removed findpeaks, it was in the repository to fix a bug in an earlier version of octave

calibration/src/findpeaks.m

@@ -1,280 +0,0 @@
-## Copyright (c) 2012 Juan Pablo Carbajal <carbajal@ifi.uzh.ch>
-##
-## This program is free software; you can redistribute it and/or modify
-## it under the terms of the GNU General Public License as published by
-## the Free Software Foundation; either version 3 of the License, or
-## (at your option) any later version.
-##
-## This program is distributed in the hope that it will be useful,
-## but WITHOUT ANY WARRANTY; without even the implied warranty of
-## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-## GNU General Public License for more details.
-##
-## You should have received a copy of the GNU General Public License
-## along with this program; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn  {Function File} {[@var{pks}, @var{loc}, @var{extra}] =} findpeaks (@var{data})
-## @deftypefnx {Function File} {@dots{} =} findpeaks (@dots{}, @var{property}, @var{value})
-## @deftypefnx {Function File} {@dots{} =} findpeaks (@dots{}, @asis{"DoubleSided"})
-## Finds peaks on @var{data}.
-##
-## Peaks of a positive array of data are defined as local maxima. For
-## double-sided data, they are maxima of the positive part and minima of
-## the negative part. @var{data} is expected to be a single column
-## vector.
-##
-## The function returns the value of @var{data} at the peaks in
-## @var{pks}. The index indicating their position is returned in
-## @var{loc}.
-##
-## The third output argument is a structure with additional information:
-##
-## @table @asis
-## @item "parabol"
-## A structure containing the parabola fitted to each returned peak. The
-## structure has two fields, @asis{"x"} and @asis{"pp"}. The field
-## @asis{"pp"} contains the coefficients of the 2nd degree polynomial
-## and @asis{"x"} the extrema of the intercal here it was fitted.
-##
-## @item "height"
-## The estimated height of the returned peaks (in units of @var{data}).
-##
-## @item "baseline"
-## The height at which the roots of the returned peaks were calculated
-## (in units of @var{data}).
-##
-## @item "roots"
-## The abscissa values (in index units) at which the parabola fitted to
-## each of the returned peaks crosses the @asis{"baseline"} value. The
-## width of the peak is calculated by @command{diff(roots)}.
-## @end table
-##
-## This function accepts property-value pair given in the list below:
-##
-## @table @asis
-##
-## @item "MinPeakHeight"
-## Minimum peak height (positive scalar). Only peaks that exceed this
-## value will be returned. For data taking positive and negative values
-## use the option "DoubleSided". Default value @code{2*std (abs (detrend
-## (data,0)))}.
-##
-## @item "MinPeakDistance"
-## Minimum separation between (positive integer). Peaks separated by
-## less than this distance are considered a single peak. This distance
-## is also used to fit a second order polynomial to the peaks to
-## estimate their width, therefore it acts as a smoothing parameter.
-## Default value 4.
-##
-## @item "MinPeakWidth"
-## Minimum width of peaks (positive integer). The width of the peaks is
-## estimated using a parabola fitted to the neighborhood of each peak.
-## The neighborhood size is equal to the value of
-## @asis{"MinPeakDistance"}. The width is evaluated at the half height
-## of the peak with baseline at "MinPeakHeight". Default value 2.
-##
-## @item "DoubleSided"
-## Tells the function that data takes positive and negative values. The
-## base-line for the peaks is taken as the mean value of the function.
-## This is equivalent as passing the absolute value of the data after
-## removing the mean.
-## @end table
-##
-## Run @command{demo findpeaks} to see some examples.
-## @end deftypefn
-
-function [pks idx varargout] = findpeaks (data, varargin)
-
-  ## --- Parse arguments --- #
-  __data__ = abs (detrend (data,0));
-
-  posscal = @(x)isscalar (x) && x >= 0;
-
-  parser = inputParser ();
-  parser.FunctionName = "findpeaks";
-  parser = addParamValue (parser,"MinPeakHeight", 2*std (__data__),posscal);
-  parser = addParamValue (parser,"MinPeakDistance",4,posscal);
-  parser = addParamValue (parser,"MinPeakWidth",2,posscal);
-  parser = addSwitch (parser,"DoubleSided");
-
-  parser = parse(parser,varargin{:});
-
-  minH      = parser.Results.MinPeakHeight;
-  minD      = parser.Results.MinPeakDistance;
-  minW      = parser.Results.MinPeakWidth;
-  dSided    = parser.Results.DoubleSided;
-
-  clear parser posscal
-  ## ------ #
-
-  if dSided
-    [data __data__] = deal (__data__, data);
-  elseif min(data)<0
-    error ("findpeaks:InvalidArgument",
-           'Data contains negative values. You may want to "DoubleSided" option');
-  endif
-
-  ## Rough estimates of first and second derivative
-  df1 = diff (data,1)([1; (1:end)']);
-  df2 = diff (data,2)([1; 1; (1:end)']);
-
-  ## check for changes of sign of 1st derivative and negativity of 2nd
-  ## derivative.
-  idx = find (df1.*[df1(2:end); 0]<0 & [df2(2:end); 0]<0);
-
-  ## Get peaks that are beyond given height
-  tf  = data(idx) > minH;
-  idx = idx(tf);
-
-  ## sort according to magnitude
-  [~,tmp] = sort(data(idx),"descend");
-  idx_s = idx(tmp);
-
-  ## Treat peaks separated less than minD as one
-  # http://savannah.gnu.org/bugs/?43691
-  # D = abs (idx_s - idx_s');
-  D = bsxfun (@minus, idx_s, idx_s');
-  if any(D(:) < minD)
-
-    i = 1;
-    peak = cell ();
-    node2visit = 1:size(D,1);
-    visited = [];
-    idx_pruned = idx_s;
-
-    ## debug
-##    h = plot(1:length(data),data,"-",idx_s,data(idx_s),'.r',idx_s,data(idx_s),'.g');
-##    set(h(3),"visible","off");
-
-    while ~isempty (node2visit)
-
-      d = D(node2visit(1),:);
-
-      visited = [visited node2visit(1)];
-      node2visit(1) = [];
-
-      neighs  = setdiff (find (d < minD), visited);
-      if ~isempty (neighs)
-        ## debug
-##        set(h(3),"xdata",idx_s(neighs),"ydata",data(idx_s(neighs)),"visible","on")
-##        pause(0.2)
-##        set(h(3),"visible","off");
-
-        idx_pruned   = setdiff (idx_pruned,idx_s(neighs));
-
-        visited    = [visited neighs];
-        node2visit = setdiff (node2visit,visited);
-
-        ## debug
-##        set(h(2),"xdata",idx_pruned,"ydata",data(idx_pruned))
-##        pause
-      endif
-
-    endwhile
-    idx = idx_pruned;
-  endif
-
-  ## Estimate widths of peaks and filter for:
-  ## width smaller than given.
-  ## wrong concavity.
-  ## not high enough
-  ## data at peak is lower than parabola by 1%
-  if minW > 0
-    ## debug
-##    h = plot(1:length(data),data,"-",idx,data(idx),'.r',...
-##          idx,data(idx),'og',idx,data(idx),'-m');
-##    set(h(4),"linewidth",2)
-##    set(h(3:4),"visible","off");
-
-    idx_pruned = idx;
-    n  = length(idx);
-    np = length(data);
-    struct_count = 0;
-    for i=1:n
-      ind = (round (max(idx(i)-minD/2,1)) : ...
-             round (min(idx(i)+minD/2,np)))';
-      pp = polyfit (ind,data(ind),2);
-      H  = pp(3) - pp(2)^2/(4*pp(1));
-
-      ## debug
-##      x = linspace(ind(1)-1,ind(end)+1,10);
-##      set(h(4),"xdata",x,"ydata",polyval(pp,x),"visible","on")
-##      set(h(3),"xdata",ind,"ydata",data(ind),"visible","on")
-##      pause(0.2)
-##      set(h(3:4),"visible","off");
-
-      rz = roots ([pp(1:2) pp(3)-mean([H,minH])]);
-      width = abs (diff (rz));
-      if width < minW || pp(1) > 0 || H < minH || data(idx(i)) < 0.99*H
-        idx_pruned = setdiff (idx_pruned, idx(i));
-      elseif nargout > 2
-        struct_count++;
-        extra.parabol(struct_count).x  = ind([1 end]);
-        extra.parabol(struct_count).pp = pp;
-
-        extra.roots(struct_count,1:2)    = rz;
-        extra.height(struct_count)   = H;
-        extra.baseline(struct_count) = mean([H,minH]);
-      endif
-
-      ## debug
-##      set(h(2),"xdata",idx_pruned,"ydata",data(idx_pruned))
-##      pause(0.2)
-
-    endfor
-  idx = idx_pruned;
-  endif
-
-
-  if dSided
-    pks = __data__(idx);
-  else
-    pks = data(idx);
-  endif
-
-  if nargout()>2
-    varargout{1} = extra;
-  endif
-
-endfunction
-
-%!demo
-%! t = 2*pi*linspace(0,1,1024)';
-%! y = sin(3.14*t) + 0.5*cos(6.09*t) + 0.1*sin(10.11*t+1/6) + 0.1*sin(15.3*t+1/3);
-%!
-%! data1 = abs(y); # Positive values
-%! [pks idx] = findpeaks(data1);
-%!
-%! data2 = y; # Double-sided
-%! [pks2 idx2] = findpeaks(data2,"DoubleSided");
-%! [pks3 idx3] = findpeaks(data2,"DoubleSided","MinPeakHeight",0.5);
-%!
-%! subplot(1,2,1)
-%! plot(t,data1,t(idx),data1(idx),'.m')
-%! subplot(1,2,2)
-%! plot(t,data2,t(idx2),data2(idx2),".m;>2*std;",t(idx3),data2(idx3),"or;>0.1;")
-%! legend("Location","NorthOutside","Orientation","horizontal")
-%!
-%! #----------------------------------------------------------------------------
-%! # Finding the peaks of smooth data is not a big deal!
-
-%!demo
-%! t = 2*pi*linspace(0,1,1024)';
-%! y = sin(3.14*t) + 0.5*cos(6.09*t) + 0.1*sin(10.11*t+1/6) + 0.1*sin(15.3*t+1/3);
-%!
-%! data = abs(y + 0.1*randn(length(y),1)); # Positive values + noise
-%! [pks idx] = findpeaks(data,"MinPeakHeight",1);
-%!
-%! dt = t(2)-t(1);
-%! [pks2 idx2] = findpeaks(data,"MinPeakHeight",1,...
-%!                              "MinPeakDistance",round(0.5/dt));
-%!
-%! subplot(1,2,1)
-%! plot(t,data,t(idx),data(idx),'.r')
-%! subplot(1,2,2)
-%! plot(t,data,t(idx2),data(idx2),'.r')
-%!
-%! #----------------------------------------------------------------------------
-%! # Noisy data may need tunning of the parameters. In the 2nd example,
-%! # MinPeakDistance is used as a smoother of the peaks.