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2pSTAploter.jl
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2pSTAploter.jl
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using NeuroAnalysis,Statistics,StatsBase,FileIO,Images,Plots,LsqFit,FFTW
disk = "G:"
subject = "AE7"
# recordSession = "002" #AF2
# testId = ["004", "005","006","007"]
# recordSession = "003" #AF2
# testId = ["004", "005","006","003"]
# recordSession = "004" #AF2
# testId = [ "005","006","007","004"]
# recordSession = "005" #AF2
# testId = ["003","004", "005","002"]
# recordSession = "006" #AF2
# testId = ["005","007", "008","004"]
# recordSession = "007" #AF2
# testId = ["003","005", "006","002"]
# recordSession = "003" #AF3
# testId = ["004","005","007", "003"]
# recordSession = "002" #AF4
# testId = ["008","009","010","011"] # In the order of L, M, S, and achromatic
# recordSession = "003" #AF4
# testId = ["000","001","002", "004"]
# #
# recordSession = "004" #AF4
# testId = ["001","002","003","004"]
# recordSession = "005" #AF4
# testId = ["000","001","003","004"]
# recordSession = "006" #AF4
# testId = ["000","001","002","003"]
# --------- AE7
recordSession = "003"
testId = ["002","003","004","001"]
# recordSession = "005"
# testId = ["004","005","006","003"]
# recordSession = "006"
# testId = ["004","006","007","003"]
# recordSession = "008"
# testId = ["003","004","005","002"]
# recordSession = "009"
# testId = ["002","003","004","001"]
# recordSession = "011"
# testId = ["002","004","005","001"]
# recordSession = "012"
# testId = ["002","003","004","001"]
# recordSession = "013"
# testId = ["003","004","005","002"]
#----------------------
# recordSession = "002" #AE6
# testId = ["006", "007", "008","005"] # 001
# testId = ["012", "013", "014","011"] # 000
# recordSession = "003" #AE6
# testId = ["005", "006","007","002"] # 001
# testId = ["011", "012","013","010"] # 000
# recordSession = "004" #AE6
# testId = ["003", "007", "008","002"] # 001 # In the order of L, M, S, and achromatic
# testId = ["013", "014", "015","012"] # 000
recordPlane = "000"
delays = collect(-0.066:0.033:0.4)
print(collect(delays))
lbTime = 0.198
ubTime = 0.330
blkTime = 0.099
respThres = 0.25
# cw = datasetFinal["coneweight"]
# achroResp = datasetFinal["achroResp"]
# CSV.write(joinpath(resultFolder,join([subject,"_",recordSession,"_",recordPlane,"_thres",respThres,"_sta_dataset.csv"])), cw)
# CSV.write(joinpath(resultFolder,join([subject,"_",recordSession,"_",recordPlane,"_thres",respThres,"_achrosta_dataset.csv"])), achroResp)
## Prepare data & result path
siteId=[]
for i =1:size(testId,1)
siteid = join(["$(recordSession)_", testId[i], "_$(recordPlane)"])
push!(siteId,siteid)
end
dataFolder = joinpath.(disk,subject, "2P_analysis", join(["U",recordSession]))
dataExportFolder = joinpath.(disk,subject, "2P_analysis", join(["U",recordSession]), siteId, "DataExport")
resultFolder = joinpath.(disk,subject, "2P_analysis", join(["U",recordSession]), "_Summary", "DataExport")
resultFolderPlot = joinpath.(disk,subject, "2P_analysis", join(["U",recordSession]), "_Summary", join(["plane_",recordPlane]),"0. Original maps","STA")
resultFolderPlotFit = joinpath.(disk,subject, "2P_analysis", join(["U",recordSession]), "_Summary", join(["plane_",recordPlane]),"0. Original maps","STAfit")
isdir(resultFolder) || mkpath(resultFolder)
isdir(resultFolderPlot) || mkpath(resultFolderPlot)
isdir(resultFolderPlotFit) || mkpath(resultFolderPlotFit)
## Load all stas
# testids = ["$(siteId)_HartleySubspace_$i" for i in 1:4]
dataFile=[]
for i=1:size(testId,1)
datafile=matchfile(Regex("[A-Za-z0-9]*[A-Za-z0-9]*_[A-Za-z0-9]*_[A-Za-z0-9]*_sta.jld2"), dir=dataExportFolder[i],join=true)[1]
push!(dataFile, datafile)
end
## Join STAs from L, M, S, and achromatic expts. Also find the strongest response and corresponding delay.
dataset = sbxjoinsta(load.(dataFile),lbTime,ubTime,blkTime)
## Check responsiveness of cell based on threshold and delay range, filter out low-response and cell 'response' too early or too late
dataset = sbxresponsivesta!(dataset,lbTime,ubTime,respThres)
dataset = sbxfitsta!(dataset)
dataset = sbxgetbestconesta(dataset)
datasetFinal=sbxgetconeweight(dataset)
datasetFinal = dataset
save(joinpath(resultFolder,join([subject,"_",recordSession,"_",recordPlane,"_thres",respThres,"_sta_datasetFinal.jld2"])),"datasetFinal",datasetFinal)
# stop
dataset = load(joinpath(resultFolder,join([subject,"_",recordSession,"_",recordPlane,"_thres",respThres,"_sta_datasetFinal.jld2"])),"datasetFinal")
## stas
# @manipulate for u in sort(collect(keys(dataset["usta"]))),d in eachindex(dataset["delays"])
@manipulate for u in sort(collect(keys(dataset["ulsta"])))#,d in eachindex(dataset["delays"])
usta=dataset["usta"][u]
# delays = dataset["delays"]
# d=8
imagesize = dataset["imagesize"]
stisize = dataset["stisize"]
uresponsive = dataset["uresponsive"]
clim = maximum(map(i->abs(i.ex),dataset["ucex"][u]))
xlim = [0,5]#[0,stisize[2]]
ylim = [0,5]#stisize[1]]
x = range(xlim...,length=imagesize[2])
y = range(ylim...,length=imagesize[1])
p = Plots.plot(layout=(1,4),legend=false,size=(1600,600),title="Cell_$(u)_STA_$(delays[d])_Responsive_$(uresponsive[u])")
foreach(c->Plots.heatmap!(p,subplot=c,x,y,usta[:,:,d,c],aspect_ratio=:equal,frame=:semi,color=:coolwarm,clims=(-clim,clim),
xlims=xlim,ylims=ylim,xticks=xlim,yticks=ylim,yflip=true,xlabel=dataset["color"][c]),1:4)
p
savefig("./temp/Unit_$u.png")
end
## best roi stas
# for u in sort(collect(keys(dataset["ulsta"])))
for u in sort(collect(keys(dataset["ulsta"])))
# u=11
local delays
ulsta = dataset["ulsta"][u]
delays = dataset["delays"]
imagesize = size(ulsta)[1]
stisize = dataset["stisize"]
maskradius = dataset["maskradius"]
truestimsz = stisize*maskradius*2
ucex = map(i->i.ex,dataset["ulcex"][u])
ucd = map(i->i.pd,dataset["ulcex"][u])
clim = maximum(abs.(ucex))
xylim = [0,round(truestimsz,digits=1)]
xy = range(xylim...,length=imagesize)
p = Plots.plot(layout=(1,5),legend=false,size=(1650,600))
Plots.bar!(p,subplot=1,dataset["color"],ucex,frame=:zerolines)
foreach(c->Plots.heatmap!(p,subplot=c+1,xy,xy,ulsta[:,:,ucd[c],c],aspect_ratio=:equal,frame=:grid,
color=:bwr,clims=(-clim,clim),xlims=xylim,ylims=xylim,xticks=[],yticks=[],
yflip=true,xlabel=string(dataset["color"][c]),title="Cell_$(u)_STA_$(delays[ucd[c]])"),1:4)
# foreach(c->Plots.plot!(p,subplot=c+1,[0.3,0.6,0.9,1.2,1.5], seriestype="vline", linecolor=:gray, linestyle=:dot, linewidth=1, linealpha=0.5, xticks=([0,0.3,0.6,0.9,1.2,1.5,1.8],["-0.9","-0.6","-0.3","0","0.3","0.6","0.9"]), label=""),1:4)
# foreach(c->Plots.plot!(p,subplot=c+1,[0.3,0.6,0.9,1.2,1.5], seriestype="hline", linecolor=:gray, linestyle=:dot, linewidth=1, linealpha=0.5, label=""),1:4)
# foreach(c->Plots.plot!(p,subplot=c+1,yticks=([0,0.3,0.6,0.9,1.2,1.5,1.8],["-0.9","-0.6","-0.3","0","0.3","0.6","0.9"])),1)
foreach(c->Plots.plot!(p,subplot=c+1,[0.2,0.4,0.6,0.8,1.0,1.2,1.4], seriestype="vline", linecolor=:gray, linestyle=:dot, linewidth=1, linealpha=0.5, xticks=([0,0.4,0.8,1.2,1.6],["-0.8","-0.4","0","0.4","0.8"]), label=""),1:4)
foreach(c->Plots.plot!(p,subplot=c+1,[0.2,0.4,0.6,0.8,1.0,1.2,1.4], seriestype="hline", linecolor=:gray, linestyle=:dot, linewidth=1, linealpha=0.5, label=""),1:4)
foreach(c->Plots.plot!(p,subplot=c+1,yticks=([0,0.4,0.8,1.2,1.6],["-0.8","-0.4","0","0.4","0.8"])),1)
# foreach(c->Plots.plot!(p,subplot=c+1,[0.4,0.8,1.2,1.6,2.0], seriestype="vline", linecolor=:gray, linestyle=:dot, linewidth=1, linealpha=0.5, xticks=([0,0.6,1.2,1.8,2.4],["-1.2","-0.6","0","0.6","1.2"]), label=""),1:4)
# foreach(c->Plots.plot!(p,subplot=c+1,[0.4,0.8,1.2,1.6,2.0], seriestype="hline", linecolor=:gray, linestyle=:dot, linewidth=1, linealpha=0.5, label=""),1:4)
# foreach(c->Plots.plot!(p,subplot=c+1,yticks=([0,0.6,1.2,1.8,2.4],["-1.2","-0.6","0","0.6","1.2"])),1)
p
savefig(joinpath(resultFolderPlot,join([subject,"_U",recordSession,"_Plane",recordPlane, "_Cell",u,".png"])))
end
sort(collect(keys(dataset["urf"])))
collect(keys(first(values(dataset["urf"]))))
## Plot example STA
u=399
coneIdex = 2
ulsta = dataset["ulsta"][u][:,:,10,coneIdex]
maskradius = dataset["maskradius"]
ppd=dataset["ppd"]
xi = dataset["xi"]
stisize = dataset["stisize"]
imagesize = size(ulsta)[1]
truestimsz = stisize*maskradius*2
ucex = map(i->i.ex,dataset["ulcex"][u])[coneIdex]
ucd = map(i->i.pd,dataset["ulcex"][u])[coneIdex]
clim = maximum(abs.(ucex))
xylim = [0,round(truestimsz,digits=1)]
xy = range(xylim...,length=imagesize)
p = Plots.plot(legend=false,size=(600,600))
Plots.heatmap!(p,xy,xy,ulsta,aspect_ratio=:equal,frame=:grid,
color=:bwr,clims=(-clim,clim),xlims=xylim,ylims=xylim,xticks=[],yticks=[],yflip=true)
Plots.plot!(p,[0.2,0.4,0.6,0.8,1.0,1.2,1.4], seriestype="vline", linecolor=:gray, linestyle=:dot, linewidth=1, linealpha=0.5, label="")
Plots.plot!(p,[0.2,0.4,0.6,0.8,1.0,1.2,1.4], seriestype="hline", linecolor=:gray, linestyle=:dot, linewidth=1, linealpha=0.5, label="")
# Plots.plot!(p,yticks=([0,0.4,0.8,1.2,1.6],["-0.8","-0.4","0","0.4","0.8"]))
p
save(joinpath(resultFolder,"AF4_U004_plane001_cell399_M.svg"),p)
a
# r = rand(-10:10,10,10)
# p=Plots.heatmap(r, color=:bwr)
# p
# save(joinpath(resultFolder,"bwr_colorbar.svg"),p)
## Plot fitted STA
for u in sort(collect(keys(datasetFinal["ulfit"]))),m in collect(keys(first(values(datasetFinal["ulfit"]))))
cn = length(datasetFinal["color"])
ulsta = datasetFinal["ulsta"][u]
delays = datasetFinal["delays"]
imagesize = size(ulsta)[1]
ppd = datasetFinal["ppd"]
stisize = datasetFinal["stisize"]
maskradius = datasetFinal["maskradius"]
truestimsz = stisize*maskradius*2
ucex = map(i->i.ex,datasetFinal["ulcex"][u])
ucd = map(i->i.pd,datasetFinal["ulcex"][u])
clim = maximum(abs.(ucex))
xylims = [0,round(truestimsz,digits=1)]
xy = range(xylims...,length=imagesize)
p = plot(layout=(4,cn),legend=false,size=(400cn,4*250))
foreach(c->heatmap!(p[1,c],xy,xy,ulsta[:,:,ucd[c],c],aspect_ratio=:equal,frame=:grid,color=:coolwarm,clims=(-clim,clim),
xlims=xylims,ylims=xylims,xticks=xylims,yticks=[],yflip=true,title="Unit_$(u)_STA_$(delays[ucd[c]])"),1:cn)
umfit=datasetFinal["ulfit"][u][m]
rpx = (imagesize[1]-1)/2
x=y=(-rpx:rpx)./ppd
# sr = round(x[end],digits=1)
# xylim=[-sr,sr]
xylims=[round.(extrema(x),digits=2)...]
fs = map(f->ismissing(f) ? missing : predict(f,x,y),umfit)
foreach(c->ismissing(fs[c]) ? plot!(p[2,c],frame=:none) :
heatmap!(p[2,c],x,y,fs[c],aspect_ratio=:equal,frame=:grid,color=:coolwarm,clims=(-clim,clim),
xlims=xylims,ylims=xylims,xticks=xylims,yticks=[],xlabel=datasetFinal["color"][c],title="Fit_$(m)"),1:cn)
foreach(c->ismissing(fs[c]) ? plot!(p[3,c],frame=:none) :
histogram!(p[3,c],umfit[c].resid,frame=:semi,xlabel="Residual",grid=false),1:cn)
foreach(c->ismissing(fs[c]) ? plot!(p[4,c],frame=:none) :
scatter!(p[4,c],vec(ulsta[:,:,ucd[c],c]),vec(fs[c]),frame=:semi,grid=false,
xlabel="y",ylabel="predicted y",title="r = $(round(umfit[c].r,digits=3))",markerstrokewidth=0,markersize=2),1:cn)
# isnothing(dir) ? p : savefig(joinpath(dir,"Unit_$(u)_Fit_$(m).png"))
p
savefig(joinpath(resultFolderPlotFit,join([subject,"_U",recordSession,"_Plane",recordPlane, "_Cell",u,"_", m,".png"])))
end
function getbestrf(dataset;rt=0.65)
urf = dataset["urf"]
nc = length(dataset["color"])
ubrf=Dict()
for u in keys(urf)
# u!=96 && continue
mfs=[]
for m in keys(urf[u])
push!(mfs, map(f->begin
if isnothing(f)
nothing
else
(param=f.param,r=f.r,m=m)
end
end,urf[u][m]))
end
# display(mfs)
crf = []
for i in 1:nc
push!(crf,mapreduce(mf->mf[i],(m1,m2)->begin
if isnothing(m1)
if isnothing(m2)
nothing
else
m2.r > rt ? m2 : nothing
end
else
if isnothing(m2)
m1.r > rt ? m1 : nothing
else
t = m1.r > m2.r ? m1 : m2
t.r > rt ? t : nothing
end
end
end,mfs))
end
ubrf[u]=crf
end
return ubrf
end
usrf = getbestrf(dataset)
urf = map(i->i[:gabor][2],values(dataset["urf"]))
urft = map(i->i.converged,urf)
vurf = urf[urft]
vups = mapreduce(i->i.param,hcat,vurf)
using Clustering
r = kmeans(vups[[3,5],:],4,display=:final)
r = kmeans([abs.(vups[3:3,:]./vups[5:5,:].-1);maximum(vups[[3,5],:],dims=1).*vups[7:7,:]],3,display=:final)
r = kmeans(maximum(vups[[3,5],:],dims=1).*vups[7:7,:],2,display=:final)
ci = assignments(r)
cu = map(i->us[findall(i.==ci)],1:3)
sort(cu[1])
Plots.scatter(vups[3,:],vups[7,:],group=ci)
# spike = load(joinpath(resultFolder,testids[1],"spike.jld2"),"spike")
# unitid = spike["unitid"];unitgood = spike["unitgood"];unitposition = spike["unitposition"];unitlayer = assignlayer(unitposition[:,2],layer)
us = collect(keys(dataset["ulsta"]))
# up = unitposition[indexin(us,unitid),:]
# ul = unitlayer[indexin(us,unitid)]
uc = [argmax(map(j->abs(j.ex),dataset["ucex"][u])) for u in keys(dataset["ulsta"])]
ud = map((u,c)->dataset["ucex"][u][c].d,keys(dataset["ulsta"]),uc)
ucsta = map((u,d,c)->mapcolor(dataset["ulsta"][u][:,:,d,c],dataset["minmaxcg"][c]),keys(dataset["ulsta"]),ud,uc)
ucmsta = map(i->alphamask(i,radius=0.5,sigma=10,masktype="Disk")[1],ucsta)
p=plotunitpositionimage(up,ucmsta,layer=layer)
save("UnitPosition_STA.svg",p)
p=plotunitlayerimage(ul,ucmsta,unitid=us)
save("UnitLayer_STA.svg",p)
uexs = mapreduce(u->map(j->j.ex,dataset["ucex"][u]),hcat,us)
uds = mapreduce(u->map(j->j.d,dataset["ucex"][u]),hcat,us)
function rftype(cws,cpd)
a=cws[1];l=cws[2];m=cws[3];s=cws[4];c=1;d = cpd[argmax(abs.(exs))]
t = join(filter(k->!isnothing(k),map((i,j)->i==0 ? nothing : "$j$(i > 0 ? "+" : "-")",cws,["A","L","M","S"])),"_")
if l != 0
if m != 0 # both significent l,m
if l > 0 && m < 0
t = "L+M-"
c = 2
d = cpd[argmax(abs.(cws[2:3]))+1]
elseif l < 0 && m > 0
t = "M+L-"
c = 3
d = cpd[argmax(abs.(cws[2:3]))+1]
elseif l > 0 && m > 0
if s < 0 && abs(s) > maximum(abs.(cws[2:3]))
t = "S-L+M+"
c = 4
d = cpd[4]
else
t = "+-"
c = 1
d = cpd[1]
end
elseif l < 0 && m < 0
if s > 0 && abs(s) > maximum(abs.(cws[2:3]))
t = "S+L-M-"
c = 4
d = cpd[4]
else
t = "+-"
c = 1
d = cpd[1]
end
end
else
if a ==0
t = "L+"
c = 4
d = cpd[4]
else
end
end
end
if l>0 && m<0
t = "L+M-"
c = 2
d = ds[argmax(abs.(exs[2:3]))+1]
elseif l<0 && m>0
t="M+L-"
c=3
d = ds[argmax(abs.(exs[2:3]))+1]
elseif l>0 && m>0
if s<0
t="S-L+M+"
c=4
d = ds[argmax(abs.(exs[2:end]))+1]
else
t = "+-"
c=1
d = ds[argmax(abs.(exs))]
end
elseif l<0 && m<0
if s>0
t="S+L-M-"
c=4
d = ds[argmax(abs.(exs[2:end]))+1]
else
t = "-+"
c=1
d = ds[argmax(abs.(exs))]
end
end
return (t=t,c=c,d=d)
end
tcd = [rftype(uexs[:,i],uds[:,i]) for i in 1:size(uexs,2)]
uc = map(i->i.c,tcd)
ud = map(i->i.d,tcd)
_,nsg,_,_=histrv(unitposition[unitgood,2],0,3500,binwidth=60)
_,ns,_,_=histrv(up[:,2],0,3500,binwidth=60)
bar(0:60:3500,[nsg ns],orientation=:h,bar_position=:stack,xlims=[-0.3,10])
plot([nsg ns],range(0,step=40,length=length(ns)),fillalpha=0.4,fill=true,labels=["SU" "SU with STA"],linealpha=0,xlims=[-0.3,7])
hline!([layer[k][1] for k in keys(layer)],linestyle=:dash,annotations=[(-0.3,layer[k][1],text(k,5,:gray20,:bottom)) for k in keys(layer)],linecolor=:gray30,legend=false)
savefig("unit_layer_dist.svg")
import Base:sin
sin(x,y;fx=1,fy=1) = sin(2π*(fx*x+fy*y))
t = dataset["ulsta"][92][:,:,10,2]
Plots.heatmap(t)
ft=abs.(fft(t))
Plots.heatmap(ft)
freqs = fftfreq(25,30)
lmpi = [Tuple(argmax(clc))...]
lmi = clc[:,:,lmpi[3:end]...]
heatmap(lmi,aspect_ratio=:equal,frame=:all,color=:coolwarm,yflip=true)
scatter!([lmpi[2]],[lmpi[1]],markersize=10)
seg = seeded_region_growing(lmi,[(CartesianIndex(lmpi[1:2]...),2),(CartesianIndex(1,1),1)])
lr = labels_map(seg)
heatmap(lr,aspect_ratio=:equal,frame=:all,color=:grays)
ri = mapreduce(i->[i...],hcat, Tuple.(findall(lr.>1)))
t = extrema(ri,dims=2)
tr = map(i->i[2]-i[1],t)
rsize = floor(Int,maximum(tr)/2)
lp = floor.(Int,mean.(t))[:]
hspan!([lp[1]-rsize,lp[1]+rsize],alpha=0.2)
vspan!([lp[2]-rsize,lp[2]+rsize],alpha=0.2)
@manipulate for u in uids
t1 = stas[1]["usta"][u]
t2=stas[2]["usta"][u]
t3=stas[3]["usta"][u]
t4=stas[4]["usta"][u]
sds = Array{Float64}(undef,imagesize...,41)
for d in 1:41
t = t1[d,:]
tt = fill(mean(t),imagesize)
tt[xi]=t
sds[:,:,d]= mapwindow(std,tt,(5,5))
end
mi = [Tuple(argmax(sds))...][1:2]
display(mi)
mir= map(i->filter(j->j>0,(-5:5) .+i),mi)
display(mir)
sds1 = [sum(sds[mir...,d]) for d in 1:41]
# bm1 = mean(t1[1,:])
# display(bm1)
# bsd1 = std(t1[1,:])
# display(bsd1)
# bm2 = mean(t2[1,:])
# bsd2 = std(t2[1,:])
# bm3 = mean(t3[1,:])
# bsd3 = std(t3[1,:])
# bm4 = mean(t4[1,:])
# bsd4 = std(t4[1,:])
#
#
# sds1=sum((t1.-bm1)./bsd1.^2,dims=2)[:]
# sds2=sum((t2.-bm2)./bsd2.^2,dims=2)[:]
# sds3=sum((t3.-bm3)./bsd3.^2,dims=2)[:]
# sds4=sum((t4.-bm4)./bsd4.^2,dims=2)[:]
plot(sds1)
# c = [:black :red :lightgreen :blue]
# plot([sds1 sds2 sds3 sds4],color=c,lw=2,grid=false)
# a=[sds1[[1,2,end,end-1]],sds2[[1,2,end,end-1]],sds3[[1,2,end,end-1]],sds4[[1,2,end,end-1]]]'
# hline!(mean.(a).+5*std.(a),color=c)
end
skm = pyimport("skimage.metrics")
@manipulate for u in uids
t1 = stas[1]["usta"][u][1,:]
t2 = stas[1]["usta"][u][25,:]
ti1=fill(mean(t1),imagesize)
ti1[xi]=t1
ti2=fill(mean(t2),imagesize)
ti2[xi]=t2
ssim,di=skm.structural_similarity(ti1,ti2,full=true)
heatmap(di,clims=(0.4,1))
end