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dfs_ripcontent_3state.m
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dfs_ripcontent_3state.m
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%% Sungod manu: Plot main results
animals = {'jaq','roquefort','despereaux','montague'}; %,, 'remy',};%};
epochfilter{1} = ['$ripthresh>=0 & (isequal($environment,''goal'')) & $forageassist==0 & $gooddecode==1']; % & $decode_error<=1
%epochfilter{1} = ['$session==27'];
% resultant excludeperiods will define times when velocity is high
timefilter{1} = {'ag_get2dstate', '($immobility == 1)','immobility_velocity',4,'immobility_buffer',0};
iterator = 'epochbehaveanal';
f = createfilter('animal',animals,'epochs',epochfilter,'excludetime', timefilter, 'iterator', iterator);
f = setfilterfunction(f, 'dfa_ripcontent', {'ripdecodesv2','trials','pos'});
f = runfilter(f);
animcol = [27 92 41; 25 123 100; 33 159 169; 123 225 191]./255; %ctrlcols
%% plot fraction coherent, local, salient, overall and by trial phase and taskphase [2F,3A]
clearvars -except f animals animcol
bars = figure(); set(gcf,'Position',[66 305 1853 551]);
contentthresh = .3;
for a = 1:length(animals)
eps = find(arrayfun(@(x) ~isempty(x.trips),f(a).output{1}));
for e = 1:length(eps)
tphasenum = f(a).output{1}(eps(e)).trips.taskphase;
valtrials = ~isnan(tphasenum);
tphasenum = [tphasenum(valtrials), [1:sum(valtrials)]']; % add trial numbers
homerips = f(a).output{1}(eps(e)).trips.homeripcontent(valtrials);
hometypes = f(a).output{1}(eps(e)).trips.homeripmaxtypes(valtrials);
rwrips = f(a).output{1}(eps(e)).trips.RWripcontent(valtrials);
postrwrips = f(a).output{1}(eps(e)).trips.postRWripcontent(valtrials);
rwtypes = f(a).output{1}(eps(e)).trips.RWripmaxtypes(valtrials); %
postrwtypes = f(a).output{1}(eps(e)).trips.postRWripmaxtypes(valtrials); %
outerrips = f(a).output{1}(eps(e)).trips.outerripcontent(valtrials);
outertypes = f(a).output{1}(eps(e)).trips.outerripmaxtypes(valtrials); %
goals = f(a).output{1}(eps(e)).trips.goalarm(valtrials,:);
goals(tphasenum(:,1)<=1,1) = nan; % turn currgoals during search trials into nans
goals(goals(:,1)==0,1) = nan;
outers = f(a).output{1}(eps(e)).trips.outerarm(valtrials);
pastwlock = f(a).output{1}(eps(e)).trips.prevarm(valtrials,2); % only consider the including lockout option
trialstack = [outers', pastwlock, goals,tphasenum(:,1)];
clear replays homereplays rwreplays postrwreplays outerreplays boxreplays
% salient - can be past, future, or any previously rewarded arm OR just any previously rewarded arm (p/f not salient)(as for FIG7A)
% MAXSTATES 0=nonclassified, 1=cont 2=frag 3=hover
for t=1:length(homerips) % extract valid rips and tack on trial info: [replay future past currgoal prevgoal ppgoal tphase salient local TYPE(cont/stat)]
if ~isempty(homerips{t})
[maxval,ind] = max(homerips{t},[],2); %(:,2:end)
valid = (hometypes{t}'==1 | hometypes{t}'==3) & maxval>contentthresh;
%homereplays{t} =[ind(valid)-1,repmat(trialstack(t,:),sum(valid),1), ismember(ind(valid)-1,[trialstack(t,1:2),unique(goals(1:t,1))']),ind(valid)-1==0];
homereplays{t} =[ind(valid)-1,repmat(trialstack(t,:),sum(valid),1), ismember(ind(valid)-1,[unique(goals(1:t,1))']),ind(valid)-1==0,hometypes{t}(valid)'];
else homereplays{t} = []; end
if ~isempty(rwrips{t})
[maxval,ind] = max(rwrips{t},[],2); %(:,2:end)
valid = (rwtypes{t}'==1 | rwtypes{t}'==3) & maxval>contentthresh;
%rwreplays{t} = [ind(valid)-1,repmat(trialstack(t,:),sum(valid),1), ismember(ind(valid)-1,[trialstack(t,1:2),unique(goals(1:t,1))']),ind(valid)-1==0];
rwreplays{t} = [ind(valid)-1,repmat(trialstack(t,:),sum(valid),1), ismember(ind(valid)-1,[unique(goals(1:t,1))']),ind(valid)-1==0, rwtypes{t}(valid)'];
else rwreplays{t} = []; end
if ~isempty(postrwrips{t})
[maxval,ind] = max(postrwrips{t},[],2); %(:,2:end)
valid = (postrwtypes{t}'==1 | postrwtypes{t}'==3) & maxval>contentthresh;
%postrwreplays{t} = [ind(valid)-1,repmat(trialstack(t,:),sum(valid),1), ismember(ind(valid)-1,[trialstack(t,1:2),unique(goals(1:t,1))']),ind(valid)-1==0];
postrwreplays{t} = [ind(valid)-1,repmat(trialstack(t,:),sum(valid),1), ismember(ind(valid)-1,[unique(goals(1:t,1))']),ind(valid)-1==0, postrwtypes{t}(valid)'];
else postrwreplays{t} = []; end
if ~isempty(outerrips{t}) % box is considered salient in option 1, not in option 2
[maxval,ind] = max(outerrips{t},[],2); %(:,2:end)
valid = (outertypes{t}'==1 | outertypes{t}'==3) & maxval>contentthresh;
%outerreplays{t} = [ind(valid)-1,repmat(trialstack(t,:),sum(valid),1), ismember(ind(valid)-1,[0,trialstack(t,1:2),unique(goals(1:t,1))']),ind(valid)-1==trialstack(t,1)]; % include box as salient
outerreplays{t} = [ind(valid)-1,repmat(trialstack(t,:),sum(valid),1), ismember(ind(valid)-1,[unique(goals(1:t,1))']),ind(valid)-1==trialstack(t,1),outertypes{t}(valid)']; % include box as salient
if any(valid)
outerreplays{t}(outerreplays{t}(:,9)==1,8) = 0; % correct salience to not include local events
end
else outerreplays{t} = []; end
end
allreplay = [homereplays';rwreplays'; postrwreplays'; outerreplays']; allreplay = vertcat(allreplay{:});
allhome{a}{e} = vertcat(homereplays{:}); allrw{a}{e} = vertcat(rwreplays{:}); allpostrw{a}{e} = vertcat(postrwreplays{:}); allouter{a}{e} = vertcat(outerreplays{:});
allbox{a}{e} = [homereplays';rwreplays'; postrwreplays']; allbox{a}{e} = vertcat(allbox{a}{e}{:});
fraccohall{a}(e,1) = size(allreplay,1)/size([vertcat(homerips{:});vertcat(rwrips{:});vertcat(postrwrips{:});vertcat(outerrips{:})],1);
fracremoteall{a}(e,1) = sum(allreplay(:,9)==0)/size([vertcat(homerips{:});vertcat(rwrips{:});vertcat(postrwrips{:});vertcat(outerrips{:})],1); % out of ALL detected SWRSsize(allreplay,1)
fracsalientall{a}(e,1) = sum(allreplay(:,8)==1 & allreplay(:,9)==0)/sum(allreplay(:,9)==0); % out of nonlocal events
fraccohbyphase{a}(e,:) = [size(allhome{a}{e},1)/size(vertcat(homerips{:}),1),size(allrw{a}{e},1)/size(vertcat(rwrips{:}),1), ...
size(allpostrw{a}{e},1)/size(vertcat(postrwrips{:}),1),size(allouter{a}{e},1)/size(vertcat(outerrips{:}),1)];
fracremotebyphase{a}(e,:) = [sum(allhome{a}{e}(:,9)==0)/size(allhome{a}{e},1),sum(allrw{a}{e}(:,9)==0)/size(allrw{a}{e},1),sum(allpostrw{a}{e}(:,9)==0)/size(allpostrw{a}{e},1),sum(allouter{a}{e}(:,9)==0)/size(allouter{a}{e},1)];
fracsalientbyphase{a}(e,:) = [sum(allhome{a}{e}(:,8)==1 & allhome{a}{e}(:,9)==0)/sum(allhome{a}{e}(:,9)==0),sum(allrw{a}{e}(:,8)==1 & allrw{a}{e}(:,9)==0)/sum(allrw{a}{e}(:,9)==0), ...
sum(allpostrw{a}{e}(:,8)==1 & allpostrw{a}{e}(:,9)==0)/sum(allpostrw{a}{e}(:,9)==0),sum(allouter{a}{e}(:,8)==1 & allouter{a}{e}(:,9)==0)/sum(allouter{a}{e}(:,9)==0)]; % out of nonlocal events
fraccohboxouter{a}(e,:) = [size(allbox{a}{e},1)/size([vertcat(homerips{:});vertcat(rwrips{:});vertcat(postrwrips{:})],1),size(allouter{a}{e},1)/size(vertcat(outerrips{:}),1)];
fracremoteboxouter{a}(e,:) = [sum(allbox{a}{e}(:,9)==0)/size(allbox{a}{e},1),sum(allouter{a}{e}(:,9)==0)/size(allouter{a}{e},1)];
fracsalientboxouter{a}(e,:) = [sum(allbox{a}{e}(:,8)==1 & allbox{a}{e}(:,9)==0)/sum(allbox{a}{e}(:,9)==0),sum(allouter{a}{e}(:,8)==1 & allouter{a}{e}(:,9)==0)/sum(allouter{a}{e}(:,9)==0)]; % out of nonlocal events
searchonly_fracNONsalientboxouter{a}(e,1) = sum(allbox{a}{e}(:,7)<1 & allbox{a}{e}(:,8)==0 & allbox{a}{e}(:,9)==0)/sum(allbox{a}{e}(:,7)<1 & allbox{a}{e}(:,9)==0); % out of nonlocal events
%subplot(221); hold on; plot(histcounts(allhome{a}{e}(:,1),[0:9],'Normalization','probability'),'.'); title([animals{a} 'home'])
%subplot(222); hold on; plot(histcounts(allrw{a}{e}(:,1),[0:9],'Normalization','probability'),'.'); title('rw')
%subplot(223); hold on; plot(histcounts(allpostrw{a}{e}(:,1),[0:9],'Normalization','probability'),'.'); title('postrw')
%subplot(224); hold on; plot(histcounts(allouter{a}{e}(:,1),[0:9],'Normalization','probability'),'.'); title('outer')
end
%boxfracs{a} = mean(cell2mat(cellfun(@(x) [sum(x(:,9)==1);sum(x(:,9)==0 & x(:,8)==1);sum(x(:,9)==0 & x(:,8)==0)]./size(x,1),allbox{a},'un',0)),2);
%fracs = cell2mat(cellfun(@(x) [sum(x(:,9)==1);sum(x(:,1)==x(:,5));sum(x(:,1)~=x(:,5) & x(:,8)==1); sum(x(:,9)==0 & x(:,8)==0)]./size(x,1),allbox{a},'un',0));
%subplot(2,1,1); %pie(mean(fracs,2),{'local','prevgoal','salient nonprevgoal','nonsalient'});
%bar(a,mean(fracs,2),'stacked');title([animals{a} 'box'])
% for outer need to isolate box (is considered salient). local is not salient
%outerfracs{a} = mean(cell2mat(cellfun(@(x) [sum(x(:,9)==1);sum(x(:,1)==0); sum(x(:,1)~=0 & x(:,8)==1); sum(x(:,9)==0 & x(:,8)==0 & x(:,1)~=0)]./size(x,1),allouter{a},'un',0)),2);
%outerfracs{a} = mean(cell2mat(cellfun(@(x) [sum(x(:,9)==1);sum(x(:,1)==0); sum(x(:,1)==x(:,5)); sum(x(:,1)~=x(:,5) & x(:,8)==1 & x(:,1)~=0); sum(x(:,8)==0 & x(:,9)==0 & x(:,1)~=0)]./size(x,1),allouter{a},'un',0));
%subplot(2,4,a+4); pie(mean(fracs,2),{'local','box','salient nonbox','nonsalient'}); title('outer')
% total num box local/remote + outer local/box/remote arm pie
totalnums{a} = [sum(cellfun(@(x) sum(x(:,9)==1 & x(:,10)==1),allbox{a})), sum(cellfun(@(x) sum(x(:,9)==1 & x(:,10)==3),allbox{a})), ...
sum(cellfun(@(x) sum(x(:,9)==0 & x(:,10)==1),allbox{a})), sum(cellfun(@(x) sum(x(:,9)==0 & x(:,10)==3),allbox{a})), ...
sum(cellfun(@(x) sum(x(:,9)==1),allouter{a})), sum(cellfun(@(x) sum(x(:,1)==0),allouter{a})),sum(cellfun(@(x) sum(x(:,9)==0 & x(:,1)>0),allouter{a}))] ...
./(sum(cellfun(@(x) size(x,1),allbox{a}))+sum(cellfun(@(x) size(x,1),allouter{a})));
subplot(1,4,a); pie(totalnums{a}); text(0,-1.5,['n=',num2str(sum(cellfun(@(x) size(x,1),allbox{a}))+sum(cellfun(@(x) size(x,1),allouter{a})))]);
title(animals{a})
end
legend({'box local cont','box local stat','box remote cont','box remote stat','outer local','outer box','outer remotearm'})
%subplot(1,2,1); bar(horzcat(boxfracs{:})','stacked'); title('box replays'); legend({'local','salient','nonsalient'}); ylim([0 1]); ylabel('fraction of coherent SWRs')
%subplot(1,2,2); bar(horzcat(outerfracs{:})','stacked'); title('outer replays'); legend({'local','box','salient','nonsalient'}); xlabel('subject')
figure;
subplot(3,3,1); hold on; plot4a(fraccohall,'gnames',{'coherent'}); ylim([0 1]); title('out of all swrs'); ylabel('frac of all events')
% subplot(3,3,2); hold on; plot4a(fracremoteall,'gnames',{'remote'}); ylim([0 1]); title('out of ALL SWRs')
% text([6:9],[.6 .7 .8 .9],num2str(cellfun(@(x) mean(x),fracremoteall)','%.04f')); text([7:10],[.6 .7 .8 .9],num2str(cellfun(@length,fracremoteall)'))
% subplot(3,3,3); hold on; plot4a(fracsalientall,'gnames',{'salient'}); ylim([0 1]); title('out of all remote')
% subplot(3,3,4); hold on; plot4a(fraccohbyphase,'gnames',{'home','rw','postrw','outer'}); ylim([0 1]); title('coherent'); ylabel('frac of all events')
% subplot(3,3,5); hold on; plot4a(fracremotebyphase,'gnames',{'home','rw','postrw','outer'}); ylim([0 1]); title('remote out of all coh')
% subplot(3,3,6); hold on; plot4a(fracsalientbyphase,'gnames',{'home','rw','postrw','outer'}); ylim([0 1]); title('salient')
% subplot(3,3,7); hold on; plot4a(fraccohboxouter,'gnames',{'box','outer'}); ylim([0 1]); title('coherent'); ylabel('frac of all events')
% subplot(3,3,8); hold on; plot4a(fracremoteboxouter,'gnames',{'box','outer'}); ylim([0 1]); title('remote out of all coh')
% subplot(3,3,9); hold on; plot4a(searchonly_fracNONsalientboxouter,'gnames',{'box'}); ylim([0 1]); title('NOT salient(prevrewarded) of out of all rem')
%% measure replay rate of an arm during contingencies before during/after it is goal [FIG 5]
clearvars -except f animals animcol
figure; set(gcf,'Position',[675 1 1202 973]); plt=1;
contentthresh = .3;
window = 40; %trials for trialcurves
kernel = gaussian(1,5); %[0 0 1 0 0]; %
for a = 1:length(animals)
eps = find(arrayfun(@(x) ~isempty(x.trips),f(a).output{1}));
for e = 1:length(eps)
cont = f(a).output{1}(eps(e)).trips.contingency;
valtrials = cont<=8; % don't use any part of the epoch with repeat goals
cont=cont(valtrials);
homerips = f(a).output{1}(eps(e)).trips.homeripcontent(valtrials);
hometypes = f(a).output{1}(eps(e)).trips.homeripmaxtypes(valtrials);
rwrips = f(a).output{1}(eps(e)).trips.RWripcontent(valtrials);
postrwrips = f(a).output{1}(eps(e)).trips.postRWripcontent(valtrials);
rwtypes = f(a).output{1}(eps(e)).trips.RWripmaxtypes(valtrials); %
postrwtypes = f(a).output{1}(eps(e)).trips.postRWripmaxtypes(valtrials); %
rips = cellfun(@(x,y,z) [x;y;z],homerips,rwrips,postrwrips,'un',0);
types = cellfun(@(x,y,z) [x,y,z]',hometypes,rwtypes,postrwtypes,'un',0);
outerrips = f(a).output{1}(eps(e)).trips.outerripcontent(valtrials);
outertypes = f(a).output{1}(eps(e)).trips.outerripmaxtypes(valtrials);
goals = f(a).output{1}(eps(e)).trips.goalarm(valtrials);
outers = f(a).output{1}(eps(e)).trips.outerarm(valtrials);
pastwlock = f(a).output{1}(eps(e)).trips.prevarm(valtrials,2); % only consider the including lockout option
tphasenum = [f(a).output{1}(eps(e)).trips.taskphase(valtrials), [1:length(goals)]']; % add trial numbers
clear replays outerreplays
for t=1:length(rips)
if ~isempty(rips{t})
[maxval,ind] = max(rips{t},[],2); %(:,2:end)
valid = types{t}==1 & maxval>contentthresh;
replays{t} = ind(valid)'-1; %
else replays{t} = []; end
if ~isempty(outerrips{t})
[maxval,ind] = max(outerrips{t},[],2); %(:,2:end)
valid = outertypes{t}'==1 & maxval>contentthresh;
outerreplays{t} = ind(valid)'-1; %
else outerreplays{t} = []; end
end
% smoothing is ok here bc this is just used for visualizing the curves across trial, not quantification per phase
countspertrial = zeros(8,length(outers));
countspertrial(:,~cellfun(@isempty,replays)) = cell2mat(cellfun(@(x) histcounts(x,[1:9])',replays(~cellfun(@isempty,replays)),'un',0));
for r = 1:8; countspertrial(r,:) = smoothvect(countspertrial(r,:), kernel); end
vispertrial = cell2mat(cellfun(@(x) histcounts(x,[1:9])',num2cell(outers),'un',0));
ind=1;
for c = 1:max(cont) % for each contingency, count search and repeat phases separately
searchreplays = replays(cont==c & tphasenum(:,1)<=1);
replaycount{a}{e}(ind,:) = histcounts(cell2mat(searchreplays),[1:9]);
trialcount{a}{e}(ind,:) = repmat(sum(cont==c & tphasenum(:,1)<=1),1,8);
visitcount{a}{e}(ind,:) = histcounts(outers(cont==c & tphasenum(:,1)<=1),[1:9]);
ecp{a}{e}(ind,:) = [e, c, 1]; % epoch cont phase (search=1, repeat=2);
goalarm{a}{e}(ind) = nan;
if ind==1
goalreplay{a}{e}(ind,:) = [nan sum(replaycount{a}{e}(ind,:))];
cumgoalreplay{a}{e}(ind,:) = goalreplay{a}{e}(ind,:);
else
goalsofar = goalarm{a}{e}(~isnan(goalarm{a}{e}));
goalreplay{a}{e}(ind,:) = [sum(replaycount{a}{e}(ind,goalsofar)),sum(replaycount{a}{e}(ind,~ismember([1:8],goalsofar)))]; % totalgoalreplay totalngreplay cumgoalreplay cumngreplay
cumgoalreplay{a}{e}(ind,:) = nansum([goalreplay{a}{e}(ind,:);cumgoalreplay{a}{e}(ind-1,:)]);
end
ind = ind+1;
if any(cont==c & tphasenum(:,1)==4) % needs to earn at least 4 goals for this cont to count
replaycount{a}{e}(ind,:) = histcounts(cell2mat(replays(cont==c & tphasenum(:,1)>1)),[1:9]);
trialcount{a}{e}(ind,:) = repmat(sum(cont==c & tphasenum(:,1)>1),1,8);
goalarm{a}{e}(ind) = goals(cont==c & tphasenum(:,1)==1); % convenient to use first trial to indicate goal
visitcount{a}{e}(ind,:) = histcounts(outers(cont==c & tphasenum(:,1)>1),[1:9]);
ecp{a}{e}(ind,:) = [e c 2]; % epoch cont phase (search=1, repeat=2);
goalsofar = goalarm{a}{e}(~isnan(goalarm{a}{e}));
goalreplay{a}{e}(ind,:) = [sum(replaycount{a}{e}(ind,goalsofar)),sum(replaycount{a}{e}(ind,~ismember([1:8],goalsofar)))]; % totalgoalreplay totalngreplay cumgoalreplay cumngreplay
cumgoalreplay{a}{e}(ind,:) = nansum([goalreplay{a}{e}(ind,:);cumgoalreplay{a}{e}(ind-1,:)]);
searchlength{a}{e}(c) = sum(cont==c & tphasenum(:,1)<1); % if complete cont, store search length for later
else % not enough of this goal experienced
replaycount{a}{e}(ind,:) = nan(1,8);
trialcount{a}{e}(ind,:) = nan(1,8);
visitcount{a}{e}(ind,:) = nan(1,8);
goalarm{a}{e}(ind) = nan;
ecp{a}{e}(ind,:) = [nan nan nan]; % epoch cont phase (search=1, repeat=2);
goalreplay{a}{e}(ind,:) = [nan nan]; % totalgoalreplay totalngreplay cumgoalreplay cumngreplay
cumgoalreplay{a}{e}(ind,:) = [nan nan];
end
ind = ind+1;
end
replaypertrial{a}{e} = [nan(4,8); replaycount{a}{e}./trialcount{a}{e}; nan(6,8)]; % pad with nans for alignment
goalarm{a}{e} = [nan(1,4), goalarm{a}{e},nan(1,6)];
visitcount{a}{e} = [nan(4,8); visitcount{a}{e}; nan(6,8)];
goallist = unique(goalarm{a}{e}(~isnan(goalarm{a}{e})),'stable');
nevergoals{a}{e} = find(~ismember([1:8],goallist));
%subplot(1,4,a);hold on;
%if length(goallist)>3
realignednongoal{a}{e} = [];
realignednongoalvis{a}{e} = [];
bytrial_firstrew_non{a}{e} = [];
bytrial_lastrew_non{a}{e} = [];
bytrial_start_non{a}{e} = [];
bytrial_firstrewvis_non{a}{e} = [];
bytrial_lastrewvis_non{a}{e} = [];
for c = 1:length(goallist)
inds = [-4+find(goalarm{a}{e}==goallist(c)):find(goalarm{a}{e}==goallist(c))+6];
realigned{a}{e}(c,:) = replaypertrial{a}{e}(inds,goallist(c));
realignedvis{a}{e}(c,:) = visitcount{a}{e}(inds,goallist(c));
if ~isempty(nevergoals{a}{e})
%randnongoal = randi(length(nevergoals{a}{e})); % choose a random unrewarded arm
%allbutgoal = find(goallist(c)~=[1:8]);
realignednongoal{a}{e} = [realignednongoal{a}{e}; replaypertrial{a}{e}(inds,nevergoals{a}{e})']; % add all nonrewarded arms to list
%realignednongoal{a}{e} = [realignednongoal{a}{e}; replaypertrial{a}{e}(inds,allbutgoal)']; % add all nonrewarded arms to list
realignednongoalvis{a}{e} = [realignednongoalvis{a}{e}; visitcount{a}{e}(inds,nevergoals{a}{e})'];
end
goalnum{a}{e}(c) = sum(tphasenum(cont==c,1)>=1 & mod(tphasenum(cont==c,1),1)==0);
% trialwise curves (aligned to first and last reward of cont)
start = find(cont==c & tphasenum(:,1)==1);
firstinds = start-window:start+window;
bytrial_firstrew{a}{e}(c,:) = nan(1,2*window+1);
bytrial_firstrew{a}{e}(c,find(firstinds>0 & firstinds<=length(outers)))= countspertrial(goallist(c),firstinds(firstinds>0 & firstinds<=length(outers)));
bytrial_firstrewvis{a}{e}(c,:) = nan(1,2*window+1);
bytrial_firstrewvis{a}{e}(c,find(firstinds>0 & firstinds<=length(outers)))= vispertrial(goallist(c),firstinds(firstinds>0 & firstinds<=length(outers)));
start= find(cont==c & tphasenum(:,1)==max(tphasenum(cont==c & mod(tphasenum(:,1),1)==0,1)));
lastinds = start-window:start+window;
bytrial_lastrew{a}{e}(c,:) = nan(1,2*window+1);
bytrial_lastrew{a}{e}(c,find(lastinds>0 & lastinds<=length(outers)))= countspertrial(goallist(c),lastinds(lastinds>0 & lastinds<=length(outers)));
bytrial_lastrewvis{a}{e}(c,:) = nan(1,2*window+1);
bytrial_lastrewvis{a}{e}(c,find(lastinds>0 & lastinds<=length(outers)))= vispertrial(goallist(c),lastinds(lastinds>0 & lastinds<=length(outers)));
%post-cont replay/visit count
if sum(lastinds>start & lastinds<=length(outers))==window % only collect data if all trials available
postreplaycount{a}{e}(c) = sum(countspertrial(goallist(c),lastinds(lastinds>start & lastinds<=length(outers))));
postviscount{a}{e}(c) = sum(vispertrial(goallist(c),lastinds(lastinds>start & lastinds<=length(outers))));
else
postreplaycount{a}{e}(c) = nan;
postviscount{a}{e}(c) = nan;
end
start= find(cont==c,1);
startinds = start-window:start+window;
bytrial_start{a}{e}(c,:) = nan(1,2*window+1);
bytrial_start{a}{e}(c,find(startinds>0 & startinds<=length(outers)))= countspertrial(goallist(c),startinds(startinds>0 & startinds<=length(outers)));
if ~isempty(nevergoals{a}{e})
tmp = nan(length(nevergoals{a}{e}),2*window+1);
tmp(:,find(firstinds>0 & firstinds<=length(outers)))= countspertrial(nevergoals{a}{e},firstinds(firstinds>0 & firstinds<=length(outers)));
bytrial_firstrew_non{a}{e} = [bytrial_firstrew_non{a}{e}; tmp];
tmp = nan(length(nevergoals{a}{e}),2*window+1);
tmp(:,find(firstinds>0 & firstinds<=length(outers)))= vispertrial(nevergoals{a}{e},firstinds(firstinds>0 & firstinds<=length(outers)));
bytrial_firstrewvis_non{a}{e} = [bytrial_firstrewvis_non{a}{e}; tmp];
tmp = nan(length(nevergoals{a}{e}),2*window+1);
tmp(:,find(lastinds>0 & lastinds<=length(outers)))= countspertrial(nevergoals{a}{e},lastinds(lastinds>0 & lastinds<=length(outers)));
bytrial_lastrew_non{a}{e} = [bytrial_lastrew_non{a}{e}; tmp];
tmp = nan(length(nevergoals{a}{e}),2*window+1);
tmp(:,find(lastinds>0 & lastinds<=length(outers)))= vispertrial(nevergoals{a}{e},lastinds(lastinds>0 & lastinds<=length(outers)));
bytrial_lastrewvis_non{a}{e} = [bytrial_lastrewvis_non{a}{e}; tmp];
tmp = nan(length(nevergoals{a}{e}),2*window+1);
tmp(:,find(startinds>0 & startinds<=length(outers)))= countspertrial(nevergoals{a}{e},startinds(startinds>0 & startinds<=length(outers)));
bytrial_start_non{a}{e} = [bytrial_start_non{a}{e}; tmp];
end
% plot([1:9],replaypertrial{a}{e}(inds,goallist(c)),'k-o');
end
%realigned{a}{e} = realigned{a}{e}(searchlength(1:length(goallist))>=4,:);
%realignednongoal{a}{e} = realignednongoal{a}{e}(searchlength(1:length(goallist))>=4,:);
%end
%figure; subplot(3,1,1); plot(replaypertrial{a}{e}); legend; title(['ep',num2str(e)])
%subplot(3,1,2); hold on; plot(realigned{a}{e}'); plot(realignednongoal{a}{e}',':'); title(num2str(unique(goalarm{a}{e}(~isnan(goalarm{a}{e})),'stable')));
%subplot(3,1,3); hold on; plot(nanmean(realigned{a}{e})); plot(nanmean(realignednongoal{a}{e}),':');
end
% BY TRIAL CURVES - aligned to first reward of cont
% xvals = -window:window;
% firstrewcurve = vertcat(bytrial_firstrew{a}{:}); firstrewcurve_non = vertcat(bytrial_firstrew_non{a}{:});
% %firstrewcurve = vertcat(bytrial_start{a}{:}); firstrewcurve_non = vertcat(bytrial_start_non{a}{:});
% %valeps = ~cellfun(@isempty,bytrial_firstrew{a}) & cellfun(@(x) sum(~isnan(x)),goalarm{a})>=1;
% %firstrewcurve = cell2mat(cellfun(@(x) x(1,:)',bytrial_firstrew{a}(valeps),'un',0))'; % just plot a single contingency#
% %firstrewcurve_non = cell2mat(cellfun(@(x,y) x((1-1)*length(y)+[1:length(y)],:)',bytrial_firstrew_non{a}(valeps),nevergoals{a}(valeps),'un',0))'; % just plot a single contingency#
% subplot(4,4,a+12); hold on; ylabel('replay/trial'); xlabel('trials from first rew of block')
% h = fill([xvals, fliplr(xvals)], [nanmean(firstrewcurve)-nanstd(firstrewcurve)./sqrt(sum(~isnan(firstrewcurve))), fliplr(nanmean(firstrewcurve)+nanstd(firstrewcurve)./sqrt(sum(~isnan(firstrewcurve))))],animcol(a,:),'FaceAlpha',.3);%
% set(h,'EdgeColor','none'); plot(xvals, nanmean(firstrewcurve),'Color',animcol(a,:),'Linewidth',1);
% h = fill([xvals, fliplr(xvals)], [nanmean(firstrewcurve_non)-nanstd(firstrewcurve_non)./sqrt(sum(~isnan(firstrewcurve_non))), fliplr(nanmean(firstrewcurve_non)+nanstd(firstrewcurve_non)./sqrt(sum(~isnan(firstrewcurve_non))))],animcol(a,:),'FaceAlpha',.3);%
% set(h,'EdgeColor','none'); plot(xvals, nanmean(firstrewcurve_non),':','Color',animcol(a,:),'Linewidth',1);
% ylim([0 2]); plot([0 0],[0 2],'k'); title(animals{a}); xlim([-40 40]);
% for r = 1:length(xvals)
% [p{a}(r),~]=ranksum(firstrewcurve(:,r),firstrewcurve_non(:,r)); % ttest for paired, ttest2 for unpaired
% end
% plot(xvals(p{a}<.05/length(xvals)),1.8*ones(sum(p{a}<.05/length(xvals)),1),'r.');
% firstviscurve = vertcat(bytrial_firstrewvis{a}{:}); firstviscurve_non = vertcat(bytrial_firstrewvis_non{a}{:});
% %valeps = ~cellfun(@isempty,bytrial_lastrew{a}) & cellfun(@(x) sum(~isnan(x)),goalarm{a})>=1;
% %lastrewcurve = cell2mat(cellfun(@(x) x(1,:)',bytrial_lastrew{a}(valeps),'un',0))'; % just plot a single contingency#
% %lastrewcurve_non = cell2mat(cellfun(@(x,y) x((1-1)*length(y)+[1:length(y)],:)',bytrial_lastrew_non{a}(valeps),nevergoals{a}(valeps),'un',0))'; % just plot a single contingency#
% subplot(4,4,a+12); hold on; ylabel('visits/trial'); xlabel('trials from first reward of block')
% h = fill([xvals, fliplr(xvals)], [nanmean(firstviscurve)-nanstd(firstviscurve)./sqrt(sum(~isnan(firstviscurve))), fliplr(nanmean(firstviscurve)+nanstd(firstviscurve)./sqrt(sum(~isnan(firstviscurve))))],animcol(a,:),'FaceAlpha',.3);%
% set(h,'EdgeColor','none'); plot(xvals, nanmean(firstviscurve),'Color',animcol(a,:),'Linewidth',1);
% h = fill([xvals, fliplr(xvals)], [nanmean(firstviscurve_non)-nanstd(firstviscurve_non)./sqrt(sum(~isnan(firstviscurve_non))), fliplr(nanmean(firstviscurve_non)+nanstd(firstviscurve_non)./sqrt(sum(~isnan(firstviscurve_non))))],animcol(a,:),'FaceAlpha',.3);%
% set(h,'EdgeColor','none'); plot(xvals, nanmean(firstviscurve_non),':','Color',animcol(a,:),'Linewidth',1);
% plot([0 0],[0 12],'k'); ylim([0 1.5]); title(animals{a}); xlim([-40 40]);
% for r = 1:length(xvals)
% [p{a}(r),~]=ranksum(firstviscurve(:,r),firstviscurve_non(:,r)); % ttest for paired, ttest2 for unpaired
% end
% plot(xvals(p{a}<.05/length(xvals)),1.2*ones(sum(p{a}<.05/length(xvals)),1),'r.');
% TRIALCURVES - aligned to last reward of cont
xvals = -window:window;
lastrewcurve = vertcat(bytrial_lastrew{a}{:}); lastrewcurve_non = vertcat(bytrial_lastrew_non{a}{:});
subplot(4,4,a); hold on; ylabel('replay/trial'); xlabel('trials from last rew of block')
h = fill([xvals, fliplr(xvals)], [nanmean(lastrewcurve)-nanstd(lastrewcurve)./sqrt(sum(~isnan(lastrewcurve))), fliplr(nanmean(lastrewcurve)+nanstd(lastrewcurve)./sqrt(sum(~isnan(lastrewcurve))))],animcol(a,:),'FaceAlpha',.3);%
set(h,'EdgeColor','none'); plot(xvals, nanmean(lastrewcurve),'Color',animcol(a,:),'Linewidth',1);
h = fill([xvals, fliplr(xvals)], [nanmean(lastrewcurve_non)-nanstd(lastrewcurve_non)./sqrt(sum(~isnan(lastrewcurve_non))), fliplr(nanmean(lastrewcurve_non)+nanstd(lastrewcurve_non)./sqrt(sum(~isnan(lastrewcurve_non))))],animcol(a,:),'FaceAlpha',.3);%
set(h,'EdgeColor','none'); plot(xvals, nanmean(lastrewcurve_non),':','Color',animcol(a,:),'Linewidth',1);
ylim([0 2]); plot([0 0],[0 2],'k'); title(animals{a}); xlim([-40 40]);
for r = 1:length(xvals)
[p{a}(r),~]=ranksum(lastrewcurve(:,r),lastrewcurve_non(:,r)); % ttest for paired, ttest2 for unpaired
end
plot(xvals(p{a}<.05/length(xvals)),1.8*ones(sum(p{a}<.05/length(xvals)),1),'r.');
text(-20,1.5,sprintf('n=%drew,%dunrew',size(lastrewcurve,1),size(lastrewcurve_non,1)));
%lastrewcurve = vertcat(bytrial_lastrew{a}{:}); lastrewcurve_non = vertcat(bytrial_lastrew_non{a}{:});
lastviscurve = vertcat(bytrial_lastrewvis{a}{:}); lastviscurve_non = vertcat(bytrial_lastrewvis_non{a}{:});
%valeps = ~cellfun(@isempty,bytrial_lastrew{a}) & cellfun(@(x) sum(~isnan(x)),goalarm{a})>=1;
%lastrewcurve = cell2mat(cellfun(@(x) x(1,:)',bytrial_lastrew{a}(valeps),'un',0))'; % just plot a single contingency#
%lastrewcurve_non = cell2mat(cellfun(@(x,y) x((1-1)*length(y)+[1:length(y)],:)',bytrial_lastrew_non{a}(valeps),nevergoals{a}(valeps),'un',0))'; % just plot a single contingency#
subplot(4,4,a+4); hold on; ylabel('visits/trial'); xlabel('trials from last reward of block')
h = fill([xvals, fliplr(xvals)], [nanmean(lastviscurve)-nanstd(lastviscurve)./sqrt(sum(~isnan(lastviscurve))), fliplr(nanmean(lastviscurve)+nanstd(lastviscurve)./sqrt(sum(~isnan(lastviscurve))))],animcol(a,:),'FaceAlpha',.3);%
set(h,'EdgeColor','none'); plot(xvals, nanmean(lastviscurve),'Color',animcol(a,:),'Linewidth',1);
h = fill([xvals, fliplr(xvals)], [nanmean(lastviscurve_non)-nanstd(lastviscurve_non)./sqrt(sum(~isnan(lastviscurve_non))), fliplr(nanmean(lastviscurve_non)+nanstd(lastviscurve_non)./sqrt(sum(~isnan(lastviscurve_non))))],animcol(a,:),'FaceAlpha',.3);%
set(h,'EdgeColor','none'); plot(xvals, nanmean(lastviscurve_non),':','Color',animcol(a,:),'Linewidth',1);
plot([0 0],[0 12],'k'); ylim([0 1]); title(animals{a}); xlim([-40 40]);
for r = 1:length(xvals)
[p{a}(r),~]=ranksum(lastviscurve(:,r),lastviscurve_non(:,r)); % ttest for paired, ttest2 for unpaired
end
plot(xvals(p{a}<.05/length(xvals)),.9*ones(sum(p{a}<.05/length(xvals)),1),'r.');
clear p
% BY CONTINGENCY
allgoal = vertcat(realigned{a}{:});
allnon = vertcat(realignednongoal{a}{:});
%valeps = ~cellfun(@isempty,realigned{a}) & cellfun(@(x) any(x>5),searchlength{a}); %cellfun(@(x) sum(~isnan(x)),goalarm{a})>=2
%allgoal = cell2mat(cellfun(@(x,y) x(y>5,:)',realigned{a}(valeps),searchlength{a}(valeps),'un',0))'; % just plot a single contingency#
%allgoal = cell2mat(cellfun(@(x) x(2,:)',realigned{a}(valeps),'un',0))'; % just plot a single contingency#
%allnon = cell2mat(cellfun(@(x,y) x((2-1)*length(y)+[1:length(y)],:)',realignednongoal{a}(valeps),nevergoals{a}(valeps),'un',0))'; % just plot a single contingency#
reps = 6:11; % reps = 1:9;
subplot(4,4,a+8); hold on; ylabel('replays/trial')
errorbar(1:6,nanmean(allgoal(:,reps)),nanstd(allgoal(:,reps))./sqrt(sum(~isnan(allgoal(:,reps)))),'.','Color',animcol(a,:));
bar(1:6,nanmean(allgoal(:,reps)),.3,'Facecolor',animcol(a,:));
errorbar(1.3:6.3,nanmean(allnon(:,reps)),nanstd(allnon(:,reps))./sqrt(sum(~isnan(allnon(:,reps)))),'.','Color',animcol(a,:));
bar(1.3:6.3,nanmean(allnon(:,reps)),.3,'Facecolor',animcol(a,:),'Facealpha',.3); ylim([0 1.5]); xlim([.5 7;]);
% h = fill([reps, fliplr(reps)], [nanmean(allgoal(:,reps))-nanstd(allgoal(:,reps))./sqrt(sum(~isnan(allgoal(:,reps)))), fliplr(nanmean(allgoal(:,reps))+nanstd(allgoal(:,reps))./sqrt(sum(~isnan(allgoal(:,reps)))))],animcol(a,:),'FaceAlpha',.3);%
% set(h,'EdgeColor','none'); plot(reps, nanmean(allgoal(:,reps)),'.-','Color',animcol(a,:),'Linewidth',1);
% h = fill([reps, fliplr(reps)], [nanmean(allnon(:,reps))-nanstd(allnon(:,reps))./sqrt(sum(~isnan(allnon(:,reps)))), fliplr(nanmean(allnon(:,reps))+nanstd(allnon(:,reps))./sqrt(sum(~isnan(allnon(:,reps)))))],animcol(a,:),'FaceAlpha',.3);%
% set(h,'EdgeColor','none'); plot(reps, nanmean(allnon(:,reps)),'.:','Color',animcol(a,:),'Linewidth',1);
% ylim([0 2]); plot([5 5],[0 2],'k'); title(animals{a}); xlim([1 9]);
for r = 1:length(reps)
[p{a}(r),~]=ranksum(allgoal(:,reps(r)),allnon(:,reps(r))); % ranksum because nonparametric
end
plot(reps(p{a}<.05/length(reps))-5,.9*ones(sum(p{a}<.05/length(reps)),1),'r.'); xlabel(sprintf('n=%dconts',size(allgoal,1)));
set(gca,'XTick',1:6,'XTickLabel',{'s1','r1','s2','r2','s3','r3'})
% allvis = vertcat(realignedvis{a}{:}); allnonvis = vertcat(realignednongoalvis{a}{:});
% subplot(4,4,a+12); hold on; ylabel('visits/block')
% errorbar(1:6,nanmean(allvis(:,reps)),nanstd(allvis(:,reps))./sqrt(sum(~isnan(allvis(:,reps)))),'.','Color',animcol(a,:));
% bar(1:6,nanmean(allvis(:,reps)),.3,'Facecolor',animcol(a,:));
% errorbar(1.3:6.3,nanmean(allnonvis(:,reps)),nanstd(allnonvis(:,reps))./sqrt(sum(~isnan(allnonvis(:,reps)))),'.','Color',animcol(a,:));
% bar(1.3:6.3,nanmean(allnonvis(:,reps)),.3,'Facecolor',animcol(a,:),'Facealpha',.3); ylim([0 3]); xlim([.5 7;]);
% for r = 1:length(reps)
% [p{a}(r),~]=ranksum(allvis(:,r),allnonvis(:,r)); % ranksum because nonparametric
% end
% plot(reps(p{a}<.05/length(reps)),2*ones(sum(p{a}<.05/length(reps)),1),'r.');
% % compare MANY VS FEW rews at goal
% xvals = -window:window;
% lowrew = cell2mat(cellfun(@(x,y) x(y<=10,:)', bytrial_lastrew{a},goalnum{a},'un',0))';
% highrew = cell2mat(cellfun(@(x,y) x(y>10,:)', bytrial_lastrew{a},goalnum{a},'un',0))';
% subplot(4,4,a+12); hold on; ylabel('replay/trial'); xlabel('trials from last rew of block')
% h = fill([xvals, fliplr(xvals)], [nanmean(lowrew)-nanstd(lowrew)./sqrt(sum(~isnan(lowrew))), fliplr(nanmean(lowrew)+nanstd(lowrew)./sqrt(sum(~isnan(lowrew))))],animcol(a,:),'FaceAlpha',.3);%
% set(h,'EdgeColor','none'); plot(xvals, nanmean(lowrew),'Color',animcol(a,:),'Linewidth',1);
% h = fill([xvals, fliplr(xvals)], [nanmean(highrew)-nanstd(highrew)./sqrt(sum(~isnan(highrew))), fliplr(nanmean(highrew)+nanstd(highrew)./sqrt(sum(~isnan(highrew))))],animcol(a,:),'FaceAlpha',.3);%
% set(h,'EdgeColor','none'); plot(xvals, nanmean(highrew),':','Color',animcol(a,:),'Linewidth',1); title('many vs few rews'); ylim([0 2])
% compare first vs second contingency
% xvals = -window:window;
% cont1 = cell2mat(cellfun(@(x) x(1,:)', bytrial_lastrew{a},'un',0))';
% cont2 = cell2mat(cellfun(@(x,y) x(2,:)', bytrial_lastrew{a}(cellfun(@(x) length(x),goalnum{a})>2),'un',0))';
% subplot(4,4,a+12); hold on; ylabel('replay/trial'); xlabel('trials from last rew of block')
% h = fill([xvals, fliplr(xvals)], [nanmean(cont1)-nanstd(cont1)./sqrt(sum(~isnan(cont1))), fliplr(nanmean(cont1)+nanstd(cont1)./sqrt(sum(~isnan(cont1))))],animcol(a,:),'FaceAlpha',.3);%
% set(h,'EdgeColor','none'); plot(xvals, nanmean(cont1),'Color',animcol(a,:),'Linewidth',1);
% h = fill([xvals, fliplr(xvals)], [nanmean(cont2)-nanstd(cont2)./sqrt(sum(~isnan(cont2))), fliplr(nanmean(cont2)+nanstd(cont2)./sqrt(sum(~isnan(cont2))))],animcol(a,:),'FaceAlpha',.3);%
% set(h,'EdgeColor','none'); plot(xvals, nanmean(cont2),':','Color',animcol(a,:),'Linewidth',1); title('1st vs 2nd'); ylim([0 2])
% correlate numgoals with count of replay in window post cont change
%subplot(4,4,a+12); hold on; plot(horzcat(goalnum{a}{:}),horzcat(postreplaycount{a}{:}),'.','Color',animcol(a,:)); lsline
%ylabel('replaycount'); xlabel('goalnum'); title(['window=',num2str(window)]);
%[r2,pv] = corrcoef([horzcat(goalnum{a}{:})',horzcat(postreplaycount{a}{:})'],'rows','complete'); text(5,3,sprintf('n=%d,r2=%.03f,p=%.03f',sum(~isnan(horzcat(postreplaycount{a}{:}))),r2(2)^2,pv(2)),'Color',animcol(a,:));
end
%% Calc mean rate of local, past, future, pg replay by trial (box vs outer & search vs rep) [SUP FIG 5A]
clearvars -except f animals animcol
bars = figure(); set(gcf,'Position',[675 286 570 688]);
contentthresh=.3;
for a = 1:length(animals)
eps = find(arrayfun(@(x) ~isempty(x.trips),f(a).output{1}));
for e = 1:length(eps)
tphasenum = f(a).output{1}(eps(e)).trips.taskphase;
valtrials = ~isnan(tphasenum);
tphasenum = [tphasenum(valtrials), [1:sum(valtrials)]'];
goals = f(a).output{1}(eps(e)).trips.goalarm(valtrials,:);
outers = f(a).output{1}(eps(e)).trips.outerarm(valtrials);
pastwlock = f(a).output{1}(eps(e)).trips.prevarm(valtrials,2); % only consider the including lockout option
homerips = f(a).output{1}(eps(e)).trips.homeripcontent(valtrials);
hometypes = f(a).output{1}(eps(e)).trips.homeripmaxtypes(valtrials);
rwrips = f(a).output{1}(eps(e)).trips.RWripcontent(valtrials);
postrwrips = f(a).output{1}(eps(e)).trips.postRWripcontent(valtrials);
rwtypes = f(a).output{1}(eps(e)).trips.RWripmaxtypes(valtrials); %
postrwtypes = f(a).output{1}(eps(e)).trips.postRWripmaxtypes(valtrials); %
rips = cellfun(@(x,y,z) [x;y;z],homerips,rwrips,postrwrips,'un',0);
types = cellfun(@(x,y,z) [x,y,z]',hometypes,rwtypes,postrwtypes,'un',0);
outerrips = f(a).output{1}(eps(e)).trips.outerripcontent(valtrials);
outertypes = f(a).output{1}(eps(e)).trips.outerripmaxtypes(valtrials); %
clear replays outerreplays
for t=1:length(rips)
if ~isempty(rips{t})
[maxval,ind] = max(rips{t},[],2); %(:,2:end)
valid = types{t}==1 & maxval>contentthresh;
replays{t} = ind(valid)'-1; %
else replays{t} = []; end
end
for t=1:length(outerrips)
if ~isempty(outerrips{t})
[maxval,ind] = max(outerrips{t},[],2); %(:,2:end)
valid = outertypes{t}'==1 & maxval>contentthresh;
outerreplays{t} = ind(valid)'-1; %
else outerreplays{t} = []; end
end
% valtrials = ~cellfun(@isempty, replays);
% if any(valtrials) % [replay future past pg]
% ripcomb = cellfun(@(x,y,z,g,h) [x', repmat([y,z,g,h],length(x),1)], replays(valtrials), num2cell(outers(valtrials)), ...
% num2cell(pastwlock(valtrials))', num2cell(goals(valtrials,:),2)',num2cell(tphasenum(valtrials,:),2)','un',0);
% ripcomb = vertcat(ripcomb{:});
% end
% valtrials = ~cellfun(@isempty, outerreplays);
% if any(valtrials)
% outerripcomb = cellfun(@(x,y,z,g,h) [x', repmat([y,z,g,h],length(x),1)], outerreplays(valtrials), num2cell(outers(valtrials)), ...
% num2cell(pastwlock(valtrials))', num2cell(goals(valtrials,:),2)',num2cell(tphasenum(valtrials,:),2)','un',0);
% outerripcomb = vertcat(outerripcomb{:});
% end
valtrials = ~cellfun(@isempty,replays);
countspertrial = zeros(length(valtrials),5); % [future past currentg previousg totalreplays]
countspertrial(valtrials,:) = cell2mat(cellfun(@(x,fut,pst,crg,prg) [sum(x==fut), sum(x==pst), sum(x==crg), sum(x==prg), length(x)], ...
replays(valtrials),num2cell(outers(valtrials)),num2cell(pastwlock(valtrials))',num2cell(goals(valtrials,1)'),...
num2cell(goals(valtrials,2))','un',0)');
valtrials = ~cellfun(@isempty,outerreplays);
outercountspertrial = zeros(length(valtrials),5);
outercountspertrial(valtrials,:) = cell2mat(cellfun(@(x,fut,pst,crg,prg) [sum(x==fut), sum(x==pst), sum(x==crg), sum(x==prg), length(x)], ...
outerreplays(valtrials),num2cell(outers(valtrials)),num2cell(pastwlock(valtrials))',num2cell(goals(valtrials,1)'),...
num2cell(goals(valtrials,2))','un',0)');
% boxSearch boxRep outerrew
rewouternums = tphasenum(tphasenum(:,1)>=1 & mod(tphasenum(:,1),1)==0,2);
totaleventrate{a}(e,:) = [mean(countspertrial(tphasenum(:,1)<=1,5)),mean(countspertrial(tphasenum(:,1)>1,5)),mean(outercountspertrial(rewouternums,5))];
% [future pg ]
searchboxrates{a}(e,:) = [mean(countspertrial(tphasenum(:,1)<=1 & ~isnan(goals(:,2)),1)), mean(countspertrial(tphasenum(:,1)<=1 & ~isnan(goals(:,2)),4))];
pooledsearchfuture{a}{e} = [countspertrial(tphasenum(:,1)<=1 & ~isnan(goals(:,2)),1)];
pooledsearchpg{a}{e} = [countspertrial(tphasenum(:,1)<=1 & ~isnan(goals(:,2)),4)];
repeatboxrates{a}(e,:) = [mean(countspertrial(tphasenum(:,1)>1 & ~isnan(goals(:,2)),1)), mean(countspertrial(tphasenum(:,1)>1 & ~isnan(goals(:,2)),4))];
pooledrepeatfuture{a}{e} = [countspertrial(tphasenum(:,1)>1 & ~isnan(goals(:,2)),1)];
pooledrepeatpg{a}{e} = [countspertrial(tphasenum(:,1)>1 & ~isnan(goals(:,2)),4)];
% future only, [correct err] (.9 trials count as correct
correcttrinums = tphasenum(~isnan(goals(:,2)) & tphasenum(:,1)>1 & (mod(tphasenum(:,1),1)==0 | mod(tphasenum(:,1),1)>.8),2);
errtrinums = tphasenum(~isnan(goals(:,2)) & tphasenum(:,1)>1 & (mod(tphasenum(:,1),1)>0 & mod(tphasenum(:,1),1)<.9),2);
repcorr_errboxrates{a}(e,:) = [mean(countspertrial(correcttrinums,1)), mean(countspertrial(errtrinums,1))];
end
totalp(a) = ranksum(totaleventrate{a}(:,1),totaleventrate{a}(:,2));
%boxlocalp(a) = ranksum(boxlocalrates{a}(:,1),boxlocalrates{a}(:,2));
searchboxp(a) = [ranksum(searchboxrates{a}(:,1),searchboxrates{a}(:,2))];
repeatboxp(a) = [ranksum(repeatboxrates{a}(:,1),repeatboxrates{a}(:,2))];
errboxp(a) = [ranksum(repcorr_errboxrates{a}(:,1),repcorr_errboxrates{a}(:,2))];
% subplot(1,4,1); hold on; title('total box events search vs rep'); ylim([0 15]);
% errorbar([a a+.5],mean(totaleventrate{a}(:,[1 2])),std(totaleventrate{a}(:,[1 2]))./sqrt(size(totaleventrate{a},1)),'k.')
% bar([a a+.5],mean(totaleventrate{a}(:,[1 2])),.8,'FaceColor',animcol(a,:));
% text([a+.3],[14],num2str(ranksum(totaleventrate{a}(:,1),totaleventrate{a}(:,2)),'%.02f'));
hold on; title('future replay on search/rep') %subplot(1,4,a);
% meaned by epoch
% errorbar([a a+6],mean(searchboxrates{a}),std(searchboxrates{a})./sqrt(size(searchboxrates{a},1)),'k.')
% bar([a a+6],mean(searchboxrates{a}),.05,'FaceColor',animcol(a,:));
% errorbar([a+.4 a+6.4],mean(repeatboxrates{a}),std(repeatboxrates{a})./sqrt(size(repeatboxrates{a},1)),'k.')
% bar([a+.4 a+6.4],mean(repeatboxrates{a}),.05,'FaceColor',animcol(a,:),'FaceAlpha',.3);
% text([a+.3 a+6.3],[1.7 1.7],num2str([ranksum(searchboxrates{a}(:,1),repeatboxrates{a}(:,1));ranksum(searchboxrates{a}(:,2),repeatboxrates{a}(:,2))],'%.02f'));
% pooled across epochs
errorbar([a],mean(vertcat(pooledsearchfuture{a}{:})),std(vertcat(pooledsearchfuture{a}{:}))./sqrt(length(vertcat(pooledsearchfuture{a}{:}))),'k.')
bar([a],mean(vertcat(pooledsearchfuture{a}{:})),.6,'FaceColor',animcol(a,:));
errorbar([a+6],mean(vertcat(pooledrepeatfuture{a}{:})),std(vertcat(pooledrepeatfuture{a}{:}))./sqrt(length(vertcat(pooledrepeatfuture{a}{:}))),'k.')
bar([ a+6],mean(vertcat(pooledrepeatfuture{a}{:})),.6,'FaceColor',animcol(a,:),'FaceAlpha',.3);
text([a],[1+a/5],num2str(ranksum(vertcat(pooledsearchfuture{a}{:}),vertcat(pooledrepeatfuture{a}{:}))),'Color',animcol(a,:));
text(a,1.1+a/5,sprintf('search n=%d,repeat n=%d',length(vertcat(pooledsearchfuture{a}{:})),length(vertcat(pooledrepeatfuture{a}{:}))),'Color',animcol(a,:));
% subplot(1,4,4); hold on; title('future on correct vs error')
% errorbar([a a+.5],nanmean(repcorr_errboxrates{a}),nanstd(repcorr_errboxrates{a})./sqrt(sum(~isnan(repcorr_errboxrates{a}))),'k.')
% bar([a a+.5],nanmean(repcorr_errboxrates{a}),.8,'FaceColor',animcol(a,:));
% text([a+.3],[.8 ],num2str(ranksum(repcorr_errboxrates{a}(:,1),repcorr_errboxrates{a}(:,2)),'%.04f')); ylim([0 1]); ylabel('events/trial')
end
plot([0 11],[1 1],'k:'); ylim([0 2]); set(gca,'XTick',[3 9],'XTickLabel',{'search','repeat'});
%subplot(1,4,[2 3]); set(gca,'XTick',[3 9],'XTickLabel',{'future','pg'}); ylim([0 2])
% subplot(3,2,1); hold on;
% plot4a(totaleventrate,'gnames',{'boxser','boxrep','outerrew'});
% ylabel('totalevents/trial'); ylim([0 20]); text([6 7 8 9],[16 17 18 19],num2str(totalp'))
% subplot(3,2,2); hold on;
% plot4a(boxlocalrates,'gnames',{'ser','rep'});
% ylabel('localboxev/trial'); ylim([0 15]); text([9 10 11 12],[10 11 12 13],num2str(boxlocalp'))
% subplot(3,2,3);
% plot4a(searchboxrates,'gnames',{'future','prevgoal'}); title('search')
% ylabel('box rates'); ylim([0 3]); text([12 13 14 15],[2 2.2 2.4 2.6],num2str(searchboxp','%.03f'));
% subplot(3,2,4);
% plot4a(repeatboxrates,'gnames',{'future','prevgoal'}); title('repeat')
% ylabel('box rates'); ylim([0 3]); text([12 13 14 15],[2 2.2 2.4 2.6],num2str(repeatboxp','%.03f'));
% subplot(3,2,5); %plot4a(repcorr_errboxrates,'gnames',{'correct','error'});
% ylabel('box rates'); ylim([0 3]); text([12 13 14 15],[2 2.2 2.4 2.6],num2str(errboxp','%.03f'));title('repeat correct vs error')
% %subplot(3,2,[5 6]); hold on;
% %plot4a(outerrates,'gnames',{'past','pg','current','box'}); ylabel('outer rates'); ylim([0 2]);
%% fit linear model of category predictors for each arm, for box and outer [FIG 3D,E and FIG 4 A,B and FIG 7B]
clearvars -except f animals animcol
contentthresh = .3;
all = figure(); byarm = figure(); cov = figure(); set(gcf,'Position',[90 262 1822 697]); poold = figure();
search_pooled = []; repeat_pooled = [];
for a = 1:length(animals)
eps = find(arrayfun(@(x) ~isempty(x.trips),f(a).output{1}));
for e = 1:length(eps)
tphasenum = f(a).output{1}(eps(e)).trips.taskphase;
valtrials = ~isnan(tphasenum);
tphasenum = [tphasenum(valtrials), [1:sum(valtrials)]']; % add trial numbers
homerips = f(a).output{1}(eps(e)).trips.homeripcontent(valtrials);
hometypes = f(a).output{1}(eps(e)).trips.homeripmaxtypes(valtrials);
rwrips = f(a).output{1}(eps(e)).trips.RWripcontent(valtrials);
postrwrips = f(a).output{1}(eps(e)).trips.postRWripcontent(valtrials);
rwtypes = f(a).output{1}(eps(e)).trips.RWripmaxtypes(valtrials); %
postrwtypes = f(a).output{1}(eps(e)).trips.postRWripmaxtypes(valtrials); %
rips = cellfun(@(x,y,z) [x;y;z],homerips,rwrips,postrwrips,'un',0);
types = cellfun(@(x,y,z) [x,y,z]',hometypes,rwtypes,postrwtypes,'un',0);
outerrips = f(a).output{1}(eps(e)).trips.outerripcontent(valtrials);
outertypes = f(a).output{1}(eps(e)).trips.outerripmaxtypes(valtrials);
clear replays outerreplays
for t=1:length(rips)
if ~isempty(rips{t})
[maxval,ind] = max(rips{t},[],2); %(:,2:end)
valid = types{t}==1 & maxval>contentthresh;
replays{t} = ind(valid)'-1; %
else replays{t} = []; end
if ~isempty(outerrips{t})
[maxval,ind] = max(outerrips{t},[],2); %(:,2:end)
valid = outertypes{t}'==1 & maxval>contentthresh;
outerreplays{t} = ind(valid)'-1; %
else outerreplays{t} = []; end
end
goals = f(a).output{1}(eps(e)).trips.goalarm(valtrials,:);
outers = f(a).output{1}(eps(e)).trips.outerarm(valtrials);
pastwlock = f(a).output{1}(eps(e)).trips.prevarm(valtrials,2); % only consider the including lockout option
countspertrial = zeros(8,length(outers));
countspertrial(:,~cellfun(@isempty,replays)) = cell2mat(cellfun(@(x) histcounts(x,[1:9])',replays(~cellfun(@isempty,replays)),'un',0));
future = cell2mat(cellfun(@(x) histcounts(x,[1:9])',num2cell(outers),'un',0));
past = cell2mat(cellfun(@(x) histcounts(x,[1:9])',num2cell(pastwlock'),'un',0));
prevgoal = cell2mat(cellfun(@(x) histcounts(x,[1:9])',num2cell(goals(:,2)'),'un',0));
currgoal = cell2mat(cellfun(@(x) histcounts(x,[1:9])',num2cell(goals(:,1)'),'un',0));
allsearch{a}{e} = [reshape(future(:,tphasenum(:,1)<=1),[],1),reshape(past(:,tphasenum(:,1)<=1),[],1),reshape(prevgoal(:,tphasenum(:,1)<=1),[],1) ...
reshape(countspertrial(:,tphasenum(:,1)<=1),[],1)]; % [future past prevgoal #replays];
allrepeat{a}{e} = [reshape(future(:,tphasenum(:,1)>1),[],1),reshape(past(:,tphasenum(:,1)>1),[],1),reshape(currgoal(:,tphasenum(:,1)>1),[],1) ...
,reshape(prevgoal(:,tphasenum(:,1)>1),[],1), reshape(countspertrial(:,tphasenum(:,1)>1),[],1)]; % [future past currgoal prevgoal #replays];
earlycorr = tphasenum(:,1)>1 & tphasenum(:,1)<5 & outers'==goals(:,1);
earlycorrrepeat{a}{e} = [reshape(future(:,earlycorr),[],1),reshape(past(:,earlycorr),[],1),reshape(currgoal(:,earlycorr),[],1) ...
,reshape(prevgoal(:,earlycorr),[],1), reshape(countspertrial(:,earlycorr),[],1)]; % [future past currgoal prevgoal #replays];
latecorr = tphasenum(:,1)>4 & outers'==goals(:,1);
latecorrrepeat{a}{e} = [reshape(future(:,latecorr ),[],1),reshape(past(:,latecorr ),[],1),reshape(currgoal(:,latecorr ),[],1) ...
,reshape(prevgoal(:,latecorr ),[],1), reshape(countspertrial(:,latecorr ),[],1)]; % [future past currgoal prevgoal #replays];
earlyerr = tphasenum(:,1)>1 & tphasenum(:,1)<5 & outers'~=goals(:,1);
earlyerrrepeat{a}{e} = [reshape(future(:,earlyerr),[],1),reshape(past(:,earlyerr),[],1),reshape(currgoal(:,earlyerr),[],1) ...
,reshape(prevgoal(:,earlyerr),[],1), reshape(countspertrial(:,earlyerr),[],1)]; % [future past currgoal prevgoal #replays];
lateerr = tphasenum(:,1)>4 & outers'~=goals(:,1);
lateerrrepeat{a}{e} = [reshape(future(:,lateerr ),[],1),reshape(past(:,lateerr ),[],1),reshape(currgoal(:,lateerr ),[],1) ...
,reshape(prevgoal(:,lateerr ),[],1), reshape(countspertrial(:,lateerr ),[],1)]; % [future past currgoal prevgoal #replays];
for arm = 1:8
searchbyarm{a}{arm}{e} = [future(arm,tphasenum(:,1)<=1)',past(arm,tphasenum(:,1)<=1)',prevgoal(arm,tphasenum(:,1)<=1)',countspertrial(arm,tphasenum(:,1)<=1)'];
repeatbyarm{a}{arm}{e} = [future(arm,tphasenum(:,1)>1)',past(arm,tphasenum(:,1)>1)',currgoal(arm,tphasenum(:,1)>1)',prevgoal(arm,tphasenum(:,1)>1)',countspertrial(arm,tphasenum(:,1)>1)'];
end
outercountspertrial = zeros(8,length(outers));
outercountspertrial(:,~cellfun(@isempty,outerreplays)) = cell2mat(cellfun(@(x) histcounts(x,[1:9])',outerreplays(~cellfun(@isempty,outerreplays)),'un',0));
rewd = mod(tphasenum(:,1),1)==0 & tphasenum(:,1)>0;
allouterrew{a}{e} = [reshape(past(:,rewd),[],1),reshape(currgoal(:,rewd),[],1), ...
reshape(prevgoal(:,rewd),[],1), reshape(outercountspertrial(:,rewd),[],1)]; % [ past current prevgoal #replays];
end
figure(all);
searchcat = vertcat(allsearch{a}{:});
searchtbl = table(searchcat(:,2),searchcat(:,1),searchcat(:,3),searchcat(:,4),'VariableNames',{'past','future','prevgoal','replaynum'});
s_mdl = fitglm(searchtbl,'linear','Distribution','poisson');
CI = coefCI(s_mdl,.01);
subplot(1,3,1); hold on; title('allsearch')
%errorbar(a+[0:5:19],table2array(mdl.Coefficients(:,1)), table2array(mdl.Coefficients(:,2)),'.','Color',animcol(a,:))
plot(a+[0:5:19],exp(table2array(s_mdl.Coefficients(:,1))),'.','MarkerSize',20,'Color',animcol(a,:));
plot([a+[0:5:19];a+[0:5:19]],exp(CI)','Color',animcol(a,:));
%cr = [corrcoef(searchcat(:,1),searchcat(:,2)), corrcoef(searchcat(:,1),searchcat(:,3)), corrcoef(searchcat(:,2),searchcat(:,3))];
%searchr2(a, :)=cr(1,[2 4 6]).^2;
text(5,2+.2*a,['trial n=',num2str(size(searchcat,1)/8)],'Color',animcol(a,:));
repeatcat = vertcat(allrepeat{a}{:});
reptbl = table(repeatcat(:,2),repeatcat(:,1),repeatcat(:,3),repeatcat(:,4),repeatcat(:,5),'VariableNames',{'past','future','currgoal','prevgoal','replaynum'});
r_mdl = fitglm(reptbl,'linear','Distribution','poisson');
CI = coefCI(r_mdl,.01);
subplot(1,3,2); hold on; title('allrepeat')
%errorbar(a+[0:5:24],table2array(mdl.Coefficients(:,1)), table2array(mdl.Coefficients(:,2)),'.','Color',animcol(a,:))
plot(a+[0:5:24],exp(table2array(r_mdl.Coefficients(:,1))),'.','MarkerSize',20,'Color',animcol(a,:));
plot([a+[0:5:24];a+[0:5:24]],exp(CI)','Color',animcol(a,:));
%cr = [corrcoef(repeatcat(:,1),repeatcat(:,2)), corrcoef(repeatcat(:,1),repeatcat(:,3)),corrcoef(repeatcat(:,1),repeatcat(:,4)), ...
% corrcoef(repeatcat(:,2),repeatcat(:,3)), corrcoef(repeatcat(:,2),repeatcat(:,4)),corrcoef(repeatcat(:,3),repeatcat(:,4))];
%repr2(a,:)=cr(1,[2:2:12]).^2;
%text(5,2+.2*a,['f vs cg corrcoef=',num2str(cr(2),'%.03f')],'Color',animcol(a,:));
text(5,2+.2*a,['trial n=',num2str(size(repeatcat,1)/8)],'Color',animcol(a,:));
outercat = vertcat(allouterrew{a}{:});
mdl = fitglm(outercat(:,1:3),outercat(:,4),'linear','Distribution','poisson');
CI = coefCI(mdl,.01);
subplot(1,3,3); hold on; title('outer rewarded')
plot(a+[0:5:19],exp(table2array(mdl.Coefficients(:,1))),'.','MarkerSize',20,'Color',animcol(a,:));
plot([a+[0:5:19];a+[0:5:19]],exp(CI)','Color',animcol(a,:));
text(5,2+.2*a,['trial n=',num2str(size(outercat,1)/8)],'Color',animcol(a,:));
figure(cov);
% note that predictors haven't be reordered to match main figs (future past cg pg)
subplot(2,4,a); imagesc(1:3,1:3,corrcov(s_mdl.CoefficientCovariance(2:end,2:end))); caxis([-1 1])
set(gca,'ytick',1:3,'xticklabel',{'pst','fut','pg'},'yticklabel',{'fut','pst','pg'}); colorbar; title('search')
nums = sprintfc('%.02f',corrcov(s_mdl.CoefficientCovariance(2:end,2:end)));
[x,y] = meshgrid(1:3,1:3); text(x(:),y(:),nums,'horizontalalignment','center','verticalalignment','middle')
subplot(2,4,a+4); imagesc(corrcov(r_mdl.CoefficientCovariance(2:end,2:end))); caxis([-1 1])
colorbar; set(gca,'xticklabel',{'pst','fut','cg','pg'},'yticklabel',{'fut','pst','cg','pg'}); title('rep')
nums = sprintfc('%.02f',corrcov(r_mdl.CoefficientCovariance(2:end,2:end)));
[x,y] = meshgrid(1:4,1:4); text(x(:),y(:),nums,'horizontalalignment','center','verticalalignment','middle')
search_pooled = [search_pooled;searchcat]; repeat_pooled = [repeat_pooled;repeatcat];
figure(byarm);
% subplot(2,2,[1:2]); hold on;
% reptbl = table(repeatcat(:,1),repeatcat(:,2),repeatcat(:,3),repeatcat(:,4),repeatcat(:,5),'VariableNames',{'future','past','currgoal','prevgoal','replaynum'});
% modelspec = 'replaynum ~ future + past +currgoal + prevgoal + future:currgoal'; %
% mdl = fitglm(reptbl,modelspec,'Distribution','poisson');
% coefinds = 1:6;
% plot(a/5+coefinds(table2array(mdl.Coefficients(:,4))<.05),ones(sum(table2array(mdl.Coefficients(:,4))<.05),1),'*','Color',animcol(a,:));
% plot(a/5+coefinds(table2array(mdl.Coefficients(:,4))>=.05),zeros(sum(table2array(mdl.Coefficients(:,4))>=.05),1),'o','Color',animcol(a,:));
% set(gca,'XTickLabel',{'int','future','past','currg','pg','f+cg'})
% subplot(2,2,3); hold on;
% [sims,simCI] = predict(mdl,[1 0 1 0; 1 0 0 0; 0 0 1 0; 0 0 0 0]);
% plot(a/5+[1:4],sims, 'ko'); plot(a/5+[1:4;1:4],simCI','Color',animcol(a,:)); set(gca,'XTickLabel',{'f+ cg+','f+ cg-','f- cg+','f-cg-'})
%subplot(2,2,4); hold on;
%[sims,simCI] = predict(mdl,[1 0 1; 1 0 0; 0 0 1; 0 0 0]);
%plot(a/5+[1:4],sims, 'ko'); plot(a/5+[1:4;1:4],simCI','Color',animcol(a,:)); set(gca,'XTickLabel',{'f+ pg+','f+ pg-','f- pg+','f-pg-'})
%
% reptbl = table(repeatcat(:,1),repeatcat(:,2),repeatcat(:,3),repeatcat(:,4),repeatcat(:,5),'VariableNames',{'future','past','currgoal','prevgoal','replaynum'});
% modelspec = 'replaynum ~ future + past + currgoal + prevgoal + future:currgoal + future:past+ future:prevgoal + past:currgoal +past:prevgoal'; %
% mdl = fitglm(reptbl,modelspec,'Distribution','poisson');
% coefinds = 1:10;
% plot(a/5+coefinds(table2array(mdl.Coefficients(:,4))<.05),ones(sum(table2array(mdl.Coefficients(:,4))<.05),1),'*','Color',animcol(a,:));
% plot(a/5+coefinds(table2array(mdl.Coefficients(:,4))>=.05),zeros(sum(table2array(mdl.Coefficients(:,4))>=.05),1),'o','Color',animcol(a,:));
% set(gca,'XTickLabel',{'int','future','past','currg','pg','f+p','f+cg','p+cg','f+pg','p+pg'})
% subplot(3,1,2); hold on;
% modelspec = 'replaynum ~ future + past + currgoal + prevgoal + future:currgoal + future:past';%
% mdl = fitglm(reptbl,modelspec,'Distribution','poisson');
% coefinds = 1:7;
% plot(a/5+coefinds(table2array(mdl.Coefficients(:,4))<.05),ones(sum(table2array(mdl.Coefficients(:,4))<.05),1),'*','Color',animcol(a,:));
% plot(a/5+coefinds(table2array(mdl.Coefficients(:,4))>=.05),zeros(sum(table2array(mdl.Coefficients(:,4))>=.05),1),'o','Color',animcol(a,:));
% set(gca,'XTickLabel',{'int','future','past','currg','pg','f+p','f+cg'})
% subplot(3,1,3); hold on;
% modelspec = 'replaynum ~ future + past + currgoal + prevgoal + future:currgoal ';%
% mdl = fitglm(reptbl,modelspec,'Distribution','poisson');
% coefinds = 1:6;
% plot(a/5+coefinds(table2array(mdl.Coefficients(:,4))<.05),ones(sum(table2array(mdl.Coefficients(:,4))<.05),1),'*','Color',animcol(a,:));
% plot(a/5+coefinds(table2array(mdl.Coefficients(:,4))>=.05),zeros(sum(table2array(mdl.Coefficients(:,4))>=.05),1),'o','Color',animcol(a,:));
% set(gca,'XTickLabel',{'int','future','past','currg','pg','f+cg'})
% [sims,simCI] = predict(mdl,[1 0 1 0; 1 0 0 0; 0 0 1 0; 0 0 0 0]);
% plot(1:4,sims, '.'); plot([1:4;1:4],simCI','k'); set(gca,'XTickLabel',{'f+ cg+','f+ cg-','f- cg+','f-cg-'})
% subplot(1,2,2); hold on;
% [sims,simCI] = predict(mdl,[1 1 1 0; 1 0 1 0; 0 1 1 0; 0 0 1 0]);
% plot(1:4,sims, '.'); plot([1:4;1:4],simCI','k'); set(gca,'XTickLabel',{'f+ p+','f+ p-','f- p+','f-p-'})
% mdl2 = step(mdl,'NSteps',5)
% plotSlice(mdl)
% plotInteraction(mdl,'future','past')
% CI = coefCI(mdl,.01);
% plot(a+[0:5:29],exp(table2array(mdl.Coefficients(:,1))),'.','MarkerSize',20,'Color',animcol(a,:));
% plot([a+[0:5:29];a+[0:5:29]],exp(CI)','Color',animcol(a,:)); %title('repeat f cg diff')
% OLD FIG S5
% subplot(1,2,1); hold on;
% fcgdiffcat = vertcat(fcgdiffrepeat{a}{:}); fcgsamecat = vertcat(fcgsamerepeat{a}{:});
% mdl = fitglm(fcgdiffcat(:,[2 1 3 4]),fcgdiffcat(:,5),'linear','Distribution','poisson');
% CI = coefCI(mdl,.01);
% plot(a+[0:5:24],exp(table2array(mdl.Coefficients(:,1))),'.','MarkerSize',20,'Color',animcol(a,:));
% plot([a+[0:5:24];a+[0:5:24]],exp(CI)','Color',animcol(a,:)); title('repeat f cg diff')
% text(5,2+.2*a,['n=',num2str(size(fcgdiffcat,1))],'Color',animcol(a,:));
% subplot(1,2,2); hold on;
% % Note that each time you subsample fcgsame, you will get a slightly different result
% %subset = randi(size(fcgsamecat,1),size(fcgdiffcat,1),1);
% %mdl = fitglm(fcgsamecat(subset,[2:4]),fcgsamecat(subset,5),'linear','Distribution','poisson');
% mdl = fitglm(fcgsamecat(:,[2:4]),fcgsamecat(:,5),'linear','Distribution','poisson');
% CI = coefCI(mdl,.01);
% plot(a+[0:5:19],exp(table2array(mdl.Coefficients(:,1))),'.','MarkerSize',20,'Color',animcol(a,:));
% plot([a+[0:5:19];a+[0:5:19]],exp(CI)','Color',animcol(a,:)); title('repeat f cg same')
% text(5,2+.2*a,['n=',num2str(size(fcgsamecat,1))],'Color',animcol(a,:));
% %text(5,2+.2*a,['n=',num2str(size(fcgsamecat(subset,:),1))],'Color',animcol(a,:));
subplot(3,2,1); hold on;
earlycorr = vertcat(earlycorrrepeat{a}{:}); latecorr = vertcat(latecorrrepeat{a}{:});
earlyerr = vertcat(earlyerrrepeat{a}{:}); lateerr = vertcat(lateerrrepeat{a}{:});
mdl = fitglm([earlycorr(:,[2 1 3 4]);earlyerr(:,[2 1 3 4])],[earlycorr(:,5);earlyerr(:,5)],'linear','Distribution','poisson');
CI = coefCI(mdl,.01);
plot(a+[0:5:24],exp(table2array(mdl.Coefficients(:,1))),'.','MarkerSize',20,'Color',animcol(a,:));
plot([a+[0:5:24];a+[0:5:24]],exp(CI)','Color',animcol(a,:)); title('repeat early (<5) all')
text(5,2+.2*a,['trial n=',num2str(size([earlycorr;earlyerr],1)/8)],'Color',animcol(a,:));
subplot(3,2,2); hold on;
mdl = fitglm([latecorr(:,[2 1 3 4]);lateerr(:,[2 1 3 4])],[latecorr(:,5);lateerr(:,5)],'linear','Distribution','poisson');
CI = coefCI(mdl,.01);
plot(a+[0:5:24],exp(table2array(mdl.Coefficients(:,1))),'.','MarkerSize',20,'Color',animcol(a,:));
plot([a+[0:5:24];a+[0:5:24]],exp(CI)','Color',animcol(a,:)); title('repeat late (5+) all')
text(5,2+.2*a,['trial n=',num2str(size([latecorr;lateerr],1)/8)],'Color',animcol(a,:));
subplot(3,2,3); hold on;
mdl = fitglm(earlycorr(:,[2:4]),earlycorr(:,5),'linear','Distribution','poisson');
CI = coefCI(mdl,.01);
plot(a+[0:5:19],exp(table2array(mdl.Coefficients(:,1))),'.','MarkerSize',20,'Color',animcol(a,:));
plot([a+[0:5:19];a+[0:5:19]],exp(CI)','Color',animcol(a,:)); title('repeat early (<5) corr')
text(5,2+.2*a,['trial n=',num2str(size(earlycorr,1)/8)],'Color',animcol(a,:));
subplot(3,2,4); hold on;
mdl = fitglm(latecorr(:,[2:4]),latecorr(:,5),'linear','Distribution','poisson');
CI = coefCI(mdl,.01);
plot(a+[0:5:19],exp(table2array(mdl.Coefficients(:,1))),'.','MarkerSize',20,'Color',animcol(a,:));
plot([a+[0:5:19];a+[0:5:19]],exp(CI)','Color',animcol(a,:)); title('repeat late (5+) corr')
text(5,2+.2*a,['trial n=',num2str(size(latecorr,1)/8)],'Color',animcol(a,:));
subplot(3,2,5); hold on;
mdl = fitglm(earlyerr(:,[2 1 3 4]),earlyerr(:,5),'linear','Distribution','poisson');
CI = coefCI(mdl,.01);
plot(a+[0:5:24],exp(table2array(mdl.Coefficients(:,1))),'.','MarkerSize',20,'Color',animcol(a,:));
plot([a+[0:5:24];a+[0:5:24]],exp(CI)','Color',animcol(a,:)); title('repeat early (<5) err')
text(5,2+.2*a,['trial n=',num2str(size(earlyerr,1)/8)],'Color',animcol(a,:));
subplot(3,2,6); hold on;
mdl = fitglm(lateerr(:,[2 1 3 4]),lateerr(:,5),'linear','Distribution','poisson');
CI = coefCI(mdl,.01);
plot(a+[0:5:24],exp(table2array(mdl.Coefficients(:,1))),'.','MarkerSize',20,'Color',animcol(a,:));
plot([a+[0:5:24];a+[0:5:24]],exp(CI)','Color',animcol(a,:)); title('repeat late (5+) err')
text(5,2+.2*a,['trial n=',num2str(size(lateerr,1)/8)],'Color',animcol(a,:));
% mdl = fitglm(repeatcat(:,[1,2,4]),repeatcat(:,5),'linear','Distribution','poisson');
% CI = coefCI(mdl,.01);
% plot(a+[0:5:19],exp(table2array(mdl.Coefficients(:,1))),'.','MarkerSize',20,'Color',animcol(a,:));
% plot([a+[0:5:19];a+[0:5:19]],exp(CI)','Color',animcol(a,:)); title('future only')
% text(5,2+.2*a,['AIC=',num2str(mdl.ModelCriterion.AIC,'%.03f')],'Color',animcol(a,:));
% subplot(1,2,2); hold on; title('currgoal only')
% mdl = fitglm(repeatcat(:,[2:4]),repeatcat(:,5),'linear','Distribution','poisson');
% CI = coefCI(mdl,.01);
% plot(a+[0:5:19],exp(table2array(mdl.Coefficients(:,1))),'.','MarkerSize',20,'Color',animcol(a,:));
% plot([a+[0:5:19];a+[0:5:19]],exp(CI)','Color',animcol(a,:));
% text(5,2+.2*a,['AIC=',num2str(mdl.ModelCriterion.AIC,'%.03f')],'Color',animcol(a,:));
% figure(byarm);
% for arm = 1:8
% subplot(2,4,a); hold on; title([animals{a},'searchbyarm']);
% searchcat = vertcat(searchbyarm{a}{arm}{:});
% mdl = fitglm(searchcat(:,1:3),searchcat(:,4),'linear','Distribution','poisson');
% errorbar(arm+[0:11:43],table2array(mdl.Coefficients(:,1)), table2array(mdl.Coefficients(:,2)),'.','Color',animcol(a,:))
% subplot(2,4,a+4); hold on; title([animals{a},'repbyarm']);
% repeatcat = vertcat(repeatbyarm{a}{arm}{:});
% mdl = fitglm(repeatcat(:,1:4),repeatcat(:,5),'linear','Distribution','poisson');
% errorbar(arm+[0:11:54],table2array(mdl.Coefficients(:,1)), table2array(mdl.Coefficients(:,2)),'.','Color',animcol(a,:))
% end
% subplot(2,4,a); xlim([0 43]); set(gca,'XTick',5.5+[0:11:43],'XTickLabel',{'int','fut','pst','pg'})
% subplot(2,4,a+4); xlim([0 53]);set(gca,'XTick',5.5+[0:11:54],'XTickLabel',{'int','fut','pst','cg','pg'})
end
figure(all)
subplot(1,3,1); xlim([0 20]); ylabel('exp(beta)'); set(gca,'XTick',2+[0:5:19],'XTickLabel',{'intrcpt','past','future','prevgoal'})
plot([0 20],[1 1],'k:'); set(gca,'YScale','log'); ylim([.4 3]);
subplot(1,3,2); set(gca,'YScale','log'); ylim([.4 3]); xlim([0 25]);set(gca,'XTick',2+[0:5:24],'XTickLabel',{'intrcpt','past','future','curgoal','prevgoal'})
plot([0 25],[1 1],'k:');
subplot(1,3,3); set(gca,'YScale','log'); ylim([.7 20]); xlim([0 20]);set(gca,'XTick',2+[0:5:20],'XTickLabel',{'intrcpt','past','current','prevgoal'})
plot([0 20],[1 1],'k:'); ylabel('[.7 20]')
figure(poold)
s_tbl = table(search_pooled(:,2),search_pooled(:,1),search_pooled(:,3),search_pooled(:,4),'VariableNames',{'past','future','prevgoal','replaynum'});
s_mdl = fitglm(s_tbl,'linear','Distribution','poisson');
CI = coefCI(s_mdl,.01);
subplot(1,2,1); hold on; title('pooledsearch')
plot([0:5:19],exp(table2array(s_mdl.Coefficients(:,1))),'k.','MarkerSize',20);
plot([[0:5:19];[0:5:19]],exp(CI)','k');
text(5,2,['trial n=',num2str(size(search_pooled,1)/8)]);
text(1,1.5,mat2str(exp(table2array(s_mdl.Coefficients(:,1)))',3)) %'%.03f'
text(1,1.4,mat2str(exp(CI(:,1))',3)) %'%.03f'
text(1,1.3,mat2str(exp(CI(:,2))',3)) %set(gca,'YScale','log');
r_tbl = table(repeat_pooled(:,2),repeat_pooled(:,1),repeat_pooled(:,3),repeat_pooled(:,4),repeat_pooled(:,5),'VariableNames',{'past','future','currgoal','prevgoal','replaynum'});
r_mdl = fitglm(r_tbl,'linear','Distribution','poisson');
CI = coefCI(r_mdl,.01);
subplot(1,2,2); hold on; title('pooledrepeat')
plot([0:5:24],exp(table2array(r_mdl.Coefficients(:,1))),'k.','MarkerSize',20);
plot([[0:5:24];[0:5:24]],exp(CI)','k');
text(5,2,['trial n=',num2str(size(repeat_pooled,1)/8)]);
text(1,1.5,mat2str(exp(table2array(r_mdl.Coefficients(:,1)))',3)) %'%.03f'
text(1,1.4,mat2str(exp(CI(:,1))',3)) %'%.03f'
text(1,1.3,mat2str(exp(CI(:,2))',3)) %
figure(byarm)
subplot(3,2,1); xlim([0 25]); ylabel('exp(beta)'); set(gca,'XTick',2+[0:5:24],'XTickLabel',{'intrcpt','past','future','currgoal','prevgoal'})
plot([0 25],[1 1],'k:'); set(gca,'YScale','log'); ylim([.3 3]);
subplot(3,2,2); xlim([0 25]); ylabel('exp(beta)'); set(gca,'XTick',2+[0:5:24],'XTickLabel',{'intrcpt','past','future','currgoal','prevgoal'})
plot([0 25],[1 1],'k:'); set(gca,'YScale','log'); ylim([.3 3]);
subplot(3,2,3); xlim([0 20]); ylabel('exp(beta)'); set(gca,'XTick',2+[0:5:19],'XTickLabel',{'intrcpt','past','currgoal','prevgoal'})
plot([0 20],[1 1],'k:'); set(gca,'YScale','log'); ylim([.3 4]);
subplot(3,2,4); xlim([0 20]); ylabel('exp(beta)'); set(gca,'XTick',2+[0:5:19],'XTickLabel',{'intrcpt','past','currgoal','prevgoal'})
plot([0 20],[1 1],'k:'); set(gca,'YScale','log'); ylim([.3 4]);
subplot(3,2,5); xlim([0 25]); ylabel('exp(beta)'); set(gca,'XTick',2+[0:5:24],'XTickLabel',{'intrcpt','past','future','currgoal','prevgoal'})
plot([0 25],[1 1],'k:'); set(gca,'YScale','log'); ylim([.3 4]);
subplot(3,2,6); xlim([0 25]); ylabel('exp(beta)'); set(gca,'XTick',2+[0:5:24],'XTickLabel',{'intrcpt','past','future','currgoal','prevgoal'})
plot([0 25],[1 1],'k:'); set(gca,'YScale','log'); ylim([.3 4]);
% save correlations (r2) to csv for supplementary table
%csvwrite('search_glm_r2table.txt',searchr2);
%csvwrite('repeat_glm_r2table.txt',repr2);
% sanity checks for the model
% randpred = randi([0 1],size(searchcat,1),3);
% randpred = searchcat(randperm(size(searchcat,1)),1:3);
% replay = zeros(size(searchcat,1),1); replay(searchcat(:,3)==1)=randi([1 2],sum(searchcat(:,3)==1),1); replay(searchcat(:,2)==1)=randi([1 2],sum(searchcat(:,2)==1),1);
% mdl = fitglm(searchcat(:,1:3),replay,'linear','Distribution','poisson'); CI = coefCI(mdl,.01);
% mdl = fitglm(randpred,searchcat(:,4),'linear','Distribution','poisson'); CI = coefCI(mdl,.01);
% figure; hold on;
% plot(1:4,table2array(mdl.Coefficients(:,1)),'.','MarkerSize',20,'Color',animcol(a,:));
% plot([1:4;1:4],CI','Color',animcol(a,:));
%simresponse = random(mdl,searchcat(:,1:3));
%[simres,simCI] = predict(mdl,searchcat(:,1:3));
%[simres,simCI] = predict(mdl,[0 1 0],'Alpha',.01)
% mdl = fitglm(searchcat(:,1:3),searchcat(:,4),'linear','Distribution','poisson');
% aic(1) = mdl.ModelCriterion.AIC;
% mdl = fitglm(searchcat(:,1),searchcat(:,4),'linear','Distribution','poisson');
% aic(2) = mdl.ModelCriterion.AIC;
% mdl = fitglm(searchcat(:,2),searchcat(:,4),'linear','Distribution','poisson');
% aic(3) = mdl.ModelCriterion.AIC;
% mdl = fitglm(searchcat(:,3),searchcat(:,4),'linear','Distribution','poisson');
% aic(4) = mdl.ModelCriterion.AIC;
% mdl = fitglm(searchcat(:,1:2),searchcat(:,4),'linear','Distribution','poisson');
% aic(5) = mdl.ModelCriterion.AIC;
% mdl = fitglm(searchcat(:,[1,3]),searchcat(:,4),'linear','Distribution','poisson');
% aic(6) = mdl.ModelCriterion.AIC;
% mdl = fitglm(searchcat(:,2:3),searchcat(:,4),'linear','Distribution','poisson');
% aic(7) = mdl.ModelCriterion.AIC;
% figure; plot(aic,'ro'); ylabel('AIC');
% set(gca,'XTickLabel',{'all','future','past','pg','futpast','futpg','pastpg'},'XTickLabelRotation',45)
%% Compare replay on correct/incorrect (repeat only) (used to be GLM to predict corr/err) [FIG 4C and SUP FIG 5 B,C]
clearvars -except f animals animcol
contentthresh = .3;
dists = figure(); set(gcf,'Position',[675 1 974 973]); scat = figure(); glms = figure();
reps = 100;
for a = 1:length(animals)
eps = find(arrayfun(@(x) ~isempty(x.trips),f(a).output{1}));
for e = 1:length(eps)
tphasenum = f(a).output{1}(eps(e)).trips.taskphase;
valtrials = ~isnan(tphasenum);
tphasenum = [tphasenum(valtrials), [1:sum(valtrials)]']; % add trial numbers
homerips = f(a).output{1}(eps(e)).trips.homeripcontent(valtrials);
hometypes = f(a).output{1}(eps(e)).trips.homeripmaxtypes(valtrials);
rwrips = f(a).output{1}(eps(e)).trips.RWripcontent(valtrials);
postrwrips = f(a).output{1}(eps(e)).trips.postRWripcontent(valtrials);
rwtypes = f(a).output{1}(eps(e)).trips.RWripmaxtypes(valtrials); %
postrwtypes = f(a).output{1}(eps(e)).trips.postRWripmaxtypes(valtrials); %
rips = cellfun(@(x,y,z) [x;y;z],homerips,rwrips,postrwrips,'un',0);
types = cellfun(@(x,y,z) [x,y,z]',hometypes,rwtypes,postrwtypes,'un',0);
outerrips = f(a).output{1}(eps(e)).trips.outerripcontent(valtrials);
outertypes = f(a).output{1}(eps(e)).trips.outerripmaxtypes(valtrials);
clear replays outerreplays
for t=1:length(rips)
if ~isempty(rips{t})
[maxval,ind] = max(rips{t},[],2); %(:,2:end)
valid = types{t}==1 & maxval>contentthresh;
replays{t} = ind(valid)'-1; %
else replays{t} = []; end
if ~isempty(outerrips{t})
[maxval,ind] = max(outerrips{t},[],2); %(:,2:end)
valid = outertypes{t}'==1 & maxval>contentthresh;
outerreplays{t} = ind(valid)'-1; %
else outerreplays{t} = []; end
end
goals = f(a).output{1}(eps(e)).trips.goalarm(valtrials,:);
goals(mod(tphasenum(:,1),1)>.85,:) = goals(find(mod(tphasenum(:,1),1)>.85)-1,:); % fix the last visit of cont (.9) to not reflect new goal yet
outers = f(a).output{1}(eps(e)).trips.outerarm(valtrials);
pastwlock = f(a).output{1}(eps(e)).trips.prevarm(valtrials,2); % only consider the including lockout option
countspertrial = zeros(length(outers),4);
valtrials = ~cellfun(@isempty,replays);
countspertrial(valtrials,:) = cell2mat(cellfun(@(x,fut,pst,crg,prg) [sum(x==fut), sum(x==pst), sum(x==crg), sum(x==prg)], ...
replays(valtrials),num2cell(outers(valtrials)),num2cell(pastwlock(valtrials))',num2cell(goals(valtrials,1)'),...
num2cell(goals(valtrials,2))','un',0)');
valtrials = tphasenum(:,1)>1 ; %& ~isnan(goals(:,2))
% [future past currg prevg correct/err early/late goalnum]
allrep{a}{e} = [countspertrial(valtrials,:), (mod(tphasenum(valtrials,1),1)==0 |mod(tphasenum(valtrials,1),1)>.85),tphasenum(valtrials,1)<5, tphasenum(valtrials,1)];
end
repcat = vertcat(allrep{a}{:});
%repcat(:,5) = repcat(randperm(size(repcat,1)),5); % for shufffle
corr = repcat(repcat(:,5)==1,:);
folds = 5;
for r = 1:reps
subsamp = [repcat(repcat(:,5)==0,:);corr(randperm(size(corr,1),sum(repcat(:,5)==0)),:)]; %match number of corr and err trials
cv = cvpartition(size(subsamp,1),'kfold',folds);
%cv = cvpartition(size(repcat,1),'kfold',folds);
for k = 1:folds
%mdl_int = fitglm(repcat(cv.training(k),1),repcat(cv.training(k),5),'constant','Distribution','binomial');
%sim_int = random(mdl_int,repcat(cv.test(k),1));
mdl = fitglm(subsamp(cv.training(k),1),subsamp(cv.training(k),5),'linear','Distribution','binomial');
sim = random(mdl,subsamp(cv.test(k),1));
tmp_future(r,k) = mean(sim~=subsamp(cv.test(k),5)); %-mean(sim_int==subsamp(cv.test(k),5))
mdl = fitglm(subsamp(cv.training(k),3),subsamp(cv.training(k),5),'linear','Distribution','binomial');
sim = random(mdl,subsamp(cv.test(k),3));
tmp_cg(r,k) = mean(sim~=subsamp(cv.test(k),5)); %-mean(sim_int==subsamp(cv.test(k),5))
mdl = fitglm(subsamp(cv.training(k),[1:4]),subsamp(cv.training(k),5),'linear','Distribution','binomial');
sim = random(mdl,subsamp(cv.test(k),[1:4]));
tmp_all(r,k) = mean(sim~=subsamp(cv.test(k),5)); %-mean(sim_int==subsamp(cv.test(k),5))
%mdl = fitglm(subsamp(cv.training(k),8),subsamp(cv.training(k),5),'linear','Distribution','binomial');
%sim = random(mdl,subsamp(cv.test(k),8));
%tmp_ripno(r,k) = mean(sim~=subsamp(cv.test(k),5)); %-mean(sim_int==subsamp(cv.test(k),5))
end
end
mcr_future{a} = mean(tmp_future,2);
mcr_cg{a} = mean(tmp_cg,2);
mcr_all{a} = mean(tmp_all,2);
%mcr_ripno{a} = mean(tmp_ripno,2);
%mcr_future{a} = reshape(tmp_future,[],1);
%mcr_cg{a} = reshape(tmp_cg,[],1);
%mcr_all{a} = reshape(tmp_all,[],1);
ci99 = tinv([.005 .995],reps-1);
mcr_futureCI = (std(mcr_future{a})/sqrt(reps))*ci99; %reps*
mcr_cgCI = (std(mcr_cg{a})/sqrt(reps))*ci99; %reps*
mcr_allCI = (std(mcr_all{a})/sqrt(reps))*ci99; %reps*
%mcr_ripnoCI = (std(mcr_ripno{a})/sqrt(reps))*ci99; %reps*
figure(glms); hold on;
plot(a+[0 5 10],[mean(mcr_future{a}) mean(mcr_cg{a}) mean(mcr_all{a})],'.','MarkerSize',20,'Color',animcol(a,:));
plot(repmat(a+[0 5 10],2,1),[mean(mcr_future{a})+mcr_futureCI' mean(mcr_cg{a})+mcr_cgCI' mean(mcr_all{a})+mcr_allCI'],'Color',animcol(a,:));
figure(dists);
subplot(2,4,a);hold on; title('future (incl1stcont) early and late')
earlycorr = repcat(:,5)==1 & repcat(:,6)==1;
latecorr = repcat(:,5)==1 & repcat(:,6)==0;
earlyerr = repcat(:,5)==0 & repcat(:,6)==1;
lateerr = repcat(:,5)==0 & repcat(:,6)==0;
errorbar(a+[0 .3],[mean(repcat(earlycorr|latecorr,1)),mean(repcat(earlyerr|lateerr,1))],[std(repcat(earlycorr|latecorr,1))./sqrt(sum(earlycorr|latecorr)), std(repcat(earlyerr|lateerr,1))./sqrt(sum(earlyerr|lateerr))],'k.');
bar(a+[0 .3],[mean(repcat(earlycorr|latecorr,1)),mean(repcat(earlyerr|lateerr,1))],.8,'FaceColor',animcol(a,:));
wrds = sprintf('pooled\np=%.03f\nn=%d,%d',ranksum(repcat(earlycorr|latecorr,1),repcat(earlyerr|lateerr,1)),sum(earlycorr|latecorr),sum(earlyerr|lateerr));
text(a,1.5,wrds); ylim([0 2])
%edges = [0:5];
%bar([1,2],[histcounts(repcat(earlycorr,1),edges,'Normalization','probability'); histcounts(repcat(earlyerr,1),edges,'Normalization','probability')],'stacked') %,.3,'FaceColor',animcol(a,:));
%[h,p]=kstest2(repcat(earlycorr,1),repcat(earlyerr,1));
%wrds = sprintf('kstest\np=%.03f\nn=%d,%d',p,sum(earlycorr),sum(earlyerr));
%text(1,.4,wrds); %ylim([0 .8]); xlim([-.5 4.5]); set(gca,'yTick',[0:.2:.8])
subplot(2,4,a+4);hold on; title('currgoal early and late')
errorbar(a+[0 .3],[mean(repcat(earlycorr|latecorr,3)),mean(repcat(earlyerr|lateerr,3))],[std(repcat(earlycorr|latecorr,3))./sqrt(sum(earlycorr|latecorr)), std(repcat(earlyerr|lateerr,3))./sqrt(sum(earlyerr|lateerr))],'k.');
bar(a+[0 .3],[mean(repcat(earlycorr|latecorr,3)),mean(repcat(earlyerr|lateerr,3))],.8,'FaceColor',animcol(a,:));
wrds = sprintf('pooled\np=%.03f\nn=%d,%d',ranksum(repcat(earlycorr|latecorr,3),repcat(earlyerr|lateerr,3)),sum(earlycorr|latecorr),sum(earlyerr|lateerr));
text(a,1.5,wrds); ylim([0 2])
%bar([4,5], [histcounts(repcat(latecorr,1),edges,'Normalization','probability'); histcounts(repcat(lateerr,1),edges,'Normalization','probability')],'stacked') %,.3,'FaceColor',animcol(a,:));
%bar(edges(1:end-1)-.2,histcounts(repcat(latecorr,1),edges,'Normalization','probability'),.3,'FaceColor',animcol(a,:));
%bar(edges(1:end-1)+.2, histcounts(repcat(lateerr,1),edges,'Normalization','probability'),.3,'FaceColor',animcol(a,:),'FaceAlpha',.3)
%[h,p]=kstest2(repcat(latecorr,1),repcat(lateerr,1));
%wrds = sprintf('kstest\np=%.03f\nn=%d,%d',p,sum(latecorr),sum(lateerr));
%text(4,.4,wrds); %ylim([0 .8]); xlim([-.5 4.5]);
%subplot(2,4,a+4);hold on; title('currgoal ')
%bar([1,2],[histcounts(repcat(earlycorr,3),edges,'Normalization','probability'); histcounts(repcat(earlyerr,3),edges,'Normalization','probability')],'stacked') %,.3,'FaceColor',animcol(a,:));