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Format NCscurImCF_3parfor.m

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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};
end
%*********************************19_1001***********************************
%To accomodate parfor copy par4scan thru global p4 functions inside of
%parfor loop --then outside to par4Gbl_Main8b.m
%**************************************************************************
% To accomodate parfor
% Copy par4scan thru global p4 functions inside of parfor loop --then outside to par4Gbl_Main8b.m
fileExt='.txt';
filePrefix='FitResultsComplete_';
fileNamePlate=[filePrefix fileSuffix fileExt];

133
workflow/templates/easy/NCscurImCF_3parfor.m
View File

@@ -1,25 +1,18 @@
%% CALLED BY NCfitImCFparforFailGbl2.m %%
function [resMatStd, resMat, selTimesStd, selIntensStd, FiltTimesELr, NormIntensELr] =...
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
mi=25;
miL=250;
%***********************************
rmsStg1=0;
rmsStg1I(1)=0;
slps=1;
filterTimes=[];
normIntens=[];
nn=1;
@@ -33,28 +26,17 @@ for n=1:size(dataMatrix,2)
nn=nn+1;
end
end
%------------------------------------------------------------------
%++++++++++++++++++++++++++++++++++++++++
filterTimes=filterTimes';
selTimesStd=filterTimes;
normIntens=normIntens';
selIntensStd=normIntens;
%normIntens %debugging parfor gbl 200330 good values
%afgj
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);
@@ -67,12 +49,12 @@ selIntensStd=normIntens;
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');
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
% Carry out the curve fitting process
[fitObject, errObj]=fit(filterTimes,normIntens,ftype);
coeffsArray=coeffvalues(fitObject);
rmsStg1=errObj.rsquare;
@@ -82,17 +64,15 @@ selIntensStd=normIntens;
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)
% 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);
@@ -104,34 +84,22 @@ selIntensStd=normIntens;
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
%rup %debugging parfor gbl 200330
%Klow
%asdfjj114
% {
catch %ME
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;
NANcond=1; stdNANcond=1; %stdNANcond added to relay not to attempt ELr as there is no curve to find critical point
end %end Try
%}
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); %jh diag
fitblStd=min(Growth);
end
cutTm(1:4)=1000; %-1 means cuts not used or NA
%{
l %debugging parfor
K
r
Klow
k
%}
%***Preserve for ResultsStd+++++++++++++++++++++++++++++
% Preserve for ResultsStd
resMatStd(1)=AUC;
resMatStd(2)=MSR;
resMatStd(3)=K;
@@ -147,18 +115,12 @@ 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));
resMatStd(16)=lastTptUsedStd;
if isempty(Tpt1Std)
Tpt1Std= 777; %0.000002; %
Tpt1Std=777;
end
resMatStd(17)=Tpt1Std;
resMatStd(18)=bl; %perform in the filter section of NCfitImCFparfor
@@ -167,13 +129,11 @@ 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
@@ -199,17 +159,13 @@ 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;
@@ -222,19 +178,16 @@ 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 *********
% 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
@@ -243,26 +196,18 @@ else
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 symetric logistic curve fit setup restated as COMMENTS for reference convenience----------------------------
% 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,...
@@ -280,29 +225,25 @@ try
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
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*************
% JH diagnostics
numFitTpts=nnz((normIntens(:)>=0)==1);
thresGT2=find(((normIntens(:)>2)==1), 1);
thresGT2Tm=filterTimes(thresGT2);
@@ -311,12 +252,9 @@ try
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);
@@ -327,7 +265,6 @@ try
if(isnan(Klow)||isnan(Kup)||isnan(llow)||isnan(lup)||isnan(rlow)||isnan(rup))
NANcond=1;
end
filterTimes=Tms;
normIntens=Ints;
resMat(17)=.00002;
@@ -337,9 +274,7 @@ try
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)
@@ -353,24 +288,17 @@ resMat(17)= Tpt1;
numFitTpts=numFitTptsStd;
numTptsGT2=numTptsGT2Std;
thresGT2Tm=thresGT2TmStd;
cutTm(1:4)= 1000; %-1 means cuts not used or NA
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
%rup
%asdf352
% {
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 %end Try
%}
resMat(1)=AUC;
resMat(2)=MSR;
resMat(3)=K;
@@ -385,14 +313,10 @@ 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);
@@ -407,10 +331,6 @@ end
FiltTimesELr=filterTimes;
NormIntensELr=normIntens;
%**********************************************************************************
%##########################################################################
lastTptUsed=max(filterTimes);
lastIntensUsed=normIntens(length(normIntens));
@@ -419,9 +339,10 @@ NormIntensELr= normIntens;
Growth=K ./ (1 + exp(-r.* (t - l )));
fitbl=min(Growth); % jh diag
end
%}
try
if isempty(thresGT2Tm),thresGT2Tm=0;end %jh diag
try % jh diag
if isempty(thresGT2Tm)
thresGT2Tm=0
end
catch
thresGT2Tm=0;
numTptsGT2=0;
@@ -430,8 +351,6 @@ NormIntensELr= normIntens;
resMat(21)=thresGT2Tm;
resMat(22)=numFitTpts;
resMat(23)=numTptsGT2;
end %function end
end