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rINSTCONTROL/@MyPeakFinder/MyPeakFinder.m
61276c8b2152Rohde_FSW

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MyPeakFinder.m
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classdef MyPeakFinder < handle
properties
Trace;
Peaks;
end
methods
function this=MyPeakFinder(varargin)
p=inputParser;
addParameter(p,'Trace',MyTrace());
parse(p,varargin{:})
this.Trace=p.Results.Trace;
end
function searchPeaks(this,varargin)
assert(validatePlot(this.Trace),...
'The Trace is not valid for finding peaks, make sure a proper trace is loaded');
p=inputParser;
%Some validation functions
ispositive=@(x) isnumeric(x) && isscalar(x) && (x > 0);
isnumber=@(x) isnumeric(x) && isscalar(x);
valid_sorts={'none','ascend','descend'};
sortstrcheck=@(x) any(validatestring(x,valid_sorts));
addParameter(p,'FindMinima',false);
addParameter(p,'MinPeakDistance',0,ispositive);
addParameter(p,'MinPeakHeight',-Inf,isnumber);
addParameter(p,'MinPeakWidth',0,ispositive);
addParameter(p,'MaxPeakWidth',Inf,ispositive);
addParameter(p,'MinPeakProminence',0.1,isnumber);
addParameter(p,'SortStr','none',sortstrcheck);
addParameter(p,'Threshold',0);
addParameter(p,'ClearPeaks',true);
addParameter(p,'Limits',[min(this.Trace.x),max(this.Trace.x)])
addParameter(p,'NPeaks',0);
parse(p,varargin{:});
%Sets the indices to be searched
x_lim=p.Results.Limits;
ind=(this.Trace.x<x_lim(2)) & (this.Trace.x>x_lim(1));
%Sets the minimum peak prominence, which is always normalized
min_peak_prominence=p.Results.MinPeakProminence*...
peak2peak(this.Trace.y(ind));
%We must condition the Trace such that x is increasing and has
%no NaNs
nan_ind=isnan(this.Trace.x) | isnan(this.Trace.y);
this.Trace.x(nan_ind)=[];
this.Trace.y(nan_ind)=[];
ind(nan_ind)=[];
if ~issorted(this.Trace.x)
this.Trace.x=flipud(this.Trace.x);
this.Trace.y=flipud(this.Trace.y);
%If it is still not sorted, we sort it
if ~issorted(this.Trace.x)
'Sorting';
[this.Trace.x,sort_ind]=sort(this.Trace.x);
this.Trace.y=this.Trace.y(sort_ind);
end
end
%If we are looking for minima, we invert the trace
if p.Results.FindMinima
y=-this.Trace.y;
else
y=this.Trace.y;
end
%As there is no way to tell it to look for infinite numbers of
%peaks when you specify NPeaks, we only specify this parameter
%if we need to
if p.Results.NPeaks
extra_args={'NPeaks',p.Results.NPeaks};
else
extra_args={};
end
%We now search for the peaks using the specified parameters
[pks,locs,wdth,prom]=findpeaks(y(ind),...
this.Trace.x(ind),...
'MinPeakDistance',p.Results.MinPeakDistance,...
'MinPeakheight',p.Results.MinPeakHeight,...
'MinPeakWidth',p.Results.MinPeakWidth,...
'MaxPeakWidth',p.Results.MaxPeakWidth,...
'SortStr',p.Results.SortStr,...
'MinPeakProminence',min_peak_prominence,...
'Threshold',p.Results.Threshold,extra_args{:});
%We invert the results back if we are looking for minima.
if p.Results.FindMinima
pks=-pks;
prom=-prom;
end
PeakStruct=struct('Value',num2cell(pks),...
'Location',num2cell(locs),...
'Width',num2cell(wdth),...
'Prominence',num2cell(prom));
%If the clearpeaks flag is set, we delete the previous peaks.
if p.Results.ClearPeaks
clearPeaks(this);
this.Peaks=PeakStruct;
else
this.Peaks=[this.Peaks;PeakStruct];
end
end
function loadTrace(this,fullfilename)
%Finds type of file
[~,~,ext]=fileparts(fullfilename);
switch ext
case '.txt'
loadTrace(this.Trace,fullfilename);
case '.mat'
DataStruct=load(fullfilename);
fields=fieldnames(DataStruct);
%We try to find which vectors are x and y for the data,
%first we find the two longest vectors in the .mat file
vec_length=structfun(@(x) length(x), DataStruct);
max_val=max(vec_length);
ind=(max_val==vec_length);
assert(sum(ind)==2,['There are %i vectors with length %i, ',...
'cannot determine which are the data vectors'],...
sum(ind),max_val);
vec_names=fields(ind);
%Now we do some basic conditioning of these vectors:
%Make column vectors and remove NaNs.
vec{1}=DataStruct.(vec_names{1})(:);
vec{2}=DataStruct.(vec_names{2})(:);
nan_ind=isnan(vec{1}) | isnan(vec{2});
vec{1}(nan_ind)=[];
vec{2}(nan_ind)=[];
%We find what x is by looking for a sorted vector
ind_x=cellfun(@(x) issorted(x,'monotonic'),vec);
this.Trace.x=vec{ind_x};
this.Trace.y=vec{~ind_x};
otherwise
error('File type %s is not supported',ext)
end
end
function fitAllPeaks(this,varargin)
valid_fits={'Lorentzian','DoubleLorentzian','Gaussian'};
validate_fit=@(x) all(ismember(x,valid_fits));
p=inputParser;
addParameter(p,'FitNames',{'LorentzianGrad'},validate_fit);
addParameter(p,'base_dir',pwd);
addParameter(p,'session_name','placeholder');
addParameter(p,'filename','placeholder');
parse(p,varargin{:});
fit_names=p.Results.FitNames;
%We instantiate the MyFit objects used for the fitting
Fits=struct();
for i=1:length(fit_names)
Fits.(fit_names{i})=MyFit('fit_name',fit_names{i},...
'enable_gui',0);
Fits.(fit_names{i}).base_dir=p.Results.base_dir;
Fits.(fit_names{i}).session_name=p.Results.session_name;
Fits.(fit_names{i}).filename=p.Results.filename;
end
%We fit the peaks
for i=1:length(this.Peaks)
%First extract the data around the peak
[x_fit,y_fit]=extractPeak(this,i);
for j=1:length(fit_names)
Fits.(fit_names{j}).Data.x=x_fit;
Fits.(fit_names{j}).Data.y=y_fit;
genInitParams(Fits.(fit_names{j}));
fitTrace(Fits.(fit_names{j}));
saveParams(Fits.(fit_names{j}),...
'save_user_params',false,...
'save_gof',true)
end
end
end
function [x_peak,y_peak]=extractPeak(this,peak_no)
loc=this.Peaks(peak_no).Location;
w=this.Peaks(peak_no).Width;
ind=(loc-10*w<this.Trace.x) & (loc+10*w>this.Trace.x);
x_peak=this.Trace.x(ind);
y_peak=this.Trace.y(ind);
end
function save(this,varargin)
%Parse inputs for saving
p=inputParser;
addParameter(p,'filename','placeholder',@ischar);
addParameter(p,'save_dir',pwd,@ischar);
addParameter(p,'overwrite_flag',false);
addParameter(p,'save_prec',15);
parse(p,varargin{:});
%Assign shorter names
filename=p.Results.filename;
save_dir=p.Results.save_dir;
overwrite_flag=p.Results.overwrite_flag;
save_prec=p.Results.save_prec;
%Puts together the full file name
fullfilename=fullfile([save_dir,filename,'.txt']);
%Creates the file in the given folder
write_flag=createFile(save_dir,fullfilename,overwrite_flag);
%Returns if the file is not created for some reason
if ~write_flag; return; end
col_names={'Value','Location','Width','Prominence'};
n_columns=length(col_names);
%Finds appropriate column width
cw=max([cellfun(@(x) length(x),col_names), save_prec+7]);
cw_vec=repmat(cw,1,4);
pre_fmt_str=repmat('%%%is\\t',1,n_columns);
fmt_str=sprintf([pre_fmt_str,'\r\n'],cw_vec);
fileID=fopen(fullfilename,'w');
fprintf(fileID,fmt_str,col_names{:});
pre_fmt_str_nmb=repmat('%%%i.15e\\t',1,n_columns);
%Removes the tab at the end
pre_fmt_str_nmb((end-2):end)=[];
nmb_fmt_str=sprintf([pre_fmt_str_nmb,'\r\n'],cw_vec);
fprintf(fileID,nmb_fmt_str,...
[[this.Peaks.Value];[this.Peaks.Location];...
[this.Peaks.Width];[this.Peaks.Prominence]]);
fclose(fileID);
end
function loadPeaks(this,fullfilename)
assert(ischar(fullfilename),...
'File name must be a char, currently it is a %s',...
class(fullfilename));
if ~exist(fullfilename,'file')
error('File named ''%s'' does not exist, choose a different file',...
fullfilename);
end
LoadStruct=importdata(fullfilename);
headers=regexp(LoadStruct.textdata{1},'\s*(\w*)\s*','Tokens');
this.Peaks=struct(headers{1}{1},num2cell(LoadStruct.data(:,1)),...
headers{2}{1},num2cell(LoadStruct.data(:,2)),...
headers{3}{1},num2cell(LoadStruct.data(:,3)),...
headers{4}{1},num2cell(LoadStruct.data(:,4)));
end
function clearPeaks(this)
this.Peaks=struct();
end
end
end
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