353 lines
13 KiB
Matlab
Executable File
353 lines
13 KiB
Matlab
Executable File
%% CALLED BY NCfitImCFparforFailGbl2.m %%
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function [resMatStd, resMat, selTimesStd, selIntensStd, FiltTimesELr, NormIntensELr] =...
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NCscurImCF_3parfor(dataMatrix, AUCfinalTime, currSpotArea, sols, bl, minTime)
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% Preallocate
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resMatStd=zeros(1,27);
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resMat=zeros(1,27);
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% Set internal variables sent to matlab fit function
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me=200;
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meL=750;
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mi=25;
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miL=250;
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rmsStg1=0;
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rmsStg1I(1)=0;
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slps=1;
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filterTimes=[];
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normIntens=[];
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nn=1;
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numFitTpts=0;
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% Build filterTimes and normIntens from spot dataMatrix selection codes produced in filter section
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for n=1:size(dataMatrix,2)
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if (((dataMatrix(3,n)==1))||(dataMatrix(3,n)==3)||(dataMatrix(3,n)==2)...
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||(dataMatrix(3,n)==0))
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filterTimes(nn)=dataMatrix(1,n);
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normIntens(nn)=dataMatrix(4,n);
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nn=nn+1;
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end
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end
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filterTimes=filterTimes';
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selTimesStd=filterTimes;
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normIntens=normIntens';
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selIntensStd=normIntens;
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lastTptUsed=1;
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lastIntensUsed=1;
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thresGT2TmStd=0;
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try
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lastTptUsed=max(filterTimes);
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lastIntensUsed=normIntens(length(normIntens));
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lastIntensUsedStd=lastIntensUsed;
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lastTptUsedStd=lastTptUsed;
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Tpt1Std=filterTimes(1);
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numFitTptsStd=nnz((normIntens(:)>=0)==1);
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thresGT2=find(((normIntens(:)>2)==1), 1);
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if isempty(thresGT2)
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thresGT2TmStd=0;
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else
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thresGT2TmStd=filterTimes(thresGT2);
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end
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numTptsGT2Std=nnz((normIntens(:)>=2)==1); % nnz(filterTimes(find(filterTimes>=thresGT2Tm)));
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K_Guess=max(normIntens);
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numTimePts=length(filterTimes);
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opts=fitoptions('Method','Nonlinear','Robust','On','DiffMinChange',1.0E-11,'DiffMaxChange',0.001,...
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'MaxFunEvals',me, 'MaxIter', mi, 'TolFun', 1.0E-12, 'TolX', 1.0E-10, 'Lower', [K_Guess*0.5,0,0],...
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'StartPoint', [K_Guess,filterTimes(floor(numTimePts/2)),0.30], 'Upper', [K_Guess*2.0,max(filterTimes),1.0],'Display','off');
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ftype=fittype('K / (1 + exp(-r* (t - l )))','independent','t','dependent',['K','r','l'],'options',opts);
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% Carry out the curve fitting process
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[fitObject, errObj]=fit(filterTimes,normIntens,ftype);
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coeffsArray=coeffvalues(fitObject);
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rmsStg1=errObj.rsquare;
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rmsStg1I(slps)=errObj.rsquare;
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sDat(slps,1)=errObj.rsquare;
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K=coeffsArray(1); sDat(slps,2)=coeffsArray(1); % Carrying Capacity
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l=coeffsArray(2); sDat(slps,3)=coeffsArray(2); % lag time
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r=coeffsArray(3); sDat(slps,4)=coeffsArray(3); % rateS
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% Integrate (from first to last time point)
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numVals=size(filterTimes);
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numVals=numVals(1);
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t_begin=0;
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t_end=AUCfinalTime;
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AUC=(K/r*log(1+exp(-r*(t_end-l)))-K/r*log(exp(-r*(t_end-l)))) - (K/r*log(1+exp(-r*(t_begin-l)))-K/r*log(exp(-r*(t_begin-l))));
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MSR=r;
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rsquare=errObj.rsquare;
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confObj=confint(fitObject,0.9); % get the 90% confidence
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NANcond=0; stdNANcond=0; % stdNANcond added to relay not to attempt ELr as there is no curve to find critical point
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confObj_filtered=confObj;
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Klow=confObj(1,1); sDat(slps,5)=confObj(1,1);
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Kup=confObj(2,1); sDat(slps,6)=confObj(2,1);
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llow=confObj(1,2); sDat(slps,7)=confObj(1,2);
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lup=confObj(2,2); sDat(slps,8)=confObj(2,2);
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rlow=confObj(1,3); sDat(slps,9)=confObj(1,3);
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rup=confObj(2,3); sDat(slps,10)=confObj(2,3);
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if(isnan(Klow)||isnan(Kup)||isnan(llow)||isnan(lup)||isnan(rlow)||isnan(rup))
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NANcond=1; stdNANcond=1; % stdNANcond added to relay not to attempt ELr as there is no curve to find critical point
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end
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catch
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% if no data is given, return zeros
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AUC=0;MSR=0;K=0;r=0;l=0;rsquare=0;Klow=0;Kup=0;
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rlow=0;rup=0;lup=0;llow=0;
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NANcond=1; stdNANcond=1; %stdNANcond added to relay not to attempt ELr as there is no curve to find critical point
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end
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if (exist('K','var')&& exist('r','var') && exist('l','var'))
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t=(0:1:200);
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Growth=K ./ (1 + exp(-r.* (t - l )));
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fitblStd=min(Growth);
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end
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cutTm(1:4)=1000; %-1 means cuts not used or NA
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% Preserve for ResultsStd
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resMatStd(1)=AUC;
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resMatStd(2)=MSR;
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resMatStd(3)=K;
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resMatStd(4)=r;
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resMatStd(5)=l;
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resMatStd(6)=rsquare;
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resMatStd(7)=Klow;
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resMatStd(8)=Kup;
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resMatStd(9)=rlow;
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resMatStd(10)=rup;
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resMatStd(11)=llow;
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resMatStd(12)=lup;
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resMatStd(13)=currSpotArea;
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resMatStd(14)=lastIntensUsedStd; % filtNormIntens(length(filtNormIntens));
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maxRateTime=0; %[]; %Std shows []; ELr shows 0; %parfor
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resMatStd(15)=0; %maxRateTimestdMeth;
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resMatStd(16)=lastTptUsedStd;
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if isempty(Tpt1Std)
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Tpt1Std=777;
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end
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resMatStd(17)=Tpt1Std;
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resMatStd(18)=bl; % perform in the filter section of NCfitImCFparfor
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resMatStd(19)=fitblStd; % taken from NCfil... and not affected by NCscur...changes
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resMatStd(20)=minTime; % not affected by changes made in NCscur...for refined 'r'
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resMatStd(21)=thresGT2TmStd;
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resMatStd(22)=numFitTptsStd;
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resMatStd(23)=numTptsGT2Std;
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resMatStd(24)=999; % yhe Standard method has no cuts .:.no cutTm
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resMatStd(25)=999;
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resMatStd(26)=999;
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resMatStd(27)=999;
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% ELr New Experimental data through L+deltaS Logistic fit for 'Improved r' Fitting
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FiltTimesELr=[]; %{ii}=filterTimes;
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NormIntensELr=[]; %{ii}=normIntens;
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normIntens=selIntensStd;
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filterTimes=selTimesStd;
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stdIntens=selIntensStd;
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tmpIntens=selIntensStd;
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stdTimes=selTimesStd;
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if stdNANcond==0
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% Determine critical points and offsets for selecting Core Data based on
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% Standard curve fit run. Put diff into NImStartupImCF02.m calling source
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% to reduce repeated execution since it doesn't change.
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% fd4=diff(sym('K / (1 + exp(-r* (t - l )))'),4);
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% sols=solve(fd4);
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tc1=eval(sols(2));
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tc2=eval(sols(3));
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LL=l; %eval(sols(1)); %exactly the same as 'l' from std. fit method-Save time
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rsTmStd=LL-tc1; %%riseTime (first critical point to L)
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deltS=rsTmStd/2;
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tc1Early=tc1-deltS; %AKA- tc1AdjTm %2*tc1 -LL
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L_Late=LL+deltS;
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tc1EdatPt=find(filterTimes>(tc1Early),1,'first');
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cutTm(1)=filterTimes(2);
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cutDatNum(1)=2;
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cutTm(2)=tc1Early;
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cutDatNum(2)=tc1EdatPt-1;
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L_LDatPt=find(filterTimes< L_Late,1,'last');
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tc2LdatPt=find(filterTimes< tc2+rsTmStd,1,'last');
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cutTm(3)=L_Late;
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cutDatNum(3)=L_LDatPt;
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% Select Core Data Set (Remove Early data before critical point)
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ints=[];
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ints(1:L_LDatPt-tc1EdatPt+2)=(tmpIntens(L_LDatPt));
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ints(2:end)=tmpIntens(tc1EdatPt:L_LDatPt);
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ints(1)=tmpIntens(1);
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tms=[];
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tms(1:L_LDatPt-tc1EdatPt+2)=(stdTimes(L_LDatPt));
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tms(2:end)=stdTimes(tc1EdatPt:L_LDatPt);
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tms(1)=stdTimes(1);
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% Include/Keep late data that define K
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if length(tmpIntens(tc2LdatPt:end))> 4
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KlastInts=tmpIntens(tc2LdatPt:end);
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KlastTms=stdTimes(tc2LdatPt:end);
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lengthKlast=length(tmpIntens(tc2LdatPt:end));
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ints(end:(end+ lengthKlast-1))=KlastInts;
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tms(end:(end+ lengthKlast-1 ))=KlastTms;
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cutTm(4)=tc2+rsTmStd;
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cutDatNum(4)=tc2LdatPt-1;
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else
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lengthKlast=length(tmpIntens(tc2LdatPt-1:end));
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if lengthKlast>1
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KlastInts=tmpIntens(end-(lengthKlast-1):end);
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KlastTms=stdTimes(end-(lengthKlast-1):end);
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ints(end:(end+ lengthKlast-1 ))=KlastInts;
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tms(end:(end+ lengthKlast-1 ))=KlastTms;
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end
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cutTm(4)=stdTimes(tc2LdatPt-1);
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cutDatNum(4)=tc2LdatPt-2; %length(stdTimes(end-(lengthKlast-1):end));
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end
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Ints=[];
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Tms=[];
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Ints=ints';
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Tms=tms';
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try
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filterTimes=Tms; filterTimes4=Tms;
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normIntens=Ints; normIntens4=Ints;
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% Classic symmetric logistic curve fit setup restated as COMMENTS for reference convenience
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% opts=fitoptions is the same as for Std and so is redundant
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% opts=fitoptions('Method','Nonlinear','Robust','On',...
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% 'DiffMinChange',1.0E-11,'DiffMaxChange',0.001,...
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% 'MaxFunEvals',me, 'MaxIter', mi, 'TolFun', 1.0E-12, 'TolX', 1.0E-10, 'Lower', [K_Guess*0.5,0,0], 'StartPoint', [K_Guess,filterTimes(floor(numTimePts/2)),0.30], 'Upper', [K_Guess*2.0,max(filterTimes),1.0]);
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ftype=fittype('K / (1 + exp(-r* (t - l )))','independent','t','dependent',['K','l','r'],'options',opts);
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fitObject=[]; errObj=[];
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% carry out the curve fitting process
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[fitObject, errObj]=fit(Tms,Ints,ftype);
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coeffsArray=coeffvalues(fitObject);
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r3=coeffsArray(3); % sDat(slps,4)=coeffsArray(3); % rateS
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if (exist('K','var')&& exist('r','var') && exist('l','var'))
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t=(0:1:200);
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GrowthELr=K ./ (1 + exp(-r.* (t - l )));
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fitblELr=min(GrowthELr); %jh diag
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end
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catch
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% if no data is given, return zeros
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AUC=0;MSR=0;K=0;r=0;l=0;rsquare=0;Klow=0;Kup=0;
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rlow=0;rup=0;lup=0;llow=0; %normIntens=[];
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end
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end
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% Update values if r is better(higher) with removal of early data
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try
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if r3>r && stdNANcond==0
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r=r3; sDat(slps,4)=sDat(slps,4); % rateS
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K=coeffsArray(1); sDat(slps,2)=coeffsArray(1); % Carrying Capacity
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l=coeffsArray(2); sDat(slps,3)=coeffsArray(2); % lag time
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coeffsArray=coeffvalues(fitObject);
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rmsStg1=errObj.rsquare;
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rmsStg1I(slps)=errObj.rsquare;
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sDat(slps,1)=errObj.rsquare;
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% JH diagnostics
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numFitTpts=nnz((normIntens(:)>=0)==1);
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thresGT2=find(((normIntens(:)>2)==1), 1);
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thresGT2Tm=filterTimes(thresGT2);
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numTptsGT2=nnz((normIntens(:)>=2)==1);
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numTimePts=length(filterTimes);
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AUC=(K/r*log(1+exp(-r*(t_end-l)))-K/r*log(exp(-r*(t_end-l)))) - (K/r*log(1+exp(-r*(t_begin-l)))-K/r*log(exp(-r*(t_begin-l))));
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MSR=r3;
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rsquare=errObj.rsquare;
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confObj=confint(fitObject,0.9); % get the 90% confidence
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NANcond=0;
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confObj_filtered=confObj;
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Klow=confObj(1,1); sDat(slps,5)=confObj(1,1);
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Kup=confObj(2,1); sDat(slps,6)=confObj(2,1);
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llow=confObj(1,2); sDat(slps,7)=confObj(1,2);
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lup=confObj(2,2); sDat(slps,8)=confObj(2,2);
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rlow=confObj(1,3); sDat(slps,9)=confObj(1,3);
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rup=confObj(2,3); sDat(slps,10)=confObj(2,3);
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if(isnan(Klow)||isnan(Kup)||isnan(llow)||isnan(lup)||isnan(rlow)||isnan(rup))
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NANcond=1;
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end
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filterTimes=Tms;
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normIntens=Ints;
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resMat(17)=.00002;
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resMat(18)=bl;
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resMat(19)=fitblELr;
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resMat(20)=minTime;
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else % r is better than r3 so use the Std data in the ELr result sheet
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filterTimes=selTimesStd;
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normIntens=selIntensStd;
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lastTptUsed=lastTptUsedStd; % Reinstall Std values for jh diags
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Tpt1=filterTimes(1);
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try
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if isempty(Tpt1)
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Tpt1=0.00002; %777;
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end
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catch
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Tpt1=0.00002; %777;
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end
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resMat(17)=Tpt1;
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numFitTpts=numFitTptsStd;
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numTptsGT2=numTptsGT2Std;
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thresGT2Tm=thresGT2TmStd;
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cutTm(1:4)=1000; % 1 means cuts not used or NA
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resMat(18)=bl; % only applicable to Std curve Fit; ELr superceeds and makes meaningless
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resMat(19)=fitblStd; % only applicable to Std curve Fit; ELr superceeds and makes meaningless
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resMat(20)=minTime; % only applicable to Std curve Fit; ELr superceeds and makes meaningless
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end % if r3>r1
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catch
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% if no data is given, return zeros
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AUC=0;MSR=0;K=0;r=0;l=0;rsquare=0;Klow=0;Kup=0;
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rlow=0;rup=0;lup=0;llow=0; % normIntens=[];
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end
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resMat(1)=AUC;
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resMat(2)=MSR;
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resMat(3)=K;
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resMat(4)=r;
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resMat(5)=l;
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resMat(6)=rsquare;
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resMat(7)=Klow;
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resMat(8)=Kup;
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resMat(9)=rlow;
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resMat(10)=rup;
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resMat(11)=llow;
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resMat(12)=lup;
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resMat(13)=currSpotArea;
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resMat(14)=lastIntensUsed; %filtNormIntens(length(filtNormIntens));
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% spline fit unneccessary and removed therfor no max spline rate time->set 0
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maxRateTime=0; % ELr will show 0; Std will show []
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resMat(15)=maxRateTime;
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resMat(16)=lastTptUsed; % filterTimes(length(filterTimes));
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try % if Std fit used no cuts .:. no cutTm
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resMat(24)=cutTm(1);
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resMat(25)=cutTm(2);
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resMat(26)=cutTm(3);
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resMat(27)=cutTm(4);
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catch
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resMat(24)=999; % if Std fit used no cuts .:. no cutTm
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resMat(25)=999;
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resMat(26)=999;
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resMat(27)=999;
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end
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FiltTimesELr=filterTimes;
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NormIntensELr=normIntens;
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lastTptUsed=max(filterTimes);
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lastIntensUsed=normIntens(length(normIntens));
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if (exist('K','var')&& exist('r','var') && exist('l','var'))
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t=(0:1:200);
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Growth=K ./ (1 + exp(-r.* (t - l )));
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fitbl=min(Growth); % jh diag
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end
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try % jh diag
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if isempty(thresGT2Tm)
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thresGT2Tm=0
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end
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catch
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thresGT2Tm=0;
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numTptsGT2=0;
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end
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resMat(21)=thresGT2Tm;
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resMat(22)=numFitTpts;
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resMat(23)=numTptsGT2;
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end
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