AnalyzeEducageTableVocalizations

load('C:\Users\owner\Dropbox\lab\progs\Maor et al\Educage\EducageTableVocalizations.mat')

mice=unique(EducageTable.mouse_num);
for nn=1:length(mice)
   micename{nn,1}= EducageTable.mouse_name(find(EducageTable.mouse_num==mice(nn),1,'first'),:);
end

%%
%d prime of all mice in first level
numtrials=1000;
alldp = NaN(50);
numS=40
binsize=20;
clear sTR
clear sNTR
sTR=nan(length(mice),numtrials/binsize);
sNTR=nan(length(mice),numtrials/binsize);
 
clear m
AllSpikeind=nan(1,40)
AllmaxD=nan(1,40)
for n=1:length(mice)
        clear dp
    
score{n}=EducageTable.score(EducageTable.mouse_num==n);
      Level{n}=EducageTable.level(EducageTable.mouse_num==n);
      allL=unique(EducageTable.level(EducageTable.mouse_num==n));
levels2work=1;

chosen_trials=find(Level{n}==levels2work,numtrials,'first');
if isempty(chosen_trials)
    continue
end
biningL=Level{n}(chosen_trials);
bining=zeros(1,length(biningL));

 binind=1:binsize:length(bining);
for b=1:length(binind)-1
    bining(binind(b):end)=bining(binind(b):end)+1;
end

bining=bining';
  [hit, miss,fa,cr,trL,ntrL,newcorrect,correct]=find_ratios4(bining,score{n}(chosen_trials));  
dp=find_dprime(trL,ntrL,binsize);
thresh=1;
spike_thresh = find((dp(2:end-1)>thresh))+1;

    spike_max= find(dp(spike_thresh)>dp(spike_thresh-1) & dp(spike_thresh)>dp(spike_thresh+1),1,'first'); 
        spike_ind=spike_thresh(spike_max); 
        [dMax, dMaxInd]=max(dp);

alldp(n,1:length(dp))=dp;
if ~isempty(spike_ind)
AllSpikeind(n)=spike_ind;
end
AllmaxD(n)=dMax;

m(n)=(dp(spike_ind)-dp(1))/(spike_ind*binsize);

 set(gca,'fontsize',12,'box','off','ytick',[0:3])
end
figure('name','Fig. S4b')
hold all
plot(smooth(nanmean(alldp(:,1:numS)),1),'color','k','linewidth',2)
errorbar(nanmean(alldp(:,1:numS)),nanstd(alldp(:,1:numS))/sqrt(length(mice)),'color','k','linewidth',2)
 set(gca,'fontsize',12,'box','off','xtick',[])

xlim([0 numS])
ylim([-1 3.5])

%%

binsize=40;
dPrimeL=nan(length(mice),9);
dPrimeLmiddle=nan(length(mice),9);
dPrimeLfirst=nan(length(mice),9);
BiasLfirst=nan(length(mice),9);
BiasLlast=nan(length(mice),9);

    for n=1:length(mice)
    cur_t=EducageTable(EducageTable.mouse_num==n,:);
    ff=unique(cur_t.freq_played);

allL=unique(cur_t.level);

levels2work=allL(allL>=1&allL<7);
levels2work=allL(allL>=1);
levels2work=allL([3 4 7 8]);

%1st third
chosen_trials=[];
for ct=1:length(levels2work)
    cv=find(cur_t.level==levels2work(ct));
   th=floor(length(cv)/3);
chosen_trials=[chosen_trials;cv(1:th)];
end
biningL=cur_t.level(chosen_trials);
  [hit, miss,fa,cr,trL,ntrL,newcorrect,correct]=find_ratios4(biningL,cur_t.score(chosen_trials)); 
 
  [temp c_bias]=find_dprime(trL,ntrL,binsize);

dPrimeLfirst(n,levels2work)=temp;
BiasLfirst(n,levels2work)=c_bias;
%2nd third
chosen_trials=[];
for ct=1:length(levels2work)
    cv=find(cur_t.level==levels2work(ct));
   th=floor(length(cv)/3);
chosen_trials=[chosen_trials;cv(th+1:th*2)];
end
biningL=cur_t.level(chosen_trials);
  [hit, miss,fa,cr,trL,ntrL,newcorrect,correct]=find_ratios4(biningL,cur_t.score(chosen_trials)); 
  [temp c_bias]=find_dprime(trL,ntrL,binsize);
dPrimeLmiddle(n,levels2work)=temp;

%last third
chosen_trials=[];
for ct=1:length(levels2work)
 cv=find(cur_t.level==levels2work(ct));
   th=floor(length(cv)/3);
chosen_trials=[chosen_trials;cv(th*2+1:th*3)];
end
biningL=cur_t.level(chosen_trials);
  [hit, miss,fa,cr,trL,ntrL,newcorrect,correct]=find_ratios4(biningL,cur_t.score(chosen_trials));  
  [temp c_bias]=find_dprime(trL,ntrL,binsize);
  dPrimeL(n,levels2work)=temp;
  BiasLlast(n,levels2work)=c_bias;
end

figure('name','Fig. 4d')

hold all
meand=nanmean(dPrimeL(:,1:6));
stdd=nanstd(dPrimeL(:,1:6));

yx=meand(~isnan(meand));
yxx=stdd(~isnan(stdd));

errorbar(1:length(yx),yx,yxx,'Color','k','LineWidth',2,'linestyle','none')
 
 for n=1:length(mice)
        y=dPrimeL(n,1:6);
    yy=y(~isnan(y));
        for nn=1:length(yy)
    plot(nn-0.1,yy(nn),'marker','o','markerfacecolor','none','markeredgecolor','k','markersize',8) 
    end
end
set(gca,'Ytick',[0:0.5:4.5],'Xtick',[],'fontsize',12,'box','off')
ylabel('d''','fontsize',15)
xlim([0 7])
ylim([-0.4 4.5])

%%
%All learninig curves

numtrials=1800;
binsize=50;
clear sTR
clear sNTR
sTR=nan(length(mice),length(levels2work),numtrials/binsize);
sNTR=nan(length(mice),length(levels2work),numtrials/binsize);
clear dPrime
 for n=1:length(mice); 
     %n=2
    score{n}=EducageTable.score(EducageTable.mouse_num==n);
      Level{n}=EducageTable.level(EducageTable.mouse_num==n);

      allL=unique(EducageTable.level(EducageTable.mouse_num==n));
     
      
levels2work=allL(allL>0&allL<7);
levels2work=allL([3 4 7 8]);

  figure('name','Fig. 4c')
  
ha = tight_subplot(1,length(levels2work),[.01 .03],[.2 .04],[.03 .03])
          set(gcf,'color',[1 1 1])
for ct=1:length(levels2work)
chosen_trials=find(Level{n}==levels2work(ct),numtrials,'first');

biningL=Level{n}(chosen_trials);
bining=zeros(1,length(biningL));

 binind=1:binsize:length(bining);
for b=1:length(binind)-1
    bining(binind(b):end)=bining(binind(b):end)+1;
end

bining=bining';
  [hit, miss,fa,cr,trL,ntrL,newcorrect,correct]=find_ratios4(bining,score{n}(chosen_trials));  
[dPrime{n}{ct} c_bias]=find_dprime(trL,ntrL,binsize);

axes(ha(ct))
plot(smooth(trL,3),'color','k','linewidth',2,'linestyle','-')%,'linewidth',2)
 hold on 
 plot(smooth(ntrL,3),'color','k','linewidth',2,'linestyle',':')%,'linewidth',2)
 set(ha(ct),'Ytick',[0.5 1],'yticklabel',{'',''},'xtick',length(trL),'xticklabel',num2str((length(trL))*binsize),'fontsize',10,'box','off')
 
ylim([0 1])
  sTR(n,ct,1:length(smooth(trL,5)))=smooth(trL,5);
   sNTR(n,ct,1:length(smooth(ntrL,5)))=smooth(ntrL,5);
title(num2str(levels2work(ct)))
end
 end
ylim([0 1])



%%
%IR and Licks
TD=3200;% trial duration
TD1=3200/20;% divide by the magnitude of down sampling
%prepere variable with maximal number of levels
DT=nan(length(mice),9);AlllastIRCR=nan(length(mice),9);LT=nan(length(mice),9);LastlickCR=nan(length(mice),9);FirstlickHit=nan(length(mice),9);IRTm=nan(length(mice),TD,9);IRNm=nan(length(mice),TD,9);LickNm=nan(length(mice),TD1,9);LickTm=nan(length(mice),TD1,9);
DT1=nan(length(mice),9);AlllastIRCR1=nan(length(mice),9);
DT2=nan(length(mice),9);AlllastIRCR2=nan(length(mice),9);

for n=1:length(mice)
    
 cur_t=EducageTable(EducageTable.mouse_num==n,:);
   allL=unique(cur_t.level);
 levels2work=allL(allL>=1&allL<7);

for ct=1:length(levels2work)
    cv=find(cur_t.level==levels2work(ct));
   th=floor(length(cv)/3);
%chosen_trials=cv(th*2+1:end);
chosen_trials=cv;
[DT(n,levels2work(ct)), AlllastIRCR(n,levels2work(ct)),LT(n,levels2work(ct)),LastlickCR(n,levels2work(ct)),FirstlickHit(n,levels2work(ct)),IRTm(n,:,levels2work(ct)),IRNm(n,:,levels2work(ct)),LickTm(n,:,levels2work(ct)),LickNm(n,:,levels2work(ct))]=find_IR_licks(cur_t,chosen_trials,TD);%
end
end
%detection time of p value licks:

clear DTCLEAN
DTCLEAN(:,:,1)=[LT(:,[1:3],1)];
figure('name','Fig. S4a')
hold all
meand=nanmean(DTCLEAN(:,:,1));
yx=meand(~isnan(meand));
 errorbar(yx,nanstd(DTCLEAN(:,:,1))./sqrt(sum(~isnan(DTCLEAN(:,:,1)))),'color','k','LineWidth',2,'linestyle','none')
 for nn=1:length(yx)
        plot(nn-0.2,DTCLEAN(:,nn),'marker','o','markerfacecolor','none','markeredgecolor','k','markersize',8) 

 end
    set(gca,'Xtick',[],'fontsize',12,'box','off')
ylabel('detection time of p value licks (ms)','fontsize',15)
xlim([0 4])