key);
+    if iscell(val), val = val{1}; end
+    if islogical(val), val = tern(val,'w/','w/o'); end
+    if key == "equalizer_structure"
+        key = '';
+        val = upper(val);
+        val = strrep(val,'_',' ');
+    end
+    if key == "pre_emph"
+        val = [val, ' pre-emph.'];
+        key = '';
+    end
+    parts(i) = sprintf('%s %s', key, string(val));
+end
+s = strjoin(parts, ', ');
+end

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/compare_ber_best_results.m

@@ -86,8 +86,9 @@ cfg.filters = struct('is_mpi',0,'pam_level',4, ...
 cfg.show_precoded = 1;
 a = plot_measurements_gpt(dataTable, cfg);
 ngmi_pam4 = a.lines(1).YData;
+grossrates = a.lines(1).XData;
 tp = TransmissionPerformance;
-netrates_vnle = tp.calculateNetRate(baudrate.* m, ...
+netrates_vnle = tp.calculateNetRate(grossrates, ...
                                       'NGMI', ngmi_pam4, ...
                                       'BER', BER_VNLE);
 
@@ -151,10 +152,26 @@ cfg.fec_lines = [];
 cfg.agg = 'max'; 
 
 cfg.figure_number = 47;
-% fig = figure(cfg.figure_number); 
 
 lambda = 1310;
 
+% cfg = struct;
+cfg.filters = struct('is_mpi',0,'pam_level',4, ...
+    'equalizer_structure',equalizer_structure.vnle_db_mlse, ...
+    'pre_emph',0,'wavelength',lambda);
+cfg.x_axis   = 'grossrate';
+cfg.y_axis   = 'NGMI';      % or 'NGMI', etc.
+cfg.show_precoded = 1;
+
+[M, cfg] = analyze_measurements_gpt(dataTable, cfg);
+
+grossrates = M.x;          % aggregated X
+ber       = M.y;           % aggregated Y (BER or NGMI)
+ber_prec  = M.y_precoded;  % precoded BER (if available)
+
+[h, M] = plot_measurements_gpt(dataTable, cfg);
+
+
 % PAM 4 
 cfg.plot.custom_colors = clr.Paired.red;
 cfg.plot.custom_linetypes = {'-'};

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/compare_ber_complete_results.m

@@ -1,170 +0,0 @@
-database_type   = 'mysql';
-dataBase   = 'labor_highspeed';
-db = DBHandler("dataBase", [dataBase], "type", database_type);
-
-
-% M = 4;
-fp = QueryFilter();
-% fp.where('Runs', 'pam_level','EQUALS', M);
-fp.where('Runs', 'fiber_length','EQUALS', 2);
-fp.where('Runs', 'wavelength','EQUALS', 1310);
-% fp.where('Runs', 'db_mode','EQUALS', 1);         % 0 == high preemphasis // 1 == low preemphasis
-fp.where('Runs', 'rop_attenuation','EQUALS', 0);
-
-fields = db.getTableFieldNames('power_state_info');
-fields = [fields; db.getTableFieldNames('dashboard_ungrouped_alltime')]; %dashboard_ungrouped_after_nov_2025  dashboard_ungrouped_aug_nov_2025 
-[dataTable,~] = db.queryDB(fp, fields);
-
-%% 
-cfg = struct;
-cfg.x_axis = 'grossrate';          % 'symbol rate' | 'bitrate' | 'wavelength'  grossrate
-cfg.y_axis = 'BER';              % 'BER' | 'GMI' | 'AIR' | ...
-
-cfg.y_scale = 'auto';            % auto -> log for BER*, linear otherwise
-cfg.outlier = 'mad';             % simple, robust; 'none' or 'pctl' also available
-cfg.show_raw = false;
-cfg.show_spread = 'none';        % 'none' or 'iqr' or minmax
-cfg.agg = 'min';                % or 'median'
-cfg.show_precoded = 0;
-
-% cfg.fec_lines = [2.2e-4 4.85e-3 2e-2]; % optional
-cfg.fec_lines = [];
-cfg.plot.custom_colors = [
-    clr.Paired.red;
-    clr.Paired.blue;
-    clr.Paired.green;
-    clr.Paired.orange;
-    clr.Paired.purple
-];
-
-cfg.plot.custom_colors_scatter = [
-    clr.Paired.lightred;
-    clr.Paired.lightblue;
-    clr.Paired.lightgreen;
-    clr.Paired.lightorange;
-    clr.Paired.lightpurple
-];
-
-
-% New styling knobs
-cfg.plot.use_cbrewer2 = true;
-cfg.plot.colormap = 'Paired';
-cfg.plot.paired_dark_first = false;        % dark for lines, light for scatter
-cfg.plot.lineWidth = 2.0;
-cfg.plot.errWidth  = 1.2;
-cfg.plot.scatterAlpha = 0.35;
-cfg.plot.legendLocation = 'best';
-cfg.plot.fecLineWidth = 2.4;              % thicker FEC limits
-cfg.plot.lineStyle_pre_emph_on = '-';
-cfg.plot.lineStyle_pre_emph_off = '-';
-
-
-%% Grid configuration
-rows = 3;             % PAM 4,6,8
-cols = 4;             % DSP schemes
-pam  = [4 6 8];
-
-cfg.figure_number = 46;
-fig = figure(cfg.figure_number); clf;
-
-% Create extremely compact tile layout
-t = tiledlayout(rows, cols, ...
-    'TileSpacing','compact', ...
-    'Padding','compact');
-
-% Common cfg
-cfg.show_precoded = 1;
-cfg.group_by = {'equalizer_structure','pre_emph'};
-cfg.x_axis = 'grossrate';
-cfg.y_axis = 'BER';
-cfg.y_scale = 'log';
-cfg.plot.custom_colors_scatter = [];
-cfg.plot.use_cbrewer2 = false;
-cfg.fec_lines = [];
-% DSP scheme definitions ---------------------------------------------
-DSP(1).name  = 'vnle';
-DSP(1).eq    = equalizer_structure.vnle;
-DSP(1).color = clr.Paired.red;
-DSP(1).lightcolor = clr.Paired.lightred;
-
-DSP(2).name  = 'VNLE PF MLSE';
-DSP(2).eq    = equalizer_structure.vnle_pf_mlse;
-DSP(2).color = clr.Paired.green;
-DSP(2).lightcolor = clr.Paired.lightgreen;
-
-DSP(3).name  = 'VNLE DB MLSE';
-DSP(3).eq    = equalizer_structure.vnle_db_mlse;
-DSP(3).color = clr.Paired.blue;
-DSP(3).lightcolor = clr.Paired.lightblue;
-
-DSP(4).name  = 'ML MLSE';
-DSP(4).eq    = equalizer_structure.ml_mlse;
-DSP(4).color = clr.Paired.purple;
-DSP(4).lightcolor = clr.Paired.lightpurple;
-
-
-% === Main nested loop: rows = PAM format, columns = DSP scheme ===
-for r = 1:rows
-    M = pam(r);
-
-    for c = 1:cols
-        ax = nexttile(t, (r-1)*cols + c);
-        cfg.ax = ax;
-
-        % ---- pre-emph = 1 (solid) ----
-        cfg.plot.custom_colors = DSP(c).lightcolor;
-        cfg.plot.custom_linetypes = {'-'};
-        cfg.filters = struct('is_mpi',0,'pam_level',M, ...
-            'equalizer_structure',DSP(c).eq, ...
-            'pre_emph',1);
-        plot_measurements_gpt(dataTable, cfg);
-
-        % ---- pre-emph = 0 (dashed) ----
-        cfg.plot.custom_colors = DSP(c).color;
-        cfg.plot.custom_linetypes = {'-'};
-        cfg.filters.pre_emph = 0;
-        plot_measurements_gpt(dataTable, cfg);
-
-        if M == 4
-            ylim([1e-5 0.2]);
-        else
-            ylim([6e-4 0.2]);
-        end
-
-        % % FEC limits
-        yline([2.2e-4 4.85e-3 2e-2],'LineWidth',1.1,'Color',[0.2 0.2 0.2], ...
-            'LineStyle',':','HandleVisibility','off');
-
-        % Axes prettification
-        beautifyBERplot;
-
-        % ===== Remove redundant labels =====
-        if c > 1
-            ax.YLabel = [];
-        end
-        if r < rows
-            ax.XLabel = [];
-        end
-
-        grid(ax,'on'); box(ax,'on');
-    end
-end
-
-%this is just a ranom size but fits and is fixed now!
-pos = 1e3.*[0.0983    0.6110    1.4113    0.4733];
-set(fig, 'Position', pos)
-
-
-
-
-%% === EXPORT TO TIKZ ===
-outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\compare_pre_emphasis.tikz';
-
-matlab2tikz(outfile, ...
-    'width','\fwidth', ...
-    'height','\fheight', ...
-    'showInfo',false, ...
-    'extraAxisOptions',{ ...
-        'legend style={font=\footnotesize}', ...
-        'legend columns=1' ...
-    } );

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/example_usage.m

@@ -0,0 +1,92 @@
+% ============================================================
+% MINIMAL EXAMPLE: Query → Analyze → Plot → Extract X/Y data
+% ============================================================
+
+%% === Load from database ===
+db = DBHandler("dataBase","labor_highspeed","type","mysql");
+
+fp = QueryFilter();
+fp.where('Runs','fiber_length','EQUALS',2);
+% fp.where('Runs','pam_level','EQUALS',6);        % PAM-4
+% fp.where('Runs','db_mode','EQUALS',0);         % w/o pre-emph
+
+fields = db.getTableFieldNames('dashboard_ungrouped_alltime');
+[dataTable,~] = db.queryDB(fp, fields);
+
+
+
+
+
+%% === Define config ===
+
+for m = [4,6,8]
+
+    cfg = struct;
+    cfg.x_axis   = 'symbolrate';
+    cfg.y_axis   = 'Alpha';
+    cfg.group_by = {'equalizer_structure','pre_emph'};
+    cfg.filters = struct('is_mpi',0,'pam_level',m, ...
+                                 'equalizer_structure',equalizer_structure.vnle_pf_mlse, ...
+                                 'pre_emph',0);
+    cfg.agg      = 'max';
+    cfg.outlier  = 'mad';
+    cfg.show_raw = false;
+    cfg.show_precoded = 0;
+    
+    % Plot cosmetics (minimal)
+    cfg.plot = struct;
+    cfg.plot.custom_colors = linspecer(8);
+    cfg.plot.custom_linetypes = {'-'};
+    cfg.plot.lineWidth = 2;
+    
+    
+    % ============================================================
+    % === ANALYSIS ONLY (no plotting) =============================
+    % ============================================================
+    A = analyze_measurements_gpt(dataTable, cfg);
+    
+    % Now you have:
+    %  A.raw.x         = raw x-values
+    %  A.raw.y         = raw BER values
+    %  A.group{1}.x    = unique sorted x-values
+    %  A.group{1}.y    = aggregated BER for each x
+    
+    x_values = A.group{1}.x;
+    y_values = A.group{1}.y;
+    
+    
+    % ============================================================
+    % === PLOT ====================================================
+    % ============================================================
+    
+    figure(10);hold on
+    cfg.ax = gca;        % optional: plot into existing axes
+    plot(x_values,y_values,...
+        'LineWidth', 2, ...
+        'Color', clr.Set1.red, ...
+        'MarkerSize', 5, ...
+        'MarkerFaceColor', clr.Set1.red,...
+        'Marker','o');
+    % [h, ~] = plot_measurements_gpt(dataTable, cfg); 
+    
+    title('Minimal VNLE BER Example')
+    xlabel('Grossrate [Gb/s]')
+    ylabel('BER')
+    xticks(100:30:220)
+    xlim([100,220]);
+    ylim([0,1]);
+
+end
+% outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\alphas.tikz';
+% matlab2tikz(outfile, ...
+%     'width','\fwidth', ...
+%     'height','\fheight', ...
+%     'showInfo',false, ...
+%     'extraAxisOptions',{ ...
+%         'legend style={font=\footnotesize}', ...
+%         'legend columns=1' ...
+%          'every axis/.append style={font=\scriptsize}',...
+%          'minor grid style={line width=0.2pt, solid, color=black!10}',...
+%          'grid style={line width=0.4pt, solid, color=black!20}',...
+%         'grid style={dashed}',...
+%     });

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/generate_spectrum_plots.m

@@ -65,7 +65,7 @@ Scpe_sig.spectrum("fignum",201,"normalizeTo0dB",0,"displayname",'Rx');
 
 
 
-if 0 
+if 1 
     %% show freuqncy response of filter
     
     measure = 1;
@@ -74,7 +74,7 @@ if 0
     %
     Digi_sig = freqresp.buildOFDM();
     
-    Digi_sig.spectrum("fignum",1112,"displayname",['maxamp:',num2str(maxamp)]);
+    % Digi_sig.spectrum("fignum",1112,"displayname",['maxamp:',num2str(maxamp)]);
     
     Digi_sig = Filter('filtdegree',3,"f_cutoff",70e9,"fs",256e9,"filterType",filtertypes.butterworth,"active",true).process(Digi_sig);
     
@@ -97,4 +97,15 @@ if 0
     
     fprintf('Plotting: %s\n', precomp_filename);
     freqresp.plot();
+
+    outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\spectrum_2.tikz';
+matlab2tikz(outfile, ...
+    'width','\fwidth', ...
+    'height','\fheight', ...
+    'showInfo',false, ...
+    'extraAxisOptions',{ ...
+        'legend style={font=\footnotesize}', ...
+        'legend columns=1' ...
+    });
+
 end

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/plot_measurements_gpt.m

@@ -1,111 +1,51 @@
-function h = plot_measurements_gpt(T, cfg)
+function [h, M, cfg] = plot_measurements_gpt(T, cfg)
-% Versatile plotting from your DB table (with cbrewer2 'Paired' palette).
+% PLOT_MEASUREMENTS_GPT
+% Plot measurements, using analyze_measurements_gpt for data handling.
 %
 % Usage:
-%   h = plot_measurements_flex(dataTable, cfg)
+%   h = plot_measurements_gpt(dataTable, cfg);
+%   [h, M] = plot_measurements_gpt(dataTable, cfg);
+%
+% For values only, without plotting, call:
+%   [M, cfg] = analyze_measurements_gpt(dataTable, cfg);
 
-%% ---- Defaults
 if nargin < 2, cfg = struct; end
-defaults = struct( ...
-    'x_axis'      , 'symbolrate', ...
-    'y_axis'      , 'BER', ...
-    'y_scale'     , 'auto', ...
-    'group_by'    , {{'equalizer_structure','pre_emph'}}, ...
-    'filters'     , struct, ...
-    'agg'         , 'mean', ...
-    'outlier'     , 'auto', ...
-    'mad_z'       , 4, ...
-    'pct_limits'  , [2.5 97.5], ...
-    'min_pts_x'   , 3, ...
-    'show_raw'    , true, ...
-    'show_precoded', [], ...
-    'show_spread' , 'none', ...
-    'fec_lines'   , [], ...
-    'plot', struct() ...
-);
-cfg = filldefaults(cfg, defaults);
 
-% ---- Plot defaults (new)
+% --- First: run analysis (filtering, grouping, aggregation) ---
+[M, cfg] = analyze_measurements_gpt(T, cfg);
+
+nG = M.nGroups;
+
+%% ---- Plot defaults ----
 plotdefs = struct( ...
     'use_cbrewer2'          , true, ...
-    'colormap'              , 'Paired', ...     % ColorBrewer 'Paired'
+    'colormap'              , 'Paired', ...
-    'paired_dark_first'     , true, ...         % dark for lines, light for scatter
+    'paired_dark_first'     , true, ...
     'lineWidth'             , 1.8, ...
     'errWidth'              , 1.0, ...
     'scatterSize'           , 14, ...
     'scatterAlpha'          , 0.35, ...
     'marker'                , 'o', ...
     'marker_precoded'       , 's', ...
-    'lineStyle_pre_emph_on' , '--', ...
-    'lineStyle_pre_emph_off', '-', ...
     'legendLocation'        , 'best', ...
-    'fecLineWidth'          , 2.2, ...          % thicker FEC limits
+    'fecLineWidth'          , 2.2, ...
     'fecColor'              , [0.25 0.25 0.25], ...
     'capSize'               , 6, ...
-    'lineStyle_default'      , '-', ...
+    'lineStyle_default'     , '-', ...
-    'use_pre_emph_styling'   , true ...
+    'custom_colors'         , [], ...
+    'custom_colors_scatter' , [] ...
 );
+if ~isfield(cfg,'plot') || isempty(cfg.plot)
+    cfg.plot = struct;
+end
 cfg.plot = filldefaults(cfg.plot, plotdefs);
 
-%% ---- Derived/prep columns
+%% ---- Colors ----
-if ~ismember('pre_emph', T.Properties.VariableNames)
-    if ~ismember('db_mode', T.Properties.VariableNames)
-        error('Missing column "db_mode" for pre_emph derivation.');
-    end
-    T.pre_emph = T.db_mode == 0;
-end
-if ~ismember(cfg.y_axis, T.Properties.VariableNames)
-    error('y_axis "%s" not found in table.', cfg.y_axis);
-end
-
-isBER = startsWith(cfg.y_axis, "BER", 'IgnoreCase', true);
-if strcmpi(cfg.y_scale,'auto'), cfg.y_scale = tern(isBER, 'log', 'linear'); end
-if strcmpi(cfg.outlier,'auto'), cfg.outlier = tern(isBER, 'mad', 'none'); end
-if isempty(cfg.show_precoded)
-    cfg.show_precoded = isBER && ismember('BER_precoded', T.Properties.VariableNames);
-end
-
-%% ---- Filters
-T = applyFilters(T, cfg.filters);
-[x_raw, x_label] = computeX(T, cfg.x_axis);
-y_raw = T.(cfg.y_axis);
-
-validXY = isfinite(x_raw) & isfinite(y_raw);
-T   = T(validXY, :);
-x_raw = x_raw(validXY);
-y_raw = y_raw(validXY);
-
-if mean(abs(y_raw)) > 1e8
-    %giga values
-    y_raw = y_raw.*1e-9;
-end
-
-if cfg.show_precoded && ismember('BER_precoded', T.Properties.VariableNames)
-    y_raw_p = T.BER_precoded(validXY);
-else
-    y_raw_p = [];
-end
-
-%% ---- Grouping
-group_by = cfg.group_by;
-if ~all(ismember(group_by, T.Properties.VariableNames))
-    error('Some group_by columns are missing in table.');
-end
-[G, grpTbl] = findgroups(T(:, group_by));
-nG = max(G);
-
-% ==== Colors (cbrewer2 'Paired' with dark/ light pairs) ====
 [cols_line, cols_scatter] = buildGroupColors(nG, cfg.plot);
 
-%% ---- Axes / Figure handling (new unified logic)
+%% ---- Axes / Figure handling ----
-
-% Priority:
-% 1) cfg.ax  → use existing axes (subplots/tiles)
-% 2) cfg.figure_number → select/create figure
-% 3) fallback: create new figure
-
 if isfield(cfg,'ax') && ~isempty(cfg.ax) && isgraphics(cfg.ax,'axes')
-    ax = cfg.ax;                     % use caller-provided axes
+    ax = cfg.ax;
     set(gcf,'CurrentAxes',ax);
 else
     if isfield(cfg,'figure_number') && ~isempty(cfg.figure_number)
@@ -113,143 +53,97 @@ else
     else
         figure;
     end
-    ax = gca;                        % active axes
+    ax = gca;
 end
-
 hold(ax,'on');
 grid(ax,'on');
 
-
 h.lines   = gobjects(nG,1);
 h.err     = gobjects(nG,1);
 h.scat    = gobjects(nG,1);
 h.lines_p = gobjects(nG,1);
 
+%% ---- Plot each group ----
 for gi = 1:nG
-    idx = (G==gi);
+    g   = M.group{gi};
-    Ti  = T(idx,:);
+    xu  = g.x;
-    xi  = x_raw(idx);
+    yu  = g.y;
-    yi  = y_raw(idx);
+    ylo = g.y_lo;
+    yhi = g.y_hi;
 
-    % Aggregate per unique x
+    % --- Linestyle selection ---
-    [xu, ia, iu] = unique(xi);
-    yu   = nan(size(xu));
-    ylo  = nan(size(xu));
-    yhi  = nan(size(xu));
-
-    for k = 1:numel(xu)
-        bin = (iu==k);
-        yy  = yi(bin);
-        yy  = yy(isfinite(yy));
-        if isempty(yy), continue; end
-        km = outlierMask(yy, cfg, strcmpi(cfg.y_scale,'log'));
-        if nnz(km) < cfg.min_pts_x, km = true(size(yy)); end
-        yy = yy(km);
-
-        if strcmpi(cfg.agg,'median'), yu(k)=median(yy,'omitnan'); elseif strcmpi(cfg.agg,'mean'), yu(k)=mean(yy,'omitnan'); elseif strcmpi(cfg.agg,'min'), yu(k)=min(yy); elseif strcmpi(cfg.agg,'max'), yu(k)=max(yy); end
-        if strcmpi(cfg.show_spread,'iqr')
-            q = prctile(yy,[25 75]);
-            ylo(k) = max(yu(k)-q(1), eps);
-            yhi(k) = max(q(2)-yu(k), eps);
-        elseif strcmpi(cfg.show_spread,'minmax')
-            ylo(k) = min(yy);
-            yhi(k) = max(yy);
-        end
-    end
-
-    % sort
-    [xu, ord] = sort(xu);
-    yu  = yu(ord); 
-    ylo = ylo(ord); 
-    yhi = yhi(ord);
-
-    % Styles
-    % Decide if we style by pre_emph
-    % --- LINE TYPE SELECTION (no pre-emphasis logic) ---
     ls = cfg.plot.lineStyle_default;
-
-    % User-defined override (cycled)
     if isfield(cfg.plot,'custom_linetypes') && ~isempty(cfg.plot.custom_linetypes)
         L = cfg.plot.custom_linetypes;
         ls = L{ mod(gi-1, numel(L)) + 1 };
     end
 
-    lbl = buildLabel(grpTbl(gi,:), group_by);
-
-    % Main line (dark)
     colL = cols_line(gi,:);
-    h.lines(gi) = plot(xu, yu, ...
+    lbl  = g.label;
+
+    % Main line
+    h.lines(gi) = plot(ax, xu, yu, ...
         'LineWidth', cfg.plot.lineWidth, ...
         'Marker', cfg.plot.marker, 'MarkerSize', 3, ...
         'Color', colL, 'LineStyle', ls, ...
         'DisplayName', char(lbl));
 
-    % Spread (IQR) in line color
+    % Spread
     if any(isfinite(ylo)) && any(isfinite(yhi))
-        h.err(gi) = errorbar(xu, yu, ylo, yhi, 'LineStyle','none', ...
+        h.err(gi) = errorbar(ax, xu, yu, ylo, yhi, 'LineStyle','none', ...
             'Color', colL, 'CapSize', cfg.plot.capSize, 'HandleVisibility','off');
         h.err(gi).LineWidth = cfg.plot.errWidth;
     end
 
-    % Raw kept scatter (light)
+    % Raw scatter
     if cfg.show_raw
-        keep_all = false(size(yi));
+        % reuse stored raw data (no extra filtering)
-        for k = 1:numel(xu)
+        xi = g.x_raw;
-            bin = (iu==k);
+        yi = g.y_raw;
-            yy  = yi(bin);
-            km  = outlierMask(yy, cfg, strcmpi(cfg.y_scale,'log'));
-            if nnz(km) < cfg.min_pts_x, km = true(size(yy)); end
-            keep_all(bin) = km;
-        end
         colS = cols_scatter(gi,:);
-        scatter(xi(keep_all), yi(keep_all), cfg.plot.scatterSize, colS, 'filled', ...
+        scatter(ax, xi, yi, cfg.plot.scatterSize, colS, 'filled', ...
-            'MarkerFaceAlpha', cfg.plot.scatterAlpha, 'MarkerEdgeAlpha', cfg.plot.scatterAlpha, ...
+            'MarkerFaceAlpha', cfg.plot.scatterAlpha, ...
+            'MarkerEdgeAlpha', cfg.plot.scatterAlpha, ...
             'HandleVisibility','off');
     end
 
-    % Precoded overlay (dotted, squares), in line color
+    % Precoded overlay
-    if cfg.show_precoded && ~isempty(y_raw_p) && strcmpi(cfg.y_axis,'BER')
+    if cfg.show_precoded && ~isempty(g.y_precoded) && strcmpi(M.y_axis,'BER')
-        ypi = y_raw_p(idx);
+        ypu = g.y_precoded;
-        ypu = nan(size(xu));
+        h.lines_p(gi) = plot(ax, xu, ypu, ...
-        for k = 1:numel(xu)
-            bin = (iu==k);
-            yy  = ypi(bin);
-            yy  = yy(isfinite(yy));
-            if isempty(yy), continue; end
-            km = outlierMask(yy, cfg, true);
-            if nnz(km) < cfg.min_pts_x, km = true(size(yy)); end
-            yy = yy(km);
-            if strcmpi(cfg.agg,'median'), ypu(k)=median(yy,'omitnan'); elseif strcmpi(cfg.agg,'mean'), ypu(k)=mean(yy,'omitnan'); elseif strcmpi(cfg.agg,'min'), ypu(k)=min(yy); elseif strcmpi(cfg.agg,'max'), ypu(k)=max(yy); end
-        end
-        h.lines_p(gi) = plot(xu, ypu, ...
             'LineWidth', max(1.2, cfg.plot.lineWidth-0.2), ...
             'Marker', cfg.plot.marker_precoded, 'MarkerSize', 3, ...
             'Color', colL, 'LineStyle', ':', ...
             'DisplayName', [char(lbl) ' (precoded)']);
     end
-
-    
 end
 
-%% ---- Axes / Labels / FEC
+%% ---- Axes / Labels / FEC ----
-ylabel(cfg.y_axis, 'Interpreter','none');
+ylabel(ax, M.y_axis, 'Interpreter','none');
-xlabel(x_label,    'Interpreter','none');
+xlabel(ax, M.x_label, 'Interpreter','none');
-set(gca, 'YScale', cfg.y_scale, 'FontSize', 11);
+set(ax, 'YScale', cfg.y_scale, 'FontSize', 11);
-% legend('Location', cfg.plot.legendLocation); box on;
 
-xticks(floor(xu));
+% X ticks/limits using all group x-values
-xlim([min(xu), max(xu)])
+allX = cellfun(@(g) g.x(:), M.group, 'UniformOutput', false);
+allX = unique(vertcat(allX{:}));
+if ~isempty(allX)
+    xticks(ax, allX);
+    xticklabels(cellstr(num2str(round(allX,1), '%.4f')))
+    xlim(ax, [min(allX), max(allX)]);
+end
 
-if startsWith(cfg.y_axis,"BER",'IgnoreCase',true)
+if startsWith(M.y_axis,"BER",'IgnoreCase',true)
     for v = cfg.fec_lines
-        yline(v, '--', 'Color', cfg.plot.fecColor, ...
+        yline(ax, v, '--', 'Color', cfg.plot.fecColor, ...
             'LineWidth', cfg.plot.fecLineWidth, 'HandleVisibility','off');
     end
-    ylim([1e-5, 0.5]);
+    ylim(ax, [1e-5, 0.5]);
-    yticks([1e-5, 1e-4, 1e-3, 1e-2, 1e-1]);
+    yticks(ax, [1e-5, 1e-4, 1e-3, 1e-2, 1e-1]);
 end
 
+% legend(ax, 'Location', cfg.plot.legendLocation); % if you want legends
 
+box(ax,'on');
 
 end % ===== main =====
 
@@ -263,120 +157,14 @@ for i = 1:numel(fn)
     if ~isfield(cfg, f) || isempty(cfg.(f))
         cfg.(f) = defs.(f);
     elseif isstruct(defs.(f)) && isstruct(cfg.(f))
-        cfg.(f) = filldefaults(cfg.(f), defs.(f)); % recursive for structs
+        cfg.(f) = filldefaults(cfg.(f), defs.(f));
     end
 end
 end
 
-function out = tern(cond, a, b)
-    if cond
-        out = a;
-    else
-        out = b;
-    end
-end
-
-function T2 = applyFilters(T, filters)
-if isempty(filters), T2 = T; return; end
-keep = true(height(T),1);
-fns  = fieldnames(filters);
-for i = 1:numel(fns)
-    name = fns{i};
-    if ~ismember(name, T.Properties.VariableNames)
-        warning('Filter column "%s" not found. Ignored.', name); %#ok<*WNTAG>
-        continue
-    end
-    val = filters.(name);
-    col = T.(name);
-    if isa(val,'function_handle')
-        m = val(col);
-        if ~islogical(m) || ~isequal(size(m), size(col))
-            error('Filter for %s must return logical mask of same size.', name);
-        end
-        keep = keep & m;
-    else
-        keep = keep & ismember(col, val);
-    end
-end
-T2 = T(keep,:);
-end
-
-function [x, label] = computeX(T, whichX)
-switch lower(whichX)
-    case {'symbolrate','baudrate'}
-        x = T.symbolrate * 1e-9;
-        label = 'Symbol rate [GBd]';
-    case 'bitrate'
-        if ~ismember('pam_level', T.Properties.VariableNames)
-            error('bitrate requires "pam_level" column.');
-        end
-        bits = floor(log2(double(T.pam_level))*10)/10;
-        x = (T.symbolrate .* bits) * 1e-9;
-        label = 'Grossrate [Gb/s]';
-        case 'grossrate'
-            x = (T.grossrate) * 1e-9;
-            label = 'Grossrate [Gb/s]';
-    otherwise
-        if ~ismember(whichX, T.Properties.VariableNames)
-            error('x_axis "%s" not found in table.', whichX);
-        end
-        x = T.(whichX);
-        label = whichX;
-end
-x = double(x(:));
-end
-
-function keep = outlierMask(y, cfg, useLog)
-if isempty(y), keep = false(size(y)); return; end
-y = y(:);
-switch lower(cfg.outlier)
-    case 'none'
-        keep = true(size(y)); return
-    case 'mad'
-        z = tern(useLog, log10(y), y);
-        med = median(z,'omitnan');
-        madv = median(abs(z-med),'omitnan');
-        if ~(isfinite(madv) && madv>0)
-            keep = true(size(y)); return
-        end
-        sigma = 1.4826*madv;
-        zz = tern(useLog, log10(y), y);
-        keep = abs(zz - med) <= cfg.mad_z*sigma;
-    case 'pctl'
-        pr = prctile(y, cfg.pct_limits);
-        keep = (y >= pr(1)) & (y <= pr(2));
-    otherwise
-        error('Unknown outlier mode "%s".', cfg.outlier);
-end
-end
-
-function s = buildLabel(grpRow, group_by)
-parts = strings(1, numel(group_by));
-for i = 1:numel(group_by)
-    key = group_by{i};
-    val = grpRow.(key);
-    if iscell(val), val = val{1}; end
-    if islogical(val), val = tern(val,'w/','w/o'); end
-    if key == "equalizer_structure"
-        key = '';
-        val = upper(val);
-        val = strrep(val,'_',' ');
-    end
-    
-    if key == "pre_emph"
-        % key = strrep(key,'_','-');
-        val = [val, ' pre-emph.'];
-        key = ''; 
-    end
-    
-    parts(i) = sprintf('%s %s', key, string(val));
-end
-s = strjoin(parts, ', ');
-end
-
 function [cols_line, cols_scatter] = buildGroupColors(nG, plotcfg)
 
-% --- 1) User-provided custom colors -------------------------------
+% 1) User-provided custom colors
 if isfield(plotcfg,'custom_colors') && ~isempty(plotcfg.custom_colors)
     C = plotcfg.custom_colors;
     if size(C,1) < nG
@@ -384,7 +172,6 @@ if isfield(plotcfg,'custom_colors') && ~isempty(plotcfg.custom_colors)
     end
     cols_line = C(1:nG, :);
 
-    % Scatter colors: either user-provided or lightened
     if isfield(plotcfg,'custom_colors_scatter') && ~isempty(plotcfg.custom_colors_scatter)
         Cs = plotcfg.custom_colors_scatter;
         if size(Cs,1) < nG
@@ -392,7 +179,6 @@ if isfield(plotcfg,'custom_colors') && ~isempty(plotcfg.custom_colors)
         end
         cols_scatter = Cs(1:nG, :);
     else
-        % auto-lighten scatter colors
         cols_scatter = zeros(nG,3);
         for i = 1:nG
             cols_scatter(i,:) = lightenColor(cols_line(i,:), 0.40);
@@ -401,7 +187,7 @@ if isfield(plotcfg,'custom_colors') && ~isempty(plotcfg.custom_colors)
     return;
 end
 
-% --- 2) Standard behavior (using cbrewer2 or fallback) ------------
+% 2) Standard behavior
 useBrewer = plotcfg.use_cbrewer2 && exist('cbrewer2','file')==2;
 if useBrewer
     N = max(2*nG, 12);

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/plot_measurements_gpt_old.m

@@ -0,0 +1,436 @@
+function h = plot_measurements_gpt_old(T, cfg)
+% Versatile plotting from your DB table (with cbrewer2 'Paired' palette).
+%
+% Usage:
+%   h = plot_measurements_flex(dataTable, cfg)
+
+%% ---- Defaults
+if nargin < 2, cfg = struct; end
+defaults = struct( ...
+    'x_axis'      , 'symbolrate', ...
+    'y_axis'      , 'BER', ...
+    'y_scale'     , 'auto', ...
+    'group_by'    , {{'equalizer_structure','pre_emph'}}, ...
+    'filters'     , struct, ...
+    'agg'         , 'mean', ...
+    'outlier'     , 'auto', ...
+    'mad_z'       , 4, ...
+    'pct_limits'  , [2.5 97.5], ...
+    'min_pts_x'   , 3, ...
+    'show_raw'    , true, ...
+    'show_precoded', [], ...
+    'show_spread' , 'none', ...
+    'fec_lines'   , [], ...
+    'plot', struct() ...
+);
+cfg = filldefaults(cfg, defaults);
+
+% ---- Plot defaults (new)
+plotdefs = struct( ...
+    'use_cbrewer2'          , true, ...
+    'colormap'              , 'Paired', ...     % ColorBrewer 'Paired'
+    'paired_dark_first'     , true, ...         % dark for lines, light for scatter
+    'lineWidth'             , 1.8, ...
+    'errWidth'              , 1.0, ...
+    'scatterSize'           , 14, ...
+    'scatterAlpha'          , 0.35, ...
+    'marker'                , 'o', ...
+    'marker_precoded'       , 's', ...
+    'lineStyle_pre_emph_on' , '--', ...
+    'lineStyle_pre_emph_off', '-', ...
+    'legendLocation'        , 'best', ...
+    'fecLineWidth'          , 2.2, ...          % thicker FEC limits
+    'fecColor'              , [0.25 0.25 0.25], ...
+    'capSize'               , 6, ...
+    'lineStyle_default'      , '-', ...
+    'use_pre_emph_styling'   , true ...
+);
+cfg.plot = filldefaults(cfg.plot, plotdefs);
+
+%% ---- Derived/prep columns
+if ~ismember('pre_emph', T.Properties.VariableNames)
+    if ~ismember('db_mode', T.Properties.VariableNames)
+        error('Missing column "db_mode" for pre_emph derivation.');
+    end
+    T.pre_emph = T.db_mode == 0;
+end
+if ~ismember(cfg.y_axis, T.Properties.VariableNames)
+    error('y_axis "%s" not found in table.', cfg.y_axis);
+end
+
+isBER = startsWith(cfg.y_axis, "BER", 'IgnoreCase', true);
+if strcmpi(cfg.y_scale,'auto'), cfg.y_scale = tern(isBER, 'log', 'linear'); end
+if strcmpi(cfg.outlier,'auto'), cfg.outlier = tern(isBER, 'mad', 'none'); end
+if isempty(cfg.show_precoded)
+    cfg.show_precoded = isBER && ismember('BER_precoded', T.Properties.VariableNames);
+end
+
+%% ---- Filters
+T = applyFilters(T, cfg.filters);
+[x_raw, x_label] = computeX(T, cfg.x_axis);
+y_raw = T.(cfg.y_axis);
+
+validXY = isfinite(x_raw) & isfinite(y_raw);
+T   = T(validXY, :);
+x_raw = x_raw(validXY);
+y_raw = y_raw(validXY);
+
+if mean(abs(y_raw)) > 1e8
+    %giga values
+    y_raw = y_raw.*1e-9;
+end
+
+if cfg.show_precoded && ismember('BER_precoded', T.Properties.VariableNames)
+    y_raw_p = T.BER_precoded(validXY);
+else
+    y_raw_p = [];
+end
+
+%% ---- Grouping
+group_by = cfg.group_by;
+if ~all(ismember(group_by, T.Properties.VariableNames))
+    error('Some group_by columns are missing in table.');
+end
+[G, grpTbl] = findgroups(T(:, group_by));
+nG = max(G);
+
+% ==== Colors (cbrewer2 'Paired' with dark/ light pairs) ====
+[cols_line, cols_scatter] = buildGroupColors(nG, cfg.plot);
+
+%% ---- Axes / Figure handling (new unified logic)
+
+% Priority:
+% 1) cfg.ax  → use existing axes (subplots/tiles)
+% 2) cfg.figure_number → select/create figure
+% 3) fallback: create new figure
+
+if isfield(cfg,'ax') && ~isempty(cfg.ax) && isgraphics(cfg.ax,'axes')
+    ax = cfg.ax;                     % use caller-provided axes
+    set(gcf,'CurrentAxes',ax);
+else
+    if isfield(cfg,'figure_number') && ~isempty(cfg.figure_number)
+        figure(cfg.figure_number);
+    else
+        figure;
+    end
+    ax = gca;                        % active axes
+end
+
+hold(ax,'on');
+grid(ax,'on');
+
+
+h.lines   = gobjects(nG,1);
+h.err     = gobjects(nG,1);
+h.scat    = gobjects(nG,1);
+h.lines_p = gobjects(nG,1);
+
+for gi = 1:nG
+    idx = (G==gi);
+    Ti  = T(idx,:);
+    xi  = x_raw(idx);
+    yi  = y_raw(idx);
+
+    % Aggregate per unique x
+    [xu, ia, iu] = unique(xi);
+    yu   = nan(size(xu));
+    ylo  = nan(size(xu));
+    yhi  = nan(size(xu));
+
+    for k = 1:numel(xu)
+        bin = (iu==k);
+        yy  = yi(bin);
+        yy  = yy(isfinite(yy));
+        if isempty(yy), continue; end
+        km = outlierMask(yy, cfg, strcmpi(cfg.y_scale,'log'));
+        if nnz(km) < cfg.min_pts_x, km = true(size(yy)); end
+        yy = yy(km);
+
+        if strcmpi(cfg.agg,'median'), yu(k)=median(yy,'omitnan'); elseif strcmpi(cfg.agg,'mean'), yu(k)=mean(yy,'omitnan'); elseif strcmpi(cfg.agg,'min'), yu(k)=min(yy); elseif strcmpi(cfg.agg,'max'), yu(k)=max(yy); end
+        if strcmpi(cfg.show_spread,'iqr')
+            q = prctile(yy,[25 75]);
+            ylo(k) = max(yu(k)-q(1), eps);
+            yhi(k) = max(q(2)-yu(k), eps);
+        elseif strcmpi(cfg.show_spread,'minmax')
+            ylo(k) = min(yy);
+            yhi(k) = max(yy);
+        end
+    end
+
+    % sort
+    [xu, ord] = sort(xu);
+    yu  = yu(ord); 
+    ylo = ylo(ord); 
+    yhi = yhi(ord);
+
+    % Styles
+    % Decide if we style by pre_emph
+    % --- LINE TYPE SELECTION (no pre-emphasis logic) ---
+    ls = cfg.plot.lineStyle_default;
+
+    % User-defined override (cycled)
+    if isfield(cfg.plot,'custom_linetypes') && ~isempty(cfg.plot.custom_linetypes)
+        L = cfg.plot.custom_linetypes;
+        ls = L{ mod(gi-1, numel(L)) + 1 };
+    end
+
+    lbl = buildLabel(grpTbl(gi,:), group_by);
+
+    % Main line (dark)
+    colL = cols_line(gi,:);
+    h.lines(gi) = plot(xu, yu, ...
+        'LineWidth', cfg.plot.lineWidth, ...
+        'Marker', cfg.plot.marker, 'MarkerSize', 3, ...
+        'Color', colL, 'LineStyle', ls, ...
+        'DisplayName', char(lbl));
+
+    % Spread (IQR) in line color
+    if any(isfinite(ylo)) && any(isfinite(yhi))
+        h.err(gi) = errorbar(xu, yu, ylo, yhi, 'LineStyle','none', ...
+            'Color', colL, 'CapSize', cfg.plot.capSize, 'HandleVisibility','off');
+        h.err(gi).LineWidth = cfg.plot.errWidth;
+    end
+
+    % Raw kept scatter (light)
+    if cfg.show_raw
+        keep_all = false(size(yi));
+        for k = 1:numel(xu)
+            bin = (iu==k);
+            yy  = yi(bin);
+            km  = outlierMask(yy, cfg, strcmpi(cfg.y_scale,'log'));
+            if nnz(km) < cfg.min_pts_x, km = true(size(yy)); end
+            keep_all(bin) = km;
+        end
+        colS = cols_scatter(gi,:);
+        scatter(xi(keep_all), yi(keep_all), cfg.plot.scatterSize, colS, 'filled', ...
+            'MarkerFaceAlpha', cfg.plot.scatterAlpha, 'MarkerEdgeAlpha', cfg.plot.scatterAlpha, ...
+            'HandleVisibility','off');
+    end
+
+    % Precoded overlay (dotted, squares), in line color
+    if cfg.show_precoded && ~isempty(y_raw_p) && strcmpi(cfg.y_axis,'BER')
+        ypi = y_raw_p(idx);
+        ypu = nan(size(xu));
+        for k = 1:numel(xu)
+            bin = (iu==k);
+            yy  = ypi(bin);
+            yy  = yy(isfinite(yy));
+            if isempty(yy), continue; end
+            km = outlierMask(yy, cfg, true);
+            if nnz(km) < cfg.min_pts_x, km = true(size(yy)); end
+            yy = yy(km);
+            if strcmpi(cfg.agg,'median'), ypu(k)=median(yy,'omitnan'); elseif strcmpi(cfg.agg,'mean'), ypu(k)=mean(yy,'omitnan'); elseif strcmpi(cfg.agg,'min'), ypu(k)=min(yy); elseif strcmpi(cfg.agg,'max'), ypu(k)=max(yy); end
+        end
+        h.lines_p(gi) = plot(xu, ypu, ...
+            'LineWidth', max(1.2, cfg.plot.lineWidth-0.2), ...
+            'Marker', cfg.plot.marker_precoded, 'MarkerSize', 3, ...
+            'Color', colL, 'LineStyle', ':', ...
+            'DisplayName', [char(lbl) ' (precoded)']);
+    end
+
+    
+end
+
+%% ---- Axes / Labels / FEC
+ylabel(cfg.y_axis, 'Interpreter','none');
+xlabel(x_label,    'Interpreter','none');
+set(gca, 'YScale', cfg.y_scale, 'FontSize', 11);
+% legend('Location', cfg.plot.legendLocation); box on;
+
+xticks(floor(xu));
+xlim([min(xu), max(xu)])
+
+if startsWith(cfg.y_axis,"BER",'IgnoreCase',true)
+    for v = cfg.fec_lines
+        yline(v, '--', 'Color', cfg.plot.fecColor, ...
+            'LineWidth', cfg.plot.fecLineWidth, 'HandleVisibility','off');
+    end
+    ylim([1e-5, 0.5]);
+    yticks([1e-5, 1e-4, 1e-3, 1e-2, 1e-1]);
+end
+
+
+
+end % ===== main =====
+
+
+%% ===================== Helpers =====================
+
+function cfg = filldefaults(cfg, defs)
+fn = fieldnames(defs);
+for i = 1:numel(fn)
+    f = fn{i};
+    if ~isfield(cfg, f) || isempty(cfg.(f))
+        cfg.(f) = defs.(f);
+    elseif isstruct(defs.(f)) && isstruct(cfg.(f))
+        cfg.(f) = filldefaults(cfg.(f), defs.(f)); % recursive for structs
+    end
+end
+end
+
+function out = tern(cond, a, b)
+    if cond
+        out = a;
+    else
+        out = b;
+    end
+end
+
+function T2 = applyFilters(T, filters)
+if isempty(filters), T2 = T; return; end
+keep = true(height(T),1);
+fns  = fieldnames(filters);
+for i = 1:numel(fns)
+    name = fns{i};
+    if ~ismember(name, T.Properties.VariableNames)
+        warning('Filter column "%s" not found. Ignored.', name); %#ok<*WNTAG>
+        continue
+    end
+    val = filters.(name);
+    col = T.(name);
+    if isa(val,'function_handle')
+        m = val(col);
+        if ~islogical(m) || ~isequal(size(m), size(col))
+            error('Filter for %s must return logical mask of same size.', name);
+        end
+        keep = keep & m;
+    else
+        keep = keep & ismember(col, val);
+    end
+end
+T2 = T(keep,:);
+end
+
+function [x, label] = computeX(T, whichX)
+switch lower(whichX)
+    case {'symbolrate','baudrate'}
+        x = T.symbolrate * 1e-9;
+        label = 'Symbol rate [GBd]';
+    case 'bitrate'
+        if ~ismember('pam_level', T.Properties.VariableNames)
+            error('bitrate requires "pam_level" column.');
+        end
+        bits = floor(log2(double(T.pam_level))*10)/10;
+        x = (T.symbolrate .* bits) * 1e-9;
+        label = 'Grossrate [Gb/s]';
+        case 'grossrate'
+            x = (T.grossrate) * 1e-9;
+            label = 'Grossrate [Gb/s]';
+    otherwise
+        if ~ismember(whichX, T.Properties.VariableNames)
+            error('x_axis "%s" not found in table.', whichX);
+        end
+        x = T.(whichX);
+        label = whichX;
+end
+x = double(x(:));
+end
+
+function keep = outlierMask(y, cfg, useLog)
+if isempty(y), keep = false(size(y)); return; end
+y = y(:);
+switch lower(cfg.outlier)
+    case 'none'
+        keep = true(size(y)); return
+    case 'mad'
+        z = tern(useLog, log10(y), y);
+        med = median(z,'omitnan');
+        madv = median(abs(z-med),'omitnan');
+        if ~(isfinite(madv) && madv>0)
+            keep = true(size(y)); return
+        end
+        sigma = 1.4826*madv;
+        zz = tern(useLog, log10(y), y);
+        keep = abs(zz - med) <= cfg.mad_z*sigma;
+    case 'pctl'
+        pr = prctile(y, cfg.pct_limits);
+        keep = (y >= pr(1)) & (y <= pr(2));
+    otherwise
+        error('Unknown outlier mode "%s".', cfg.outlier);
+end
+end
+
+function s = buildLabel(grpRow, group_by)
+parts = strings(1, numel(group_by));
+for i = 1:numel(group_by)
+    key = group_by{i};
+    val = grpRow.(key);
+    if iscell(val), val = val{1}; end
+    if islogical(val), val = tern(val,'w/','w/o'); end
+    if key == "equalizer_structure"
+        key = '';
+        val = upper(val);
+        val = strrep(val,'_',' ');
+    end
+    
+    if key == "pre_emph"
+        % key = strrep(key,'_','-');
+        val = [val, ' pre-emph.'];
+        key = ''; 
+    end
+    
+    parts(i) = sprintf('%s %s', key, string(val));
+end
+s = strjoin(parts, ', ');
+end
+
+function [cols_line, cols_scatter] = buildGroupColors(nG, plotcfg)
+
+% --- 1) User-provided custom colors -------------------------------
+if isfield(plotcfg,'custom_colors') && ~isempty(plotcfg.custom_colors)
+    C = plotcfg.custom_colors;
+    if size(C,1) < nG
+        error('custom_colors must have at least nG=%d rows.', nG);
+    end
+    cols_line = C(1:nG, :);
+
+    % Scatter colors: either user-provided or lightened
+    if isfield(plotcfg,'custom_colors_scatter') && ~isempty(plotcfg.custom_colors_scatter)
+        Cs = plotcfg.custom_colors_scatter;
+        if size(Cs,1) < nG
+            error('custom_colors_scatter must have at least nG=%d rows.', nG);
+        end
+        cols_scatter = Cs(1:nG, :);
+    else
+        % auto-lighten scatter colors
+        cols_scatter = zeros(nG,3);
+        for i = 1:nG
+            cols_scatter(i,:) = lightenColor(cols_line(i,:), 0.40);
+        end
+    end
+    return;
+end
+
+% --- 2) Standard behavior (using cbrewer2 or fallback) ------------
+useBrewer = plotcfg.use_cbrewer2 && exist('cbrewer2','file')==2;
+if useBrewer
+    N = max(2*nG, 12);
+    C = cbrewer2(plotcfg.colormap, N);
+    cols_line    = zeros(nG,3);
+    cols_scatter = zeros(nG,3);
+    for i = 1:nG
+        if plotcfg.paired_dark_first
+            dark  = C(2*i-1, :);
+            light = C(2*i,   :);
+        else
+            light = C(2*i-1, :);
+            dark  = C(2*i,   :);
+        end
+        cols_line(i,:)    = dark;
+        cols_scatter(i,:) = light;
+    end
+else
+    C = lines(max(nG,7));
+    cols_line    = C(1:nG,:);
+    cols_scatter = zeros(nG,3);
+    for i = 1:nG
+        cols_scatter(i,:) = lightenColor(cols_line(i,:), 0.50);
+    end
+end
+
+end
+
+function c2 = lightenColor(c, fracTowardWhite)
+c = c(:).';
+c2 = (1-fracTowardWhite)*c + fracTowardWhite*1;
+end

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/plot_rates.m

@@ -1,34 +0,0 @@
-
-path = "C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\HighSpeedExperiment_2024\Auswertung_JLT\final\rates.csv";
-
-%% === Read CSV into a table ===
-T = readtable(path, 'Delimiter', ';', 'DecimalSeparator', ',');
-
-%% === Column definitions ===
-x = T.X_Werte;
-pam_cols = {'PAM_2','PAM_4','PAM_6','PAM_8','PAM_12'};
-titles   = {'PAM-2','PAM-4','PAM-6','PAM-8','PAM-12'};
-
-% === Create subplots ===
-figure;hold on
-c = linspecer(6);
-for k = 1:numel(pam_cols)
-    
-    y = T.(pam_cols{k});
-    valid = ~isnan(y);
-
-    
-    % Scatter plot
-    s = scatter(x(valid), y(valid), 25, 'filled','MarkerEdgeColor',c(k,:),'MarkerFaceColor',c(k,:),'DisplayName',titles{k});
-
-    % Fit (2nd-order polynomial)
-    p = polyfit(x(valid), y(valid), 2);
-    xfit = linspace(min(x(valid)), max(x(valid)), 200);
-    yfit = polyval(p, xfit);
-
-    % Plot fit curve
-    plot(xfit, yfit, 'LineWidth', 1,'linestyle',':','Color',c(k,:),'HandleVisibility','off');
-
-    xlabel('baud rate [GBd]');
-    ylabel('net rate [Gb/s]');
-end

projects/HighSpeedExperiment_2024/Auswertung_JLT/run_dsp_from_db.m

@@ -1,6 +1,6 @@
 % === SETTINGS ===
-dsp_options.append_to_db = 0;
+dsp_options.append_to_db = 1;
-dsp_options.max_occurences = 5;
+dsp_options.max_occurences = 1;
 
 experiment = "highspeed_2024";
 dsp_options.mode            = "load_run_id"; % 'simulate' & 'load_files'
@@ -40,25 +40,25 @@ end
 
 fp = QueryFilter();
 % fp.where('Runs', 'run_id','EQUALS', 2776);
-M = 4;
+M = 6;
 fp.where('Runs', 'pam_level','EQUALS', M);     
-fp.where('Runs', 'bitrate','EQUALS', 390e9);%360,390
+% fp.where('Runs', 'bitrate','EQUALS', 390e9);%360,390
 % fp.where('Runs', 'symbolrate','EQUALS', 195e9);
-fp.where('Runs', 'fiber_length','EQUALS', 2);   
+fp.where('Runs', 'fiber_length','EQUALS', 10);   
-% fp.where('Runs', 'is_mpi','EQUALS', 0);                
+fp.where('Runs', 'is_mpi','EQUALS', 0);                
 % fp.where('Runs', 'interference_path_length','EQUALS', 1000);      
 % fp.where('Runs', 'loop_id','GREATER_THAN', 11);   
 % fp.where('Runs', 'sir','EQUALS',18);  
-fp.where('Runs', 'wavelength','EQUALS', 1310);         
+% fp.where('Runs', 'wavelength','EQUALS', 1310);         
 fp.where('Runs', 'db_mode','EQUALS', 0);              
 fp.where('Runs', 'rop_attenuation','EQUAL', 0);      
-% fp.where('Runs', 'power_pd_in','GREATER_THAN', 7);    
+% fp.where('Runs', 'power_pd_in','LESS_THAN', 7);    
 
 [dataTable,~] = db.queryDB(fp, db.getTableFieldNames('Runs'));
 
 % === Set LOOPS & Initialize DataStorage ===
 dsp_options.parameters = struct();
-% dsp_options.parameters.pf_ncoeffs = [1,2];%[0,logspace(-4,0,10)];
+% dsp_options.parameters.pf_ncoeffs = [1,2];%s[0,logspace(-4,0,10)];
 
 wh = DataStorage(dsp_options.parameters);
 wh.addStorage("ffe_package");
@@ -70,10 +70,7 @@ wh.addStorage("mlmlse_package");
 
 %% === RUN IT ===
 
-[results,wh] = submitJobs(dataTable.run_id(:), dsp_options, "serial", 'wh', wh, 'waitbar', true);
+[results,wh] = submitJobs(dataTable.run_id(:), dsp_options, "parallel", 'wh', wh, 'waitbar', true);
-
-
-
 
 
 

projects/HighSpeedExperiment_2024/a_minimal_example.m

@@ -0,0 +1,118 @@
+
+
+dsp_options.storage_path    = 'Z:\2024\sioe_labor\';
+dsp_options.max_occurences = 1;
+db    = DBHandler("dataBase", 'labor_highspeed', "type",  'mysql' );
+
+fp = QueryFilter();
+
+fp.where('Runs','fiber_length','EQUALS', 2);
+fp.where('Runs','wavelength','EQUALS', 1310);
+fp.where('Runs','bitrate','EQUALS', 300e9);
+fp.where('Runs','pam_level','EQUALS', 4);
+fp.where('Runs','rop_attenuation','EQUALS', 0);
+fp.where('Runs','is_mpi','EQUALS', 0);
+fp.where('Runs', 'db_mode','EQUALS', 0); 
+fields = db.getTableFieldNames('Runs');
+[dataTable,~] = db.queryDB(fp, fields);
+
+
+fsym        = dataTable.symbolrate;
+M           = double(dataTable.pam_level);
+duob_mode   = db_mode(strrep(dataTable.db_mode,'"',''));
+
+
+% Load and Sync signal data from DB
+[Tx_bits, Symbols, Scpe_cell, ~] = loadAndSyncSignalDataFromDb(dataTable, dsp_options);
+
+% Preprocess signal
+Scpe_sig = preprocessSignal(Scpe_cell{1}, Symbols, fsym);
+
+% Show spectrum
+Scpe_sig.spectrum("fignum",1,"displayname",'Rx')
+
+
+
+
+
+
+%% simple FFE
+
+mu_ffe      = [0.0001, 0.0008, 0.001];
+mu_dfe      = 0.0004;
+ffe_order   = [50, 0, 0];
+eq_dfe    = EQ("Ne",ffe_order,"Nb",[0,0,0],"training_length",4096,"training_loops",5,"dd_loops",5,"K",2,"DCmu",0.005,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
+
+ffe_results = ffe(eq_dfe,M,Scpe_sig,Symbols,Tx_bits,...
+    "precode_mode",duob_mode,...
+    'showAnalysis',0,...
+    "postFFE",[],...
+    "eth_style_symbol_mapping",0);
+
+
+ffe_results.metrics.print("description",'FFE');
+ffe_results.config.equalizer_structure = "ffe";
+
+%% a) VNLE // b) concatenated VNLE + MLSE
+
+pf_ncoeffs  = 1;
+ffe_order   = [50, 5, 5];
+dfe_order   = [0,0,0];
+eq_    = EQ("Ne",ffe_order,"Nb",dfe_order,"training_length",4096,"training_loops",5,"dd_loops",5,"K",2,"DCmu",0.005,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
+pf_    = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
+
+
+
+if duob_mode == db_mode.no_db && M == 6 %only for PAM-6 and no duobinary precoding, otherwise leads to false sequence estimation
+    trellexlusion = 1;
+else
+    trellexlusion = 0;
+end
+
+%state_mode 3 -> stat lvl; state_mode 2 -> use target lvls
+%scale_mode 2 -> mmse adaption
+
+mlse_  = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels,'scale_mode',2,'trellis_exclusion',trellexlusion,'trellis_state_mode',2);
+
+[vnle_results, mlse_results] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Scpe_sig, Symbols, Tx_bits, ...
+    "precode_mode", duob_mode,...
+    'showAnalysis', 0, ...
+    "postFFE", [],...
+    "eth_style_symbol_mapping", 0);
+
+vnle_results.metrics.print("description",'VNLE');
+mlse_results.metrics.print("description",'VNLE + PF + MLSE');
+
+
+%% Duobinary Equalization
+
+
+if duob_mode == db_mode.no_db && M == 6 %only for PAM-6 and no duobinary precoding, otherwise leads to false sequence estimation
+    trellexlusion = 1;
+else
+    trellexlusion = 0;
+end
+mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels,'scale_mode',2,'trellis_exclusion',trellexlusion,'trellis_state_mode',3);
+
+ffe_order   = [50, 5, 5];
+dfe_order   = [0,0,0];
+eq_    = EQ("Ne",ffe_order,"Nb",dfe_order,"training_length",4096,"training_loops",5,"dd_loops",5,"K",2,"DCmu",0.005,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
+
+dbt_results = duobinary_target(eq_, mlse_db_, M, Scpe_sig, Symbols, Tx_bits, ...
+    "precode_mode", duob_mode, ...
+    'showAnalysis', 0,...
+    "postFFE", []);
+
+dbt_results.metrics.print("description",'Duobinary EQ');
+
+%% Ml based Viterbi 
+
+%ML-based MLSE (L=2)
+mu_ml = 0.01; training_epochs = 100;
+ml_mlse_equalizer = ML_MLSE("epochs_tr",training_epochs,"epochs_dd",1, ...
+    "len_tr",length(Scpe_sig),"mu_dd",mu_ml,"mu_tr",mu_ml,"order",11,"sps",2, ...
+    "traceback_depth",128,"L",1,"delta",4,"adaptive_mu",0);
+
+[ml_mlse_results] = ml_mlse(ml_mlse_equalizer, M, Scpe_sig, Symbols, Tx_bits,"precode_mode",duob_mode);
+
+ml_mlse_results.metrics.print("description",'ML pre Eq. + Viterbi')

projects/HighSpeedExperiment_2024/auswertung/only_show_precomp.m

@@ -8,84 +8,115 @@ db_coding_approach  = 0;
 fsym = 160e9;
 fdac = 256e9;
 random_key = 0;
-M = 4;
+pams = [4];
 
-if (db_precode==1)&&(db_coding_approach==0)
+cols = cbrewer2('Paired',6);
+for i = 1:length(pams)
+    M = pams(i);
+    if (db_precode==1)&&(db_coding_approach==0)
 
-    if M == 4
+        if M == 4
-        pulsef=1;
+            pulsef=1;
-        precomp_amp_max = -50;
+            precomp_amp_max = -50;
-    elseif M == 6
+            fsym = 196e9;
-        pulsef=0;
+        elseif M == 6
-        precomp_amp_max = -50;
+            pulsef=0;
-    elseif M == 8
+            precomp_amp_max = -50;
-        pulsef=0;
+            fsym = 180e9;
-        precomp_amp_max = -50;
+        elseif M == 8
+            pulsef=0;
+            precomp_amp_max = -50;
+            fsym = 160e9;
+        end
+
+    elseif (db_precode==1)&&(db_coding_approach==1)
+
+        if M == 4
+            pulsef=1;
+            precomp_amp_max = -38;
+            pulsef = 1;
+        elseif M == 6
+            pulsef=0;
+            precomp_amp_max = -38;
+            pulsef = 1;
+        elseif M == 8
+            pulsef=0;
+            precomp_amp_max = -38;
+            pulsef = 1;
+        end
+
+    elseif (db_precode==0)&&(db_coding_approach==0)
+
+        if M == 4
+            pulsef=1;
+            precomp_amp_max = -37;
+            pulsef = 1;
+            fsym = 196e9;
+        elseif M == 6
+            pulsef=0;
+            precomp_amp_max = -34;
+            pulsef = 1;
+            fsym = 180e9;
+        elseif M == 8
+            pulsef=0;
+            precomp_amp_max = -34;
+            pulsef = 0;
+            fsym = 160e9;
+        end
     end
 
-elseif (db_precode==1)&&(db_coding_approach==1)
 
-    if M == 4
+    rcalpha     = 0.05;
-        pulsef=1;
+    Pform       = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"alpha",rcalpha);
-        precomp_amp_max = -38;
+
-        pulsef = 1;
+    Pamsource = PAMsource(...
-    elseif M == 6
+        "fsym",fsym,"M",M,"order",19,"useprbs",0,...
-        pulsef=0;
+        "fs_out",fdac,...
-        precomp_amp_max = -38;
+        "applyclipping",0,"clipfactor",1.2,...
-        pulsef = 1;
+        "applypulseform",pulsef,"pulseformer",Pform,...
-    elseif M == 8
+        "randkey",random_key,...
-        pulsef=0;
+        "db_precode",db_precode,"db_encode",db_coding_approach,...
-        precomp_amp_max = -38;
+        "mrds_code",0,"mrds_blocklength",512);
-        pulsef = 1;
+
+    [Digi_sig,Symbols,Bits] = Pamsource.process();
+
+    Digi_sig = Digi_sig.normalize("mode","rms");
+
+    precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',Digi_sig.fs);
+
+    % maxampdb = [-30:-3:-50,precomp_amp_max];
+    maxampdb = precomp_amp_max;%sort(maxampdb);
+    cols_ = cbrewer2('spectral',15);
+    
+    for j = 1:length(maxampdb)
+
+        if maxampdb(j) == precomp_amp_max
+            color=clr.Set1.green;
+        else
+            color=cols_(j,:);
+        end
+
+        Digi_sig_pre = precomp_est.precomp(Digi_sig,'maxampdb',maxampdb(j),'loadPath',precomp_path,'fileName',precomp_fn);
+    
+        % Digi_sig_pre = Digi_sig_pre.normalize("mode","rms");
+    
+        Digi_sig_pre = Digi_sig_pre.resample("fs_out",fdac);
+    
+        Digi_sig_pre= Digi_sig_pre.normalize("mode","rms");
+    
+        Digi_sig_pre.spectrum("displayname","Strong Precomp","fignum",2223,"normalizeToNyquist",0,"normalizeTo0dB",0,"color",color,"linestyle",'-','addDCoffset',27);
+
     end
 
-elseif (db_precode==0)&&(db_coding_approach==0)
+     Digi_sig.spectrum("displayname","No Precomp","fignum",2223,"normalizeToNyquist",0,"normalizeTo0dB",0,"color",cols(2*i,:),"linestyle",'-','addDCoffset',27);
 
-    if M == 4
-        pulsef=1;
-        precomp_amp_max = -37;
-        pulsef = 1;
-    elseif M == 6
-        pulsef=0;
-        precomp_amp_max = -34;
-        pulsef = 1;
-    elseif M == 8
-        pulsef=0;
-        precomp_amp_max = -34;
-        pulsef = 0;
-    end
 end
 
+ylim([-25,10]);
+xlim([0,105]);
+xticks(0:20:110);
+yticks(-30:10:10);
 
-rcalpha     = 0.05;
+fig = gcf;
-Pform       = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"alpha",rcalpha);
+pos = [536.3333 879 450 222];
-
+set(fig, 'Position', pos);
-Pamsource = PAMsource(...
-    "fsym",fsym,"M",M,"order",19,"useprbs",0,...
-    "fs_out",fdac,...
-    "applyclipping",0,"clipfactor",1.2,...
-    "applypulseform",pulsef,"pulseformer",Pform,...
-    "randkey",random_key,...
-    "db_precode",db_precode,"db_encode",db_coding_approach,...
-    "mrds_code",0,"mrds_blocklength",512);
-
-[Digi_sig,Symbols,Bits] = Pamsource.process();
-
-Digi_sig = Digi_sig.normalize("mode","rms");
-
-Digi_sig.spectrum("displayname","No Precomp","fignum",2223,"normalizeToNyquist",0,"normalizeTo0dB",0);
-
-precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',Digi_sig.fs);
-
-Digi_sig = precomp_est.precomp(Digi_sig,'maxampdb',precomp_amp_max,'loadPath',precomp_path,'fileName',precomp_fn);
-
-Digi_sig = Digi_sig.normalize("mode","rms");
-
-Digi_sig = Digi_sig.resample("fs_out",fdac);
-
-Digi_sig= Digi_sig.normalize("mode","rms");
-
-Digi_sig.spectrum("displayname","Strong Precomp","fignum",2223,"normalizeToNyquist",0,"normalizeTo0dB",0);
-
-ylim([-30,3]);
-xlim([-5,100]);

projects/HighSpeedExperiment_2024/bias_evaluation.m

@@ -30,7 +30,7 @@ for l =  1:numel(lambda_vals)
     for m = 1:numel(M_vals)
        
         ber_ffe      = wh.getStoValue('ber_ffe',v_bias_vals,awg_vpp_vals(1),precomp_amp_max_vals(1),rop_atten_vals(1),M_vals(m),lambda_vals(l));
-        ber         = wh.getStoValue('ber_collect',v_bias_vals,awg_vpp_vals(1),precomp_amp_max_vals(1),rop_atten_vals(1),M_vals(m),lambda_vals(l));
+        ber         = wh.getStoValue('ber_ffe',v_bias_vals,awg_vpp_vals(1),precomp_amp_max_vals(1),rop_atten_vals(1),M_vals(m),lambda_vals(l));
         exfo         = wh.getStoValue('exfo',v_bias_vals,awg_vpp_vals(1),precomp_amp_max_vals(1),rop_atten_vals(1),M_vals(m),lambda_vals(l));
 
         lb         = wh.getStoValue('exfo',v_bias_vals,awg_vpp_vals(1),precomp_amp_max_vals(1),rop_atten_vals(1),M_vals(m),lambda_vals(l));

projects/IMDD_base_system/simulation_bwl_2.m

@@ -18,14 +18,14 @@ u_pi            = 2.9;
 vbias           = -vbias_rel*u_pi;
 laser_wavelength    = 1310;
 laser_linewidth     = 0;
-tx_bw_nyquist   = 0.7;
+tx_bw_nyquist   = 1;
 
 % Channel
 link_length     = 1;
 
 % RX
-rop = -8;
+rop = 0;
-rx_bw_nyquist = 0.7;
+rx_bw_nyquist = 0.99;
 
 vnle_order1 = 50;
 vnle_order2 = 3;
@@ -59,7 +59,7 @@ Pform       = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rc","pulselength",1
 
 db_precode = 0;
 db_encode = 0;
-duob_mode = db_mode.no_db;
+duob_mode = db_mode.db_precoded;
 apply_pulsef = 1;
 [Digi_sig,Symbols,Tx_bits] = PAMsource(...
     "fsym",fsym,"M",M,"order",18,"useprbs",0,...
@@ -67,12 +67,21 @@ apply_pulsef = 1;
     "applyclipping",0,"clipfactor",1.5,...
     "applypulseform",apply_pulsef,"pulseformer",Pform,...
     "randkey",random_key,...
-    "db_precode",db_precode,"db_encode",db_encode,...
     "mrds_code",0,"mrds_blocklength",512,"duobinary_mode",duob_mode).process();
 
 Digi_sig.spectrum("displayname",'Digi Spectrum','fignum',10,'normalizeTo0dB',1);
 
 
+%% proof of concept 
+Symbols_db = Duobinary().encode(Symbols);
+mim_decoded = Duobinary().decode(Symbols_db,"M",M);
+rx_bits_mim_decoded = PAMmapper(M,0,"eth_style",0).demap(mim_decoded);
+rx_bits_mim_decoded_.signal =  circshift(rx_bits_mim_decoded.signal,0);
+[~,~,ber_mim_decode,~]  = calc_ber(rx_bits_mim_decoded_.signal,Tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
+fprintf('BER mim: %.2e \n',ber_mim_decode);
+
+%%
+
 
 %%%%% AWG
 % El_sig = M8199A("kover",kover).process(Digi_sig);
@@ -127,14 +136,57 @@ pf_    = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
 % mlse_  = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
 mlse_  = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
 
-% FFE or VNLE
+if duob_mode == db_mode.no_db
-[eq_signal_sd, eq_noise] = eq_.process(Scpe_cell{1}, Symbols);
 
-% Hard decision on VNLE output
+    % FFE or VNLE
-eq_signal_hd = PAMmapper(M, 0).quantize(eq_signal_sd);
+    [eq_signal_sd, eq_noise] = eq_.process(Scpe_cell{1}, Symbols);
+    
+    % Hard decision on VNLE output
+    eq_signal_hd = PAMmapper(M, 0).quantize(eq_signal_sd);
+    
+    % Process through postfilter and MLSE
+    [mlse_sig_sd,whitened_noise] = pf_.process(eq_signal_sd, eq_noise);
+    mlse_.DIR = pf_.coefficients;
+    mlse_sig_sd = mlse_.process(mlse_sig_sd,Symbols);
+    
+    
+    % BER
+    rx_bits = PAMmapper(M,0,"eth_style",0).demap(eq_signal_hd);
+    [~,tot_err,ber_vnle,a] = calc_ber(rx_bits.signal,Tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
+    
+    rx_bits = PAMmapper(M,0,"eth_style",0).demap(mlse_sig_sd);
+    [~,tot_err,ber_mlse,a] = calc_ber(rx_bits.signal,Tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
 
-% Process through postfilter and MLSE
+elseif duob_mode == db_mode.db_precoded
-[mlse_sig_sd,whitened_noise] = pf_.process(eq_signal_sd, eq_noise);
+
-mlse_.DIR = pf_.coefficients;
+    %%
-mlse_sig_sd = mlse_.process(mlse_sig_sd,Symbols);
+    [EQ_sig, Noi] = eq_.process(Scpe_cell{1},Duobinary().encode(Symbols));
-mlse_sig_hd = PAMmapper(M, 0, "eth_style", 0).quantize(mlse_sig_sd);
+
+    showLevelHistogram(EQ_sig,Duobinary().encode(Symbols),"displayname",101);
+
+    mim_decoded = Duobinary().decode(EQ_sig,"M",M);
+
+    showLevelHistogram(mim_decoded,Symbols,"displayname",101);
+
+    rx_bits_mim_decoded = PAMmapper(M,0,"eth_style",0).demap(mim_decoded);
+
+    rx_bits_mim_decoded_.signal =  circshift(rx_bits_mim_decoded.signal,0);
+
+    [~,~,ber_mim_decode,~]  = calc_ber(rx_bits_mim_decoded_.signal,Tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
+
+    fprintf('BER mim: %.2e \n',ber_mim_decode);
+
+    %%
+    mlse_sig_hd = MLSE("DIR",[1,1],"duobinary_output",1,"M",M,"trellis_states",PAMmapper(M,0).levels).process(EQ_sig,Symbols);
+
+    mlse_sig_hd_decoded = Duobinary().encode(mlse_sig_hd,"M",M);
+
+    mlse_sig_hd_decoded = Duobinary().decode(mlse_sig_hd_decoded,"M",M);
+
+    rx_bits_mlse_decoded = PAMmapper(M,0,"eth_style",0).demap(mlse_sig_hd_decoded);
+
+    [~,errors_db_diff_precoded,ber_db_diff_precoded,a]  = calc_ber(rx_bits_mlse_decoded.signal,Tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
+
+    %%
+
+end

projects/ML_based_MLSE/experimental_data.m

@@ -35,8 +35,9 @@ mlse_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M
 db_ref_sequence = Duobinary().encode(Symbols);
 db_ref_constellation = unique(db_ref_sequence.signal);
 [eq_signal, eq_noise] = eq_.process(Scpe_sig,db_ref_sequence);
+
 %%
-if 0
+if 1
     [mlse_sig_sd,LLR,GMI_MLSE] = mlse_.process(eq_signal,Symbols);
 else
     % Ml MLSE

projects/WDM/WDM_model.m

@@ -142,7 +142,7 @@ for realiz = 1:s.num_realiz
         Opt_sig_wdm_demux = Optical_Demultiplex("attenuation",0,"B",200e9,"filtype",1,"fs_out",Opt_sig_wdm_rx.fs/upsample_pow,"fs_in",Opt_sig_wdm_rx.fs,"lambda_center",1310).process(Opt_sig_wdm_rx);
         
         PD_cell = {};
-        parfor l = 1:N
+        for l = 1:N
         
             %%%%%% PD Square Law %%%%%%
             assert(fdac*kover==Opt_sig_wdm_demux{l}.fs,'Sampling Frequencies do not match! Check previous steps');