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rd_ncFiles.R
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# read in SPLs for SanctSound Project
library(ncdf4)
library(lubridate)
library(reshape)
library(ggplot2)
library(tidyverse)
library(gridExtra)
rm(list=ls())
dirSPL = setwd( "E:\\RESEARCH\\SanctSound\\") #dirSPL = dirs[ grepl("data", dirs) ]
list.files(dirSPL)
ncFiles= list.files(dirSPL, pattern = "ships.nc" ,full.names = TRUE)
#test a file
r = nc_open(ncFiles[1])
r$var$time_stamp$longname
tm <- ncvar_get(r, "time_stamp")
tmF = as.POSIXct ( gsub(":000Z","", gsub("T", " ", tm)), tz="GMT" )
tb <- ncvar_get(r, "time_bounds")/1000
tb/1000
options(scipen =999)
r$var$time_bounds$units
#rows are stare/end, columns
nrow(tb)
ncol(tb)
as.POSIXct(tb[1,1], origin = "1970-01-01", tz = "GMT") #"50921-04-26 19:03:20 GMT"
#remove last three zeros
tedit = substr( as.character(tb[1,1]), 1, nchar(as.character(tb[1,1]))-3 )
as.POSIXct(as.numeric(tedit), origin = "1970-01-01", tz = "GMT") #"2018-12-14 02:26:35 GMT"
info = NULL
infoT = NULL
quants <- c(.01,0.25,0.50,0.75,0.99)
perSum = NULL
for (ff in 1: length(SPL_OL) ) { # ff = 1
#1) meta data-- details
r = nc_open(SPL_OL[ff])
fname = gsub(".nc", "", basename( SPL_OL[ff] ))
siteDep = substr(fname, start = 9 , stop = 15)
site = substr(fname, start = 9 , stop = 12)
deploy = substr(fname, start = 14 , stop = 15)
cat("Processing : ", siteDep, "\n")
#r$is_GMT
#tmpT = r$var$timeStamp$dim[[1]]$vals
#x = as.POSIXct(tmpT,origin = "1970-01-01",tz = "GMT")
#2) read in data
OTB = r$dim$frequency$vals
spl <- as.data.frame (ncvar_get(r, "sound_pressure_levels")) #rows = frequency, columns = time
tm <- ncvar_get(r, "timeStamp")
#format data tm[1]
tmF = as.POSIXct ( gsub(":000Z","", gsub("T", " ", tm)),tz="GMT" )
cdata = rbind(as.character(tmF),spl)
rownames(cdata) = c("dtime", as.character(OTB))
cdata = as.data.frame( t(cdata) )
cdata$date = as.Date(cdata$dtime)
#4) truncate data to time period of interest- between March 1 and May 1, 30 days (is this possible??)
# and calculate percentiles on truncated data and save to new matrix
#condition for each deployment...
###sites to start at the beginning of deployment
if (siteDep == "CI04_02") {
dwindow = c( min(cdata$date), min(cdata$date) + 30) #start at beginning + 30 days
tst = cdata[cdata$date >= dwindow[1] & cdata$date <= dwindow[2],]
ndys = length( unique(tst$date) )
inD = tst[, 2:11]
indx <- sapply(inD, is.factor)
inD[indx] <- lapply(inD[indx], function(x) as.numeric(as.character(x)))
outD = as.data.frame( apply( inD, 2 , quantile, probs = quants ))
outD = cbind(outD,quants,siteDep)
#spectrogram, tile plot
tst2 = reshape2 :: melt(tst, id.var = "dtime", measure.vars = as.character(OTB) )
tst2$dtime2 = as.POSIXct(tst2$dtime ,tz = "GMT")
plot1 = ggplot(tst2, aes(dtime2, variable, fill= as.numeric(value))) +
geom_tile() +
scale_fill_gradient(low="white", high="black",limits=c(70,120) ) +
#scale_y_discrete(labels = ulabs) +
labs(fill = "Sound Pressure Levels", title = siteDep) +
xlab("") +
ylab("Frequency [Hz]")
#cumulative dynamic range, line plot (per frequency use inD, columns)
cumV = NULL
fqID = colnames(inD)
for (vv in 1:length(fqID)){
x = inD[,fqID[vv]] #should I un-dB?
n <- seq_along(x) ## cumulative sample size
m <- cumsum(x) / n ## cumulative mean check: inD[1,vv]+inD[2,vv]
m2<- cumsum(x * x) / n ## cumulative squared mean plot(m2)
v <- (m2 - m * m) * (n / (n - 1)) ## cumulative variance
s <- sqrt(v) ## cumulative standard deviation
cumV = rbind(cumV, as.data.frame(cbind(fqID[vv],x,n,m,m2,v,s)))
rm(x,n,m,m2,v,s)
}
cumV = as.data.frame(cumV)
unique(cumV$V1)
plot3 = ggplot(cumV, aes(as.numeric(as.character(n)), as.numeric(as.character(s)),
color = as.factor(V1), group = (V1)) )+
geom_line()+
scale_color_discrete(name = "OTB")+
labs(y = "cumulative variance",x ="hourly SPL", title=siteDep, fill = "OTB")
#outputs
pv = grid.arrange(plot1, plot3, ncol=1, nrow =2)
#outputs
perSum = rbind(perSum, melt(outD, id.vars = c("quants","siteDep"), measure.vars = as.character(OTB) ))
infoT = rbind(infoT, c( fname, ndys, as.character(dwindow ) ) )
ggsave(filename = paste0("D:\\RESEARCH\\SanctSound\\plots\\", fname, "_OTB_hSPLcumVar.png"), pv, dpi=300)
ggsave(filename = paste0("D:\\RESEARCH\\SanctSound\\plots\\", fname, "_OTB_cumVar.png"), plot3, dpi=300)
#clean up
rm(tst,ndys,inD,outD)
}
if (siteDep == "CI01_02") {
dwindow = c( min(cdata$date), min(cdata$date) + 30) #start at beginning + 30 days
tst = cdata[cdata$date >= dwindow[1] & cdata$date <= dwindow[2],]
ndys = length( unique(tst$date) )
inD = tst[, 2:11]
indx <- sapply(inD, is.factor)
inD[indx] <- lapply(inD[indx], function(x) as.numeric(as.character(x)))
outD = as.data.frame( apply( inD, 2 , quantile, probs = quants ))
outD = cbind(outD,quants,siteDep)
#spectrogram, tile plot
tst2 = reshape2 :: melt(tst, id.var = "dtime", measure.vars = as.character(OTB) )
tst2$dtime2 = as.POSIXct(tst2$dtime ,tz = "GMT")
plot1 = ggplot(tst2, aes(dtime2, variable, fill= as.numeric(value))) +
geom_tile() +
scale_fill_gradient(low="white", high="black",limits=c(70,120) ) +
#scale_y_discrete(labels = ulabs) +
labs(fill = "Sound Pressure Levels", title = siteDep) +
xlab("") +
ylab("Frequency [Hz]")
#mean-median, tile plot
medD = NULL
for (mm in 1:ncol(inD)){ # mm = 1
medD = cbind(medD, outD[3,mm] - inD[,mm]) # median - SPL
}
medD = as.data.frame( cbind(as.character(tst$dtime), medD) )
colnames(medD) = c("dtime", as.character(OTB))
medDm = reshape2 :: melt(medD, id.var = "dtime", measure.vars = as.character(OTB) )
medDm$dtime2 = as.POSIXct(medDm$dtime ,tz = "GMT")
plot2 = ggplot(medDm, aes(dtime2, variable, fill= as.numeric(value))) +
geom_tile() +
scale_fill_gradient(low="white", high="black") +
#scale_y_discrete(labels = ulabs) +
labs(fill = "Median-SPL", title = "") +
xlab("") +
ylab("Frequency [Hz]")
#cumulative dynamic range, line plot (per frequency use inD, columns)
cumV = NULL
fqID = colnames(inD)
for (vv in 1:length(fqID)){
x = inD[,fqID[vv]] #should I un-dB?
n <- seq_along(x) ## cumulative sample size
m <- cumsum(x) / n ## cumulative mean check: inD[1,vv]+inD[2,vv]
m2<- cumsum(x * x) / n ## cumulative squared mean plot(m2)
v <- (m2 - m * m) * (n / (n - 1)) ## cumulative variance
s <- sqrt(v) ## cumulative standard deviation
cumV = rbind(cumV, as.data.frame(cbind(fqID[vv],x,n,m,m2,v,s)))
rm(x,n,m,m2,v,s)
}
cumV = as.data.frame(cumV)
unique(cumV$V1)
plot3 = ggplot(cumV, aes(as.numeric(as.character(n)), as.numeric(as.character(s)),
color = as.factor(V1), group = (V1)) )+
geom_line()+
scale_color_discrete(name = "OTB")+
labs(y = "cumulative variance",x ="hourly SPL", title=siteDep, fill = "OTB")
#ggsave(filename = paste0("D:\\RESEARCH\\SanctSound\\plots\\", fname, "_cumVar.png"), plot3, dpi=300)
#outputs
pv = grid.arrange(plot1, plot3, ncol=1, nrow =2)
#outputs
perSum = rbind(perSum, melt(outD, id.vars = c("quants","siteDep"), measure.vars = as.character(OTB) ))
infoT = rbind(infoT, c( fname, ndys, as.character(dwindow ) ) )
ggsave(filename = paste0("D:\\RESEARCH\\SanctSound\\plots\\", fname, "_OTB_hSPLcumVar.png"), pv, dpi=300)
ggsave(filename = paste0("D:\\RESEARCH\\SanctSound\\plots\\", fname, "_OTB_cumVar.png"), plot3, dpi=300)
#clean up
rm(tst,ndys,inD,outD)
}
if (siteDep == "OC02_01") {
dwindow = c( min(cdata$date), min(cdata$date) + 30) #start at beginning + 30 days
tst = cdata[cdata$date >= dwindow[1] & cdata$date <= dwindow[2],]
ndys = length( unique(tst$date) )
inD = tst[, 2:11]
indx <- sapply(inD, is.factor)
inD[indx] <- lapply(inD[indx], function(x) as.numeric(as.character(x)))
outD = as.data.frame( apply( inD, 2 , quantile, probs = quants ))
outD = cbind(outD,quants,siteDep)
#spectrogram, tile plot
tst2 = reshape2 :: melt(tst, id.var = "dtime", measure.vars = as.character(OTB) )
tst2$dtime2 = as.POSIXct(tst2$dtime ,tz = "GMT")
plot1 = ggplot(tst2, aes(dtime2, variable, fill= as.numeric(value))) +
geom_tile() +
scale_fill_gradient(low="white", high="black",limits=c(70,120) ) +
#scale_y_discrete(labels = ulabs) +
labs(fill = "Sound Pressure Levels", title = siteDep) +
xlab("") +
ylab("Frequency [Hz]")
#cumulative dynamic range, line plot (per frequency use inD, columns)
cumV = NULL
fqID = colnames(inD)
for (vv in 1:length(fqID)){
x = inD[,fqID[vv]] #should I un-dB?
n <- seq_along(x) ## cumulative sample size
m <- cumsum(x) / n ## cumulative mean check: inD[1,vv]+inD[2,vv]
m2<- cumsum(x * x) / n ## cumulative squared mean plot(m2)
v <- (m2 - m * m) * (n / (n - 1)) ## cumulative variance
s <- sqrt(v) ## cumulative standard deviation
cumV = rbind(cumV, as.data.frame(cbind(fqID[vv],x,n,m,m2,v,s)))
rm(x,n,m,m2,v,s)
}
cumV = as.data.frame(cumV)
unique(cumV$V1)
plot3 = ggplot(cumV, aes(as.numeric(as.character(n)), as.numeric(as.character(s)),
color = as.factor(V1), group = (V1)) )+
geom_line()+
scale_color_discrete(name = "OTB")+
labs(y = "cumulative variance",x ="hourly SPL", title=siteDep, fill = "OTB")
#outputs
pv = grid.arrange(plot1, plot3, ncol=1, nrow =2)
#outputs
perSum = rbind(perSum, melt(outD, id.vars = c("quants","siteDep"), measure.vars = as.character(OTB) ))
infoT = rbind(infoT, c( fname, ndys, as.character(dwindow ) ) )
ggsave(filename = paste0("D:\\RESEARCH\\SanctSound\\plots\\", fname, "_OTB_hSPLcumVar.png"), pv, dpi=300)
ggsave(filename = paste0("D:\\RESEARCH\\SanctSound\\plots\\", fname, "_OTB_cumVar.png"), plot3, dpi=300)
#clean up
rm(tst,ndys,inD,outD)
}
if (siteDep == "OC01_01") {
dwindow = c( min(cdata$date), min(cdata$date) + 30) #start at beginning + 30 days
tst = cdata[cdata$date >= dwindow[1] & cdata$date <= dwindow[2],]
ndys = length( unique(tst$date) )
inD = tst[, 2:11]
indx <- sapply(inD, is.factor)
inD[indx] <- lapply(inD[indx], function(x) as.numeric(as.character(x)))
outD = as.data.frame( apply( inD, 2 , quantile, probs = quants ))
outD = cbind(outD,quants,siteDep)
#spectrogram, tile plot
tst2 = reshape2 :: melt(tst, id.var = "dtime", measure.vars = as.character(OTB) )
tst2$dtime2 = as.POSIXct(tst2$dtime ,tz = "GMT")
plot1 = ggplot(tst2, aes(dtime2, variable, fill= as.numeric(value))) +
geom_tile() +
scale_fill_gradient(low="white", high="black",limits=c(70,120) ) +
#scale_y_discrete(labels = ulabs) +
labs(fill = "Sound Pressure Levels", title = siteDep) +
xlab("") +
ylab("Frequency [Hz]")
#cumulative dynamic range, line plot (per frequency use inD, columns)
cumV = NULL
fqID = colnames(inD)
for (vv in 1:length(fqID)){
x = inD[,fqID[vv]] #should I un-dB?
n <- seq_along(x) ## cumulative sample size
m <- cumsum(x) / n ## cumulative mean check: inD[1,vv]+inD[2,vv]
m2<- cumsum(x * x) / n ## cumulative squared mean plot(m2)
v <- (m2 - m * m) * (n / (n - 1)) ## cumulative variance
s <- sqrt(v) ## cumulative standard deviation
cumV = rbind(cumV, as.data.frame(cbind(fqID[vv],x,n,m,m2,v,s)))
rm(x,n,m,m2,v,s)
}
cumV = as.data.frame(cumV)
unique(cumV$V1)
plot3 = ggplot(cumV, aes(as.numeric(as.character(n)), as.numeric(as.character(s)),
color = as.factor(V1), group = (V1)) )+
geom_line()+
scale_color_discrete(name = "OTB")+
labs(y = "cumulative variance",x ="hourly SPL", title=siteDep, fill = "OTB")
#outputs
pv = grid.arrange(plot1, plot3, ncol=1, nrow =2)
#outputs
perSum = rbind(perSum, melt(outD, id.vars = c("quants","siteDep"), measure.vars = as.character(OTB) ))
infoT = rbind(infoT, c( fname, ndys, as.character(dwindow ) ) )
ggsave(filename = paste0("D:\\RESEARCH\\SanctSound\\plots\\", fname, "_OTB_hSPLcumVar.png"), pv, dpi=300)
ggsave(filename = paste0("D:\\RESEARCH\\SanctSound\\plots\\", fname, "_OTB_cumVar.png"), plot3, dpi=300)
#clean up
rm(tst,ndys,inD,outD)
}
###sites to start on March 01
if (siteDep == "FK04_01") {
dwindow = c( as.Date("2019-03-01",format="%Y-%m-%d"), as.Date("2019-03-01",format="%Y-%m-%d") + 30) #start at beginning + 30 days
tst = cdata[cdata$date >= dwindow[1] & cdata$date <= dwindow[2],]
ndys = length( unique(tst$date) )
inD = tst[, 2:11]
indx <- sapply(inD, is.factor)
inD[indx] <- lapply(inD[indx], function(x) as.numeric(as.character(x)))
outD = as.data.frame( apply( inD, 2 , quantile, probs = quants ))
outD = cbind(outD,quants,siteDep)
#spectrogram, tile plot
tst2 = reshape2 :: melt(tst, id.var = "dtime", measure.vars = as.character(OTB) )
tst2$dtime2 = as.POSIXct(tst2$dtime ,tz = "GMT")
plot1 = ggplot(tst2, aes(dtime2, variable, fill= as.numeric(value))) +
geom_tile() +
scale_fill_gradient(low="white", high="black",limits=c(70,120) ) +
#scale_y_discrete(labels = ulabs) +
labs(fill = "Sound Pressure Levels", title = siteDep) +
xlab("") +
ylab("Frequency [Hz]")
#cumulative dynamic range, line plot (per frequency use inD, columns)
cumV = NULL
fqID = colnames(inD)
for (vv in 1:length(fqID)){
x = inD[,fqID[vv]] #should I un-dB?
n <- seq_along(x) ## cumulative sample size
m <- cumsum(x) / n ## cumulative mean check: inD[1,vv]+inD[2,vv]
m2<- cumsum(x * x) / n ## cumulative squared mean plot(m2)
v <- (m2 - m * m) * (n / (n - 1)) ## cumulative variance
s <- sqrt(v) ## cumulative standard deviation
cumV = rbind(cumV, as.data.frame(cbind(fqID[vv],x,n,m,m2,v,s)))
rm(x,n,m,m2,v,s)
}
cumV = as.data.frame(cumV)
unique(cumV$V1)
plot3 = ggplot(cumV, aes(as.numeric(as.character(n)), as.numeric(as.character(s)),
color = as.factor(V1), group = (V1)) )+
geom_line()+
scale_color_discrete(name = "OTB")+
labs(y = "cumulative variance",x ="hourly SPL", title=siteDep, fill = "OTB")
#outputs
pv = grid.arrange(plot1, plot3, ncol=1, nrow =2)
#outputs
perSum = rbind(perSum, melt(outD, id.vars = c("quants","siteDep"), measure.vars = as.character(OTB) ))
infoT = rbind(infoT, c( fname, ndys, as.character(dwindow ) ) )
ggsave(filename = paste0("D:\\RESEARCH\\SanctSound\\plots\\", fname, "_OTB_hSPLcumVar.png"), pv, dpi=300)
ggsave(filename = paste0("D:\\RESEARCH\\SanctSound\\plots\\", fname, "_OTB_cumVar.png"), plot3, dpi=300)
#clean up
rm(tst,ndys,inD,outD)
}
if (siteDep == "GR01_01") {
dwindow = c( as.Date("2019-03-01",format="%Y-%m-%d"), as.Date("2019-03-01",format="%Y-%m-%d") + 30) #start at beginning + 30 days
tst = cdata[cdata$date >= dwindow[1] & cdata$date <= dwindow[2],]
ndys = length( unique(tst$date) )
inD = tst[, 2:11]
indx <- sapply(inD, is.factor)
inD[indx] <- lapply(inD[indx], function(x) as.numeric(as.character(x)))
outD = as.data.frame( apply( inD, 2 , quantile, probs = quants ))
outD = cbind(outD,quants,siteDep)
#spectrogram, tile plot
tst2 = reshape2 :: melt(tst, id.var = "dtime", measure.vars = as.character(OTB) )
tst2$dtime2 = as.POSIXct(tst2$dtime ,tz = "GMT")
plot1 = ggplot(tst2, aes(dtime2, variable, fill= as.numeric(value))) +
geom_tile() +
scale_fill_gradient(low="white", high="black",limits=c(70,120) ) +
#scale_y_discrete(labels = ulabs) +
labs(fill = "Sound Pressure Levels", title = siteDep) +
xlab("") +
ylab("Frequency [Hz]")
#cumulative dynamic range, line plot (per frequency use inD, columns)
cumV = NULL
fqID = colnames(inD)
for (vv in 1:length(fqID)){
x = inD[,fqID[vv]] #should I un-dB?
n <- seq_along(x) ## cumulative sample size
m <- cumsum(x) / n ## cumulative mean check: inD[1,vv]+inD[2,vv]
m2<- cumsum(x * x) / n ## cumulative squared mean plot(m2)
v <- (m2 - m * m) * (n / (n - 1)) ## cumulative variance
s <- sqrt(v) ## cumulative standard deviation
cumV = rbind(cumV, as.data.frame(cbind(fqID[vv],x,n,m,m2,v,s)))
rm(x,n,m,m2,v,s)
}
cumV = as.data.frame(cumV)
unique(cumV$V1)
plot3 = ggplot(cumV, aes(as.numeric(as.character(n)), as.numeric(as.character(s)),
color = as.factor(V1), group = (V1)) )+
geom_line()+
scale_color_discrete(name = "OTB")+
labs(y = "cumulative variance",x ="hourly SPL", title=siteDep, fill = "OTB")
#outputs
pv = grid.arrange(plot1, plot3, ncol=1, nrow =2)
#outputs
perSum = rbind(perSum, melt(outD, id.vars = c("quants","siteDep"), measure.vars = as.character(OTB) ))
infoT = rbind(infoT, c( fname, ndys, as.character(dwindow ) ) )
ggsave(filename = paste0("D:\\RESEARCH\\SanctSound\\plots\\", fname, "_OTB_hSPLcumVar.png"), pv, dpi=300)
ggsave(filename = paste0("D:\\RESEARCH\\SanctSound\\plots\\", fname, "_OTB_cumVar.png"), plot3, dpi=300)
#clean up
rm(tst,ndys,inD,outD)
}
###sites to start on March 01-- enough data?
if (siteDep == "HI01_01") { #!!! not enough days, start sooner?
dwindow = c( as.Date("2019-03-01",format="%Y-%m-%d"), as.Date("2019-03-01",format="%Y-%m-%d") + 30) #start at beginning + 30 days
tst = cdata[cdata$date >= dwindow[1] & cdata$date <= dwindow[2],]
ndys = length( unique(tst$date) )
inD = tst[, 2:11]
indx <- sapply(inD, is.factor)
inD[indx] <- lapply(inD[indx], function(x) as.numeric(as.character(x)))
outD = as.data.frame( apply( inD, 2 , quantile, probs = quants ))
outD = cbind(outD,quants,siteDep)
#spectrogram, tile plot
tst2 = reshape2 :: melt(tst, id.var = "dtime", measure.vars = as.character(OTB) )
tst2$dtime2 = as.POSIXct(tst2$dtime ,tz = "GMT")
plot1 = ggplot(tst2, aes(dtime2, variable, fill= as.numeric(value))) +
geom_tile() +
scale_fill_gradient(low="white", high="black",limits=c(70,120) ) +
#scale_y_discrete(labels = ulabs) +
labs(fill = "Sound Pressure Levels", title = siteDep) +
xlab("") +
ylab("Frequency [Hz]")
#cumulative dynamic range, line plot (per frequency use inD, columns)
cumV = NULL
fqID = colnames(inD)
for (vv in 1:length(fqID)){
x = inD[,fqID[vv]] #should I un-dB?
n <- seq_along(x) ## cumulative sample size
m <- cumsum(x) / n ## cumulative mean check: inD[1,vv]+inD[2,vv]
m2<- cumsum(x * x) / n ## cumulative squared mean plot(m2)
v <- (m2 - m * m) * (n / (n - 1)) ## cumulative variance
s <- sqrt(v) ## cumulative standard deviation
cumV = rbind(cumV, as.data.frame(cbind(fqID[vv],x,n,m,m2,v,s)))
rm(x,n,m,m2,v,s)
}
cumV = as.data.frame(cumV)
unique(cumV$V1)
plot3 = ggplot(cumV, aes(as.numeric(as.character(n)), as.numeric(as.character(s)),
color = as.factor(V1), group = (V1)) )+
geom_line()+
scale_color_discrete(name = "OTB")+
labs(y = "cumulative variance",x ="hourly SPL", title=siteDep, fill = "OTB")
#outputs
pv = grid.arrange(plot1, plot3, ncol=1, nrow =2)
#outputs
perSum = rbind(perSum, melt(outD, id.vars = c("quants","siteDep"), measure.vars = as.character(OTB) ))
infoT = rbind(infoT, c( fname, ndys, as.character(dwindow ) ) )
ggsave(filename = paste0("D:\\RESEARCH\\SanctSound\\plots\\", fname, "_OTB_hSPLcumVar.png"), pv, dpi=300)
ggsave(filename = paste0("D:\\RESEARCH\\SanctSound\\plots\\", fname, "_OTB_cumVar.png"), plot3, dpi=300)
#clean up
rm(tst,ndys,inD,outD)
}
if (siteDep == "MB02_01") {
dwindow = c( as.Date("2019-03-01",format="%Y-%m-%d"), as.Date("2019-03-01",format="%Y-%m-%d") + 30) #start at beginning + 30 days
tst = cdata[cdata$date >= dwindow[1] & cdata$date <= dwindow[2],]
ndys = length( unique(tst$date) )
inD = tst[, 2:11]
indx <- sapply(inD, is.factor)
inD[indx] <- lapply(inD[indx], function(x) as.numeric(as.character(x)))
outD = as.data.frame( apply( inD, 2 , quantile, probs = quants ))
outD = cbind(outD,quants,siteDep)
#spectrogram, tile plot
tst2 = reshape2 :: melt(tst, id.var = "dtime", measure.vars = as.character(OTB) )
tst2$dtime2 = as.POSIXct(tst2$dtime ,tz = "GMT")
plot1 = ggplot(tst2, aes(dtime2, variable, fill= as.numeric(value))) +
geom_tile() +
scale_fill_gradient(low="white", high="black",limits=c(70,120) ) +
#scale_y_discrete(labels = ulabs) +
labs(fill = "Sound Pressure Levels", title = siteDep) +
xlab("") +
ylab("Frequency [Hz]")
#cumulative dynamic range, line plot (per frequency use inD, columns)
cumV = NULL
fqID = colnames(inD)
for (vv in 1:length(fqID)){
x = inD[,fqID[vv]] #should I un-dB?
n <- seq_along(x) ## cumulative sample size
m <- cumsum(x) / n ## cumulative mean check: inD[1,vv]+inD[2,vv]
m2<- cumsum(x * x) / n ## cumulative squared mean plot(m2)
v <- (m2 - m * m) * (n / (n - 1)) ## cumulative variance
s <- sqrt(v) ## cumulative standard deviation
cumV = rbind(cumV, as.data.frame(cbind(fqID[vv],x,n,m,m2,v,s)))
rm(x,n,m,m2,v,s)
}
cumV = as.data.frame(cumV)
unique(cumV$V1)
plot3 = ggplot(cumV, aes(as.numeric(as.character(n)), as.numeric(as.character(s)),
color = as.factor(V1), group = (V1)) )+
geom_line()+
scale_color_discrete(name = "OTB")+
labs(y = "cumulative variance",x ="hourly SPL", title=siteDep, fill = "OTB")
#outputs
pv = grid.arrange(plot1, plot3, ncol=1, nrow =2)
#outputs
perSum = rbind(perSum, melt(outD, id.vars = c("quants","siteDep"), measure.vars = as.character(OTB) ))
infoT = rbind(infoT, c( fname, ndys, as.character(dwindow ) ) )
ggsave(filename = paste0("D:\\RESEARCH\\SanctSound\\plots\\", fname, "_OTB_hSPLcumVar.png"), pv, dpi=300)
ggsave(filename = paste0("D:\\RESEARCH\\SanctSound\\plots\\", fname, "_OTB_cumVar.png"), plot3, dpi=300)
#clean up
rm(tst,ndys,inD,outD)
}
if (siteDep == "SB01_02") {
dwindow = c( as.Date("2019-03-01",format="%Y-%m-%d"), as.Date("2019-03-01",format="%Y-%m-%d") + 30) #start at beginning + 30 days
tst = cdata[cdata$date >= dwindow[1] & cdata$date <= dwindow[2],]
ndys = length( unique(tst$date) )
inD = tst[, 3:12] #frequency is different for this deployent? 16 Hz included- does not start at 31.5
indx <- sapply(inD, is.factor)
inD[indx] <- lapply(inD[indx], function(x) as.numeric(as.character(x)))
outD = as.data.frame( apply( inD, 2 , quantile, probs = quants ))
outD = cbind(outD,quants,siteDep)
#spectrogram, tile plot
tst2 = reshape2 :: melt(tst, id.var = "dtime", measure.vars = as.character(OTB) )
tst2$dtime2 = as.POSIXct(tst2$dtime ,tz = "GMT")
plot1 = ggplot(tst2, aes(dtime2, variable, fill= as.numeric(value))) +
geom_tile() +
scale_fill_gradient(low="white", high="black",limits=c(70,120) ) +
#scale_y_discrete(labels = ulabs) +
labs(fill = "Sound Pressure Levels", title = siteDep) +
xlab("") +
ylab("Frequency [Hz]")
#cumulative dynamic range, line plot (per frequency use inD, columns)
cumV = NULL
fqID = colnames(inD)
for (vv in 1:length(fqID)){
x = inD[,fqID[vv]] #should I un-dB?
n <- seq_along(x) ## cumulative sample size
m <- cumsum(x) / n ## cumulative mean check: inD[1,vv]+inD[2,vv]
m2<- cumsum(x * x) / n ## cumulative squared mean plot(m2)
v <- (m2 - m * m) * (n / (n - 1)) ## cumulative variance
s <- sqrt(v) ## cumulative standard deviation
cumV = rbind(cumV, as.data.frame(cbind(fqID[vv],x,n,m,m2,v,s)))
rm(x,n,m,m2,v,s)
}
cumV = as.data.frame(cumV)
unique(cumV$V1)
plot3 = ggplot(cumV, aes(as.numeric(as.character(n)), as.numeric(as.character(s)),
color = as.factor(V1), group = (V1)) )+
geom_line()+
scale_color_discrete(name = "OTB")+
labs(y = "cumulative variance",x ="hourly SPL", title=siteDep, fill = "OTB")
#outputs
OTB = as.character(OTB)[2:11] #need to turncate because this site has measurement at 16 Hz!?
pv = grid.arrange(plot1, plot3, ncol=1, nrow =2)
perSum = rbind(perSum, melt(outD, id.vars = c("quants","siteDep"), measure.vars = (OTB) ))
infoT = rbind(infoT, c( fname, ndys, as.character(dwindow ) ) )
ggsave(filename = paste0("D:\\RESEARCH\\SanctSound\\plots\\", fname, "_OTB_hSPLcumVar.png"), pv, dpi=300)
ggsave(filename = paste0("D:\\RESEARCH\\SanctSound\\plots\\", fname, "_OTB_cumVar.png"), plot3, dpi=300)
#clean up
rm(tst,ndys,inD,outD)
}
#not processing... MB02_02 or GR01_02
#3) simple tile plot all data for deployment: x = time, y = frequency, fill = SPL (save out with title of deployment)
#combine for plotting- rows = time, columns = frequency
tst = reshape2 :: melt(cdata, id.vars = "dtime" , measure.vars = as.character(OTB) )
tst$dtime2 = as.POSIXct(tst$dtime ,tz = "GMT")
p1 = ggplot(tst, aes(dtime2, variable, fill= as.numeric(value))) +
geom_tile() +
scale_fill_gradient(low="white", high="black",limits=c(70,120) ) +
#scale_y_discrete(labels = ulabs) +
labs(fill = "Sound Pressure Levels", title = fname) +
xlab("") +
ylab("Frequency [Hz]")
ggsave(filename = paste0("D:\\RESEARCH\\SanctSound\\plots\\", fname, ".png"), p1, dpi=300)
#5) details for gantt chart
info = rbind(info, c( fname, as.character(min(tmF)), as.character(max(tmF)), length(unique(as.Date(tmF)) )) )
rm(p1,tst,cdata,tmF,OTB,fname)
}
#---------------------------------------------------
# GANTT chart for all deployments
#---------------------------------------------------
#https://www.molecularecologist.com/2019/01/simple-gantt-charts-in-r-with-ggplot2-and-the-tidyverse/
head(info)
colnames(info) = c("site","start","end","days")
info = as.data.frame(info)
gantt = reshape2 :: melt(info, id.vars = "site", measure.vars = c("start","end") )
site2 = substr(gantt$site, start = 9 , stop = 15)
site3 = substr(gantt$site, start = 9 , stop = 12)
deploy = substr(gantt$site, start = 14 , stop = 15)
gantt$date = as.Date(gantt$value)
gantt = cbind(gantt,site2,site3,deploy)
# Data window of analysis
D2plotS = c(as.Date("2019-03-01",format="%Y-%m-%d"),as.Date("2019-05-01",format="%Y-%m-%d"))
D2plotS = cbind(D2plotS, c(0,0))
colnames(D2plotS) = c("dateS","value")
D2plotC = as.data.frame(D2plotS)
ggplot(gantt, aes(date, site3,color = deploy)) +
geom_line(size=10)+
labs(x = "2019", y = "", title = "Deployment period")+
geom_vline(xintercept = D2plotC$dateS[1],linetype="dashed",
color = "black", size=.3) +
geom_vline(xintercept = D2plotC$dateS[2],linetype="dashed",
color = "black", size=.3)
# GANTT chart for all deployments, only period of analysis
head(infoT)
colnames(infoT) = c("site","days", "start","end")
infoT = as.data.frame(infoT)
gantt = reshape2 :: melt(infoT, id.vars = "site", measure.vars = c("start","end") )
site2 = substr(gantt$site, start = 9 , stop = 15)
site3 = substr(gantt$site, start = 9 , stop = 12)
deploy = substr(gantt$site, start = 14 , stop = 15)
gantt$date = as.Date(gantt$value)
gantt = cbind(gantt,site2,site3,deploy)
ggplot(gantt, aes(date, site3, color = deploy)) +
geom_line(size=10)+
labs(x = "2019", y = "",title = "Analysis period")+
geom_vline(xintercept = D2plotC$dateS[1],linetype="dashed",
color = "black", size=.3) +
geom_vline(xintercept = D2plotC$dateS[2],linetype="dashed",
color = "black", size=.3)
#---------------------------------------------------
# PLOT OF Percentiles from SPLS across octave bands
#---------------------------------------------------
head(perSum)
perSum$siteDep
ggplot(perSum, aes(variable, value, color = as.factor(quants), group = (quants)) ) +
geom_line(size=2)+
#geom_point(size=1)+
scale_color_discrete(name = "Percentiles")+
facet_grid(~as.factor(siteDep))+
theme_light() +
theme(axis.text.x = element_text(angle = 90)) +
labs(x = "Frequency", y = "Sound Pressure Level dB re: 1uPa",title = "")