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dfs_ripcontent_contentthresh.m
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dfs_ripcontent_contentthresh.m
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%% Sungod manu: Plot main results WITH more permissive definition of remote content
% original def: majority PD across all segments, & PD in segment >=.3
% this def: majority of all segments excluding current segment, & PD in seg >=.3
% ie events that will change classification box->arm are those with remote content .3-.5
animals = {'jaq','roquefort','despereaux','montague'}; %,, 'remy',};%};
epochfilter{1} = ['$ripthresh>=0 & (isequal($environment,''goal'')) & $forageassist==0 & $gooddecode==1']; %& $epoch==2
%epochfilter{1} = ['$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', {'ripdecodesv3','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
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
for t=1:length(homerips) % extract valid rips and tack on trial info: [replay future past currgoal prevgoal ppgoal tphase salient local]
if ~isempty(homerips{t})
[maxval,ind] = max(homerips{t}(:,2:end),[],2); % max of arms only
ind(maxval<contentthresh) = 0; % if arm max <contentthresh, ind should be box; no longer need ind-1
maxval(maxval<contentthresh) = homerips{t}(maxval<contentthresh,1);
valid = hometypes{t}'==1 & maxval>=contentthresh;
homereplays{t} =[ind(valid),repmat(trialstack(t,:),sum(valid),1), ismember(ind(valid),[trialstack(t,1:2),unique(goals(1:t,1))']),ind(valid)==0];
else homereplays{t} = []; end
if ~isempty(rwrips{t})
[maxval,ind] = max(rwrips{t}(:,2:end),[],2); %
ind(maxval<contentthresh) = 0; % if <contentthresh in arms, ind should be box; no longer need ind-1
maxval(maxval<contentthresh) = rwrips{t}(maxval<contentthresh,1);
valid = rwtypes{t}'==1 & maxval>=contentthresh;
rwreplays{t} = [ind(valid),repmat(trialstack(t,:),sum(valid),1), ismember(ind(valid),[trialstack(t,1:2),unique(goals(1:t,1))']),ind(valid)==0];
else rwreplays{t} = []; end
if ~isempty(postrwrips{t})
[maxval,ind] = max(postrwrips{t}(:,2:end),[],2); %(:,2:end)
ind(maxval<contentthresh) = 0; % if <contentthresh in arms, ind should be box; no longer need ind-1
maxval(maxval<contentthresh) = postrwrips{t}(maxval<contentthresh,1);
valid = postrwtypes{t}'==1 & maxval>=contentthresh;
postrwreplays{t} = [ind(valid),repmat(trialstack(t,:),sum(valid),1), ismember(ind(valid),[trialstack(t,1:2),unique(goals(1:t,1))']),ind(valid)==0];
else postrwreplays{t} = []; end
if ~isempty(outerrips{t})
[maxval,ind] = max(outerrips{t},[],2);
for r = find(ind-1==outers(t) & maxval<(1-contentthresh)) % if max is localarm
newmax = max(outerrips{t}(r,~ismember([1:9],outers(t)+1)));
if newmax>contentthresh
ind(r) = find(outerrips{t}(r,:)==newmax);
maxval(r) = newmax;
end
end
valid = outertypes{t}'==1 & 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
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
%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),allbox{a})), sum(cellfun(@(x) sum(x(:,9)==0),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','box remote','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'))
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')
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')
subplot(3,3,9); hold on; plot4a(fracsalientboxouter,'gnames',{'box','outer'}); ylim([0 1]); title('salient')
%% make Violins and bars of content relationships with various trial categories, vs rand shuffs [HOME / RW]
clearvars -except f animals animcol
bars = figure(); set(gcf,'Position',[66 305 1853 551]);
reps = 5000;
contentthresh = .3;
for a = 1:length(animals)
%figure; set(gcf,'Position',[1008 1009 663 833]);
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);
clear replays
for t=1:length(rips)
if ~isempty(rips{t})
[maxval,ind] = max(rips{t}(:,2:end),[],2); % max of arms only
ind(maxval<contentthresh) = 0; % if arm max <contentthresh, ind should be box; no longer need ind-1
maxval(maxval<contentthresh) = rips{t}(maxval<contentthresh,1);
valid = types{t}==1 & maxval>=contentthresh;
replays{t} =[ind(valid)];
else replays{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
valtrials = ~cellfun(@isempty, replays);
if any(valtrials)
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{:});
salient = ripcomb(:,1)>0 & (ripcomb(:,1)==ripcomb(:,2) | ripcomb(:,1)==ripcomb(:,3) | table2array(rowfun(@(x) ismember(x(1),goals(1:x(8),1)),table(ripcomb))));
contentfracs{a}(e,:) = [length(vertcat(rips{:}))-size(ripcomb,1), sum(ripcomb(:,1)==0), sum(salient), sum(ripcomb(:,1)>0 & ~salient)]./length(vertcat(rips{:}));
% all ripples during search trials; f vs past vs pgoal
search = ripcomb((ripcomb(:,7) <= 1 & ~isnan(ripcomb(:,5))),:); %~isnan(abovethresh(:,4)) & & ~isnan(abovethresh(:,6))
alluniqueinds = table2array(rowfun(@(x) length(unique(x([2,3,5])))==3,table(search)));
allunique = search(alluniqueinds,:); %
if length(unique(allunique(:,8)))>=1
search_future{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,2));
search_past{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,3));
search_prevgoal{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,5));
search_other{a}(e) = sum(allunique(:,1)>0 & allunique(:,1)~=allunique(:,2) & allunique(:,1)~=allunique(:,3) & allunique(:,1)~=allunique(:,5));
for r = 1:reps
randlist = randi([1 8],size(allunique,1),1);
search_randshuff{a}(:,:,e,r) = replaymatrix(allunique(:,1),randlist);
end
end
search_rtcounts{a}(e,:) = [size(allunique,1), length(unique(allunique(:,8))), sum(tphasenum(:,1)<1)]; % #rips #trials #totalsearchtrials
% all ripples during search trials; f==pgoal vs past
alluniqueinds = search(:,2)==search(:,5) & search(:,2)~=search(:,4) ;
allunique = search(alluniqueinds,:); %
if length(unique(allunique(:,8)))>=1
search2_futurepg{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,2));
search2_past{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,3));
search2_other{a}(e) = sum(allunique(:,1)>0 & allunique(:,1)~=allunique(:,2) & allunique(:,1)~=allunique(:,4));
for r = 1:reps
randlist = randi([1 8],size(allunique,1),1);
search2_randshuff{a}(:,:,e,r) = replaymatrix(allunique(:,1),randlist);
end
end
search2_rtcounts{a}(e,:) = [size(allunique,1), length(unique(allunique(:,8))), sum(tphasenum(:,1)<1)]; % #rips #trials #totalsearchtrials
% all ripples during repeat trials; future==past==goal vs pg
repeat = ripcomb((ripcomb(:,7) > 1 & ~isnan(ripcomb(:,5))),:); %~isnan(abovethresh(:,4)) & & ~isnan(abovethresh(:,6))
alluniqueinds = table2array(rowfun(@(x) length(unique(x([2,3,4])))==1,table(repeat))) & repeat(:,5)~=repeat(:,2);
allunique = repeat(alluniqueinds,:); %
if length(unique(allunique(:,8)))>=1
rep1_pfg{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,2));
rep1_prevgoal{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,5));
rep1_other{a}(e) = sum(allunique(:,1)>0 & allunique(:,1)~=allunique(:,2) & allunique(:,1)~=allunique(:,5));
for r = 1:reps
randlist = randi([1 8],size(allunique,1),1);
rep1_randshuff{a}(:,:,e,r) = replaymatrix(allunique(:,1),randlist);
end
end
rep1_rtcounts{a}(e,:) = [size(allunique,1), length(unique(allunique(:,8))), sum(tphasenum(:,1)>=1)]; % #rips #trials #totalreptrials
% all ripples during repeat trials; future==goal vs past vs pg
alluniqueinds = table2array(rowfun(@(x) length(unique(x([2,3,5])))==3,table(repeat))) & repeat(:,2)==repeat(:,4);
allunique = repeat(alluniqueinds,:); %
if length(unique(allunique(:,8)))>=1
rep2_futureg{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,2));
rep2_past{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,3));
rep2_prevgoal{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,5));
rep2_other{a}(e) = sum(allunique(:,1)>0 & allunique(:,1)~=allunique(:,2) & allunique(:,1)~=allunique(:,3) & allunique(:,1)~=allunique(:,5));
for r = 1:reps
randlist = randi([1 8],size(allunique,1),1);
rep2_randshuff{a}(:,:,e,r) = replaymatrix(allunique(:,1),randlist);
end
end
rep2_rtcounts{a}(e,:) = [size(allunique,1), length(unique(allunique(:,8))), sum(tphasenum(:,1)>=1)]; % #rips #trials #totalreptrials
% all ripples during repeat trials; past==curgoal vs future vs pg
alluniqueinds = table2array(rowfun(@(x) length(unique(x([2,3,5])))==3,table(repeat))) & repeat(:,3)==repeat(:,4);
allunique = repeat(alluniqueinds,:); %
if length(unique(allunique(:,8)))>=1
rep3_future{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,2));
rep3_pastg{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,3));
rep3_prevgoal{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,5));
rep3_other{a}(e) = sum(allunique(:,1)>0 & allunique(:,1)~=allunique(:,2) & allunique(:,1)~=allunique(:,3) & allunique(:,1)~=allunique(:,5));
for r = 1:reps
randlist = randi([1 8],size(allunique,1),1);
rep3_randshuff{a}(:,:,e,r) = replaymatrix(allunique(:,1),randlist);
end
end
rep3_rtcounts{a}(e,:) = [size(allunique,1), length(unique(allunique(:,8))), sum(tphasenum(:,1)>=1)]; % #rips #trials #totalreptrials
% all ripples during repeat trials; past vs curgoal vs future vs pg
alluniqueinds = table2array(rowfun(@(x) length(unique(x([2,3,4,5])))==4,table(repeat)));
allunique = repeat(alluniqueinds,:); %
if length(unique(allunique(:,8)))>=1
rep4_future{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,2));
rep4_past{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,3));
rep4_currg{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,4));
rep4_prevgoal{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,5));
rep4_other{a}(e) = sum(allunique(:,1)>0 & allunique(:,1)~=allunique(:,2) & allunique(:,1)~=allunique(:,3) & allunique(:,1)~=allunique(:,4) & allunique(:,1)~=allunique(:,5));
for r = 1:reps
randlist = randi([1 8],size(allunique,1),1);
rep4_randshuff{a}(:,:,e,r) = replaymatrix(allunique(:,1),randlist);
end
end
rep4_rtcounts{a}(e,:) = [size(allunique,1), length(unique(allunique(:,8))), sum(tphasenum(:,1)>=1)]; % #rips #trials #totalreptrials
end
end
diag = logical(eye(8));
eptot_future = sum(search_future{a},3);eventtotal = sum(sum(eptot_future)); frac_future = sum(eptot_future(diag))/eventtotal;% will be same total for all
eptot_past = sum(search_past{a},3); frac_past = sum(eptot_past(diag))/eventtotal;
eptot_prevgoal = sum(search_prevgoal{a},3); frac_prevgoal = sum(eptot_prevgoal(diag))/eventtotal;
frac_other = (sum(search_other{a})/eventtotal)/5;
eptot_randshuff = sum(search_randshuff{a},3); frac_randshuff = sum(reshape(eptot_randshuff(repmat(diag,1,1,reps)),[8 reps]))/eventtotal;
frac_search_randshuff_norm{a} = frac_randshuff'-.125;
figure(bars); subplot(2,5,6); hold on;
bar(a/5+[1:4],[frac_future,frac_past,frac_prevgoal,frac_other]-.125,'FaceColor',animcol(a,:),'BarWidth',.2)
pvals =[permutationp(frac_future,frac_randshuff,'tails',2,'reps',reps),permutationp(frac_past,frac_randshuff,'tails',2,'reps',reps),permutationp(frac_prevgoal,frac_randshuff,'tails',2,'reps',reps),permutationp(frac_other,frac_randshuff,'tails',2,'reps',reps)];
ntext = sprintf('n=%devents;%d/%dsearchtrials',eventtotal,sum(search_rtcounts{a}(:,2)),sum(search_rtcounts{a}(:,3))); text(1,a/20+.125,ntext,'Color',animcol(a,:));
text(3/5+-.3+[1:4],a/20+[.1 .1 .1 .1],num2str(pvals','%.04f'),'Color',animcol(a,:));
% eptot_future = sum(search2_futurepg{a},3);eventtotal = sum(sum(eptot_future)); frac_future = sum(eptot_future(diag))/eventtotal;% will be same total for all
% eptot_past = sum(search2_past{a},3); frac_past = sum(eptot_past(diag))/eventtotal;
% eptot_randshuff = sum(search2_randshuff{a},3); frac_randshuff = sum(reshape(eptot_randshuff(repmat(diag,1,1,reps)),[8 reps]))/eventtotal;
% frac_search2_randshuff_norm{a} = frac_randshuff'-.125;
% figure(bars); subplot(2,5,7); hold on;
% bar(a/5+[1:2],[frac_future,frac_past]-.125,'FaceColor',animcol(a,:),'BarWidth',.2)
% pvals =[permutationp(frac_future,frac_randshuff,'tails',2,'reps',reps),permutationp(frac_past,frac_randshuff,'tails',2,'reps',reps)];
% ntext = sprintf('n=%devents;%d/%dsearchtrials',eventtotal,sum(search2_rtcounts{a}(:,2)),sum(search2_rtcounts{a}(:,3))); text(1,a/20+.125,ntext,'Color',animcol(a,:));
% text(3/5+-.3+[1:2],a/20+[.1 .1 ],num2str(pvals','%.04f'),'Color',animcol(a,:));
%
eptot_future = sum(rep1_pfg{a},3);eventtotal = sum(sum(eptot_future)); frac_future = sum(eptot_future(diag))/eventtotal;% will be same total for all
eptot_prevgoal = sum(rep1_prevgoal{a},3); frac_prevgoal = sum(eptot_prevgoal(diag))/eventtotal;
eptot_randshuff = sum(rep1_randshuff{a},3); frac_randshuff = sum(reshape(eptot_randshuff(repmat(diag,1,1,reps)),[8 reps]))/eventtotal;
frac_other = (sum(rep1_other{a})/eventtotal)/6;
frac_rep1_randshuff_norm{a} = frac_randshuff'-.125;
figure(bars); subplot(2,5,7); hold on;
bar(a/5+[1:3],[frac_future,frac_prevgoal,frac_other]-.125,'FaceColor',animcol(a,:),'BarWidth',.2)
pvals =[permutationp(frac_future,frac_randshuff,'tails',2,'reps',reps),permutationp(frac_prevgoal,frac_randshuff,'tails',2,'reps',reps),permutationp(frac_other,frac_randshuff,'tails',2,'reps',reps)];
ntext = sprintf('n=%devents;%d/%dreptrials',eventtotal,sum(rep1_rtcounts{a}(:,2)),sum(rep1_rtcounts{a}(:,3))); text(1,a/20+.125,ntext,'Color',animcol(a,:));
text(3/5+-.3+[1:3],a/20+[.1 .1 .1],num2str(pvals','%.04f'),'Color',animcol(a,:));
eptot_future = sum(rep2_futureg{a},3);eventtotal = sum(sum(eptot_future)); frac_future = sum(eptot_future(diag))/eventtotal;% will be same total for all
eptot_past = sum(rep2_past{a},3); frac_past = sum(eptot_past(diag))/eventtotal;
eptot_prevgoal = sum(rep2_prevgoal{a},3); frac_prevgoal = sum(eptot_prevgoal(diag))/eventtotal;
eptot_randshuff = sum(rep2_randshuff{a},3); frac_randshuff = sum(reshape(eptot_randshuff(repmat(diag,1,1,reps)),[8 reps]))/eventtotal;
frac_rep2_randshuff_norm{a} = frac_randshuff'-.125;
figure(bars); subplot(2,5,8); hold on;
bar(a/5+[1:3],[frac_future,frac_past,frac_prevgoal]-.125,'FaceColor',animcol(a,:),'BarWidth',.2)
pvals =[permutationp(frac_future,frac_randshuff,'tails',2,'reps',reps),permutationp(frac_past,frac_randshuff,'tails',2,'reps',reps),permutationp(frac_prevgoal,frac_randshuff,'tails',2,'reps',reps)];
ntext = sprintf('n=%devents;%d/%dreptrials',eventtotal,sum(rep2_rtcounts{a}(:,2)),sum(rep2_rtcounts{a}(:,3))); text(1,a/20+.125,ntext,'Color',animcol(a,:));
text(3/5+-.3+[1:3],a/20+[.1 .1 .1 ],num2str(pvals','%.04f'),'Color',animcol(a,:));
eptot_future = sum(rep3_future{a},3);eventtotal = sum(sum(eptot_future)); frac_future = sum(eptot_future(diag))/eventtotal;% will be same total for all
eptot_past = sum(rep3_pastg{a},3); frac_past = sum(eptot_past(diag))/eventtotal;
eptot_prevgoal = sum(rep3_prevgoal{a},3); frac_prevgoal = sum(eptot_prevgoal(diag))/eventtotal;
eptot_randshuff = sum(rep3_randshuff{a},3); frac_randshuff = sum(reshape(eptot_randshuff(repmat(diag,1,1,reps)),[8 reps]))/eventtotal;
frac_rep3_randshuff_norm{a} = frac_randshuff'-.125;
figure(bars); subplot(2,5,9); hold on;
bar(a/5+[1:3],[frac_future,frac_past,frac_prevgoal]-.125,'FaceColor',animcol(a,:),'BarWidth',.2)
pvals =[permutationp(frac_future,frac_randshuff,'tails',2,'reps',reps),permutationp(frac_past,frac_randshuff,'tails',2,'reps',reps),permutationp(frac_prevgoal,frac_randshuff,'tails',2,'reps',reps)];
ntext = sprintf('n=%devents;%d/%dreptrials',eventtotal,sum(rep3_rtcounts{a}(:,2)),sum(rep3_rtcounts{a}(:,3))); text(1,a/20+.125,ntext,'Color',animcol(a,:));
text(3/5+-.3+[1:3],a/20+[.1 .1 .1],num2str(pvals','%.04f'),'Color',animcol(a,:));
eptot_future = sum(rep4_future{a},3);eventtotal = sum(sum(eptot_future)); frac_future = sum(eptot_future(diag))/eventtotal;% will be same total for all
eptot_past = sum(rep4_past{a},3); frac_past = sum(eptot_past(diag))/eventtotal;
eptot_currg = sum(rep4_currg{a},3); frac_currg = sum(eptot_currg(diag))/eventtotal;
eptot_prevgoal = sum(rep4_prevgoal{a},3); frac_prevgoal = sum(eptot_prevgoal(diag))/eventtotal;
eptot_randshuff = sum(rep4_randshuff{a},3); frac_randshuff = sum(reshape(eptot_randshuff(repmat(diag,1,1,reps)),[8 reps]))/eventtotal;
frac_rep4_randshuff_norm{a} = frac_randshuff'-.125;
figure(bars); subplot(2,5,10); hold on;
bar(a/5+[1:4],[frac_future,frac_past,frac_currg,frac_prevgoal]-.125,'FaceColor',animcol(a,:),'BarWidth',.2)
pvals =[permutationp(frac_future,frac_randshuff,'tails',2,'reps',reps),permutationp(frac_past,frac_randshuff,'tails',2,'reps',reps),permutationp(frac_currg,frac_randshuff,'tails',2,'reps',reps),permutationp(frac_prevgoal,frac_randshuff,'tails',2,'reps',reps)];
ntext = sprintf('n=%devents;%d/%dreptrials',eventtotal,sum(rep4_rtcounts{a}(:,2)),sum(rep4_rtcounts{a}(:,3))); text(1,a/20+.125,ntext,'Color',animcol(a,:));
text(3/5+-.3+[1:4],a/20+[.1 .1 .1 .1],num2str(pvals','%.04f'),'Color',animcol(a,:));
end
subplot(2,5,1); violin(frac_search_randshuff_norm,'medc',[],'facecolor',[.3 .3 .3]); ylim([-.15 .4]); title('randshuff'); ylabel('fraction replays, p vs randshuff')
set(gca,'yticklabel',{'0.025','0.125','0.225','0.325','0.425',''})
subplot(2,5,6); xlim([1 5]); ylim([-.15 .4]);
set(gca,'xtick',[1.5:1:4.5],'xticklabel',{'future','past','prevgoal','other'}); title('search'); set(gca,'yticklabel',{'0.025','0.125','0.225','0.325','0.425',''})
%subplot(2,5,2); violin(frac_search2_randshuff_norm,'medc',[],'facecolor',[.3 .3 .3]); ylim([-.15 .4]); title('randshuff'); ylabel('fraction replays, p vs randshuff')
%set(gca,'yticklabel',{'0.025','0.125','0.225','0.325','0.425',''})
%subplot(2,5,7); xlim([1 4]); ylim([-.15 .4]);
%set(gca,'xtick',[1.5:1:2.5],'xticklabel',{'f/prevgoal','past'}); title('search'); set(gca,'yticklabel',{'0.025','0.075','0.125','0.175','0.225','0.275','0.325','',''})
subplot(2,5,2); violin(frac_rep1_randshuff_norm,'medc',[],'facecolor',[.3 .3 .3]); ylim([-.15 .4]); title('randshuff'); ylabel('fraction replays, p vs randshuff')
set(gca,'yticklabel',{'0.025','0.125','0.225','0.325','0.425',''})
subplot(2,5,7); xlim([1 4]); ylim([-.15 .4]);
set(gca,'xtick',[1.5:1:3.5],'xticklabel',{'pfg','prevgoal','other'}); title('repeat'); set(gca,'yticklabel',{'0.025','0.125','0.225','0.325','0.425',''})
subplot(2,5,3); violin(frac_rep2_randshuff_norm,'medc',[],'facecolor',[.3 .3 .3]); ylim([-.15 .4]); title('randshuff'); ylabel('fraction replays, p vs randshuff')
set(gca,'yticklabel',{'0.025','0.125','0.225','0.325','0.425',''})
subplot(2,5,8); xlim([1 4]); ylim([-.15 .4]);
set(gca,'xtick',[1.5:1:3.5],'xticklabel',{'future/g','past','prevgoal'}); title('repeat'); set(gca,'yticklabel',{'0.025','0.125','0.225','0.325','0.425',''})
subplot(2,5,4); violin(frac_rep3_randshuff_norm,'medc',[],'facecolor',[.3 .3 .3]); ylim([-.15 .4]); title('randshuff'); ylabel('fraction replays, p vs randshuff')
set(gca,'yticklabel',{'0.025','0.125','0.225','0.325','0.425',''})
subplot(2,5,9); xlim([1 4]); ylim([-.15 .4]);
set(gca,'xtick',[1.5:1:3.5],'xticklabel',{'future','past/g','prevgoal'}); title('repeat'); set(gca,'yticklabel',{'0.025','0.125','0.225','0.325','0.425',''})
subplot(2,5,5); violin(frac_rep4_randshuff_norm,'medc',[],'facecolor',[.3 .3 .3]); ylim([-.15 .4]); title('randshuff'); ylabel('fraction replays, p vs randshuff')
set(gca,'yticklabel',{'0.025','0.125','0.225','0.325','0.425',''})
subplot(2,5,10); xlim([1 5]); ylim([-.15 .4]);
set(gca,'xtick',[1.5:1:4.5],'xticklabel',{'future','past','currg','prevgoal'}); title('repeat'); set(gca,'yticklabel',{'0.025','0.125','0.225','0.325','0.425',''})
%figure; plot4a(contentfracs,'gnames',{'incoh','local','salient','other'}); ylim([0 .75]); title('box replays')
% figure; subplot(1,5,1); plot4a(cellfun(@(x) x(:,1)./x(:,2),search_rtcounts,'Un',0)); ylim([0 20]);
% subplot(1,5,2); plot4a(cellfun(@(x) x(:,1)./x(:,2),search2_rtcounts,'Un',0));ylim([0 20]);
% subplot(1,5,3); plot4a(cellfun(@(x) x(:,1)./x(:,2),rep1_rtcounts,'Un',0));ylim([0 20]);
% subplot(1,5,4); plot4a(cellfun(@(x) x(:,1)./x(:,2),rep2_rtcounts,'Un',0));ylim([0 20]);
% subplot(1,5,5); plot4a(cellfun(@(x) x(:,1)./x(:,2),rep3_rtcounts,'Un',0));ylim([0 20]);
%figname = 'allbox-maxcont';
%print(figname,'-depsc','-painters')
%% make Violins and bars of content relationships with various trial categories, vs rand shuffs [OUTER]
clearvars -except f animals animcol
bars = figure(); set(gcf,'Position',[66 305 1853 551]);
reps = 5000;
contentthresh=.3;
for a = 1:length(animals)
%figure; set(gcf,'Position',[1008 1009 663 833]);
eps = find(arrayfun(@(x) ~isempty(x.trips),f(a).output{1}));
for e = 1:length(eps)
rips = f(a).output{1}(eps(e)).trips.outerripcontent;
types = f(a).output{1}(eps(e)).trips.outerripmaxtypes; %
goals = f(a).output{1}(eps(e)).trips.goalarm;
outers = f(a).output{1}(eps(e)).trips.outerarm;
pastwlock = f(a).output{1}(eps(e)).trips.prevarm(:,2); % only consider the including lockout option
tphasenum = [f(a).output{1}(eps(e)).trips.taskphase, [1:length(goals)]']; % add trial numbers
clear replays
for t=1:length(rips)
if ~isempty(rips{t})
[maxval,ind] = max(rips{t},[],2);
for r = find(ind-1==outers(t) & maxval<(1-contentthresh)) % if max is localarm
newmax = max(rips{t}(r,~ismember([1:9],outers(t)+1)));
if newmax>contentthresh
ind(r) = find(rips{t}(r,:)==newmax);
maxval(r) = newmax;
end
end
valid = types{t}'==1 & maxval>=contentthresh;
replays{t} = [ind(valid)-1];
else replays{t} = []; end
end
valtrials = ~cellfun(@isempty, replays);
if any(valtrials)
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{:});
%salient - past or previously rewarded arms only, not box/future
salient = ~(ripcomb(:,1)==ripcomb(:,2)) & (ripcomb(:,1)==ripcomb(:,3) | table2array(rowfun(@(x) ismember(x(1),goals(1:x(8),1)),table(ripcomb))));
contentfracs{a}(e,:) = [length(vertcat(rips{:}))-size(ripcomb,1), sum(ripcomb(:,1)==ripcomb(:,2)), sum(ripcomb(:,1)==0), sum(salient), sum(ripcomb(:,1)~=ripcomb(:,2) & ripcomb(:,1)>0 & ~salient)]./length(vertcat(rips{:}));
nonlocal = ripcomb(ripcomb(:,1)~=ripcomb(:,2),:); % get rid of local events
nonlocal(:,9) = zeros(size(nonlocal,1),1); % add a new column for future: box (zeros) in addition to current location
trialfracs{a}(e,:) = [sum(~valtrials' & tphasenum(:,1)>=1), sum(valtrials' & tphasenum(:,1)>=1)-length(unique(nonlocal(:,8))),length(unique(nonlocal(:,8)))]/sum(tphasenum(:,1)>=1); % of repeat trials, fractrials no replay, frac local only, frac with nonlocal ,
% all ripples during repeat trials; future=box, prevarm=currarm/goal vs prevgoal
%repeat = nonlocal((nonlocal(:,7) >= 1 & ~isnan(nonlocal(:,5))),:); %~isnan(abovethresh(:,4)) & & ~isnan(abovethresh(:,6))
repeat = ripcomb((ripcomb(:,7) >= 1 & ~isnan(ripcomb(:,5))),:); %~isnan(abovethresh(:,4)) & & ~isnan(abovethresh(:,6))
alluniqueinds = repeat(:,2)==repeat(:,3) & repeat(:,3)==repeat(:,4) & repeat(:,4)~=repeat(:,5);
allunique = repeat(alluniqueinds,:); %
if length(unique(allunique(:,8)))>=1
% rep1_future{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,9),'withbox',1);
% rep1_pcg{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,3),'withbox',1);
% rep1_prevgoal{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,5),'withbox',1);
% for r = 1:reps
% randlist = randi([0 8],size(allunique,1),1);
% while any(randlist==allunique(:,2)) % remove any localarms from randlist
% randlist(randlist==allunique(:,2)) = randi([0 8],sum(randlist==allunique(:,2)),1);
% end
% rep1_randshuff{a}(:,:,e,r) = replaymatrix(allunique(:,1),randlist,'withbox',1);
% end
rep1_pcg{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,3));
rep1_prevgoal{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,5));
rep1_other{a}(:,:,e) = sum(allunique(:,1)>0 & allunique(:,1)~=allunique(:,2) & allunique(:,1)~=allunique(:,5));
for r = 1:reps
randlist = randi([1 8],size(allunique,1),1);
rep1_randshuff{a}(:,:,e,r) = replaymatrix(allunique(:,1),randlist);
end
end
rep1_rtcounts{a}(e,:) = [size(allunique,1), length(unique(allunique(:,8))), sum(tphasenum(:,1)>=1)]; % #rips #trials #totalreptrials
% all ripples during repeat trials; future==goal vs past vs pg (past error, not to pg)
alluniqueinds = table2array(rowfun(@(x) length(unique(x([3,4,5])))==3,table(repeat)));
allunique = repeat(alluniqueinds,:); %
if length(unique(allunique(:,8)))>=1
rep2_past{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,3));
rep2_cg{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,4));
rep2_prevgoal{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,5));
for r = 1:reps
randlist = randi([1 8],size(allunique,1),1);
rep2_randshuff{a}(:,:,e,r) = replaymatrix(allunique(:,1),randlist);
end
end
rep2_rtcounts{a}(e,:) = [size(allunique,1), length(unique(allunique(:,8))), sum(tphasenum(:,1)>=1)]; % #rips #trials #totalreptrials
% all ripples during repeat trials; future==goal vs past=pg (past error to pg)
% alluniqueinds = repeat(:,7)==1; %repeat(:,3)==repeat(:,5) & repeat(:,4)~=repeat(:,3);
% allunique = repeat(alluniqueinds,:); %
% if length(unique(allunique(:,8)))>=1
% rep3_box{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,9),'withbox',1);
% rep3_pgoal{a}(:,:,e) = replaymatrix(allunique(:,1),allunique(:,5),'withbox',1);
% for r = 1:reps
% randlist = randi([0 8],size(allunique,1),1);
% while any(randlist==allunique(:,2)) % remove any localarms from randlist
% randlist(randlist==allunique(:,2)) = randi([0 8],sum(randlist==allunique(:,2)),1);
% end
% rep3_randshuff{a}(:,:,e,r) = replaymatrix(allunique(:,1),randlist,'withbox',1);
% end
% end
% rep3_rtcounts{a}(e,:) = [size(allunique,1), length(unique(allunique(:,8))), sum(tphasenum(:,1)>=1)]; % #rips #trials #totalreptrials
%
end
end
diag = logical(eye(8));
%eptot_future = sum(rep1_future{a},3); frac_future = sum(eptot_future(diag))/eventtotal;% will be same total for all
eptot_pcg = sum(rep1_pcg{a},3); eventtotal = sum(sum(eptot_pcg)); frac_pcg = sum(eptot_pcg(diag))/eventtotal;
eptot_prevgoal = sum(rep1_prevgoal{a},3); frac_prevgoal = sum(eptot_prevgoal(diag))/eventtotal;
eptot_randshuff = sum(rep1_randshuff{a},3); frac_randshuff = sum(reshape(eptot_randshuff(repmat(diag,1,1,reps)),[8 reps]))/eventtotal;
frac_other = (sum(rep1_other{a})/eventtotal)/6;
frac_rep1_randshuff_norm{a} = frac_randshuff'-.125; %
figure(bars); subplot(2,3,4); hold on;
bar(a/5+[1:3],[frac_pcg,frac_prevgoal,frac_other]-.125,'FaceColor',animcol(a,:),'BarWidth',.2)
pvals =[permutationp(frac_pcg,frac_randshuff,'tails',2,'reps',reps),permutationp(frac_prevgoal,frac_randshuff,'tails',2,'reps',reps), permutationp(frac_other,frac_randshuff,'tails',2,'reps',reps)];
ntext = sprintf('n=%devents;%d/%dreptrials',eventtotal,sum(rep1_rtcounts{a}(:,2)),sum(rep1_rtcounts{a}(:,3))); text(1,a/20+.125,ntext,'Color',animcol(a,:));
text(3/5+-.3+[1:3],a/20+[.1 .1 .1],num2str(pvals','%.04f'),'Color',animcol(a,:));
%eptot_future = sum(rep2_future{a},3); frac_future = sum(eptot_future(diag))/eventtotal;% will be same total for all
eptot_past = sum(rep2_past{a},3); eventtotal = sum(sum(eptot_past)); frac_past = sum(eptot_past(diag))/eventtotal;
eptot_cg = sum(rep2_cg{a},3); frac_cg = sum(eptot_cg(diag))/eventtotal;
eptot_prevgoal = sum(rep2_prevgoal{a},3); frac_prevgoal = sum(eptot_prevgoal(diag))/eventtotal;
eptot_randshuff = sum(rep2_randshuff{a},3); frac_randshuff = sum(reshape(eptot_randshuff(repmat(diag,1,1,reps)),[8 reps]))/eventtotal;
frac_rep2_randshuff_norm{a} = frac_randshuff'-.125;
figure(bars); subplot(2,3,5); hold on;
bar(a/5+[1:3],[frac_past,frac_cg,frac_prevgoal]-.125,'FaceColor',animcol(a,:),'BarWidth',.2)
pvals =[permutationp(frac_past,frac_randshuff,'tails',2,'reps',reps),permutationp(frac_cg,frac_randshuff,'tails',2,'reps',reps),permutationp(frac_prevgoal,frac_randshuff,'tails',2,'reps',reps)];
ntext = sprintf('n=%devents;%d/%dreptrials',eventtotal,sum(rep2_rtcounts{a}(:,2)),sum(rep2_rtcounts{a}(:,3))); text(1,a/20+.125,ntext,'Color',animcol(a,:));
text(3/5+-.3+[1:3],a/20+[.1 .1 .1],num2str(pvals','%.04f'),'Color',animcol(a,:));
% eptot_future = sum(rep3_box{a},3);eventtotal = sum(sum(eptot_future)); frac_future = sum(eptot_future(diag))/eventtotal;% will be same total for all
% eptot_pgoal = sum(rep3_pgoal{a},3); frac_pgoal = sum(eptot_pgoal(diag))/eventtotal;
% eptot_randshuff = sum(rep3_randshuff{a},3); frac_randshuff = sum(reshape(eptot_randshuff(repmat(diag,1,1,reps)),[9 reps]))/eventtotal;
% frac_rep3_randshuff_norm{a} = frac_randshuff'-.125;
% figure(bars); subplot(2,3,6); hold on;
% bar(a/5+[1:2],[frac_future,frac_pgoal]-.125,'FaceColor',animcol(a,:),'BarWidth',.2)
% pvals =[permutationp(frac_future,frac_randshuff,'tails',2),permutationp(frac_pgoal,frac_randshuff,'tails',2)];
% ntext = sprintf('n=%devents;%d/%dreptrials',eventtotal,sum(rep3_rtcounts{a}(:,2)),sum(rep3_rtcounts{a}(:,3))); text(1,a/20+.125,ntext,'Color',animcol(a,:));
% text(3/5+-.3+[1:2],a/20+[.1 .1],num2str(pvals','%.04f'),'Color',animcol(a,:));
%
end
subplot(2,3,1); violin(frac_rep1_randshuff_norm,'medc',[],'facecolor',[.3 .3 .3]); ylim([-.15 .45]); title('randshuff'); ylabel('fraction replays')
set(gca,'yticklabel',{'0.025','0.125','0.225','0.325','0.425','0.525'})
subplot(2,3,4); xlim([1 5]); ylim([-.15 .45]); set(gca,'yticklabel',{'0.025','0.125','0.225','0.325','0.425','0.525'})
set(gca,'xtick',[1.5:1:3.5],'xticklabel',{'current','prevgoal','other'}); title('repeat');
subplot(2,3,2); violin(frac_rep2_randshuff_norm,'medc',[],'facecolor',[.3 .3 .3]); ylim([-.15 .45]); title('randshuff'); ylabel('fraction replays')
set(gca,'yticklabel',{'0.025','0.125','0.225','0.325','0.425','0.525'})
subplot(2,3,5); xlim([1 5]); ylim([-.15 .45]);
set(gca,'xtick',[1.5:1:3.5],'xticklabel',{'past','current','prevgoal'}); title('repeat')
set(gca,'yticklabel',{'0.025','0.125','0.225','0.325','0.425','0.525'})
% subplot(2,3,3); violin(frac_rep3_randshuff_norm,'medc',[],'facecolor',[.3 .3 .3]); ylim([-.15 .4]); title('randshuff'); ylabel('fraction replays')
% set(gca,'yticklabel',{'0.025','0.125','0.225','0.325','0.425',''})
% subplot(2,3,6); xlim([1 4]); ylim([-.15 .4]);
% set(gca,'xtick',[1.5:1:2.5],'xticklabel',{'box','pgoal'}); title('repeat');
% set(gca,'yticklabel',{'0.025','0.125','0.225','0.325','0.425',''})
%figure; %plot4a(contentfracs,'gnames',{'incoh','localarm','box','salient','other'}); ylim([0 .75]); title('outer replays')
subplot(2,3,6); plot4a(trialfracs,'gnames',{'norips','localonly','hasremote'}); ylim([0 1]); title('repeat frac norips/localonly')
%% measure replay rate of an arm during contingencies before during/after it is goal
clearvars -except f animals animcol
figure; set(gcf,'Position',[675 1 1202 973]); plt=1;
contentthresh = .3;
window = 20; %trials for trialcurves
kernel = gaussian(1,5);
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:end),[],2);
ind(maxval<contentthresh) = 0; % if <contentthresh in arms, ind should be box; no longer need ind-1
maxval(maxval<contentthresh) = rips{t}(maxval<contentthresh,1);
valid = types{t}==1 & maxval>=contentthresh;
replays{t} = [ind(valid)]';
else replays{t} = []; end
if ~isempty(outerrips{t})
[maxval,ind] = max(outerrips{t},[],2);
for r = find(ind-1==outers(t) & maxval<(1-contentthresh)) % if max is localarm
newmax = max(outerrips{t}(r,~ismember([1:9],outers(t)+1)));
if newmax>contentthresh
ind(r) = find(outerrips{t}(r,:)==newmax);
maxval(r) = newmax;
end
end
valid = outertypes{t}'==1 & maxval>=contentthresh;
outerreplays{t} = [ind(valid)-1]';
else outerreplays{t} = []; end
end
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+8); 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.');
%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,:));
%figure;
%visxrep = cell2mat(cellfun(@(x,y,z) [reshape(x([5:2:end-4],y),1,[]);reshape(z([1:2:end],y),1,[])],visitcount{a},nevergoals{a},replaycount{a},'un',0));
%boxplot(visxrep(2,:),visxrep(1,:))
end
%% fit linear model of category predictors for each arm, for box and outer
clearvars -except f animals animcol
contentthresh = .3;
all = figure(); byarm = figure();
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);
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
clear replays outerreplays
for t=1:length(rips)
if ~isempty(rips{t})
[maxval,ind] = max(rips{t}(:,2:end),[],2);
ind(maxval<contentthresh) = 0; % if <contentthresh in arms, ind should be box; no longer need ind-1
maxval(maxval<contentthresh) = rips{t}(maxval<contentthresh,1);
valid = types{t}==1 & maxval>=contentthresh;
replays{t} = [ind(valid)]';
else replays{t} = []; end
if ~isempty(outerrips{t})
[maxval,ind] = max(outerrips{t},[],2);
for r = find(ind-1==outers(t) & maxval<(1-contentthresh)) % if max is localarm
newmax = max(outerrips{t}(r,~ismember([1:9],outers(t)+1)));
if newmax>contentthresh
ind(r) = find(outerrips{t}(r,:)==newmax);
maxval(r) = newmax;
end
end
valid = outertypes{t}'==1 & maxval>=contentthresh;
outerreplays{t} = [ind(valid)-1]';
else outerreplays{t} = []; end
end
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];
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}{:});
mdl = fitglm(searchcat(:,1:3),searchcat(:,4),'linear','Distribution','poisson');
CI = coefCI(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(mdl.Coefficients(:,1))),'.','MarkerSize',20,'Color',animcol(a,:));
plot([a+[0:5:19];a+[0:5:19]],exp(CI)','Color',animcol(a,:));
repeatcat = vertcat(allrepeat{a}{:});
mdl = fitglm(repeatcat(:,1:4),repeatcat(:,5),'linear','Distribution','poisson');
CI = coefCI(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(mdl.Coefficients(:,1))),'.','MarkerSize',20,'Color',animcol(a,:));
plot([a+[0:5:24];a+[0:5:24]],exp(CI)','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,:));
% 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','future','past','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','future','past','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]')
% 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)
%% measure lag between replay and future visit
clearvars -except f animals animcol
bars = figure(); reg = figure(); set(gcf,'Position',[675 1 1202 486]);
contentthresh = .3;
plt=1;
sinceedges = [-40:1];
untiledges = [0:40];
kernel = gaussian(1,5);
window =50;
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); %
goals = f(a).output{1}(eps(e)).trips.goalarm(valtrials,:);