151 lines
5.1 KiB
Matlab
151 lines
5.1 KiB
Matlab
function plotFitParameterPDF(fitResults, scanValues, paramName, varargin)
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%% plotFitParameterPDF
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% Author: Karthik
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% Date: 2025-10-06
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% Version: 1.0
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%
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% Description:
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% Plots 2D PDF (heatmap) of any parameter from two-Gaussian fit results
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% for multiple scan parameters, with optional overlay of mean ± SEM.
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%
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% Inputs:
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% fitResults - struct array from fitTwoGaussianCurves
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% scanValues - vector of scan parameter values
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% paramName - string specifying the parameter to plot:
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% 'mu1', 'sigma1', 'A2', 'mu2', 'sigma2'
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%
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% Optional name-value pairs (same as before, plus OverlayMeanSEM):
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% 'OverlayMeanSEM' - logical, overlay mean ± SEM (default: true)
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% --- Parse optional inputs ---
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p = inputParser;
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addParameter(p, 'Title', '', @(x) ischar(x) || isstring(x));
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addParameter(p, 'XLabel', '', @(x) ischar(x) || isstring(x));
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addParameter(p, 'YLabel', '', @(x) ischar(x) || isstring(x));
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addParameter(p, 'FigNum', 1, @(x) isscalar(x));
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addParameter(p, 'FontName', 'Arial', @ischar);
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addParameter(p, 'FontSize', 14, @isnumeric);
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addParameter(p, 'SkipSaveFigures', false, @islogical);
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addParameter(p, 'SaveFileName', 'FitParameterPDF.fig', @ischar);
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addParameter(p, 'SaveDirectory', pwd, @ischar);
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addParameter(p, 'NumPoints', 200, @(x) isscalar(x));
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addParameter(p, 'DataRange', [], @(x) isempty(x) || numel(x)==2);
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addParameter(p, 'XLim', [], @(x) isempty(x) || numel(x)==2);
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addParameter(p, 'Colormap', @jet);
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addParameter(p, 'PlotType', 'histogram', @(x) any(validatestring(x,{'kde','histogram'})));
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addParameter(p, 'NumberOfBins', 50, @isscalar);
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addParameter(p, 'NormalizeHistogram', true, @islogical);
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addParameter(p, 'OverlayMeanSEM', true, @islogical);
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parse(p, varargin{:});
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opts = p.Results;
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% --- Map paramName to index ---
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paramMap = struct('mu1',1,'sigma1',2,'A2',3,'mu2',4,'sigma2',5);
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if ~isfield(paramMap,paramName)
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error('Invalid paramName. Must be one of: mu1, sigma1, A2, mu2, sigma2');
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end
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paramIdx = paramMap.(paramName);
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% --- Determine repetitions and scan parameters ---
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N_params = numel(scanValues);
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N_total = numel(fitResults);
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N_reps = N_total / N_params;
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% --- Extract chosen parameter values ---
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paramValues = nan(N_reps, N_params);
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for k = 1:N_total
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paramIdxScan = mod(k-1, N_params) + 1;
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repIdx = floor((k-1)/N_params) + 1;
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if fitResults(k).isValid
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paramValues(repIdx, paramIdxScan) = fitResults(k).pFit(paramIdx);
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end
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end
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% --- Prepare data per scan parameter ---
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dataCell = cell(N_params,1);
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for i = 1:N_params
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dataCell{i} = paramValues(:,i);
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end
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% --- Determine y-range ---
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if isempty(opts.DataRange)
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allData = cell2mat(dataCell(:));
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y_min = min(allData);
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y_max = max(allData);
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else
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y_min = opts.DataRange(1);
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y_max = opts.DataRange(2);
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end
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% --- Prepare PDF grid/matrix ---
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if strcmpi(opts.PlotType,'kde')
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y_grid = linspace(y_min, y_max, opts.NumPoints);
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pdf_matrix = zeros(numel(y_grid), N_params);
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else
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edges = linspace(y_min, y_max, opts.NumberOfBins+1);
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binCenters = (edges(1:end-1) + edges(2:end))/2;
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pdf_matrix = zeros(numel(binCenters), N_params);
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end
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% --- Compute PDFs ---
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for i = 1:N_params
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data = dataCell{i};
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data = data(~isnan(data));
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if isempty(data), continue; end
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if strcmpi(opts.PlotType,'kde')
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f = ksdensity(data, y_grid);
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pdf_matrix(:,i) = f;
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else
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counts = histcounts(data, edges);
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if opts.NormalizeHistogram
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binWidth = edges(2) - edges(1);
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counts = counts / (sum(counts) * binWidth);
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end
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pdf_matrix(:,i) = counts(:);
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end
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end
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% --- Plot heatmap ---
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fig = figure(opts.FigNum); clf(fig);
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set(fig, 'Color', 'w', 'Position', [100 100 950 750]);
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if strcmpi(opts.PlotType,'kde')
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imagesc(scanValues, y_grid, pdf_matrix);
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else
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imagesc(scanValues, binCenters, pdf_matrix);
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end
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set(gca, 'YDir', 'normal', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
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xlabel(opts.XLabel, 'FontSize', opts.FontSize, 'FontName', opts.FontName);
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ylabel(opts.YLabel, 'FontSize', opts.FontSize, 'FontName', opts.FontName);
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title(opts.Title, 'FontSize', opts.FontSize+2, 'FontWeight', 'bold');
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colormap(feval(opts.Colormap));
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c = colorbar;
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if strcmpi(opts.PlotType,'kde') || opts.NormalizeHistogram
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ylabel(c, 'Probability Density', 'Rotation', -90, 'FontName', opts.FontName, 'FontSize', opts.FontSize);
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else
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ylabel(c, 'Counts', 'Rotation', -90, 'FontName', opts.FontName, 'FontSize', opts.FontSize);
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end
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if ~isempty(opts.XLim)
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xlim(opts.XLim);
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end
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% --- Overlay mean ± SEM if requested ---
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if opts.OverlayMeanSEM
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meanParam = nanmean(paramValues,1);
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semParam = nanstd(paramValues,0,1) ./ sqrt(sum(~isnan(paramValues),1));
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hold on;
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xVec = reshape(scanValues, 1, []); % ensures 1 × N_params
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fill([xVec, fliplr(xVec)], [meanParam - semParam, fliplr(meanParam + semParam)], ...
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[0.2 0.4 0.8], 'FaceAlpha',0.2, 'EdgeColor','none');
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plot(scanValues, meanParam, 'k-', 'LineWidth', 2);
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end
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% --- Save figure ---
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if ~opts.SkipSaveFigures
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if ~exist(opts.SaveDirectory,'dir'), mkdir(opts.SaveDirectory); end
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savefig(fig, fullfile(opts.SaveDirectory, opts.SaveFileName));
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end
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end
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