#' @import rhdf5
#' @import data.table
#' @import signal
#' @import TTR
#' @import zoo
#' @export
matthew <- function(filename, cfreqs = c(600, 1200), srate = 50000) {
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 = " ")))
}
bpfilt <- butter(n = 4, W = c(cfreqs[1]/(50000/2), cfreqs[2]/(50000/2)), type = "pass", plane = "z")
samprate <- 1/srate
metadata <- data.table(h5ls(filename))
chandt <- data.table( "chan01" = h5read(filename, metadata[, name])[,1])
h5closeAll()
t <- seq.int(from = 0,
to = length(chandt[, chan01])*samprate,
length.out = length(chandt[, chan01]))
dt <- cbind(t, chandt)
moltendt <- melt(dt,
measure.vars = c("chan01"),
variable.name = "channel")
## perform DC remove of each channel
moltendt[, remsat := ifelse(value == -Inf, -1,
ifelse(value == Inf, 1, value))]
moltendt[, DC := removeDC(remsat, 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 > 500]]
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 = 500, by = 250,
FUN = rollingFitfreq,
srate = samprate,
stime = min(t),
partial = FALSE,
align = "left"),
rsqr = rollapply(data = bpfiltered,
width = 500, by = 250,
FUN = rollingFitR,
srate = samprate,
stime = min(t),
partial = FALSE,
align = "left"),
fitime = rollapply(data = t,
width = 500, by = 250,
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]]
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")]
fwrite(x = unique(sum_dt),
file = paste0(tools::file_path_sans_ext(filename), "_out.txt"))
return(print(paste(tools::file_path_sans_ext(filename), "finished processing @", Sys.time(), sep = " ")))
}
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