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- %% 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')
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