Format NCscurImCF_3parfor.m
This commit is contained in:
@@ -218,11 +218,8 @@ function [par4scanselIntensStd,par4scanselTimesStd,par4scanTimesELr,par4scanInte
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outCstd(ii,:)=resMatStd; %{ii, par4resMatStd};
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end
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%*********************************19_1001***********************************
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%To accomodate parfor copy par4scan thru global p4 functions inside of
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%parfor loop --then outside to par4Gbl_Main8b.m
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%**************************************************************************
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% To accomodate parfor
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% Copy par4scan thru global p4 functions inside of parfor loop --then outside to par4Gbl_Main8b.m
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fileExt='.txt';
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filePrefix='FitResultsComplete_';
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fileNamePlate=[filePrefix fileSuffix fileExt];
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@@ -1,437 +1,356 @@
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%% 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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%Major revision for Early-Late data cuts to improve accuracof 'r'. Removed legacy iterative method.
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%Significant Modification for Parfor
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%***************************************************************
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%##########################################################################
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%******************************************* New Stage 1***************************************************************
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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; %50
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miL=250;
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%***********************************
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rmsStg1=0;
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rmsStg1I(1)= 0;
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slps=1;
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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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% 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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||(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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%------------------------------------------------------------------
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%++++++++++++++++++++++++++++++++++++++++
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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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%normIntens %debugging parfor gbl 200330 good values
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%afgj
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lastTptUsed= 1;
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lastIntensUsed= 1;
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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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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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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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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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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',...
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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],'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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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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% 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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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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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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% 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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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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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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%rup %debugging parfor gbl 200330
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%Klow
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%asdfjj114
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% {
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catch %ME
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catch ME
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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 %end Try
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%}
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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); %jh diag
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end
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cutTm(1:4)= 1000; %-1 means cuts not used or NA
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%{
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l %debugging parfor
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K
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r
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Klow
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k
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%}
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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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%spline fit unneccessary and removed;therefore No maxRateTime assoc'd w/spline fit
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%try
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%resMatStd(15)= maxRateTime;
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%catch
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maxRateTime= 0; %[]; %Std shows []; ELr shows 0; %parfor
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resMatStd(15)= 0; %maxRateTimestdMeth;
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%end
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resMatStd(16)= lastTptUsedStd; %filterTimes(length(filterTimes));
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if isempty(Tpt1Std)
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Tpt1Std= 777; %0.000002; %
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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; %The 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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%if SwitchEvsEL==3 %Remove 'SwitchEvsEL==...' temporary SWITCH when Hartman decides what he wants
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%*********************************************************************************
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%ELr New Experimental data through L+deltaS Logistic fit for 'Improved r' Fitting
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%*********************************************************************************
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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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%-----------------------------------------------
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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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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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cutTm(4)= stdTimes(tc2LdatPt-1);
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cutDatNum(4)= tc2LdatPt-2; %length(stdTimes(end-(lengthKlast-1):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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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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%************************************************
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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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maxRateTime=0; %[]; %Std shows []; ELr shows 0; %parfor
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resMatStd(15)=0; %maxRateTimestdMeth;
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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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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; %The 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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%----------------------------------------------------------------------------------------------
|
||||
%if SwitchEvsEL==3 %Remove 'SwitchEvsEL==...' temporary SWITCH when Hartman decides what he wants
|
||||
%*********************************************************************************
|
||||
%ELr New Experimental data through L+deltaS Logistic fit for 'Improved r' Fitting
|
||||
%*********************************************************************************
|
||||
FiltTimesELr=[]; %{ii}= filterTimes;
|
||||
NormIntensELr=[]; %{ii}= normIntens;
|
||||
normIntens=selIntensStd;
|
||||
filterTimes=selTimesStd;
|
||||
stdIntens=selIntensStd;
|
||||
tmpIntens=selIntensStd;
|
||||
stdTimes=selTimesStd;
|
||||
|
||||
%classic symetric logistic curve fit setup restated as COMMENTS for reference convenience----------------------------
|
||||
%opts= fitoptions is the same as for Std and so is redundant
|
||||
%opts = fitoptions('Method','Nonlinear','Robust','On',...
|
||||
% 'DiffMinChange',1.0E-11,'DiffMaxChange',0.001,...
|
||||
% '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]);
|
||||
if stdNANcond==0
|
||||
% Determine critical points and offsets for selecting Core Data based on
|
||||
% Standard curve fit run. Put diff into NImStartupImCF02.m calling source
|
||||
% to reduce repeated execution since it doesn't change.
|
||||
% fd4=diff(sym('K / (1 + exp(-r* (t - l )))'),4);
|
||||
% sols=solve(fd4);
|
||||
tc1=eval(sols(2));
|
||||
tc2=eval(sols(3));
|
||||
LL=l; %eval(sols(1)); %exactly the same as 'l' from std. fit method-Save time
|
||||
rsTmStd=LL-tc1; %%riseTime (first critical point to L)
|
||||
deltS=rsTmStd/2;
|
||||
tc1Early=tc1-deltS; %AKA- tc1AdjTm %2*tc1 -LL
|
||||
L_Late=LL+deltS;
|
||||
tc1EdatPt=find(filterTimes>(tc1Early),1,'first');
|
||||
cutTm(1)=filterTimes(2);
|
||||
cutDatNum(1)=2;
|
||||
cutTm(2)=tc1Early;
|
||||
cutDatNum(2)=tc1EdatPt-1;
|
||||
L_LDatPt=find(filterTimes< L_Late,1,'last');
|
||||
tc2LdatPt=find(filterTimes< tc2+rsTmStd,1,'last');
|
||||
cutTm(3)=L_Late;
|
||||
cutDatNum(3)=L_LDatPt;
|
||||
|
||||
ftype = fittype('K / (1 + exp(-r* (t - l )))','independent','t','dependent',['K','l','r'],'options',opts);
|
||||
fitObject=[]; errObj=[];
|
||||
% carry out the curve fitting process
|
||||
[fitObject, errObj] = fit(Tms,Ints,ftype);
|
||||
coeffsArray = coeffvalues(fitObject);
|
||||
r3 = coeffsArray(3); %sDat(slps,4)= coeffsArray(3); % rateS
|
||||
% Select Core Data Set (Remove Early data before critical point)
|
||||
ints=[];
|
||||
ints(1:L_LDatPt-tc1EdatPt+2)=(tmpIntens(L_LDatPt));
|
||||
ints(2:end)=tmpIntens(tc1EdatPt:L_LDatPt);
|
||||
ints(1)=tmpIntens(1);
|
||||
tms=[];
|
||||
tms(1:L_LDatPt-tc1EdatPt+2)=(stdTimes(L_LDatPt));
|
||||
tms(2:end)=stdTimes(tc1EdatPt:L_LDatPt);
|
||||
tms(1)=stdTimes(1);
|
||||
|
||||
if (exist('K','var')&& exist('r','var') && exist('l','var'))
|
||||
t = (0:1:200);
|
||||
GrowthELr = K ./ (1 + exp(-r.* (t - l )));
|
||||
fitblELr= min(GrowthELr); %jh diag
|
||||
end
|
||||
|
||||
catch ME
|
||||
% if no data is given, return zeros
|
||||
AUC = 0;MSR = 0;K = 0;r = 0;l = 0;rsquare = 0;Klow = 0;Kup = 0;
|
||||
rlow = 0;rup = 0;lup = 0;llow = 0; %normIntens=[];
|
||||
|
||||
end %end Try
|
||||
|
||||
end %if stdNANcond=0
|
||||
%++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
|
||||
%Update values if r is better(higher) with removal of early data
|
||||
try
|
||||
if r3>r && stdNANcond==0
|
||||
r = r3; sDat(slps,4)= sDat(slps,4); % rateS
|
||||
K = coeffsArray(1); sDat(slps,2)= coeffsArray(1); % Carrying Capacity
|
||||
l = coeffsArray(2); sDat(slps,3)= coeffsArray(2); %lag time
|
||||
|
||||
coeffsArray = coeffvalues(fitObject);
|
||||
rmsStg1=errObj.rsquare;
|
||||
rmsStg1I(slps)= errObj.rsquare;
|
||||
sDat(slps,1)=errObj.rsquare;
|
||||
|
||||
%jh diagnostics*************
|
||||
numFitTpts=nnz((normIntens(:)>=0)==1);
|
||||
thresGT2 = find(((normIntens(:)>2)==1), 1);
|
||||
thresGT2Tm = filterTimes(thresGT2);
|
||||
numTptsGT2 = nnz((normIntens(:)>=2)==1);
|
||||
numTimePts = length(filterTimes);
|
||||
|
||||
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))));
|
||||
MSR = r3;
|
||||
%***************************
|
||||
|
||||
rsquare = errObj.rsquare;
|
||||
confObj = confint(fitObject,0.9); % get the 90% confidence
|
||||
NANcond=0;
|
||||
|
||||
confObj_filtered=confObj;
|
||||
Klow = confObj(1,1); sDat(slps,5)=confObj(1,1);
|
||||
Kup = confObj(2,1); sDat(slps,6)=confObj(2,1);
|
||||
llow = confObj(1,2); sDat(slps,7)=confObj(1,2);
|
||||
lup = confObj(2,2); sDat(slps,8)=confObj(2,2);
|
||||
rlow = confObj(1,3); sDat(slps,9)=confObj(1,3);
|
||||
rup = confObj(2,3); sDat(slps,10)=confObj(2,3);
|
||||
if(isnan(Klow)||isnan(Kup)||isnan(llow)||isnan(lup)||isnan(rlow)||isnan(rup))
|
||||
NANcond=1;
|
||||
% Include/Keep late data that define K
|
||||
if length(tmpIntens(tc2LdatPt:end))> 4
|
||||
KlastInts=tmpIntens(tc2LdatPt:end);
|
||||
KlastTms=stdTimes(tc2LdatPt:end);
|
||||
lengthKlast=length(tmpIntens(tc2LdatPt:end));
|
||||
ints(end:(end+ lengthKlast-1))=KlastInts;
|
||||
tms(end:(end+ lengthKlast-1 ))=KlastTms;
|
||||
cutTm(4)=tc2+rsTmStd;
|
||||
cutDatNum(4)=tc2LdatPt-1;
|
||||
else
|
||||
lengthKlast=length(tmpIntens(tc2LdatPt-1:end));
|
||||
if lengthKlast>1
|
||||
KlastInts=tmpIntens(end-(lengthKlast-1):end);
|
||||
KlastTms=stdTimes(end-(lengthKlast-1):end);
|
||||
ints(end:(end+ lengthKlast-1 ))=KlastInts;
|
||||
tms(end:(end+ lengthKlast-1 ))=KlastTms;
|
||||
end
|
||||
cutTm(4)=stdTimes(tc2LdatPt-1);
|
||||
cutDatNum(4)=tc2LdatPt-2; %length(stdTimes(end-(lengthKlast-1):end));
|
||||
end
|
||||
Ints=[];
|
||||
Tms=[];
|
||||
Ints=ints';
|
||||
Tms=tms';
|
||||
try
|
||||
filterTimes=Tms; filterTimes4=Tms;
|
||||
normIntens=Ints; normIntens4=Ints;
|
||||
|
||||
filterTimes= Tms;
|
||||
normIntens= Ints;
|
||||
resMat(17)= .00002;
|
||||
resMat(18)= bl;
|
||||
resMat(19)= fitblELr;
|
||||
resMat(20)= minTime;
|
||||
else % r is better than r3 so use the Std data in the ELr result sheet
|
||||
filterTimes=selTimesStd;
|
||||
normIntens=selIntensStd;
|
||||
% Classic symmetric logistic curve fit setup restated as COMMENTS for reference convenience
|
||||
% opts=fitoptions is the same as for Std and so is redundant
|
||||
% opts=fitoptions('Method','Nonlinear','Robust','On',...
|
||||
% 'DiffMinChange',1.0E-11,'DiffMaxChange',0.001,...
|
||||
% '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]);
|
||||
|
||||
lastTptUsed= lastTptUsedStd; %Reinstall Std values for jh diags
|
||||
ftype=fittype('K / (1 + exp(-r* (t - l )))','independent','t','dependent',['K','l','r'],'options',opts);
|
||||
fitObject=[]; errObj=[];
|
||||
% carry out the curve fitting process
|
||||
[fitObject, errObj]=fit(Tms,Ints,ftype);
|
||||
coeffsArray=coeffvalues(fitObject);
|
||||
r3=coeffsArray(3); %sDat(slps,4)=coeffsArray(3); % rateS
|
||||
|
||||
Tpt1=filterTimes(1);
|
||||
try
|
||||
if isempty(Tpt1)
|
||||
Tpt1= 0.00002; %777;
|
||||
end
|
||||
catch
|
||||
Tpt1= 0.00002; %777;
|
||||
end
|
||||
if (exist('K','var')&& exist('r','var') && exist('l','var'))
|
||||
t=(0:1:200);
|
||||
GrowthELr = K ./ (1 + exp(-r.* (t - l )));
|
||||
fitblELr= min(GrowthELr); %jh diag
|
||||
end
|
||||
catch ME
|
||||
% if no data is given, return zeros
|
||||
AUC = 0;MSR = 0;K = 0;r = 0;l = 0;rsquare = 0;Klow = 0;Kup = 0;
|
||||
rlow = 0;rup = 0;lup = 0;llow = 0; %normIntens=[];
|
||||
end
|
||||
end
|
||||
|
||||
resMat(17)= Tpt1;
|
||||
numFitTpts= numFitTptsStd;
|
||||
numTptsGT2= numTptsGT2Std;
|
||||
thresGT2Tm = thresGT2TmStd;
|
||||
% Update values if r is better(higher) with removal of early data
|
||||
try
|
||||
if r3>r && stdNANcond==0
|
||||
r=r3; sDat(slps,4)=sDat(slps,4); % rateS
|
||||
K=coeffsArray(1); sDat(slps,2)=coeffsArray(1); % Carrying Capacity
|
||||
l=coeffsArray(2); sDat(slps,3)=coeffsArray(2); % lag time
|
||||
coeffsArray=coeffvalues(fitObject);
|
||||
rmsStg1=errObj.rsquare;
|
||||
rmsStg1I(slps)=errObj.rsquare;
|
||||
sDat(slps,1)=errObj.rsquare;
|
||||
|
||||
cutTm(1:4)= 1000; %-1 means cuts not used or NA
|
||||
% JH diagnostics
|
||||
numFitTpts=nnz((normIntens(:)>=0)==1);
|
||||
thresGT2=find(((normIntens(:)>2)==1), 1);
|
||||
thresGT2Tm=filterTimes(thresGT2);
|
||||
numTptsGT2=nnz((normIntens(:)>=2)==1);
|
||||
numTimePts=length(filterTimes);
|
||||
|
||||
resMat(18)= bl; %only applicable to Std curve Fit; ELr superceeds and makes meaningless
|
||||
resMat(19)= fitblStd; %only applicable to Std curve Fit; ELr superceeds and makes meaningless
|
||||
resMat(20)= minTime; %only applicable to Std curve Fit; ELr superceeds and makes meaningless
|
||||
end %if r3>r1
|
||||
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))));
|
||||
MSR=r3;
|
||||
rsquare=errObj.rsquare;
|
||||
confObj=confint(fitObject,0.9); % get the 90% confidence
|
||||
NANcond=0;
|
||||
confObj_filtered=confObj;
|
||||
Klow=confObj(1,1); sDat(slps,5)=confObj(1,1);
|
||||
Kup=confObj(2,1); sDat(slps,6)=confObj(2,1);
|
||||
llow=confObj(1,2); sDat(slps,7)=confObj(1,2);
|
||||
lup=confObj(2,2); sDat(slps,8)=confObj(2,2);
|
||||
rlow=confObj(1,3); sDat(slps,9)=confObj(1,3);
|
||||
rup=confObj(2,3); sDat(slps,10)=confObj(2,3);
|
||||
if(isnan(Klow)||isnan(Kup)||isnan(llow)||isnan(lup)||isnan(rlow)||isnan(rup))
|
||||
NANcond=1;
|
||||
end
|
||||
filterTimes=Tms;
|
||||
normIntens=Ints;
|
||||
resMat(17)=.00002;
|
||||
resMat(18)=bl;
|
||||
resMat(19)=fitblELr;
|
||||
resMat(20)=minTime;
|
||||
else % r is better than r3 so use the Std data in the ELr result sheet
|
||||
filterTimes=selTimesStd;
|
||||
normIntens=selIntensStd;
|
||||
lastTptUsed=lastTptUsedStd; % Reinstall Std values for jh diags
|
||||
Tpt1=filterTimes(1);
|
||||
try
|
||||
if isempty(Tpt1)
|
||||
Tpt1=0.00002; %777;
|
||||
end
|
||||
catch
|
||||
Tpt1=0.00002; %777;
|
||||
end
|
||||
|
||||
%rup
|
||||
%asdf352
|
||||
% {
|
||||
catch ME
|
||||
resMat(17)=Tpt1;
|
||||
numFitTpts=numFitTptsStd;
|
||||
numTptsGT2=numTptsGT2Std;
|
||||
thresGT2Tm=thresGT2TmStd;
|
||||
cutTm(1:4)=1000; % 1 means cuts not used or NA
|
||||
resMat(18)=bl; % only applicable to Std curve Fit; ELr superceeds and makes meaningless
|
||||
resMat(19)=fitblStd; % only applicable to Std curve Fit; ELr superceeds and makes meaningless
|
||||
resMat(20)=minTime; % only applicable to Std curve Fit; ELr superceeds and makes meaningless
|
||||
end % if r3>r1
|
||||
catch ME
|
||||
% if no data is given, return zeros
|
||||
AUC = 0;MSR = 0;K = 0;r = 0;l = 0;rsquare = 0;Klow = 0;Kup = 0;
|
||||
rlow = 0;rup = 0;lup = 0;llow = 0; %normIntens=[];
|
||||
AUC=0;MSR=0;K=0;r=0;l=0;rsquare=0;Klow=0;Kup=0;
|
||||
rlow=0;rup=0;lup=0;llow=0; % normIntens=[];
|
||||
end
|
||||
|
||||
end %end Try
|
||||
%}
|
||||
resMat(1)= AUC;
|
||||
resMat(2)= MSR;
|
||||
resMat(3)= K;
|
||||
resMat(4)= r;
|
||||
resMat(5)= l;
|
||||
resMat(6)= rsquare;
|
||||
resMat(7)= Klow;
|
||||
resMat(8)= Kup;
|
||||
resMat(9)= rlow;
|
||||
resMat(10)= rup;
|
||||
resMat(11)= llow;
|
||||
resMat(12)= lup;
|
||||
resMat(13)= currSpotArea;
|
||||
resMat(14)= lastIntensUsed; %filtNormIntens(length(filtNormIntens));
|
||||
|
||||
%spline fit unneccessary and removed therfor no max spline rate time->set 0
|
||||
maxRateTime=0; %ELr will show 0; Std will show []
|
||||
resMat(15)= maxRateTime;
|
||||
|
||||
resMat(16)= lastTptUsed; %filterTimes(length(filterTimes));
|
||||
|
||||
|
||||
try %if Std fit used no cuts .:. no cutTm
|
||||
resMat(24)= cutTm(1);
|
||||
resMat(25)= cutTm(2);
|
||||
resMat(26)= cutTm(3);
|
||||
resMat(27)= cutTm(4);
|
||||
catch
|
||||
resMat(24)= 999; %if Std fit used no cuts .:. no cutTm
|
||||
resMat(25)= 999;
|
||||
resMat(26)= 999;
|
||||
resMat(27)= 999;
|
||||
end
|
||||
|
||||
FiltTimesELr= filterTimes;
|
||||
NormIntensELr= normIntens;
|
||||
|
||||
%**********************************************************************************
|
||||
%##########################################################################
|
||||
resMat(1)=AUC;
|
||||
resMat(2)=MSR;
|
||||
resMat(3)=K;
|
||||
resMat(4)=r;
|
||||
resMat(5)=l;
|
||||
resMat(6)=rsquare;
|
||||
resMat(7)=Klow;
|
||||
resMat(8)=Kup;
|
||||
resMat(9)=rlow;
|
||||
resMat(10)=rup;
|
||||
resMat(11)=llow;
|
||||
resMat(12)=lup;
|
||||
resMat(13)=currSpotArea;
|
||||
resMat(14)=lastIntensUsed; %filtNormIntens(length(filtNormIntens));
|
||||
% spline fit unneccessary and removed therfor no max spline rate time->set 0
|
||||
maxRateTime=0; % ELr will show 0; Std will show []
|
||||
resMat(15)=maxRateTime;
|
||||
resMat(16)=lastTptUsed; % filterTimes(length(filterTimes));
|
||||
try % if Std fit used no cuts .:. no cutTm
|
||||
resMat(24)=cutTm(1);
|
||||
resMat(25)=cutTm(2);
|
||||
resMat(26)=cutTm(3);
|
||||
resMat(27)=cutTm(4);
|
||||
catch
|
||||
resMat(24)=999; % if Std fit used no cuts .:. no cutTm
|
||||
resMat(25)=999;
|
||||
resMat(26)=999;
|
||||
resMat(27)=999;
|
||||
end
|
||||
|
||||
FiltTimesELr=filterTimes;
|
||||
NormIntensELr=normIntens;
|
||||
lastTptUsed=max(filterTimes);
|
||||
lastIntensUsed=normIntens(length(normIntens));
|
||||
|
||||
if (exist('K','var')&& exist('r','var') && exist('l','var'))
|
||||
t = (0:1:200);
|
||||
Growth = K ./ (1 + exp(-r.* (t - l )));
|
||||
fitbl= min(Growth); %jh diag
|
||||
if (exist('K','var')&& exist('r','var') && exist('l','var'))
|
||||
t=(0:1:200);
|
||||
Growth=K ./ (1 + exp(-r.* (t - l )));
|
||||
fitbl=min(Growth); % jh diag
|
||||
end
|
||||
try % jh diag
|
||||
if isempty(thresGT2Tm)
|
||||
thresGT2Tm=0
|
||||
end
|
||||
%}
|
||||
try
|
||||
if isempty(thresGT2Tm),thresGT2Tm=0;end %jh diag
|
||||
catch
|
||||
thresGT2Tm= 0;
|
||||
numTptsGT2= 0;
|
||||
thresGT2Tm=0;
|
||||
numTptsGT2=0;
|
||||
end
|
||||
|
||||
resMat(21)= thresGT2Tm;
|
||||
resMat(22)= numFitTpts;
|
||||
resMat(23)= numTptsGT2;
|
||||
|
||||
|
||||
end %function end
|
||||
resMat(21)=thresGT2Tm;
|
||||
resMat(22)=numFitTpts;
|
||||
resMat(23)=numTptsGT2;
|
||||
end
|
||||
|
||||
|
||||
Reference in New Issue
Block a user