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