>> help measurepeaks
  Peak detection and height and area measurement, similar syntax as
  findpeaksSG with the addition of a 7th input argument that controls
  plotting (0=no plots, 1=plots). The 3rd to 6th input arguments can be
  scalars or vectors with one entry for each segment.
  M=measurepeaks(x,y,SlopeThreshold,AmpThreshold,SmoothWidth,FitWidth,plots)
  Locates and measures the positive peaks in a noisy x-y time series data.
  Detects peaks by looking for downward zero-crossings in the first
  derivative whose upward slopes exceed SlopeThreshold. Returns matrix (M)
  containing peak number, the absolute peak height, peak-valley difference,
  perpendicular drop area, and the tangent skim area of each peak.
  Arguments "SlopeThreshold", "AmpThreshold" and "SmoothWidth" control peak
  sensitivity of each segment. Higher values will neglect smaller features.
  "SmoothWidth" is a vector of the widths of the smooths applied before
  peak detection; larger values ignore narrow peaks. If smoothwidth=0, no
  smoothing is performed. "FitWidth" is a vector of the number of points
  around the top part of the peak that are taken for initial estimate of
  the peak center and width (minimum 3). If plots = 0, the function does
  not plot; if plots=1, the function plots the signal and numbers the peaks
  in figure 1 and plots each peak separately and marks the valley positions
  in an array of subplots in figure 2). Note: this function uses
  SmoothWidth only for peak detection; it performs measurements on the raw
  unsmoothed y data. If the data are noisy, it may be beneficial to smooth
  the y data yourself before calling measurepeaks.m, using any smooth
  function of your choice.
 
  See http://terpconnect.umd.edu/~toh/spectrum/Smoothing.html and
  http://terpconnect.umd.edu/~toh/spectrum/PeakFindingandMeasurement.htm
  (c) T.C. O'Haver, 2016.  Version 1.1, December, 2016
 
  Example 1: sin(x).^2 has theoretical peaks at x=0.5pi, 1.5pi, 2.5pi,
  3.5pi..., with peak heights of 1.0 and peak areas of pi/2 = 1.5708.
   disp('          Peak      Position    PeakMax    Peak-valley    Perp drop   Tan skim')
   x=[0:.01:20]';y=sin(x).^2;measurepeaks(x,y,0,0,5,5,1)
 
  Example 2: The built-in "humps" function has two peaks, no noise.
   measurepeaks(0:.01:2,humps(0:.01:2),0,0,1,1,1)
 
  Example 3: Series of peaks that get progressively taller and wider.
   x=[0:.01:5]';y=x.*sin(x.^2).^2;measurepeaks(x,y,0,0,5,5,1)
 
  Example 4: Like example 3, with random white noise added.
   x=[0:.01:5]';y=.1.*randn(size(x))+x.*sin(-x.^2).^2;
   measurepeaks(x,y,.001,.5,15,15,1)
 
  Example 5; Like example 3, with added rising baseline.
   x=[0:.01:5]';y=x+x.*sin(x.^2).^2;measurepeaks(x,y,0,0,5,5,1)
 
  Example 6: Gaussian on linear baseline, theoretical area = 1.7725
   x=[0:.1:10];y=2+x/10+exp(-(x-5).^2)+.01.*randn(size(x));
   disp('          Peak      Position    PeakMax    Peak-valley    Perp drop   Tan skim')
   disp(measurepeaks(x,y,0.0001,0.3,3,5,1))
   Peak-valley and Tan skim are most accurate height and area measures.
 
  Example 7: Two overlapping Gaussians, zero baseline, theoretical area = 1.7725
    x=[0:.05:10];y=exp(-(x-6).^2)+exp(-(x-3.5).^2)+.01.*randn(size(x));
   disp('          Peak      Position    PeakMax    Peak-valley    Perp drop   Tan skim')
   disp(measurepeaks(x,y,0.0001,0.5,8,6,1))
   PeakMax and Perp drop are most accurate height and area measures.
  
  Example 8: Narrow Gaussian peak on sloping linear baseline, followed by
  much broader peak, theoretical heights 1 and 1, areas 1.59 and 31.9.
  x=1:.2:150; 
  y=(150-x)./200+gaussian(x,30,1.5)+gaussian(x,90,30);
  measurepeaks(x,y,[0.001 .00005],[.6 .6],[5 120],[8 150],1) 
  Example 6: Noisy Gaussian on linear baseline, theoretical area = 1.7725
  >> x=[0:.1:10];y=2+x/10+exp(-(x-5).^2)+.01.*randn(size(x));
  >> disp('          Peak      Position    PeakMax    Peak-valley    Perp drop   Tan skim')
  >> disp(measurepeaks(x,y,0.0001,0.3,3,5,1))
 
  Example 7: Two overlapping noisy Gaussians, theoretical area = 1.7725
  >> x=[0:.1:10];y=exp(-(x-6).^2)+exp(-(x-4).^2)+.01.*randn(size(x));
  >> disp('          Peak      Position    PeakMax    Peak-valley    Perp drop   Tan skim')
  >> disp(measurepeaks(x,y,0.0001,0.5,8,6,1))