theory silas diss

2026-02-20 09:51:01 +01:00
parent 2a724b833f
commit c0a0a415a8
19 changed files with 2624 additions and 7120 deletions
--- a/Functions/Theory/Dissertation/loss_curve_digitized.m
+++ b/Functions/Theory/Dissertation/loss_curve_digitized.m
@@ -0,0 +1,120 @@
+function loss_curve_digitized(filename)
+% PLOT_WPD_DATASETS Reads and plots scattering data from a CSV file.
+%   filename: String containing the path to the CSV file (e.g., 'wpd_datasets.csv')
+%
+%   This function expects a CSV with the following structure:
+%   Row 1: Dataset Names (every 2nd column)
+%   Row 2: Variable Names (X, Y, X, Y...)
+%   Row 3+: Numeric Data (potentially with NaNs for unequal lengths)
+
+    % --- 1. Import Data ---
+    if nargin < 1
+        filename = 'wpd_datasets.csv'; % Default filename
+    end
+
+    % Read the numeric data, skipping the first 2 header lines
+    % 'TreatAsMissing' ensures empty cells become NaNs
+    raw_data = readmatrix(filename, 'NumHeaderLines', 2);
+
+    % Read the first line separately to parse Dataset Names
+    fid = fopen(filename, 'r');
+    if fid == -1
+        error('Could not open file: %s', filename);
+    end
+    header_line = fgetl(fid);
+    fclose(fid);
+    
+    % Split header by comma to get names
+    raw_names = split(header_line, ',');
+    
+    % Extract non-empty names (assuming names are in col 1, 3, 5...)
+    dataset_names = raw_names(~cellfun('isempty', raw_names));
+    
+    % --- 2. Setup Plot ---
+    figure('Color', 'w', 'Position', [100, 100, 800, 600]);
+    ax = gca;
+    hold(ax, 'on');
+   
+    
+    line_styles = {'-', '-', '-', '-'};
+    
+    % --- 3. Iterate and Plot Each Dataset ---
+    num_datasets = length(dataset_names);
+    
+    for i = 1:num_datasets
+        % Calculate column indices for X and Y
+        % Dataset 1: Cols 1,2 | Dataset 2: Cols 3,4 | etc.
+        col_x = (i-1)*2 + 1;
+        col_y = (i-1)*2 + 2;
+        
+        % Extract data
+        if col_y > size(raw_data, 2)
+            warning('Data columns missing for dataset %d', i);
+            break;
+        end
+        
+        X = raw_data(:, col_x);
+        Y = raw_data(:, col_y);
+
+        
+        
+        % Remove NaNs (missing data due to unequal lengths)
+        valid_mask = ~isnan(X) & ~isnan(Y);
+        X = X(valid_mask);
+        Y = Y(valid_mask);
+
+        [X, sortIdx] = sort(X);
+        Y = Y(sortIdx);
+        
+        % 3. Smooth the Data
+
+        if numel(X) > 20
+        if 1
+            Y = smoothdata(Y, 'sgolay', 15); 
+        else
+            Y = movmean(Y, 15); 
+        end
+        end
+
+        
+        % Plotting
+        % Using semilogy because scattering data often spans orders of magnitude
+        % (Adjust to 'plot' if linear scale is preferred)
+        p = plot(ax, X, Y, ...
+            'LineStyle', line_styles{mod(i-1, length(line_styles)) + 1}, ...
+            'Marker', 'none', ...
+            'Color', 'black', ...
+            'LineWidth', 1.5, ...
+            'MarkerSize', 6, ...
+            'DisplayName', dataset_names{i});
+            
+        % Optional: Fill marker faces for better visibility
+        % p.MarkerFaceColor = p.Color;
+        % p.MarkerFaceAlpha = 0.3; % Semi-transparent fill
+    end
+    
+    % --- 4. Styling and Formatting ---
+    
+    % Axis Labels (Inferred from typical scattering plots)
+    xlabel(ax, 'Wavelength (\mu m)', 'FontSize', 12, 'FontWeight', 'bold');
+    ylabel(ax, 'Intensity / Cross-Section (a.u.)', 'FontSize', 12, 'FontWeight', 'bold');
+    
+    % Title
+    title(ax, 'Dataset Comparison', 'FontSize', 14);
+    
+    % Legend
+    legend(ax, 'Location', 'best', 'Interpreter', 'none', 'Box', 'on');
+    
+    % Grid
+    grid(ax, 'on');
+    ax.GridAlpha = 0.3;
+    ax.MinorGridAlpha = 0.1;
+    
+    % Set Log Scale for Y (likely required for this data type)
+    set(ax, 'YScale', 'log'); 
+    
+    % Enhance axis appearance
+    set(ax, 'Box', 'on', 'LineWidth', 1.2, 'FontSize', 10);
+    
+    hold(ax, 'off');
+end