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main_RADM.R
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main_RADM.R
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require(ncdf4)
require(stringr)
require(stringi)
require(parallel)
require(ggplot2)
require(plyr)
require(viridis)
require(gridExtra)
require(gtools)
require(nortest)
main_RADM <- function(WMO, index_ifremer, index_greylist, path_to_netcdf, n_cores=detectCores()) {
choice = my_menu(title = paste0("What kind of radiometry sensor is used in this float ? ",
"(0 to abandon and quit)"),
choices = c("OCR504 (PEEK)", "OCR504 (Aluminium)")
)
switch (choice + 1,
return(0),
{sensor_type = "PEEK"},
{sensor_type = "Aluminium"}
)
cat("Preparing file lists, dates, and greylist...")
files = as.character(index_ifremer$file) #retrieve the path of each netcfd file
ident = strsplit(files,"/") #separate the different roots of the files paths
ident = matrix(unlist(ident), ncol=4, byrow=TRUE)
dac = ident[,1] #retrieve the DAC of all profiles as a vector
wod = ident[,2] #retrieve the WMO of all profiles as a vector
prof_id = ident[,4] #retrieve all profiles name as a vector
variables = as.character(index_ifremer$parameters) #retrieve the list of variables available in
#each file
#variables = strsplit(variables," ") #separate the different available variables of each profile
lat = index_ifremer$latitude #retrieve the latitude of all profiles as a vector
lon = index_ifremer$longitude #retrieve the longitude of all profiles as a vector
prof_date = index_ifremer$date #retrieve the date of all profiles as a vector
subset = which(wod==WMO & substr(prof_id,14,14)!="D"
& !is.na(prof_date) & grepl("DOWNWELLING_PAR", variables) )
subset_date_miss = which( wod==WMO & substr(prof_id,14,14)!="D" & is.na(prof_date)
& grepl("DOWNWELLING_PAR", variables) )
subset_copy = which(wod==WMO
& ( substr(prof_id,14,14)=="D" | !grepl("DOWNWELLING_PAR", variables) ) )
subset_pos_miss = which( wod==WMO & substr(prof_id,14,14)!="D" &
grepl("DOWNWELLING_PAR", variables) & (is.na(lat) | is.na(lon)) )
profile_list = substr(prof_id[subset], 3, 14)
files_list = files[subset]
lat_list = lat[subset]
lon_list = lon[subset]
prof_date_list = prof_date[subset]
month_list = as.numeric(str_sub(prof_date_list,5,6))
day_list = as.numeric(str_sub(prof_date_list,7,8))
hour_list = as.numeric(str_sub(prof_date_list,9,10))
minute_list = as.numeric(str_sub(prof_date_list,11,12))
second_list = as.numeric(str_sub(prof_date_list,13,14))
tu_list = (hour_list + minute_list/60 + second_list/3600)
M = mapply(possol.vec, month=month_list, jday=day_list, tu=tu_list, xlon=lon_list, xlat=lat_list)
solar_elev = unlist(M, use.names=F)
night = which(solar_elev < -5)
day = which(solar_elev >= -5)
profile_night = profile_list[night]
files_night = files_list[night]
date_night = as.Date(as.character(prof_date_list[night]), format="%Y%m%d%H%M%S", tz="UTC")
date_night = julian(date_night, origin=as.Date("1950-01-01", tz="UTC"))
profile_day = profile_list[day]
files_day = files_list[day]
date_day = as.Date(as.character(prof_date_list[day]), format="%Y%m%d%H%M%S", tz="UTC")
date_day = julian(date_day, origin=as.Date("1950-01-01", tz="UTC"))
date_list = as.Date(as.character(prof_date_list), format="%Y%m%d%H%M%S", tz="UTC")
date_list = julian(date_list, origin=as.Date("1950-01-01", tz="UTC"))
greylist_starts = as.Date(as.character(index_greylist$START_DATE), format="%Y%m%d", tz="UTC")
index_greylist$START_JULD = julian(greylist_starts, origin=as.Date("1950-01-01", tz="UTC"))
greylist_ends = as.Date(as.character(index_greylist$END_DATE), format="%Y%m%d", tz="UTC")
index_greylist$END_JULD = julian(greylist_ends, origin=as.Date("1950-01-01", tz="UTC"))
index_greylist$END_JULD[which(is.na(index_greylist$END_JULD))] = Inf
PARAM_NAMES = c("DOWNWELLING_PAR", "DOWN_IRRADIANCE380",
"DOWN_IRRADIANCE412", "DOWN_IRRADIANCE490")
is_greylisted <- function(julian_day, WMO, PARAMETER_NAME) {
relevant_lines = which(index_greylist$PLATFORM_CODE==WMO &
index_greylist$PARAMETER_NAME==PARAMETER_NAME)
greylisted_bool = any(index_greylist$START_JULD[relevant_lines] <= julian_day &
index_greylist$END_JULD[relevant_lines] >= julian_day)
return(greylisted_bool)
}
cat("DONE\nImporting and extracting drift data...")
####################################
### Start of drift considerations
####################################
traj_name_B = paste(path_to_netcdf, dac[subset[1]], "/", WMO, "/", WMO, "_BRtraj.nc", sep="")
traj_name_C = paste(path_to_netcdf, dac[subset[1]], "/", WMO, "/", WMO, "_Rtraj.nc", sep="")
fnc_B = nc_open(traj_name_B)
fnc_C = nc_open(traj_name_C)
temp = ncvar_get(fnc_C, "TEMP")
juld_B = ncvar_get(fnc_B, "JULD")
juld_C = ncvar_get(fnc_C, "JULD")
code = ncvar_get(fnc_B, "MEASUREMENT_CODE")
drift_C = which(!is.na(temp) & code==290) #290 may not be the only correct code
DRIFT = numeric(0)
DRIFT_Ts = numeric(0)
DRIFT_date = numeric(0)
DRIFT_name = character(0)
for (param_name in PARAM_NAMES) {
irr = ncvar_get(fnc_B, param_name)
drift_B = which(!is.na(irr) & code==290) #290 may not be the only correct code
if ( length(drift_B)==0 ) {
next
}
drift_match = rep(NA, length(drift_B))
for (i in 1:length(drift_B)) {
match_dist = abs(juld_B[drift_B[i]] - juld_C[drift_C])
drift_match[i] = min(which( match_dist == min(match_dist) ))
}
DRIFT = c(DRIFT, irr[drift_B])
DRIFT_Ts = c(DRIFT_Ts, temp[drift_C[drift_match]])
DRIFT_date = c(DRIFT_date, juld_B[drift_B])
DRIFT_name = c(DRIFT_name, rep(param_name, length(drift_B)))
}
nc_close(fnc_B)
nc_close(fnc_C)
drift_dataf = data.frame("PARAM"=DRIFT, "PARAM_Ts"=DRIFT_Ts,
"PARAM_date"=DRIFT_date, "PARAM_name"=DRIFT_name)
cat("DONE\nExtracting dark data for method B...")
files_drift_PARALLEL = rep(files_list, 4)
date_drift_PARALLEL = rep(date_list, 4)
PARAM_NAMES_drift_PARALLEL = rep(PARAM_NAMES, each=length(files_list))
DRIFT_MATCH = mcmapply(get_profile_match, file_name=files_drift_PARALLEL,
param_name=PARAM_NAMES_drift_PARALLEL, path_to_netcdf=path_to_netcdf,
PROFILE_DATE=date_drift_PARALLEL, mc.cores=n_cores, USE.NAMES=FALSE,
MoreArgs=list(method="day", material=sensor_type))
DRIFT_dataf = data.frame("PARAM"=unlist(DRIFT_MATCH[1,]),
"PARAM_Ts"=unlist(DRIFT_MATCH[2,]),
"PARAM_date"=unlist(DRIFT_MATCH[3,]),
"PARAM_name"=unlist(DRIFT_MATCH[4,]))
cat("DONE\nFlagging outliers and greylist...")
### remove drift outliers
is_drift_outlier = rep(NA, length(drift_dataf$PARAM))
is_DRIFT_outlier = rep(NA, length(DRIFT_dataf$PARAM))
for (param_name in PARAM_NAMES) {
param_axis = which(drift_dataf$PARAM_name == param_name)
quartiles = quantile(drift_dataf$PARAM[param_axis], probs=c(0.25, 0.75))
margin = as.numeric(1.5 * (quartiles[2] - quartiles[1]))
outliers_lim = c(quartiles[1] - margin, quartiles[2] + margin)
is_drift_outlier[param_axis] = (drift_dataf$PARAM[param_axis] < outliers_lim[1] |
drift_dataf$PARAM[param_axis] > outliers_lim[2])
### Alternative method
param_axis = which(DRIFT_dataf$PARAM_name == param_name)
quartiles = quantile(DRIFT_dataf$PARAM[param_axis], probs=c(0.25, 0.75))
margin = as.numeric(1.5 * (quartiles[2] - quartiles[1]))
outliers_lim = c(quartiles[1] - margin, quartiles[2] + margin)
is_DRIFT_outlier[param_axis] = (DRIFT_dataf$PARAM[param_axis] < outliers_lim[1] |
DRIFT_dataf$PARAM[param_axis] > outliers_lim[2])
}
drift_dataf$is_drift_outlier = is_drift_outlier
DRIFT_dataf$is_drift_outlier = is_DRIFT_outlier
drift_dataf$PARAM_date_squared = (drift_dataf$PARAM_date)^2
DRIFT_dataf$PARAM_date_squared = (DRIFT_dataf$PARAM_date)^2
drift_dataf$PARAM_date_0 = drift_dataf$PARAM_date - date_list[1]
DRIFT_dataf$PARAM_date_0 = DRIFT_dataf$PARAM_date - date_list[1]
drift_dataf$is_greylisted = mcmapply(is_greylisted, julian_day=drift_dataf$PARAM_date,
PARAMETER_NAME=as.character(drift_dataf$PARAM_name),
MoreArgs=list(WMO=WMO), mc.cores=n_cores)
DRIFT_dataf$is_greylisted = mcmapply(is_greylisted, julian_day=DRIFT_dataf$PARAM_date,
PARAMETER_NAME=as.character(DRIFT_dataf$PARAM_name),
MoreArgs=list(WMO=WMO), mc.cores=n_cores)
cat("DONE\n")
do_quadratic_fit = c(F, F, F, F)
repeat { # loop until the user finds an adequate fit or quits
cat("Fitting both methods...")
A_axis_drift = rep(NA, 4)
B_axis_drift = rep(NA, 4)
C_axis_drift = rep(NA, 4)
Q_axis_drift = rep(0, 4)
drift_dataf_5C = drift_dataf
fitted_coeff_drift = NULL
A_axis_DRIFT = rep(NA, 4)
B_axis_DRIFT = rep(NA, 4)
C_axis_DRIFT = rep(NA, 4)
DRIFT_dataf_5C = DRIFT_dataf
fitted_coeff_DRIFT = NULL
Q_axis_DRIFT = rep(0, 4)
for (i in 1:4) {
subset_PAR = which(drift_dataf$PARAM_name == PARAM_NAMES[i])
subset_fit = which(drift_dataf$PARAM_name == PARAM_NAMES[i] & !drift_dataf$is_greylisted &
!drift_dataf$is_drift_outlier)
if ( length(subset_fit)==0 ) { next }
if (do_quadratic_fit[i]) {
fit_ABC = lm(PARAM ~ PARAM_Ts + PARAM_date + PARAM_date_squared, data=drift_dataf,
subset=subset_fit)
Q_axis_drift[i] = fit_ABC$coefficients[4]
} else {
fit_ABC = lm(PARAM ~ PARAM_Ts + PARAM_date, data=drift_dataf, subset=subset_fit)
}
fitted_coeff_drift[[PARAM_NAMES[i]]] = fit_ABC$coefficients
A_axis_drift[i] = fit_ABC$coefficients[1]
B_axis_drift[i] = fit_ABC$coefficients[2]
C_axis_drift[i] = fit_ABC$coefficients[3]
drift_dataf_5C$PARAM[subset_PAR] = drift_dataf_5C$PARAM[subset_PAR] -
B_axis_drift[i] * (drift_dataf_5C$PARAM_Ts[subset_PAR] - 5)
}
for (i in 1:4) {
### Alternative drift
subset_PAR = which(DRIFT_dataf$PARAM_name == PARAM_NAMES[i])
subset_fit = which(DRIFT_dataf$PARAM_name == PARAM_NAMES[i] & !DRIFT_dataf$is_greylisted &
!DRIFT_dataf$is_drift_outlier)
if (length(subset_fit) == 0) { next }
if (do_quadratic_fit[i]) {
fit_ABC = lm(PARAM ~ PARAM_Ts + PARAM_date + PARAM_date_squared, data=DRIFT_dataf,
subset=subset_fit)
Q_axis_DRIFT[i] = fit_ABC$coefficients[4]
} else {
fit_ABC = lm(PARAM ~ PARAM_Ts + PARAM_date, data=DRIFT_dataf, subset=subset_fit)
}
fitted_coeff_DRIFT[[PARAM_NAMES[i]]] = fit_ABC$coefficients
A_axis_DRIFT[i] = fit_ABC$coefficients[1]
B_axis_DRIFT[i] = fit_ABC$coefficients[2]
C_axis_DRIFT[i] = fit_ABC$coefficients[3]
DRIFT_dataf_5C$PARAM[subset_PAR] = DRIFT_dataf_5C$PARAM[subset_PAR] -
B_axis_DRIFT[i] * (DRIFT_dataf_5C$PARAM_Ts[subset_PAR] - 5)
}
cat("DONE\nCreating drift plots...")
good_drift = which(!drift_dataf$is_greylisted & !drift_dataf$is_drift_outlier)
range_time = seq(min(c(drift_dataf$PARAM_date[good_drift], date_list)),
max(c(drift_dataf$PARAM_date[good_drift], date_list)), length.out=100 )
data_fit_drift = data.frame(PARAM_name = rep(PARAM_NAMES, each=2),
x = rep(range_time - date_list[1], 4),
y = rep(A_axis_drift, each=2) + rep(B_axis_drift, each=2) * 5
+ rep(C_axis_drift, each=2) * rep(range_time, 4)
+ rep(Q_axis_drift, each=2) * rep(range_time^2, 4)
)
data_fit_DRIFT = data.frame(PARAM_name = rep(PARAM_NAMES, each=2),
x = rep(range_time - date_list[1], 4),
y = rep(A_axis_DRIFT, each=2) + rep(B_axis_DRIFT, each=2) * 5
+ rep(C_axis_DRIFT, each=2) * rep(range_time, 4)
+ rep(Q_axis_DRIFT, each=2) * rep(range_time^2, 4)
)
irr_labels = c("PAR", "E[d](380)", "E[d](412)", "E[d](490)")
names(irr_labels) = PARAM_NAMES
g4 = ggplot(na.omit(drift_dataf), aes(x=PARAM_date_0, y=PARAM, color=PARAM_Ts)) +
geom_point(data=function(x){x[!x$is_greylisted & !x$is_drift_outlier, ]}) +
geom_point(data=function(x){x[x$is_greylisted & !x$is_drift_outlier, ]}, color="red") +
scale_color_viridis() +
scale_y_continuous(labels = scientific_formatter) +
facet_wrap(~PARAM_name, scale="free_y",
labeller = labeller(PARAM_name = as_labeller(irr_labels, default=label_parsed))) +
theme_bw() +
labs(x="Days from the deployment",
y=TeX(paste0("$E_d(\\lambda)$ ($W\\, m^{-2}\\, nm^{-1}$) ;",
"PAR ($\\mu mol\\, m^{-2}\\, s^{-1}$)")),
colour="Sensor\ntemperature (°C)")
g5 = ggplot(na.omit(drift_dataf_5C), aes(x=PARAM_date_0, y=PARAM, color=PARAM_Ts)) +
geom_point(data=function(x){x[!x$is_greylisted & !x$is_drift_outlier, ]}) +
geom_point(data=function(x){x[x$is_greylisted & !x$is_drift_outlier, ]}, color="red") +
#geom_point(data=function(x){x[x$is_drift_outlier & !x$is_greylisted, ]},
# color="red", shape=4) +
scale_color_viridis() +
scale_y_continuous(labels = scientific_formatter) +
facet_wrap(~PARAM_name, scale="free_y",
labeller = labeller(PARAM_name = as_labeller(irr_labels, default=label_parsed))) +
theme_bw() +
labs(x="Days from the deployment",
y=TeX(paste0("$E_d(\\lambda)$ ($W\\, m^{-2}\\, nm^{-1}$) ;",
"PAR ($\\mu mol\\, m^{-2}\\, s^{-1}$)")),
colour="Sensor\ntemperature (°C)")
g6 = ggplot(na.omit(DRIFT_dataf), aes(x=PARAM_date_0, y=PARAM, color=PARAM_Ts)) +
geom_point(data=function(x){x[!x$is_greylisted & !x$is_drift_outlier, ]}) +
geom_point(data=function(x){x[x$is_greylisted & !x$is_drift_outlier, ]}, color="red") +
scale_color_viridis() +
scale_y_continuous(labels = scientific_formatter) +
facet_wrap(~PARAM_name, scale="free_y",
labeller = labeller(PARAM_name = as_labeller(irr_labels, default=label_parsed))) +
theme_bw() +
labs(x="Days from the deployment",
y=TeX(paste0("$E_d(\\lambda)$ ($W\\, m^{-2}\\, nm^{-1}$) ;",
"PAR ($\\mu mol\\, m^{-2}\\, s^{-1}$)")),
colour="Sensor\ntemperature (°C)")
g7 = ggplot(na.omit(DRIFT_dataf_5C), aes(x=PARAM_date_0, y=PARAM, color=PARAM_Ts)) +
geom_point(data=function(x){x[!x$is_greylisted & !x$is_drift_outlier, ]}) +
geom_point(data=function(x){x[x$is_greylisted & !x$is_drift_outlier, ]}, color="red") +
scale_color_viridis() +
scale_y_continuous(labels = scientific_formatter) +
facet_wrap(~PARAM_name, scale="free_y",
labeller = labeller(PARAM_name = as_labeller(irr_labels, default=label_parsed))) +
theme_bw() +
labs(x="Days from the deployment",
y=TeX(paste0("$E_d(\\lambda)$ ($W\\, m^{-2}\\, nm^{-1}$) ;",
"PAR ($\\mu mol\\, m^{-2}\\, s^{-1}$)")),
colour="Sensor\ntemperature (°C)")
#g4_fit = g4 + geom_line(data=data_fit_drift, mapping=aes(x=x,y=y), color="red")
g5_fit = g5 + geom_line(data=data_fit_drift, mapping=aes(x=x,y=y), color="red")
g5_fit_2 = g5_fit + geom_line(data=data_fit_DRIFT, mapping=aes(x=x,y=y), color="black")
#g6_fit = g6 + geom_line(data=data_fit_DRIFT, mapping=aes(x=x,y=y), color="black")
g7_fit = g7 + geom_line(data=data_fit_DRIFT, mapping=aes(x=x,y=y), color="black")
g7_fit_2 = g7_fit + geom_line(data=data_fit_drift, mapping=aes(x=x,y=y), color="red")
if ( dim(drift_dataf)[1] != 0 ) {
x11(xpos=0, ypos=0)
plot(g4)
x11(xpos=0, ypos=100)
plot(g5_fit)
x11(xpos=0, ypos=200)
plot(g5_fit_2)
plot_name=paste0(WMO, "_regr_drift_A.png")
png(filename=plot_name, width=800, height=600)
plot(g5_fit)
dev.off()
plot_name=paste0(WMO, "_regr_drift_uncorrected_A.png")
png(filename=plot_name, width=800, height=600)
plot(g4)
dev.off()
} else {
cat("No valid drift data points were found...")
}
if ( dim(DRIFT_dataf)[1] != 0 ) {
x11(xpos=-1, ypos=0)
plot(g6)
x11(xpos=-1, ypos=100)
plot(g7_fit)
x11(xpos=-1, ypos=200)
plot(g7_fit_2)
plot_name=paste0(WMO, "_regr_drift_B.png")
png(filename=plot_name, width=800, height=600)
plot(g7_fit)
dev.off()
plot_name=paste0(WMO, "_regr_drift_uncorrected_B.png")
png(filename=plot_name, width=800, height=600)
plot(g6)
dev.off()
} else {
cat("The alternative drift method found no valid dark data points...")
}
cat("DONE\n")
choice = my_menu(title = "What is the next step ? (0 to abandon and quit)",
choices = c("Continue with correction from method A",
"Continue with correction from method B",
"Change to quadratic fits for some parameters",
"Continue without a drift correction"))
switch (choice + 1,
return(0),
{fitted_coeff_drift_corr = fitted_coeff_drift
A_axis_drift_corr = A_axis_drift
B_axis_drift_corr = B_axis_drift
C_axis_drift_corr = C_axis_drift
Q_axis_drift_corr = Q_axis_drift
break},
{fitted_coeff_drift_corr = fitted_coeff_DRIFT
A_axis_drift_corr = A_axis_DRIFT
B_axis_drift_corr = B_axis_DRIFT
C_axis_drift_corr = C_axis_DRIFT
Q_axis_drift_corr = Q_axis_DRIFT
break},
{ },
{fitted_coeff_drift_corr = NULL
A_axis_drift_corr = c(0, 0, 0, 0)
B_axis_drift_corr = c(0, 0, 0, 0)
C_axis_drift_corr = c(0, 0, 0, 0)
Q_axis_drift_corr = c(0, 0, 0, 0)
break}
)
do_quadratic_fit = c(F, F, F, F)
for (i in 1:4) {
choice = my_menu(title = paste("Use a quadratic fit for", PARAM_NAMES[i], "?"),
choices = c("Yes", "No"))
do_quadratic_fit[i] = (choice == 1)
}
while(dev.cur() > 1) {dev.off()}
}
while(dev.cur() > 1) {dev.off()}
###############################################
#### Start of radiometry/Ts matches extraction
###############################################
cat("Extracting day and night profiles...")
files_night_PARALLEL = rep(files_night, 4)
date_night_PARALLEL = rep(date_night, 4)
PARAM_NAMES_night_PARALLEL = rep(PARAM_NAMES, each=length(files_night))
A_axis_drift_night_PARALLEL = rep(A_axis_drift_corr, each=length(files_night))
C_axis_drift_night_PARALLEL = rep(C_axis_drift_corr, each=length(files_night))
Q_axis_drift_night_PARALLEL = rep(Q_axis_drift_corr, each=length(files_night))
files_day_PARALLEL = rep(files_day, 4)
date_day_PARALLEL = rep(date_day, 4)
PARAM_NAMES_day_PARALLEL = rep(PARAM_NAMES, each=length(files_day))
A_axis_drift_day_PARALLEL = rep(A_axis_drift_corr, each=length(files_day))
C_axis_drift_day_PARALLEL = rep(C_axis_drift_corr, each=length(files_day))
Q_axis_drift_day_PARALLEL = rep(Q_axis_drift_corr, each=length(files_day))
NIGHT_MATCH = mcmapply(get_profile_match, file_name=files_night_PARALLEL,
param_name=PARAM_NAMES_night_PARALLEL, PROFILE_DATE=date_night_PARALLEL,
drift_A=A_axis_drift_night_PARALLEL, drift_C=C_axis_drift_night_PARALLEL,
drift_Q=Q_axis_drift_night_PARALLEL, mc.cores=n_cores, USE.NAMES=FALSE,
MoreArgs=list(path_to_netcdf=path_to_netcdf, material=sensor_type))
DAY_MATCH = mcmapply(get_profile_match, file_name=files_day_PARALLEL,
param_name=PARAM_NAMES_day_PARALLEL, PROFILE_DATE=date_day_PARALLEL,
drift_A=A_axis_drift_day_PARALLEL, drift_C=C_axis_drift_day_PARALLEL,
drift_Q=Q_axis_drift_day_PARALLEL, mc.cores=n_cores, USE.NAMES=FALSE,
MoreArgs=list(method="day", path_to_netcdf=path_to_netcdf,
material=sensor_type))
cat("DONE\nFlagging greylisted profiles and outliers in day profiles...")
### find dark median outliers in day profiles
all_param_names = unlist(lapply(DAY_MATCH[4,], `[[`, 1))
# extract the first element of each profile
is_dark_outlier = rep(NA, length(all_param_names))
all_dark_median = unlist(DAY_MATCH[5,])
for (param_name in PARAM_NAMES) {
param_axis = which(all_param_names == param_name)
quartiles = quantile(all_dark_median[param_axis], probs=c(0.25, 0.75))
margin = as.numeric(1.5 * (quartiles[2] - quartiles[1]))
outliers_lim = c(quartiles[1] - margin, quartiles[2] + margin)
is_dark_outlier[param_axis] = (all_dark_median[param_axis] < outliers_lim[1] |
all_dark_median[param_axis] > outliers_lim[2])
}
all_lengths = mapply(length, DAY_MATCH[1,])
all_lengths = all_lengths[which(all_lengths != 0)]
is_dark_outlier_full = rep(is_dark_outlier, times=all_lengths)
if (length(NIGHT_MATCH) == 0) {
PAR_dataf = data.frame("PARAM"=numeric(0), "PARAM_Ts"=numeric(0),
"PARAM_date"=numeric(0), "PARAM_name"=character(0),
"PARAM_pres"=numeric(0), "is_greylisted"=logical(0))
} else {
PAR_dataf = data.frame("PARAM"=unlist(NIGHT_MATCH[1,]),
"PARAM_Ts"=unlist(NIGHT_MATCH[2,]),
"PARAM_date"=unlist(NIGHT_MATCH[3,]),
"PARAM_name"=unlist(NIGHT_MATCH[4,]),
"PARAM_pres"=unlist(NIGHT_MATCH[5,]))
PAR_dataf$is_greylisted = mcmapply(is_greylisted, julian_day=PAR_dataf$PARAM_date,
PARAMETER_NAME=as.character(PAR_dataf$PARAM_name),
MoreArgs=list(WMO=WMO), mc.cores=n_cores)
}
DAY_dataf = data.frame("PARAM"=unlist(DAY_MATCH[1,]),
"PARAM_Ts"=unlist(DAY_MATCH[2,]),
"PARAM_date"=unlist(DAY_MATCH[3,]),
"PARAM_name"=unlist(DAY_MATCH[4,]),
"is_dark_outlier"=is_dark_outlier_full,
"PARAM_pres"=unlist(DAY_MATCH[7,]))
DAY_dataf$is_greylisted = mcmapply(is_greylisted, julian_day=DAY_dataf$PARAM_date,
PARAMETER_NAME=as.character(DAY_dataf$PARAM_name),
MoreArgs=list(WMO=WMO), mc.cores=n_cores)
cat("DONE\nExtracting full Ts range...")
TEST_MATCH = mcmapply(get_profile_match, file_name=files_drift_PARALLEL,
param_name=PARAM_NAMES_drift_PARALLEL, path_to_netcdf=path_to_netcdf,
PROFILE_DATE=date_drift_PARALLEL, mc.cores=n_cores, USE.NAMES=FALSE,
MoreArgs=list(method="test", material=sensor_type))
TEST_dataf = data.frame("PARAM"=unlist(TEST_MATCH[1,]),
"PARAM_Ts"=unlist(TEST_MATCH[2,]),
"PARAM_date"=unlist(TEST_MATCH[3,]),
"PARAM_name"=unlist(TEST_MATCH[4,]))
#T_PAR = TEST_dataf$PARAM[which(TEST_dataf$PARAM_name=="DOWNWELLING_PAR")]
#T_380 = TEST_dataf$PARAM[which(TEST_dataf$PARAM_name=="DOWN_IRRADIANCE380")]
#T_412 = TEST_dataf$PARAM[which(TEST_dataf$PARAM_name=="DOWN_IRRADIANCE412")]
#T_490 = TEST_dataf$PARAM[which(TEST_dataf$PARAM_name=="DOWN_IRRADIANCE490")]
#nna = which(!is.na(T_PAR) & !is.na(T_380) & !is.na(T_412) & !is.na(T_490))
Ts_range = range(c(TEST_dataf$PARAM_Ts), na.rm=T)
cat("DONE\n")
###########################
### Start of Ts fitting
###########################
pres_cutoff = -Inf
repeat {
cat("Fitting Ts to radiometry parameters...")
fitted_coeff = NULL
A_axis = rep(NA, 4)
B_axis = rep(NA, 4)
fitted_coeff_day = NULL
A_axis_day = rep(NA, 4)
B_axis_day = rep(NA, 4)
for (i in 1:length(PARAM_NAMES)) {
## night
subset_PAR = which(PAR_dataf$PARAM_name==PARAM_NAMES[i] & !is.na(PAR_dataf$PARAM_Ts)
& !PAR_dataf$is_greylisted & PAR_dataf$PARAM_pres>pres_cutoff)
if ( length(subset_PAR)==0 ) { next }
fit_AB = lm(PARAM ~ PARAM_Ts, data=PAR_dataf, subset=subset_PAR)
fitted_coeff[[PARAM_NAMES[i]]] = fit_AB$coefficients
A_axis[i] = fit_AB$coefficients[[1]]
B_axis[i] = fit_AB$coefficients[[2]]
}
for (i in 1:length(PARAM_NAMES)) {
## day
subset_DAY = which(DAY_dataf$PARAM_name==PARAM_NAMES[i] & !is.na(DAY_dataf$PARAM_Ts)
& !DAY_dataf$is_greylisted & !DAY_dataf$is_dark_outlier
& DAY_dataf$PARAM_pres>pres_cutoff)
fit_param = DAY_dataf$PARAM[subset_DAY]
fit_Ts = DAY_dataf$PARAM_Ts[subset_DAY]
fit_param = fit_param[order(fit_Ts)]
fit_Ts = fit_Ts[order(fit_Ts)]
run_min = running(fit_param, fun=min, width=51, pad=TRUE, align="center")
run_min = run_min[26:(length(fit_param)+25)]
data_run = data.frame("run_min"=run_min, "fit_Ts"=fit_Ts)
fit_AB_day = lm(run_min ~ fit_Ts, data=data_run)
fitted_coeff_day[[PARAM_NAMES[i]]] = fit_AB_day$coefficients
A_axis_day[i] = fit_AB_day$coefficients[[1]]
B_axis_day[i] = fit_AB_day$coefficients[[2]]
}
cat("DONE\nCreating Ts/Irr plots...")
data_fit = data.frame(
PARAM_name = rep(PARAM_NAMES, each=2),
x = rep(Ts_range, 4),
y = rep(A_axis, each=2) + rep(B_axis, each=2) * rep(Ts_range, 4)
)
data_fit_day = data.frame(
PARAM_name = rep(PARAM_NAMES, each=2),
x = rep(Ts_range, 4),
y = rep(A_axis_day, each=2) + rep(B_axis_day, each=2) * rep(Ts_range, 4)
)
g1 = ggplot(na.omit(PAR_dataf), aes(x=PARAM_Ts, y=PARAM, color=PARAM_pres, group=PARAM_name)) +
geom_point(data=function(x){x[!x$is_greylisted & x$PARAM_pres>pres_cutoff, ]}) +
#geom_point(data=function(x){x[x$is_greylisted, ]}, color="red") +
scale_color_viridis(trans='reverse') +
scale_y_continuous(labels = scientific_formatter) +
facet_wrap(~PARAM_name, scale="free_y",
labeller = labeller(PARAM_name = as_labeller(irr_labels, default=label_parsed))) +
theme_bw() +
labs(x="Sensor temperature (°C)",
y=TeX("$E_d(\\lambda)$ ($W\\, m^{-2}\\, nm^{-1}$) ;
PAR ($\\mu mol\\, m^{-2}\\, s^{-1}$)"),
colour="Pressure (dbar)")#, title="Data from night profiles")
g1_day = ggplot(na.omit(DAY_dataf),
aes(x=PARAM_Ts, y=PARAM, color=PARAM_pres, group=PARAM_name)) +
geom_point(data=function(x){x[!x$is_greylisted & !x$is_dark_outlier
& x$PARAM_pres>pres_cutoff, ]}) +
#geom_point(data=function(x){x[x$is_greylisted & !x$is_dark_outlier, ]}, color="red") +
scale_color_viridis(trans='reverse') +
scale_y_continuous(labels = scientific_formatter) +
facet_wrap(~PARAM_name, scale="free_y",
labeller = labeller(PARAM_name = as_labeller(irr_labels, default=label_parsed))) +
theme_bw() +
labs(x="Sensor temperature (°C)",
y=TeX("$E_d(\\lambda)$ ($W\\, m^{-2}\\, nm^{-1}$) ;
PAR ($\\mu mol\\, m^{-2}\\, s^{-1}$)"),
colour="Pressure (dbar)")#, title="Data from night profiles")
g2 = g1 + geom_line(data=data_fit, mapping=aes(x=x,y=y), color="red")
g3 = g2 + geom_line(data=data_fit_day, mapping=aes(x=x,y=y), color="black")
g2_day = g1_day + geom_line(data=data_fit_day, mapping=aes(x=x,y=y), color="black")
g3_day = g2_day + geom_line(data=data_fit, mapping=aes(x=x,y=y), color="red")
x11(xpos=0, ypos=0)
plot(g2)
x11(xpos=0, ypos=-1)
plot(g3)
x11(xpos=-1, ypos=0)
plot(g2_day)
x11(xpos=-1, ypos=-1)
plot(g3_day)
plot_name=paste0(WMO, "_regr_day.png")
png(filename=plot_name, width=800, height=600)
plot(g3_day)
dev.off()
plot_name=paste0(WMO, "_regr_night.png")
png(filename=plot_name, width=800, height=600)
plot(g3)
dev.off()
plot_name=paste0(WMO, "_regr_day_only.png")
png(filename=plot_name, width=800, height=600)
plot(g2_day)
dev.off()
cat("DONE\n")
plot_name=paste0(WMO, "_regr_night_only.png")
png(filename=plot_name, width=800, height=600)
plot(g2)
dev.off()
choice = my_menu(title = "Which correction should be used ? (0 to abandon and quit)",
choices = c("Continue with correction from night method",
"Continue with correction from day method",
"Add or change the pressure cutoff for data selection"))
switch (choice + 1,
return(0),
{fitted_coeff_corr = fitted_coeff
A_axis_corr = A_axis
B_axis_corr = B_axis
break},
{fitted_coeff_corr = fitted_coeff_day
A_axis_corr = A_axis_day
B_axis_corr = B_axis_day
break},
{ }
)
cat("New pressure cutoff in decibars: ")
if (interactive()) {
pres_cutoff <- as.numeric(readLines(stdin(), 1))
} else {
pres_cutoff <- as.numeric(readLines(file("stdin"), 1))
}
while(dev.cur() > 1) {dev.off()} # clear all windows
}
while(dev.cur() > 1) {dev.off()} # clear all windows
best_QC = 2
#### Deal with missing dates
prof_num_date_miss = substr(prof_id[subset_date_miss], 11, 13)
if ( length(prof_num_date_miss) != 0 ) {
question = paste0("Profiles with missing date : ", paste(prof_num_date_miss, collapse = ", "),
"\nWhat should be done with them ? (0 to abandon and quit)")
choice = my_menu(title = question,
choices = c("Attempt to find a suited date for DM correction",
"Ignore the profiles (just copy them)"))
date_list_missing = rep(NA, length(prof_num_date_miss))
if ( choice == 0 ) {
return(0)
} else if (choice == 1) {
for (i in 1:length(prof_num_date_miss)) {
date_prev = date_list[which(as.numeric(substr(prof_id[subset], 11, 13))
== (as.numeric(prof_num_date_miss[1]) - 1) )]
date_next = date_list[which(as.numeric(substr(prof_id[subset], 11, 13))
== (as.numeric(prof_num_date_miss[1]) + 1) )]
question2 = c("For profile ", prof_num_date_miss[i], " :\n Date of preceding profile : ",
NA, "\n Date of following profile : ", NA,
"\nWhat should be done with this profile ?
(If any date is unknown, do not use it)")
if (length(date_prev) != 0) {
question2[4] = as.character(as.Date(date_prev, origin=as.Date("1950-01-01", tz="UTC")))
} else {
question2[4] = "Unknown"
}
if (length(date_next) != 0) {
question2[6] = as.character(as.Date(date_next, origin=as.Date("1950-01-01", tz="UTC")))
} else {
question2[6] = "Unknown"
}
question2 = paste(question2, collapse="")
choice2 = my_menu(title = question2,
choices = c("Use the preceding date",
"Use the following date",
"Use the interpolation of the dates",
"Ignore this profile (just copy it)"))
switch(choice2 + 1,
return(0),
{ date_list_missing[i] = date_prev },
{ date_list_missing[i] = date_next },
{ date_list_missing[i] = (date_prev + date_next) / 2 }
)
}
}
# move the profiles with no alternative date to the just_copy list
subset_copy = c(subset_copy, subset_date_miss[ which(is.na(date_list_missing)) ])
subset_date_miss = subset_date_miss[ which(!is.na(date_list_missing)) ]
date_list_missing = date_list_missing[ which(!is.na(date_list_missing)) ]
} else {
cat("No profiles with missing date\n")
}
### Mention missing positions to user
if ( length(subset_pos_miss) != 0 ) {
cat(paste0("Profiles with missing position : ",
paste(substr(prof_id[subset_pos_miss], 11, 13), collapse = ", "), "\n")
)
} else {
cat("No profiles with missing position\n")
}
####################################
### Apply correction to netcdf
####################################
date_update = Sys.time()
DATE = stri_datetime_format(date_update, format="uuuuMMddHHmmss", tz="UTC")
corr_file <- function(file_name, PROFILE_DATE, path_to_netcdf, just_copy=FALSE) {
path_sep = unlist(strsplit(file_name, "/"))
if (!just_copy) { str_sub(path_sep[4], 2, 2) = "D" }
file_name_out = paste(path_sep[1], path_sep[2], path_sep[3], "RADM/RADM_profiles", path_sep[4],
sep="/")
full_file_name_out = paste(path_to_netcdf, file_name_out, sep="")
full_file_name_in = paste(path_to_netcdf, file_name, sep="")
system2("cp", c(full_file_name_in, full_file_name_out))
if (just_copy) { return(rep(0, 4)) }
exit = rep(NA, 4)
for (i in 1:length(PARAM_NAMES)) {
matchup = get_Ts_match(file_name=file_name, path_to_netcdf=path_to_netcdf,
PARAM_NAME=PARAM_NAMES[i])
match_day = get_profile_match(file_name=file_name, param_name=PARAM_NAMES[i],
path_to_netcdf=path_to_netcdf, PROFILE_DATE=PROFILE_DATE,
method="day", drift_A=A_axis_drift_corr[i],
drift_C=C_axis_drift_corr[i], drift_Q=Q_axis_drift_corr[i],
material=sensor_type)
### Flags
corr_qc = matchup$PARAM_QC
corr_qc[match_day$MATCH_which] = rep(as.character(best_QC), length(match_day$MATCH_which))
#if which==NULL --> no change
corr_qc[which(matchup$PARAM_QC=="3" | matchup$PARAM_QC=="4")] = "4"
corr_qc[which(matchup$PRES_B_QC=="3" | matchup$PRES_B_QC=="4")] = "4"
corr_qc[which(is.na(matchup$Ts) & !is.na(matchup$PARAM))] = "4"
corr_qc_word = paste(corr_qc, collapse="")
blank_qc = paste(rep(" ", matchup$n_levels), collapse="")
corr_qc_array = rep(blank_qc, matchup$n_prof)
corr_qc_array[matchup$id_prof] = corr_qc_word
### Adjusted parameter
param_undrifted = as.numeric( matchup$PARAM - A_axis_drift_corr[i]
- C_axis_drift_corr[i] * PROFILE_DATE
- Q_axis_drift_corr[i] * PROFILE_DATE^2 )
corr = param_undrifted - ( A_axis_corr[i] + B_axis_corr[i] * matchup$Ts )
corr[which(corr_qc == "4")] = NA #filechecker requirement
corr_array = array(NA, c(matchup$n_levels, matchup$n_prof))
corr_array[, matchup$id_prof] = corr
### Error
if (PARAM_NAMES[i] == "DOWNWELLING_PAR") {
ramp_error = 0.05 # 5%
abs_error = 3e-2 # umol/m²/s # TODO : validate
} else {
ramp_error = 0.02 # 2%
abs_error = 2.5e-5 # W/m²/nm
}
corr_error_array = pmax(ramp_error*abs(corr_array), abs_error)
### Scientific comment
scientific_comment = str_pad(paste0(PARAM_NAMES[i], " dark correction. Uses JULD to correct drift and SENSOR_TEMP to correct temperature variance. SENSOR_TEMP is reconstructed from the TEMP axis of the core file following [https://doi.org/10.3390/s21186217]"),
256, "right")
### Scientific coefficient
A_total = A_axis_drift_corr[i] + A_axis_corr[i]
scientific_coefficient = paste0("A = ", signif(A_total,4),", B = ", signif(B_axis_corr[i],4),
", C = ", signif(C_axis_drift_corr[i],4))
if (Q_axis_drift_corr[i] != 0) {
scientific_coefficient = paste0(scientific_coefficient, ", Q = ",
signif(Q_axis_drift_corr[i],4))
}
### Scientific equation
scientific_equation = paste0(PARAM_NAMES[i], "_ADJUSTED = ", PARAM_NAMES[i],
" - A - B*SENSOR_TEMP - C*JULD")
if (Q_axis_drift_corr[i] != 0) {
scientific_equation = paste0(scientific_equation, " - Q*JULD^2")
}
### comment_dmqc_operator_PRIMARY
comment_dmqc_operator_PRIMARY =
"PRIMARY | https://orcid.org/0000-0001-9992-5334 | Raphaelle Sauzede, CNRS"
#"PRIMARY | https://orcid.org/0000-0003-3931-4675 | Giorgio Dall'Olmo, PML"
### comment_dmqc_operator_PARAM
comment_dmqc_operator_PARAM = paste(PARAM_NAMES[i], "| https://orcid.org/0000-0002-1230-164X | Catherine Schmechtig, CNRS")
### HISTORY_SOFTWARE
HISTORY_SOFTWARE = "RADM"
### HISTORY_SOFTWARE_RELEASE
HISTORY_SOFTWARE_RELEASE = "1.04"
### write to file
exit[i] = write_DM(file_out=full_file_name_out, param_name=PARAM_NAMES[i], DATE=DATE,
scientific_comment=scientific_comment,
scientific_coefficient=scientific_coefficient,
scientific_equation=scientific_equation,
comment_dmqc_operator_PRIMARY=comment_dmqc_operator_PRIMARY,
comment_dmqc_operator_PARAM=comment_dmqc_operator_PARAM,
HISTORY_SOFTWARE=HISTORY_SOFTWARE,
HISTORY_SOFTWARE_RELEASE=HISTORY_SOFTWARE_RELEASE,
param_adjusted=corr_array, param_adjusted_qc=corr_qc_array,
param_adjusted_error=corr_error_array)
if (exit[i] != 0) {
cat(paste0("\nWarning: write_DM() exited with error code ", exit[i], " on file ",
file_name, " with parameter ", PARAM_NAMES[i], " !"))
}
}
return(exit)
}
cat("Applying correction to netcdf files...")
#corr_file(files_list[1], PROFILE_DATE=date_list[1], path_to_netcdf=path_to_netcdf)
corr_all = mcmapply(corr_file, file_name=files_list, PROFILE_DATE=date_list, mc.cores=n_cores,
SIMPLIFY=FALSE, MoreArgs=list(path_to_netcdf=path_to_netcdf))
if ( length(subset_date_miss) != 0 ) {
corr_date_miss = mcmapply(corr_file, file_name=files[subset_date_miss],
PROFILE_DATE=date_list_missing, mc.cores=n_cores, SIMPLIFY=FALSE,
MoreArgs=list(path_to_netcdf=path_to_netcdf))
}
corr_copy = mcmapply(corr_file, file_name=files[subset_copy], mc.cores=n_cores, SIMPLIFY=FALSE,
MoreArgs=list(path_to_netcdf=path_to_netcdf, PROFILE_DATE=NA,
just_copy=TRUE))
cat("DONE\n")
return(0)
}