| V=findvalleys(x,y,SlopeThreshold,AmpThreshold,smoothwidth,peakgroup,smoothtype)
|
function V=findvalleys(x,y,SlopeThreshold,AmpThreshold,smoothwidth,peakgroup,smoothtype)
% function P=findvalleys(x,y,SlopeThreshold,AmpThreshold,smoothwidth,peakgroup,smoothtype)
% Function to locate the valleys (mimnima) in a noisy x-y time series data
% set. Detects valleys by looking for upward zero-crossings
% in the first derivative that exceed SlopeThreshold.
% Returns list (V) containing valley number and position,
% depth, and width of each valley. Arguments "slopeThreshold",
% "ampThreshold" and "smoothwidth" control sensitivity.
% Higher values will neglect smaller features. "Smoothwidth" is
% the width of the smooth applied before valley detection; larger
% values ignore narrow features. "Peakgroup" is the number points
% around the bottom part of the valley that are fit to a parabola to
% determine the valley vertex (x and y at lowest point) and width.
% The argument "smoothtype" determines the smooth algorithm:
% If smoothtype=1, rectangular (sliding-average or boxcar)
% If smoothtype=2, triangular (2 passes of sliding-average)
% If smoothtype=3, pseudo-Gaussian (3 passes of sliding-average)
% See http://terpconnect.umd.edu/~toh/spectrum/Smoothing.html and
% http://terpconnect.umd.edu/~toh/spectrum/PeakFindingandMeasurement.htm
% T. C. O'Haver, Version 2=1, September, 2011
if nargin~=7;smoothtype=1;end % smoothtype=1 if not specified in argument
if smoothtype>3;smoothtype=3;end
if smoothtype<1;smoothtype=1;end
smoothwidth=round(smoothwidth);
peakgroup=round(peakgroup);
d=fastsmooth(deriv(y),smoothwidth,smoothtype);
n=round(peakgroup/2+1);
V=[0 0 0 0];
vectorlength=length(y);
peak=1;
AmpTest=AmpThreshold;
for j=smoothwidth:length(y)-smoothwidth,
if sign(d(j)) < sign (d(j+1)), % Detects zero-crossing
if d(j+1)-d(j) > SlopeThreshold*y(j), % if slope of derivative is larger than SlopeThreshold
if y(j) > AmpTest, % if height of valley is larger than AmpThreshold
xx=zeros(size(peakgroup));yy=zeros(size(peakgroup));
for k=1:peakgroup, % Create sub-group of points near valley
groupindex=j+k-n+1;
if groupindex<1, groupindex=1;end
if groupindex>vectorlength, groupindex=vectorlength;end
xx(k)=x(groupindex);yy(k)=y(groupindex);
end
[coef,S]=polyfit(xx,yy,2); % Fit parabola to sub-group with centering and scaling
c1=coef(3);c2=coef(2);c3=coef(1);
valleyX=-c2/(2*c3); % Compute valley position and height of fitted parabola
valleyY=(c1-(c2*c2/(4*c3)));
MeasuredWidth=norm(2.35703/(sqrt(2)*sqrt(-1*c3)));
% if the valley is too narrow for least-squares technique to work
% well, just use the min value of y in the sub-group of points near valley.
if peakgroup<5,
valleyY=min(yy);
pindex=val2ind(yy,valleyY);
valleyX=xx(pindex(1));
end
% Construct matrix P. One row for each valley
% detected, containing the valley number, valley
% position (x-value) and valley depth (y-value).
V(peak,:) = [round(peak) valleyX valleyY MeasuredWidth];
peak=peak+1;
end
end
end
end
% ----------------------------------------------------------------------
function [index,closestval]=val2ind(x,val)
% Returns the index and the value of the element of vector x that is closest to val
% If more than one element is equally close, returns vectors of indicies and values
% Tom O'Haver (toh@umd.edu) October 2006
% Examples: If x=[1 2 4 3 5 9 6 4 5 3 1], then val2ind(x,6)=7 and val2ind(x,5.1)=[5 9]
% [indices values]=val2ind(x,3.3) returns indices = [4 10] and values = [3 3]
dif=abs(x-val);
index=find((dif-min(dif))==0);
closestval=x(index);
function d=deriv(a)
% First derivative of vector using 2-point central difference.
% T. C. O'Haver, 1988.
n=length(a);
d(1)=a(2)-a(1);
d(n)=a(n)-a(n-1);
for j = 2:n-1;
d(j)=(a(j+1)-a(j-1)) ./ 2;
end
function SmoothY=fastsmooth(Y,w,type,ends)
% fastbsmooth(Y,w,type,ends) smooths vector Y with smooth
% of width w. Version 2.0, May 2008.
% The argument "type" determines the smooth type:
% If type=1, rectangular (sliding-average or boxcar)
% If type=2, triangular (2 passes of sliding-average)
% If type=3, pseudo-Gaussian (3 passes of sliding-average)
% The argument "ends" controls how the "ends" of the signal
% (the first w/2 points and the last w/2 points) are handled.
% If ends=0, the ends are zero. (In this mode the elapsed
% time is independent of the smooth width). The fastest.
% If ends=1, the ends are smoothed with progressively
% smaller smooths the closer to the end. (In this mode the
% elapsed time increases with increasing smooth widths).
% fastsmooth(Y,w,type) smooths with ends=0.
% fastsmooth(Y,w) smooths with type=1 and ends=0.
% Example:
% fastsmooth([1 1 1 10 10 10 1 1 1 1],3)= [0 1 4 7 10 7 4 1 1 0]
% fastsmooth([1 1 1 10 10 10 1 1 1 1],3,1,1)= [1 1 4 7 10 7 4 1 1 1]
% T. C. O'Haver, May, 2008.
if nargin==2, ends=0; type=1; end
if nargin==3, ends=0; end
switch type
case 1
SmoothY=sa(Y,w,ends);
case 2
SmoothY=sa(sa(Y,w,ends),w,ends);
case 3
SmoothY=sa(sa(sa(Y,w,ends),w,ends),w,ends);
end
function SmoothY=sa(Y,smoothwidth,ends)
w=round(smoothwidth);
SumPoints=sum(Y(1:w));
s=zeros(size(Y));
halfw=round(w/2);
L=length(Y);
for k=1:L-w,
s(k+halfw-1)=SumPoints;
SumPoints=SumPoints-Y(k);
SumPoints=SumPoints+Y(k+w);
end
s(k+halfw)=sum(Y(L-w+1:L));
SmoothY=s./w;
% Taper the ends of the signal if ends=1.
if ends==1,
startpoint=(smoothwidth + 1)/2;
SmoothY(1)=(Y(1)+Y(2))./2;
for k=2:startpoint,
SmoothY(k)=mean(Y(1:(2*k-1)));
SmoothY(L-k+1)=mean(Y(L-2*k+2:L));
end
SmoothY(L)=(Y(L)+Y(L-1))./2;
end
% ----------------------------------------------------------------------
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