#' @import rhdf5
#' @import data.table
#' @import signal
#' @import TTR
#' @import zoo
#' @export
wavProcessR <- function(filename, cfreqs = c(600, 1200)) {
if (file.exists(paste0(tools::file_path_sans_ext(filename), "_out.txt"))) {
return(message(paste(basename(filename), "has already been processed. Results are available in", paste0(tools::file_path_sans_ext(filename), "_out.txt"), sep = " ")))
}
names <- data.table(h5ls(filename))
## read in file channel information
channames <- data.table(name = names[group == "/", name])
samprate <<- h5read(filename, paste0(channames[1, name], "/interval/"))[1,]
stime <- h5read(filename, paste0(channames[1, name], "/start/"))[,1]
len <- h5read(filename, paste0(channames[1, name], "/length/"))[,1]
channames[, c("date", "recID", "treatment", "chan") := tstrsplit(name, "_")]
chandetails <- channames[complete.cases(channames)]
bpfilt <- butter(n = 4, W = c(cfreqs[1]*samprate*2, cfreqs[2]*samprate*2), type = "pass", plane = "z")
#message(idx_lists[1])
chan01 <- as.vector(h5read(filename,
paste0(chandetails[chan == "Ch1", name], "/values/")))
h5closeAll()
chan02 <- as.vector(h5read(filename,
paste0(chandetails[chan == "Ch2", name], "/values/")))
h5closeAll()
chan03 <- as.vector(h5read(filename,
paste0(chandetails[chan == "Ch3", name], "/values/")))
h5closeAll()
chan04 <- as.vector(h5read(filename,
paste0(chandetails[chan == "Ch4", name], "/values/")))
h5closeAll()
chan05 <- as.vector(h5read(filename,
paste0(chandetails[chan == "Ch5", name], "/values/")))
h5closeAll()
chan06 <- as.vector(h5read(filename,
paste0(chandetails[chan == "Ch6", name], "/values/")))
h5closeAll()
t_seg <- seq.int(from = 0,
to = length(chan01)*samprate,
length.out = length(chan01))
dt <- data.table(t = t_seg,
chan01 = chan01,
chan02 = chan02,
chan03 = chan03,
chan04 = chan04,
chan05 = chan05,
chan06 = chan06)
rm(t_seg, chan01, chan02, chan03, chan04, chan05, chan06)
moltendt <- melt(dt,
measure.vars = c("chan01",
"chan02",
"chan03",
"chan04",
"chan05",
"chan06"),
variable.name = "channel")
## perform DC remove of each channel
moltendt[, DC := removeDC(value, 50),
by = "channel"]
## run band pass filter on DC remove signal
moltendt[, bpfiltered := as.vector(filtfilt(bpfilt, x = DC)),
by = "channel"]
moltendt[, enved := envelopeR(bpfiltered, samprate = samprate),
by = "channel"]
moltendt[, envsd := mean(abs(bpfiltered), na.rm = TRUE),
by = c("channel")]
moltendt[, group_no := eventGroupR(enved, 2*envsd),
by = "channel"]
## select events that meet smooth threshold
dt_thresh <- moltendt[enved > 2*envsd]
## calculate length of each event but taking the length of time above threshold
event_length <- dt_thresh[, .(n_dp = length(t)),
by = c("channel", "group_no")]
## merge thresholded events with event length
data <- merge(dt_thresh, event_length, by = c("channel", "group_no"))
## select events longer than 10ms (0.01s) 0.01*50000 sample rate
data <- data[data[,n_dp > 0.01/samprate]]
## save output of fly-by events that pass threshold
if (!dir.exists(paste0(dirname(filename), "/raw_events/"))) {
dir.create(paste0(dirname(filename), "/raw_events/"), recursive = TRUE)
}
fwrite(data, file = paste0(paste0(dirname(filename), "/raw_events/", gsub(".mat", ".txt", basename(filename)))))
if (dim(data)[1] > 0) {
data_sum <- data[, .(mint = min(t),
maxt = max(t),
minDC = min(DC),
maxDC = max(DC),
minfilt = min(bpfiltered),
maxfilt = max(bpfiltered)),
by = c("channel", "group_no")]
data_sum <- unique(data_sum)
## use rollapply instead of winScan
rol_win <- data[, .(fitfreq = rollapply(data = bpfiltered,
width = 0.01/samprate,
by = ceiling(0.01/samprate/2),
FUN = rollingFitfreq,
srate = samprate,
stime = min(t),
partial = FALSE,
align = "left"),
rsqr = rollapply(data = bpfiltered,
width = 0.01/samprate,
by = ceiling(0.01/samprate/2),
FUN = rollingFitR,
srate = samprate,
stime = min(t),
partial = FALSE,
align = "left"),
fitime = rollapply(data = t,
width = 0.01/samprate,
by = ceiling(0.01/samprate/2),
FUN = min,
partial = FALSE,
align = "left")),
by = c("channel", "group_no")]
rol_merge <- merge(rol_win, data_sum, by = c("channel", "group_no"))
## select fits over 0.9
rol_sig <- rol_merge[rol_merge[, rsqr > 0.90]]
if (!dir.exists(paste0(dirname(filename), "/fit_events/"))) {
dir.create(paste0(dirname(filename), "/fit_events/"), recursive = TRUE)
}
fwrite(rol_sig, file = paste0(paste0(dirname(filename), "/fit_events/", gsub(".mat", "_fit.txt", basename(filename)))))
rol_molten <- melt(data = rol_sig,
measure.vars = c("fitfreq",
"rsqr"))
sum_dt <- rol_molten[, .(mean = mean(value, na.rm = TRUE),
median = median(value, na.rm = TRUE),
stdev = sd(value, na.rm = TRUE),
se = se(value, na.rm = TRUE),
n_fits = length(value),
min_t = mint,
max_t = maxt,
evlength = maxt - mint,
min_DC = minDC,
max_DC = maxDC,
min_filt = minfilt,
max_filt = maxfilt),
by = c("channel", "group_no" ,"variable")]
sum_dt <- unique(sum_dt)
fwrite(x = sum_dt,
file = paste0(tools::file_path_sans_ext(filename), "_out.txt"))
return()
} else {
return(message(paste0(basename(filename), " contains no identifiable flyby events")))
}
}
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