Merge remote-tracking branch 'refs/remotes/KISN/master'

LICENSE

@@ -0,0 +1,402 @@
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README.md

@@ -0,0 +1,20 @@
+# Theta_acceleration_data
+
+Dataset for the paper:
+
+Frequency of theta rhythm is controlled by acceleration but not speed in running rats
+
+Kropff E., Carmichael J.E., Moser E.I. and Moser M-B.
+
+
+
+
+The raw data in Axona format was compressed using 7-zip. It includes one folder per animal with tracked position (.pos), one 4.8 KHz sampled eeg per tetrode (.egfx), setup file (.set) and log files for car experiments (.log). For animals with 2 .egfx files per session, all data is entorhinal. For animals with 3 .egfx files per session, .egf is hippocampal while .egf2 and .egf3 are entorhinal. Always .egf is for tetrodes 1 to 4, .egf2 for tetrodes 5 to 8 and .egf3 for tetrodes 9 to 12. Filenames contain the date and session number following the format YYMMDDSS (YY = year, MM = month, DD = day, SS = session number).
+
+Log files for car sessions have two columns. The first column indicates the timestamp in milliseconds. The second column indicates the type of event occurring at that timestamp. Events can be either alarm_x, start_x, end_x or a number indicating car speed, x taking values r (right) or l (left). The alarm event corresponds to the beginning of a sound played for 6 seconds before the car starts moving. Consecutive start and end events demark one run along the 4 m track in either left or right direction. Each number event is a signal sent at that moment to the motor controlling the car. It is expressed in arbitrary units that can be converted to actual car speed multiplying the value by a factor of 0.35.
+
+The spreadsheet 'all sessions.xlsx' contains one sheet per experimental animal, with a column for the date (YYMMDD) and six columns indicating type of session for each session number (blank indicates no more sessions conducted that day). Session types are A,B or S for open field, C and V for 2-speed and 4-speed protocol (correspondingly) regular car experiments, P and D for passive transport and dark (correspondingly) car experiments. Other session types are not relevant for this paper.
+
+The 'spike_timestamp_data.db' file contains a single MATLAB structure 'db' saved in '-mat' format. It contains spike timestamps for 2497 entorhinal cells (all sessions on a given day, one day per cell). The fields of the structure are: 'cell_type' (grid, hd, speed, border, putative interneuron), 'timestamps' in seconds relative the start of each session, 'session_type' for each session, rat, date and tetrode. Session types are coded as in 'all sessions.xlsx'.
+
+The 'codes' folder has MATLAB scripts for individual figures.

code/Fig_1/Fig_1.m

@@ -0,0 +1,40 @@
+load('data_fig_1.db','-mat');
+figure;
+
+%% theta frequency
+frwin=3;
+for a=1:2
+    rx=size(data.unwrapped_phase_right{a},1);
+    freq_right{a}=([data.unwrapped_phase_right{a}(:,1+frwin:end) nan(rx,frwin)]-[nan(rx,frwin) data.unwrapped_phase_right{a}(:,1:end-frwin)])/2/pi/0.02/frwin/2;
+    rx=size(data.unwrapped_phase_left{a},1);
+    freq_left{a}=([data.unwrapped_phase_left{a}(:,1+frwin:end) nan(rx,frwin)]-[nan(rx,frwin) data.unwrapped_phase_left{a}(:,1:end-frwin)])/2/pi/0.02/frwin/2;    
+end
+
+%% acceleration
+win=10;    %gfr=fr;
+
+
+    rx=size(data.x_right,1);
+    acc_right=-([nan(rx,win) data.x_right(:,1:end-win)]+[data.x_right(:,1+win:end) nan(rx,win)]-2*data.x_right)/0.02/0.02/win/win;
+    rx=size(data.x_left,1);    
+    acc_left=([nan(rx,win) data.x_left(:,1:end-win)]+[data.x_left(:,1+win:end) nan(rx,win)]-2*data.x_left)/0.02/0.02/win/win;    
+
+
+ fct=0; 
+th=8; axlim=[-120  220]; 
+subplot(2,1,1)
+
+gacc=acc_right;
+gfr=freq_right;
+do_plots;
+
+set(AX(2),'XTick',[]);
+
+subplot(2,1,2)
+    gfr=freq_left; 
+    gacc=acc_left;    
+
+do_plots;
+set(AX(2),'XTick',[]);
+
+

code/Fig_1/Fig_1.m~

@@ -0,0 +1,48 @@
+load('data_fig_1.db','-mat');
+
+
+subplot(2,1,1)
+
+%% theta frequency
+frwin=3;
+for a=1:3
+    rx=size(data.unwrapped_phase_right{a},1);
+    freq_right{a}=([data.unwrapped_phase_right{a}(:,1+frwin:end) nan(rx,frwin)]-[nan(rx,frwin) data.unwrapped_phase_right{a}(:,1:end-frwin)])/2/pi/0.02/frwin/2;
+    rx=size(data.unwrapped_phase_left{a},1);
+    freq_left{a}=([data.unwrapped_phase_left{a}(:,1+frwin:end) nan(rx,frwin)]-[nan(rx,frwin) data.unwrapped_phase_left{a}(:,1:end-frwin)])/2/pi/0.02/frwin/2;    
+end
+
+%% acceleration
+win=10;    %gfr=fr;
+
+
+    rx=size(x,1);
+    acc_right=-([nan(rx,win) x(:,1:end-win)]+[x(:,1+win:end) nan(rx,win)]-2*x)/0.02/0.02/win/win;
+
+
+axlim=0; fct=0; th=0; 
+th=ths(r); axlim=[-120  220]; %fct=0.0127;
+new_figfig
+% set(AX(2),'XTickLabel',[]);
+% set(AX(1),'XTickLabel',[]);
+
+set(AX(2),'XTick',[]);
+% L=legend('acceleration','CA1','MEC','Location', 'NorthEast');
+% set(L,'Color','none')
+% set(L,'box','off')
+% set(L,'FontSize',10,'FontName','Arial')
+subplot(2,1,2)
+% win=5;
+if r==3
+    gfr=data.unwrapped_phase_left; gacc=lacc;
+    rx=size(lx,1);
+    gacc=([nan(rx,win) lx(:,1:end-win)]+[lx(:,1+win:end) nan(rx,win)]-2*lx)/0.02/0.02/win/win;    
+else
+    gfr=fr; gacc=acc; 
+    rx=size(x,1);
+    gacc=-([nan(rx,win) x(:,1:end-win)]+[x(:,1+win:end) nan(rx,win)]-2*x)/0.02/0.02/win/win;    
+end
+% axlim=[-160 260];
+new_figfig
+set(AX(2),'XTick',[]);
+

code/Fig_1/data_fig_1.db

@@ -0,0 +1 @@
+/annex/objects/MD5-s23588531--4e064a672abd8adcfb19ca2e5e57e4f9

code/Fig_1/do_plots.m

@@ -0,0 +1,62 @@
+
+% selx=423:624;
+selx=451:601;
+bx=(-500:500)/50;
+col1=.1*[1 1 1];%[.0 .6 .6];
+col2=[255 175 3]/255;%[77 255 255]/255;%[255 195 77]/255;%[225 54 255]/255;%[255 110 238]/255;[0 .63 .54];
+col3=[255 0 0]/255;%[106 77 255]/255;%[77 255 106]/255;%[.60 .65 .0];%[77 255 106]/255;%[0 1 1]*.9;
+xxx=gacc(:,selx);
+mxxx=nanmean(xxx);
+sxxx=nanstd(xxx)/2;
+hold on;
+
+
+[AX,H1,H2]=plotyy(bx(selx),nanmean(gacc(:,selx)),bx(selx),[nanmean(gfr{1}(:,selx))' nanmean(gfr{2}(:,selx))']);
+uistack(AX(1), 'top')
+
+set(AX(1),'Color','none')
+set(AX(1),'FontName','Arial');
+set(AX(2),'FontName','Arial');
+if th==0
+    th=nanmean(nanmean(gfr{2}(:,[423:450 600:624])));
+    th=.5*round(th/.5);
+end
+if fct==0
+    fct= (nanmean([max(nanmean(gfr{2}(:,selx))) max(nanmean(gfr{1}(:,selx)))])-th)/max(mxxx);
+end
+set(H1,'LineWidth',1);set(H2,'LineWidth',2);
+set(H1,'LineStyle','--','Color',col1);
+ set(H2(1),'Color',col2)
+set(H2(2),'Color',col3)
+if axlim==0
+    axlim=1.01*[1.15*min(mxxx-sxxx) max(mxxx+sxxx)];
+end
+axs=-1000:100:1000;
+set(AX(1),'YLim',axlim);
+set(AX(1),'YLim',axlim); %axlim=get(AX(2),'YLim');
+set(AX(1),'YTick',axs);
+set(AX(1),'XTick',-3:1:3);
+set(AX(2),'YLim',axlim*fct+th-0.0);%[6.332 10]);
+set(AX(2),'YTick',6:11);
+set(AX(1),'XLim',[-1 2]);
+set(AX(2),'XLim',[-1 2]);
+set(AX(1),'box','off');
+set(AX(1),'YColor','k');
+set(AX(2),'YColor',col3);
+AX(1)=reg_plots(AX(1));
+AX(2)=reg_plots(AX(2));
+
+
+
+AX_annotations = copyobj(AX(1),get(AX(1),'Parent'));
+A=area(AX_annotations,bx(selx),[mxxx-sxxx; 2*sxxx]'); 
+set(A(1),'Visible','off');
+set(A(2),'FaceColor',col1+(1-col1)*.8);
+set(A(2),'EdgeColor','none');
+uistack(AX_annotations, 'bottom');
+set(AX_annotations,'Color',[1 1 1]);
+set(AX_annotations,'XTick',[],'YTick',[]);
+dsp=diff(axlim)/200*120;
+ set(AX(1),'dataaspectratio',[1 dsp 1]);
+ set(AX(2),'dataaspectratio',[1 dsp*fct 1]);
+ set(AX_annotations,'dataaspectratio',[1 dsp 1]);

code/Fig_1/reg_plots.m

@@ -0,0 +1,3 @@
+function ax=reg_plots(ax)
+set(ax,'FontSize',13);
+set(ax,'FontName','Arial');

code/Fig_1b/Fig_1c.m

@@ -0,0 +1,60 @@
+load('data_fig_1c.db','-mat');
+
+ecp=bsxfun(@rdivide,bsxfun(@minus,eeghp.ecL.p,nanmean(eeghp.ecL.p)),nanmean(amp.ecL.p));
+ecm=bsxfun(@rdivide,bsxfun(@minus,eeghp.ecL.m,nanmean(eeghp.ecL.m)),nanmean(amp.ecL.m));
+
+egp=bsxfun(@rdivide,bsxfun(@minus,eeg.ecL.p,nanmean(eeg.ecL.p)),nanmean(amp.ecL.p));
+egm=bsxfun(@rdivide,bsxfun(@minus,eeg.ecL.m,nanmean(eeg.ecL.m)),nanmean(amp.ecL.m));
+%%
+ks=20;
+sel=4801:14400;
+aux=ecp(sel,:);
+[idx,Cp]=kmeans(aux',ks);
+aux=ecm(sel,:);
+[idxm,Cm]=kmeans(aux',ks);
+%%
+close all;
+f=figure;
+pp=zeros(19201,ks);
+pm=pp;
+for j=1:ks
+   pp(:,j)=mean(ecp(:,idx==j),2);
+   pm(:,j)=mean(ecm(:,idxm==j),2);
+end
+
+C=corr(pp(sel(2401:4800),:),pm(sel(2401:4800),:));
+C(1:ks+1:end)=-1;
+nsp=8;
+fp=zeros(1,nsp);
+fm=fp;
+phm=fp;
+for j=1:nsp
+    [mC,rw]=max(C);
+    [~,cl]=max(mC);
+    rw=rw(cl);
+  
+
+fp(j)=rw;
+fm(j)=cl;
+   
+   C(:,cl)=-1;
+   C(rw,:)=-1;
+   phm(j)=angle(mean(exp(1i*phase.ecL.m(9601,bsxfun(@eq,idxm,fm(j)))),2));
+end
+[~,sindex]=sort(phm,'descend');
+for k=1:nsp
+    j=sindex(k);
+    subplot(nsp,1,k)
+    rw=fp(j); cl=fm(j);
+    disp([C(rw,cl),corr(pp(sel(2401:4800),rw),pm(sel(2401:4800),cl)),sum(idx==rw),sum(idxm==cl)])
+
+    auxp=mean(ecp(:,idx==fp(j)),2); 
+    plot((-9600:9600)/4800,auxp,'c','LineWidth',2);
+   hold on;
+   auxm=mean(ecm(:,idxm==fm(j)),2);
+   plot((-9600:9600)/4800,auxm,'b','LineWidth',2);
+   axis([-.5 .5 1.4*[-1 1]]);   
+   set(gca,'XTick',-0.5:.5:.5,'XTickLabel',[]);
+   golden(.3)
+end
+set(gca,'XTick',-0.5:.5:.5,'XTickLabel',-0.5:.5:.5,'FontName','Arial');

code/Fig_1b/data_fig_1c.db

@@ -0,0 +1 @@
+/annex/objects/MD5-s1715354492--3acf785d1a3a26cefc5fae02b184747d

code/Fig_2/Fig_2.m

@@ -0,0 +1,225 @@
+load('data_fig_2.db','-mat');
+
+res={};
+bins=(1:300)*.002;
+res.allcells.positive=0*bins;
+res.allcells.negative=0*bins;
+res.allcells.shuff=zeros(1000,numel(bins));
+typ='allcells';
+delta_r=5.0666667;
+delta_l=30.4;
+if 1
+
+    for i=1:numel(alldata.ts)
+        
+        
+        data=alldata.pos{i};
+        ts=alldata.ts{i};
+        
+        %%
+        nlaps=min(numel(data.log.end_r),numel(data.log.end_l));
+        
+        right=(data.log.start_r(1:nlaps)+delta_r)';
+        left=(data.log.start_l(1:nlaps)+delta_l)';  
+
+        dwin=.5;
+
+        %% speed
+        selfast=is_inside(ts',data.log.start_r'+1,data.log.start_r'+4) | is_inside(ts',data.log.start_l'+31.5,data.log.start_l'+34.5);
+        selslow=is_inside(ts',data.log.start_l'+5,data.log.start_l'+25) | is_inside(ts',data.log.start_r'+8,data.log.start_r'+28);
+        res.(typ).fast(i,:)=hist(autoc2(ts(selfast)),bins)/6;    
+        res.(typ).slow(i,:)=hist(autoc2(ts(selslow)),bins)/40;    
+        
+        %% acceleration
+
+        res.(typ).negative=res.(typ).negative + hist(autoc2(ts(is_inside(ts',right,right+dwin))),bins);    
+        res.(typ).positive=res.(typ).positive + hist(autoc2(ts(is_inside(ts',left,left+dwin))),bins);                       
+        
+        for j=1:1000
+            shuff_fast=data.log.start_r'+1+3*rand(1,nlaps);
+            shuff_slow=data.log.start_l'+5+20*rand(1,nlaps);          
+            shuff=[shuff_fast shuff_slow];
+            shuff=shuff(randperm(numel(shuff),numel(shuff)/2));
+            res.(typ).shuff(j,:)=res.(typ).shuff(j,:)+hist(autoc2(ts(is_inside(ts',shuff,shuff+dwin))),bins);
+        end       
+    
+    end
+end
+
+%%
+%%
+close all
+
+bins=(1:150)*.004;
+
+
+%%
+sel=bins>0.01 & bins<0.3;
+types={'allcells'};
+nshuf=1000;
+sname='shuff_speed.mat';
+if exist(sname,'file')
+    load(sname,'-mat')
+else
+    sh_f1={};sh_f2={};
+    for ty=1:numel(types)
+        typ=types{ty};
+    sh_f1.(typ)=zeros(1,nshuf);
+    sh_f2.(typ)=zeros(1,nshuf);      
+for j=1:nshuf
+    if mod(j,100)==0
+        disp(j);
+    end
+    sel=rand(1,size(res.(typ).fast,1))>.5;
+    
+    aux1=mean([reshape(res.(typ).fast(sel,:),[],150); reshape(res.(typ).slow(~sel,:),[],150)]);
+    aux2=mean([reshape(res.(typ).fast(~sel,:),[],150); reshape(res.(typ).slow(sel,:),[],150)]);
+    aux1(end)=0;
+    aux2(end)=0;
+    aux1=aux1/max(aux1);    
+    aux2=aux2/max(aux2);    
+    c1=do_fit(bins,aux1);
+    c2=do_fit(bins,aux2);
+    
+    sh_f1.(typ)(j)=c1.f;
+    sh_f2.(typ)(j)=c2.f;
+ 
+end
+
+    end
+save(sname,'sh_f1','sh_f2','-mat');
+end
+%% speed
+
+for ty=numel(types)
+    typ=types{ty};
+    f=figure;
+    hold on;
+    aux=mean(reshape(res.(typ).fast,[],150))/8;
+    aux(end)=0;
+    mxaux=max(aux);
+    aux=aux/max(aux);
+ffast=do_plot(bins,aux,[0 1 0],[.5 1 .5]);    
+    auxs=mean(reshape(res.(typ).slow,[],150))/40;
+    auxs(end)=0;
+    mxauxs=max(auxs);
+
+    auxs=auxs/max(auxs);
+    fslow=do_plot(bins,auxs,[1 0 0],[1 .5*[1 1]]); 
+
+subplot(2,2,1)
+axis([0 .3 0.3 1.2]);
+gl=1.5;
+
+set(gca,'YTick',0:.5:2,'FontName','Arial');
+subplot(2,2,2)
+axis([0 .3 .15*[-1 1] ]);
+
+set(gca,'YTick',-.6:.1:.6,'FontName','Arial');
+
+
+subplot(2,2,3)
+fbins=7.5:.01:8.5;
+h=hist([sh_f1.(typ) sh_f2.(typ)],fbins);
+b=bar(fbins,h,'LineStyle','none','FaceColor',.5*[1 2 1],'BarWidth',1.0);
+mx=ceil(2*max(h));
+hold on;
+legend off;
+line((fslow)*[1 1],[0 mx],'LineWidth',2,'Color',[1 1 1]*.5);
+line(ffast*[1 1],[0 mx],'LineWidth',2,'Color',[1 .5 .5]);
+axis([7.6 8.0 0 380]);
+axis([7.5 9.5 0 380]);
+
+set(gca,'YTick',0:100:300)
+
+subplot(224)
+fbins=7.5:.01:8.5;
+h=hist([sh_f1.(typ) sh_f2.(typ)],fbins);
+b=bar(fbins,h,'LineStyle','none','FaceColor',.5*[1 2 1],'BarWidth',1.0);
+mx=ceil(2*max(h));
+hold on;
+legend off;
+line((fslow)*[1 1],[0 mx],'LineWidth',2,'Color',[1 1 1]*.5);
+line(ffast*[1 1],[0 mx],'LineWidth',2,'Color',[1 .5 .5]);
+axis([7.6 8.0 0 300]);
+
+set(gca,'YTick',0:100:300)
+
+
+end
+
+%% Acceleration
+gs=0;
+figure;
+for ty=numel(types)
+    typ=types{ty};
+
+    hold on;
+    aux=gauss_boxcar(mean(reshape(res.(typ).negative,2,150)),gs);
+    aux=aux/max(aux);
+fm=do_plot(bins,aux,[0 0 0],.5*[1 1 1]);    
+    aux=gauss_boxcar(mean(reshape(res.(typ).shuff(1,:),2,150)),gs);
+    aux=aux/max(aux);
+    fc=do_plot(bins,aux,[0 1 0],[.5 1 .5]);    
+    aux=gauss_boxcar(mean(reshape(res.(typ).positive,2,150)),gs);
+    aux=aux/max(aux);
+    fp=do_plot(bins,aux,[1 0 0],[1 .5*[1 1]]); 
+end
+subplot(2,2,1)
+axis([0 .3 0.3 1.2]);
+
+set(gca,'YTick',0:.5:2,'FontName','Arial');
+subplot(2,2,2)
+axis([0 .3 .15*[-1 1] ]);
+
+set(gca,'YTick',-.6:.1:.6,'FontName','Arial');
+
+
+sname='shuff_acc.mat';
+if exist(sname,'file')
+    load(sname,'-mat')
+else
+    
+for j=1:nshuf
+    if mod(j,100)==0
+        disp(j);
+    end
+
+    acc_f(j)=all_fit(res.(typ).shuff(j,:));
+
+end
+
+save(sname,'acc_f','-mat');
+end
+%%
+subplot(2,2,3)
+fbins=7:.05:9;
+h=hist(acc_f,fbins);
+h=h;%/sum(h);
+b=bar(fbins,h,'LineStyle','none','FaceColor',.5*[1 2 1],'BarWidth',1.0);
+mx=ceil(2.1*max(h));
+gg=fit(fbins',h','gauss1');
+hold on;
+p1=plot(gg);
+set(p1,'Color',[.5 1 .5]-.5);
+legend off;
+% line(fc*[1 1],[0 mx],'LineWidth',2,'Color',[0 1 0]);
+line(fm*[1 1],[0 mx],'LineWidth',2,'Color',[1 1 1]*.5);
+line(fp*[1 1],[0 mx],'LineWidth',2,'Color',[1 .5 .5]);
+axis([7.5 9.5 0 280]);
+set(gca,'YTick',0:100:300);
+p_n=(1-erf((fm-gg.b1)/gg.c1)); 
+p_p=(1-erf((fp-gg.b1)/gg.c1));
+
+
+title(['neg: ' num2str(1-erf(abs(fm-gg.b1)/gg.c1)) '   pos: ' num2str(p_p)]);
+
+
+
+
+
+%%
+
+
+
+

code/Fig_2/Fig_2.m~

@@ -0,0 +1,196 @@
+load('data_fig_2.db','-mat');
+
+res={};
+bins=(1:300)*.002;
+res.allcells.positive=0*bins;
+res.allcells.negative=0*bins;
+res.allcells.shuff=zeros(1000,numel(bins));
+typ='allcells';
+delta_r=5.0666667;
+delta_l=30.4;
+if 1
+
+    for i=1:numel(alldata.ts)
+        
+        
+        data=alldata.pos{i};
+        ts=alldata.ts{i};
+        
+        %%
+        nlaps=min(numel(data.log.end_r),numel(data.log.end_l));
+        
+        right=(data.log.start_r(1:nlaps)+delta_r)';
+        left=(data.log.start_l(1:nlaps)+delta_l)';  
+
+        dwin=.5;
+
+        %% speed
+        selfast=is_inside(ts',data.log.start_r'+1,data.log.start_r'+4) | is_inside(ts',data.log.start_l'+31.5,data.log.start_l'+34.5);
+        selslow=is_inside(ts',data.log.start_l'+5,data.log.start_l'+25) | is_inside(ts',data.log.start_r'+8,data.log.start_r'+28);
+        res.(typ).fast(i,:)=hist(autoc2(ts(selfast)),bins)/6;    
+        res.(typ).slow(i,:)=hist(autoc2(ts(selslow)),bins)/40;    
+        
+        %% acceleration
+
+        res.(typ).negative=res.(typ).negative + hist(autoc2(ts(is_inside(ts',right,right+dwin))),bins);    
+        res.(typ).positive=res.(typ).positive + hist(autoc2(ts(is_inside(ts',left,left+dwin))),bins);                       
+        
+        for j=1:1000
+            shuff_fast=data.log.start_r'+1+3*rand(1,nlaps);
+            shuff_slow=data.log.start_l'+5+20*rand(1,nlaps);          
+            shuff=[shuff_fast shuff_slow];
+            shuff=shuff(randperm(numel(shuff),numel(shuff)/2));
+            res.(typ).shuff(j,:)=res.(typ).shuff(j,:)+hist(autoc2(ts(is_inside(ts',shuff,shuff+dwin))),bins);
+        end       
+    
+    end
+    save('partial_res.db','res','-mat');
+end
+
+%%
+%%
+close all
+
+bins=(1:150)*.004;
+
+
+%%
+sel=bins>0.01 & bins<0.3;
+types={'allcells'};
+nshuf=1000;
+sname='shuff_speed.mat';
+if 0%exist(sname,'file')
+    load(sname,'-mat')
+else
+    sh_f1={};sh_f2={};
+    for ty=1:numel(types)
+        typ=types{ty};
+    sh_f1.(typ)=zeros(1,nshuf);
+    sh_f2.(typ)=zeros(1,nshuf);      
+%     aux_all=[.5*(res.(typ).fast(:,1:2:end)+res.(typ).fast(:,2:2:end)); .5*(res.(typ).slow(:,1:2:end)+res.(typ).slow(:,2:2:end)); ]
+for j=1:nshuf
+    if mod(j,100)==0
+        disp(j);
+    end
+    sel=rand(1,size(res.(typ).fast,1))>.5;
+    
+    aux1=mean([reshape(res.(typ).fast(sel,:),[],150); reshape(res.(typ).slow(~sel,:),[],150)]);
+    aux2=mean([reshape(res.(typ).fast(~sel,:),[],150); reshape(res.(typ).slow(sel,:),[],150)]);
+    aux1(end)=0;
+    aux2(end)=0;
+    aux1=aux1/max(aux1);    
+    aux2=aux2/max(aux2);    
+    c1=do_fit(bins,aux1);
+    c2=do_fit(bins,aux2);
+    
+    sh_f1.(typ)(j)=c1.f;
+    sh_f2.(typ)(j)=c2.f;
+ 
+end
+
+    end
+save(sname,'sh_f1','sh_f2','-mat');
+end
+%% speed
+
+
+
+
+for ty=numel(types)
+    typ=types{ty};
+    f=figure;
+    hold on;
+    aux=mean(reshape(res.(typ).fast,[],150))/8;
+    aux(end)=0;
+    mxaux=max(aux);
+    aux=aux/max(aux);
+ffast=do_plot(bins,aux,[0 1 0],[.5 1 .5]);    
+    auxs=mean(reshape(res.(typ).slow,[],150))/40;
+    auxs(end)=0;
+    mxauxs=max(auxs);
+
+    auxs=auxs/max(auxs);
+    fslow=do_plot2(bins,auxs,[1 0 0],[1 .5*[1 1]]); 
+
+subplot(2,2,1)
+axis([0 .3 0.3 1.2]);
+gl=1.5;
+%golden(gl);
+set(gca,'YTick',0:.5:2,'FontName','Arial');
+subplot(2,2,2)
+axis([0 .3 .15*[-1 1] ]);
+%golden(gl);
+set(gca,'YTick',-.6:.1:.6,'FontName','Arial');
+
+
+subplot(2,2,3)
+fbins=7.5:.01:8.5;
+h=hist([sh_f1.(typ) sh_f2.(typ)],fbins);
+b=bar(fbins,h,'LineStyle','none','FaceColor',.5*[1 2 1],'BarWidth',1.0);
+mx=ceil(2*max(h));
+hold on;
+legend off;
+line((fslow)*[1 1],[0 mx],'LineWidth',2,'Color',[1 1 1]*.5);
+line(ffast*[1 1],[0 mx],'LineWidth',2,'Color',[1 .5 .5]);
+axis([7.6 8.0 0 380]);
+axis([7.5 9.5 0 380]);
+
+set(gca,'YTick',0:100:300)
+
+subplot(224)
+fbins=7.5:.01:8.5;
+h=hist([sh_f1.(typ) sh_f2.(typ)],fbins);
+b=bar(fbins,h,'LineStyle','none','FaceColor',.5*[1 2 1],'BarWidth',1.0);
+mx=ceil(2*max(h));
+hold on;
+legend off;
+line((fslow)*[1 1],[0 mx],'LineWidth',2,'Color',[1 1 1]*.5);
+line(ffast*[1 1],[0 mx],'LineWidth',2,'Color',[1 .5 .5]);
+axis([7.6 8.0 0 300]);
+
+set(gca,'YTick',0:100:300)
+
+
+end
+
+%% Acceleration
+gs=0;
+for ty=numel(types)
+    typ=types{ty};
+%     f=figure;
+%     plot(bins,gauss_boxcar(sum(res.(typ).right.pre,1),gs),'Color',[1 .5 .5]);
+    hold on;
+    aux=gauss_boxcar(mean(reshape(res.(typ).right,2,150)),gs);
+    aux=aux/max(aux);
+fm=myplot33(bins,aux,[0 0 0],.5*[1 1 1]);    
+%     plot(bins,gauss_boxcar(sum(res.(typ).left.pre,1),gs),'Color',.5*[1 1 1]);
+
+
+
+%%
+
+
+
+   
+%     plot(bins,aux/max(aux),'Color',[1 0 0]);
+    aux=gauss_boxcar(mean(reshape(res.(typ).control(1,:),2,150)),gs);
+    aux=aux/max(aux);
+    fc=myplot3(bins,aux,[0 1 0],[.5 1 .5]);    
+%     plot(bins,aux/max(aux),'Color',[0 1 0]);
+
+    aux=gauss_boxcar(mean(reshape(res.(typ).left,2,150)),gs);
+    aux=aux/max(aux);
+    fp=myplot3(bins,aux,[1 0 0],[1 .5*[1 1]]); 
+end
+subplot(2,2,1)
+axis([0 .3 0.3 1.2]);
+gl=1.5;
+golden(gl);
+set(gca,'YTick',0:.5:2,'FontName','Arial');
+subplot(2,2,2)
+axis([0 .3 .15*[-1 1] ]);
+golden(gl);
+set(gca,'YTick',-.6:.1:.6,'FontName','Arial');
+
+
+

code/Fig_2/all_fit.m

@@ -0,0 +1,8 @@
+function f=all_fit(aux)
+autoc_step=.002;
+autoc_bins=1:300; 
+bins=(autoc_bins)*autoc_step*2; bins=bins(1:150);
+    aux=mean(reshape(aux,[],150));
+    aux=aux/max(aux);    
+    c=do_fit(bins,aux);
+    f=c.f;

code/Fig_2/autoc2.m

@@ -0,0 +1,19 @@
+function ht=autoc2(ts,mn,mx)%,autoc_bins,autoc_step)
+    if ~exist('mn','var')
+        mn=0;
+        mx=1;
+    end
+
+    overl=5000;
+    ksel=1:min(numel(ts),overl); %memory overload
+    ht=[];
+    if numel(ts)>1
+
+        while ~isempty(ksel)
+            haux=bsxfun(@minus,ts(ksel),ts(ksel)');
+            ht=[ht; haux(haux>mn & haux<mx)];
+            ksel=ksel(end)+1:min(numel(ts),ksel(end)+overl);
+        end
+    end
+
+end

code/Fig_2/data_fig_2.db

@@ -0,0 +1 @@
+/annex/objects/MD5-s1008483337--77108f3ab8132a9e1122cb1ff7829911

code/Fig_2/do_fit.m

@@ -0,0 +1,14 @@
+function [c,g]=do_fit(bins,aux)
+
+sel=bins>0.015 & bins<0.3;
+
+
+fo = fitoptions('Method','NonlinearLeastSquares',...
+               'Lower',[0.2,0,0,0,6,-1,-1],...
+               'Upper',[2,20,1,Inf,12,1,1],...
+               'StartPoint',[1,3,.2,10,8,0,0]);
+% ft = fittype('a-b*x+c*cos(2*pi*f*x)*exp(-d*x)','options',fo);
+ft = fittype('a*exp(-b*x)+c*cos(2*pi*f*x)*exp(-d*x)+g+h*x','options',fo);
+
+
+[c, g] = fit(bins(sel)',aux(sel)',ft);

code/Fig_2/do_plot.m

@@ -0,0 +1,45 @@
+function ff=do_plot(bins,aux,col,col2)    
+% aux=aux/max(aux);
+% 
+% sel=bins>0.015 & bins<0.3;
+% 
+% 
+% fo = fitoptions('Method','NonlinearLeastSquares',...
+%                'Lower',[0.2,0,0,0,6,-1,-1],...
+%                'Upper',[2,20,1,Inf,12,1,1],...
+%                'StartPoint',[1,3,.2,10,8,0,0]);
+% % ft = fittype('a-b*x+c*cos(2*pi*f*x)*exp(-d*x)','options',fo);
+% ft = fittype('a*exp(-b*x)+c*cos(2*pi*f*x)*exp(-d*x)+g+h*x','options',fo);
+
+% aux=aux/max(aux);
+[curve2, ~] = do_fit(bins,aux);
+
+aux=aux/feval(curve2,0);
+[curve2, gof2] = do_fit(bins,aux);
+
+
+subplot(2,2,1)
+p1=plot(curve2);
+set(p1,'Color',col2,'LineWidth',2);
+hold on;
+plot(bins,aux,'Color',col);
+plot(bins,curve2.a*exp(-curve2.b*bins)+curve2.g+curve2.h*bins,'c');
+
+legend off;
+axis([0 .3 0 1.2])
+
+subplot(2,2,2)
+
+plot(bins,curve2.c.*cos(2*pi*curve2.f.*bins).*exp(-curve2.d.*bins),'Color',col2,'LineWidth',2);
+hold on;
+plot(bins,aux-feval(curve2,bins)'+curve2.c.*cos(2*pi*curve2.f.*bins).*exp(-curve2.d.*bins),'Color',col);
+
+% disp(curve2.f);
+% curve2
+% gof2
+disp(['freq: ' trunc(curve2.f,3) '  R^{2}: ' trunc(gof2.rsquare,3)]);
+ff=curve2.f;
+axis([0 .3 -.2 .2])
+
+
+% plot(bins,.2+.9*exp(-6*bins).*(1+.1*cos(2*pi*bins*9)),'b');

code/Fig_3/Fig_3.m

@@ -0,0 +1,149 @@
+close all;
+% load('N:\kropff\analyisis\figs\fig2a\all open data_q95.db','-mat');
+% load('N:\kropff\analyisis\figs\fig2a\all car data_q95.db','-mat');
+% % % load('all open data.db','-mat');
+% % % load('all car data.db','-mat');
+% db_all.car=db_car;
+% db_all.open=db_open;
+% clear db_car; clear db_open;
+%     addpath('/gpfs/work/kropff/common_functions/');
+
+dats={'ec', 'hc'};
+% Haccum={};
+w1=0; w2=3;
+jet2=interp1(1:64,jet,(1+w1:(64-w1-w2-1)/(64-1):64-w2));
+
+% load(['all ' dat ' data.db'],'-mat')
+dang=pi/50;
+dacc=20;%15;
+accbins=0:dacc:200; %350
+angbins=-pi:dang:pi; angbins(end)=[]; angbins=angbins+dang/2;  
+cols=[ [1 0 0]; [255 175 3]/255];
+
+
+for d=1:2%1:2
+% dat='open';
+% dat='car';
+dat=dats{d};
+
+% close all;
+
+
+
+ 
+% close all;
+histlim=5*dacc;
+
+
+
+angbins=-pi:dang:pi; angbins(end)=[]; angbins=angbins+dang/2;  
+Ha=zeros(numel(angbins),numel(accbins)); Oa=Ha;
+Hva=Ha;
+
+myfl=['data_fig_3.db'];
+
+nr=numel(unique(who('-file',myfl)));
+L={};
+L.Fr=cell(1,-1+2*numel(accbins));
+L.BFr=cell(-1+2*numel(accbins),7);
+% L.Occ=zeros(1,21);
+% L.Fr=L.Occ; L.BFr=L.Occ;
+for i=[1 3:nr] %2 hippocampal drive not in hippocampus
+    rat=['rat' num2str(i)];
+    db_all.(dat)=load(myfl,rat,'-mat');
+    db_all.(dat)=db_all.(dat).(rat);
+
+    sel=true(size(db_all.(dat).accel));
+    if isempty(sel)
+        warning([num2str(i) 'empty']);
+        continue;
+    end
+    
+
+    sel=sel&db_all.(dat).t_dist>5;
+
+    sel1=conv(double(sel), ones(25,1)/25,'same');
+    sel=sel1>.99;
+    selx=abs(cos(db_all.(dat).anga-db_all.(dat).angv))>.9;
+    xxx=db_all.(dat).accel.*sign(cos(db_all.(dat).anga-db_all.(dat).angv));
+
+    frw=50*5;
+%     [Hs,Occs,Hv]=freq_hist(db_all.(dat).accel(sel),mod(db_all.(dat).anga(sel)-db_all.(dat).angv(sel)+pi,2*pi)-pi,db_all.(dat).freq(sel)-fr_baseline(sel),accbins,angbins);
+    [Hs,Occs,Hv]=freq_hist(db_all.(dat).accel(sel),mod(db_all.(dat).anga(sel)-db_all.(dat).angv(sel)+pi,2*pi)-pi,db_all.(dat).freq(sel),accbins,angbins);
+
+    % Linear results
+    
+    rxxx=round(xxx/dacc);
+%     L.Occ=arrayfun(@(x) sum(rxxx(sel & selx)==x),-10:10);
+    ws=50*[1 2 5 10 20 50 200];
+    aend=accbins(end)/dacc;
+    for w=1:numel(ws)
+        fr_baseline=nanconv(db_all.(dat).freq,ones(ws(w),1)/ws(w)); %10 s window
+        
+        for x=-aend:aend
+            xj=aend+1+x;
+            if w==1
+                L.Fr{xj}=[L.Fr{xj}; db_all.(dat).freq(rxxx==x & sel & selx)];
+            end
+            L.BFr{xj,w}=[L.BFr{xj,w}; fr_baseline(rxxx==x & sel & selx)];        
+        end    
+    end
+    
+    Ha=Ha+Hs.*Occs; Oa=Oa+Occs; Hva=Hva+Hv.*Occs;
+    Haccum{d}=Ha;
+    Oaccum{d}=Oa;
+    Laccum{d}=L;
+
+end
+
+end
+%%
+close all
+for d=1:2
+angbins(101)=angbins(1)+2*pi;
+
+f=figure;
+hold off;
+H=Haccum{d}; O=Oaccum{d};
+H(O>0)=H(O>0)./O(O>0);
+histlim=max(O(:))/10000;
+
+H(O<histlim)=nan;
+Hs=gauss_smoothing_2D(repmat(H,3,1),[1.5 3]);
+h1=size(H,1);
+Hs=Hs(h1+(1:h1),:);
+
+polar3d(flipud(Hs'),angbins(1),angbins(end),accbins(1),accbins(size(Hs,2)),1,'surf'); shading flat;view(2); 
+axis equal; view([-90,90]); axis tight; 
+hold on;
+
+colormap(jet2);
+
+cc=colorbar;
+set(cc,'YTick',7.5:.5:10);
+name=['fig2a_' dat];
+plot3([0 100],200*[1 1],[15 15],'k')
+caxis([8.1 9.7])
+
+
+
+name=['colormap_' dats{d}];
+end
+%%
+f=figure;
+accbins2=[fliplr(-accbins(2:end)) accbins];
+for d=2
+    L=Laccum{d};
+    sel=cellfun(@numel,L.Fr);
+    sel=sel>max(sel)/10000;
+    mn=cellfun(@mean,L.Fr);
+    eom=cellfun(@std,L.Fr)./sqrt(cellfun(@numel,L.Fr));
+    errorbar(accbins2(sel),mn(sel),eom(sel),'Color',cols(d,:));
+    hold on;
+    plot(accbins2(sel),mn(sel),'Color',cols(d,:),'LineWidth',3);
+    dat_acc=arrayfun(@(x) accbins2(x)*ones(size(L.Fr{x})),1:numel(L.Fr),'UniformOutput',0);
+ 
+end
+axis([-220 240 7.95 10]);
+set(gca,'YTick',6:.5:12)
+

code/Fig_3/Fig_3.m~

@@ -0,0 +1,180 @@
+close all;
+% load('N:\kropff\analyisis\figs\fig2a\all open data_q95.db','-mat');
+% load('N:\kropff\analyisis\figs\fig2a\all car data_q95.db','-mat');
+% % % load('all open data.db','-mat');
+% % % load('all car data.db','-mat');
+% db_all.car=db_car;
+% db_all.open=db_open;
+% clear db_car; clear db_open;
+%     addpath('/gpfs/work/kropff/common_functions/');
+
+dats={'ec', 'hc'};
+% Haccum={};
+w1=0; w2=3;
+jet2=interp1(1:64,jet,(1+w1:(64-w1-w2-1)/(64-1):64-w2));
+
+% load(['all ' dat ' data.db'],'-mat')
+dang=pi/50;
+dacc=20;%15;
+accbins=0:dacc:200; %350
+angbins=-pi:dang:pi; angbins(end)=[]; angbins=angbins+dang/2;  
+cols=[ [1 0 0]; [255 175 3]/255];
+
+
+for d=1:2%1:2
+% dat='open';
+% dat='car';
+dat=dats{d};
+
+% close all;
+
+
+
+ 
+% close all;
+histlim=5*dacc;
+
+
+
+angbins=-pi:dang:pi; angbins(end)=[]; angbins=angbins+dang/2;  
+Ha=zeros(numel(angbins),numel(accbins)); Oa=Ha;
+Hva=Ha;
+
+myfl=['data_fig_3.db'];
+
+nr=numel(unique(who('-file',myfl)));
+L={};
+L.Fr=cell(1,-1+2*numel(accbins));
+L.BFr=cell(-1+2*numel(accbins),7);
+% L.Occ=zeros(1,21);
+% L.Fr=L.Occ; L.BFr=L.Occ;
+for i=[1 3:nr] %2 hippocampal drive not in hippocampus
+    rat=['rat' num2str(i)];
+    db_all.(dat)=load(myfl,rat,'-mat');
+    db_all.(dat)=db_all.(dat).(rat);
+
+    sel=true(size(db_all.(dat).accel));
+    if isempty(sel)
+        warning([num2str(i) 'empty']);
+        continue;
+    end
+    
+
+    sel=sel&db_all.(dat).t_dist>5;
+
+    sel1=conv(double(sel), ones(25,1)/25,'same');
+    sel=sel1>.99;
+    selx=abs(cos(db_all.(dat).anga-db_all.(dat).angv))>.9;
+    xxx=db_all.(dat).accel.*sign(cos(db_all.(dat).anga-db_all.(dat).angv));
+
+    frw=50*5;
+%     [Hs,Occs,Hv]=freq_hist(db_all.(dat).accel(sel),mod(db_all.(dat).anga(sel)-db_all.(dat).angv(sel)+pi,2*pi)-pi,db_all.(dat).freq(sel)-fr_baseline(sel),accbins,angbins);
+    [Hs,Occs,Hv]=freq_hist(db_all.(dat).accel(sel),mod(db_all.(dat).anga(sel)-db_all.(dat).angv(sel)+pi,2*pi)-pi,db_all.(dat).freq(sel),accbins,angbins);
+
+    % Linear results
+    
+    rxxx=round(xxx/dacc);
+%     L.Occ=arrayfun(@(x) sum(rxxx(sel & selx)==x),-10:10);
+    ws=50*[1 2 5 10 20 50 200];
+    aend=accbins(end)/dacc;
+    for w=1:numel(ws)
+        fr_baseline=nanconv(db_all.(dat).freq,ones(ws(w),1)/ws(w)); %10 s window
+        
+        for x=-aend:aend
+            xj=aend+1+x;
+            if w==1
+                L.Fr{xj}=[L.Fr{xj}; db_all.(dat).freq(rxxx==x & sel & selx)];
+            end
+            L.BFr{xj,w}=[L.BFr{xj,w}; fr_baseline(rxxx==x & sel & selx)];        
+        end    
+    end
+    
+    Ha=Ha+Hs.*Occs; Oa=Oa+Occs; Hva=Hva+Hv.*Occs;
+    Haccum{d}=Ha;
+    Oaccum{d}=Oa;
+    Laccum{d}=L;
+
+end
+
+end
+%%
+close all
+for d=1:2
+angbins(101)=angbins(1)+2*pi;
+
+f=figure;
+hold off;
+H=Haccum{d}; O=Oaccum{d};
+H(O>0)=H(O>0)./O(O>0);
+histlim=max(O(:))/10000;
+
+H(O<histlim)=nan;
+Hs=gauss_smoothing_2D(repmat(H,3,1),[1.5 3]);
+h1=size(H,1);
+Hs=Hs(h1+(1:h1),:);
+
+polar3d(flipud(Hs'),angbins(1),angbins(end),accbins(1),accbins(size(Hs,2)),1,'surf'); shading flat;view(2); 
+axis equal; view([-90,90]); axis tight; 
+hold on;
+
+colormap(jet2);
+
+cc=colorbar;
+set(cc,'YTick',7.5:.5:10);
+name=['fig2a_' dat];
+plot3([0 100],200*[1 1],[15 15],'k')
+caxis([8.1 9.7])
+
+
+
+name=['colormap_' dats{d}];
+end
+%%
+f=figure;
+accbins2=[fliplr(-accbins(2:end)) accbins];
+for d=2
+    L=Laccum{d};
+    sel=cellfun(@numel,L.Fr);
+    sel=sel>max(sel)/10000;
+    mn=cellfun(@mean,L.Fr);
+    eom=cellfun(@std,L.Fr)./sqrt(cellfun(@numel,L.Fr));
+    errorbar(accbins2(sel),mn(sel),eom(sel),'Color',cols(d,:));
+    hold on;
+    plot(accbins2(sel),mn(sel),'Color',cols(d,:),'LineWidth',3);
+    dat_acc=arrayfun(@(x) accbins2(x)*ones(size(L.Fr{x})),1:numel(L.Fr),'UniformOutput',0);
+ 
+end
+axis([-220 240 7.95 10]);
+set(gca,'YTick',6:.5:12)
+
+%%
+f=figure;
+accbins2=[fliplr(-accbins(2:end)) accbins];
+for d=2
+    subplot(1,2,d)
+    for j=[3 ]
+        
+    L=Laccum{d};
+    L.Fr2=arrayfun(@(x) L.Fr{x}-L.BFr{x,j},1:numel(L.Fr),'UniformOutput',0);
+    sel=cellfun(@numel,L.Fr2);
+    sel=sel>max(sel)/10000 & abs(accbins2)<210;
+    mn=cellfun(@mean,L.Fr2);
+    eom=cellfun(@std,L.Fr2)./sqrt(cellfun(@numel,L.Fr2));
+    errorbar(accbins2(sel),mn(sel),eom(sel),'Color',cols(d,:)*(j-1)/6);
+    hold on;
+    plot(accbins2(sel),mn(sel),'Color',cols(d,:)*(j-1)/6,'LineWidth',3);
+    end
+
+    
+    axis([-220 240 -.25 2]);
+    golden(2.5)
+set(gca,'YTick',-2:.5:12)
+
+    
+    dat_acc=arrayfun(@(x) accbins2(x)*ones(size(L.Fr2{x})),1:numel(L.Fr2),'UniformOutput',0);    
+%     [cr_neg,p_neg]=corr(cell2mat(dat_acc(sel & accbins2<0)'),cell2mat(L.Fr2(sel & accbins2<0)'))
+%     [cr_neg,p_neg]=corr(accbins2(sel & accbins2<0)',mn(sel & accbins2<0)');
+    lneg_bas = fitlm(accbins2(sel & accbins2<0)',mn(sel & accbins2<0)','linear')
+    lpos_bas = fitlm(accbins2(sel & accbins2>0)',mn(sel & accbins2>0)','linear')
+    
+end

code/Fig_3/data_fig_3.db

@@ -0,0 +1 @@
+/annex/objects/MD5-s1831968181--c6324a1e14a92575b78e9b2d687066c3

code/Fig_3/gauss_smoothing_2D.m

@@ -0,0 +1,12 @@
+function map=gauss_smoothing_2D(map, sigma)
+% filt=fspecial('gaussian',[ceil(3*sigma(1)) ceil(3*sigma(2))]);%,sigma(1));
+l1=ceil(3*sigma(1));
+l2=ceil(3*sigma(2));
+[X,Y]=meshgrid(-l1:l1,-l2:l2);
+filt=exp(-.5*((X/sigma(1)).^2+(Y/sigma(2)).^2));
+filt=filt/sum(filt(:));
+[m1,m2]=size(map);
+
+% map=repmat(map,3,3); % angular data can be tricky
+map=nanconv(map,filt,'same','edge','2d' );
+% map=map(m1+(1:m1),m2+(1:m2));

code/Fig_3d/Fig_3d.m

@@ -0,0 +1,116 @@
+close all;
+res3={};
+fcol=figure;
+    acclim=50; 
+    savename=['intrinsic_results_ns_res' num2str(acclim) '.mat'];
+    
+    
+
+    load(savename,'-mat');
+    types=fields(res2);
+    for ty=1:numel(types)
+    typ=types{ty};
+
+ f1=figure
+
+    gs=1;
+    res=res2;
+
+
+        
+    autoc_step=.002;
+    autoc_bins=1:300; 
+    bins=(autoc_bins)*autoc_step;
+    phbins_norm=(pi/60:pi/60:5*pi)/2/pi;
+
+%%
+
+    hold on;
+    aux=nanmean(res.(typ).right);
+    aux=aux/max(aux);
+    fm=do_plot(bins,aux,[0 0 0],.5*[1 1 1]);    
+
+   
+    aux=nanmean(res.(typ).left);
+    aux=aux/max(aux);
+    fc=do_plot(bins,aux,[1 0 0],[1 .5 .5]);    
+    
+    aux=res.(typ).control2(1,:);
+    aux=aux/max(aux);
+    fneut=do_plot(bins,aux,[0 122 255]/255,[51 226 217]/255);     
+
+    subplot(2,2,1)
+    set(gca,'XTick',0:.1:.6);
+    axis([0 .3 0 1]);
+    set(gca,'YTick',0:.5:2,'FontName','Arial');
+    ss=subplot(2,2,2)
+    set(gca,'XTick',0:.1:.6);
+    axis([0.0 .2 0.25*[-1 1] ]);
+    set(gca,'YTick',-.6:.2:.6,'FontName','Arial');
+    title(typ);
+
+    %%
+        fsh=res.(typ).fcontrol2(:);
+    
+    %%
+        subplot(2,2,4)
+    fbins=6:.02:9;
+        h=hist(fsh,fbins);
+
+        h=h/sum(h);
+        bar(fbins,h,'EdgeColor','none','FaceColor',[51 226 217]/255,'BarWidth',.8);
+
+        gg=fit(fbins',h(:),'gauss1');
+        hold on;
+        p1=plot(gg);
+        set(p1,'Color',[0 122 255]/255);
+        legend off;
+        mx=ceil(1.1*max(max(h),gg.a1));
+        line(fm*[1 1],[0 mx],'LineWidth',2,'Color',[1 1 1]*0);
+        line(fc*[1 1],[0 mx],'LineWidth',2,'Color',[1 0 0]);
+        axis([7.3 9 0 0.3]);
+        set(gca,'XTick',7:.5:10,'YTick',0:0.1:1);
+
+
+%%
+if ty<numel(types)
+figure(fcol)
+ subplot(2,3,ty)
+        h=hist(fsh,fbins);
+ 
+
+        h=h/sum(h);
+        bar(fbins,h,'EdgeColor','none','FaceColor',[51 226 217]/255,'BarWidth',1.0);
+
+        gg=fit(fbins',h(:),'gauss1');
+        hold on;
+        p1=plot(gg);
+        set(p1,'Color',[0 122 255]/255);
+        legend off;
+        mx=ceil(1.1*max(max(h),gg.a1));
+        line(fm*[1 1],[0 mx],'LineWidth',1,'Color',[1 1 1]*0);
+        line(fc*[1 1],[0 mx],'LineWidth',1,'Color',[1 0 0]);
+        axis([7.3 9 0 0.3]);
+
+        set(gca,'XTick',7:.5:9,'XTickLabel',{7,'',8,'',9});
+else
+
+    ylim([0 .5]);
+    xlim([7.9 9])
+        subplot(2,2,3)
+
+
+                
+                subplot(2,2,2)
+                axis([.09 .15 -.05 .08])
+             
+                
+
+end
+  end
+ 
+  
+    
+
+
+%%

code/Fig_3d/Fig_3d.m~

@@ -0,0 +1,120 @@
+close all;
+res3={};
+fcol=figure;
+    acclim=50; 
+    savename=['intrinsic_results_ns_res' num2str(acclim) '.mat'];
+    
+    
+
+    load(savename,'-mat');
+    types=fields(res2);
+    for ty=1:numel(types)
+    typ=types{ty};
+%         vs={'pn','pm','cfm','cfc','cshm','cshst','cshp99','cshp95','cshp5','cshp1'};
+%         for j=1:numel(vs)
+%             eval([vs{j} '=nan*acclist;']);
+%         end
+ f1=figure
+
+    gs=1;
+    res=res2;
+
+
+        
+    autoc_step=.002;
+    autoc_bins=1:300; 
+    bins=(autoc_bins)*autoc_step;
+    phbins_norm=(pi/60:pi/60:5*pi)/2/pi;
+
+%%
+
+    hold on;
+    aux=nanmean(res.(typ).right);
+    aux=aux/max(aux);
+    fm=do_plot(bins,aux,[0 0 0],.5*[1 1 1]);    
+
+   
+    aux=nanmean(res.(typ).left);
+    aux=aux/max(aux);
+    fc=do_plot(bins,aux,[1 0 0],[1 .5 .5]);    
+    
+    aux=res.(typ).control2(1,:);
+    aux=aux/max(aux);
+    fneut=do_plot(bins,aux,[0 122 255]/255,[51 226 217]/255);     
+
+    subplot(2,2,1)
+    set(gca,'XTick',0:.1:.6);
+    axis([0 .3 0 1]);
+    set(gca,'YTick',0:.5:2,'FontName','Arial');
+    ss=subplot(2,2,2)
+    set(gca,'XTick',0:.1:.6);
+    axis([0.0 .2 0.25*[-1 1] ]);
+    set(gca,'YTick',-.6:.2:.6,'FontName','Arial');
+    title(typ);
+
+    %%
+        fsh=res.(typ).fcontrol2(:);
+    
+    %%
+        subplot(2,2,4)
+    fbins=6:.02:9;
+        h=hist(fsh,fbins);
+
+        h=h/sum(h);
+        bar(fbins,h,'EdgeColor','none','FaceColor',[51 226 217]/255,'BarWidth',.8);
+
+        gg=fit(fbins',h(:),'gauss1');
+        hold on;
+        p1=plot(gg);
+        set(p1,'Color',[0 122 255]/255);%[.5 1 .5]-.5);
+        legend off;
+        mx=ceil(1.1*max(max(h),gg.a1));
+        line(fm*[1 1],[0 mx],'LineWidth',2,'Color',[1 1 1]*0);
+        line(fc*[1 1],[0 mx],'LineWidth',2,'Color',[1 0 0]);
+        axis([7.3 9 0 0.3]);
+        set(gca,'XTick',7:.5:10,'YTick',0:0.1:1);
+
+
+%%
+if ty<numel(types)
+figure(fcol)
+ subplot(2,3,ty)
+        h=hist(fsh,fbins);
+ 
+
+        h=h/sum(h);
+        bar(fbins,h,'EdgeColor','none','FaceColor',[51 226 217]/255,'BarWidth',1.0);
+
+        gg=fit(fbins',h(:),'gauss1');
+        hold on;
+        p1=plot(gg);
+        set(p1,'Color',[0 122 255]/255);
+        legend off;
+        mx=ceil(1.1*max(max(h),gg.a1));
+        line(fm*[1 1],[0 mx],'LineWidth',1,'Color',[1 1 1]*0);
+        line(fc*[1 1],[0 mx],'LineWidth',1,'Color',[1 0 0]);
+        axis([7.3 9 0 0.3]);
+
+        set(gca,'XTick',7:.5:9,'XTickLabel',{7,'',8,'',9});%,'YTick',0:0.5:1);
+
+else
+
+    ylim([0 .5]);
+    xlim([7.9 9])
+        subplot(2,2,3)
+
+
+                
+                subplot(2,2,2)
+                axis([.09 .15 -.05 .08])
+             
+                
+
+end
+  end
+ 
+  
+    
+
+
+%%

code/Fig_3d/do_fit.m

@@ -0,0 +1,18 @@
+function [c,g]=do_fit(bins,aux)
+c={};
+c.f=nan;
+g=nan;
+
+if ~any(isnan(aux))
+sel=bins>0.015 & bins<0.3;
+
+
+fo = fitoptions('Method','NonlinearLeastSquares',...
+               'Lower',[0.2,0,0,0,6,-1,-1],...
+               'Upper',[2,20,1,Inf,12,1,1],...
+               'StartPoint',[1,3,.2,1,8,0,0]);
+ft = fittype('a*exp(-b*x)+c*cos(2*pi*f*x)*exp(-d*x)+g+h*x','options',fo);
+
+
+[c, g] = fit(bins(sel)',aux(sel)',ft);
+end

code/Fig_3d/do_plot.m

@@ -0,0 +1,29 @@
+function ff=do_plot(bins,aux,col,col2)    
+
+[curve2, ~] = do_fit(bins,aux);
+
+aux=aux/feval(curve2,0);
+[curve2, gof2] = do_fit(bins,aux);
+
+
+subplot(2,2,1)
+p1=plot(curve2);
+set(p1,'Color',col2,'LineWidth',2);
+hold on;
+plot(bins,aux,'Color',col);
+
+legend off;
+axis([0 .3 0 1.2])
+
+subplot(2,2,2)
+
+plot(bins,curve2.c.*cos(2*pi*curve2.f.*bins).*exp(-curve2.d.*bins),'Color',col2,'LineWidth',2);
+hold on;
+plot(bins,aux-feval(curve2,bins)'+curve2.c.*cos(2*pi*curve2.f.*bins).*exp(-curve2.d.*bins),'Color',col);
+
+
+disp(['freq: ' trunc(curve2.f,3) '  R^{2}: ' trunc(gof2.rsquare,3)]);
+ff=curve2.f;
+axis([0 .3 -.2 .2])
+
+

code/Fig_5/Fig_5.m

@@ -0,0 +1,44 @@
+function Fig_5()
+x=-50:1:50; % 50 samples per second
+close all;
+f2=figure;
+speed=.92*exp(-(x/15).^2);
+acc=get_acceleration(x,0,speed)/5;
+acc0=get_acceleration0(x,0,acc);
+th=smooth_theta2(acc,1-exp(-.1));
+ff=90; fg=.8;
+
+subplot(2,3,1)
+plot(x,[speed*fg; acc*ff*fg*.8; acc*ff]');
+
+set(gca,'YTick',-1:1,'XTick',-100:50:100);
+axis([-50 50 -1 1])
+axis square
+% hold on;
+
+subplot(2,3,2)
+plot(x,[speed*fg; acc*ff*fg*.8; acc0*ff]');
+set(gca,'YTick',-1:1,'XTick',-100:50:100);
+axis([-50 50 -1 1])
+axis square
+
+subplot(2,3,3)
+plot(x,[speed*fg; acc*ff*fg*.8; th*ff]');
+
+set(gca,'YTick',-1:1,'XTick',-100:50:100);
+axis([-50 50 -1 1])
+axis square
+[corr(speed',acc') corr(acc',acc');
+corr(speed',acc0') corr(acc',acc0');
+corr(speed',th') corr(acc',th')]
+
+
+
+
+function acc=get_acceleration(x,x0,speed)
+x=x-x0;
+acc=[0 speed(3:end)-speed(1:end-2) 0]/2;
+
+
+function acc=get_acceleration0(x,x0,acc)
+acc(acc<0)=0;

code/Fig_5/Fig_5.m~

@@ -0,0 +1,44 @@
+function Fig_5()
+x=-50:1:50;
+close all;
+f2=figure;
+speed=.92*exp(-(x/15).^2);
+acc=get_acceleration(x,0,speed)/5;
+acc0=get_acceleration0(x,0,acc);
+th=smooth_theta2(acc,1-exp(-.1));%r(i)*(1+decay)>th(i-1)
+ff=90; fg=.8;
+
+subplot(2,3,1)
+plot(x,[speed*fg; acc*ff*fg*.8; acc*ff]');
+
+set(gca,'YTick',-1:1,'XTick',-100:50:100);
+axis([-50 50 -1 1])
+axis square
+% hold on;
+
+subplot(2,3,2)
+plot(x,[speed*fg; acc*ff*fg*.8; acc0*ff]');
+set(gca,'YTick',-1:1,'XTick',-100:50:100);
+axis([-50 50 -1 1])
+axis square
+
+subplot(2,3,3)
+plot(x,[speed*fg; acc*ff*fg*.8; th*ff]');
+
+set(gca,'YTick',-1:1,'XTick',-100:50:100);
+axis([-50 50 -1 1])
+axis square
+[corr(speed',acc') corr(acc',acc');
+corr(speed',acc0') corr(acc',acc0');
+corr(speed',th') corr(acc',th')]
+
+
+
+
+function acc=get_acceleration(x,x0,speed)
+x=x-x0;
+acc=[0 speed(3:end)-speed(1:end-2) 0]/2;
+
+
+function acc=get_acceleration0(x,x0,acc)
+acc(acc<0)=0;

code/Fig_5/smooth_theta2.m

@@ -0,0 +1,12 @@
+function th=smooth_theta2(acc0,decay)
+
+racc=acc0;
+racc(acc0<0)=0;
+th=racc(1)*ones(size(racc));
+for i=2:numel(racc)
+    if racc(i)*(1+decay)>th(i-1) 
+        th(i)=racc(i); 
+    else        
+        th(i)=th(i-1)*(1-decay);    
+    end
+end

code/Fig_6/Fig_6.m

@@ -0,0 +1,67 @@
+load('data_fig_6.db','-mat');
+close all;
+
+
+%%
+f=figure;
+subplot(2,1,1)
+sel=1:numel(data.v);
+speeds=data.v(sel)';
+accs=[0 speeds(3:end)-speeds(1:end-2) 0]/.04;
+ths=smooth_theta2(accs,1-exp(-.1)); 
+
+raccs=accs;
+raccs(raccs<0)=0;
+
+plot(sel/50,(speeds)/std(speeds),'r');
+hold on;
+
+
+plot(sel/50,accs/std(accs),'b');
+plot(sel/50,ths/std(accs),'g');
+xlim([0 10])
+
+
+ths=8+ths/100;
+da=20; abins=-5:5;
+dv=5; vbins=0:12;
+rv=round(speeds/dv);
+ra=round(accs/da);
+vfr=arrayfun(@(x) mean(ths(rv==x)),vbins);
+afr=arrayfun(@(x) mean(ths(ra==x)),abins);
+m8=vfr(1);
+[th_f5,th_s5]=mybp(ths-m8,0.5);
+[th_f1,th_s1]=mybp(ths-m8,0.1);
+[th_f3,th_s3]=mybp(ths-m8,0.3);
+
+vfr_f=arrayfun(@(x) mean(th_f5(rv==x))+m8,vbins);
+vfr_s=arrayfun(@(x) mean(th_s5(rv==x))+m8,vbins);
+
+afr_f=arrayfun(@(x) mean(th_f1(ra==x))+m8,abins);
+afr_s=arrayfun(@(x) mean(th_s1(ra==x))+m8,abins);
+
+
+subplot(2,2,3)
+plot(vbins*dv,[vfr; vfr_s; vfr_f]');
+axis([0 50 7.8 9.1])
+golden(2.5)
+
+subplot(2,2,4)
+plot(abins*da,[afr; afr_s; afr_f]');
+axis([-120 140 7.8 9.1])
+set(gca,'YTick',6:.5:12);
+
+
+
+[corr(speeds',ths') corr(accs',ths');
+corr(speeds',th_f5') corr(accs',th_f1');
+corr(speeds',th_s5') corr(accs',th_s1')]
+drawnow;
+
+
+
+
+function [th_f,th_s]=mybp(ths,f1)
+    th_f=real(bandpass_and_hilbert(ths',f1,100,50)');
+    th_s=real(bandpass_and_hilbert(ths',0,f1,50)');
+end

code/Fig_6/Fig_6.m~

@@ -0,0 +1,79 @@
+% f1=figure;
+mypath='/mnt/Vol2/kropff/analyisis/figs/theta_new/fig2abc-openfield theta/clean_test/';
+files=dir([mypath 'peak_matr_final_f2_*.db']);
+
+close all;
+for ifile=14%1:numel(files) % 14 10
+p=load([mypath files(ifile).name],'-mat');%3 6 8
+data=p.out.data{1};
+
+%%
+% close all;
+f=figure;
+subplot(2,1,1)
+sel=1:numel(data.v);
+speeds=data.v(sel)';
+accs=[0 speeds(3:end)-speeds(1:end-2) 0]/.04;
+% ths=smooth_theta(accs,0.1); %0.06
+ths=smooth_theta2(accs,1-exp(-.1)); %0.06
+
+raccs=accs;
+raccs(raccs<0)=0;
+
+plot(sel/50,(speeds)/std(speeds),'r');
+hold on;
+
+
+plot(sel/50,accs/std(accs),'b');
+plot(sel/50,ths/std(accs),'g');
+xlim([0 10])
+
+
+%
+% sel2=true(size(speeds));
+ths=8+ths/100;
+da=20; abins=-5:5;
+dv=5; vbins=0:12;
+rv=round(speeds/dv);
+ra=round(accs/da);
+vfr=arrayfun(@(x) mean(ths(rv==x)),vbins);
+afr=arrayfun(@(x) mean(ths(ra==x)),abins);
+m8=vfr(1);%mean(ths);
+[th_f5,th_s5]=mybp(ths-m8,0.5);
+[th_f1,th_s1]=mybp(ths-m8,0.1);
+[th_f3,th_s3]=mybp(ths-m8,0.3);
+
+vfr_f=arrayfun(@(x) mean(th_f5(rv==x))+m8,vbins);
+vfr_s=arrayfun(@(x) mean(th_s5(rv==x))+m8,vbins);
+
+afr_f=arrayfun(@(x) mean(th_f1(ra==x))+m8,abins);
+afr_s=arrayfun(@(x) mean(th_s1(ra==x))+m8,abins);
+
+
+subplot(2,2,3)
+plot(vbins*dv,[vfr; vfr_s; vfr_f]');
+axis([0 50 7.8 9.1])
+golden(2.5)
+
+subplot(2,2,4)
+plot(abins*da,[afr; afr_s; afr_f]');
+axis([-120 140 7.8 9.1])
+set(gca,'YTick',6:.5:12);
+
+
+
+[corr(speeds',ths') corr(accs',ths');
+corr(speeds',th_f5') corr(accs',th_f1');
+corr(speeds',th_s5') corr(accs',th_s1')]
+drawnow;
+end
+
+
+
+
+function [th_f,th_s]=mybp(ths,f1)
+    th_f=real(bandpass_and_hilbert(ths',f1,100,50)');
+    th_f=abs(th_f).*cos(angle(th_f));
+    th_s=bandpass_and_hilbert(ths',0,f1,50)';
+    th_s=abs(th_s).*cos(angle(th_s));
+end

code/Fig_6/bandpass_and_hilbert.m

@@ -0,0 +1,65 @@
+%%% copyright 2012, Emilio Kropff
+%%% output:  first dim = number of frequencies
+%%%         second dim = number of trials
+%%%          third dim = number of timestamps
+
+
+
+function h=bandpass_and_hilbert(x,flow,fhigh,sampling)
+    l=size(x,1); 
+
+    NFFT= 2^nextpow2(l); % power of 2 is a faster fft 
+
+    x_fft=fft(x,NFFT);
+
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    % Hilbert Transform, taken from the MATLAB function hilbert(). h is
+    % the 'filter' applied on the fft of the signal. The filter
+    % consists in multiplying the fft by i*sign(freq). But the output
+    % is hilbert(x)=x-i*transform(x), which results in a total filter that multyplies
+    % by 2 positive frequencies and by 0 negative frequencies (and by 1 the
+    % two central frequencies).
+    % http://en.wikipedia.org/wiki/Hilbert_transform#Relationship_with_
+    % the_Fourier_transform
+    hilbert_filter  = zeros(NFFT,1); 
+    % NFFT is even (it is a power of 2) so there are two central frequencies
+    hilbert_filter([1 NFFT/2+1]) = 1; 
+    hilbert_filter(2:NFFT/2) = 2;   %positive freqs (upper part of the circle)        
+
+    
+    fft_freqs=sampling/2*(linspace(0,1,NFFT/2+1))'; %This includes only the upper part of the circle
+    
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    % Bandpass Filter
+    bandpass_filter=bsxfun(@gt,fft_freqs,flow) & bsxfun(@lt,fft_freqs,fhigh);
+    bandpass_filter=[bandpass_filter; bandpass_filter(end-1:-1:2,:)]; 
+
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    % Finally, perform the bandpass and hilbert transform in one step
+    % (blocked)
+    
+%     nfreqmax=floor(.5*10^8/numel(x_fft));
+    nfreqmax=floor(.1*10^8/numel(x_fft));
+    fstart=1; fend=min(numel(flow),fstart-1+nfreqmax);
+    h=NaN(l,numel(flow));
+    while fstart<=fend
+        haux=ifft(bsxfun(@times,x_fft,bsxfun(@times,bandpass_filter(:,fstart:fend),hilbert_filter)));%combined_filter(:,ones(1,car.nlaps)));
+        h(:,fstart:fend)=haux(1:l,:);
+        fstart=fend+1;
+        fend=min(numel(flow),fstart-1+nfreqmax);
+    end
+
+%     h=squeeze(h);
+%     if nargout>1
+%         bpx=zeros(nf,nx,l);
+%         for f=1:nf
+%             bandpass_filter=(fft_freqs>flow).*(fft_freqs<fhigh);
+%             bandpass_filter=[bandpass_filter; bandpass_filter(end-1:-1:2)];             
+%             hilbert_transf=ifft(bsxfun(@times,x_fft,bandpass_filter));
+%             bpx(f,:,:)=hilbert_transf(1:l,:);
+%         end
+%         bpx=squeeze(bpx);
+%     end    
+
+
+ 

code/Fig_6/data_fig_6.db

@@ -0,0 +1 @@
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code/Fig_6/smooth_theta2.m

@@ -0,0 +1,12 @@
+function th=smooth_theta2(acc0,decay)
+
+racc=acc0;
+racc(acc0<0)=0;
+th=racc(1)*ones(size(racc));
+for i=2:numel(racc)
+    if racc(i)*(1+decay)>th(i-1) 
+        th(i)=racc(i); 
+    else        
+        th(i)=th(i-1)*(1-decay);    
+    end
+end

datacite.yml

@@ -0,0 +1,84 @@
+# Metadata for DOI registration according to DataCite Metadata Schema 4.1.
+# For detailed schema description see https://doi.org/10.5438/0014
+
+## Required fields
+
+# The main researchers involved. Include digital identifier (e.g., ORCID)
+# if possible, including the prefix to indicate its type.
+authors:
+  -
+    firstname: "Emilio"
+    lastname: "Kropff"
+    
+  -
+    firstname: "James E."
+    lastname: "Carmichael"
+
+  -
+    firstname: "Edvard I."
+    lastname: "Moser"
+
+  -
+    firstname: "May-Britt"
+    lastname: "Moser"
+
+# A title to describe the published resource.
+title: "Dataset - Frequency of theta rhythm is controlled by acceleration but not speed in running rats"
+
+# Additional information about the resource, e.g., a brief abstract.
+description: |
+  Dataset corresponding to the paper
+  Frequency of theta rhythm is controlled by acceleration but not speed in running rats
+
+# Lit of keywords the resource should be associated with.
+# Give as many keywords as possible, to make the resource findable.
+keywords:
+  - Speed
+  - Acceleration
+  - Theta rhythm
+  - Entorhinal cortex
+  - Hippocampus
+  - Space
+  - Grid cells
+  - Head direction cells
+  - Border cells
+  - Speed cells
+
+# License information for this resource. Please provide the license name and/or a link to the license.
+# Please add also a corresponding LICENSE file to the repository.
+license:
+  name: "Creative Commons CC BY-NC-ND Attribution-NonCommercial-NoDerivatives 4.0 International"
+  url: "https://creativecommons.org/licenses/by-nc-nd/4.0/"
+
+
+
+## Optional Fields
+
+# Funding information for this resource.
+# Separate funder name and grant number by comma.
+funding:
+  - "EU, Advanced Investigator Grants from the European Research Council (‘CIRCUIT’, Grant Agreement N°232608; ‘GRIDCODE’, Grant Agreement N°338865)"
+  - "EU, FP7 FET Proactive Programme on Neuro-Bio-Inspired Systems (Grant Agreement 600725)"
+  - "EU, FP7 collaborative project (‘SPACEBRAIN’, Grant Agreement N°200873)"
+  - "Norway, Centre of Excellence scheme and the National Infrastructure Scheme of the Research Council of Norway RCN (Centre for Neural Computation, grant number 223262; NORBRAIN, grant number 197467 and 295721)"
+  - "Norway, RCN FRIPRO grants (grant numbers 286225 and 300394)"
+  - "Argentina, PICT 2015-1273"
+  - "Kavli Foundation"
+
+# Related publications. reftype might be: IsSupplementTo, IsDescribedBy, IsReferencedBy.
+# Please provide digital identifier (e.g., DOI) if possible.
+# Add a prefix to the ID, separated by a colon, to indicate the source.
+# Supported sources are: DOI, arXiv, PMID
+# In the citation field, please provide the full reference, including title, authors, journal etc.
+references:
+  -
+    id: "10.1016/j.neuron.2021.01.017"
+    reftype: "IsSupplementTo"
+    citation: "Kropff E., Carmichael J.E., Moser E.I. and Moser M-B., 2021, Frequency of theta rhythm is controlled by acceleration but not speed in running rats, Neuron"
+
+
+# Resource type. Default is Dataset, other possible values are Software, DataPaper, Image, Text.
+resourcetype: Dataset
+
+# Do not edit or remove the following line
+templateversion: 1.2

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