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functions.R
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functions.R
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#############
# Functions #
#############
# labels
label.help <- function(label,id){
shiny::HTML(paste0(label,actionLink(id,label=NULL,icon=icon('question-circle'))))
}
# Get neurons that have been selected
get_neurons<-function(input, db){
skel.type = input$SkeletonType
if(grepl("xample",input$Type)){
cts = "PD2a1"
skel.type = "FlyCircuit"
}else if (is_lhn_type(input$Type)){
if(sum(grepl("all",input$CT))>0){
if(sum(grepl("all",input$AG))>0){
AG = subset(db[,],pnt%in%input$PNT)[,"anatomy.group"]
}else{
AG = input$AG
}
CT = subset(db[,],anatomy.group%in%AG)[,"cell.type"]
}else{
CT = input$CT
}
if(sum(grepl("all",input$lhns))>0){
lhn.cts = sort(unique(db[,"cell.type"]))
}else{
lhn.cts = input$lhns
}
cts = unique(c(lhn.cts,CT))
cts = cts[cts%in%db[,"cell.type"]]
}else if(input$Type=="MBON"){
cts = input$MBON
}else if (grepl("IN",input$Type)){
cts = input$PN
}else{
cts = NULL
}
neurons = subset(db,skeleton.type%in%skel.type&cell.type%in%cts)
neurons[,"colour"] = darjeeling(length(neurons)) # Assign darjeeling colours
neurons
}
# Define a function for a frontal view of the brain
frontalView<-function(zoom=0.6){
um=structure(c(1, 0, 0, 0, 0, -1, 0, 0, 0, 0, -1, 0, 0, 0, 0, 1), .Dim = c(4L, 4L))
rgl.viewpoint(userMatrix=um,zoom=zoom)
}
# Plot a selection of pnts
plot_pnt <- function (pnts = "PD2") {
plot.pnts = lhlite::primary.neurite.tracts[pnts]
rgl::plot3d(plot.pnts, soma = T, lwd = 5, col = "grey20",skipRedraw = TRUE)
pxyz = t(sapply(plot.pnts, function(x) nat::xyzmatrix(x)[nat::rootpoints(x),]))
rownames(pxyz) = gsub(pattern = "LH|lh", "", names(plot.pnts))
shift <- matrix(c(-3, 3, 0), nrow(pxyz), 3, byrow = TRUE)
rgl::text3d(pxyz + shift, texts = rownames(pxyz))
}
# Wrapper function for dotprops.character to handle some checks/restrictions that are quite specific to shiny usage
dotprops_from_nrrd<-function(f, ...) {
ni <- read.im3d(f, ReadData = F)
imsize=prod(unlist(attr(ni,'datablock')[c("n","size")]))
if(imsize > 150e6)
stop("Nrrd image files must be <= 150 Mb uncompressed. Try downsampling to ~ 1 x 1 x 1 µm voxel size.")
# read the image
im=read.im3d(f, ReadByteAsRaw = TRUE)
coords=ind2coord(im)
if(nrow(coords) > 1e5)
stop("Nrrd image contains > 100,000 non-zero voxels. Please use a skeletonised/binarised image as produced by http://fiji.sc/Skeletonize3D")
dotprops(coords, ...)
}
# Work around to log into CATMAID via R Shiny
shiny_catmaid_login <- function (conn = NULL, ..., Cache = TRUE, Force = FALSE)
{
if (is.null(conn)) {
if (!length(pairlist(...))) {
conn = catmaid:::catmaid_last_connection()
}
if (is.null(conn))
conn = shiny_catmaid_connection(...)
}
if (!Force) {
if (!is.null(conn$authresponse))
return(invisible(conn))
cached_conn = catmaid:::catmaid_cached_connection(conn)
if (!is.null(cached_conn))
return(invisible(cached_conn))
}
if (isTRUE(conn$nologin)) {
conn$authresponse = GET(url = conn$server)
stop_for_status(conn$authresponse)
res_cookies = cookies(conn$authresponse)
csrf_row = grepl("csrf", res_cookies$name)
if (any(csrf_row)) {
token_value = res_cookies$value[csrf_row][1]
conn$config = httr::add_headers(`X-CSRFToken` = token_value)
}
else warning("I can't seem to find a CSRF token.", "You will not be able to POST to this site!")
}
else {
body <- if (is.null(conn$token))
list(name = conn$username, pwd = conn$password)
else NULL
conn$authresponse = POST(url = paste0(conn$server, "/accounts/login"),
body = body, config = conn$config)
stop_for_status(conn$authresponse)
}
conn$cookies = unlist(cookies(conn$authresponse))
conn$config = c(conn$config, set_cookies(conn$cookies))
if (Cache)
catmaid:::catmaid_cache_connection(conn)
invisible(conn)
}
# R Shiny work-around
shiny_catmaid_connection <-function (server, username = NULL, password = NULL, authname = NULL,
authpassword = NULL, token = NULL, authtype = NULL)
{
arglist = formals(fun = sys.function())
argnames = names(arglist)
conn = list(server = server, username = username, password = password, authname = authname, authpassword = authpassword, token = token, authtype = authtype)
class(conn) = "catmaid_connection"
if (is.null(conn$server) || !grepl("^http[s]{0,1}", conn$server))
stop("Must provide a valid https server")
if (is.null(conn$username) && is.null(conn$token)) {
conn$nologin = TRUE
return(invisible(conn))
}
if (is.null(conn$authname)) {
conn$config = config()
}
else {
if (is.null(conn$authtype))
conn$authtype = "basic"
conn$config = authenticate(conn$authname, conn$authpassword,
type = conn$authtype)
}
if (!is.null(conn$token))
conn$config = c(conn$config, add_headers(`X-Authorization` = paste("Token",
conn$token)))
invisible(conn)
}
vfb_url <- function(neuron_name, style=c("dev", "old")) {
style=match.arg(style, c("dev", "old"))
vfb_id <- as.character(vfb_ids[vfb_ids$Name %in% neuron_name, 'vfbid'])
if(style=='old'){
paste0("http://www.virtualflybrain.org/site/tools/view_stack/3rdPartyStack.htm?json=FlyCircuit2012/", neuron_name, "/wlz_meta/tiledImageModelData.jso&type=THIRD_PARTY_STACK&tpbid=", vfb_id)
} else {
paste0("http://www.virtualflybrain.org/site/stacks/index.htm?add=", paste0(vfb_id, collapse=','), "&clear=true")
}
}
vfb_link <- function(neuron_name) {
url <- vfb_url(neuron_name)
paste0("<a target='_blank' href='", url, "'>View in Virtual Fly Brain stack browser</a>")
}
is_lhn_type <- function(type){
sum(sapply(c("^ON","^LN","^LHN"),grepl,type))>0
}
downloadskeletons <- function (nl, dir, format = "swc", subdir = NULL, INDICES = names(nl), files = NULL, Force = TRUE, ...){
if (grepl("\\.zip", dir)) {
zip_file = dir
if (file.exists(zip_file)) {
if (!Force){
stop("Zip file: ", zip_file, "already exists")
}else{
unlink(zip_file)
}
}
zip_dir = tools::file_path_as_absolute(dirname(zip_file))
zip_file = file.path(zip_dir, basename(zip_file))
dir <- file.path(tempfile("user_neurons"))
} else {
zip_file = NULL
}
if (!file.exists(dir)){
dir.create(dir)
}
df = attr(nl, "df")
ee = substitute(subdir)
subdirs = NULL
if (!is.null(ee) && !is.character(ee)) {
if (!is.null(df))
df = df[INDICES, ]
subdirs = file.path(dir, eval(ee, df, parent.frame()))
names(subdirs) = INDICES
}
ff = substitute(files)
if (!is.null(ff)) {
if (!is.character(ff))
files = eval(ff, df, parent.frame())
if (is.null(names(files)))
names(files) = INDICES
}
written = structure(rep("", length(INDICES)+1), .Names = c(INDICES,"metadata"))
for (nn in INDICES) {
n = nl[[nn]]
thisdir = dir
if (is.null(subdirs)) {
if (!is.null(subdir)) {
propval = n[[subdir]]
if (!is.null(propval))
thisdir = file.path(dir, propval)
}
}
else {
thisdir = subdirs[nn]
}
if (!file.exists(thisdir))
dir.create(thisdir, recursive = TRUE)
written[nn] = nat::write.neuron(n, dir = thisdir, file = files[nn],
format = format, Force = Force)
}
# Save metadata
utils::write.csv(df,file = paste0(dir,"/neurons_metadata.csv"),row.names = FALSE)
written["metadata"] = paste0(dir,"_metadata.csv")
if (!is.null(zip_file)) {
owd = setwd(dir)
on.exit(setwd(owd))
zip(zip_file, files = dir(dir, recursive = TRUE))
unlink(dir, recursive = TRUE)
written <- zip_file
}
invisible(written)
}
# downloadskeletons <- function(nl, dir, subdir = NULL, INDICES = names(nl), files = NULL){
# fs <- c()
# tmpdir <- tempdir()
# setwd(tempdir())
# for (i in INDICES) {
# path <- paste0(tmpdir, i, ".swc")
# fs <- c(fs, path)
# utils::write.csv(nl[i][[1]]$d, path)
# }
# zip(zipfile=dir, files=fs)
# }
download_all_mophologies <- function(dir, ...){
all.neurons = subset(lhlite::all.lh.neurons,skeleton.type%in%c("FlyCircuit", "DyeFill", "MCFO", "EM", "FijiTracing", "JeanneDyeFill"))
most.lhins.pnt = subset(all.neurons,type=="PN")
most.lhins.pnt[,"pnt"] = most.lhins.pnt[,"tract"]
neurons = c(subset(all.neurons,type!="PN"),most.lhins.pnt)
attr(neurons,"df") = neurons[,c("cell.type", "anatomy.group", "pnt", "tract","type", "skeleton.type", "coreLH", "id")]
neurons[,"skeleton.type_pnt"] = paste0(neurons[,"skeleton.type"],"_",neurons[,"pnt"])
downloadskeletons(neurons,dir = dir,subdir = skeleton.type_pnt,format="swc",files = paste0(cell.type,"_",id),Force = TRUE, ...)
}
resamplecatmaidneuron <- function (x, stepsize = 1, ...) {
r = nat:::resample.neuron(x, stepsize = stepsize, ...)
c = catmaid::connectors(x)
c$treenode_id = nabor::knn(data = nat::xyzmatrix(r), query = nat::xyzmatrix(c),
k = 1)$nn.idx
r$connectors = c
r
}