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Batch_SpikeGLX.jl
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using Statistics,StatsBase,StatsPlots,Mmap,Images,ePPR
function process_flash_spikeglx(files,param;uuid="",log=nothing,plot=true)
dataset = prepare(joinpath(param[:dataexportroot],files))
ex = dataset["ex"];subject = ex["Subject_ID"];recordsession = ex["RecordSession"];recordsite = ex["RecordSite"]
siteid = join(filter(!isempty,[subject,recordsession,recordsite]),"_")
datadir = joinpath(param[:dataroot],subject,siteid)
siteresultdir = joinpath(param[:resultroot],subject,siteid)
# testid = join(filter(!isempty,[siteid,ex["TestID"]]),"_")
testid = splitdir(splitext(ex["source"])[1])[2]
resultdir = joinpath(siteresultdir,testid)
isdir(resultdir) || mkpath(resultdir)
# Condition Tests
envparam = ex["EnvParam"];exparam = ex["Param"];preicidur = ex["PreICI"];conddur = ex["CondDur"];suficidur = ex["SufICI"]
spike = dataset["spike"];unitspike = spike["unitspike"];unitid = spike["unitid"];unitgood=spike["unitgood"];unitposition=spike["unitposition"]
condon = ex["CondTest"]["CondOn"]
condoff = ex["CondTest"]["CondOff"]
condidx = ex["CondTest"]["CondIndex"]
minconddur=minimum(condoff-condon)
ugs = map(i->i ? "Single-" : "Multi-",unitgood)
layer = haskey(param,:layer) ? param[:layer] : nothing
figfmt = haskey(param,:figfmt) ? param[:figfmt] : [".png"]
# Prepare Conditions
ctc = condtestcond(ex["CondTestCond"])
cond = condin(ctc)
factors = finalfactor(ctc)
# Prepare LFP
lfbin = matchfile(Regex("^$(testid)[A-Za-z0-9_]*.imec.lf.bin"),dir = datadir,join=true)[1]
lfmeta = dataset["lf"]["meta"]
nsavedch = lfmeta["nSavedChans"]
nsample = lfmeta["nFileSamp"]
fs = lfmeta["fs"]
nch = lfmeta["snsApLfSy"][2]
hx,hy,hz = lfmeta["probespacing"]
exchmask = exchmasknp(dataset,type="lf")
pnrow,pncol = size(exchmask)
depths = hy*(0:(pnrow-1))
mmlf = Mmap.mmap(lfbin,Matrix{Int16},(nsavedch,nsample),0)
# Depth LFP and CSD
epochdur = timetounit(150)
epoch = [0 epochdur]
epochs = condon.+epoch
# All LFP epochs, gain corrected(voltage), line noise(60,120,180Hz) removed, bandpass filtered, in the same shape of probe where excluded channels are replaced with local average
ys = reshape2mask(epochsamplenp(mmlf,fs,epochs,1:nch,meta=lfmeta,bandpass=[1,100]),exchmask)
if plot
for c in 1:pncol
cmcys = Dict(condstring(r)=>dropdims(mean(ys[:,c,:,r.i],dims=3),dims=3) for r in eachrow(cond))
cmccsd = Dict(condstring(r)=>dropdims(mean(csd(ys[:,c,:,r.i],h=hy),dims=3),dims=3) for r in eachrow(cond))
for k in keys(cmcys)
plotanalog(cmcys[k],fs=fs,cunit=:uv)
foreach(ext->savefig(joinpath(resultdir,"Column_$(c)_$(k)_MeanLFP$ext")),figfmt)
plotanalog(imfilter(cmccsd[k],Kernel.gaussian((1,1))),fs=fs)
foreach(ext->savefig(joinpath(resultdir,"Column_$(c)_$(k)_MeanCSD$ext")),figfmt)
end
end
end
# Mean LFP and ΔCSD of combined columns
pys = cat((ys[:,i,:,:] for i in 1:pncol)...,dims=3)
cmys = Dict(condstring(r)=>dropdims(mean(pys[:,:,[r.i;r.i.+nrow(ctc)]],dims=3),dims=3) for r in eachrow(cond))
pcsd = csd(pys,h=hy)
basedur = timetounit(15)
baseindex = epoch2sampleindex([0 basedur],fs)
dcsd = stfilter(pcsd,temporaltype=:sub,ti=baseindex)
cmdcsd = Dict(condstring(r)=>dropdims(mean(dcsd[:,:,[r.i;r.i.+nrow(ctc)]],dims=3),dims=3) for r in eachrow(cond))
x = (1/fs/SecondPerUnit)*(0:(size(dcsd,2)-1))
if plot
for k in keys(cmys)
plotanalog(cmys[k],fs=fs,cunit=:uv)
foreach(ext->savefig(joinpath(resultdir,"$(k)_MeanLFP$ext")),figfmt)
plotanalog(imfilter(cmdcsd[k],Kernel.gaussian((1,1))),fs=fs,y=depths,color=:RdBu)
foreach(ext->savefig(joinpath(resultdir,"$(k)_MeandCSD$ext")),figfmt)
end
end
# Depth Power Spectrum
epochdur = timetounit(300)
epoch = [0 epochdur]
epochs = condon.+epoch
nw = 2
ys = reshape2mask(epochsamplenp(mmlf,fs,epochs,1:nch,meta=lfmeta,bandpass=[1,100]),exchmask)
pys = cat((ys[:,i,:,:] for i in 1:pncol)...,dims=3)
ps,freq = powerspectrum(pys,fs,freqrange=[1,100],nw=nw)
epoch = [-epochdur 0]
epochs = condon.+epoch
ys = reshape2mask(epochsamplenp(mmlf,fs,epochs,1:nch,meta=lfmeta,bandpass=[1,100]),exchmask)
pys = cat((ys[:,i,:,:] for i in 1:pncol)...,dims=3)
bps,freq = powerspectrum(pys,fs,freqrange=[1,100],nw=nw)
pcs = ps./bps.-1
cmpcs = Dict(condstring(r)=>dropdims(mean(pcs[:,:,[r.i;r.i.+nrow(ctc)]],dims=3),dims=3) for r in eachrow(cond))
if plot
fcmpcs = Dict(k=>imfilter(cmpcs[k],Kernel.gaussian((1,1))) for k in keys(cmpcs))
lim = mapreduce(pc->maximum(abs.(pc)),max,values(fcmpcs))
for k in keys(fcmpcs)
plotanalog(fcmpcs[k],x=freq,y=depths,xlabel="Freqency",xunit="Hz",timeline=[],clims=(-lim,lim),color=:vik)
foreach(ext->savefig(joinpath(resultdir,"$(k)_PowerContrast$ext")),figfmt)
end
end
save(joinpath(resultdir,"lfp.jld2"),"cmlfp",cmys,"cmcsd",cmdcsd,"cmpc",cmpcs,"freq",freq,"time",x,"depth",depths,"fs",fs,
"siteid",siteid,"log",ex["Log"],"color","$(exparam["ColorSpace"])_$(exparam["Color"])")
# Unit Position
if plot
plotunitposition(unitposition,unitgood=unitgood,chposition=spike["chposition"],unitid=unitid,layer=layer)
foreach(ext->savefig(joinpath(resultdir,"UnitPosition$ext")),figfmt)
end
save(joinpath(resultdir,"spike.jld2"),"spike",spike,"siteid",siteid)
# Unit Spike Train
if plot
epochext = preicidur
for u in eachindex(unitspike)
ys,ns,ws,is = epochspiketrain(unitspike[u],condon.-epochext,condoff.+epochext,isminzero=true,shift=epochext)
title = "$(ugs[u])Unit_$(unitid[u])_SpikeTrian"
plotspiketrain(ys,timeline=[0,conddur],title=title)
foreach(ext->savefig(joinpath(resultdir,"$title$ext")),figfmt)
end
end
# Single Unit Depth PSTH
epochdur = timetounit(150)
epoch = [0 epochdur]
epochs = condon.+epoch
bw = timetounit(2)
psthbins = epoch[1]:bw:epoch[2]
baseindex = epoch2sampleindex([0 basedur],1/(bw*SecondPerUnit))
normfun = x->x.-mean(x[baseindex])
unitepochpsth = map(st->psthspiketrains(epochspiketrain(st,epochs,isminzero=true).y,psthbins,israte=true,ismean=false),unitspike[unitgood])
cmdepthpsth = Dict(condstring(r)=>spacepsth(map(pm->(vmeanse(pm.mat[r.i,:],normfun=normfun)...,pm.x),unitepochpsth),unitposition[unitgood,:],hy*(0:pnrow).-(hy/2)) for r in eachrow(cond))
if plot
for k in keys(cmdepthpsth)
cdp = cmdepthpsth[k]
plotpsth(imfilter(cdp.psth,Kernel.gaussian((1,1))),cdp.x,cdp.y,n=cdp.n)
foreach(ext->savefig(joinpath(resultdir,"$(k)_DepthPSTH$ext")),figfmt)
end
end
save(joinpath(resultdir,"depthpsth.jld2"),"cmdepthpsth",cmdepthpsth,"siteid",siteid)
# Single Unit Binary Spike Train of Condition Tests
bepochext = timetounit(-300)
bepoch = [-bepochext minconddur]
bepochs = condon.+bepoch
spikebins = bepoch[1]:timetounit(1):bepoch[2] # 1ms bin histogram will convert spike times to binary spike train
bst = map(st->permutedims(psthspiketrains(epochspiketrain(st,bepochs,isminzero=true,shift=bepochext).y,spikebins,israte=false,ismean=false).mat),unitspike[unitgood]) # nSpikeBin x nEpoch for each unit
# Single Unit Correlogram and Circuit
lag=50
ccgs,x,ccgis,projs,eunits,iunits,projweights = circuitestimate(bst,lag=lag,unitid=unitid[unitgood],condis=cond.i)
if !isempty(projs)
if plot
for i in eachindex(ccgs)
title = "Correlogram between single unit $(ccgis[i][1]) and $(ccgis[i][2])"
bar(x,ccgs[i],bar_width=1,legend=false,color=:gray15,linecolor=:match,title=title,xlabel="Time (ms)",ylabel="Coincidence/Spike",grid=(:x,0.4),xtick=[-lag,0,lag])
foreach(ext->savefig(joinpath(resultdir,"$title$ext")),figfmt)
end
plotcircuit(unitposition,unitid,projs,unitgood=unitgood,eunits=eunits,iunits=iunits,layer=layer)
foreach(ext->savefig(joinpath(resultdir,"UnitPosition_Circuit$ext")),figfmt)
end
save(joinpath(resultdir,"circuit.jld2"),"projs",projs,"eunits",eunits,"iunits",iunits,"projweights",projweights,"siteid",siteid)
end
end
function process_hartley_spikeglx(files,param;uuid="",log=nothing,plot=true)
dataset = prepare(joinpath(param[:dataexportroot],files))
ex = dataset["ex"];subject = ex["Subject_ID"];recordsession = ex["RecordSession"];recordsite = ex["RecordSite"]
siteid = join(filter(!isempty,[subject,recordsession,recordsite]),"_")
datadir = joinpath(param[:dataroot],subject,siteid)
siteresultdir = joinpath(param[:resultroot],subject,siteid)
# testid = join(filter(!isempty,[siteid,ex["TestID"]]),"_")
testid = splitdir(splitext(ex["source"])[1])[2]
resultdir = joinpath(siteresultdir,testid)
isdir(resultdir) || mkpath(resultdir)
# Condition Tests
envparam = ex["EnvParam"];exparam = ex["Param"];preicidur = ex["PreICI"];conddur = ex["CondDur"];suficidur = ex["SufICI"]
spike = dataset["spike"];unitspike = spike["unitspike"];unitid = spike["unitid"];unitgood=spike["unitgood"];unitposition=spike["unitposition"]
condon = ex["CondTest"]["CondOn"]
condoff = ex["CondTest"]["CondOff"]
condidx = ex["CondTest"]["CondIndex"]
ugs = map(i->i ? "Single-" : "Multi-",unitgood)
layer = haskey(param,:layer) ? param[:layer] : nothing
figfmt = haskey(param,:figfmt) ? param[:figfmt] : [".png"]
# Prepare Conditions
condtable = DataFrame(ex["Cond"])
ctc = condtestcond(ex["CondTestCond"])
cond = condin(ctc)
factors = finalfactor(ctc)
blank = haskey(param,:blank) ? param[:blank] : :SpatialFreq=>0
ci = ctc[!,blank.first].!==blank.second
cctc = ctc[ci,factors]
ccond = condin(cctc)
ccondon = condon[ci]
ccondoff = condoff[ci]
ccondidx = condidx[ci]
bi = .!ci
bctc = ctc[bi,factors]
bcondon = condon[bi]
bcondoff = condoff[bi]
bcondidx = condidx[bi]
isblank = !isempty(bcondon)
# Unit Position
if plot
plotunitposition(unitposition,unitgood=unitgood,chposition=spike["chposition"],unitid=unitid,layer=layer)
foreach(ext->savefig(joinpath(resultdir,"UnitPosition$ext")),figfmt)
end
save(joinpath(resultdir,"spike.jld2"),"spike",spike,"siteid",siteid)
# Prepare Imageset
bgcolor = RGBA(getparam(envparam,"BGColor")...)
maxcolor = RGBA(getparam(envparam,"MaxColor")...)
mincolor = RGBA(getparam(envparam,"MinColor")...)
masktype = getparam(envparam,"MaskType")
maskradius = getparam(envparam,"MaskRadius")
masksigma = getparam(envparam,"Sigma")
diameter = 10#getparam(envparam,"Diameter")
ppd = haskey(param,:ppd) ? param[:ppd] : 45
ii = round(Int,4.5ppd):round(Int,8.5ppd)
jj = range(round(Int,5.5ppd),length=length(ii))
d=round(length(ii)/ppd,digits=1)
sizedeg = (diameter,diameter)
imagesetname = splitext(splitdir(ex["CondPath"])[2])[1] * "_size$(sizedeg)_ppd$ppd" # hartley subspace, degree size and ppd define a unique image set
if !haskey(param,imagesetname)
imageset = Dict{Any,Any}(:image => map(i->GrayA.(grating(θ=deg2rad(i.Ori),sf=i.SpatialFreq,phase=i.SpatialPhase,size=sizedeg,ppd=ppd)[ii,jj]),eachrow(condtable)))
imageset[:sizepx] = size(imageset[:image][1])
param[imagesetname] = imageset
end
sizedeg=(d,d)
# Prepare Image Stimuli
imageset = param[imagesetname]
bgcolor = oftype(imageset[:image][1][1],bgcolor)
imagestimuliname = "bgcolor$(bgcolor)_masktype$(masktype)_maskradius$(maskradius)_masksigma$(masksigma)" # bgcolor and mask define a unique masking on an image set
if !haskey(imageset,imagestimuliname)
imagestimuli = Dict{Any,Any}(:stimuli => map(i->alphablend.(alphamask(i,radius=maskradius,sigma=masksigma,masktype=masktype)[1],[bgcolor]),imageset[:image]))
imagestimuli[:unmaskindex] = alphamask(imageset[:image][1],radius=maskradius,sigma=masksigma,masktype=masktype)[2]
imageset[imagestimuliname] = imagestimuli
end
imagestimuli = imageset[imagestimuliname]
# imgfile = joinpath(param[:dataroot],"$(testid)_image.mat")
# imgs = readmat(imgfile)["imageArray"]
# mat2julia!(imgs)
# px=613;py=135;rp=32
#
# rawimageset = map(i->dropdims(mean(i[py-rp:py+rp,px-rp:px+rp,:],dims=3),dims=3),imgs)
# imageset = map(i->gaussian_pyramid(i, 1, 4, 1.5)[2],rawimageset)
#
# imagesize = size(imageset[1])
# x = Array{Float64}(undef,length(ccondidx),prod(imagesize))
# foreach(i->x[i,:]=vec(imageset[ccondidx[i]]),1:size(x,1))
if :STA in param[:model]
sizepx = imageset[:sizepx]
xi = imagestimuli[:unmaskindex]
uy=Dict();usta = Dict()
delays = -30:5:210
if isblank
uci = unique(ccondidx)
ucii = map(i->findall(condidx.==i),uci)
buci = unique(bcondidx)
bucii = mapreduce(i->findall(condidx.==i),append!,buci)
bx = mean(mapreduce(i->gray.(imagestimuli[:stimuli][i][xi]),hcat,buci),dims=2)[:]
x = Array{Float64}(undef,length(uci),length(xi))
foreach(i->x[i,:]=gray.(imagestimuli[:stimuli][uci[i]][xi]).-bx,1:size(x,1))
for u in eachindex(unitspike)
!unitgood[u] && continue
ys = Array{Float64}(undef,length(delays),length(uci))
stas = Array{Float64}(undef,length(delays),length(xi))
for d in eachindex(delays)
y = epochspiketrainresponse_ono(unitspike[u],condon.+delays[d],condoff.+delays[d],israte=true,isnan2zero=true)
by = mean(y[bucii])
y = map(i->mean(y[i])-by,ucii)
ys[d,:] = y
stas[d,:]=sta(x,y)
end
uy[unitid[u]]=ys
usta[unitid[u]]=stas
if plot
r = [extrema(stas)...]
for d in eachindex(delays)
title = "$(ugs[u])Unit_$(unitid[u])_STA_$(delays[d])"
p = plotsta(stas[d,:],sizepx=sizepx,sizedeg=sizedeg,index=xi,title=title,r=r)
foreach(ext->save(joinpath(resultdir,"$title$ext"),p),figfmt)
end
end
end
else
uci = unique(condidx)
ucii = map(i->findall(condidx.==i),uci)
x = Array{Float64}(undef,length(uci),length(xi))
foreach(i->x[i,:]=gray.(imagestimuli[:stimuli][uci[i]][xi]),1:size(x,1))
for u in eachindex(unitspike)
!unitgood[u] && continue
ys = Array{Float64}(undef,length(delays),length(uci))
stas = Array{Float64}(undef,length(delays),length(xi))
for d in eachindex(delays)
y = epochspiketrainresponse_ono(unitspike[u],condon.+delays[d],condoff.+delays[d],israte=true,isnan2zero=true)
y = map(i->mean(y[i]),ucii)
ys[d,:] = y
stas[d,:]=sta(x,y)
end
uy[unitid[u]]=ys
usta[unitid[u]]=stas
if plot
r = [extrema(stas)...]
for d in eachindex(delays)
title = "$(ugs[u])Unit_$(unitid[u])_STA_$(delays[d])"
p = plotsta(stas[d,:],sizepx=sizepx,sizedeg=sizedeg,index=xi,title=title,r=r)
foreach(ext->save(joinpath(resultdir,"$title$ext"),p),figfmt)
end
end
end
end
save(joinpath(resultdir,"sta.jld2"),"sizepx",sizepx,"x",x,"xi",xi,"xcond",condtable[uci,:],"uy",uy,"usta",usta,"delays",delays,
"siteid",siteid,"sizedeg",sizedeg,"log",ex["Log"],"color","$(exparam["ColorSpace"])_$(exparam["Color"])","maxcolor",maxcolor,"mincolor",mincolor)
end
if :ePPR in param[:model]
sizepx = imageset[:sizepx]
xi = imagestimuli[:unmaskindex]
cell = haskey(param,:cell) ? param[:cell] : nothing
roir = haskey(param,:roir) ? param[:roir] : 16
xscale=255
bg = xscale*gray(bgcolor)
xname = "x_upleft[1, 1]_size$(sizepx)"
if !haskey(imagestimuli,xname)
x = Array{Float64}(undef,nrow(ctc),prod(sizepx))
foreach(i->x[i,:]=xscale*gray.(imagestimuli[:stimuli][condidx[i]]),1:size(x,1))
imagestimuli[xname]=Dict(:x=>x,:xsize=>sizepx)
end
# if isnothing(site)
#
# else
# i = findfirst(site.site.==siteid)
# c = round.(Int,site.roicenter[i]*ppd)
# r = round(Int,site.roiradius[i]*ppd)
# r = clamproi(c,r,sizepx)
# xsize = (2r+1,2r+1)
# xname = "x_center$(c)_size$(xsize)_scale$(xscale)"
# if !haskey(imagestimuli,xname)
# x = Array{Float64}(undef,nrow(ctc),prod(xsize))
# foreach(i->x[i,:]=xscale*gray.(imagestimuli[:stimuli][condidx[i]][map(i->(-r:r).+i,c)...]),1:size(x,1))
# imagestimuli[xname]=Dict(:x=>x,:xsize=>xsize)
# end
# end
for u in eachindex(unitspike)
!unitgood[u] && continue
# unitid[u]!=51 && continue
d=60
if isnothing(cell)
x = imagestimuli[xname][:x]
xsize = imagestimuli[xname][:xsize]
else
id = "$(siteid)_SU$(unitid[u])"
i = findfirst(cell.id.==id)
if isnothing(i) || ismissing(cell.roicenter[i])
continue
x = imagestimuli[xname][:x]
xsize = imagestimuli[xname][:xsize]
else
c = round.(Int,cell.roicenter[i]*ppd)
r = round(Int,cell.roiradius[i]*ppd)
r = clamproi(c,r,sizepx)
ur = min(r,roir)
xsize = (2ur+1,2ur+1)
x = Array{Float64}(undef,nrow(ctc),prod(xsize))
foreach(i->x[i,:]=xscale*gray.(imresize(imagestimuli[:stimuli][condidx[i]][map(i->(-r:r).+i,c)...],xsize)),1:size(x,1))
end
end
y = epochspiketrainresponse_ono(unitspike[u],condon.+d,condoff.+d,israte=true,isnan2zero=true)
unitresultdir = joinpath(resultdir,"$(ugs[u])Unit_$(unitid[u])_ePPR_$d")
rm(unitresultdir,force=true,recursive=true)
log = ePPRLog(debug=true,plot=plot,dir=unitresultdir)
hp = ePPRHyperParams(xsize...,blankcolor=bg,ndelay=param[:eppr_ndelay],nft=param[:eppr_nft],lambda=param[:eppr_lambda])
model,models = epprcv(x,y,hp,log)
if plot && !isnothing(model)
log(plotmodel(model,hp),file="Model_Final (λ=$(hp.lambda)).png")
end
end
# save(joinpath(resultdir,"eppr.jld2"),"sizepx",sizepx,"x",x,"xi",xi,"xcond",condtable[uci,:],"uy",uy,"usta",usta,"delays",delays,
# "siteid",siteid,"sizedeg",sizedeg,"log",ex["Log"],"color","$(exparam["ColorSpace"])_$(exparam["Color"])","maxcolor",maxcolor,"mincolor",mincolor)
end
end
function process_condtest_spikeglx(files,param;uuid="",log=nothing,plot=true)
dataset = prepare(joinpath(param[:dataexportroot],files))
ex = dataset["ex"];subject = ex["Subject_ID"];recordsession = ex["RecordSession"];recordsite = ex["RecordSite"]
siteid = join(filter(!isempty,[subject,recordsession,recordsite]),"_")
datadir = joinpath(param[:dataroot],subject,siteid)
siteresultdir = joinpath(param[:resultroot],subject,siteid)
# testid = join(filter(!isempty,[siteid,ex["TestID"]]),"_")
testid = splitdir(splitext(ex["source"])[1])[2]
resultdir = joinpath(siteresultdir,testid)
isdir(resultdir) || mkpath(resultdir)
# Condition Tests
envparam = ex["EnvParam"];exparam = ex["Param"];preicidur = ex["PreICI"];conddur = ex["CondDur"];suficidur = ex["SufICI"]
spike = dataset["spike"];unitspike = spike["unitspike"];unitid = spike["unitid"];unitgood = spike["unitgood"];unitposition=spike["unitposition"]
condon = ex["CondTest"]["CondOn"]
condoff = ex["CondTest"]["CondOff"]
condidx = ex["CondTest"]["CondIndex"]
minconddur=minimum(condoff-condon)
ugs = map(i->i ? "Single-" : "Multi-",unitgood)
layer = haskey(param,:layer) ? param[:layer] : nothing
figfmt = haskey(param,:figfmt) ? param[:figfmt] : [".png"]
# Prepare Conditions
ctc = condtestcond(ex["CondTestCond"])
cond = condin(ctc)
factors = finalfactor(ctc)
blank = :SpatialFreq=>0
if ex["ID"] == "Color"
factors = [:HueAngle]
blank = :Color=>36
end
haskey(param,:blank) && (blank = param[:blank])
ci = ctc[!,blank.first].!==blank.second
cctc = ctc[ci,factors]
ccond = condin(cctc)
ccondon=condon[ci]
ccondoff=condoff[ci]
bi = .!ci
bctc = ctc[bi,factors]
bcondon=condon[bi]
bcondoff=condoff[bi]
isblank = !isempty(bcondon)
# Prepare LFP
lfbin = matchfile(Regex("^$(testid)[A-Za-z0-9_]*.imec.lf.bin"),dir = datadir,join=true)[1]
lfmeta = dataset["lf"]["meta"]
nsavedch = lfmeta["nSavedChans"]
nsample = lfmeta["nFileSamp"]
fs = lfmeta["fs"]
nch = lfmeta["snsApLfSy"][2]
hx,hy,hz = lfmeta["probespacing"]
exchmask = exchmasknp(dataset,type="lf")
pnrow,pncol = size(exchmask)
depths = hy*(0:(pnrow-1))
mmlf = Mmap.mmap(lfbin,Matrix{Int16},(nsavedch,nsample),0)
# Depth Power Spectrum
epochdur = timetounit(preicidur)
epoch = [0 epochdur]
epochs = ccondon.+epoch
nw = 2
ys = reshape2mask(epochsamplenp(mmlf,fs,epochs,1:nch,meta=lfmeta,bandpass=[1,100]),exchmask)
pys = cat((ys[:,i,:,:] for i in 1:pncol)...,dims=3)
ps,freq = powerspectrum(pys,fs,freqrange=[1,100],nw=nw)
epoch = [-epochdur 0]
epochs = ccondon.+epoch
ys = reshape2mask(epochsamplenp(mmlf,fs,epochs,1:nch,meta=lfmeta,bandpass=[1,100]),exchmask)
pys = cat((ys[:,i,:,:] for i in 1:pncol)...,dims=3)
bps,freq = powerspectrum(pys,fs,freqrange=[1,100],nw=nw)
pcs = ps./bps.-1
cmpcs = Dict(condstring(r)=>dropdims(mean(pcs[:,:,[r.i;r.i.+nrow(cctc)]],dims=3),dims=3) for r in eachrow(ccond))
if plot
fcmpcs = Dict(k=>imfilter(cmpcs[k],Kernel.gaussian((1,1))) for k in keys(cmpcs))
lim = mapreduce(pc->maximum(abs.(pc)),max,values(fcmpcs))
for k in keys(fcmpcs)
plotanalog(fcmpcs[k],x=freq,y=depths,xlabel="Freqency",xunit="Hz",timeline=[],clims=(-lim,lim),color=:vik)
foreach(ext->savefig(joinpath(resultdir,"$(k)_PowerContrast$ext")),figfmt)
end
end
save(joinpath(resultdir,"lfp.jld2"),"cmpcs",cmpcs,"depth",depths,"freq",freq,"siteid",siteid)
# Unit Position
if plot
plotunitposition(unitposition,unitgood=unitgood,chposition=spike["chposition"],unitid=unitid,layer=layer)
foreach(ext->savefig(joinpath(resultdir,"UnitPosition$ext")),figfmt)
end
save(joinpath(resultdir,"spike.jld2"),"spike",spike,"siteid",siteid)
# Unit Spike Trian
if plot
epochext = preicidur
for u in eachindex(unitspike)
ys,ns,ws,is = epochspiketrain(unitspike[u],condon.-epochext,condoff.+epochext,isminzero=true,shift=epochext)
title = "$(ugs[u])Unit_$(unitid[u])_SpikeTrian"
plotspiketrain(ys,timeline=[0,conddur],title=title)
foreach(ext->savefig(joinpath(resultdir,"$title$ext")),figfmt)
end
end
# Single Unit Condition Response
sui=findall(unitgood)
responsedelay = haskey(param,:responsedelay) ? param[:responsedelay] : timetounit(15)
minresponse = haskey(param,:minresponse) ? param[:minresponse] : 5
ubr=[];uresponsive=[];umodulative=[]
for i in sui
rs = epochspiketrainresponse_ono(unitspike[i],ccondon.+responsedelay,ccondoff.+responsedelay,israte=true)
prs = epochspiketrainresponse_ono(unitspike[i],ccondon.+(responsedelay-preicidur),ccondon.+responsedelay,israte=true)
push!(uresponsive,isresponsive(prs,rs,ccond.i))
push!(umodulative,ismodulative([DataFrame(Y=rs) cctc]))
if isblank
br = subrvr(unitspike[i],bcondon.+responsedelay,bcondoff.+responsedelay)
mseuc = condresponse(br,condin(bctc))
push!(ubr,(m=mseuc[1,:m],se=mseuc[1,:se]))
end
if plot && length(factors)==2
plotcondresponse(rs,cctc)
foreach(ext->savefig(joinpath(resultdir,"Single-Unit_$(unitid[i])_CondResponse$ext")),figfmt)
end
end
# Condition Response in Factor Space
fms,fses,fa=factorresponse(unitspike[sui],ccond,ccondon.+responsedelay,ccondoff.+responsedelay)
pfms,pfses,fa=factorresponse(unitspike[sui],ccond,ccondon.+(responsedelay-preicidur),ccondon.+responsedelay)
sfms,sfses,fa=factorresponse(unitspike[sui],ccond,ccondoff.+responsedelay,ccondoff.+(responsedelay+suficidur))
uenoughresponse=[];ufrf = Dict(k=>[] for k in keys(fa));uoptfi=Dict(k=>[] for k in keys(fa))
for i in eachindex(fms)
oi = Any[Tuple(argmax(replace(fms[i],missing=>-Inf)))...]
push!(uenoughresponse,fms[i][oi...]>=minresponse)
for f in keys(fa)
fd = findfirst(f.==keys(fa))
fdn = length(fa[f])
fi = deepcopy(oi)
fi[fd]=1:fdn
push!(uoptfi[f],fi)
rdf=DataFrame(m=fms[i][fi...],se=fses[i][fi...],u=fill(unitid[sui[i]],fdn),ug=fill("SU",fdn))
rdf[:,f]=fa[f]
prdf=DataFrame(m=pfms[i][fi...],se=pfses[i][fi...],u=fill(unitid[sui[i]],fdn),ug=fill("Pre_SU",fdn))
prdf[:,f]=fa[f]
srdf=DataFrame(m=sfms[i][fi...],se=sfses[i][fi...],u=fill(unitid[sui[i]],fdn),ug=fill("Suf_SU",fdn))
srdf[:,f]=fa[f]
push!(ufrf[f],umodulative[i] ? factorresponsefeature(rdf[!,f],rdf[!,:m],factor=f) : missing)
if plot
proj = f in [:Ori,:Ori_Final,:HueAngle] ? :polar : :cartesian
df = [rdf;prdf;srdf]
plotcondresponse(dropmissing(df),color=[:black,:gray70,:gray35],linewidth=[3,1,3],grid=true,projection=proj,response=isblank ? ubr[i:i] : [])
foreach(ext->savefig(joinpath(resultdir,"Single-Unit_$(unitid[sui[i]])_$(f)_Tuning$ext")),figfmt)
end
end
end
save(joinpath(resultdir,"factorresponse.jld2"),"fms",fms,"fses",fses,"pfms",pfms,"pfses",pfses,"sfms",sfms,"sfses",sfses,"fa",fa,
"optfi",uoptfi,"responsive",uresponsive,"modulative",umodulative,"enoughresponse",uenoughresponse,"factorresponsefeature",ufrf,
"siteid",siteid,"unitid",unitid[sui],"log",ex["Log"],"color","$(exparam["ColorSpace"])_$(exparam["Color"])")
# Single Unit Binary Spike Trian of Condition Tests
bepochext = timetounit(-300)
bepoch = [-bepochext minconddur]
bepochs = ccondon.+bepoch
spikebins = bepoch[1]:timetounit(1):bepoch[2]
bst = map(st->permutedims(psthspiketrains(epochspiketrain(st,bepochs,isminzero=true,shift=bepochext).y,spikebins,israte=false,ismean=false).mat),unitspike[unitgood]) # nSpikeBin x nEpoch for each unit
# Single Unit Correlogram and Circuit
lag=50
ccgs,x,ccgis,projs,eunits,iunits,projweights = circuitestimate(bst,lag=lag,unitid=unitid[unitgood],condis=ccond.i)
if !isempty(projs)
if plot
for i in eachindex(ccgs)
title = "Correlogram between single unit $(ccgis[i][1]) and $(ccgis[i][2])"
bar(x,ccgs[i],bar_width=1,legend=false,color=:gray15,linecolor=:match,title=title,xlabel="Time (ms)",ylabel="Coincidence/Spike",grid=(:x,0.4),xtick=[-lag,0,lag])
foreach(ext->savefig(joinpath(resultdir,"$title$ext")),figfmt)
end
plotcircuit(unitposition,unitid,projs,unitgood=unitgood,eunits=eunits,iunits=iunits,layer=layer)
foreach(ext->savefig(joinpath(resultdir,"UnitPosition_Circuit$ext")),figfmt)
end
save(joinpath(resultdir,"circuit.jld2"),"projs",projs,"eunits",eunits,"iunits",iunits,"projweights",projweights,"siteid",siteid)
end
end