DataAvailability_eLIFE2019/scripts/D_GenerateTRN_matrice.m

%% generate TRN datafile by extraction of significant responses pre/post events.

load('Rearlytraining.mat');

Erefnames=R.Erefnames(1:7); %DT5 - include lever insertion

    for i=1:size(R.Ses,2) %include sessions DT and DT5
        A=[];B=[];
        A(1:length(R.Ses(i).Coord),1:length(Erefnames))=NaN(length(R.Ses(i).Coord),length(Erefnames));
        B(1:length(R.Ses(i).Coord),1:length(Erefnames))=NaN(length(R.Ses(i).Coord),length(Erefnames));
       
        for j=1:length(Erefnames)
            
            IDXrespPost=find(R.Ses(i).Ev(j).ttestPostEvent(:,1)<0.01);
            A(:,j)=zeros(length(R.Ses(i).Ev(j).RespDirPost),1);
            A(IDXrespPost,j)=R.Ses(i).Ev(j).RespDirPost(IDXrespPost,1);
            
            IDXrespPre=find(R.Ses(i).Ev(j).ttestPreEvent(:,1)<0.01);
            B(:,j)=zeros(length(R.Ses(i).Ev(j).RespDirPre),1);
            B(IDXrespPre,j)=R.Ses(i).Ev(j).RespDirPre(IDXrespPre,1);
        end
        TRNevent=[B(:,1:6) A(:,[1:5 7])]; 
        sesTRN(i).TRN(:,1)=nansum(abs(TRNevent),2);
        sesTRN(i).TRNmatrice=TRNevent;
    end
    
save('TRN_DT5_earlytrain.mat','sesTRN') 

%%
clear R
load('Rextendedtraining.mat');


A=[];B=[];
A(1:length(R.Coord),1:length(Erefnames))=NaN(length(R.Coord),length(Erefnames));
B(1:length(R.Coord),1:length(Erefnames))=NaN(length(R.Coord),length(Erefnames));

for j=1:length(Erefnames)
    
    IDXrespPost=find(R.Ev(j).ttestPostEvent(:,1)<0.01);
    A(:,j)=zeros(length(R.Ev(j).RespDirPost),1);
    A(IDXrespPost,j)=R.Ev(j).RespDirPost(IDXrespPost,1);
    
    IDXrespPre=find(R.Ev(j).ttestPreEvent(:,1)<0.01);
    B(:,j)=zeros(length(R.Ev(j).RespDirPre),1);
    B(IDXrespPre,j)=R.Ev(j).RespDirPre(IDXrespPre,1);
end
TRNevent=[B(:,1:6) A(:,[1:5 7])];
TRN(:,1)=nansum(abs(TRNevent),2);
TRNmatrice=TRNevent;

save('TRN_DT5_extendedtrain.mat','TRNmatrice','TRN')