Question about using the "fit_mutiple_gaussians" from File Exchange

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Teshan Rezel
Teshan Rezel 2022-7-21
回答： Mathieu NOE 2022-7-21
Hi folks,
The file in question can be found here.
I am sure I'm using it wrong so please bear with me!
I am trying to input my own data to analyse, but the result looks wrong, as per the image below.
I tried to inout my data as follows, replacing the "x" parameter with my data. May I ask if anyone can point out what I'm doing wrong please?
numGaussians = 4;
myPath = 'F:\Histogram Analysis.xlsx';
data = readtable(myPath, 'sheet', 'Percent-Anisotropic');
centers = randi(100, 1, numGaussians);
% Make widths in the range 0 - 20
sigmas = randi(20, 1, numGaussians);
% Make amplitudes in the range 0 - 40
amplitudes = randi([10, 40], 1, numGaussians);
% Make signal that is the sum of all Gaussians
% g = gaussian(x, peakPosition, width)
x = data.(1)(2:end);
% x = linspace(0, 150, 1000);
y = zeros(1, numel(x));
% y = data.(1)(2:end);
hFig = figure;
  2 个评论
Mathieu NOE
Mathieu NOE 2022-7-21
hi
don't know if you posted an image below your code, for any obscure reason it would not display in my browser
anyway, it woud help if you could also share the data to test the code

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Mathieu NOE
Mathieu NOE 2022-7-21
here the code a bit simplified
but the results with a 4 gaussian model does not really fit well. I think this come mostly from the distorded shape of the major peak , so another model should be used IMHO
clearvars
% Demo to fit multiple Gaussians to data.
clc; % Clear the command window.
fprintf('Beginning to run %s.m.\n', mfilename);
clear global;
workspace; % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 20;
% load data
load('x.mat')
y = x;
x = 1:numel(y);
% First specify how many Gaussians there will be.
numGaussians = 4;
% estimated parameters
% peaks y values
[PKS,LOCS]= findpeaks(y,'NPeaks',numGaussians);
amplitudes = PKS;
centers = x(LOCS);
sigmas = x(LOCS)/30;
% Put all the parameters into a table for convenience in looking at, and using, the results.
tActual = table((1:numGaussians)', amplitudes(:), centers(:), sigmas(:), 'VariableNames', {'Number', 'Amplitude', 'Mean', 'Width'});
% Now sort parameters in order of increasing mean, just so it's easier to think about (though it's not required).
tActual = sortrows(tActual, 3);
tActual.Number = (1:numGaussians)' % Unsort the first column of numbers.
% Sum up the component curves to make our test signal that we will analyze to try to guess the component curves from.
legendStrings = cell(numGaussians, 1);
%----------------------------------------------------------------------------------------------------------------------------------
% Initial Gaussian Parameters
initialGuesses = [tActual.Mean(:), tActual.Width(:)];
% Add a little noise so that our first guess is not dead on accurate.
initialGuesses = initialGuesses + 2 * rand(size(initialGuesses))
startingGuesses = reshape(initialGuesses', 1, [])
global c NumTrials TrialError
% warning off
% Initializations
NumTrials = 0; % Track trials
TrialError = 0; % Track errors
% t and y must be row vectors.
tFit = reshape(x, 1, []);
y = reshape(y, 1, []);
%-------------------------------------------------------------------------------------------------------------------------------------------
% Perform an iterative fit using the FMINSEARCH function to optimize the height, width and center of the multiple Gaussians.
options = optimset('TolX', 1e-4, 'MaxFunEvals', 10^12); % Determines how close the model must fit the data
% First, set some options for fminsearch().
options.TolFun = 1e-4;
options.TolX = 1e-4;
options.MaxIter = 100000;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% HEAVY LIFTING DONE RIGHT HERE:
% Run optimization
[parameter, fval, flag, output] = fminsearch(@(lambda)(fitgauss(lambda, tFit, y)), startingGuesses, options);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%----------------------------------------------------------------------------------------------------------------
% Now plot results.
yhat = PlotComponentCurves(x, y, tFit, c, parameter);
% Compute the residuals between the actual y and the estimated y and put that into the graph's title.
meanResidual = mean(abs(y - yhat));
fprintf('The mean of the absolute value of the residuals is %f.\n', meanResidual);
caption = sprintf('Estimation of %d Gaussian Curves that will fit data. Mean Residual = %f.', numGaussians, meanResidual);
title(caption, 'FontSize', fontSize, 'Interpreter', 'none');
drawnow;
% Make table for the fitted, estimated results.
% First make numGaussians row by 3 column matrix: Column 1 = amplitude, column 2 = mean, column 3 = width.
% parameter % Print to command window.
estimatedMuSigma = reshape(parameter, 2, [])';
gaussianParameters = [c, estimatedMuSigma];
% Now sort parameters in order of increasing mean
gaussianParameters = sortrows(gaussianParameters, 2);
tActual % Display actual table in the command window.
% Create table of the output parameters and display it below the actual, true parameters.
tEstimate = table((1:numGaussians)', c(:), estimatedMuSigma(:, 1), estimatedMuSigma(:, 2), 'VariableNames', {'Number', 'Amplitude', 'Mean', 'Width'})
% Plot the error as a function of trial number.
hFigError = figure();
hFigError.Name = 'Errors';
semilogy(TrialError, 'b-');
% hFigError.WindowState = 'maximized';
grid on;
xlabel('Trial Number', 'FontSize', fontSize)
ylabel('Error', 'FontSize', fontSize)
caption = sprintf('Errors for all %d trials.', length(TrialError));
title(caption, 'FontSize', fontSize, 'Interpreter', 'none');
message = sprintf('Done!\nHere is the result!\nNote: there could be multiple ways\n(multiple sets of Gaussians)\nthat you could achieve the same sum (same test curve).');
fprintf('Done running %s.m.\n', mfilename);
msgbox(message);
%=======================================================================================================================================================
function yhat = PlotComponentCurves(x, y, t, c, parameter)
try
fontSize = 20;
% Get the means and widths.
means = parameter(1 : 2 : end);
widths = parameter(2 : 2 : end);
% Now plot results.
hFig2 = figure;
hFig2.Name = 'Fitted Component Curves';
% plot(x, y, '--', 'LineWidth', 2)
hold on;
yhat = zeros(1, length(t));
numGaussians = length(c);
legendStrings = cell(numGaussians + 2, 1);
for k = 1 : numGaussians
% Get each component curve.
thisEstimatedCurve = c(k) .* gaussian(t, means(k), widths(k));
% Plot component curves.
plot(x, thisEstimatedCurve, '-', 'LineWidth', 2);
hold on;
% Overall curve estimate is the sum of the component curves.
yhat = yhat + thisEstimatedCurve;
legendStrings{k} = sprintf('Estimated Gaussian %d', k);
end
% Plot original summation curve, that is the actual curve.
plot(x, y, 'r-', 'LineWidth', 1)
% Plot estimated summation curve, that is the estimate of the curve.
plot(x, yhat, 'k--', 'LineWidth', 2)
grid on;
xlabel('X', 'FontSize', fontSize)
ylabel('Y', 'FontSize', fontSize)
caption = sprintf('Estimation of %d Gaussian Curves that will fit data.', numGaussians);
title(caption, 'FontSize', fontSize, 'Interpreter', 'none');
grid on
legendStrings{numGaussians+1} = sprintf('Actual original signal');
legendStrings{numGaussians+2} = sprintf('Sum of all %d Gaussians', numGaussians);
legend(legendStrings);
xlim(sort([x(1) x(end)]));
hFig2.WindowState = 'maximized';
drawnow;
catch ME
% Some error happened if you get here.
callStackString = GetCallStack(ME);
errorMessage = sprintf('Error in program %s.\nTraceback (most recent at top):\n%s\nError Message:\n%s', ...
mfilename, callStackString, ME.message);
WarnUser(errorMessage);
end
end % of PlotComponentCurves
%=======================================================================================================================================================
function theError = fitgauss(lambda, t, y)
% Fitting function for multiple overlapping Gaussians, with statements
% added (lines 18 and 19) to slow the progress and plot each step along the
% way, for educational purposes.
% Author: T. C. O'Haver, 2006
global c NumTrials TrialError
try
A = zeros(length(t), round(length(lambda) / 2));
for j = 1 : length(lambda) / 2
A(:,j) = gaussian(t, lambda(2 * j - 1), lambda(2 * j))';
end
c = A \ y';
z = A * c;
theError = norm(z - y');
% Penalty so that heights don't become negative.
if sum(c < 0) > 0
theError = theError + 1000000;
end
NumTrials = NumTrials + 1;
TrialError(NumTrials) = theError;
catch ME
% Some error happened if you get here.
callStackString = GetCallStack(ME);
errorMessage = sprintf('Error in program %s.\nTraceback (most recent at top):\n%s\nError Message:\n%s', ...
mfilename, callStackString, ME.message);
WarnUser(errorMessage);
end
end % of fitgauss()
%=======================================================================================================================================================
function g = gaussian(x, peakPosition, width)
% gaussian(x,pos,wid) = gaussian peak centered on pos, half-width=wid
% x may be scalar, vector, or matrix, pos and wid both scalar
% T. C. O'Haver, 1988
% Examples: gaussian([0 1 2],1,2) gives result [0.5000 1.0000 0.5000]
% plot(gaussian([1:100],50,20)) displays gaussian band centered at 50 with width 20.
g = exp(-((x - peakPosition) ./ (0.60056120439323 .* width)) .^ 2);
end % of gaussian()
%=======================================================================================================================================================
% Gets a string describing the call stack where each line is the filename, function name, and line number in that file.
% Sample usage
% try
% % Some code that might throw an error......
% catch ME
% callStackString = GetCallStack(ME);
% errorMessage = sprintf('Error in program %s.\nTraceback (most recent at top):\n%s\nError Message:\n%s', ...
% mfilename, callStackString, ME.message);
% WarnUser(errorMessage);
% end
function callStackString = GetCallStack(errorObject)
try
theStack = errorObject.stack;
callStackString = '';
stackLength = length(theStack);
% Get the date of the main, top level function:
% d = dir(theStack(1).file);
% fileDateTime = d.date(1:end-3);
if stackLength <= 3
% Some problem in the OpeningFcn
% Only the first item is useful, so just alert on that.
[folder, baseFileName, ext] = fileparts(theStack(1).file);
baseFileName = sprintf('%s%s', baseFileName, ext); % Tack on extension.
callStackString = sprintf('%s in file %s, in the function %s, at line %d\n', callStackString, baseFileName, theStack(1).name, theStack(1).line);
else
% Got past the OpeningFcn and had a problem in some other function.
for k = 1 : length(theStack)-3
[folder, baseFileName, ext] = fileparts(theStack(k).file);
baseFileName = sprintf('%s%s', baseFileName, ext); % Tack on extension.
callStackString = sprintf('%s in file %s, in the function %s, at line %d\n', callStackString, baseFileName, theStack(k).name, theStack(k).line);
end
end
catch ME
errorMessage = sprintf('Error in program %s.\nTraceback (most recent at top):\nError Message:\n%s', ...
mfilename, ME.message);
WarnUser(errorMessage);
end
end % from callStackString
%==========================================================================================================================
% Pops up a warning message, and prints the error to the command window.
function WarnUser(warningMessage)
if nargin == 0
return; % Bail out if they called it without any arguments.
end
try
fprintf('%s\n', warningMessage);
uiwait(warndlg(warningMessage));
% Write the warning message to the log file
folder = 'C:\Users\Public\Documents\MATLAB Settings';
if ~exist(folder, 'dir')
mkdir(folder);
end
fullFileName = fullfile(folder, 'Error Log.txt');
fid = fopen(fullFileName, 'at');
if fid >= 0
fprintf(fid, '\nThe error below occurred on %s.\n%s\n', datestr(now), warningMessage);
fprintf(fid, '-------------------------------------------------------------------------------\n');
fclose(fid);
end
catch ME
message = sprintf('Error in WarnUser():\n%s', ME.message);
fprintf('%s\n', message);
uiwait(warndlg(message));
end
end % from WarnUser()

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