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Bryan Roessler
2024-09-10 13:47:29 -04:00
commit 8ebb6ad265
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qhtcp-workflow/apps/matlab/easy/NCscurImCF_3parfor.m Executable file
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%% CALLED BY NCfitImCFparforFailGbl2.m %%
function [resMatStd, resMat, selTimesStd, selIntensStd, FiltTimesELr, NormIntensELr] =...
NCscurImCF_3parfor(dataMatrix, AUCfinalTime, currSpotArea, sols, bl, minTime)
% 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=[];
normIntens=[];
nn=1;
numFitTpts=0;
% Build filterTimes and normIntens from spot dataMatrix selection codes produced in filter section
for n=1:size(dataMatrix,2)
if (((dataMatrix(3,n)==1))||(dataMatrix(3,n)==3)||(dataMatrix(3,n)==2)...
||(dataMatrix(3,n)==0))
filterTimes(nn)=dataMatrix(1,n);
normIntens(nn)=dataMatrix(4,n);
nn=nn+1;
end
end
filterTimes=filterTimes';
selTimesStd=filterTimes;
normIntens=normIntens';
selIntensStd=normIntens;
lastTptUsed=1;
lastIntensUsed=1;
thresGT2TmStd=0;
try
lastTptUsed=max(filterTimes);
lastIntensUsed=normIntens(length(normIntens));
lastIntensUsedStd=lastIntensUsed;
lastTptUsedStd=lastTptUsed;
Tpt1Std=filterTimes(1);
numFitTptsStd=nnz((normIntens(:)>=0)==1);
thresGT2=find(((normIntens(:)>2)==1), 1);
if isempty(thresGT2)
thresGT2TmStd=0;
else
thresGT2TmStd=filterTimes(thresGT2);
end
numTptsGT2Std=nnz((normIntens(:)>=2)==1); % nnz(filterTimes(find(filterTimes>=thresGT2Tm)));
K_Guess=max(normIntens);
numTimePts=length(filterTimes);
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],'Display','off');
ftype=fittype('K / (1 + exp(-r* (t - l )))','independent','t','dependent',['K','r','l'],'options',opts);
% Carry out the curve fitting process
[fitObject, errObj]=fit(filterTimes,normIntens,ftype);
coeffsArray=coeffvalues(fitObject);
rmsStg1=errObj.rsquare;
rmsStg1I(slps)=errObj.rsquare;
sDat(slps,1)=errObj.rsquare;
K=coeffsArray(1); sDat(slps,2)=coeffsArray(1); % Carrying Capacity
l=coeffsArray(2); sDat(slps,3)=coeffsArray(2); % lag time
r=coeffsArray(3); sDat(slps,4)=coeffsArray(3); % rateS
% Integrate (from first to last time point)
numVals=size(filterTimes);
numVals=numVals(1);
t_begin=0;
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))));
MSR=r;
rsquare=errObj.rsquare;
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
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; stdNANcond=1; % stdNANcond added to relay not to attempt ELr as there is no curve to find critical point
end
catch
% 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;
NANcond=1; stdNANcond=1; %stdNANcond added to relay not to attempt ELr as there is no curve to find critical point
end
if (exist('K','var')&& exist('r','var') && exist('l','var'))
t=(0:1:200);
Growth=K ./ (1 + exp(-r.* (t - l )));
fitblStd=min(Growth);
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
resMatStd(15)=0; %maxRateTimestdMeth;
resMatStd(16)=lastTptUsedStd;
if isempty(Tpt1Std)
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; % yhe Standard method has no cuts .:.no cutTm
resMatStd(25)=999;
resMatStd(26)=999;
resMatStd(27)=999;
% 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
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;
% 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]);
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
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
% 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
% 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;
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
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
% 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
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
end
try % jh diag
if isempty(thresGT2Tm)
thresGT2Tm=0
end
catch
thresGT2Tm=0;
numTptsGT2=0;
end
resMat(21)=thresGT2Tm;
resMat(22)=numFitTpts;
resMat(23)=numTptsGT2;
end