├── Examples ├── VO2_Raman.mat ├── VO2_Raman.png ├── Er_PL_in_YSO.mat ├── Er_PL_in_YSO.png ├── Er_PL_in_YSO.m └── VO2_Raman.m ├── @PeakFit ├── fngaussian.m ├── fnlorentzian.m ├── computearea.m ├── computewidth.m ├── computeheight.m ├── model.m ├── transformpolycoeff.m ├── findmaxima.m ├── disp.m ├── convertunit.m ├── fit.m └── PeakFit.m ├── movmean (for MATLAB older than R2016a).m ├── README.md └── LICENSE /Examples/VO2_Raman.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/heriantolim/PeakFit/HEAD/Examples/VO2_Raman.mat -------------------------------------------------------------------------------- /Examples/VO2_Raman.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/heriantolim/PeakFit/HEAD/Examples/VO2_Raman.png -------------------------------------------------------------------------------- /Examples/Er_PL_in_YSO.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/heriantolim/PeakFit/HEAD/Examples/Er_PL_in_YSO.mat -------------------------------------------------------------------------------- /Examples/Er_PL_in_YSO.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/heriantolim/PeakFit/HEAD/Examples/Er_PL_in_YSO.png -------------------------------------------------------------------------------- /@PeakFit/fngaussian.m: -------------------------------------------------------------------------------- 1 | function y = fngaussian(x, c, h, w) 2 | %% Gaussian function with center c, height h, and FWHM w 3 | % 4 | % Copyright: Herianto Lim (https://heriantolim.com) 5 | % Licensing: GNU General Public License v3.0 6 | % First created: 04/04/2013 7 | % Last modified: 04/04/2013 8 | 9 | y = h * exp(-4 * log(2) * (x - c).^2 / w^2); 10 | 11 | end 12 | -------------------------------------------------------------------------------- /@PeakFit/fnlorentzian.m: -------------------------------------------------------------------------------- 1 | function y = fnlorentzian(x, c, h, w) 2 | %% Lorentzian function with center c, height h, and FWHM w 3 | % 4 | % Copyright: Herianto Lim (https://heriantolim.com) 5 | % Licensing: GNU General Public License v3.0 6 | % First created: 04/04/2013 7 | % Last modified: 04/04/2013 8 | 9 | y = h * (w / 2)^2 ./ ((x - c).^2 + (w / 2)^2); 10 | 11 | end 12 | -------------------------------------------------------------------------------- /@PeakFit/computearea.m: -------------------------------------------------------------------------------- 1 | function area = computearea(peakShape, height, width) 2 | %% Compute Peak Area 3 | % area = PeakFit.computearea(peakShape, height, width) computes the area 4 | % of a peak function of the specified peakShape, given its heights and 5 | % widths. 6 | % 7 | % Copyright: Herianto Lim (https://heriantolim.com) 8 | % Licensing: GNU General Public License v3.0 9 | % First created: 25/03/2013 10 | % Last modified: 25/03/2013 11 | 12 | assert(isintegerscalar(peakShape), ... 13 | 'PeakFit:computearea:InvalidInput', ... 14 | 'Input to the peak shape must be an integer scalar.'); 15 | assert(isrealarray(height) && isrealarray(width), ... 16 | 'PeakFit:computearea:InvalidInput', ... 17 | 'Input to the height and width must be arrays of real numbers.'); 18 | assert(all(size(height) == size(width)), ... 19 | 'PeakFit:computearea:InvalidInput', ... 20 | 'The height and width arrays must have the same dimensions.'); 21 | 22 | switch peakShape 23 | case 1 24 | area = pi / 2 * height .* width; 25 | case 2 26 | area = sqrt(pi / log(2)) / 2 * height .* width; 27 | otherwise 28 | error('PeakFit:computearea:UnexpectedInput', ... 29 | 'The specified peak shape is not recognized.'); 30 | end 31 | 32 | end 33 | -------------------------------------------------------------------------------- /@PeakFit/computewidth.m: -------------------------------------------------------------------------------- 1 | function width = computewidth(peakShape, area, height) 2 | %% Compute Peak Width 3 | % width = PeakFit.computewidth(peakShape, area, height) computes the width 4 | % of a peak function of the specified peakShape, given its area and 5 | % height. 6 | % 7 | % Copyright: Herianto Lim (https://heriantolim.com) 8 | % Licensing: GNU General Public License v3.0 9 | % First created: 25/03/2013 10 | % Last modified: 25/03/2013 11 | 12 | assert(isintegerscalar(peakShape), ... 13 | 'PeakFit:computewidth:InvalidInput', ... 14 | 'Input to the peak shape must be an integer scalar.'); 15 | assert(isrealarray(area) && isrealarray(height), ... 16 | 'PeakFit:computewidth:InvalidInput', ... 17 | 'Input to the area and height must be arrays of real numbers.'); 18 | assert(all(size(area) == size(height)), ... 19 | 'PeakFit:computewidth:InvalidInput', ... 20 | 'The area and height arrays must have the same dimensions.'); 21 | 22 | switch peakShape 23 | case 1 24 | width = 2 / pi * area ./ height; 25 | case 2 26 | width = 2 / sqrt(pi / log(2)) * area ./ height; 27 | otherwise 28 | error('PeakFit:computewidth:UnexpectedInput', ... 29 | 'The specified peak shape is not recognized.'); 30 | end 31 | 32 | end 33 | -------------------------------------------------------------------------------- /@PeakFit/computeheight.m: -------------------------------------------------------------------------------- 1 | function height = computeheight(peakShape, area, width) 2 | %% Compute Peak Height 3 | % height = PeakFit.computeheight(peakShape, area, width) computes the 4 | % height of a peak function of the specified peakShape, given its areas 5 | % and widths. 6 | % 7 | % Copyright: Herianto Lim (https://heriantolim.com) 8 | % Licensing: GNU General Public License v3.0 9 | % First created: 25/03/2013 10 | % Last modified: 25/03/2013 11 | 12 | assert(isintegerscalar(peakShape), ... 13 | 'PeakFit:computeheight:InvalidInput', ... 14 | 'Input to the peak shape must be an integer scalar.'); 15 | assert(isrealarray(area) && isrealarray(width), ... 16 | 'PeakFit:computeheight:InvalidInput', ... 17 | 'Input to the area and width must be arrays of real numbers.'); 18 | assert(all(size(area) == size(width)), ... 19 | 'PeakFit:computeheight:InvalidInput', ... 20 | 'The area and width arrays must have the same dimensions.'); 21 | 22 | switch peakShape 23 | case 1 24 | height = 2 / pi * area ./ width; 25 | case 2 26 | height = 2 / sqrt(pi / log(2)) * area ./ width; 27 | otherwise 28 | error('PeakFit:computeheight:UnexpectedInput', ... 29 | 'The specified peak shape is not recognized.'); 30 | end 31 | 32 | end 33 | -------------------------------------------------------------------------------- /movmean (for MATLAB older than R2016a).m: -------------------------------------------------------------------------------- 1 | function u = movmean(v, varargin) 2 | %% Moving Mean 3 | % This function is provided for backwards compatibility. It does the same 4 | % job as the builtin function, movmean, which was introduced in MATLAB 5 | % R2016a. It can be safely deleted on newer MATLAB. 6 | % 7 | % u = movmean(v) computes the simple moving average of the vector v using 8 | % sample width = 1. 9 | % 10 | % u = movmean(v,w) computes the simple moving average of the vector v 11 | % using sample width = w. 12 | % 13 | % Tested on: 14 | % - MATLAB R2013b 15 | % - MATLAB R2015b 16 | % 17 | % Copyright: Herianto Lim 18 | % https://heriantolim.com/ 19 | % First created: 22/03/2013 20 | % Last modified: 20/10/2016 21 | 22 | %% Input Validation and Parsing 23 | assert(isvector(v) && isreal(v), ... 24 | 'Input to the sample vector must be a real vector.'); 25 | 26 | if isempty(varargin) 27 | w = 1; 28 | elseif isscalar(varargin{1}) && isreal(varargin{1}) && varargin{1} >= 0 29 | w = varargin{1}; 30 | else 31 | error('Input to the sample width must be a positive real scalar.'); 32 | end 33 | 34 | %% Simple Moving Average 35 | u = v; 36 | 37 | if w == 0 38 | return 39 | end 40 | 41 | n = numel(v); 42 | w = ceil(w / 2); 43 | 44 | for i = 2:w 45 | u(i) = mean(v(1:2 * i - 1)); 46 | u(n - i + 1) = mean(v(n - 2 * i + 2:n)); 47 | end 48 | 49 | n1 = 1 + w; 50 | n2 = n - w; 51 | 52 | for i = n1:n2 53 | u(i) = mean(v(i - w:i + w)); 54 | end 55 | 56 | end 57 | -------------------------------------------------------------------------------- /Examples/Er_PL_in_YSO.m: -------------------------------------------------------------------------------- 1 | %% PeakFit Example #1: Er PL in YSO 2 | % 3 | % Copyright: Herianto Lim (https://heriantolim.com) 4 | % Licensing: GNU General Public License v3.0 5 | % First created: 28/10/2018 6 | % Last modified: 28/10/2018 7 | 8 | % Add the required packages using MatVerCon. 9 | % addpackage('MatCommon','MatGraphics','PeakFit'); 10 | 11 | % Clear workspace variables. 12 | clear; 13 | 14 | % Load data. If fails, adjust the file path supplied to the argument. 15 | S = load('Er_PL_in_YSO.mat'); 16 | 17 | % Perform Lorentzian peak fitting with default values. 18 | % The algorithm will attempt to find all the peaks in the spectrum. 19 | S.Fit = PeakFit(S.Data, 'PeakShape', 'Lorentzian'); 20 | 21 | %% Plotting 22 | % Settings. 23 | Groot.usedefault(); 24 | Groot.usedefault('latex', 8, .6); 25 | RESOLUTION = 300; 26 | AXES_SIZE = [12, 4]; 27 | TICK_LENGTH = .2; 28 | 29 | % Plot data. 30 | xData = S.Fit.XData; 31 | yData = S.Fit.YData; 32 | xLim = S.Fit.Window; 33 | xModel = linspace(xLim(1), xLim(2), ceil(RESOLUTION / 2.54 * AXES_SIZE(1))); 34 | [yModel, yPeak, yBaseline] = S.Fit.model(xModel); 35 | yLim = [min(min(yData), min(yBaseline)), max(max(yData), max(yModel))]; 36 | 37 | % Figure. 38 | fig = docfigure(AXES_SIZE); 39 | 40 | % Axes. 41 | pos = [0, 0, AXES_SIZE]; 42 | ax = axes('Position', pos, 'XLim', xLim, 'YLim', yLim, 'YTickLabel', ''); 43 | xlabel('Wavenumber (cm$^{-1}$)'); 44 | ylabel('Intensity (arb. unit)'); 45 | fixticklength(.2); 46 | 47 | % Plots. 48 | N = S.Fit.NumPeaks; 49 | h = plot(xData, yData, 'Color', 'b'); 50 | 51 | for j = 1:N 52 | plot(xModel, yBaseline + yPeak{j}, 'Color', 'r', 'LineWidth', .3); 53 | end 54 | 55 | h(2) = plot(xModel, yModel, 'Color', 'g', 'LineWidth', .3); 56 | 57 | % Peak labels. 58 | h = Label.peak(S.Fit.Center(1, :), 'StringFormat', '%.1f', ... 59 | 'FontColor', [.3, .3, .3], 'LineWidth', 0, ... 60 | 'MinYPos', .1, 'MinYDist', .01, 'PlotLine', h); 61 | 62 | % Reconfigure the axes. 63 | extent = vertcat(h.Extent); 64 | yLim(2) = max(extent(:, 2) + extent(:, 4)); 65 | yLim(2) = yLim(1) + diff(yLim) / (1 - TICK_LENGTH / AXES_SIZE(2)); 66 | ax.YLim = yLim; 67 | 68 | % Reconfigure the layout. 69 | margin = ax.TightInset + .1; 70 | ax.Position = pos + [margin(1), margin(2), 0, 0]; 71 | pos = pos + [0, 0, margin(1) + margin(3), margin(2) + margin(4)]; 72 | set(fig, {'Position', 'PaperPosition', 'PaperSize'}, {pos, pos, pos(3:4)}); 73 | 74 | % Saving. 75 | print(fig, 'Er_PL_in_YSO.png', '-dpng', sprintf('-r%d', RESOLUTION)); 76 | close(fig); 77 | -------------------------------------------------------------------------------- /@PeakFit/model.m: -------------------------------------------------------------------------------- 1 | function [yModel, yPeak, yBaseline] = model(obj, varargin) 2 | %% Fit Model 3 | % [yModel, yPeak, yBaseline] = obj.model() returns a set of fit models 4 | % evaluated at the data points. 5 | % 6 | % [yModel, yPeak, yBaseline] = obj.model(x) evaluates the fit models at x 7 | % instead. 8 | % 9 | % Outputs: 10 | % yModel: The reconstructed data points from the fit results. 11 | % 12 | % yPeak: A cell containing the resconstructed data points of each peak. 13 | % 14 | % yBaseline: The reconstructed baseline points. 15 | % 16 | % Requires package: 17 | % - MatCommon_v1.0.0+ 18 | % 19 | % Tested on: 20 | % - MATLAB R2013b 21 | % 22 | % Copyright: Herianto Lim (https://heriantolim.com) 23 | % Licensing: GNU General Public License v3.0 24 | % First created: 04/04/2013 25 | % Last modified: 25/10/2016 26 | 27 | yModel = []; 28 | yPeak = []; 29 | yBaseline = []; 30 | 31 | numPeaks = obj.NumPeaks; 32 | center = obj.Center; 33 | height = obj.Height; 34 | width = obj.Width; 35 | 36 | if numPeaks == 0 || isempty(center) || isempty(height) || isempty(width) 37 | return 38 | end 39 | 40 | %% Parse Inputs 41 | if nargin == 1 42 | xModel = obj.XData; 43 | elseif nargin > 2 44 | error('PeakfFit:model:TooManyInput', ... 45 | 'At most one input argument can be taken.'); 46 | elseif isrealvector(varargin{1}) 47 | xModel = varargin{1}; 48 | else 49 | error('PeakFit:model:InvalidInput', ... 50 | 'Input to the domains for the model must be a vector of real numbers.'); 51 | end 52 | 53 | %% Construct Baseline 54 | baseline = obj.Baseline; 55 | 56 | if obj.BaselinePolyOrder < 0 || isempty(baseline) 57 | yBaseline = zeros(size(xModel)); 58 | else 59 | yBaseline = polyval(baseline(1, :), xModel); 60 | end 61 | 62 | %% Construct Peak 63 | yPeak = cell(1, numPeaks); 64 | 65 | for i = 1:numPeaks 66 | 67 | switch obj.PeakShape(i) 68 | case 1 69 | yPeak{i} = PeakFit.fnlorentzian(xModel, ... 70 | center(1, i), height(1, i), width(1, i)); 71 | case 2 72 | yPeak{i} = PeakFit.fngaussian(xModel, ... 73 | center(1, i), height(1, i), width(1, i)); 74 | otherwise 75 | error('PeakFit:model:UnexpectedInput', ... 76 | 'The specified peak shape is not recognized.'); 77 | end 78 | 79 | end 80 | 81 | %% Construct Model 82 | yModel = yBaseline; 83 | 84 | for i = 1:numPeaks 85 | yModel = yModel + yPeak{i}; 86 | end 87 | 88 | end 89 | -------------------------------------------------------------------------------- /Examples/VO2_Raman.m: -------------------------------------------------------------------------------- 1 | %% PeakFit Example #2: VO2 Raman 2 | % 3 | % Copyright: Herianto Lim (https://heriantolim.com) 4 | % Licensing: GNU General Public License v3.0 5 | % First created: 29/10/2018 6 | % Last modified: 29/10/2018 7 | 8 | % Add the required packages using MatVerCon. 9 | % addpackage('MatCommon','MatGraphics','PeakFit'); 10 | 11 | % Clear workspace variables. 12 | clear; 13 | 14 | % Load data. If fails, adjust the file path supplied to the argument. 15 | S = load('VO2_Raman.mat'); 16 | 17 | % Perform Lorentzian peak fitting. 18 | S.Fit = PeakFit(S.Data, 'Window', [100, 900], 'PeakShape', 'Lorentzian', ... 19 | 'CenterLow', [521, 145, 198, 224, 258, 304, 310, 336, 387, 394, 430, 503, 588, 615, 665, 827], ... 20 | 'CenterUp', [524, 151, 202, 228, 263, 306, 315, 342, 394, 402, 450, 506, 597, 625, 675, 837], ... 21 | 'WidthUp', [4, 14, 10, 8, 10, 10, 10, 10, 18, 20, 40, 20, 30, 40, 20, 30], ... 22 | 'BaselinePolyOrder', 1); 23 | 24 | %% Plotting 25 | % Settings. 26 | Groot.usedefault(); 27 | Groot.usedefault('latex', 8, .6); 28 | RESOLUTION = 300; 29 | AXES_SIZE = [12, 4]; 30 | TICK_LENGTH = .2; 31 | CLIP_RANGE = [500, 540]; 32 | 33 | % Plot data. 34 | xData = S.Fit.XData; 35 | yData = S.Fit.YData; 36 | xLim = S.Fit.Window; 37 | ix = (xData >= xLim(1) & xData <= xLim(2)) ... 38 | & (xData < CLIP_RANGE(1) | xData > CLIP_RANGE(2)); 39 | xModel = linspace(xLim(1), xLim(2), ceil(RESOLUTION / 2.54 * AXES_SIZE(1))); 40 | [yModel, yPeak, yBaseline] = S.Fit.model(xModel); 41 | yLim = [min(min(yData), min(yBaseline)), max(yData(ix)) / .6]; 42 | 43 | % Figure. 44 | fig = docfigure(AXES_SIZE); 45 | 46 | % Axes. 47 | pos = [0, 0, AXES_SIZE]; 48 | ax = axes('Position', pos, 'XLim', xLim, 'YLim', yLim, ... 49 | 'YTickLabel', '', 'XDir', 'reverse'); 50 | xlabel('Raman shift (cm$^{-1}$)'); 51 | ylabel('Intensity (arb. unit)'); 52 | fixticklength(.2); 53 | 54 | % Plots. 55 | N = S.Fit.NumPeaks; 56 | h = plot(xData, yData, 'Color', 'b'); 57 | 58 | for j = 1:N 59 | plot(xModel, yBaseline + yPeak{j}, 'Color', 'r', 'LineWidth', .3); 60 | end 61 | 62 | h(2) = plot(xModel, yModel, 'Color', 'g', 'LineWidth', .3); 63 | 64 | % Peak labels. 65 | h = Label.peak(S.Fit.Center(1, :), 'StringFormat', '%.1f', ... 66 | 'FormatSpec', [2, ones(1, N - 1)], ... 67 | 'FontColor', {[.3, .3, .3], [1, .5, 0]}, 'LineWidth', 0, ... 68 | 'MinYPos', .1, 'MinYDist', .01, 'PlotLine', h, 'ClipRange', CLIP_RANGE); 69 | 70 | % Reconfigure the axes. 71 | extent = vertcat(h.Extent); 72 | yLim(2) = max(extent(:, 2) + extent(:, 4)); 73 | yLim(2) = yLim(1) + diff(yLim) / (1 - TICK_LENGTH / AXES_SIZE(2)); 74 | ax.YLim = yLim; 75 | 76 | % Reconfigure the layout. 77 | margin = ax.TightInset + .1; 78 | ax.Position = pos + [margin(1), margin(2), 0, 0]; 79 | pos = pos + [0, 0, margin(1) + margin(3), margin(2) + margin(4)]; 80 | set(fig, {'Position', 'PaperPosition', 'PaperSize'}, {pos, pos, pos(3:4)}); 81 | 82 | % Saving. 83 | print(fig, 'VO2_Raman.png', '-dpng', sprintf('-r%d', RESOLUTION)); 84 | close(fig); 85 | -------------------------------------------------------------------------------- /@PeakFit/transformpolycoeff.m: -------------------------------------------------------------------------------- 1 | function p = transformpolycoeff(p, varargin) 2 | %% Transform Polynomial Coefficient 3 | % This method transform a polynomial-coefficient vector p into another 4 | % polynomial-coefficient vector p' such that the corresponding polynomial 5 | % function y(x) transforms under: x -> x' = a x + b and y -> y' = c y + d. 6 | % 7 | % Syntax: 8 | % p = PeakFit.transformpolycoeff(p, a) 9 | % p = PeakFit.transformpolycoeff(p, a, b) 10 | % p = PeakFit.transformpolycoeff(p, a, b, c) 11 | % p = PeakFit.transformpolycoeff(p, a, b, c, d) 12 | % 13 | % Inputs 14 | % p: The polynomial coefficients in a row vector. It can be inputted as a 15 | % 3-row matrix, where the 1st, 2nd and 3rd row specifies the values of 16 | % p, the lower bound of p, and the upper bound of p respectively. 17 | % 18 | % a, b, c, d: The linear transformation coefficiens. 19 | % 20 | % Requires package: 21 | % - MatCommon_v1.0.0+ 22 | % 23 | % Tested on: 24 | % - MATLAB R2013b 25 | % - MATLAB R2015b 26 | % 27 | % Copyright: Herianto Lim (https://heriantolim.com) 28 | % Licensing: GNU General Public License v3.0 29 | % First created: 27/10/2016 30 | % Last modified: 27/10/2016 31 | 32 | assert(iscomplexmatrix(p) && (isrow(p) || size(p, 1) == 3), ... 33 | 'PeakFit:transformpolycoeff:InvalidInput', ... 34 | 'Input to the polynomial-coefficient vector must be a row vector or a 3-row matrix of complex numbers.'); 35 | assert(all(cellfun(@isrealscalar, varargin)), ... 36 | 'PeakFit:transformpolycoeff:InvalidInput', ... 37 | 'Input to the linear transformation coefficients must be a real scalar.'); 38 | 39 | N = nargin; 40 | 41 | if N == 1 42 | return 43 | end 44 | 45 | [m, n] = size(p); 46 | a = varargin{1}; 47 | 48 | if a == 0 49 | p = nan(m, n); 50 | return 51 | end 52 | 53 | if N > 2 54 | b = varargin{2}; 55 | else 56 | b = 0; 57 | end 58 | 59 | if N > 3 60 | c = varargin{3}; 61 | 62 | if c == 0 63 | p = zeros(m, n); 64 | return 65 | end 66 | 67 | else 68 | c = 1; 69 | end 70 | 71 | if N > 4 72 | d = varargin{4}; 73 | else 74 | d = 0; 75 | end 76 | 77 | if n > 1 78 | % compute the upper triangular matrix of the combinatorials 79 | A = zeros(n); 80 | N = n - 1; 81 | 82 | for i = 0:N 83 | 84 | for j = 0:i 85 | A(n - i, n - i + j) = A(n - i, n - i + j) ... 86 | + nchoosek(i, j) * a^(-i) * (-b)^j; 87 | end 88 | 89 | end 90 | 91 | % transform the polynomial coefficients 92 | p(1, :) = p(1, :) * A; 93 | 94 | % transform the lower and upper bounds 95 | if m > 2 96 | p2 = zeros(1, n); 97 | p3 = zeros(1, n); 98 | 99 | for j = 1:n 100 | 101 | for i = 1:j 102 | 103 | if A(i, j) < 0 104 | p2(j) = p2(j) + p(3, j) * A(i, j); 105 | p3(j) = p3(j) + p(2, j) * A(i, j); 106 | else 107 | p2(j) = p2(j) + p(2, j) * A(i, j); 108 | p3(j) = p3(j) + p(3, j) * A(i, j); 109 | end 110 | 111 | end 112 | 113 | end 114 | 115 | p(2, :) = p2; 116 | p(3, :) = p3; 117 | end 118 | 119 | end 120 | 121 | p(:, n) = p(:, n) + d / c; 122 | p = p * c; 123 | 124 | if m > 2 && c < 0 125 | p([2, 3], :) = p([3, 2], :); 126 | end 127 | 128 | end 129 | -------------------------------------------------------------------------------- /@PeakFit/findmaxima.m: -------------------------------------------------------------------------------- 1 | function [xm, ym] = findmaxima(obj, varargin) 2 | %% Find Maxima 3 | % [xm, ym] = obj.findmaxima() locates the maxima in the curve data stored 4 | % in the object and returns the coordinates of the maxima. 5 | % 6 | % [xm, ym] = obj.findmaxima(window) locates the maxima within the 7 | % specified x window. window is a real vector of length two. 8 | % 9 | % [xm, ym] = obj.findmaxima(n) locates exactly n maxima. 10 | % 11 | % [xm, ym] = obj.findmaxima(window, n) locates n maxima within the window. 12 | % 13 | % [xm, ym] = obj.findmaxima(x, y, ...) use the x and y vector as the data 14 | % points instead of the data stored in the object. 15 | % 16 | % The curve is smoothed first prior to finding the maxima with a simple 17 | % moving mean. The MovMeanWidth property is used as the sample 18 | % width of the moving mean. 19 | % 20 | % Requires package: 21 | % - MatCommon_v1.0.0+ 22 | % 23 | % Tested on: 24 | % - MATLAB R2013b 25 | % - MATLAB R2015b 26 | % 27 | % Copyright: Herianto Lim (https://heriantolim.com) 28 | % Licensing: GNU General Public License v3.0 29 | % First created: 27/03/2013 30 | % Last modified: 05/11/2016 31 | 32 | %% Input Validation and Parsing 33 | N = nargin - 1; 34 | 35 | if N == 0 36 | x = obj.XData; 37 | y = obj.YData; 38 | n = 0; 39 | else 40 | k = N; 41 | 42 | if isintegerscalar(varargin{k}) && varargin{k} >= 0 43 | n = varargin{k}; 44 | k = k - 1; 45 | else 46 | n = 0; 47 | end 48 | 49 | if k > 0 && isrealvector(varargin{k}) && numel(varargin{k}) == 2 50 | w = sort(varargin{k}); 51 | k = k - 1; 52 | else 53 | w = []; 54 | end 55 | 56 | if k > 1 && isrealvector(varargin{k}) && isrealvector(varargin{k - 1}) ... 57 | && numel(varargin{k}) == numel(varargin{k - 1}) 58 | y = varargin{k}; 59 | x = varargin{k - 1}; 60 | k = k - 2; 61 | else 62 | x = obj.XData; 63 | y = obj.YData; 64 | end 65 | 66 | if k > 0 67 | error('PeakFit:findmaxima:UnexpectedInput', ... 68 | 'One or more inputs are not recognized.'); 69 | end 70 | 71 | end 72 | 73 | % Trim data points 74 | if ~isempty(w) 75 | ix = x >= w(1) & x <= w(2); 76 | x = x(ix); 77 | y = y(ix); 78 | end 79 | 80 | %% Finding the Maxima 81 | N = numel(x); 82 | 83 | if N == 0 84 | % No data, return empty 85 | xm = []; 86 | ym = []; 87 | elseif n == 1 || N < 4 88 | % The tallest point is always the maxima 89 | [ym, k] = max(y); 90 | xm = x(k); 91 | else 92 | % The gradient of the smoothed curve 93 | w = obj.MovMeanWidth; 94 | 95 | if w < 1 96 | w = ceil(w * N); 97 | end 98 | 99 | y1 = diff(movmean(y, w)) ./ diff(x); 100 | 101 | % Find all maxima and give each a rank according to the average of their 102 | % adjacent absolute gradients 103 | k = N - 2; 104 | ix = zeros(1, k); 105 | rank = zeros(1, k); 106 | m = 0; 107 | 108 | for i = 1:k 109 | 110 | if y1(i) > 0 && y1(i + 1) < 0 111 | m = m + 1; 112 | ix(m) = i + 1; 113 | rank(m) = mean(abs(y1(i:i + 1))); 114 | end 115 | 116 | end 117 | 118 | ix = ix(1:m); 119 | rank = rank(1:m); 120 | 121 | if m == 0 122 | % y is either monotonically increasing or decreasing 123 | [ym, k] = max(y); 124 | xm = x(k); 125 | else 126 | 127 | if n > 0 128 | 129 | if m > n 130 | [~, k] = sort(rank, 'descend'); 131 | ix = ix(k); 132 | ix = sort(ix(1:n)); 133 | elseif m < n 134 | warning('PeakFit:find:FoundFewerPeaks', ... 135 | 'Requested to find %d peaks, but only %d peaks are found.', ... 136 | n, m); 137 | end 138 | 139 | end 140 | 141 | xm = x(ix); 142 | ym = y(ix); 143 | end 144 | 145 | end 146 | 147 | end 148 | -------------------------------------------------------------------------------- /@PeakFit/disp.m: -------------------------------------------------------------------------------- 1 | function disp(obj) 2 | %% Display Object Properties 3 | % 4 | % Copyright: Herianto Lim (https://heriantolim.com) 5 | % Licensing: GNU General Public License v3.0 6 | % First created: 08/04/2013 7 | % Last modified: 25/10/2016 8 | 9 | fprintf('\nFit Options:\n'); 10 | fprintf('\t%18s: %s\n', 'Method', obj.Method); 11 | fprintf('\t%18s: %s\n', 'Robust', obj.Robust); 12 | fprintf('\t%18s: %s\n', 'Algorithm', obj.Algorithm); 13 | fprintf('\t%18s: %.2e\n', 'DiffMaxChange', obj.DiffMaxChange); 14 | fprintf('\t%18s: %.2e\n', 'DiffMinChange', obj.DiffMinChange); 15 | fprintf('\t%18s: %d\n', 'MaxFunEvals', obj.MaxFunEvals); 16 | fprintf('\t%18s: %d\n', 'MaxIters', obj.MaxIters); 17 | fprintf('\t%18s: %.2e\n', 'TolFun', obj.TolFun); 18 | fprintf('\t%18s: %.2e\n', 'TolX', obj.TolX); 19 | 20 | exitFlag = obj.ExitFlag; 21 | 22 | if ~isempty(exitFlag) 23 | numPeaks = obj.NumPeaks; 24 | header = ' Lower Bound Start Point Upper Bound Lower CI Convergence Upper CI RelUncert\n'; 25 | lineFormat = ' %11.4e %11.4e %11.4e %11.4e %11.4e %11.4e %11.4e\n'; 26 | 27 | fprintf('\nArea:\n\t Peak'); 28 | fprintf(header); 29 | lower = obj.AreaLow; 30 | start = obj.AreaStart; 31 | upper = obj.AreaUp; 32 | x = obj.Area; 33 | 34 | for i = 1:numPeaks 35 | fprintf('\t%5d', i); 36 | fprintf(lineFormat, lower(i), start(i), upper(i), x(2, i), ... 37 | x(1, i), x(3, i), abs((x(3, i) - x(2, i)) / x(1, i)) / 2); 38 | end 39 | 40 | fprintf('\nCenter:\n\t Peak'); 41 | fprintf(header); 42 | lower = obj.CenterLow; 43 | start = obj.CenterStart; 44 | upper = obj.CenterUp; 45 | x = obj.Center; 46 | 47 | for i = 1:numPeaks 48 | fprintf('\t%5d', i); 49 | fprintf(lineFormat, lower(i), start(i), upper(i), x(2, i), ... 50 | x(1, i), x(3, i), abs((x(3, i) - x(2, i)) / x(1, i)) / 2); 51 | end 52 | 53 | fprintf('\nHeight:\n\t Peak'); 54 | fprintf(header); 55 | lower = obj.HeightLow; 56 | start = obj.HeightStart; 57 | upper = obj.HeightUp; 58 | x = obj.Height; 59 | 60 | for i = 1:numPeaks 61 | fprintf('\t%5d', i); 62 | fprintf(lineFormat, lower(i), start(i), upper(i), x(2, i), ... 63 | x(1, i), x(3, i), abs((x(3, i) - x(2, i)) / x(1, i)) / 2); 64 | end 65 | 66 | fprintf('\nWidth:\n\t Peak'); 67 | fprintf(header); 68 | lower = obj.WidthLow; 69 | start = obj.WidthStart; 70 | upper = obj.WidthUp; 71 | x = obj.Width; 72 | 73 | for i = 1:numPeaks 74 | fprintf('\t%5d', i); 75 | fprintf(lineFormat, lower(i), start(i), upper(i), x(2, i), ... 76 | x(1, i), x(3, i), abs((x(3, i) - x(2, i)) / x(1, i)) / 2); 77 | end 78 | 79 | Q = obj.BaselinePolyOrder + 1; 80 | 81 | if Q > 0 82 | fprintf('\nBaseline:\n\tCoeff'); 83 | fprintf(header); 84 | lower = obj.BaselineLow; 85 | start = obj.BaselineStart; 86 | upper = obj.BaselineUp; 87 | x = obj.Baseline; 88 | 89 | for i = 1:Q 90 | fprintf('\t%5s', sprintf('p%d', i)); 91 | fprintf(lineFormat, lower(i), start(i), upper(i), x(2, i), ... 92 | x(1, i), x(3, i), abs((x(3, i) - x(2, i)) / x(1, i)) / 2); 93 | end 94 | 95 | end 96 | 97 | fprintf('\nGoodness of Fit:\n'); 98 | fprintf('\t%18s: %.4g\n', 'RelStDev', obj.RelStDev); 99 | fprintf('\t%18s: %.4g\n', 'CoeffDeterm', obj.CoeffDeterm); 100 | fprintf('\t%18s: %.4g\n', 'AdjCoeffDeterm', obj.AdjCoeffDeterm); 101 | 102 | fprintf('\nIteration Report:\n'); 103 | fprintf('\t%18s: %d\n', 'NumFunEvals', obj.NumFunEvals); 104 | fprintf('\t%18s: %d\n', 'NumIters', obj.NumIters); 105 | 106 | if exitFlag > 0 107 | exitStatus = 'converged'; 108 | elseif exitFlag < 0 109 | exitStatus = 'diverged'; 110 | elseif obj.NumFunEvals > obj.MaxFunEvals 111 | exitStatus = 'MaxFunEvals was exceeded'; 112 | elseif obj.NumIters > obj.MaxIters 113 | exitStatus = 'MaxIters was exceeded'; 114 | else 115 | exitStatus = 'fail'; 116 | end 117 | 118 | fprintf('\t%18s: %d (%s)\n', 'ExitFlag', exitFlag, exitStatus); 119 | end 120 | 121 | end 122 | -------------------------------------------------------------------------------- /@PeakFit/convertunit.m: -------------------------------------------------------------------------------- 1 | function obj = convertunit(obj, unitFrom, unitTo, varargin) 2 | %% Convert Units 3 | % This method converts the values of XData, Center, Baseline, and other 4 | % related properties in a PeakFit object according to a unit conversion. 5 | % 6 | % Available units to be converted from or to are: 7 | % 'eV' : electron volt 8 | % 'percm' : cm^{-1} (wavenumber) 9 | % 'Ramanshift': cm^{-1} (Raman shift) 10 | % When converting to or from Raman shift, an additional argument is 11 | % required that specifies the excitation wavelength in nanometer. 12 | % 13 | % Examples: 14 | % obj=convertunit(obj, 'nm', 'eV'); 15 | % converts from nanometer to electron volt 16 | % obj=convertunit(obj, 'eV', 'Ramanshift', 532); 17 | % converts from electron volt to Raman shift at 532nm excitation. 18 | % 19 | % Requires package: 20 | % - MatCommon_v1.0.0+ 21 | % - PhysConst_v1.0.0+ 22 | % 23 | % Tested on: 24 | % - MATLAB R2013b 25 | % - MATLAB R2015b 26 | % 27 | % Copyright: Herianto Lim (https://heriantolim.com) 28 | % Licensing: GNU General Public License v3.0 29 | % First created: 27/10/2016 30 | % Last modified: 27/10/2016 31 | 32 | %% Constants 33 | DEF_UNITS = {'eV', 'percm', 'Ramanshift'}; 34 | 35 | %% Input Validation and Parsing 36 | assert(isstringscalar(unitFrom) && isstringscalar(unitTo), ... 37 | 'PeakFit:convertunit:InvalidInput', ... 38 | 'Input to the units name must be a string scalar.'); 39 | 40 | if strcmp(unitFrom, unitTo) 41 | return 42 | end 43 | 44 | assert(any(strcmp(unitFrom, DEF_UNITS)) && any(strcmp(unitTo, DEF_UNITS)), ... 45 | 'PeakFit:convertunit:UnexpectedCase', ... 46 | 'Conversion of the units from %s to %s has not been defined in the code.', ... 47 | unitFrom, unitTo); 48 | 49 | %% Conversion Coefficients 50 | ME1 = MException('PeakFit:convertunit:TooFewInput', ... 51 | 'Additional input arguments are required when converting from %s to %s.', ... 52 | unitFrom, unitTo); 53 | ME2 = MException('PeakFit:convertunit:InvalidInput', ... 54 | 'The additional input arguments failed validation.'); 55 | 56 | switch unitFrom 57 | case 'eV' 58 | 59 | switch unitTo 60 | case 'percm' 61 | a = Constant.ElementaryCharge / Constant.Planck ... 62 | / Constant.LightSpeed / 100; 63 | b = 0; 64 | case 'Ramanshift' 65 | a = -Constant.ElementaryCharge / Constant.Planck ... 66 | / Constant.LightSpeed / 100; 67 | 68 | if nargin < 4 69 | throw(ME1); 70 | elseif ~isrealscalar(varargin{1}) && varargin{1} <= 0 71 | throw(ME2); 72 | end 73 | 74 | b = 1e7 / varargin{1}; 75 | end 76 | 77 | case 'percm' 78 | 79 | switch unitTo 80 | case 'eV' 81 | a = 1e2 * Constant.Planck * Constant.LightSpeed ... 82 | / Constant.ElementaryCharge; 83 | b = 0; 84 | case 'Ramanshift' 85 | a = -1; 86 | 87 | if nargin < 4 88 | throw(ME1); 89 | elseif ~isrealscalar(varargin{1}) && varargin{1} <= 0 90 | throw(ME2); 91 | end 92 | 93 | b = 1e7 / varargin{1}; 94 | end 95 | 96 | case 'Ramanshift' 97 | 98 | if nargin < 4 99 | throw(ME1); 100 | elseif ~isrealscalar(varargin{1}) && varargin{1} <= 0 101 | throw(ME2); 102 | end 103 | 104 | switch unitTo 105 | case 'eV' 106 | a = -1e2 * Constant.Planck * Constant.LightSpeed ... 107 | / Constant.ElementaryCharge; 108 | b = -a * 1e7 / varargin{1}; 109 | case 'percm' 110 | a = -1; 111 | b = 1e7 / varargin{1}; 112 | end 113 | 114 | end 115 | 116 | %% Change the Object Properties 117 | obj.XData = a * obj.XData + b; 118 | obj.Window = a * obj.Window + b; 119 | 120 | obj.CenterStart = a * obj.CenterStart + b; 121 | 122 | if a < 0 123 | obj.Center = a * obj.Center([1, 3, 2], :) + b; 124 | obj.CenterLow = a * obj.CenterUp + b; 125 | obj.CenterUp = a * obj.CenterLow + b; 126 | else 127 | obj.Center = a * obj.Center + b; 128 | obj.CenterLow = a * obj.CenterLow + b; 129 | obj.CenterUp = a * obj.CenterUp + b; 130 | end 131 | 132 | if obj.BaselinePolyOrder >= 0 133 | obj.Baseline = PeakFit.transformpolycoeff(obj.Baseline, a, b); 134 | B = [obj.BaselineStart; obj.BaselineLow; obj.BaselineUp]; 135 | B = PeakFit.transformpolycoeff(B, a, b); 136 | obj.BaselineStart = B(1, :); 137 | obj.BaselineLow = B(2, :); 138 | obj.BaselineUp = B(3, :); 139 | end 140 | 141 | a = abs(a); 142 | obj.Width = a * obj.Width; 143 | obj.WidthStart = a * obj.WidthStart; 144 | obj.WidthLow = a * obj.WidthLow; 145 | obj.WidthUp = a * obj.WidthUp; 146 | obj.AreaStart = a * obj.AreaStart; 147 | obj.AreaLow = a * obj.AreaLow; 148 | obj.AreaUp = a * obj.AreaUp; 149 | 150 | end 151 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # PeakFit 2 | **PeakFit** provides a tool to fit spectral data with a linear combination of symmetric peak functions such as Gaussian or Lorentzian. The background component of the spectra can be optionally taken into account by specifying the related parameter. In which case, the algorithm will attempt to fit the background with a polynomial function of the given order. 3 | 4 | The fit model is given by: 5 | 6 | f(*x*) ~ peak1(*x*) + peak2(*x*) + ... + peakN(*x*) + polynomial(*x*) 7 | 8 | Each peak function is characterized by three fit coefficients: `Center`, `Width`, and either one of `Area` and `Height`. The polynomial function is characterized by n+1 fit coefficients, where n is the order of the polynomial. 9 | 10 | ## Licensing 11 | This software is licensed under the GNU General Public License (version 3). 12 | 13 | ## Tested On 14 | - MATLAB R2013b - R2018a 15 | 16 | ## Requirements 17 | - MATLAB Curve Fitting Toolbox 18 | - [MatCommon](https://github.com/heriantolim/MatCommon) 19 | - [MatGraphics](https://github.com/heriantolim/MatGraphics) - required for doing the examples 20 | - [PhysConst](https://github.com/heriantolim/PhysConst) - required for the `convertunit` method 21 | 22 | ## Setting Up 23 | 1. Download or git-clone this repository and other repositories listed in the [Requirements](https://github.com/heriantolim/PeakFit#requirements). 24 | 2. Add the repositories to the MATLAB's search path via `addpath(genpath( ... ))` OR this [version control system](https://github.com/heriantolim/MatlabVerCon). 25 | 26 | ## Usage 27 | Construct the PeakFit object in the following ways, and the fit results will be populated in the object's [public properties](https://github.com/heriantolim/PeakFit#public-properties). 28 | 29 | ```MATLAB 30 | obj = PeakFit(Data, ... Name-Value ...) 31 | ``` 32 | 33 | OR 34 | 35 | ```MATLAB 36 | obj = PeakFit(XData, YData, ... Name-Value ...) 37 | ``` 38 | 39 | OR 40 | 41 | ```MATLAB 42 | % Create an empty PeakFit object. 43 | obj = PeakFit(); 44 | 45 | % Specify the data points and peak-fit settings via property assignments. 46 | obj.XData = ... ; 47 | obj.YData = ... ; 48 | obj.Property1Name = Value1; 49 | obj.Property2Name = Value2; 50 | ... 51 | 52 | % Perform the peak fitting by reinstantiating the PeakFit object. 53 | obj = PeakFit(obj); 54 | ``` 55 | 56 | `Data` must be specified as a two-column (or two-row) matrix where the first column (or first row) is the X data points and the second column (or second row) is the Y data points. In the alternative syntax, `XData` and `YData` are respectively the X and the Y data points, specified as vectors, of the curve to be fitted. 57 | 58 | The peak-fit settings can be specified after the mandatory arguments in Name-Value syntax, e.g. `PeakFit(Data, 'Window', [100,900], 'NumPeaks', 3)`. If no settings are specified, the algorithm will attempt to fit all peaks in the data using the default settings. The default behavior may not be optimal for noisy data. See [Best Practices](https://github.com/heriantolim/PeakFit#best-practices) for recommendations in making an optimal fit. 59 | 60 | ### Name-Value Pair Arguments 61 | Any of the [public properties](https://github.com/heriantolim/PeakFit#public-properties) can be specified as arguments in Name-Value syntax during the object construction. Specifying other things as Name-Value arguments will return an error. 62 | 63 | ## Examples 64 | ### Default Behavior 65 | If the `PeakFit` is called without specifying the number of peaks, start points, or lower or upper bounds, then the algorithm will attempt to fit all peaks that it can guess. The following image is a photoluminescence spectrum of Er3+ in Y2SiO5 at near-liquid N2 temperature. The spectrum was fitted using the command: 66 | ```MATLAB 67 | Fit = PeakFit(Data, 'PeakShape', 'Lorentzian'); 68 | ``` 69 | The full code is given in [Examples/Er_PL_in_YSO.m](/Examples/Er_PL_in_YSO.m). It can be seen that many of the peaks were not resolved properly, and only the tallest peaks were correctly identified. 70 | 71 | ![Er PL in YSO](/Examples/Er_PL_in_YSO.png) 72 | 73 | ### Best Practices 74 | The PeakFit algorithm works best if the lower and upper bound of the peak `Center`, the upper bound of the peak `Width`, and the baseline polynomial order are specified, as done in the following example. The following Raman spectrum of VO2, taken at near-liquid N2 temperature ([Lim2014](https://doi.org/10.1063/1.4867481)), was fitted using the command: 75 | ```MATLAB 76 | Fit = PeakFit(Data, 'PeakShape', 'Lorentzian', ... 77 | 'CenterLow', [...], ... 78 | 'CenterUp', [...], ... 79 | 'WidthUp', [...], ... 80 | 'BaselinePolyOrder', n); 81 | ``` 82 | The full code is given in [Examples/VO2_Raman.m](/Examples/VO2_Raman.m). Constructing the constraints for the fitting often require guess work, but the constraints do not have to be narrow to get a good accuracy. In some cases, the approximate locations of the peaks are known in the literature, and the fit constraints can be defined from this knowledge. 83 | 84 | ![VO2 Raman](/Examples/VO2_Raman.png) 85 | 86 | ## Public Properties 87 | - `Data`, `XData`, `YData`: The data points of the curve to be fitted. Please ensure that the Y data points are all positive, otherwise the peak fitting may not work properly. 88 | - `Window`: A vector of length two [*a*, *b*] that limits the fitting to only the data points whose X coordinates lies within [*a*, *b*]. 89 | - `NumPeaks`: The number of peaks wished to be fitted. When the fitting peak shapes, start points, lower, or upper bounds are set with vectors of length greater than `NumPeaks`, then `NumPeaks` will be incremented to adjust to the maximum length of these vectors. When the maximum length of these vectors is less, then these vectors will be expanded and filled with the default values. When `NumPeaks`=0 and all the start point, lower, and upper are not set, then the algorithm will attempt to fit all peaks that it can guess in the curve. Defaults to 0. 90 | - `PeakShape`: A string vector that specifies the peak shape of each peak. The choices of `PeakShape` currently are: 'Lorentzian' (1) and 'Gaussian' (2). `PeakShape` may be set with an integer, the initial of these names, e.g. 'L' or 'G', or the full name, e.g. 'Lorentzian'. When the length of `PeakShape` is less than `NumPeaks`, the remaining peaks will be set with the default `PeakShape`, which is 'Lorentzian'. If PeakShape contains only one element, then the default value is the same as that element. 91 | - `BaselinePolyOrder`: An integer that specifies the order of the polynomial function used to fit the background of the spectrum. Defaults to 0, which means a constant polynomial. Set this to a negative value to exclude the polynomial from the fit model. 92 | 93 | ### Fit Results 94 | - (Read-only) `Peak`: A struct containing the fit results for each peak. 95 | - (Read-only) `Base`: A struct containing the fit results for the baseline. 96 | - (Read-only) `Area`, `Center`, `Height`, `Width`: A 3-by-`NumPeaks` matrix that stores the fit results for the area, center, height, and width, respectively; with each column correspond to the fit results for a particular peak. The first row holds the values at convergence, and the second (third) row holds the 95% CI lower (upper) bounds. Note that `Area` (or `Height`) is a redundant variable to the fit model, as it can be computed from `Width` and `Height` (or `Area`). Hence, it would be enough to specify the start points, lower or upper bounds for the `Area` or `Height` alone, as one can be computed from the other for any given `Width`. 97 | - (Read-only) `Baseline`: A 3-by-(n+1) matrix, where n=`BaselinePolyOrder`, that stores the fit results for the baseline. The elements in the i-th column correspond to the fit results for the x^(n-i+1) polynomial coefficient. The first row holds the values at convergence, and the second (third) row holds the 95% CI lower (upper) bounds. 98 | - (Read-only) `RelStDev`: The relative standard deviation of the fit results. 99 | - (Read-only) `CoeffDeterm`: The coefficient of determination of the fit results. 100 | - (Read-only) `AdjCoeffDeterm`: The degree-of-freedom adjusted coefficient of determination of the fit results. 101 | - (Read-only) `NumFunEvals`: The number of function evaluations. 102 | - (Read-only) `NumIters`: The number of iterations. 103 | - (Read-only) `ExitFlag`: The exit condition of the algorithm. Positive flags indicate convergence, within tolerances. Zero flags indicate that the maximum number of function evaluations or iterations was exceeded. Negative flags indicate that the algorithm did not converge to a solution. 104 | 105 | ### Fitting Start Points 106 | - `AreaStart`, `CenterStart`, `WidthStart`, `HeightStart`, `BaselineStart`: A vector of initial values for the area, center, width, height, and baseline coefficients, respectively. The default values are determined heuristically. To make certain properties default, set their values to NaN. They will be then replaced with the default values upon fitting. 107 | 108 | ### Fitting Lower Bounds 109 | - `AreaLow`, `CenterLow`, `WidthLow`, `HeightLow`, `BaselineLow`: A vector of lower bounds for the area, center, width, height, and baseline coefficients, respectively. The default values are determined heuristically. To make certain properties default, set their values to -Inf. They will be then replaced with the default values upon fitting. 110 | 111 | ### Fitting Upper Bounds 112 | - `AreaUp`, `CenterUp`, `WidthUp`, `HeightUp`, `BaselineUp`: A vector of upper bounds for the area, center, width, height, and baseline coefficients, respectively. The default values are determined heuristically. To make certain properties default, set their values to Inf. They will be then replaced with the default values upon fitting. 113 | 114 | ### Algorithm Parameters 115 | - (Read-only) `Method`='NonLinearLeastSquares'; The method used for the fitting. 116 | - `Robust`: The type of the least-squares method to be used in the fitting. Avaliable values are 'off', 'LAR' (least absolute residual method), and 'Bisquare' (bisquare weight method). Defaults to 'off'. 117 | - `Algorithm`: The algorithm to be used in the fitting. Available values are 'Lavenberg-Marquardt', 'Gauss-Newton', or 'Trust-Region'. Defaults to 'Trust-Region'. 118 | - `MovMeanWidth`: The window width of the moving average used for smoothing the curve in order to filter out the noise before finding the maximas. This parameter is used only when CenterStart is not given. The value of this can be set as a positive integer which specifies the width in terms of the number of data points, OR a real scalar between 0 and 1 which specifies the width as a fraction of the total number of data points. Defaults to 0.02. 119 | - `DiffMaxChange`: The maximum change in coefficients for finite difference gradients. Defaults to 0.1. 120 | - `DiffMinChange`: The minimum change in coefficients for finite difference gradients. Defaults to 1e-8. 121 | - `MaxFunEvals`: The allowed maximum number of evaluations of the model. Defaults to 1e5. 122 | - `MaxIters`: The maximum number of iterations allowed for the fit. Defaults to 1e3. 123 | - `TolFun`: The termination tolerance on the model value. Defaults to 1e-6. 124 | - `TolX`: The termination tolerance on the coefficient values. Defaults to 1e-6. 125 | 126 | ## Public Methods 127 | - `disp`: Displays the options, results, error and performance of the fitting. 128 | - `model`: Returns the reconstructed data points (model) using the fit results. See [@PeakFit/model.m](/@PeakFit/model.m) for more info. 129 | - `convertunit`: Converts the units of the data points and the fit results. Available units to be converted from or to are: 'eV' (electron volt), 'percm'(wavenumber in cm^-1), 'Ramanshift' (Raman shift in cm^-1). When converting to or from Raman shift, an additional argument is required that specifies the excitation wavelength in nanometer. See [@PeakFit/convertunit.m](/@PeakFit/convertunit.m) for more info. 130 | 131 | ### Static Methods 132 | - `fnlorentzian`: The Lorentzian function. See [@PeakFit/fnlorentzian.m](/@PeakFit/fnlorentzian.m) for more info. 133 | - `fngaussian`: The Gaussian function. See [@PeakFit/fngaussian.m](/@PeakFit/fngaussian.m) for more info. 134 | 135 | ## See Also 136 | - [BiErfFit](https://github.com/heriantolim/BiErfFit) 137 | -------------------------------------------------------------------------------- /@PeakFit/fit.m: -------------------------------------------------------------------------------- 1 | function obj = fit(obj) 2 | %% Peak Fitting 3 | % This method performs a curve fitting to a spectral data with a linear 4 | % combination of peak functions using the MATLAB Curve Fitting toolbox. 5 | % 6 | % The fit results and statistics will be stored in the object properties 7 | % after a successful calling of this method. 8 | % 9 | % The object properties related to the start points and constraints for 10 | % the fit parameters will also be initialized. 11 | % 12 | % This algorithm is far from perfect. A lot of things need fixing to adapt 13 | % to different scenarios or be more efficient. 14 | % 15 | % Tested on: 16 | % - MATLAB R2013b 17 | % - MATLAB R2015b 18 | % 19 | % Copyright: Herianto Lim (https://heriantolim.com) 20 | % Licensing: GNU General Public License v3.0 21 | % First created: 15/03/2013 22 | % Last modified: 30/12/2020 23 | 24 | x = obj.XData; 25 | y = obj.YData; 26 | 27 | if isempty(x) || isempty(y) 28 | return 29 | end 30 | 31 | %% Defaults 32 | HEIGHT_START = .85; 33 | HEIGHT_UP = 1.35; 34 | WIDTH_START = .5; 35 | WIDTH_UP = 1; 36 | 37 | %% Initialize the Fit Data 38 | assert(numel(x) == numel(y), ... 39 | 'PeakFit:fit:InconsistentNumPoints', ... 40 | 'The number of X and Y data points must be equal.'); 41 | 42 | % Sort the points (x,y) in ascending order 43 | [x, ix] = sort(x); 44 | y = y(ix); 45 | 46 | % Update the object data 47 | obj.XData = x; 48 | obj.YData = y; 49 | 50 | % Trim data points 51 | if ~isempty(obj.Window) 52 | ix = x >= obj.Window(1) & x <= obj.Window(2); 53 | assert(sum(ix) >= obj.MinNumPoints, ... 54 | 'PeakFit:fit:InsufficientNumPoints', ... 55 | 'The number of data points within the fit window must be at least %d.', ... 56 | obj.MinNumPoints); 57 | x = x(ix); 58 | y = y(ix); 59 | else 60 | obj.Window = [x(1), x(end)]; 61 | end 62 | 63 | % Perform linear mapping from [x(1),x(end)]->[0,1] and [min(y),max(y)]->[0,1] 64 | bp = obj.BaselinePolyOrder + 1; 65 | a = min(y); 66 | b = max(y); 67 | 68 | if bp < 1 && a > 0 69 | a = 0; 70 | end 71 | 72 | c = 1 / (b - a); 73 | d = -c * a; 74 | a = 1 / (x(end) - x(1)); 75 | b = -a * x(1); 76 | x = a * x + b; 77 | y = c * y + d; 78 | 79 | %% Initialize the Fit Parameters 80 | % Find the number of peaks 81 | n = [numel(obj.CenterStart), numel(obj.CenterLow), numel(obj.CenterUp), ... 82 | numel(obj.HeightStart), numel(obj.HeightLow), numel(obj.HeightUp), ... 83 | numel(obj.WidthStart), numel(obj.WidthLow), numel(obj.WidthUp), ... 84 | numel(obj.AreaStart), numel(obj.AreaLow), numel(obj.AreaUp), ... 85 | obj.NumPeaks]; 86 | np = numel(obj.PeakShape); 87 | 88 | if np > 1 89 | np = max(max(n), np); 90 | else 91 | 92 | if np == 0 93 | obj.PeakShape = obj.DefaultPeakShape; 94 | end 95 | 96 | np = max(n); 97 | end 98 | 99 | % Initialize the constraint and peak shape arrays 100 | if np == 0 101 | % Find all possible maxima 102 | [xm, ym] = obj.findmaxima(x, y); 103 | np = numel(xm); 104 | center = [xm; nan(2, np)]; 105 | height = [HEIGHT_START * ym; nan(2, np)]; 106 | width = nan(3, np); 107 | area = nan(3, np); 108 | ps = repmat(obj.PeakShape, 1, np); 109 | else 110 | % Fill the blank constraints with NaN 111 | n = np - n; 112 | center = [a * obj.CenterStart + b, nan(1, n(1)); 113 | a * obj.CenterLow + b, nan(1, n(2)); 114 | a * obj.CenterUp + b, nan(1, n(3))]; 115 | height = [c * obj.HeightStart, nan(1, n(4)); 116 | c * obj.HeightLow, nan(1, n(5)); 117 | c * obj.HeightUp, nan(1, n(6))]; 118 | width = [a * obj.WidthStart, nan(1, n(7)); 119 | a * obj.WidthLow, nan(1, n(8)); 120 | a * obj.WidthUp, nan(1, n(9))]; 121 | area = [a * c * obj.AreaStart, nan(1, n(10)); 122 | a * c * obj.AreaLow, nan(1, n(11)); 123 | a * c * obj.AreaUp, nan(1, n(12))]; 124 | 125 | if n(13) == 0 126 | ps = repmat(obj.PeakShape, 1, np); 127 | else 128 | ps = [obj.PeakShape, repmat(obj.DefaultPeakShape, 1, n(13))]; 129 | end 130 | 131 | % If lower bounds exceed upper bounds, swap them 132 | for j = 1:np 133 | 134 | if center(2, j) > center(3, j) 135 | center([2, 3], j) = center([3, 2], j); 136 | end 137 | 138 | if height(2, j) > height(3, j) 139 | height([2, 3], j) = height([3, 2], j); 140 | end 141 | 142 | if width(2, j) > width(3, j) 143 | width([2, 3], j) = width([3, 2], j); 144 | end 145 | 146 | if area(2, j) > area(3, j) 147 | area([2, 3], j) = area([3, 2], j); 148 | end 149 | 150 | end 151 | 152 | end 153 | 154 | % If center starts are known, fill the blank height starts 155 | for j = 1:np 156 | 157 | if isfinite(center(1, j)) &&~isfinite(height(1, j)) 158 | height(1, j) = HEIGHT_START * interp1(x, y, center(1, j)); 159 | end 160 | 161 | end 162 | 163 | % If the lower and/or upper bounds exist, fill the blank start points 164 | for j = 1:np 165 | 166 | if all(isfinite(center(:, j)) == [0; 1; 1]) 167 | [center(1, j), ym] = obj.findmaxima(x, y, center(2:3, j), 1); 168 | 169 | if ~isfinite(height(1, j)) 170 | height(1, j) = HEIGHT_START * ym; 171 | end 172 | 173 | end 174 | 175 | if ~isfinite(height(1, j)) 176 | 177 | if isfinite(height(3, j)) 178 | 179 | if isfinite(height(2, j)) 180 | height(1, j) = height(2, j) ... 181 | + HEIGHT_START * (height(3, j) - height(2, j)); 182 | else 183 | height(1, j) = HEIGHT_START * height(3, j); 184 | end 185 | 186 | end 187 | 188 | end 189 | 190 | if ~isfinite(width(1, j)) 191 | 192 | if isfinite(width(3, j)) 193 | 194 | if isfinite(width(2, j)) 195 | width(1, j) = width(2, j) ... 196 | + WIDTH_START * (width(3, j) - width(2, j)); 197 | else 198 | width(1, j) = WIDTH_START * width(3, j); 199 | end 200 | 201 | end 202 | 203 | end 204 | 205 | if ~isfinite(area(1, j)) 206 | 207 | if isfinite(area(3, j)) 208 | 209 | if isfinite(area(2, j)) 210 | area(1, j) = area(2, j) ... 211 | + HEIGHT_START * WIDTH_START * (area(3, j) - area(2, j)); 212 | else 213 | area(1, j) = HEIGHT_START * WIDTH_START * area(3, j); 214 | end 215 | 216 | end 217 | 218 | end 219 | 220 | end 221 | 222 | % Fill the remaining blank center starts. 223 | % This block needs revision because center starts may not be initially ordered. 224 | ix = ~isfinite(center(1, :)); 225 | 226 | if any(ix) 227 | ix = find(ix); 228 | w = diff(ix); 229 | s = ix([2, w] > 1); 230 | t = ix([w, 2] > 1); 231 | n = numel(s); 232 | w = zeros(n, 2); 233 | 234 | if s(1) > 1 235 | w(1, 1) = center(1, s(1) - 1) + obj.TolX; 236 | else 237 | w(1, 1) = x(1) - obj.TolX; 238 | end 239 | 240 | if t(n) < np 241 | w(n, 2) = center(1, t(n) + 1) - obj.TolX; 242 | else 243 | w(n, 2) = x(end) + obj.TolX; 244 | end 245 | 246 | for i = 2:n 247 | w(i, 1) = center(1, s(i) - 1) + obj.TolX; 248 | w(i - 1, 2) = center(1, t(i - 1) + 1) - obj.TolX; 249 | end 250 | 251 | for i = 1:n 252 | [xm, ym] = obj.findmaxima(x, y, w(i, :), t(i) - s(i) + 1); 253 | center(1, s(i):t(i)) = xm; 254 | 255 | for j = s(i):t(i) 256 | 257 | if ~isfinite(height(1, j)) ... 258 | &&~(isfinite(area(1, j)) && isfinite(width(1, j))) 259 | height(1, j) = HEIGHT_START * ym(j - s(i) + 1); 260 | end 261 | 262 | end 263 | 264 | end 265 | 266 | end 267 | 268 | % Compute the width or height constraints from the area constraints 269 | n = [1, 3, 2]; 270 | 271 | for i = 1:3 272 | 273 | for j = 1:np 274 | 275 | if isfinite(area(i, j)) 276 | 277 | if ~isfinite(width(i, j)) && isfinite(height(n(i), j)) 278 | width(i, j) = PeakFit.computewidth(ps(j), ... 279 | area(i, j), height(n(i), j)); 280 | elseif ~isfinite(height(i, j)) && isfinite(width(n(i), j)) 281 | 282 | if area(i, j) < 0 283 | height(i, j) = PeakFit.computeheight(ps(j), ... 284 | area(n(i), j), width(i, j)); 285 | else 286 | height(i, j) = PeakFit.computeheight(ps(j), ... 287 | area(i, j), width(n(i), j)); 288 | end 289 | 290 | end 291 | 292 | end 293 | 294 | end 295 | 296 | end 297 | 298 | % Replace the blank width constraints with defaults 299 | width(3, ~isfinite(width(3, :))) = WIDTH_UP / sqrt(np); 300 | width(2, ~isfinite(width(2, :))) = 2 * mean(diff(x)); 301 | ix = ~isfinite(width(1, :)); 302 | width(1, ix) = max(width(2, ix), WIDTH_START * width(3, ix)); 303 | 304 | % Replace the blank center constraints with defaults 305 | ix = ~isfinite(center(1, :)); 306 | center(1, ix) = rand(1, sum(ix)); 307 | ix = ~isfinite(center(2, :)); 308 | center(2, ix) = center(1, ix) - width(1, ix); 309 | ix = ~isfinite(center(3, :)); 310 | center(3, ix) = center(1, ix) + width(1, ix); 311 | 312 | % Replace the blank height constraints with defaults 313 | ix = ~isfinite(height(1, :)); 314 | height(1, ix) = rand(1, sum(ix)); 315 | w = 2 * obj.TolFun; 316 | 317 | for j = 1:np 318 | 319 | if height(1, j) < 0 320 | 321 | if ~isfinite(height(2, j)) 322 | height(2, j) = -HEIGHT_UP ... 323 | * min(y(x >= center(2, j) & x <= center(3, j))); 324 | end 325 | 326 | if ~isfinite(height(3, j)) 327 | height(3, j) = -w; 328 | end 329 | 330 | else 331 | 332 | if ~isfinite(height(2, j)) 333 | height(2, j) = w; 334 | end 335 | 336 | if ~isfinite(height(3, j)) 337 | height(3, j) = HEIGHT_UP ... 338 | * max(y(x >= center(2, j) & x <= center(3, j))); 339 | end 340 | 341 | end 342 | 343 | end 344 | 345 | % Compute the area from the height and width 346 | for i = 1:3 347 | 348 | for j = 1:np 349 | 350 | if ~isfinite(area(i, j)) 351 | area(i, j) = PeakFit.computearea(ps(j), ... 352 | height(i, j), width(i, j)); 353 | end 354 | 355 | end 356 | 357 | end 358 | 359 | % Set the baseline constraints 360 | if bp > 0 361 | % Set the y-axis intersect as the start point for the baseline 362 | baseline = zeros(1, bp); 363 | w = obj.MovMeanWidth; 364 | 365 | if w >= 1 366 | w = w / numel(x); 367 | end 368 | 369 | baseline(bp) = mean(y(x <= w / 2)); 370 | 371 | % Use a fibonacci sequence to define the lower and upper bounds 372 | n = bp + 1; 373 | w = ones(1, n); 374 | 375 | for i = 3:n 376 | w(i) = w(i - 1) + w(i - 2); 377 | end 378 | 379 | w = flip(w); 380 | w(n) = []; 381 | baseline = [baseline; baseline - w; baseline + w]; 382 | else 383 | baseline = zeros(3, 0); 384 | end 385 | 386 | %% Construct the Fit Coefficients and Expression 387 | coeff = cell(1, 3 * np); 388 | 389 | for i = 1:np 390 | coeff{i} = sprintf('c%d', i); 391 | coeff{np + i} = sprintf('h%d', i); 392 | coeff{2 * np + i} = sprintf('w%d', i); 393 | end 394 | 395 | if bp > 0 396 | coeff = [coeff, cell(1, bp)]; 397 | 398 | for i = 1:bp 399 | coeff{3 * np + i} = sprintf('p%d', i); 400 | end 401 | 402 | end 403 | 404 | expr = cell(1, np); 405 | 406 | for i = 1:np 407 | 408 | switch ps(i) 409 | case 1 410 | expr{i} = sprintf('h%1$d*(w%1$d/2)^2/((x-c%1$d).^2+(w%1$d/2)^2)', i); 411 | case 2 412 | expr{i} = sprintf('h%1$d*exp(-4*log(2)*(x-c%1$d).^2/w%1$d^2)', i); 413 | otherwise 414 | error('PeakFit:fit:UnexpectedCase', ... 415 | 'Something went wrong. Please review the code.'); 416 | end 417 | 418 | end 419 | 420 | expr = strjoin(expr, '+'); 421 | 422 | if bp > 0 423 | n = bp - 2; 424 | 425 | for i = 1:n 426 | expr = [expr, sprintf('+p%d*x.^%d', i, bp - i)]; %#ok 427 | end 428 | 429 | if bp > 1 430 | expr = [expr, sprintf('+p%d*x', bp - 1)]; 431 | end 432 | 433 | expr = [expr, sprintf('+p%d', bp)]; 434 | end 435 | 436 | %% MATLAB Curve Fitting ToolBox 437 | FitOption = fitoptions('Method', obj.Method, ... 438 | 'Robust', obj.Robust, ... 439 | 'StartPoint', [center(1, :), height(1, :), width(1, :), baseline(1, :)], ... 440 | 'Lower', [center(2, :), height(2, :), width(2, :), baseline(2, :)], ... 441 | 'Upper', [center(3, :), height(3, :), width(3, :), baseline(3, :)], ... 442 | 'Algorithm', obj.Algorithm, ... 443 | 'DiffMaxChange', obj.DiffMaxChange, ... 444 | 'DiffMinChange', obj.DiffMinChange, ... 445 | 'MaxFunEvals', obj.MaxFunEvals, ... 446 | 'MaxIter', obj.MaxIters, ... 447 | 'TolFun', obj.TolFun, ... 448 | 'TolX', obj.TolX, ... 449 | 'Display', 'off'); 450 | FitType = fittype(expr, ... 451 | 'coefficients', coeff, ... 452 | 'options', FitOption); 453 | warning('off', 'all'); 454 | [FitObj, gof, FitOutput] = fit(x', y', FitType); 455 | warning('on', 'all'); 456 | fitResult = [coeffvalues(FitObj); confint(FitObj)]; 457 | 458 | %% Update Object Properties 459 | % Update the shape and number of peaks 460 | obj.NumPeaks = np; 461 | obj.PeakShape = ps; 462 | 463 | % Reverse mapping coefficients 464 | a = 1 / a; 465 | b = -a * b; 466 | c = 1 / c; 467 | d = -c * d; 468 | 469 | % Update the center, height, and width, after a reverse mapping 470 | center = a * center + b; 471 | height = c * height; 472 | width = a * width; 473 | area = a * c * area; 474 | obj.Center = a * fitResult(:, 1:np) + b; 475 | obj.Height = c * fitResult(:, np + 1:2 * np); 476 | obj.Width = a * fitResult(:, 2 * np + 1:3 * np); 477 | obj.CenterStart = center(1, :); 478 | obj.CenterLow = center(2, :); 479 | obj.CenterUp = center(3, :); 480 | obj.HeightStart = height(1, :); 481 | obj.HeightLow = height(2, :); 482 | obj.HeightUp = height(3, :); 483 | obj.WidthStart = width(1, :); 484 | obj.WidthLow = width(2, :); 485 | obj.WidthUp = width(3, :); 486 | obj.AreaStart = area(1, :); 487 | obj.AreaLow = area(2, :); 488 | obj.AreaUp = area(3, :); 489 | 490 | % Update the baseline, after a reverse mapping 491 | if bp > 0 492 | baseline = PeakFit.transformpolycoeff(baseline, a, b, c, d); 493 | obj.Baseline = PeakFit.transformpolycoeff(fitResult(:, 3 * np + 1:3 * np + bp), a, b, c, d); 494 | obj.BaselineStart = baseline(1, :); 495 | obj.BaselineLow = baseline(2, :); 496 | obj.BaselineUp = baseline(3, :); 497 | end 498 | 499 | % Update the goodness of fit 500 | obj.RelStDev = gof.rmse; % due to the transformation to [0,1], 501 | % the rmse is effectively a relative standard deviation 502 | obj.CoeffDeterm = gof.rsquare; 503 | obj.AdjCoeffDeterm = gof.adjrsquare; 504 | 505 | % Update the performance stats of the fitting 506 | obj.NumFunEvals = FitOutput.funcCount; 507 | obj.NumIters = FitOutput.iterations; 508 | obj.ExitFlag = FitOutput.exitflag; 509 | 510 | end 511 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /@PeakFit/PeakFit.m: -------------------------------------------------------------------------------- 1 | classdef PeakFit 2 | %% PeakFit Class 3 | % Fit a spectral curve with a linear combination of symmetric peak 4 | % functions such as Gaussian, Lorentzian, etc. A baseline polynomial 5 | % function may be included to represent a 'background' contribution to the 6 | % spectra. 7 | % 8 | % Constructions: 9 | % A PeakFit object can be constructed in two ways. 10 | % 11 | % The first is by creating an empty PeakFit object and specifying the 12 | % fitting parameters via property assignments as follows: 13 | % 14 | % obj = PeakFit(); % creates an empty PeakFit 15 | % obj.XData = [...]; % specify the X data points 16 | % obj.YData = [...]; % specify the Y data points 17 | % obj.CenterStart = [...]; % specify the start points of the peak centers 18 | % obj.WidthStart = [...]; % specify the start points for the peak widths 19 | % 20 | % To perform the peak fitting, call another PeakFit with the prepared obj 21 | % as the input argument: 22 | % 23 | % obj = PeakFit(obj); 24 | % 25 | % The fit results will be stored in the object properties, which can be 26 | % displayed into the console by calling: 27 | % 28 | % disp(obj); 29 | % 30 | % The second is by specifying the data points and fitting parameters 31 | % together in one call as follows: 32 | % 33 | % obj = PeakFit(Data, ... 34 | % Window, [...], ... 35 | % CenterLow, [...], ... 36 | % CenterUp, [...], ... 37 | % WidthUp, [...], ... 38 | % BaselinePolyOrder, [.]); 39 | % 40 | % The data points can also be specified in the following way: 41 | % 42 | % obj = PeakFit(XData, YData, ...); 43 | % 44 | % The peak fitting will be performed automatically after the object 45 | % construction, provided that the data points have been supplied. The data 46 | % points are the only mandatory inputs for a PeakFit object. 47 | % 48 | % In most cases, specifying the following parameters: Window, CenterLow, 49 | % CenterUp, WidthUp, and BaselinePolyOrder, would improve the accuracy of 50 | % the peak fitting significantly. 51 | % 52 | % Public Properties: 53 | % Data: The data points of the curve. An alternative to XData and YData. 54 | % Data must be a two-column or two-row matrix. 55 | % 56 | % XData: The X data points of the curve. 57 | % 58 | % YData: The Y data points of the curve. The peak fitting may not work 59 | % properly if some Y data points are negative. 60 | % 61 | % Window: A vector of length two [a,b] that limits the fitting to only the 62 | % data points whose X coordinates lies within [a,b]. 63 | % 64 | % NumPeaks: The number of peaks wished to be fitted. When the fitting peak 65 | % shape, start point, lower, or upper bound are set with vectors 66 | % of length greater than NumPeaks, then NumPeaks will be 67 | % incremented to adjust to the maximum length of these vectors. 68 | % When the maximum length of these vectors is less, then these 69 | % vectors will be expanded and filled with the default values. 70 | % When NumPeaks=0 and all the start point, lower, and upper are 71 | % not set, then the program attempts to fit all peaks found in 72 | % the curve. 73 | % 74 | % PeakShape: A string vector that specifies the peak shape of each peak. 75 | % Available PeakShape in this version are: 'Lorentzian' (1) and 76 | % 'Gaussian' (2). PeakShape may be set with an integer, the 77 | % initial of these names, e.g. 'L' or 'G', or the full name, 78 | % e.g. 'Lorentzian'. When the length of PeakShape is less than 79 | % NumPeaks, the remaining peaks will have a default PeakShape, 80 | % which is 'Lorentzian'. If PeakShape contains only one 81 | % element, then the default value is the same as that element. 82 | % 83 | % BaselinePolyOrder: An integer that specifies the order of the polynomial 84 | % function used to fit the background of the spectrum. 85 | % Defaults to 0 (a constant polynomial). Set this to a 86 | % negative value to exclude the polynomial from the fit 87 | % model. 88 | % 89 | % ...Start: A vector of initial values for the ... coefficients, where the 90 | % blanks are one of the {Area, Center, Width, Height, Baseline}. 91 | % The default values are determined heuristically. To make 92 | % certain properties default, set their values to NaN. They will 93 | % be then replaced with the default values upon fitting. 94 | % 95 | % ...Low: A vector of lower bounds for the ... coefficients, where the 96 | % blanks are one of the {Area, Center, Width, Height, Baseline}. 97 | % The default values are determined heuristically. To make certain 98 | % properties default, set their values to -Inf. They will be then 99 | % replaced with the default values upon fitting. 100 | % 101 | % ...Up: A vector of upper bounds for the ... coefficients, where the 102 | % blanks are one of the {Area, Center, Width, Height, Baseline}. 103 | % The default values are determined heuristically. To make certain 104 | % properties default, set their values to Inf. They will be then 105 | % replaced with the default values upon fitting. 106 | % 107 | % Robust: The type of the least-squares method to be used in the fitting. 108 | % Available values are 'off', 'LAR' (least absolute residual), and 109 | % 'Bisquare' (bisquare weights). Defaults to 'off'. 110 | % 111 | % Algorithm: The algorithm to be used in the fitting. Available values are 112 | % 'Lavenberg-Marquardt', 'Gauss-Newton', or 'Trust-Region'. 113 | % Defaults to 'Trust-Region'. 114 | % 115 | % MovMeanWidth: The window width of the moving average used for smoothing 116 | % the curve in order to filter out the noise before finding 117 | % the maximas. This parameter is used only when CenterStart 118 | % is not given. The value of this can be set as a positive 119 | % integer which specifies the width in terms of the number 120 | % of data points, OR a real scalar between [0,1] which 121 | % specifies the width as a fraction of the total number of 122 | % data points. 123 | % 124 | % DiffMaxChange: The maximum change in coefficients for finite difference 125 | % gradients. Defaults to 0.1. 126 | % 127 | % DiffMinChange: The minimum change in coefficients for finite difference 128 | % gradients. Defaults to 1e-8. 129 | % 130 | % MaxFunEvals: The allowed maximum number of evaluations of the model. 131 | % Defaults to 1e5. 132 | % 133 | % MaxIters: The maximum number of iterations allowed for the fit. Defaults 134 | % to 1e3. 135 | % 136 | % TolFun: The termination tolerance on the model value. Defaults to 10^-6. 137 | % 138 | % TolX: The termination tolerance on the coefficient values. Defaults to 139 | % 10^-6. 140 | % 141 | % Read-only Properties: 142 | % Method: The method used for the fitting, which is 143 | % 'NonLinearLeastSquares'. 144 | % 145 | % Peak: A struct containing the fit results for each peak. 146 | % 147 | % Base: A struct containing the fit results for the baseline. 148 | % 149 | % Area, Center, Height, Width, Baseline: A 3-by-NumPeaks matrix that 150 | % stores the fit results for the area,... , respectively. The first 151 | % row is the values at convergence; the second row is the 95% CI 152 | % lower bounds; and the third row is the 95% CI upper bounds. 153 | % 154 | % RelStDev: The relative standard deviation of the fit results. 155 | % 156 | % CoeffDeterm: The coefficient of determination of the fit results. 157 | % 158 | % AdjCoeffDeterm: The degree-of-freedom adjusted coefficient of 159 | % determination of the fit results. 160 | % 161 | % NumFunEvals: The number of function evaluations. 162 | % 163 | % NumIters: The number of iterations. 164 | % 165 | % ExitFlag: The exit condition of the algorithm. Positive flags indicate 166 | % convergence, within tolerances. Zero flags indicate that the 167 | % maximum number of function evaluations or iterations was 168 | % exceeded. Negative flags indicate that the algorithm did not 169 | % converge to a solution. 170 | % 171 | % Public Methods: 172 | % disp: Displays the options, results, error and performance of the 173 | % fitting. 174 | % 175 | % model: Returns the reconstructed data points (model) using the fit 176 | % results. 177 | % 178 | % convertunit: Converts the units of the data points and the fit results. 179 | % Available units to be converted from or to are: 180 | % 'eV' : electron volt 181 | % 'percm' : cm^{-1} (wavenumber) 182 | % 'Ramanshift': cm^{-1} (Raman shift) 183 | % When converting to or from Raman shift, an additional 184 | % argument is required that specifies the excitation 185 | % wavelength in nanometer. 186 | % 187 | % Static Methods: 188 | % fnlorentzian: The lorentzian function. 189 | % 190 | % fngaussian: The gaussian function. 191 | % 192 | % Requires package: 193 | % - MatCommon_v1.0.0+ 194 | % - PhysConst_v1.0.0+ (for the convertunit method only) 195 | % 196 | % Tested on: 197 | % - MATLAB R2013b 198 | % - MATLAB R2015b 199 | % - MATLAB R2017b 200 | % - MATLAB R2018a 201 | % 202 | % Copyright: Herianto Lim (https://heriantolim.com) 203 | % Licensing: GNU General Public License v3.0 204 | % First created: 25/03/2013 205 | % Last modified: 04/11/2016 206 | 207 | %% Properties 208 | % Data, start points, and constraints for fitting 209 | properties (Dependent = true) 210 | Data 211 | end 212 | 213 | properties 214 | XData 215 | YData 216 | Window 217 | NumPeaks = 0; 218 | PeakShape 219 | AreaStart 220 | AreaLow 221 | AreaUp 222 | CenterStart 223 | CenterLow 224 | CenterUp 225 | HeightStart 226 | HeightLow 227 | HeightUp 228 | WidthStart 229 | WidthLow 230 | WidthUp 231 | BaselinePolyOrder = 0; 232 | end 233 | 234 | % The start point and constraints for the baseline are set automatically 235 | properties (SetAccess = protected) 236 | BaselineStart 237 | BaselineLow 238 | BaselineUp 239 | end 240 | 241 | % Fitting options 242 | properties (Constant = true) 243 | Method = 'NonlinearLeastSquares'; 244 | end 245 | 246 | properties 247 | Robust = 'off'; 248 | Algorithm = 'Trust-Region'; 249 | MovMeanWidth = .02; 250 | DiffMaxChange = .1; 251 | DiffMinChange = 1e-8; 252 | MaxFunEvals = 1e5; 253 | MaxIters = 1e3; 254 | TolFun = 1e-6; 255 | TolX = 1e-6; 256 | end 257 | 258 | % Fit results 259 | properties (SetAccess = protected) 260 | Center 261 | Height 262 | Width 263 | Baseline 264 | end 265 | 266 | properties (Dependent = true) 267 | Area 268 | Peak 269 | Base 270 | end 271 | 272 | % Fit errors and performance 273 | properties (SetAccess = protected) 274 | RelStDev = 0; 275 | CoeffDeterm = 0; 276 | AdjCoeffDeterm = 0; 277 | NumFunEvals = 0; 278 | NumIters = 0; 279 | ExitFlag 280 | end 281 | 282 | % Class dictionary 283 | properties (Constant = true, GetAccess = protected) 284 | DefaultPeakShape = 1; 285 | MinNumPoints = 10; 286 | PeakShapeTable = table(... 287 | [1; 2], ... 288 | {'L'; 'G'}, ... 289 | {'Lorentzian'; 'Gaussian'}, ... 290 | 'VariableNames', {'ID', 'Initial', 'Name'}); 291 | RobustList = {'on', 'off', 'LAR', 'Bisquare'}; 292 | AlgorithmList = {'Levenberg-Marquardt', 'Trust-Region'}; 293 | end 294 | 295 | %% Methods 296 | methods 297 | % Constructor 298 | function obj = PeakFit(varargin) 299 | N = nargin; 300 | 301 | if N == 0 302 | return 303 | end 304 | 305 | % Copy the PeakFit object given in the first argument if any 306 | k = 1; 307 | 308 | if isa(varargin{k}, class(obj)) 309 | obj = varargin{k}; 310 | k = k + 1; 311 | end 312 | 313 | % Parse input to Data points 314 | if k <= N && isrealmatrix(varargin{k}) 315 | 316 | if isvector(varargin{k}) 317 | 318 | if k < N && isrealvector(varargin{k + 1}) 319 | obj.XData = varargin{k}; 320 | obj.YData = varargin{k + 1}; 321 | k = k + 2; 322 | end 323 | 324 | else 325 | obj.Data = varargin{k}; 326 | k = k + 1; 327 | end 328 | 329 | end 330 | 331 | % Parse inputs to the parameters 332 | P = properties(obj); 333 | 334 | while k < N 335 | 336 | if ~isstringscalar(varargin{k}) 337 | break 338 | end 339 | 340 | ix = strcmpi(varargin{k}, P); 341 | 342 | if any(ix) 343 | obj.(P{ix}) = varargin{k + 1}; 344 | k = k + 2; 345 | else 346 | break 347 | end 348 | 349 | end 350 | 351 | assert(k > N, ... 352 | 'PeakFit:UnexpectedInput', ... 353 | 'One or more inputs are not recognized.'); 354 | 355 | % Perform peak fitting 356 | obj = fit(obj); 357 | end 358 | 359 | % Get Methods 360 | function x = get.Data(obj) 361 | 362 | if numel(obj.XData) == numel(obj.YData) 363 | x = [obj.XData; obj.YData]; 364 | else 365 | x = []; 366 | end 367 | 368 | end 369 | 370 | function x = get.Area(obj) 371 | peakShape = obj.PeakShape; 372 | height = obj.Height; 373 | width = obj.Width; 374 | 375 | if isempty(peakShape) || isempty(height) || isempty(width) 376 | x = []; 377 | else 378 | numPeaks = numel(peakShape); 379 | x = zeros(3, numPeaks); 380 | 381 | for i = 1:numPeaks 382 | x(:, i) = PeakFit.computearea(peakShape(i), ... 383 | height(:, i), width(:, i)); 384 | end 385 | 386 | end 387 | 388 | end 389 | 390 | function x = get.Peak(obj) 391 | x = struct(); 392 | area = obj.Area; 393 | center = obj.Center; 394 | height = obj.Height; 395 | width = obj.Width; 396 | 397 | if isempty(area) || isempty(center) || isempty(height) || isempty(width) 398 | return 399 | end 400 | 401 | numPeaks = obj.NumPeaks; 402 | 403 | for i = 1:numPeaks 404 | x(i).Area.Value = area(1, i); 405 | x(i).Area.CI = area(2:3, i).'; 406 | x(i).Center.Value = center(1, i); 407 | x(i).Center.CI = center(2:3, i).'; 408 | x(i).Height.Value = height(1, i); 409 | x(i).Height.CI = height(2:3, i).'; 410 | x(i).Width.Value = width(1, i); 411 | x(i).Width.CI = width(2:3, i).'; 412 | end 413 | 414 | end 415 | 416 | function x = get.Base(obj) 417 | x = struct(); 418 | baseline = obj.Baseline; 419 | 420 | if ~isempty(baseline) 421 | P = obj.BaselinePolyOrder; 422 | Q = P + 1; 423 | 424 | for i = 1:Q 425 | x.(sprintf('p%d', i)).Value = baseline(1, i); 426 | x.(sprintf('p%d', i)).CI = baseline(2:3, i).'; 427 | end 428 | 429 | end 430 | 431 | end 432 | 433 | % Set Methods 434 | function obj = set.Data(obj, x) 435 | 436 | if isempty(x) 437 | obj.XData = []; 438 | obj.YData = []; 439 | return 440 | end 441 | 442 | ME = MException('PeakFit:InvalidInput', ... 443 | 'Input to set the Data must be a real matrix of size [n,2] or [2,n].'); 444 | 445 | if isrealmatrix(x) 446 | [m, n] = size(x); 447 | 448 | if m == 2 449 | obj.XData = x(1, :); 450 | obj.YData = x(2, :); 451 | elseif n == 2 452 | obj.XData = x(:, 1); 453 | obj.YData = x(:, 2); 454 | else 455 | throw(ME); 456 | end 457 | 458 | else 459 | throw(ME); 460 | end 461 | 462 | end 463 | 464 | function obj = set.XData(obj, x) 465 | 466 | if isempty(x) 467 | obj.XData = []; 468 | elseif isrealvector(x) && all(isfinite(x)) 469 | 470 | if numel(x) < obj.MinNumPoints 471 | error('PeakFit:setXData:InsufficientNumPoints', ... 472 | 'Input vector to the XData must have at least %d elements.', ... 473 | obj.MinNumPoints); 474 | else 475 | obj.XData = x(:).'; 476 | end 477 | 478 | else 479 | error('PeakFit:setXData:InvalidInput', ... 480 | 'Input to set the XData must be a vector of finite real numbers.'); 481 | end 482 | 483 | end 484 | 485 | function obj = set.YData(obj, x) 486 | 487 | if isempty(x) 488 | obj.YData = []; 489 | elseif isrealvector(x) && all(isfinite(x)) 490 | 491 | if numel(x) < obj.MinNumPoints 492 | error('PeakFit:setYData:InsufficientNumPoints', ... 493 | 'Input vector to the YData must have at least %d elements.', ... 494 | obj.MinNumPoints); 495 | else 496 | obj.YData = x(:).'; 497 | end 498 | 499 | else 500 | error('PeakFit:setYData:InvalidInput', ... 501 | 'Input to set the YData must be a vector of finite real numbers.'); 502 | end 503 | 504 | end 505 | 506 | function obj = set.Window(obj, x) 507 | 508 | if isempty(x) 509 | obj.Window = []; 510 | elseif isrealvector(x) && numel(x) == 2 511 | obj.Window = sort(x(:)).'; 512 | else 513 | error('PeakFit:setWindow:InvalidInput', ... 514 | 'Input to set the Window must be a real vector of length two.'); 515 | end 516 | 517 | end 518 | 519 | function obj = set.NumPeaks(obj, x) 520 | 521 | if isintegerscalar(x) && x >= 0 522 | obj.NumPeaks = x; 523 | else 524 | error('PeakFit:setNumPeaks:InvalidInput', ... 525 | 'Input to set the NumPeaks must be a positive integer scalar.'); 526 | end 527 | 528 | end 529 | 530 | function obj = set.PeakShape(obj, x) 531 | 532 | if isempty(x) 533 | obj.PeakShape = []; 534 | elseif isintegervector(x) 535 | obj.PeakShape = x(:).'; 536 | else 537 | 538 | if isstringscalar(x) 539 | x = {x}; 540 | elseif ~isstringvector(x) 541 | error('PeakFit:setPeakShape:InvalidInput', ... 542 | 'Input to PeakShape must be a string scalar or vector.'); 543 | end 544 | 545 | ME = MException('PeakFit:setPeakShape:UnexpectedInput', ... 546 | 'The specified peak shape has not been defined in this class.'); 547 | N = numel(x); 548 | y = zeros(1, N); 549 | 550 | for n = 1:N 551 | 552 | if numel(x{n}) == 1 553 | tf = strcmpi(x{n}, obj.PeakShapeTable.Initial); 554 | else 555 | tf = strcmpi(x{n}, obj.PeakShapeTable.Name); 556 | end 557 | 558 | if any(tf) 559 | y(n) = obj.PeakShapeTable.ID(tf); 560 | else 561 | throw(ME); 562 | end 563 | 564 | end 565 | 566 | obj.PeakShape = y; 567 | end 568 | 569 | end 570 | 571 | function obj = set.AreaStart(obj, x) 572 | 573 | if isempty(x) 574 | obj.AreaStart = []; 575 | elseif isrealvector(x) 576 | obj.AreaStart = x(:).'; 577 | else 578 | error('PeakFit:setAreaStart:InvalidInput', ... 579 | 'Input to set the AreaStart must be a vector of real numbers.'); 580 | end 581 | 582 | end 583 | 584 | function obj = set.AreaLow(obj, x) 585 | 586 | if isempty(x) 587 | obj.AreaLow = []; 588 | elseif isrealvector(x) 589 | obj.AreaLow = x(:).'; 590 | else 591 | error('PeakFit:setAreaLow:InvalidInput', ... 592 | 'Input to set the AreaLow must be a vector of real numbers.'); 593 | end 594 | 595 | end 596 | 597 | function obj = set.AreaUp(obj, x) 598 | 599 | if isempty(x) 600 | obj.AreaUp = []; 601 | elseif isrealvector(x) 602 | obj.AreaUp = x(:).'; 603 | else 604 | error('PeakFit:setAreaUp:InvalidInput', ... 605 | 'Input to set the AreaUp must be a vector of real numbers.'); 606 | end 607 | 608 | end 609 | 610 | function obj = set.CenterStart(obj, x) 611 | 612 | if isempty(x) 613 | obj.CenterStart = []; 614 | elseif isrealvector(x) 615 | obj.CenterStart = x(:).'; 616 | else 617 | error('PeakFit:setCenterStart:InvalidInput', ... 618 | 'Input to set the CenterStart must be a vector of real numbers.'); 619 | end 620 | 621 | end 622 | 623 | function obj = set.CenterLow(obj, x) 624 | 625 | if isempty(x) 626 | obj.CenterLow = []; 627 | elseif isrealvector(x) 628 | obj.CenterLow = x(:).'; 629 | else 630 | error('PeakFit:setCenterLow:InvalidInput', ... 631 | 'Input to set the CenterLow must be a vector of real numbers.'); 632 | end 633 | 634 | end 635 | 636 | function obj = set.CenterUp(obj, x) 637 | 638 | if isempty(x) 639 | obj.CenterUp = []; 640 | elseif isrealvector(x) 641 | obj.CenterUp = x(:).'; 642 | else 643 | error('PeakFit:setCenterUp:InvalidInput', ... 644 | 'Input to set the CenterUp must be a vector of real numbers.'); 645 | end 646 | 647 | end 648 | 649 | function obj = set.HeightStart(obj, x) 650 | 651 | if isempty(x) 652 | obj.HeightStart = []; 653 | elseif isrealvector(x) 654 | obj.HeightStart = x(:).'; 655 | else 656 | error('PeakFit:setHeightStart:InvalidInput', ... 657 | 'Input to set the HeightStart must be a vector of real numbers.'); 658 | end 659 | 660 | end 661 | 662 | function obj = set.HeightLow(obj, x) 663 | 664 | if isempty(x) 665 | obj.HeightLow = []; 666 | elseif isrealvector(x) 667 | obj.HeightLow = x(:).'; 668 | else 669 | error('PeakFit:setHeightLow:InvalidInput', ... 670 | 'Input to set the HeightLow must be a vector of real numbers.'); 671 | end 672 | 673 | end 674 | 675 | function obj = set.HeightUp(obj, x) 676 | 677 | if isempty(x) 678 | obj.HeightUp = []; 679 | elseif isrealvector(x) 680 | obj.HeightUp = x(:).'; 681 | else 682 | error('PeakFit:setHeightUp:InvalidInput', ... 683 | 'Input to set the HeightUp must be a vector of real numbers.'); 684 | end 685 | 686 | end 687 | 688 | function obj = set.WidthStart(obj, x) 689 | 690 | if isempty(x) 691 | obj.WidthStart = []; 692 | elseif isrealvector(x) && all(isnan(x) | x >= 0) 693 | obj.WidthStart = x(:).'; 694 | else 695 | error('PeakFit:setWidthStart:InvalidInput', ... 696 | 'Input to set the WidthStart must be a vector of positive real numbers.'); 697 | end 698 | 699 | end 700 | 701 | function obj = set.WidthLow(obj, x) 702 | 703 | if isempty(x) 704 | obj.WidthLow = []; 705 | elseif isrealvector(x) && all(isnan(x) | x >= 0) 706 | obj.WidthLow = x(:).'; 707 | else 708 | error('PeakFit:setWidthLow:InvalidInput', ... 709 | 'Input to set the WidthLow must be a vector of positive real numbers.'); 710 | end 711 | 712 | end 713 | 714 | function obj = set.WidthUp(obj, x) 715 | 716 | if isempty(x) 717 | obj.WidthUp = []; 718 | elseif isrealvector(x) && all(isnan(x) | x >= 0) 719 | obj.WidthUp = x(:).'; 720 | else 721 | error('PeakFit:setWidthUp:InvalidInput', ... 722 | 'Input to set the WidthUp must be a vector of positive real numbers.'); 723 | end 724 | 725 | end 726 | 727 | function obj = set.BaselinePolyOrder(obj, x) 728 | 729 | if isintegerscalar(x) 730 | obj.BaselinePolyOrder = x; 731 | else 732 | error('PeakFit:setBaselinePolyOrder:InvalidInput', ... 733 | 'Input to set the BaselinePolyOrder must be an integer scalar.'); 734 | end 735 | 736 | end 737 | 738 | function obj = set.BaselineStart(obj, x) 739 | 740 | if isempty(x) 741 | obj.BaselineStart = []; 742 | elseif isrealvector(x) 743 | obj.BaselineStart = x(:).'; 744 | else 745 | error('PeakFit:setBaselineStart:InvalidInput', ... 746 | 'Input to set the BaselineStart must be a vector of real numbers.'); 747 | end 748 | 749 | end 750 | 751 | function obj = set.BaselineLow(obj, x) 752 | 753 | if isempty(x) 754 | obj.BaselineLow = []; 755 | elseif isrealvector(x) 756 | obj.BaselineLow = x(:).'; 757 | else 758 | error('PeakFit:setBaselineLow:InvalidInput', ... 759 | 'Input to set the BaselineLow must be a vector of real numbers.'); 760 | end 761 | 762 | end 763 | 764 | function obj = set.BaselineUp(obj, x) 765 | 766 | if isempty(x) 767 | obj.BaselineUp = []; 768 | elseif isrealvector(x) 769 | obj.BaselineUp = x(:).'; 770 | else 771 | error('PeakFit:setBaselineUp:InvalidInput', ... 772 | 'Input to set the BaselineUp must be a vector of real numbers.'); 773 | end 774 | 775 | end 776 | 777 | function obj = set.Robust(obj, x) 778 | ME = MException('PeakFit:setRobust:InvalidInput', ... 779 | 'Input to set the Robust must be either: %s.', ... 780 | strjoin(obj.RobustList, ', ')); 781 | 782 | if isstringscalar(x) 783 | tf = strcmpi(x, obj.RobustList); 784 | 785 | if any(tf) 786 | obj.Robust = obj.RobustList{tf}; 787 | else 788 | throw(ME); 789 | end 790 | 791 | else 792 | throw(ME); 793 | end 794 | 795 | end 796 | 797 | function obj = set.Algorithm(obj, x) 798 | ME = MException('PeakFit:setAlgorithm:InvalidInput', ... 799 | 'Input to set the Algorithm must be either: %s.', ... 800 | strjoin(obj.AlgorithmList, ', ')); 801 | 802 | if isstringscalar(x) 803 | tf = strcmpi(x, obj.AlgorithmList); 804 | 805 | if any(tf) 806 | obj.Algorithm = obj.AlgorithmList{tf}; 807 | else 808 | throw(ME); 809 | end 810 | 811 | else 812 | throw(ME); 813 | end 814 | 815 | end 816 | 817 | function obj = set.MovMeanWidth(obj, x) 818 | ME = MException('PeakFit:setMovMeanWidth:InvalidInput', ... 819 | 'Input to set the MovMeanWidth must be a positive integer scalar or a real scalar between [0,1].'); 820 | 821 | if isrealscalar(x) && x >= 0 822 | 823 | if isintegerscalar(x) || x <= 1 824 | obj.MovMeanWidth = x; 825 | else 826 | throw(ME); 827 | end 828 | 829 | else 830 | throw(ME); 831 | end 832 | 833 | end 834 | 835 | function obj = set.DiffMaxChange(obj, x) 836 | 837 | if isrealscalar(x) && x > 0 838 | obj.DiffMaxChange = x; 839 | else 840 | error('PeakFit:setDiffMaxChange:InvalidInput', ... 841 | 'Input to set the DiffMaxChange must be a positive real scalar.'); 842 | end 843 | 844 | end 845 | 846 | function obj = set.DiffMinChange(obj, x) 847 | 848 | if isrealscalar(x) && x > 0 849 | obj.DiffMinChange = x; 850 | else 851 | error('PeakFit:setDiffMinChange:InvalidInput', ... 852 | 'Input to set the DiffMinChange must be a positive real scalar.'); 853 | end 854 | 855 | end 856 | 857 | function obj = set.MaxFunEvals(obj, x) 858 | 859 | if isintegerscalar(x) && x > 0 860 | obj.MaxFunEvals = x; 861 | else 862 | error('PeakFit:setMaxFunEvals:InvalidInput', ... 863 | 'Input to set the MaxFunEvals must be a positive integer scalar.'); 864 | end 865 | 866 | end 867 | 868 | function obj = set.MaxIters(obj, x) 869 | 870 | if isintegerscalar(x) && x > 0 871 | obj.MaxIters = x; 872 | else 873 | error('PeakFit:setMaxIters:InvalidInput', ... 874 | 'Input to set the MaxIters must be a positive integer scalar.'); 875 | end 876 | 877 | end 878 | 879 | function obj = set.TolFun(obj, x) 880 | 881 | if isrealscalar(x) && x > 0 882 | obj.TolFun = x; 883 | else 884 | error('PeakFit:setTolFun:InvalidInput', ... 885 | 'Input to set the TolFun must be a positive real scalar.'); 886 | end 887 | 888 | end 889 | 890 | function obj = set.TolX(obj, x) 891 | 892 | if isrealscalar(x) && x > 0 893 | obj.TolX = x; 894 | else 895 | error('PeakFit:setTolX:InvalidInput', ... 896 | 'Input to set the TolX must be a positive real scalar.'); 897 | end 898 | 899 | end 900 | 901 | function obj = set.Center(obj, x) 902 | 903 | if ~isempty(x) 904 | 905 | if isrealmatrix(x) && size(x, 1) == 3 906 | obj.Center = x; 907 | else 908 | error('PeakFit:setCenter:InvalidInput', ... 909 | 'Input to set the Center must be a real matrix with 3 rows.'); 910 | end 911 | 912 | end 913 | 914 | end 915 | 916 | function obj = set.Height(obj, x) 917 | 918 | if ~isempty(x) 919 | 920 | if isrealmatrix(x) && size(x, 1) == 3 921 | obj.Height = x; 922 | else 923 | error('PeakFit:setHeight:InvalidInput', ... 924 | 'Input to set the Height must be a real matrix with 3 rows.'); 925 | end 926 | 927 | end 928 | 929 | end 930 | 931 | function obj = set.Width(obj, x) 932 | 933 | if ~isempty(x) 934 | 935 | if isrealmatrix(x) && size(x, 1) == 3 936 | obj.Width = x; 937 | else 938 | error('PeakFit:setWidth:InvalidInput', ... 939 | 'Input to set the Width must be a real matrix with 3 rows.'); 940 | end 941 | 942 | end 943 | 944 | end 945 | 946 | function obj = set.Baseline(obj, x) 947 | 948 | if ~isempty(x) 949 | 950 | if isrealmatrix(x) && size(x, 1) == 3 951 | obj.Baseline = x; 952 | else 953 | error('PeakFit:setBaseline:InvalidInput', ... 954 | 'Input to set the Baseline must be a real matrix with 3 rows.'); 955 | end 956 | 957 | end 958 | 959 | end 960 | 961 | function obj = set.RelStDev(obj, x) 962 | 963 | if isrealscalar(x) && x >= 0 964 | obj.RelStDev = x; 965 | else 966 | error('PeakFit:setRelStDev:InvalidInput', ... 967 | 'Input to set the RelStDev must be a positive real scalar.'); 968 | end 969 | 970 | end 971 | 972 | function obj = set.CoeffDeterm(obj, x) 973 | 974 | if isrealscalar(x) 975 | obj.CoeffDeterm = x; 976 | else 977 | error('PeakFit:setCoeffDeterm:InvalidInput', ... 978 | 'Input to set the CoeffDeterm must be a real scalar.'); 979 | end 980 | 981 | end 982 | 983 | function obj = set.AdjCoeffDeterm(obj, x) 984 | 985 | if isrealscalar(x) 986 | obj.AdjCoeffDeterm = x; 987 | else 988 | error('PeakFit:setAdjCoeffDeterm:InvalidInput', ... 989 | 'Input to set the AdjCoeffDeterm must be a real scalar.'); 990 | end 991 | 992 | end 993 | 994 | function obj = set.NumFunEvals(obj, x) 995 | 996 | if isintegerscalar(x) && x >= 0 997 | obj.NumFunEvals = x; 998 | else 999 | error('PeakFit:setNumFunEvals:InvalidInput', ... 1000 | 'Input to set the NumFunEvals must be a positive integer scalar.'); 1001 | end 1002 | 1003 | end 1004 | 1005 | function obj = set.NumIters(obj, x) 1006 | 1007 | if isintegerscalar(x) && x >= 0 1008 | obj.NumIters = x; 1009 | else 1010 | error('PeakFit:setNumIters:InvalidInput', ... 1011 | 'Input to set the NumIters must be a positive integer scalar.'); 1012 | end 1013 | 1014 | end 1015 | 1016 | function obj = set.ExitFlag(obj, x) 1017 | 1018 | if isempty(x) 1019 | obj.ExitFlag = []; 1020 | elseif isintegerscalar(x) 1021 | obj.ExitFlag = x; 1022 | else 1023 | error('PeakFit:setExitFlag:InvalidInput', ... 1024 | 'Input to set the ExitFlag must be an integer scalar.'); 1025 | end 1026 | 1027 | end 1028 | 1029 | % display the object properties 1030 | disp(obj) 1031 | 1032 | % construct a fit model from the fit results 1033 | [yModel, yPeak, yBaseline] = model(obj, varargin) 1034 | 1035 | % convert units 1036 | obj = convertunit(obj, unitFrom, unitTo, varargin) 1037 | end 1038 | 1039 | methods (Access = protected) 1040 | % fit peaks 1041 | obj = fit(obj) 1042 | 1043 | % find maxima 1044 | [xm, ym] = findmaxima(obj, varargin) 1045 | end 1046 | 1047 | methods (Static = true) 1048 | % lorentzian function 1049 | y = fnlorentzian(x, c, h, w) 1050 | 1051 | % gaussian function 1052 | y = fngaussian(x, c, h, w) 1053 | end 1054 | 1055 | methods (Static = true, Access = protected) 1056 | % compute the peak area 1057 | area = computearea(peakShape, height, width) 1058 | 1059 | % compute the peak height 1060 | height = computeheight(peakShape, area, width) 1061 | 1062 | % compute the peak width 1063 | width = computewidth(peakShape, area, height) 1064 | 1065 | % transform polynomial coefficients 1066 | p = transformpolycoeff(p, varargin) 1067 | end 1068 | 1069 | end 1070 | --------------------------------------------------------------------------------