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BuildBWPsychRCa.m.bak
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BuildBWPsychRCa.m.bak
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function [kernel, details] = BuildBWPsychRCa(Expt, varargin)
% [kernel, details] = BuildBWPsychRC(Expt, varargin)
% Builds Psych and STA kernels in Fourier domain.
% [kernel, details] = BuildBWPsychRC(Expt, 'sprc') to build the STA
% [kernel, details] = BuildBWPsychRC(Expt) to build psych only
%BuildBWPsychRC(details,kernel) to plot a previously calcualted result
%
%BuildBWPsychRC(Expt,'minseedcount',n) changes mininum numnbe of repeated
%stim required to build kernel (default 200)
rotate = 0;
smoothw = 2;
nbins = 6;
seedoffset = 815; %works for lemM079
seedoffsets = 815;
sprc = 0;
delays = [0 300:50:1000];
permute = 0;
spermute = 0;
ftpwr = [];
fts = [];
framerate = 166.7;
bw = 130;
or = [];
sf = 2;
wi = 1;
savemode = 'none';
minseedcount = 200; %need to lower this for recording sessions
nresample = 0;
meanimage = 0;
noplot = 0;
plotarg = {};
pargs = {};
pj = 0;
plottype = 'BWRC';
mksigkernel = 1;
predictall = 0;
TrialRange = [];
recorded=0;
j = 1;
while j <= length(varargin)
if strncmpi(varargin{j},'bw',2);
j = j+1;
bw = varargin{j};
elseif strncmpi(varargin{j},'blocks',4);
j = j+1;
blocks = varargin{j};
if isfield(Expt,'Header') && isfield(Expt.Header,'BlockStart')
TrialRange = [];
bs = Expt.Header.BlockStart;
bs(length(bs)+1) = length(Expt.Trials);
for j = 1:length(blocks)
TrialRange = [TrialRange bs(blocks(j)):bs(blocks(j)+1)];
end
fprintf('Using Trials %d to %d\n',min(TrialRange),max(TrialRange));
break
end
elseif strncmpi(varargin{j},'delay',4);
j = j+1;
delays = varargin{j};
elseif strncmpi(varargin{j},'mean',4);
meanimage = 1;
elseif strncmpi(varargin{j},'nojack',4);
pj = pj+1;
pargs{pj} = varargin{j};
elseif strncmpi(varargin{j},'bjack',4);
pj = pj+1;
pargs{pj} = varargin{j};
elseif strncmpi(varargin{j},'nosigkernel',6);
mksigkernel = 0;
elseif strncmpi(varargin{j},'noplot',4);
pj = pj+1;
pargs{pj} = varargin{j};
plotarg = 'noplot';
noplot = 1;
elseif strncmpi(varargin{j},'or',2);
j = j+1;
or = varargin{j};
elseif strncmpi(varargin{j},'minseedcount',6)
j = j+1;
minseedcount = varargin{j};
elseif strncmpi(varargin{j},'predictall',10)
predictall = 1;
elseif strncmpi(varargin{j},'predict',4)
j = j+1;
kernel = varargin{j};
j = j+1;
details = varargin{j};
if isempty(ftpwr)
ftpwr = abs(details.fts);
end
pid = find([Expt.Trials.RespDir] == 1 & [Expt.Trials.ob] == 130);
nid = find([Expt.Trials.RespDir] == -1 & [Expt.Trials.ob] == 130);
zid = [pid nid];
ses = [Expt.Trials(zid).Seedseq];
choices = [Expt.Trials(zid).RespDir];
details = PredictChoice(kernel, ftpwr, ses, choices, details, varargin{j+1:end});
ppid = find(details.pkresp >= details.criterion(1));
pnid = find(details.pkresp < details.criterion(1));
details.ccp(4) = CalcCP([Expt.Trials(ppid).count],[Expt.Trials(pnid).count]);
if isfield(details,'pkaresp') %used jacknife
ppid = find(details.pkaresp >= details.criterion(2));
pnid = find(details.pkaresp < details.criterion(2));
details.ccp(5) = CalcCP([Expt.Trials(ppid).count],[Expt.Trials(pnid).count]);
end
return;
elseif strncmpi(varargin{j},'permute',4)
permute = 1;
if length(varargin) > j && isnumeric(varargin{j+1})
j = j+1;
permute = varargin{j};
end
elseif strncmpi(varargin{j},'resample',4)
if length(varargin) > j && isnumeric(varargin{j+1})
j = j+1;
nresample= varargin{j};
end
elseif strncmpi(varargin{j},'save',4)
savemode = 'short';
elseif strncmpi(varargin{j},'spermute',4)
spermute = 1;
if length(varargin) > j && isnumeric(varargin{j+1})
j = j+1;
spermute = varargin{j};
end
elseif strncmpi(varargin{j},'plot',4)
j = j+1;
plottype = varargin{j};
elseif strncmpi(varargin{j},'rotate',4);
j = j+1;
rotate = varargin{j} .* pi/180;
elseif strncmpi(varargin{j},'sprc',4);
sprc = 1;
elseif strncmpi(varargin{j},'ftpwr',3);
j = j+1;
ftpwr = varargin{j};
elseif strncmpi(varargin{j},'fts',3);
j = j+1;
fts = varargin{j};
for j = 1:size(fts,1)
ftpwr{j} = squeeze(abs(fts(j,:,:)));
end
elseif strncmpi(varargin{j},'rweight',6);
plotarg = {plotarg{:} varargin{j}};
elseif strncmpi(varargin{j},'smoothw',6);
j = j+1;
smoothw = varargin{j};
elseif strncmpi(varargin{j},'Trials',6);
j = j+1;
TrialRange = varargin{j};
elseif strncmpi(varargin{j},'recorded',3);
j = j+1;
recorded=1;
recorddate = varargin{j};
end
j = j+1;
end
if iscell(Expt) && isfield(Expt{1},'kernel') && isfield(Expt{1},'predchoice')
smw = smoothw-1;
if plottype == 'PKOR'
for j = 1:length(Expt)-smw
[a,b] = BuildBWPsychRCa(Expt(j:j+smw));
pkors(j,:) = b.pkor;
peaks(j) = GetAngle(b.pkor);
end
hold off;
imagesc(pkors);
hold on;
plot(peaks,1:j,'w');
kernel = a;
details = b;
else
kernel = zeros(size(Expt{1}.kernel));
n = 0;
details.choices = [];
%details.pkresp = [];
details.choiceors = [];
allor = [];
details.pkresp = [];
F = gcf;
for j = 1:length(Expt)
figure(F);
PlotKernel(Expt{j}.kernel, Expt{j}, plottype, varargin{1:end},'plotrow',[length(Expt) 2 j]);
kernel = kernel + Expt{j}.kernel * length(Expt{j}.choices);
details.choices = [details.choices Expt{j}.choices];
details.choiceors = [details.choiceors Expt{j}.choiceors];
details.pkresp = [details.pkresp Expt{j}.pkresp];
allor = cat(1,allor, Expt{j}.pkor);
n = n+length(Expt{j}.choices);
end
GetFigure('MeanKernel');
% [details.pkor, a] = AddOriSum(kernel');
details.pkvar = var(kernel(:));
details.filename = 'Combined';
[kernel, details] = PlotKernel(kernel, details, plottype);
end
return;
elseif iscellstr(Expt)
for j = 1:length(Expt)
[a, res{j}] = BuildBWPsychRCa(Expt{j},varargin);
res{j}.kernel = a;
end
% kernel = BuildBWPsychRCa(res);
kernel = a;
details = res;
return;
elseif ischar(Expt)
ts = now;
[a, Ex] = ReadSerialFile(Expt,'readexpt');
%E
fprintf('Read Serial Took %.2f\n',mytoc(ts));
[kernel, details] = BuildBWPsychRCa(Ex,varargin{:});
details.filename = Expt;
details.Header = Ex.Header;
details.Stimvals = Ex.Stimvals;
if strmatch(savemode,'short')
outname = [Expt '.mat'];
Kernel = rmfields(details,{'fts' 'pim' 'nim' 'sigkernel' 'allmean' 'kb'});
Kernel.kernel = kernel;
if recorded
Kernel.recorddate=recorddate;
end
% Kernel.Trials = rmfield(Kernel.Trials,'Seedseq');
save(outname,'Kernel');
end
return;
elseif isfield(Expt,'predchoice') & length(varargin) > 0 & isnumeric(varargin{1}) %its a result file.
[kernel, details] = PlotKernel(varargin{1}, Expt, plottype, varargin{2:end});
return;
elseif isfield(Expt,'predchoice') & isfield(Expt,'kernel') %its a result file with kernel
[kernel, details] = PlotKernel(Expt.kernel, Expt, plottype, varargin{1:end});
return;
elseif isfield(Expt,'predchoice') & isfield(Expt,'fts') %its a result file with powers
if isempty(ftpwr)
for j = 1:size(Expt.fts,1)
ftpwr{j} = squeeze(abs(Expt.fts(j,:,:)));
end
end
else
details.plotr = 0;
end
px = 0.0188; %was size on Berlioz for V1 expts, and on Ravel.
if length(or) > 1 && meanimage %build FFT plots for several oris with signal, to get signs right
[nr,nc] = Nsubplots(length(or));
for j = 1:length(or)
nfts = filterim([sf sf/2], [or(j) bw], wi, 'seedoffset', seedoffset, 'pix2deg', px,'nseeds', 10, 'getft','noplot');
sigkernel = fftshift(squeeze(mean(abs(nfts),1)));
subplot(nr,nc,j);
imagesc([2 257],[1 256],fliplr(sigkernel'));
title(sprintf('%.0f',or(j)));
end
return;
end
if ~isfield(Expt.Trials,'ob')
fprintf('%s Not an ORBW file\n',Expt.Header.name);
kernel = [];
details.err = 'Not ORBW';
return;
end
sf = Expt.Stimvals.sf;
wi = Expt.Stimvals.wi;
if isfield(Expt.Stimvals,'impx') & Expt.Stimvals.impx < 1
px = Expt.Stimvals.impx;
end
if isempty(or)
or = Expt.Stimvals.or;
ors = unique([Expt.Trials.or]);
or = min(ors);
end
if isfield(Expt.Trials,'seedoffset')
id = find([Expt.Trials.ob] > 120);
seedoffset = median([Expt.Trials.ob]);
end
for j = 1:length(Expt.Trials)
lens(j) = length(Expt.Trials(j).Seedseq);
if isempty(Expt.Trials(j).ob)
Expt.Trials(j).ob = 0;
end
end
gid = lens >= prctile(lens,90);
badid = find(lens < prctile(lens,90));
pid = find([Expt.Trials.RespDir] == 1 & [Expt.Trials.ob] == 130 & gid);
nid = find([Expt.Trials.RespDir] == -1 & [Expt.Trials.ob] == 130 &gid);
spid = find([Expt.Trials.RespDir] == 1 & [Expt.Trials.ob] <= 75 & gid);
snid = find([Expt.Trials.RespDir] == -1 & [Expt.Trials.ob] <= 75 & gid);
RespOris = [median([Expt.Trials(snid).or]) median([Expt.Trials(spid).or]); or or+90];
OriCounts = Counts([Expt.Trials([pid nid]).or]);
[a,b] = Counts(lens);
nf = 200;
errs = find(ismember([Expt.Trials.RespDir],[-1 1]) & lens < nf);
for j = 1:length(errs)
Expt.Trials(errs(j)).Seedseq(lens(errs(j))+1:nf) = 1;
end
if length(pid) == 0 | length(nid) == 0
kernel = NaN;
return;
end
if length(TrialRange)
pid = intersect(pid,TrialRange);
nid = intersect(nid,TrialRange);
end
zid = [pid nid];
badid = intersect(badid,zid);
for j = 1:length(badid)
fprintf('Trials %d only %d seeds\n',badid(j),lens(badid(j)));
end
if isfield(Expt.Trials,'imseed')
seedoffset = median([Expt.Trials(zid).imseed]);
[seedcounts, details.seedoffsets] = Counts([Expt.Trials(zid).imseed]);
seedoffsets = details.seedoffsets;
end
if isempty(ftpwr)
if mksigkernel
sig = min([Expt.Trials.ob]);
nfts = filterim([sf sf/2], [or sig], wi, 'seedoffset', seedoffset, 'pix2deg', px,'nseeds', 1000, 'getft','noplot');
pfts = filterim([sf sf/2], [or+90 sig], wi, 'seedoffset', seedoffset, 'pix2deg', px,'nseeds', 1000, 'getft','noplot');
sigkernel = fftshift(squeeze(mean(abs(pfts),1)-mean(abs(nfts),1)));
end
%restrict to trials with enough reps to be worth calculating images
if length(seedcounts) > 1
xid = find(seedcounts < minseedcount);
xxid = [];
for j = 1:length(xid)
xxid = [xxid find([Expt.Trials(zid).imseed] == seedoffsets(xid(j)))];
end
nid = setdiff(nid, zid(xxid));
pid = setdiff(pid, zid(xxid));
end
zid = [pid nid];
sid = find(seedcounts >= minseedcount);
if isempty(sid) %not enough trials with one image set to be useful
fprintf('Too Few Trials (%d) to be useful\n',max(seedcounts));
kernel = [];
details.err = 'Too Few Trials';
return;
end
alltid = [];
for j = 1:length(sid)
seed = seedoffsets(sid(j));
fts = filterim([sf sf/2], [or bw], wi, 'seedoffset', seed, 'pix2deg', px,'nseeds', 1000, 'getft','noplot');
for j = 1:size(fts,1)
pwr{j} = squeeze(abs(fts(j,:,:)));
end
tid = find([Expt.Trials(zid).imseed] == seed);
alltid = [alltid tid];
[X, ses] = CalcTrialPower(Expt, pwr, zid(tid));
for k = 1:length(tid)
tpwr{tid(k)} = X{k};
idlist(tid(k)) = Expt.Trials(zid(tid(k))).id;
end
end
else
pwr = ftpwr;
[tpwr, ses] = CalcTrialPower(Expt, pwr, zid);
end
choices = [ones(size(pid)) ones(size(nid)).*-1];
if meanimage
kernel = fftshift(squeeze(mean(pwr)));
a = kernel'; %gives vertial blob for vertical ori
imagesc([2 257],[1 256],fliplr(a));
aor = AddOriSum(a, plotarg);
[a,b] = GetAngle(aor);
title(sprintf('OR= %.0f->%.0f (%.0f), bw = %.1f',or,a,b,bw));
details.meanor = aor;
return;
end
sevals = unique(ses);
%kernel is respdir ==1 - respdir == -1
[kernel, details] = CalcPKernel(Expt,pid, nid, pwr, tpwr);
details.choiceors = RespOris;
details.OriCounts = OriCounts;
details = PredictChoice(kernel, tpwr, ses, choices, details,pargs{:});
details.choices = choices;
details.zid = zid;
details.impx = px;
details.seedoffset = seedoffset; %for 0 signal
details.seedoffsets = seedoffsets; %all
details.seedcounts = seedcounts; %all
details.filename = Expt.Header.name;
%store enough in Trials so that can build PSFs with ExptPsych
for j = length(Expt.Trials):-1:1
Trials(j).or = Expt.Trials(j).or;
Trials(j).ob = Expt.Trials(j).ob;
Trials(j).RespDir = Expt.Trials(j).RespDir;
Trials(j).se = Expt.Trials(j).se;
Trials(j).Seedseq = Expt.Trials(j).Seedseq;
Trials(j).imseed = Expt.Trials(j).imseed;
Trials(j).id = Expt.Trials(j).id;
end
for j = 1:length(zid)
Trials(zid(j)).pkresp = details.pkaresp(j);
end
if length(TrialRange)
Trials = Trials(TrialRange);
end
if predictall
ors = unique([Trials.or]);
bws = unique([Trials.ob]);
% bws = bws(bws < 120);
for j = 1:length(ors)
for k = 1:length(bws)
fprintf('Or %.0f, bw %.0f: ',ors(j),bws(k));
id = find(ismember([Expt.Trials.RespDir],[-1 1]) & [Expt.Trials.ob] == bws(k) & [Expt.Trials.or] == ors(j));
if length(id)
nfts = filterim([sf sf/2], [ors(j) bws(k)], wi, 'seedoffset', seedoffset, 'pix2deg', px,'nseeds', 1000, 'getft','noplot');
for m= 1:size(fts,1)
pwr{m} = squeeze(abs(nfts(m,:,:)));
end
schoices = [Expt.Trials(id).RespDir];
[tpwr, ses] = CalcTrialPower(Expt, pwr, id, 'absolute');
X = PredictChoice(kernel, tpwr, ses, schoices, details,'nojacknife');
for t = 1:length(id)
Trials(id(t)).pkresp = X.pkresp(t);
end
end
end
end
[tpwr, ses] = CalcTrialPower(Expt, pwr, zid);
end
details.Trials = Trials;
pup = sum(choices ==-1)./length(choices);
rand('state',1001);
checkpermute = 0; %set to 1 for debugging
for p = permute:-1:1
id = randperm(length(zid));
ichoices = choices(id);
% ichoices = ((rand(1,131) < pup)*2) - 1
ipid = zid(ichoices==1);
inid = zid(ichoices==-1);
%need to put tpwr in the same order as ichoices, so that ichoices(tid)
% is [1 1 1 1 1 1 1 ..... -1 -1 -1 -1 -1] becuase CalcPk needs this
%is removed in the jacknife, the correct image is removed from the kernel.
tid = [find(ismember(zid,ipid)) find(ismember(zid,inid))];
% tid = id;
[ki, a] = CalcPKernel(Expt,ipid, inid, pwr,tpwr(tid));
if checkpermute == 1
tic;
for j = 1:length(ichoices)
if ichoices(j) == 1
iid = setdiff(ipid, zid(j));
[ka, b] = CalcPKernel(Expt,iid, inid, pwr,tpwr(tid));
else
iid = setdiff(inid, zid(j));
[ka, b] = CalcPKernel(Expt,ipid, iid, pwr,tpwr(tid));
end
ka = fftshift(b.psum-b.nsum);
% ka = fftshift(ka);
resp(j) = sum(tpwr{j}(:) .* ka(:));
end
crit = prctile(resp,100 * sum(ichoices==1)/length(choices));
pc(resp <= crit) = -1;
pc(resp > crit) = 1;
score = sum(pc==ichoices)./length(choices);
if 0
k = j;
for j = 1:length(inid)
iid = setdiff(inid, inid(j));
[ka, b] = CalcPKernel(Expt,ipid, iid, pwr,tpwr(tid));
ka = fftshift(ka);
resp(j+k) = sum(tpwr{j+k}(:) .* ka(:));
end
end
dur(1) = toc;
end
kerrs(p) = a.err;
ks(p,:,:) = ki;
xc = corrcoef(kernel(:),ki(:));
xcs(p,1)= xc(1,2);
xcs(p,3)= sum(ichoices == choices);
tic;
a = PredictChoice(ki,tpwr,ses, ichoices, a);
dur(2) = toc;
xc = corrcoef(details.pkresp,a.pkresp);
xcs(p,2)= xc(1,2);
pperf(p) = a.predchoice(1);
pvar(p) = var((ki(:)));
end
oldf = gcf;
for p = nresample:-1:1
E = Expt;
id = Bresample(1:length(zid));
E.Trials = Expt.Trials(zid(id));
izid = 1:length(id);
ichoices = choices(id);
% ichoices = ((rand(1,131) < pup)*2) - 1
ipid = find(ichoices==1);
inid = find(ichoices==-1);
%need to put tpwr in the same order as ichoices, so that ichoices(tid)
% is [1 1 1 1 1 1 1 ..... -1 -1 -1 -1 -1] becuase CalcPk needs this
%is removed in the jacknife, the correct image is removed from the kernel.
tid = [find(ismember(izid,ipid)) find(ismember(izid,inid))];
tid = [id(ipid) id(inid)];
% tid = id;
[ki, a] = CalcPKernel(E,ipid, inid, pwr,tpwr(tid));
x = AddOriSum(ki','noplot',plotarg{:});
GetFigure('Resamples');
if p == nresample
hold off;
else
hold on;
end
plot(x);
[kor(p),c,kradius(p)] = GetAngle(x);
end
if nresample
hold off;
plot(kradius.*cos(kor .*pi/180),kradius.*sin(kor .*pi/180),'o');
hold on; plot(kradius.*cos(pi +(kor .*pi/180)),kradius.*sin(pi+(kor .*pi/180)),'o');
figure(oldf);
end
if permute
end
%kernel, details] = CalcPKernel(Expt,pid, nid, pwr, tpwr);
if mksigkernel & exist('sigkernel','var');
details.sigkernel = sigkernel;
end
details.kb = CalcPKernel(Expt,pid(2:end), nid, pwr, tpwr(2:end));
if permute
details.kernelsd = squeeze(std(ks,[],1));
details.permpred = pperf;
details.permpkvar = pvar;
end
if nresample
details.resampleors = kor;
details.resampler = kradius;
end
for j = 1:size(ses,2)
alltriggers{j} = [];
end
for j = 1:size(sevals,1) %what size(ses,2) < size(sevals,1)
[ij,ik] = find(ses == sevals(j));
for k = 1:length(ik)
alltriggers{ik(k)}= {alltriggers{ik(k)} ij(k)};
end
end
[details.pkor, a] = AddOriSum(kernel',plotarg);
details.pkor = a.ork;
details.sfk = a.sfk;
if sprc
[secounts, sevals] = Counts(ses);
sxmean = zeros(size(fts,2),size(fts,3));
for j = 1:length(sevals);
sxmean = sxmean + squeeze(fts(sevals(j),:,:)) .* secounts(j);
end
details.allsxmean = sxmean./sum(secounts);
details.delays = delays;
for di = 1:length(delays)
delay = delays(di);
spseeds = [];
nf = size(ses,1);
k = 1:length(zid);
for j = 1:length(zid)
frames = floor((Expt.Trials(zid(j)).Spikes-delay)./166);
frames = frames(find(frames >0 & frames <= nf));
allframes{j} = frames;
spseeds = [spseeds ses(frames,k(j))']; %seeds associated with spikes
end
spsum = zeros(size(pwr{1}));
spsxsum = zeros(size(pwr{1}));
[nspk, sv] = Counts(spseeds);
for j = 1:length(sv)
se = sv(j);
spsum = spsum + nspk(j) .* pwr{se};
spsxsum = spsxsum + nspk(j) .* squeeze(fts(se,:,:));
end
spmean = spsum./sum(nspk) - details.allmean;
details.spmean(di,:,:) = fftshift(spmean);
details.spvar(di) = var(spmean(:));
sxmean = spsxsum./sum(nspk) - details.allsxmean;
details.sxmean(di,:,:) = fftshift(sxmean);
details.sxvar(di) = var(abs(sxmean(:)));
nspks(di,sv) = nspk;
end
ns = length(ses(:));
nf = sum(nspks(1,:));
for ip = 1:spermute
id = ceil(rand(nf,1) .* ns);
[pnspk, psv] = Counts(ses(id));
spsum = pnspk(1) .* pwr{psv(1)};
for j = 2:length(psv)
se = psv(j);
spsum = spsum + pnspk(j) .* pwr{se};
end
pm = spsum./nf - details.allmean;
permvar(ip) = var(pm(:));
end
if spermute
details.permvar = permvar;
end
[v,sxt] = max(details.sxvar);
[v,t] = max(details.spvar);
nspk = nspks(t,:);
spk = fftshift(details.spmean(t,:,:)); %%undo fftshift above
sxspk = fftshift(details.sxmean(t,:,:)); %%undo fftshift above
for j = 1:length(sevals)
id = find(sv == sevals(j));
sid = find(sevals == j);
if isempty(id) | isempty(sid)
ps(sevals(j)) = 0;
else
ps(sevals(j)) = nspk(sevals(j))./secounts(j); %p(Spike|seed)
pss(sevals(j),:) = nspks(:,sevals(j))./secounts(j);
end
fpwr(sevals(j)) = sum(pwr{sevals(j)}(:) .* spk(:));
sxpwr(sevals(j)) = abs(sum(sum(fts(sevals(j),:,:) .* sxspk)));
end
sevars = var(pss);
%have <= for <, incase the percentile resp is 0;
[lo,a] = MeanIm(pwr, find(ps <= prctile(ps,30)));
[hi, b] = MeanIm(pwr, find(ps > prctile(ps,70)));
details.Imret = [a b];
for j = 1:length(delays)
p = pss(:,j);
lo = MeanIm(pwr, find(ps <= prctile(ps,30)));
hi = MeanIm(pwr, find(ps > prctile(ps,70)));
psmean(j,:,:) = fftshift(squeeze(hi-lo));
end
[a,b] = sort(fpwr(sevals)); %sorted by power
k = 1;
nf = 0;
%if some seeds are missing, than length(fwr) = largest seed, not # of
%seeds
bincount = sum(secounts)./nbins;
for j = 1:length(sevals)
porder(sevals(b(j))) = k;
nf = nf+ secounts(b(j));
if nf > bincount
nf = 0;
k = k+1;
end
end
if sum(porder == k) < 100;
porder(porder == k) = k-1;
end
[a,b] = sort(ps(sevals));
k = 1; nf = 0;
for j = 1:length(sevals)
forder(sevals(b(j))) = k;
nf = nf+ secounts(b(j));
if nf > bincount
nf = 0;
k = k+1;
end
end
if sum(forder == k) < 100;
forder(forder == k) = k-1;
end
[a,b] = sort(sxpwr(sevals));%sorted by product with STA
k = 1; nf = 0;
for j = 1:length(sevals)
sorder(sevals(b(j))) = k;
nf = nf+ secounts(b(j));
if nf > bincount
nf = 0;
k = k+1;
end
end
if sum(sorder == k) < 100;
forder(sorder == k) = k-1;
end
pscale = std(ps(sevals))./std(fpwr(sevals));
pfit = polyfit(fpwr(sevals).*pscale,ps(sevals),2);
%nextspk is perdicetd resp from power law fit to (spk pwr kernel .* image) vs rate
netspk(sevals) = polyval(pfit,fpwr(sevals).*pscale);
if noplot == 0
subplot(2,2,1);
hold off;
plot(fpwr(sevals),ps(sevals),'o');
hold on;
[a,b] = sort(fpwr(sevals));
plot(a,netspk(b),'linewidth',2);
[x,y] = xysmooth(fpwr(sevals),ps(sevals),100);
plot(x,y,'r');
[x,y] = xysmooth(fpwr(sevals),ps(sevals),10);
plot(x,y,'g');
end
latency = 500;
for j = zid
Expt.Trials(j).Predcount(1) = sum(netspk(Expt.Trials(j).Seedseq)); %from Spike Triggered power spec
Expt.Trials(j).Predcount(2) = sum(ps(Expt.Trials(j).Seedseq)); %from Spike trigged response to seeds
Expt.Trials(j).count = sum(Expt.Trials(j).Spikes > latency & Expt.Trials(j).Spikes < Expt.Trials(j).dur + latency);
Expt.Trials(j).sepower = porder(Expt.Trials(j).Seedseq); %binned/ordered by image power .* power kernel
Expt.Trials(j).frpower = forder(Expt.Trials(j).Seedseq);
Expt.Trials(j).sxpower = sorder(Expt.Trials(j).Seedseq);
end
cfig = gcf;
details.nspks = sum([Expt.Trials(zid).count]);
if isfield(Expt.Header,'frameperiod')
frameperiod = Expt.Header.frameperiod;
Expt.Stimvals.fz = 10000/frameperiod;
else
frameperiod = 166.6;
end
details.rc = PlotRevCorAny(Expt,'exp','sepower','Trials',zid,'filltimes',frameperiod,'nmin',100,'box','sdfw',framerate,'noplot');
netspk = PlotRC(details.rc,'netspk','sdsmooth',5,'timerange',[200 1000],'noplot');
details.predcount = cat(2,Expt.Trials(zid).Predcount);
for j = 1:max(porder)
id = find(porder == j);
netspkx(j) = mean(fpwr(id));
end
for j = zid
Expt.Trials(j).Predcount(3) = sum(netspk(Expt.Trials(j).sepower)); %from subspace map to binned power groups
end
pc = cat(1,Expt.Trials.Predcount)';
if noplot == 0
figure(cfig);
subplot(2,2,2);
plot(netspkx,netspk,'ro-');
hold off;
plot(pc(1,:),[Expt.Trials(zid).count],'o');
hold on;
plot(pc(2,:),[Expt.Trials(zid).count],'go');
plot(pc(3,:),[Expt.Trials(zid).count],'ro');
subplot(2,2,3);
imagesc(fliplr(squeeze(details.spmean(t,:,:))'));
end
xc = corrcoef(kernel(:), details.spmean(t,:,:));
details.psxcorr(1) = xc(2,1);
[details.spkor, spring] = AddOriSum(squeeze(details.spmean(t,:,:))',plotarg);
xc = corrcoef(details.pkor, details.spkor);
details.psxcorr(2) = xc(2,1);
[a,b,c] = GetAngle(details.spkor);
title(sprintf('VarRatio %.2f Pkxc %.3f,%.3f Or %.0f',details.spvar(t)./details.spvar(1),details.psxcorr(1),details.psxcorr(2),a));
details.cp = CalcCP([Expt.Trials(pid).count],[Expt.Trials(nid).count]);
details.ccp(1) = CalcCP([Expt.Trials(pid).count]-pc(1,ismember(zid,pid)),...
[Expt.Trials(nid).count]-pc(1,ismember(zid,nid))); %cp after adjust counts using STA of power
details.ccp(2) = CalcCP([Expt.Trials(pid).count]-pc(3,ismember(zid,pid)),...
[Expt.Trials(nid).count]-pc(3,ismember(zid,nid))); %adjusted using subspace map to binned power
details.ccp(4) = CalcCP([Expt.Trials(details.fixpid).count],[Expt.Trials(details.fixnid).count]);
details.ccp(3) = CalcCP([Expt.Trials(pid).count]-pc(2,ismember(zid,pid)),...
[Expt.Trials(nid).count]-pc(2,ismember(zid,nid)));
ppid = find(details.pkresp >= details.criterion(1));
pnid = find(details.pkresp < details.criterion(1));
%ccp(5) is the CP based on predicted choices
details.ccp(5) = CalcCP([Expt.Trials(zid(ppid)).count],[Expt.Trials(zid(pnid)).count]);
details.ccp(6) = CalcCP(pc(1,ppid),pc(1,pnid)); %pred from p(spike|power)
details.ccp(7) = CalcCP(pc(3,ppid),pc(3,pnid)); %pred from subspace map to binned powers
details.ccp(8) = CalcCP(pc(2,ppid),pc(2,pnid)); %from spike triggered resp to seeds.
%ccp(9) is the Cp based on overpredicted choices (not using jacknife)
if isfield(details,'pkaresp') %used jacknife
ppid = find(details.pkaresp >= details.criterion(2));
pnid = find(details.pkaresp < details.criterion(2));
details.ccp(9) = CalcCP([Expt.Trials(ppid).count],[Expt.Trials(pnid).count]);
end
if noplot == 0
subplot(2,2,4);
end
else
spring.r = 0;
end
if noplot == 0
if rotate
[xi, yi] = meshgrid([1:wi],[1:wi]);
xr = (xi-128) .* cos(pi/4) + (yi-128) .* sin(pi/4);
yr = (yi-128) .* cos(pi/20) - (xi-128) .* sin(pi/20);
end
hold off;
imagesc([2 257],[1 256],fliplr(kernel'));
end
[details.pkor, b] = AddOriSum(kernel',plotarg);
details.pkor = b.ork;
if b.r < spring.r & noplot == 0
imagesc([2 257],[1 256],fliplr(kernel'));
AddOriSum(kernel','maxr',spring.r);
end
[a,b,c] = GetAngle(details.pkor);
if isfield(details,'cp') & noplot == 0
title(sprintf('CP %.2f, %.2f, %.2f Or%.0f',details.cp,details.ccp(1),details.ccp(2),a));
end
details.pkvar = var(kernel(:));
details.fts = fts;
function [tpwr, ses] = CalcTrialPower(Expt, pwr, zid, varargin)
scoretype = 0;
j = 1;
while j <= length(varargin)
if strncmpi(varargin{j},'absolute',4)
scoretype = 1;
end
j = j+1;
end
if isempty(zid)
fprintf('No Trials \n');
end
ses = [Expt.Trials(zid).Seedseq];
nse = find(sum(ses') > 0); %remove trailing zeros
ses = ses(nse,:);
for j = 1:length(Expt.Trials)
if length(Expt.Trials(j).Seedseq) > max(nse)
Expt.Trials(j).Seedseq = Expt.Trials(j).Seedseq(1:max(nse));
end
end
isum = zeros(size(pwr{1}));
for j = 1:length(zid)
tpwr{j} = MeanIm(pwr,ses(:,j));
isum = isum+tpwr{j};
end
if scoretype == 0
isum = isum./length(zid);
for j = 1:length(zid)
tpwr{j} = tpwr{j} - isum;
end
end
function [mim, good] = MeanIm(pwr, id)
if isempty(id)
mim = zeros(size(pwr{1}));
fprintf('Empty list for Meanim');
good = 0;
return;
end
if length(id) > 5 % bizarerely this is much faster, even if n = 200
mim = pwr{id(1)};
for j = 2:length(id)
mim =mim + pwr{id(j)};
end
good = j;
mim = mim./j;
else
mim = mean(cat(3,pwr{id}),3);
end
function [kernel, details] = CalcPKernel(Expt, pid, nid, pwr, tpwr)
%new method uses only the mean power spectrum for each Trial
%makes it easier to deal with combined Expts using different image sets
psum = zeros(size(pwr{1}));
nsum = zeros(size(pwr{1}));
pz = 0;
nz = 0;
for j = 1:length(pid)
if isempty(tpwr{j})
pz = pz+1;
else
psum = psum + tpwr{j}; %in order as zid = [pid nid]
end
end
for j = length(pid)+1:length(pid)+length(nid)
if isempty(tpwr{j})
nz = nz+1;
else
nsum = nsum + tpwr{j}; %in order as zid = [pid nid]
end
end
pn = length(pid)-pz;
nn = length(nid)-nz;
pim = psum./pn;
nim = nsum./nn;
details.pim = pim;
details.nim = nim;
details.psum = psum;
details.nsum = nsum;
details.allmean = (nsum+psum)./(pn+nn);
kernel = fftshift(pim-nim); %kerel is + choice - -choice
details.nframes = [pn nn];
details.err = NaN;
function [kernel, details] = oldCalcPKernel(Expt, pid, nid, pwr, tpwr)
checktrialpwr = 1;
[a,b] = Counts([Expt.Trials(pid).Seedseq]);
[c,d] = Counts([Expt.Trials(nid).Seedseq]);
psum = zeros(size(pwr{1}));
nsum = zeros(size(pwr{1}));
for j = 1:length(a)
se = b(j);
psum = psum + pwr{se} .* a(j);
end
for j = 1:length(c)
nsum = nsum + pwr{d(j)} .* c(j);
end
pim = psum./sum(a);
nim = nsum./sum(c);
details.pim = pim;
details.nim = nim;
details.psum = psum;
details.nsum = nsum;
details.nframes = [sum(a) sum(c)];
kernel = fftshift(pim-nim); %kerel is + choice - -choice
aim = (nsum+psum)./(sum(a)+sum(c));
details.allmean = aim;
if checktrialpwr
psum = zeros(size(pwr{1}));
nsum = zeros(size(pwr{1}));
for j = 1:length(pid)
psum = psum + tpwr{j}; %in order as zid = [pid nid]
end
for j = length(pid)+1:length(pid)+length(nid)
nsum = nsum + tpwr{j}; %in order as zid = [pid nid]
end
pn = length(pid);
nn = length(nid);
tk = psum./pn-nsum./nn;
diffs = fftshift(tk)-kernel;
details.err = sum(abs(diffs(:)));
else
details.err = NaN;
end
function [kernel, details] = PlotKernel(kernel, details, plottype, varargin)
j = 1;
plotrow = [];
plotargs = {};
while j <= length(varargin)
if strncmpi(varargin{j},'plotrow',6)
j = j+1;
plotrow = varargin{j};
elseif strncmpi(varargin{j},'var',3)
else
plotargs = {plotargs{:} varargin{j}};
end
j = j+1;
end
ntrials = length(details.pkresp);
if strncmpi(plottype,'RCVar',4)
subplot(2,2,1);
hold off;
[a,t] = max(details.spvar);
sk = squeeze(details.spmean(t,:,:))';
imagesc([2 257],[1 256],fliplr(sk));
[a,sb] = AddOriSum(sk);
[a,b,c] = GetAngle(a);
title(sprintf('PWR %d trials. Or %.0f, peak %.0f r%.2f',ntrials,a,b,c));
subplot(2,2,2);
hold off;
plot(details.delays./10,details.spvar./details.spvar(1));
hold on;
plot(details.delays./10,details.sxvar./details.sxvar(1),'r');
legend('pwr','sta');
subplot(2,2,3);
hold off;
[a,t] = max(details.sxvar);
sk = squeeze(abs(details.sxmean(t,:,:)))';
imagesc([2 257],[1 256],fliplr(sk));
[a,sb] = AddOriSum(sk);
[a,b,c] = GetAngle(a);
title(sprintf('STA. Or %.0f, peak %.0f r%.2f',a,b,c));
subplot(2,2,4);
hold off;
imagesc(ifft2(squeeze(details.sxmean(t,:,:))));
elseif strncmpi(plottype,'BWRCall',4)
if isfield(details,'spvar') %has spike data
nr = 2;
nc = 3;
else
nr = 1;