library(simbaR)
library(rhdf5)
library(data.table)
library(signal)
library(windowscanr)
library(ggplot2)
library(biosignalEMG)
library(cowplot)
library(zoo)
#bandpass filter design
bpfilt <- butter(n = 4, W = c(600/(50000/2), 1200/(50000/2)), type = "pass", plane = "z")
filename <- file.choose()
resfile <- file.choose()
## read in file channel information
meta <- hdf5metaReadR(filename)
names <- data.table(h5ls(filename))
samprate <- meta$samprate
chandetails <- meta$chandetails
codedt <- meta$codedt
idx_lists <- meta$idx_lists
## segment to analyse
idx <- idx_lists[[61]]
out_dt <- segmentProcessR(idx_lists = idx,
filename = filename,
samprate = samprate,
chandetails = chandetails,
codedt = codedt)
# create data.table out of data channels
chan01 <- as.vector(h5read(filename,
paste0(chandetails[chan == "Ch1", name], "/values/"),
index = list(idx[1]:idx[2],1)))
chan02 <- as.vector(h5read(filename,
paste0(chandetails[chan == "Ch2", name], "/values/"),
index = list(idx[1]:idx[2],1)))
chan03 <- as.vector(h5read(filename,
paste0(chandetails[chan == "Ch3", name], "/values/"),
index = list(idx[1]:idx[2],1)))
chan04 <- as.vector(h5read(filename,
paste0(chandetails[chan == "Ch4", name], "/values/"),
index = list(idx[1]:idx[2],1)))
t_seg <- seq.int(from = codedt[t_idx == idx[1], time],
to = codedt[t_idx == idx[1], time] + length(chan01)*samprate,
length.out = length(chan01))
dt <- data.table(t = t_seg,
chan01 = chan01,
chan02 = chan02,
chan03 = chan03,
chan04 = chan04)
rm(t_seg, chan01, chan02, chan03, chan04)
moltendt <- melt(dt,
measure.vars = c("chan01",
"chan02",
"chan03",
"chan04"),
variable.name = "channel")
## add letter codes and start time to each segment
moltendt[, code := codedt[t_idx == idx[1], lettercode]]
moltendt[, time := codedt[t_idx == idx[1], time]]
## 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"]
## group using custom envelope function
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"]
noise01 <- moltendt[t %between% c(2, 3), c("t", "channel", "value", "bpfiltered")]
save(noise01, file = "./data/noise01.rda", compress = TRUE)
uscaled <- round(scales::rescale(u, to = c(-7500000,7500000)))
flyby <- moltendt[channel == "chan01"][group_no == 44, value]
white_n <- noise(kind = "white", duration = 1, samp.rate = 50000, stereo = FALSE, xunit = "time")@left
pink_n <- noise(kind = "pink", duration = 1, samp.rate = 50000, stereo = FALSE, xunit = "time")@left
red_n <- noise(kind = "red", duration = 1, samp.rate = 50000, stereo = FALSE, xunit = "time")@left
chan01_n <- noise01[channel == "chan01", value]
chan02_n <- noise01[channel == "chan02", value]
chan03_n <- noise01[channel == "chan03", value]
chan04_n <- noise01[channel == "chan04", value]
uscaled <- c(round(scales::rescale(chan01_n, to = c(-7500000,7500000))),
round(scales::rescale(flyby, to = c(-7500000,7500000))),
round(scales::rescale(chan01_n, to = c(-7500000,7500000))))
uscaled <- round(scales::rescale(c(chan01_n, flyby, chan01_n), to = c(-7500000,7500000)))
w <- Wave(uscaled, samp.rate = 50000, bit = 24) #make the wave variable
x <- stereo(w,w)
play(x, player = "/usr/bin/vlc", "--play-and-stop --audio-visual visual")
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]]
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", paste0("_", idx[1], ".txt"), basename(filename)))))
data_sum <- data[, .(mint = min(t),
maxt = max(t),
minDC = min(DC),
maxDC = max(DC),
minfilt = min(bpfiltered),
maxfilt = max(bpfiltered),
time = time,
code = code),
by = c("channel", "group_no")]
data_sum <- unique(data_sum)
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]]
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", paste0("_fit_", idx[1], ".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,
time = time,
code = code),
by = c("channel", "group_no" ,"variable")]
sum_dt <- unique(sum_dt)
orig_rec <- moltendt
chan <- "chan01"
grp_no <- 21
event_start <- min(orig_rec[channel == chan][group_no == grp_no, t]) - 0.1
event_end <- max(orig_rec[channel == chan][group_no == grp_no, t]) + 0.1
ggplot(data = orig_rec[t %between% c(event_start, event_end)], aes(x = t)) +
geom_rect(inherit.aes = FALSE, aes(xmin = min(orig_rec[channel == chan][group_no == grp_no, t]),
xmax = max(orig_rec[channel == chan][group_no == grp_no, t]),
ymin = -Inf, ymax = Inf, fill = "group"), fill = "#ffeda0") +
#geom_line(aes(y = DC, colour = "DC removed"), size = 0.2) +
geom_line(aes(y = bpfiltered, colour = "bp_filtered"), size = 0.5) +
geom_line(aes(y = enved, colour = "envelope")) +
geom_line(aes(y = 2*envsd, colour = "threshold")) +
facet_wrap(. ~ channel) +
ggtitle(paste(chan, grp_no, sep = "-"))
ggplot(data = orig_rec[t %between% c(1420,1420.5)], aes(x = t)) +
geom_line(aes(y = bpfiltered, colour = "bp_filtered"), size = 0.5) +
geom_line(aes(y = enved, colour = "envelope")) +
geom_line(aes(y = 2*envsd, colour = "threshold")) +
facet_wrap(. ~ channel) +
ggtitle(paste(chan, grp_no, sep = "-"))
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