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Format NCscurImCF_3parfor.m

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This commit is contained in:
Bryan Roessler
2024-07-25 14:35:27 -04:00
parent df3f9e324e
commit e8f86da392
2 changed files with 314 additions and 398 deletions
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7
workflow/templates/easy/NCfitImCFparforFailGbl2.m
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@@ -218,11 +218,8 @@ function [par4scanselIntensStd,par4scanselTimesStd,par4scanTimesELr,par4scanInte
outCstd(ii,:)=resMatStd; %{ii, par4resMatStd}; outCstd(ii,:)=resMatStd; %{ii, par4resMatStd};
end end
%*********************************19_1001*********************************** % To accomodate parfor
%To accomodate parfor copy par4scan thru global p4 functions inside of % Copy par4scan thru global p4 functions inside of parfor loop --then outside to par4Gbl_Main8b.m
%parfor loop --then outside to par4Gbl_Main8b.m
%**************************************************************************
fileExt='.txt'; fileExt='.txt';
filePrefix='FitResultsComplete_'; filePrefix='FitResultsComplete_';
fileNamePlate=[filePrefix fileSuffix fileExt]; fileNamePlate=[filePrefix fileSuffix fileExt];

683
workflow/templates/easy/NCscurImCF_3parfor.m
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@@ -1,437 +1,356 @@
%% CALLED BY NCfitImCFparforFailGbl2.m %% %% CALLED BY NCfitImCFparforFailGbl2.m %%
function [resMatStd, resMat, selTimesStd, selIntensStd, FiltTimesELr, NormIntensELr] =... function [resMatStd, resMat, selTimesStd, selIntensStd, FiltTimesELr, NormIntensELr] =...
NCscurImCF_3parfor(dataMatrix, AUCfinalTime, currSpotArea, sols, bl, minTime) NCscurImCF_3parfor(dataMatrix, AUCfinalTime, currSpotArea, sols, bl, minTime)
%Major revision for Early-Late data cuts to improve accuracof 'r'. Removed legacy iterative method.
%Significant Modification for Parfor
%***************************************************************
%##########################################################################
%******************************************* New Stage 1***************************************************************
%Preallocate
resMatStd= zeros(1,27);
resMat= zeros(1,27);
%Set internal variables sent to matlab fit function
me=200;
meL=750;
mi=25; %50
miL=250;
%***********************************
rmsStg1=0;
rmsStg1I(1)= 0;
slps=1;
% Preallocate
resMatStd=zeros(1,27);
resMat=zeros(1,27);
% Set internal variables sent to matlab fit function
me=200;
meL=750;
mi=25;
miL=250;
rmsStg1=0;
rmsStg1I(1)=0;
slps=1;
filterTimes=[]; filterTimes=[];
normIntens=[]; normIntens=[];
nn=1; nn=1;
numFitTpts=0; numFitTpts=0;
%Build filterTimes and normIntens from spot dataMatrix selection codes produced in filter section % Build filterTimes and normIntens from spot dataMatrix selection codes produced in filter section
for n=1:size(dataMatrix,2) for n=1:size(dataMatrix,2)
if (((dataMatrix(3,n)==1))||(dataMatrix(3,n)==3)||(dataMatrix(3,n)==2)... if (((dataMatrix(3,n)==1))||(dataMatrix(3,n)==3)||(dataMatrix(3,n)==2)...
||(dataMatrix(3,n)==0)) ||(dataMatrix(3,n)==0))
filterTimes(nn)= dataMatrix(1,n); filterTimes(nn)=dataMatrix(1,n);
normIntens(nn)=dataMatrix(4,n); normIntens(nn)=dataMatrix(4,n);
nn=nn+1; nn=nn+1;
end end
end end
filterTimes=filterTimes';
%------------------------------------------------------------------ selTimesStd=filterTimes;
normIntens=normIntens';
%++++++++++++++++++++++++++++++++++++++++ selIntensStd=normIntens;
lastTptUsed=1;
filterTimes=filterTimes'; lastIntensUsed=1;
selTimesStd=filterTimes;
normIntens=normIntens';
selIntensStd=normIntens;
%normIntens %debugging parfor gbl 200330 good values
%afgj
lastTptUsed= 1;
lastIntensUsed= 1;
thresGT2TmStd=0; thresGT2TmStd=0;
try try
lastTptUsed=max(filterTimes); lastTptUsed=max(filterTimes);
lastIntensUsed=normIntens(length(normIntens)); lastIntensUsed=normIntens(length(normIntens));
lastIntensUsedStd= lastIntensUsed; lastIntensUsedStd=lastIntensUsed;
lastTptUsedStd=lastTptUsed;
lastTptUsedStd= lastTptUsed; Tpt1Std=filterTimes(1);
Tpt1Std= filterTimes(1);
numFitTptsStd=nnz((normIntens(:)>=0)==1); numFitTptsStd=nnz((normIntens(:)>=0)==1);
thresGT2 = find(((normIntens(:)>2)==1), 1); thresGT2=find(((normIntens(:)>2)==1), 1);
if isempty(thresGT2) if isempty(thresGT2)
thresGT2TmStd=0; thresGT2TmStd=0;
else else
thresGT2TmStd = filterTimes(thresGT2); thresGT2TmStd=filterTimes(thresGT2);
end end
numTptsGT2Std = nnz((normIntens(:)>=2)==1); %nnz(filterTimes(find(filterTimes>=thresGT2Tm))); numTptsGT2Std=nnz((normIntens(:)>=2)==1); % nnz(filterTimes(find(filterTimes>=thresGT2Tm)));
K_Guess = max(normIntens); K_Guess=max(normIntens);
numTimePts = length(filterTimes); numTimePts=length(filterTimes);
opts = fitoptions('Method','Nonlinear','Robust','On',... opts=fitoptions('Method','Nonlinear','Robust','On','DiffMinChange',1.0E-11,'DiffMaxChange',0.001,...
'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],...
'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'); 'StartPoint', [K_Guess,filterTimes(floor(numTimePts/2)),0.30], 'Upper', [K_Guess*2.0,max(filterTimes),1.0],'Display','off');
ftype = fittype('K / (1 + exp(-r* (t - l )))','independent','t','dependent',['K','r','l'],'options',opts); ftype=fittype('K / (1 + exp(-r* (t - l )))','independent','t','dependent',['K','r','l'],'options',opts);
% carry out the curve fitting process % Carry out the curve fitting process
[fitObject, errObj] = fit(filterTimes,normIntens,ftype); [fitObject, errObj]=fit(filterTimes,normIntens,ftype);
coeffsArray = coeffvalues(fitObject); coeffsArray=coeffvalues(fitObject);
rmsStg1=errObj.rsquare; rmsStg1=errObj.rsquare;
rmsStg1I(slps)= errObj.rsquare; rmsStg1I(slps)=errObj.rsquare;
sDat(slps,1)=errObj.rsquare; sDat(slps,1)=errObj.rsquare;
K = coeffsArray(1); sDat(slps,2)= coeffsArray(1); % Carrying Capacity K=coeffsArray(1); sDat(slps,2)=coeffsArray(1); % Carrying Capacity
l = coeffsArray(2); sDat(slps,3)= coeffsArray(2); %lag time l=coeffsArray(2); sDat(slps,3)=coeffsArray(2); % lag time
r = coeffsArray(3); sDat(slps,4)= coeffsArray(3); % rateS r=coeffsArray(3); sDat(slps,4)=coeffsArray(3); % rateS
% integrate (from first to last time point) % Integrate (from first to last time point)
numVals = size(filterTimes); numVals=size(filterTimes);
numVals = numVals(1); numVals=numVals(1);
t_begin = 0; t_begin=0;
t_end = AUCfinalTime; t_end=AUCfinalTime;
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)))); 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 = r; MSR=r;
rsquare=errObj.rsquare;
rsquare = errObj.rsquare; confObj=confint(fitObject,0.9); % get the 90% confidence
confObj = confint(fitObject,0.9); % get the 90% confidence NANcond=0; stdNANcond=0; % stdNANcond added to relay not to attempt ELr as there is no curve to find critical point
NANcond=0; stdNANcond=0; %stdNANcond added to relay not to attempt ELr as there is no curve to find critical point
confObj_filtered=confObj; confObj_filtered=confObj;
Klow = confObj(1,1); sDat(slps,5)=confObj(1,1); Klow=confObj(1,1); sDat(slps,5)=confObj(1,1);
Kup = confObj(2,1); sDat(slps,6)=confObj(2,1); Kup=confObj(2,1); sDat(slps,6)=confObj(2,1);
llow = confObj(1,2); sDat(slps,7)=confObj(1,2); llow=confObj(1,2); sDat(slps,7)=confObj(1,2);
lup = confObj(2,2); sDat(slps,8)=confObj(2,2); lup=confObj(2,2); sDat(slps,8)=confObj(2,2);
rlow = confObj(1,3); sDat(slps,9)=confObj(1,3); rlow=confObj(1,3); sDat(slps,9)=confObj(1,3);
rup = confObj(2,3); sDat(slps,10)=confObj(2,3); rup=confObj(2,3); sDat(slps,10)=confObj(2,3);
if(isnan(Klow)||isnan(Kup)||isnan(llow)||isnan(lup)||isnan(rlow)||isnan(rup)) if(isnan(Klow)||isnan(Kup)||isnan(llow)||isnan(lup)||isnan(rlow)||isnan(rup))
NANcond=1; stdNANcond=1; %stdNANcond added to relay not to attempt ELr as there is no curve to find critical point NANcond=1; stdNANcond=1; % stdNANcond added to relay not to attempt ELr as there is no curve to find critical point
end end
catch ME
%rup %debugging parfor gbl 200330
%Klow
%asdfjj114
% {
catch %ME
% if no data is given, return zeros % if no data is given, return zeros
AUC = 0;MSR = 0;K = 0;r = 0;l = 0;rsquare = 0;Klow = 0;Kup = 0; AUC=0;MSR=0;K=0;r=0;l=0;rsquare=0;Klow=0;Kup=0;
rlow = 0;rup = 0;lup = 0;llow = 0; rlow=0;rup=0;lup=0;llow=0;
NANcond=1; stdNANcond=1; %stdNANcond added to relay not to attempt ELr as there is no curve to find critical point NANcond=1; stdNANcond=1; %stdNANcond added to relay not to attempt ELr as there is no curve to find critical point
end %end Try
%}
if (exist('K','var')&& exist('r','var') && exist('l','var'))
t = (0:1:200);
Growth = K ./ (1 + exp(-r.* (t - l )));
fitblStd= min(Growth); %jh diag
end
cutTm(1:4)= 1000; %-1 means cuts not used or NA
%{
l %debugging parfor
K
r
Klow
k
%}
%***Preserve for ResultsStd+++++++++++++++++++++++++++++
resMatStd(1)= AUC;
resMatStd(2)= MSR;
resMatStd(3)= K;
resMatStd(4)= r;
resMatStd(5)= l;
resMatStd(6)= rsquare;
resMatStd(7)= Klow;
resMatStd(8)= Kup;
resMatStd(9)= rlow;
resMatStd(10)= rup;
resMatStd(11)= llow;
resMatStd(12)= lup;
resMatStd(13)= currSpotArea;
resMatStd(14)= lastIntensUsedStd; %filtNormIntens(length(filtNormIntens));
%spline fit unneccessary and removed;therefore No maxRateTime assoc'd w/spline fit
%try
%resMatStd(15)= maxRateTime;
%catch
maxRateTime= 0; %[]; %Std shows []; ELr shows 0; %parfor
resMatStd(15)= 0; %maxRateTimestdMeth;
%end
resMatStd(16)= lastTptUsedStd; %filterTimes(length(filterTimes));
if isempty(Tpt1Std)
Tpt1Std= 777; %0.000002; %
end
resMatStd(17)= Tpt1Std;
resMatStd(18)= bl; %perform in the filter section of NCfitImCFparfor
resMatStd(19)= fitblStd; %Taken from NCfil... and not affected by NCscur...changes
resMatStd(20)= minTime; %Not affected by changes made in NCscur...for refined 'r'
resMatStd(21)= thresGT2TmStd;
resMatStd(22)= numFitTptsStd;
resMatStd(23)= numTptsGT2Std;
resMatStd(24)= 999; %The Standard method has no cuts .:.no cutTm
resMatStd(25)= 999;
resMatStd(26)= 999;
resMatStd(27)= 999;
%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;
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;
%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);
%-----------------------------------------------
%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 end
cutTm(4)= stdTimes(tc2LdatPt-1); if (exist('K','var')&& exist('r','var') && exist('l','var'))
cutDatNum(4)= tc2LdatPt-2; %length(stdTimes(end-(lengthKlast-1):end)); t=(0:1:200);
Growth=K ./ (1 + exp(-r.* (t - l )));
fitblStd=min(Growth);
end
end cutTm(1:4)=1000; %-1 means cuts not used or NA
% Preserve for ResultsStd
resMatStd(1)=AUC;
resMatStd(2)=MSR;
resMatStd(3)=K;
resMatStd(4)=r;
resMatStd(5)=l;
resMatStd(6)=rsquare;
resMatStd(7)=Klow;
resMatStd(8)=Kup;
resMatStd(9)=rlow;
resMatStd(10)=rup;
resMatStd(11)=llow;
resMatStd(12)=lup;
resMatStd(13)=currSpotArea;
resMatStd(14)=lastIntensUsedStd; % filtNormIntens(length(filtNormIntens));
%************************************************ maxRateTime=0; %[]; %Std shows []; ELr shows 0; %parfor
Ints=[]; resMatStd(15)=0; %maxRateTimestdMeth;
Tms=[];
Ints= ints';
Tms= tms';
try resMatStd(16)=lastTptUsedStd;
filterTimes= Tms; filterTimes4= Tms; if isempty(Tpt1Std)
normIntens= Ints; normIntens4=Ints; Tpt1Std=777;
end
resMatStd(17)=Tpt1Std;
resMatStd(18)=bl; %perform in the filter section of NCfitImCFparfor
resMatStd(19)=fitblStd; %Taken from NCfil... and not affected by NCscur...changes
resMatStd(20)=minTime; %Not affected by changes made in NCscur...for refined 'r'
resMatStd(21)=thresGT2TmStd;
resMatStd(22)=numFitTptsStd;
resMatStd(23)=numTptsGT2Std;
resMatStd(24)=999; %The Standard method has no cuts .:.no cutTm
resMatStd(25)=999;
resMatStd(26)=999;
resMatStd(27)=999;
%---------------------------------------------------------------------------------------------- %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---------------------------- if stdNANcond==0
%opts= fitoptions is the same as for Std and so is redundant % Determine critical points and offsets for selecting Core Data based on
%opts = fitoptions('Method','Nonlinear','Robust','On',... % Standard curve fit run. Put diff into NImStartupImCF02.m calling source
% 'DiffMinChange',1.0E-11,'DiffMaxChange',0.001,... % to reduce repeated execution since it doesn't change.
% '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]); % 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); % Select Core Data Set (Remove Early data before critical point)
fitObject=[]; errObj=[]; ints=[];
% carry out the curve fitting process ints(1:L_LDatPt-tc1EdatPt+2)=(tmpIntens(L_LDatPt));
[fitObject, errObj] = fit(Tms,Ints,ftype); ints(2:end)=tmpIntens(tc1EdatPt:L_LDatPt);
coeffsArray = coeffvalues(fitObject); ints(1)=tmpIntens(1);
r3 = coeffsArray(3); %sDat(slps,4)= coeffsArray(3); % rateS 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')) % Include/Keep late data that define K
t = (0:1:200); if length(tmpIntens(tc2LdatPt:end))> 4
GrowthELr = K ./ (1 + exp(-r.* (t - l ))); KlastInts=tmpIntens(tc2LdatPt:end);
fitblELr= min(GrowthELr); %jh diag KlastTms=stdTimes(tc2LdatPt:end);
end lengthKlast=length(tmpIntens(tc2LdatPt:end));
ints(end:(end+ lengthKlast-1))=KlastInts;
catch ME tms(end:(end+ lengthKlast-1 ))=KlastTms;
% if no data is given, return zeros cutTm(4)=tc2+rsTmStd;
AUC = 0;MSR = 0;K = 0;r = 0;l = 0;rsquare = 0;Klow = 0;Kup = 0; cutDatNum(4)=tc2LdatPt-1;
rlow = 0;rup = 0;lup = 0;llow = 0; %normIntens=[]; else
lengthKlast=length(tmpIntens(tc2LdatPt-1:end));
end %end Try if lengthKlast>1
KlastInts=tmpIntens(end-(lengthKlast-1):end);
end %if stdNANcond=0 KlastTms=stdTimes(end-(lengthKlast-1):end);
%++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ ints(end:(end+ lengthKlast-1 ))=KlastInts;
%Update values if r is better(higher) with removal of early data tms(end:(end+ lengthKlast-1 ))=KlastTms;
try end
if r3>r && stdNANcond==0 cutTm(4)=stdTimes(tc2LdatPt-1);
r = r3; sDat(slps,4)= sDat(slps,4); % rateS cutDatNum(4)=tc2LdatPt-2; %length(stdTimes(end-(lengthKlast-1):end));
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;
end end
Ints=[];
Tms=[];
Ints=ints';
Tms=tms';
try
filterTimes=Tms; filterTimes4=Tms;
normIntens=Ints; normIntens4=Ints;
filterTimes= Tms; % Classic symmetric logistic curve fit setup restated as COMMENTS for reference convenience
normIntens= Ints; % opts=fitoptions is the same as for Std and so is redundant
resMat(17)= .00002; % opts=fitoptions('Method','Nonlinear','Robust','On',...
resMat(18)= bl; % 'DiffMinChange',1.0E-11,'DiffMaxChange',0.001,...
resMat(19)= fitblELr; % '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]);
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 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); if (exist('K','var')&& exist('r','var') && exist('l','var'))
try t=(0:1:200);
if isempty(Tpt1) GrowthELr = K ./ (1 + exp(-r.* (t - l )));
Tpt1= 0.00002; %777; fitblELr= min(GrowthELr); %jh diag
end end
catch catch ME
Tpt1= 0.00002; %777; % if no data is given, return zeros
end 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; % Update values if r is better(higher) with removal of early data
numFitTpts= numFitTptsStd; try
numTptsGT2= numTptsGT2Std; if r3>r && stdNANcond==0
thresGT2Tm = thresGT2TmStd; 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 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))));
resMat(19)= fitblStd; %only applicable to Std curve Fit; ELr superceeds and makes meaningless MSR=r3;
resMat(20)= minTime; %only applicable to Std curve Fit; ELr superceeds and makes meaningless rsquare=errObj.rsquare;
end %if r3>r1 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 resMat(17)=Tpt1;
%asdf352 numFitTpts=numFitTptsStd;
% { numTptsGT2=numTptsGT2Std;
catch ME 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 % if no data is given, return zeros
AUC = 0;MSR = 0;K = 0;r = 0;l = 0;rsquare = 0;Klow = 0;Kup = 0; 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=[]; rlow=0;rup=0;lup=0;llow=0; % normIntens=[];
end
end %end Try resMat(1)=AUC;
%} resMat(2)=MSR;
resMat(1)= AUC; resMat(3)=K;
resMat(2)= MSR; resMat(4)=r;
resMat(3)= K; resMat(5)=l;
resMat(4)= r; resMat(6)=rsquare;
resMat(5)= l; resMat(7)=Klow;
resMat(6)= rsquare; resMat(8)=Kup;
resMat(7)= Klow; resMat(9)=rlow;
resMat(8)= Kup; resMat(10)=rup;
resMat(9)= rlow; resMat(11)=llow;
resMat(10)= rup; resMat(12)=lup;
resMat(11)= llow; resMat(13)=currSpotArea;
resMat(12)= lup; resMat(14)=lastIntensUsed; %filtNormIntens(length(filtNormIntens));
resMat(13)= currSpotArea; % spline fit unneccessary and removed therfor no max spline rate time->set 0
resMat(14)= lastIntensUsed; %filtNormIntens(length(filtNormIntens)); maxRateTime=0; % ELr will show 0; Std will show []
resMat(15)=maxRateTime;
%spline fit unneccessary and removed therfor no max spline rate time->set 0 resMat(16)=lastTptUsed; % filterTimes(length(filterTimes));
maxRateTime=0; %ELr will show 0; Std will show [] try % if Std fit used no cuts .:. no cutTm
resMat(15)= maxRateTime; resMat(24)=cutTm(1);
resMat(25)=cutTm(2);
resMat(16)= lastTptUsed; %filterTimes(length(filterTimes)); resMat(26)=cutTm(3);
resMat(27)=cutTm(4);
catch
try %if Std fit used no cuts .:. no cutTm resMat(24)=999; % if Std fit used no cuts .:. no cutTm
resMat(24)= cutTm(1); resMat(25)=999;
resMat(25)= cutTm(2); resMat(26)=999;
resMat(26)= cutTm(3); resMat(27)=999;
resMat(27)= cutTm(4); end
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;
%**********************************************************************************
%##########################################################################
FiltTimesELr=filterTimes;
NormIntensELr=normIntens;
lastTptUsed=max(filterTimes); lastTptUsed=max(filterTimes);
lastIntensUsed=normIntens(length(normIntens)); lastIntensUsed=normIntens(length(normIntens));
if (exist('K','var')&& exist('r','var') && exist('l','var')) if (exist('K','var')&& exist('r','var') && exist('l','var'))
t = (0:1:200); t=(0:1:200);
Growth = K ./ (1 + exp(-r.* (t - l ))); Growth=K ./ (1 + exp(-r.* (t - l )));
fitbl= min(Growth); %jh diag fitbl=min(Growth); % jh diag
end
try % jh diag
if isempty(thresGT2Tm)
thresGT2Tm=0
end end
%}
try
if isempty(thresGT2Tm),thresGT2Tm=0;end %jh diag
catch catch
thresGT2Tm= 0; thresGT2Tm=0;
numTptsGT2= 0; numTptsGT2=0;
end end
resMat(21)= thresGT2Tm; resMat(21)=thresGT2Tm;
resMat(22)= numFitTpts; resMat(22)=numFitTpts;
resMat(23)= numTptsGT2; resMat(23)=numTptsGT2;
end
end %function end