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Moment_of_Correlation_2DPIV_uncertainty/postprocess_codes/plot_MC_uncertainty.m

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function [covxMC,covyMC,covxIM,covyIM,covxCS,covyCS,covP,covD]=plot_MC_uncertainty(A,casename,outputdir,savematplot,printfig,runCS,runIM,runMC)
width=3;
height=3;
left=200;
bottom=200;
maxyl=40;
% Set the defaults for saving/printing to a file
set(0,'defaultFigureInvertHardcopy','on'); % This is the default anyway
set(0,'defaultFigurePaperUnits','inches'); % This is the default anyway
% % defsize = get(0, 'PaperSize');
% left = (defsize(1)- width)/2;
% bottom = (defsize(2)- height)/2;
defsize = [left, bottom, width, height];
set(0, 'defaultFigurePaperPosition', defsize);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if strcmp(casename{1},'PivChal03B')
% A=load(fullfile(outputdir{1},'PivChal03B.mat'));
%% Convert to vectors
err_up=A.err_up(:);
err_vp=A.err_vp(:);
err_ud=A.err_ud(:);
err_vd=A.err_vd(:);
UMCx=A.UMCx(:);
UMCy=A.UMCy(:);
UIMx=A.UIMx(:);
UIMy=A.UIMy(:);
UCSx=A.UCSx(:);
UCSy=A.UCSy(:);
%% Eliminate bad vectors
veccutoff=0.1;
inx1=(find(abs(err_up)>veccutoff));
iny1=(find(abs(err_vp)>veccutoff));
inx2=(find(abs(err_ud)>veccutoff));
iny2=(find(abs(err_vd)>veccutoff));
err_up(inx1)=[];
err_vp(iny1)=[];
UMCx(inx1)=[];
UMCy(iny1)=[];
UIMx(inx1)=[];
UIMy(iny1)=[];
err_ud(inx2)=[];
err_vd(iny2)=[];
UCSx(inx2)=[];
UCSy(iny2)=[];
%%
% err_up=err_up-mean(err_up);
% err_vp=err_vp-mean(err_vp);
%% Calculate coverage
ngmx1=length(err_up);
ngmy1=length(err_vp);
ngmx2=length(err_ud);
ngmy2=length(err_vd);
cnt=0;
for j=1:ngmx1
if err_up(j)<=UMCx(j) && err_up(j)>-UMCx(j)
cnt=cnt+1;
end
end
covxMC=100*cnt/ngmx1;
cnt=0;
for j=1:ngmy1
if err_vp(j)<=UMCy(j) && err_vp(j)>-UMCy(j)
cnt=cnt+1;
end
end
covyMC=100*cnt/ngmy1;
cnt=0;
for j=1:ngmx1
if err_up(j)<=UIMx(j) && err_up(j)>-UIMx(j)
cnt=cnt+1;
end
end
covxIM=100*cnt/ngmx1;
cnt=0;
for j=1:ngmy1
if err_vp(j)<=UIMy(j) && err_vp(j)>-UIMy(j)
cnt=cnt+1;
end
end
covyIM=100*cnt/ngmy1;
cnt=0;
for j=1:ngmx2
if err_ud(j)<=UCSx(j) && err_ud(j)>-UCSx(j)
cnt=cnt+1;
end
end
covxCS=100*cnt/ngmx2;
cnt=0;
for j=1:ngmy2
if err_vd(j)<=UCSy(j) && err_vd(j)>-UCSy(j)
cnt=cnt+1;
end
end
covyCS=100*cnt/ngmy2;
[covxMC covyMC covxIM covyIM covxCS covyCS]
% [rms(err_up) rms(err_vp) rms(err_ud) rms(err_vd)]
% keyboard;
%% Plot Functions call
%Plot X and Y error and uncertainty histogram together for all 3 metrics
error_prana=[err_up;err_vp];
error_davis=[err_ud;err_vd];
UMC=[UMCx;UMCy];
UIM=[UIMx;UIMy];
UCS=[UCSx;UCSy];
%% Find percentage of error within Std deviation error
cutoff=0.1;%0.1%0.5
%Prana
eP=error_prana;
eP(eP>cutoff)=[];
eP=eP-mean(eP);
ePstd=std(eP);
indexP=find(abs(eP)<=ePstd);
covP=100*length(indexP)/length(eP);
%DaVis
eD=error_davis;
eD(eD>cutoff)=[];
eD=eD-mean(eD);
eDstd=std(eD);
indexD=find(abs(eD)<=eDstd);
covD=100*length(indexD)/length(eD);
[covP covD]
%%
Nbin1=40;
ler=-0.1;uer=0.1;
Nbin2=60;
lun=0;uun=0.1;
vec=linspace(ler,uer,Nbin1);
Nep=histc(error_prana,vec);
Ned=histc(error_davis,vec);
vec2=linspace(lun,uun,Nbin2);
Nmc=histc(UMC,vec2);
Nim=histc(UIM,vec2);
Ncs=histc(UCS,vec2);
Lp=length(error_prana);
Ld=length(error_davis);
Nep=(Nep/Lp)*100;
Ned=(Ned/Ld)*100;
Nmc=(Nmc/Lp)*100;
Nim=(Nim/Lp)*100;
Ncs=(Ncs/Ld)*100;
lw=1.2;fs=20;
set(0,'DefaultAxesFontName', 'Times New Roman');
crimson=rgb('crimson');
dblue=rgb('deepskyblue');
blueviolet=rgb('blueviolet');
figure;
set(gcf,'DefaultLineLineWidth',lw);set(gca,'FontSize',fs);
hold on;
plot(vec,Nep,'k-');
plot(vec,Ned,'k--');
plot(vec2,Nmc,'r-','MarkerSize',4,'color',crimson);
plot(vec2,Nim,'c-','MarkerSize',4,'color',dblue);
plot(vec2,Ncs,'g-','MarkerSize',4,'color',blueviolet);
yl=ylim(gca);
ll=linspace(0,maxyl,10);
% ll2=1:100:max(0.75*yl(2));
sigprana=rms(error_prana(abs(error_prana)<=0.1)).*ones(length(ll),1);
sigdavis=rms(error_davis(abs(error_davis)<=0.1)).*ones(length(ll),1);
sigMC=rms(UMC(UMC<=0.1)).*ones(length(ll),1);
sigIM=rms(UIM(UIM<=0.1)).*ones(length(ll),1);
sigCS=rms(UCS(UCS<=0.1)).*ones(length(ll),1);
[mean(error_prana) mean(error_davis)]
plot(sigprana,ll,'k-');
plot(sigdavis,ll,'k--');
plot(sigMC,ll,'r-','MarkerSize',4,'color',crimson);
plot(sigIM,ll,'c-.','MarkerSize',4,'color',dblue);
plot(sigCS,ll,'g--','MarkerSize',4,'color',blueviolet);
hold off;
% ylabel('No. of Data Points');
% xlabel('Error and Uncertainty (pixels)');
% lbox=legend('e_{prana}','e_{davis}','MC','PD','CS','location','northeast');
% set(lbox,'FontSize',16);
% title('Turbulent Boundary Layer \newline');
grid on;box on;
set(gcf,'color','white');
% axis square;
hold off;
if printfig==1
export_fig(gcf,fullfile(outputdir{2},[casename{1},'_Histogram.png']),'-painters','-r360');
end
if savematplot==1
save(fullfile(outputdir{1},'PivChal03B_hist.mat'),'vec','Nep','Ned','vec2','Nmc','Nim','Ncs','ll','sigprana','sigdavis','sigMC','sigIM','sigCS');
end
[sigprana(1) sigdavis(1) sigMC(1) sigIM(1) sigCS(1)]
%% Plot RMS error in uncertainty bins scatter plot
% For MC
Nbin3=8;
Ubin1=linspace(0, 0.08, Nbin3);
numcx1=zeros(Nbin3,1);
covxbin1=zeros(Nbin3,1);
rmserru1=zeros(Nbin3,1);
rmsMC=zeros(Nbin3,1);
for p=1:length(Ubin1)
if p<length(Ubin1)
[val1]= find((UMC<Ubin1(p+1)) & (UMC>=Ubin1(p)));
elseif p==length(Ubin1)
[val1]= find((UMC>=Ubin1(p)));
end
numcx1(p)=(length(val1));
tmperr=(error_prana(val1));
tempMC=UMC(val1);
cvrx1=find(tmperr<tempMC);
covxbin1(p)=100*length(cvrx1)/numcx1(p);
rmserru1(p)=rms(tmperr);
rmsMC(p)=rms(tempMC);
end
%%%%%%% FOR IM
Ubin2=linspace(0, 0.08, Nbin3);
numcx2=zeros(Nbin3,1);
covxbin2=zeros(Nbin3,1);
rmserru2=zeros(Nbin3,1);
rmsIM=zeros(Nbin3,1);
for p=1:length(Ubin2)
if p<length(Ubin2)
[val1]= find((UIM<Ubin2(p+1)) & (UIM>=Ubin2(p)));
elseif p==length(Ubin2)
[val1]= find((UIM>=Ubin2(p)));
end
numcx2(p)=(length(val1));
tmperr=(error_prana(val1));
tempMC=UIM(val1);
cvrx1=find(tmperr<tempMC);
covxbin2(p)=100*length(cvrx1)/numcx2(p);
rmserru2(p)=rms(tmperr);
rmsIM(p)=rms(tempMC);
end
%%%%% FOR CS
Ubin3=linspace(0, 0.08, Nbin3);
numcx3=zeros(Nbin3,1);
covxbin3=zeros(Nbin3,1);
rmserru3=zeros(Nbin3,1);
rmsCS=zeros(Nbin3,1);
for p=1:length(Ubin3)
if p<length(Ubin3)
[val1]= find((UCS<Ubin3(p+1)) & (UCS>=Ubin3(p)));
elseif p==length(Ubin3)
[val1]= find((UCS>=Ubin3(p)));
end
numcx3(p)=(length(val1));
tmperr=(error_davis(val1));
tempMC=UCS(val1);
cvrx1=find(tmperr<tempMC);
covxbin3(p)=100*length(cvrx1)/numcx3(p);
rmserru3(p)=rms(tmperr);
rmsCS(p)=rms(tempMC);
end
% Plots
figure;
set(gcf,'DefaultLineLineWidth',lw);set(gca,'FontSize',fs);
hold on;
plot(rmserru1(1:end-1),rmsMC(1:end-1),'r-o','color',crimson);
plot(rmserru2(1:end-1),rmsIM(1:end-1),'b-o','color',dblue);
plot(rmserru3(1:end-1),rmsCS(1:end-1),'b-o','color',blueviolet);
xl=xlim(gca);
plot(linspace(xl(1),xl(2),10),linspace(xl(1),xl(2),10),'k-');
% plot(rmserru3(1:end-1),rmserru3(1:end-1),'k--');
hold off;
% axis([0 0.06 0 0.1]);
% xlabel('RMS error(pixels)');
% ylabel('RMS Uncertainty in each bin(pixels)');
% lbox=legend('MC','PD','CS','e_{RMS}','location','northwest');
% set(lbox,'FontSize',16);
% title('Turbulent Boundary Layer \newline');
grid on;box on;
set(gcf,'color','white');
axis square;
hold off;
if printfig==1
export_fig(gcf,fullfile(outputdir{2},[casename{1},'_RMSerroruncertainty.png']),'-painters','-r360');
end
if savematplot==1
save(fullfile(outputdir{1},'PivChal03B_RMS.mat'),'rmserru1','rmserru2','rmserru3','rmsMC','rmsIM','rmsCS');
end
% keyboard;
%% Plot Covergae Histogram
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if strcmp(casename{2},'PivChal05B');
% A=load(fullfile(outputdir{1},'PivChal03B.mat'));
%% Convert to vectors
err_up=A.err_up(:);
err_vp=A.err_vp(:);
err_ud=A.err_ud(:);
err_vd=A.err_vd(:);
UMCx=A.UMCx(:);
UMCy=A.UMCy(:);
UIMx=A.UIMx(:);
UIMy=A.UIMy(:);
UCSx=A.UCSx(:);
UCSy=A.UCSy(:);
%% Eliminate bad vectors
veccutoff=0.1;
inx1=(find(abs(err_up)>veccutoff));
iny1=(find(abs(err_vp)>veccutoff));
inx2=(find(abs(err_ud)>veccutoff));
iny2=(find(abs(err_vd)>veccutoff));
err_up(inx1)=[];
err_vp(iny1)=[];
UMCx(inx1)=[];
UMCy(iny1)=[];
UIMx(inx1)=[];
UIMy(iny1)=[];
err_ud(inx2)=[];
err_vd(iny2)=[];
UCSx(inx2)=[];
UCSy(iny2)=[];
%% Calculate coverage
ngmx1=length(err_up);
ngmy1=length(err_vp);
ngmx2=length(err_ud);
ngmy2=length(err_vd);
cnt=0;
for j=1:ngmx1
if err_up(j)<=UMCx(j) && err_up(j)>-UMCx(j)
cnt=cnt+1;
end
end
covxMC=100*cnt/ngmx1;
cnt=0;
for j=1:ngmy1
if err_vp(j)<=UMCy(j) && err_vp(j)>-UMCy(j)
cnt=cnt+1;
end
end
covyMC=100*cnt/ngmy1;
cnt=0;
for j=1:ngmx1
if err_up(j)<=UIMx(j) && err_up(j)>-UIMx(j)
cnt=cnt+1;
end
end
covxIM=100*cnt/ngmx1;
cnt=0;
for j=1:ngmy1
if err_vp(j)<=UIMy(j) && err_vp(j)>-UIMy(j)
cnt=cnt+1;
end
end
covyIM=100*cnt/ngmy1;
cnt=0;
for j=1:ngmx2
if err_ud(j)<=UCSx(j) && err_ud(j)>-UCSx(j)
cnt=cnt+1;
end
end
covxCS=100*cnt/ngmx2;
cnt=0;
for j=1:ngmy2
if err_vd(j)<=UCSy(j) && err_vd(j)>-UCSy(j)
cnt=cnt+1;
end
end
covyCS=100*cnt/ngmy2;
[covxMC covyMC covxIM covyIM covxCS covyCS]
% [rms(err_up) rms(err_vp) rms(err_ud) rms(err_vd)]
% keyboard;
%% Plot Functions call
%Plot X and Y error and uncertainty histogram together for all 3 metrics
error_prana=[err_up;err_vp];
error_davis=[err_ud;err_vd];
UMC=[UMCx;UMCy];
UIM=[UIMx;UIMy];
UCS=[UCSx;UCSy];
%% Find percentage of error within Std deviation error
cutoff=0.1;%0.1
%Prana
eP=error_prana;
eP(eP>cutoff)=[];
eP=eP-mean(eP);
ePstd=std(eP);
indexP=find(abs(eP)<=ePstd);
covP=100*length(indexP)/length(eP);
%DaVis
eD=error_davis;
eD(eD>cutoff)=[];
eD=eD-mean(eD);
eDstd=std(eD);
indexD=find(abs(eD)<=eDstd);
covD=100*length(indexD)/length(eD);
[covP covD]
%%
Nbin1=40;
ler=-0.1;uer=0.1;
Nbin2=60;
lun=0;uun=0.1;
vec=linspace(ler,uer,Nbin1);
Nep=histc(error_prana,vec);
Ned=histc(error_davis,vec);
vec2=linspace(lun,uun,Nbin2);
Nmc=histc(UMC,vec2);
Nim=histc(UIM,vec2);
Ncs=histc(UCS,vec2);
Lp=length(error_prana);
Ld=length(error_davis);
Nep=(Nep/Lp)*100;
Ned=(Ned/Ld)*100;
Nmc=(Nmc/Lp)*100;
Nim=(Nim/Lp)*100;
Ncs=(Ncs/Ld)*100;
lw=1.2;fs=20;
set(0,'DefaultAxesFontName', 'Times New Roman');
crimson=rgb('crimson');
dblue=rgb('deepskyblue');
blueviolet=rgb('blueviolet');
figure;
set(gcf,'DefaultLineLineWidth',lw);set(gca,'FontSize',fs);
hold on;
plot(vec,Nep,'k-');
plot(vec,Ned,'k--');
plot(vec2,Nmc,'r-','MarkerSize',4,'color',crimson);
plot(vec2,Nim,'c-','MarkerSize',4,'color',dblue);
plot(vec2,Ncs,'g-','MarkerSize',4,'color',blueviolet);
yl=ylim(gca);
ll=linspace(0,maxyl,10);
% ll2=1:100:max(0.75*yl(2));
sigprana=rms(error_prana(abs(error_prana)<=0.1)).*ones(length(ll),1);
sigdavis=rms(error_davis(abs(error_davis)<=0.1)).*ones(length(ll),1);
sigMC=rms(UMC(UMC<=0.1)).*ones(length(ll),1);
sigIM=rms(UIM(UIM<=0.1)).*ones(length(ll),1);
sigCS=rms(UCS(UCS<=0.1)).*ones(length(ll),1);
[mean(error_prana) mean(error_davis)]
plot(sigprana,ll,'k-');
plot(sigdavis,ll,'k--');
plot(sigMC,ll,'r-','MarkerSize',4,'color',crimson);
plot(sigIM,ll,'c-.','MarkerSize',4,'color',dblue);
plot(sigCS,ll,'g--','MarkerSize',4,'color',blueviolet);
hold off;
% ylabel('No. of Data Points');
% xlabel('Error and Uncertainty (pixels)');
% lbox=legend('e_{prana}','e_{davis}','MC','PD','CS','location','northeast');
% set(lbox,'FontSize',16);
% title('Laminar Separation Bubble \newline');
grid on;box on;
set(gcf,'color','white');
% axis square;
hold off;
if printfig==1
export_fig(gcf,fullfile(outputdir{2},[casename{2},'_Histogram.png']),'-painters','-r360');
end
if savematplot==1
save(fullfile(outputdir{1},'PivChal05B_hist.mat'),'vec','Nep','Ned','vec2','Nmc','Nim','Ncs','ll','sigprana','sigdavis','sigMC','sigIM','sigCS');
end
[sigprana(1) sigdavis(1) sigMC(1) sigIM(1) sigCS(1)]
%% Plot RMS error in uncertainty bins scatter plot
% For MC
Nbin3=8;
Ubin1=linspace(0, 0.08, Nbin3);
numcx1=zeros(Nbin3,1);
covxbin1=zeros(Nbin3,1);
rmserru1=zeros(Nbin3,1);
rmsMC=zeros(Nbin3,1);
for p=1:length(Ubin1)
if p<length(Ubin1)
[val1]= find((UMC<Ubin1(p+1)) & (UMC>=Ubin1(p)));
elseif p==length(Ubin1)
[val1]= find((UMC>=Ubin1(p)));
end
numcx1(p)=(length(val1));
tmperr=(error_prana(val1));
tempMC=UMC(val1);
cvrx1=find(tmperr<tempMC);
covxbin1(p)=100*length(cvrx1)/numcx1(p);
rmserru1(p)=rms(tmperr);
rmsMC(p)=rms(tempMC);
end
%%%%%%% FOR IM
Ubin2=linspace(0, 0.08, Nbin3);
numcx2=zeros(Nbin3,1);
covxbin2=zeros(Nbin3,1);
rmserru2=zeros(Nbin3,1);
rmsIM=zeros(Nbin3,1);
for p=1:length(Ubin2)
if p<length(Ubin2)
[val1]= find((UIM<Ubin2(p+1)) & (UIM>=Ubin2(p)));
elseif p==length(Ubin2)
[val1]= find((UIM>=Ubin2(p)));
end
numcx2(p)=(length(val1));
tmperr=(error_prana(val1));
tempMC=UIM(val1);
cvrx1=find(tmperr<tempMC);
covxbin2(p)=100*length(cvrx1)/numcx2(p);
rmserru2(p)=rms(tmperr);
rmsIM(p)=rms(tempMC);
end
%%%%% FOR CS
Ubin3=linspace(0, 0.08, Nbin3);
numcx3=zeros(Nbin3,1);
covxbin3=zeros(Nbin3,1);
rmserru3=zeros(Nbin3,1);
rmsCS=zeros(Nbin3,1);
for p=1:length(Ubin3)
if p<length(Ubin3)
[val1]= find((UCS<Ubin3(p+1)) & (UCS>=Ubin3(p)));
elseif p==length(Ubin3)
[val1]= find((UCS>=Ubin3(p)));
end
numcx3(p)=(length(val1));
tmperr=(error_davis(val1));
tempMC=UCS(val1);
cvrx1=find(tmperr<tempMC);
covxbin3(p)=100*length(cvrx1)/numcx3(p);
rmserru3(p)=rms(tmperr);
rmsCS(p)=rms(tempMC);
end
% Plots
figure;
set(gcf,'DefaultLineLineWidth',lw);set(gca,'FontSize',fs);
hold on;
plot(rmserru1(1:end-1),rmsMC(1:end-1),'r-o','color',crimson);
plot(rmserru2(1:end-1),rmsIM(1:end-1),'b-o','color',dblue);
plot(rmserru3(1:end-1),rmsCS(1:end-1),'b-o','color',blueviolet);
xl=xlim(gca);
plot(linspace(xl(1),xl(2),10),linspace(xl(1),xl(2),10),'k-');
% plot(rmserru3(1:end-1),rmserru3(1:end-1),'k--');
hold off;
axis([0 0.06 0 0.1]);
% xlabel('RMS error(pixels)');
% ylabel('RMS Uncertainty in each bin(pixels)');
% lbox=legend('MC','PD','CS','e_{RMS}','location','northwest');
% set(lbox,'FontSize',16);
% title('Laminar Separation Bubble \newline');
grid on;box on;
set(gcf,'color','white');
% axis square;
hold off;
if printfig==1
export_fig(gcf,fullfile(outputdir{2},[casename{2},'_RMSerroruncertainty.png']),'-painters','-r360');
end
if savematplot==1
save(fullfile(outputdir{1},'PivChal05B_RMS.mat'),'rmserru1','rmserru2','rmserru3','rmsMC','rmsIM','rmsCS');
end
%%
% keyboard;
%% Plot Covergae Histogram
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if strcmp(casename{3},'stagnation_flow');
% A=load(fullfile(outputdir{1},'PivChal03B.mat'));
%% Convert to vectors
err_up=A.err_up(:);
err_vp=A.err_vp(:);
err_ud=A.err_ud(:);
err_vd=A.err_vd(:);
UMCx=A.UMCx(:);
UMCy=A.UMCy(:);
UIMx=A.UIMx(:);
UIMy=A.UIMy(:);
UCSx=A.UCSx(:);
UCSy=A.UCSy(:);
%% Eliminate bad vectors
veccutoff=0.3;
inx1=(find(abs(err_up)>veccutoff));
iny1=(find(abs(err_vp)>veccutoff));
inx2=(find(abs(err_ud)>veccutoff));
iny2=(find(abs(err_vd)>veccutoff));
err_up(inx1)=[];
err_vp(iny1)=[];
UMCx(inx1)=[];
UMCy(iny1)=[];
UIMx(inx1)=[];
UIMy(iny1)=[];
err_ud(inx2)=[];
err_vd(iny2)=[];
UCSx(inx2)=[];
UCSy(iny2)=[];
%% Calculate coverage
ngmx1=length(err_up);
ngmy1=length(err_vp);
ngmx2=length(err_ud);
ngmy2=length(err_vd);
cnt=0;
for j=1:ngmx1
if err_up(j)<=UMCx(j) && err_up(j)>-UMCx(j)
cnt=cnt+1;
end
end
covxMC=100*cnt/ngmx1;
cnt=0;
for j=1:ngmy1
if err_vp(j)<=UMCy(j) && err_vp(j)>-UMCy(j)
cnt=cnt+1;
end
end
covyMC=100*cnt/ngmy1;
cnt=0;
for j=1:ngmx1
if err_up(j)<=UIMx(j) && err_up(j)>-UIMx(j)
cnt=cnt+1;
end
end
covxIM=100*cnt/ngmx1;
cnt=0;
for j=1:ngmy1
if err_vp(j)<=UIMy(j) && err_vp(j)>-UIMy(j)
cnt=cnt+1;
end
end
covyIM=100*cnt/ngmy1;
cnt=0;
for j=1:ngmx2
if err_ud(j)<=UCSx(j) && err_ud(j)>-UCSx(j)
cnt=cnt+1;
end
end
covxCS=100*cnt/ngmx2;
cnt=0;
for j=1:ngmy2
if err_vd(j)<=UCSy(j) && err_vd(j)>-UCSy(j)
cnt=cnt+1;
end
end
covyCS=100*cnt/ngmy2;
[covxMC covyMC covxIM covyIM covxCS covyCS]
% [rms(err_up) rms(err_vp) rms(err_ud) rms(err_vd)]
% keyboard;
%% Plot Functions call
%Plot X and Y error and uncertainty histogram together for all 3 metrics
error_prana=[err_up;err_vp];
error_davis=[err_ud;err_vd];
UMC=[UMCx;UMCy];
UIM=[UIMx;UIMy];
UCS=[UCSx;UCSy];
%% Find percentage of error within Std deviation error
cutoff=0.2;%0.2
%Prana
eP=error_prana;
eP(eP>cutoff)=[];
eP=eP-mean(eP);
ePstd=std(eP);
indexP=find(abs(eP)<=ePstd);
covP=100*length(indexP)/length(eP);
%DaVis
eD=error_davis;
eD(eD>cutoff)=[];
eD=eD-mean(eD);
eDstd=std(eD);
indexD=find(abs(eD)<=eDstd);
covD=100*length(indexD)/length(eD);
[covP covD]
%%
Nbin1=40;
ler=-0.3;uer=0.3;
Nbin2=60;
lun=0;uun=0.3;
vec=linspace(ler,uer,Nbin1);
Nep=histc(error_prana,vec);
Ned=histc(error_davis,vec);
vec2=linspace(lun,uun,Nbin2);
Nmc=histc(UMC,vec2);
Nim=histc(UIM,vec2);
Ncs=histc(UCS,vec2);
Lp=length(error_prana);
Ld=length(error_davis);
Nep=(Nep/Lp)*100;
Ned=(Ned/Ld)*100;
Nmc=(Nmc/Lp)*100;
Nim=(Nim/Lp)*100;
Ncs=(Ncs/Ld)*100;
lw=1.2;fs=20;
set(0,'DefaultAxesFontName', 'Times New Roman');
crimson=rgb('crimson');
dblue=rgb('deepskyblue');
blueviolet=rgb('blueviolet');
figure;
set(gcf,'DefaultLineLineWidth',lw);set(gca,'FontSize',fs);
hold on;
plot(vec,Nep,'k-');
plot(vec,Ned,'k--');
plot(vec2,Nmc,'r-','MarkerSize',4,'color',crimson);
plot(vec2,Nim,'c-','MarkerSize',4,'color',dblue);
plot(vec2,Ncs,'g-','MarkerSize',4,'color',blueviolet);
yl=ylim(gca);
ll=linspace(0,maxyl,10);
% ll2=1:100:max(0.75*yl(2));
sigprana=rms(error_prana(abs(error_prana)<=0.2)).*ones(length(ll),1);
sigdavis=rms(error_davis(abs(error_davis)<=0.2)).*ones(length(ll),1);
sigMC=rms(UMC(UMC<=0.2)).*ones(length(ll),1);
sigIM=rms(UIM(UIM<=0.2)).*ones(length(ll),1);
sigCS=rms(UCS(UCS<=0.2)).*ones(length(ll),1);
[mean(error_prana) mean(error_davis)]
plot(sigprana,ll,'k-');
plot(sigdavis,ll,'k--');
plot(sigMC,ll,'r-','MarkerSize',4,'color',crimson);
plot(sigIM,ll,'c-.','MarkerSize',4,'color',dblue);
plot(sigCS,ll,'g--','MarkerSize',4,'color',blueviolet);
hold off;
% ylabel('No. of Data Points');
% xlabel('Error and Uncertainty (pixels)');
% lbox=legend('e_{prana}','e_{davis}','MC','PD','CS','location','northeast');
% set(lbox,'FontSize',16);
% title('Stagnation Flow \newline');
grid on;box on;
set(gcf,'color','white');
% axis square;
hold off;
if printfig==1
export_fig(gcf,fullfile(outputdir{2},[casename{3},'_Histogram.png']),'-painters','-r360');
end
if savematplot==1
save(fullfile(outputdir{1},'stagnation_flow_hist.mat'),'vec','Nep','Ned','vec2','Nmc','Nim','Ncs','ll','sigprana','sigdavis','sigMC','sigIM','sigCS');
end
[sigprana(1) sigdavis(1) sigMC(1) sigIM(1) sigCS(1)]
%% Plot RMS error in uncertainty bins scatter plot
% For MC
Nbin3=8;
Ubin1=linspace(0, 0.3, Nbin3);
numcx1=zeros(Nbin3,1);
covxbin1=zeros(Nbin3,1);
rmserru1=zeros(Nbin3,1);
rmsMC=zeros(Nbin3,1);
for p=1:length(Ubin1)
if p<length(Ubin1)
[val1]= find((UMC<Ubin1(p+1)) & (UMC>=Ubin1(p)));
elseif p==length(Ubin1)
[val1]= find((UMC>=Ubin1(p)));
end
numcx1(p)=(length(val1));
tmperr=(error_prana(val1));
tempMC=UMC(val1);
cvrx1=find(tmperr<tempMC);
covxbin1(p)=100*length(cvrx1)/numcx1(p);
rmserru1(p)=rms(tmperr);
rmsMC(p)=rms(tempMC);
end
%%%%%%% FOR IM
Ubin2=linspace(0, 0.3, Nbin3);
numcx2=zeros(Nbin3,1);
covxbin2=zeros(Nbin3,1);
rmserru2=zeros(Nbin3,1);
rmsIM=zeros(Nbin3,1);
for p=1:length(Ubin2)
if p<length(Ubin2)
[val1]= find((UIM<Ubin2(p+1)) & (UIM>=Ubin2(p)));
elseif p==length(Ubin2)
[val1]= find((UIM>=Ubin2(p)));
end
numcx2(p)=(length(val1));
tmperr=(error_prana(val1));
tempMC=UIM(val1);
cvrx1=find(tmperr<tempMC);
covxbin2(p)=100*length(cvrx1)/numcx2(p);
rmserru2(p)=rms(tmperr);
rmsIM(p)=rms(tempMC);
end
%%%%% FOR CS
Ubin3=linspace(0, 0.3, Nbin3);
numcx3=zeros(Nbin3,1);
covxbin3=zeros(Nbin3,1);
rmserru3=zeros(Nbin3,1);
rmsCS=zeros(Nbin3,1);
for p=1:length(Ubin3)
if p<length(Ubin3)
[val1]= find((UCS<Ubin3(p+1)) & (UCS>=Ubin3(p)));
elseif p==length(Ubin3)
[val1]= find((UCS>=Ubin3(p)));
end
numcx3(p)=(length(val1));
tmperr=(error_davis(val1));
tempMC=UCS(val1);
cvrx1=find(tmperr<tempMC);
covxbin3(p)=100*length(cvrx1)/numcx3(p);
rmserru3(p)=rms(tmperr);
rmsCS(p)=rms(tempMC);
end
% Plots
figure;
set(gcf,'DefaultLineLineWidth',lw);set(gca,'FontSize',fs);
hold on;
plot(rmserru1(1:end-1),rmsMC(1:end-1),'r-o','color',crimson);
plot(rmserru2(1:end-1),rmsIM(1:end-1),'b-o','color',dblue);
plot(rmserru3(1:end-1),rmsCS(1:end-1),'b-o','color',blueviolet);
xl=xlim(gca);
plot(linspace(xl(1),xl(2),10),linspace(xl(1),xl(2),10),'k-');
% plot(rmserru3(1:end-1),rmserru3(1:end-1),'k--');
hold off;
% axis([0 0.06 0 0.1]);
% xlabel('RMS error(pixels)');
% ylabel('RMS Uncertainty in each bin(pixels)');
% lbox=legend('MC','PD','CS','e_{RMS}','location','northwest');
% set(lbox,'FontSize',16);
% title('Stagnation Flow \newline');
grid on;box on;
set(gcf,'color','white');
% axis square;
hold off;
if printfig==1
export_fig(gcf,fullfile(outputdir{2},[casename{3},'_RMSerroruncertainty.png']),'-painters','-r360');
end
if savematplot==1
save(fullfile(outputdir{1},'stagnation_flow_RMS.mat'),'rmserru1','rmserru2','rmserru3','rmsMC','rmsIM','rmsCS');
end
%%
% keyboard;
%% Plot Covergae Histogram
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if strcmp(casename{4},'Vortex_Ring');
% A=load(fullfile(outputdir{1},'PivChal03B.mat'));
%% Convert to vectors
err_up=A.err_up(:);
err_vp=A.err_vp(:);
err_ud=A.err_ud(:);
err_vd=A.err_vd(:);
UMCx=A.UMCx(:);
UMCy=A.UMCy(:);
UIMx=A.UIMx(:);
UIMy=A.UIMy(:);
UCSx=A.UCSx(:);
UCSy=A.UCSy(:);
%% Eliminate bad vectors
veccutoff=0.2;
inx1=(find(abs(err_up)>veccutoff));
iny1=(find(abs(err_vp)>veccutoff));
inx2=(find(abs(err_ud)>veccutoff));
iny2=(find(abs(err_vd)>veccutoff));
err_up(inx1)=[];
err_vp(iny1)=[];
UMCx(inx1)=[];
UMCy(iny1)=[];
UIMx(inx1)=[];
UIMy(iny1)=[];
err_ud(inx2)=[];
err_vd(iny2)=[];
UCSx(inx2)=[];
UCSy(iny2)=[];
%% Calculate coverage
ngmx1=length(err_up);
ngmy1=length(err_vp);
ngmx2=length(err_ud);
ngmy2=length(err_vd);
cnt=0;
for j=1:ngmx1
if err_up(j)<=UMCx(j) && err_up(j)>-UMCx(j)
cnt=cnt+1;
end
end
covxMC=100*cnt/ngmx1;
cnt=0;
for j=1:ngmy1
if err_vp(j)<=UMCy(j) && err_vp(j)>-UMCy(j)
cnt=cnt+1;
end
end
covyMC=100*cnt/ngmy1;
cnt=0;
for j=1:ngmx1
if err_up(j)<=UIMx(j) && err_up(j)>-UIMx(j)
cnt=cnt+1;
end
end
covxIM=100*cnt/ngmx1;
cnt=0;
for j=1:ngmy1
if err_vp(j)<=UIMy(j) && err_vp(j)>-UIMy(j)
cnt=cnt+1;
end
end
covyIM=100*cnt/ngmy1;
cnt=0;
for j=1:ngmx2
if err_ud(j)<=UCSx(j) && err_ud(j)>-UCSx(j)
cnt=cnt+1;
end
end
covxCS=100*cnt/ngmx2;
cnt=0;
for j=1:ngmy2
if err_vd(j)<=UCSy(j) && err_vd(j)>-UCSy(j)
cnt=cnt+1;
end
end
covyCS=100*cnt/ngmy2;
[covxMC covyMC covxIM covyIM covxCS covyCS]
% [rms(err_up) rms(err_vp) rms(err_ud) rms(err_vd)]
% keyboard;
%% Plot Functions call
%Plot X and Y error and uncertainty histogram together for all 3 metrics
error_prana=[err_up;err_vp];
error_davis=[err_ud;err_vd];
UMC=[UMCx;UMCy];
UIM=[UIMx;UIMy];
UCS=[UCSx;UCSy];
%% Find percentage of error within Std deviation error
cutoff=0.2;%0.2
%Prana
eP=error_prana;
eP(eP>cutoff)=[];
eP=eP-mean(eP);
ePstd=std(eP);
indexP=find(abs(eP)<=ePstd);
covP=100*length(indexP)/length(eP);
%DaVis
eD=error_davis;
eD(eD>cutoff)=[];
eD=eD-mean(eD);
eDstd=std(eD);
indexD=find(abs(eD)<=eDstd);
covD=100*length(indexD)/length(eD);
[covP covD]
%%
Nbin1=40;
ler=-0.2;uer=0.2;
Nbin2=60;
lun=0;uun=0.2;
vec=linspace(ler,uer,Nbin1);
Nep=histc(error_prana,vec);
Ned=histc(error_davis,vec);
vec2=linspace(lun,uun,Nbin2);
Nmc=histc(UMC,vec2);
Nim=histc(UIM,vec2);
Ncs=histc(UCS,vec2);
Lp=length(error_prana);
Ld=length(error_davis);
Nep=(Nep/Lp)*100;
Ned=(Ned/Ld)*100;
Nmc=(Nmc/Lp)*100;
Nim=(Nim/Lp)*100;
Ncs=(Ncs/Ld)*100;
lw=1.2;fs=20;
set(0,'DefaultAxesFontName', 'Times New Roman');
crimson=rgb('crimson');
dblue=rgb('deepskyblue');
blueviolet=rgb('blueviolet');
figure;
set(gcf,'DefaultLineLineWidth',lw);set(gca,'FontSize',fs);
hold on;
plot(vec,Nep,'k-');
plot(vec,Ned,'k--');
plot(vec2,Nmc,'r-','MarkerSize',4,'color',crimson);
plot(vec2,Nim,'c-','MarkerSize',4,'color',dblue);
plot(vec2,Ncs,'g-','MarkerSize',4,'color',blueviolet);
yl=ylim(gca);
ll=linspace(0,maxyl,10);
% ll2=1:100:max(0.75*yl(2));
sigprana=rms(error_prana(abs(error_prana)<=0.2)).*ones(length(ll),1);
sigdavis=rms(error_davis(abs(error_davis)<=0.2)).*ones(length(ll),1);
sigMC=rms(UMC(UMC<=0.2)).*ones(length(ll),1);
sigIM=rms(UIM(UIM<=0.2)).*ones(length(ll),1);
sigCS=rms(UCS(UCS<=0.2)).*ones(length(ll),1);
[mean(error_prana(abs(error_prana)<=0.2)) mean(error_davis(abs(error_davis)<=0.2))]
plot(sigprana,ll,'k-');
plot(sigdavis,ll,'k--');
plot(sigMC,ll,'r-','MarkerSize',4,'color',crimson);
plot(sigIM,ll,'c-.','MarkerSize',4,'color',dblue);
plot(sigCS,ll,'g--','MarkerSize',4,'color',blueviolet);
hold off;
% ylabel('No. of Data Points');
% xlabel('Error and Uncertainty (pixels)');
% lbox=legend('e_{prana}','e_{davis}','MC','PD','CS','location','northeast');
% set(lbox,'FontSize',16);
% title('Vortex Ring \newline');
grid on;box on;
set(gcf,'color','white');
% axis square;
hold off;
if printfig==1
export_fig(gcf,fullfile(outputdir{2},[casename{4},'_Histogram.png']),'-painters','-r360');
end
if savematplot==1
save(fullfile(outputdir{1},'Vortex_Ring_hist.mat'),'vec','Nep','Ned','vec2','Nmc','Nim','Ncs','ll','sigprana','sigdavis','sigMC','sigIM','sigCS');
end
[sigprana(1) sigdavis(1) sigMC(1) sigIM(1) sigCS(1)]
%% Plot RMS error in uncertainty bins scatter plot
% For MC
Nbin3=8;
Ubin1=linspace(0, 0.1, Nbin3);
numcx1=zeros(Nbin3,1);
covxbin1=zeros(Nbin3,1);
rmserru1=zeros(Nbin3,1);
rmsMC=zeros(Nbin3,1);
for p=1:length(Ubin1)
if p<length(Ubin1)
[val1]= find((UMC<Ubin1(p+1)) & (UMC>=Ubin1(p)));
elseif p==length(Ubin1)
[val1]= find((UMC>=Ubin1(p)));
end
numcx1(p)=(length(val1));
tmperr=(error_prana(val1));
tempMC=UMC(val1);
cvrx1=find(tmperr<tempMC);
covxbin1(p)=100*length(cvrx1)/numcx1(p);
rmserru1(p)=rms(tmperr);
rmsMC(p)=rms(tempMC);
end
%%%%%%% FOR IM
Ubin2=linspace(0, 0.1, Nbin3);
numcx2=zeros(Nbin3,1);
covxbin2=zeros(Nbin3,1);
rmserru2=zeros(Nbin3,1);
rmsIM=zeros(Nbin3,1);
for p=1:length(Ubin2)
if p<length(Ubin2)
[val1]= find((UIM<Ubin2(p+1)) & (UIM>=Ubin2(p)));
elseif p==length(Ubin2)
[val1]= find((UIM>=Ubin2(p)));
end
numcx2(p)=(length(val1));
tmperr=(error_prana(val1));
tempMC=UIM(val1);
cvrx1=find(tmperr<tempMC);
covxbin2(p)=100*length(cvrx1)/numcx2(p);
rmserru2(p)=rms(tmperr);
rmsIM(p)=rms(tempMC);
end
%%%%% FOR CS
Ubin3=linspace(0, 0.1, Nbin3);
numcx3=zeros(Nbin3,1);
covxbin3=zeros(Nbin3,1);
rmserru3=zeros(Nbin3,1);
rmsCS=zeros(Nbin3,1);
for p=1:length(Ubin3)
if p<length(Ubin3)
[val1]= find((UCS<Ubin3(p+1)) & (UCS>=Ubin3(p)));
elseif p==length(Ubin3)
[val1]= find((UCS>=Ubin3(p)));
end
numcx3(p)=(length(val1));
tmperr=(error_davis(val1));
tempMC=UCS(val1);
cvrx1=find(tmperr<tempMC);
covxbin3(p)=100*length(cvrx1)/numcx3(p);
rmserru3(p)=rms(tmperr);
rmsCS(p)=rms(tempMC);
end
% Plots
figure;
set(gcf,'DefaultLineLineWidth',lw);set(gca,'FontSize',fs);
hold on;
plot(rmserru1(1:end-1),rmsMC(1:end-1),'r-o','color',crimson);
plot(rmserru2(1:end-1),rmsIM(1:end-1),'b-o','color',dblue);
plot(rmserru3(1:end-1),rmsCS(1:end-1),'b-o','color',blueviolet);
xl=xlim(gca);
plot(linspace(xl(1),xl(2),10),linspace(xl(1),xl(2),10),'k-');
% plot(rmserru3(1:end-1),rmserru3(1:end-1),'k--');
hold off;
% axis([0 0.06 0 0.1]);
% xlabel('RMS error(pixels)');
% ylabel('RMS Uncertainty in each bin(pixels)');
% lbox=legend('MC','PD','CS','e_{RMS}','location','northwest');
% set(lbox,'FontSize',16);
% title('Vortex Ring \newline');
grid on;box on;
set(gcf,'color','white');
% axis square;
hold off;
if printfig==1
export_fig(gcf,fullfile(outputdir{2},[casename{4},'_RMSerroruncertainty.png']),'-painters','-r360');
end
if savematplot==1
save(fullfile(outputdir{1},'Vortex_Ring_RMS.mat'),'rmserru1','rmserru2','rmserru3','rmsMC','rmsIM','rmsCS');
end
%%
% keyboard;
%% Plot Covergae Histogram
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if strcmp(casename{5},'Jetdata');
% A=load(fullfile(outputdir{1},'PivChal03B.mat'));
%% Convert to vectors
err_up=A.err_up(:);
err_vp=A.err_vp(:);
err_ud=A.err_ud(:);
err_vd=A.err_vd(:);
UMCx=A.UMCx(:);
UMCy=A.UMCy(:);
UIMx=A.UIMx(:);
UIMy=A.UIMy(:);
UCSx=A.UCSx(:);
UCSy=A.UCSy(:);
%% Eliminate bad vectors
veccutoff=0.4;
inx1=(find(abs(err_up)>veccutoff));
iny1=(find(abs(err_vp)>veccutoff));
inx2=(find(abs(err_ud)>veccutoff));
iny2=(find(abs(err_vd)>veccutoff));
err_up(inx1)=[];
err_vp(iny1)=[];
UMCx(inx1)=[];
UMCy(iny1)=[];
UIMx(inx1)=[];
UIMy(iny1)=[];
err_ud(inx2)=[];
err_vd(iny2)=[];
UCSx(inx2)=[];
UCSy(iny2)=[];
%% Calculate coverage
ngmx1=length(err_up);
ngmy1=length(err_vp);
ngmx2=length(err_ud);
ngmy2=length(err_vd);
cnt=0;
for j=1:ngmx1
if err_up(j)<=UMCx(j) && err_up(j)>-UMCx(j)
cnt=cnt+1;
end
end
covxMC=100*cnt/ngmx1;
cnt=0;
for j=1:ngmy1
if err_vp(j)<=UMCy(j) && err_vp(j)>-UMCy(j)
cnt=cnt+1;
end
end
covyMC=100*cnt/ngmy1;
cnt=0;
for j=1:ngmx1
if err_up(j)<=UIMx(j) && err_up(j)>-UIMx(j)
cnt=cnt+1;
end
end
covxIM=100*cnt/ngmx1;
cnt=0;
for j=1:ngmy1
if err_vp(j)<=UIMy(j) && err_vp(j)>-UIMy(j)
cnt=cnt+1;
end
end
covyIM=100*cnt/ngmy1;
cnt=0;
for j=1:ngmx2
if err_ud(j)<=UCSx(j) && err_ud(j)>-UCSx(j)
cnt=cnt+1;
end
end
covxCS=100*cnt/ngmx2;
cnt=0;
for j=1:ngmy2
if err_vd(j)<=UCSy(j) && err_vd(j)>-UCSy(j)
cnt=cnt+1;
end
end
covyCS=100*cnt/ngmy2;
[covxMC covyMC covxIM covyIM covxCS covyCS]
% [rms(err_up) rms(err_vp) rms(err_ud) rms(err_vd)]
% keyboard;
%% Plot Functions call
%Plot X and Y error and uncertainty histogram together for all 3 metrics
error_prana=[err_up;err_vp];
error_davis=[err_ud;err_vd];
UMC=[UMCx;UMCy];
UIM=[UIMx;UIMy];
UCS=[UCSx;UCSy];
%% Find percentage of error within Std deviation error
cutoff=0.2;%0.2
%Prana
eP=error_prana;
eP(eP>cutoff)=[];
eP=eP-mean(eP);
ePstd=std(eP);
indexP=find(abs(eP)<=ePstd);
covP=100*length(indexP)/length(eP);
%DaVis
eD=error_davis;
eD(eD>cutoff)=[];
eD=eD-mean(eD);
eDstd=std(eD);
indexD=find(abs(eD)<=eDstd);
covD=100*length(indexD)/length(eD);
[covP covD]
%%
Nbin1=40;
ler=-0.4;uer=0.4;
Nbin2=60;
lun=0;uun=0.4;
vec=linspace(ler,uer,Nbin1);
Nep=histc(error_prana,vec);
Ned=histc(error_davis,vec);
vec2=linspace(lun,uun,Nbin2);
Nmc=histc(UMC,vec2);
Nim=histc(UIM,vec2);
Ncs=histc(UCS,vec2);
Lp=length(error_prana);
Ld=length(error_davis);
Nep=(Nep/Lp)*100;
Ned=(Ned/Ld)*100;
Nmc=(Nmc/Lp)*100;
Nim=(Nim/Lp)*100;
Ncs=(Ncs/Ld)*100;
lw=1.2;fs=20;
set(0,'DefaultAxesFontName', 'Times New Roman');
crimson=rgb('crimson');
dblue=rgb('deepskyblue');
blueviolet=rgb('blueviolet');
figure;
set(gcf,'DefaultLineLineWidth',lw);set(gca,'FontSize',fs);
hold on;
plot(vec,Nep,'k-');
plot(vec,Ned,'k--');
plot(vec2,Nmc,'r-','MarkerSize',4,'color',crimson);
plot(vec2,Nim,'c-','MarkerSize',4,'color',dblue);
plot(vec2,Ncs,'g-','MarkerSize',4,'color',blueviolet);
yl=ylim(gca);
ll=linspace(0,maxyl,10);
% ll2=1:100:max(0.75*yl(2));
sigprana=rms(error_prana(abs(error_prana)<=0.2)).*ones(length(ll),1);
sigdavis=rms(error_davis(abs(error_davis)<=0.2)).*ones(length(ll),1);
sigMC=rms(UMC(UMC<=0.2)).*ones(length(ll),1);
sigIM=rms(UIM(UIM<=0.2)).*ones(length(ll),1);
sigCS=rms(UCS(UCS<=0.2)).*ones(length(ll),1);
[mean(error_prana(abs(error_prana)<=0.2)) mean(error_davis(abs(error_davis)<=0.2))]
plot(sigprana,ll,'k-');
plot(sigdavis,ll,'k--');
plot(sigMC,ll,'r-','MarkerSize',4,'color',crimson);
plot(sigIM,ll,'c-.','MarkerSize',4,'color',dblue);
plot(sigCS,ll,'g--','MarkerSize',4,'color',blueviolet);
hold off;
% ylabel('No. of Data Points');
% xlabel('Error and Uncertainty (pixels)');
% lbox=legend('e_{prana}','e_{davis}','MC','PD','CS','location','northeast');
% set(lbox,'FontSize',16);
% title('Jet Inviscid Core \newline');
grid on;box on;
set(gcf,'color','white');
% axis square;
hold off;
if printfig==1
export_fig(gcf,fullfile(outputdir{2},[casename{5},'_Histogram.png']),'-painters','-r360');
end
if savematplot==1
save(fullfile(outputdir{1},'Jetdata_hist.mat'),'vec','Nep','Ned','vec2','Nmc','Nim','Ncs','ll','sigprana','sigdavis','sigMC','sigIM','sigCS');
end
[sigprana(1) sigdavis(1) sigMC(1) sigIM(1) sigCS(1)]
%% Plot RMS error in uncertainty bins scatter plot
% For MC
Nbin3=8;
Ubin1=linspace(0, 0.12, Nbin3);
numcx1=zeros(Nbin3,1);
covxbin1=zeros(Nbin3,1);
rmserru1=zeros(Nbin3,1);
rmsMC=zeros(Nbin3,1);
for p=1:length(Ubin1)
if p<length(Ubin1)
[val1]= find((UMC<Ubin1(p+1)) & (UMC>=Ubin1(p)));
elseif p==length(Ubin1)
[val1]= find((UMC>=Ubin1(p)));
end
numcx1(p)=(length(val1));
tmperr=(error_prana(val1));
tempMC=UMC(val1);
cvrx1=find(tmperr<tempMC);
covxbin1(p)=100*length(cvrx1)/numcx1(p);
rmserru1(p)=rms(tmperr);
rmsMC(p)=rms(tempMC);
end
%%%%%%% FOR IM
Ubin2=linspace(0, 0.12, Nbin3);
numcx2=zeros(Nbin3,1);
covxbin2=zeros(Nbin3,1);
rmserru2=zeros(Nbin3,1);
rmsIM=zeros(Nbin3,1);
for p=1:length(Ubin2)
if p<length(Ubin2)
[val1]= find((UIM<Ubin2(p+1)) & (UIM>=Ubin2(p)));
elseif p==length(Ubin2)
[val1]= find((UIM>=Ubin2(p)));
end
numcx2(p)=(length(val1));
tmperr=(error_prana(val1));
tempMC=UIM(val1);
cvrx1=find(tmperr<tempMC);
covxbin2(p)=100*length(cvrx1)/numcx2(p);
rmserru2(p)=rms(tmperr);
rmsIM(p)=rms(tempMC);
end
%%%%% FOR CS
Ubin3=linspace(0, 0.12, Nbin3);
numcx3=zeros(Nbin3,1);
covxbin3=zeros(Nbin3,1);
rmserru3=zeros(Nbin3,1);
rmsCS=zeros(Nbin3,1);
for p=1:length(Ubin3)
if p<length(Ubin3)
[val1]= find((UCS<Ubin3(p+1)) & (UCS>=Ubin3(p)));
elseif p==length(Ubin3)
[val1]= find((UCS>=Ubin3(p)));
end
numcx3(p)=(length(val1));
tmperr=(error_davis(val1));
tempMC=UCS(val1);
cvrx1=find(tmperr<tempMC);
covxbin3(p)=100*length(cvrx1)/numcx3(p);
rmserru3(p)=rms(tmperr);
rmsCS(p)=rms(tempMC);
end
% Plots
figure;
set(gcf,'DefaultLineLineWidth',lw);set(gca,'FontSize',fs);
hold on;
plot(rmserru1(1:end-1),rmsMC(1:end-1),'r-o','color',crimson);
plot(rmserru2(1:end-1),rmsIM(1:end-1),'b-o','color',dblue);
plot(rmserru3(1:end-1),rmsCS(1:end-1),'b-o','color',blueviolet);
xl=xlim(gca);
plot(linspace(xl(1),xl(2),10),linspace(xl(1),xl(2),10),'k-');
% plot(rmserru3(1:end-1),rmserru3(1:end-1),'k--');
hold off;
% axis([0 0.06 0 0.1]);
% xlabel('RMS error(pixels)');
% ylabel('RMS Uncertainty in each bin(pixels)');
% lbox=legend('MC','PD','CS','e_{RMS}','location','northwest');
% set(lbox,'FontSize',16);
% title('Jet Inviscid Core \newline');
grid on;box on;
set(gcf,'color','white');
% axis square;
hold off;
if printfig==1
export_fig(gcf,fullfile(outputdir{2},[casename{5},'_RMSerroruncertainty.png']),'-painters','-r360');
end
if savematplot==1
save(fullfile(outputdir{1},'Jetdata_RMS.mat'),'rmserru1','rmserru2','rmserru3','rmsMC','rmsIM','rmsCS');
end
%%
%% Plot Covergae Histogram
end
end