To isolate the regions around the peaks, do something like this —
x = linspace(0, 150, 500); % Create Data (Row)
y = @(v) sinc((x+v)-mean((x+v))/5); % Create Data (Rows)
v = (0:10:1500).';
x = x(:); % Convert To Column
y = sum(y(v)).'; % Convert To Column
y = y + rand(size(y))*0.2; % Add Noise
figure
plot(x, y)
grid
xlabel('X')
ylabel('Y')
title('Original Data')
[pks,plocs] = findpeaks(y, 'MinPeakProminence',1); % Desired Peak & Index
for k = 1:numel(pks)
[vys,vlocs] = findpeaks(-y, 'MinPeakProminence',0.1); % Valleys & Indices
[vlc,ixv] = mink(abs(vlocs-plocs(k)),2); % Choose Two Closest Valleys To Desired Peak
vlcs(:,k) = plocs(k) + [-1; 1].*vlc; % Calculate Indices
end
% vlcs
figure
plot(x, y)
hold on
for k = 1:size(vlcs,2)
idxr = vlcs(1,k) : vlcs(2,k); % Initial Index Range
xv = x(idxr);
yv = y(idxr);
Apos(k,:) = trapz(x(yv>=0),yv(yv>=0)); % Calculate Positive Peak Areas
Mpos(k,:) = mean(yv(yv>=0)); % Calculate Mean Value
plot(x(idxr), y(idxr), '-r') % Plot Desired Peak Region
end
hold off
grid
xlabel('X')
ylabel('Y')
title('Original Data With Selected Regions Higlighted')
PeakAreas = table(x(plocs),Apos,Mpos, 'VariableNames',{'Peak X Location','Peak Area','Peak Mean'})
The regions in red in the second plot are the segments used for the ‘Apos’ area and ‘Mpos’ mean calculations.
.
