analysis/99_process_examples.R
In afsc-gap-products/gapctd: Process CTD data from bottom trawl surveys
# Process and plot deployment examples
# Sean Rohan, November 2022
#
# Processes example deployments using the gapctd package with the following methods:
# - Typical: Manfacturer recommendend temperature offset (-0.5) and CTM parameters (alpha_C = 0.04, beta_C = 1/8)
# - Typ. CTM: Estimate temperature offset and manufacturer recommended CTM parameters (alpha_C = 0.04, beta_C = 1/8)
# - Temperature-Salinity Curve Area (TSA): Estimated temperature offset and CTM parameters. CTM optimization based on area between temperature-salinity curves.
# - Minimum Salinity Gradient (MSG): Estimated temperature offset and CTM parameters. CTM optimization based on path distance of salinity curves.
#
# Prepare data -------------------------------------------------------------------------------------
library(gapctd)
# Get haul data
haul_files <- list.files(here::here("paper", "data", "haul_data"), full.names = TRUE)
ex_deployments <- read.csv(file = here::here("paper", "data", "example_deployments.csv"))
deployment_dat <- data.frame()
for(ii in 1:length(haul_files)) {
deployment_dat <- deployment_dat |>
dplyr::bind_rows(
readRDS(file = haul_files[ii]) |>
janitor::clean_names() |>
dplyr::inner_join(ex_deployments)
)
}
names(deployment_dat) <- toupper(names(deployment_dat))
saveRDS(deployment_dat,
here::here("paper", "data", "ex_hauls.rds"))
# Functions for plotting examples ------------------------------------------------------------------
derive_and_bin <- function(x, bin_width = NULL) {
x$downcast <- gapctd::derive_eos(x$downcast)
x$upcast <- gapctd::derive_eos(x$upcast)
if(!is.null(bin_width)) {
x$downcast <- gapctd:::bin_average(x$downcast,
by = "depth",
bin_width = bin_width)
x$upcast <- gapctd:::bin_average(x$upcast,
by = "depth",
bin_width = bin_width)
}
return(x)
}
make_stage_df <- function(x, stage, method) {
out <- data.frame()
if("downcast" %in% names(x)) {
out <- dplyr::bind_rows(out,
as.data.frame(x$downcast@data)|>
dplyr::mutate(cast_direction = x$downcast@metadata$cast_direction,
processing_method = method) |>
dplyr::bind_cols(x$downcast@metadata$race_metadata |>
dplyr::select(VESSEL, CRUISE, HAUL, REGION))
)
}
if("upcast" %in% names(x)) {
out <- dplyr::bind_rows(out,
as.data.frame(x$upcast@data) |>
dplyr::mutate(cast_direction = x$upcast@metadata$cast_direction,
processing_method = method) |>
dplyr::bind_cols(x$upcast@metadata$race_metadata |>
dplyr::select(VESSEL, CRUISE, HAUL, REGION)))
}
out$stage <- stage
return(out)
}
# Get names of example deployment files
ex_files <- list.files(here::here("paper", "data", "cnv"),
pattern = ".cnv",
full.names = TRUE)
haul_df <- readRDS(here::here("paper", "data", "ex_hauls.rds"))
# Make table of representative stations
ts_gradient <- readRDS(here::here("paper", "output", "TS_gradient_by_cast.rds")) |>
dplyr::group_by(vessel, cruise, haul) |>
dplyr::summarise(temperature_range = round(mean(temperature_range), 2),
salinity_range = round(mean(salinity_range), 2),
temperature_max_gradient = round(mean(temperature_max_gradient_z), 3),
salinity_max_gradient = round(mean(salinity_max_gradient_s), 3))
names(ts_gradient) <- toupper(names(ts_gradient))
representative_station_df <- haul_df |>
dplyr::mutate(LATITUDE = (START_LATITUDE+END_LATITUDE)/2,
LONGITUDE = (START_LONGITUDE+END_LONGITUDE)/2) |>
dplyr::inner_join(ts_gradient,
by = c("VESSEL", "CRUISE", "HAUL")) |>
dplyr::inner_join(
data.frame(VESSEL = c(148,
176,
148,
176,
162,
94,
162,
94,
162,
94,
162,
94),
CRUISE = c(202201,
202201,
202101,
202101,
202102,
202102,
202202,
202202,
202201,
202201,
202101,
202101),
FULL_REGION = c("Aleutian Islands",
"Aleutian Islands",
"Gulf of Alaska",
"Gulf of Alaska",
"Northern Bering Sea",
"Northern Bering Sea",
"Northern Bering Sea",
"Northern Bering Sea",
"Eastern Bering Sea",
"Eastern Bering Sea",
"Eastern Bering Sea",
"Eastern Bering Sea"
)),
by = c("VESSEL", "CRUISE")
) |>
dplyr::mutate(DATE = paste0(lubridate::month(START_TIME, label = TRUE, abbr = TRUE), " ",
lubridate::day(START_TIME), ", ",
lubridate::year(START_TIME)),
LATITUDE = format(round(LATITUDE, 2), nsmall = 2),
LONGITUDE = format(round(LONGITUDE, 2), nsmall = 2)) |>
dplyr::select(EXAMPLE, REGION, DATE, LATITUDE, LONGITUDE,
TEMPERATURE_RANGE, SALINITY_RANGE, TEMPERATURE_MAX_GRADIENT, SALINITY_MAX_GRADIENT,
DESCRIPTION) |>
dplyr::arrange(EXAMPLE)
write.csv(representative_station_df,
here::here("paper", "plots", "representative_station_table.csv"),
row.names = FALSE)
# Run data processing methods on each example deployment -------------------------------------------
process_df <- data.frame()
param_df <- data.frame()
for(ii in 1:length(ex_files)) {
sel_ctd <- read.oce(file = ex_files[ii])
# Steps that are the same across methods
stage_1 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "split") |>
derive_and_bin(bin_width = 1)
stage_2 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "median_filter") |>
derive_and_bin(bin_width = 1)
stage_3 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "lowpass_filter") |>
derive_and_bin(bin_width = 1)
# Typical method
stage_4_typical <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "align",
align_pars = list(temperature = -0.5),
ctm_pars = list(alpha_C = 0.04, beta_C = 1/8),
cor_var = "conductivity") |>
derive_and_bin(bin_width = 1)
stage_5_typical <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "ctmcorrect",
align_pars = list(temperature = -0.5),
ctm_pars = list(alpha_C = 0.04, beta_C = 1/8),
cor_var = "conductivity") |>
derive_and_bin(bin_width = 1)
stage_6_typical <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "slowdown",
align_pars = list(temperature = -0.5),
ctm_pars = list(alpha_C = 0.04, beta_C = 1/8),
cor_var = "conductivity") |>
derive_and_bin(bin_width = 1)
stage_7_typical <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "bin_average",
align_pars = list(temperature = -0.5),
ctm_pars = list(alpha_C = 0.04, beta_C = 1/8),
cor_var = "conductivity")
stage_8_typical <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "full", # w/ Density inversion check and completeness check
align_pars = list(temperature = -0.5),
ctm_pars = list(alpha_C = 0.04, beta_C = 1/8),
cor_var = "conductivity")
# Estimated temperature alignment, typical CTM parameters
round1_4 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "align",
align_pars = list(),
ctm_pars = list(alpha_C = 0.04, beta_C = 1/8)) |>
derive_and_bin(bin_width = 1)
round1_5 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
align_pars = list(),
ctm_pars = list(alpha_C = 0.04, beta_C = 1/8),
return_stage = "ctmcorrect") |>
derive_and_bin(bin_width = 1)
round1_6 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "slowdown",
ctm_pars = round1_5$downcast@metadata$ctm$both) |>
derive_and_bin(bin_width = 1)
round1_7 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "bin_average",
ctm_pars = round1_5$downcast@metadata$ctm$both)
round1_8 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "full", # w/ Density inversion check and completeness check
ctm_pars = round1_5$downcast@metadata$ctm$both)
# Estimated alignment and CTM parameters, with optimization based on T-S Area (TSA)
round2_4 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
align_pars = list(),
ctm_pars = list(),
return_stage = "align") |>
derive_and_bin(bin_width = 1)
round2_5 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
align_pars = list(),
ctm_pars = list(),
return_stage = "ctmcorrect") |>
derive_and_bin(bin_width = 1)
round2_6 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
align_pars = list(),
ctm_pars = round2_5$downcast@metadata$ctm$both,
return_stage = "slowdown") |>
derive_and_bin(bin_width = 1)
round2_7 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
align_pars = list(),
ctm_pars = round2_5$downcast@metadata$ctm$both,
return_stage = "bin_average")
round2_8 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
align_pars = list(),
ctm_pars = round2_5$downcast@metadata$ctm$both,
ctd_tz = "America/Anchorage",
return_stage = "full")
# Estimated alignment and CTM parameters, with optimization based on Minimum Salinity Gradient (MSG)
sel_downcast <- oce::ctdTrim(x = sel_ctd,
method = "range",
parameters = list(item = "timeS",
from = 0,
to = max(stage_1$downcast@data$timeS + 0.25, na.rm = TRUE)))
round3_4 <- run_gapctd(x = sel_downcast,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "align") |>
derive_and_bin(bin_width = 1)
round3_5 <- run_gapctd(x = sel_downcast,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "ctmcorrect") |>
derive_and_bin(bin_width = 1)
round3_6 <- run_gapctd(x = sel_downcast,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "slowdown",
ctm_pars = round3_5$downcast@metadata$ctm$both) |>
derive_and_bin(bin_width = 1)
round3_7 <- run_gapctd(x = sel_downcast,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "bin_average",
ctm_pars = round3_5$downcast@metadata$ctm$both)
round3_8 <- run_gapctd(x = sel_downcast,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "full", # w/ Density inversion check and completeness check
ctm_pars = round3_5$downcast@metadata$ctm$both)
sel_upcast <- oce::ctdTrim(x = sel_ctd,
method = "range",
parameters = list(item = "timeS",
from = min(stage_1$upcast@data$timeS - 0.25, na.rm = TRUE),
to = 5e6))
round3_4_uc <- run_gapctd(x = sel_upcast,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "align") |>
derive_and_bin(bin_width = 1)
round3_5_uc <- run_gapctd(x = sel_upcast,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "ctmcorrect") |>
derive_and_bin(bin_width = 1)
round3_6_uc <- run_gapctd(x = sel_upcast,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "slowdown",
ctm_pars = round3_5_uc$upcast@metadata$ctm$both) |>
derive_and_bin(bin_width = 1)
round3_7_uc <- run_gapctd(x = sel_upcast,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "bin_average",
ctm_pars = round3_5_uc$upcast@metadata$ctm$both)
round3_8_uc <- run_gapctd(x = sel_upcast,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "full", # w/ Density inversion check and completeness check
ctm_pars = round3_5_uc$upcast@metadata$ctm$both)
# Make data frame with all outputs
all_steps <- dplyr::bind_rows(make_stage_df(stage_1, stage = "split", method = "All"),
make_stage_df(stage_2, stage = "median_filter", method = "All"),
make_stage_df(stage_3, stage = "lowpass_filter", method = "All"),
make_stage_df(round1_4, stage = "align", method = "Typ. CTM"),
make_stage_df(round1_5, stage = "ctmcorrect", method = "Typ. CTM"),
make_stage_df(round1_6, stage = "slowdown", method = "Typ. CTM"),
make_stage_df(round1_7, stage = "bin_average", method = "Typ. CTM"),
make_stage_df(round1_8, stage = "slowdown", method = "Typ. CTM"),
make_stage_df(round2_4, stage = "align", method = "TSA"),
make_stage_df(round2_5, stage = "ctmcorrect", method = "TSA"),
make_stage_df(round2_6, stage = "slowdown", method = "TSA"),
make_stage_df(round2_7, stage = "bin_average", method = "TSA"),
make_stage_df(round2_8, stage = "slowdown", method = "TSA"),
make_stage_df(round3_4, stage = "align", method = "MSG"),
make_stage_df(round3_5, stage = "ctmcorrect", method = "MSG"),
make_stage_df(round3_6, stage = "slowdown", method = "MSG"),
make_stage_df(round3_7, stage = "bin_average", method = "MSG"),
make_stage_df(round3_8, stage = "slowdown", method = "MSG"),
make_stage_df(round3_4_uc, stage = "align", method = "MSG"),
make_stage_df(round3_5_uc, stage = "ctmcorrect", method = "MSG"),
make_stage_df(round3_6_uc, stage = "slowdown", method = "MSG"),
make_stage_df(round3_7_uc, stage = "bin_average", method = "MSG"),
make_stage_df(round3_8_uc, stage = "slowdown", method = "MSG"),
make_stage_df(stage_4_typical, stage = "align", method = "Typical"),
make_stage_df(stage_5_typical, stage = "ctmcorrect", method = "Typical"),
make_stage_df(stage_6_typical, stage = "slowdown", method = "Typical"),
make_stage_df(stage_7_typical, stage = "bin_average", method = "Typical"),
make_stage_df(stage_8_typical, stage = "slowdown", method = "Typical")
)
process_df <- dplyr::bind_rows(process_df, all_steps)
# Data frame with parameters used for each method
param_df <- dplyr::bind_rows(param_df,
dplyr::bind_rows(
data.frame(
T_offset_dc = round1_4$downcast@metadata$align[["temperature"]]['offset'],
TC_corr_dc = round1_4$downcast@metadata$align[["temperature"]]['corr'],
T_offset_uc = round1_4$upcast@metadata$align[["temperature"]]['offset'],
TC_corr_uc = round1_4$upcast@metadata$align[["temperature"]]['corr'],
alpha_C = round1_5$downcast@metadata$ctm['alpha_C'],
beta_C = round1_5$downcast@metadata$ctm['beta_C'],
round = 1)|>
dplyr::bind_cols(round1_8$downcast@metadata$race_metadata),
data.frame(
T_offset_dc = round2_4$downcast@metadata$align[["temperature"]]['offset'],
TC_corr_dc = round2_4$downcast@metadata$align[["temperature"]]['corr'],
T_offset_uc = round2_4$upcast@metadata$align[["temperature"]]['offset'],
TC_corr_uc = round2_4$upcast@metadata$align[["temperature"]]['corr'],
alpha_C = round2_5$downcast@metadata$ctm$both['alpha_C'],
beta_C = round2_5$downcast@metadata$ctm$both['beta_C'],
round = 2)|>
dplyr::bind_cols(round1_8$downcast@metadata$race_metadata),
data.frame(
T_offset_dc = round3_4$downcast@metadata$align[["temperature"]]['offset'],
TC_corr_dc = round3_4$downcast@metadata$align[["temperature"]]['corr'],
T_offset_uc = NA,
TC_corr_uc = NA,
alpha_C = round3_5$downcast@metadata$ctm$both['alpha_C'],
beta_C = round3_5$downcast@metadata$ctm$both['beta_C'],
round = 3)|>
dplyr::bind_cols(round1_8$downcast@metadata$race_metadata)
)
)
}
# Save outputs
saveRDS(process_df, file = here::here("paper", "data", "ex_process_df.rds"))
saveRDS(param_df, file = here::here("paper", "data", "param_df.rds"))
# Plot results -------------------------------------------------------------------------------------
library(gapctd)
process_df <- readRDS(here::here("paper", "data", "ex_process_df.rds"))
param_df <- readRDS(here::here("paper", "data", "param_df.rds"))
# profile_df <- readRDS(here::here("paper", "data", "ex_profiles.rds"))
representative_station_df <- read.csv(here::here("paper", "plots", "representative_station_table.csv"))
unique_deployments <- process_df |>
dplyr::select(VESSEL, CRUISE, HAUL) |>
unique()
process_df <- dplyr::filter(process_df, depth > 1)
offset_curves <- data.frame(processing_method = c("All", NA, "Typ. CTM", "TSA", "MSG", "Typical"),
shift_var = c(0, 0, 0.125, 0.25, 0.375, 0))
for(jj in 1:nrow(unique_deployments)) {
sel_dat <- process_df |>
dplyr::filter(VESSEL == unique_deployments$VESSEL[jj],
CRUISE == unique_deployments$CRUISE[jj],
HAUL == unique_deployments$HAUL[jj],
!(stage %in% c("bin_average"))) |>
dplyr::arrange(depth)
sel_dat$sigmat <- oce::swSigmaT(salinity = sel_dat$salinity,
temperature = sel_dat$temperature,
pressure = sel_dat$pressure,
longitude = representative_station_df$LONGITUDE[jj],
latitude = representative_station_df$LATITUDE[jj])
sel_dat$sigmatheta <- oce::swSigmaTheta(salinity = sel_dat$salinity,
temperature = sel_dat$temperature,
pressure = sel_dat$pressure,
longitude = representative_station_df$LONGITUDE[jj],
latitude = representative_station_df$LATITUDE[jj])
sel_dat$range_temperature <- diff(range(sel_dat$temperature, na.rm = TRUE))
sel_dat$range_salinity <- diff(range(sel_dat$salinity, na.rm = TRUE))
sel_dat$range_sigmat <- diff(range(sel_dat$sigmat, na.rm = TRUE))
sel_dat$range_sigmatheta <- diff(range(sel_dat$sigmat, na.rm = TRUE))
sel_dat$range_density <- diff(range(sel_dat$density-1000, na.rm = TRUE))
sel_dat <- dplyr::inner_join(sel_dat,
offset_curves,
by = "processing_method")
names(sel_dat) <- tolower(names(sel_dat))
plot_T <- ggplot() +
geom_path(data = sel_dat,
mapping = aes(y = depth,
x = temperature + range_temperature*shift_var,
color = factor(processing_method, levels = c("All", "Typical", "Typ. CTM", "TSA", "MSG")),
linetype = cast_direction),
size = rel(0.3)) +
scale_x_continuous(name = expression("Temperature ("*degree*C*')'),
guide = guide_axis(check.overlap = TRUE)) +
scale_y_reverse(name = "Depth (m)") +
scale_color_manual(guide = "none", values = ggthemes::colorblind_pal()(6)[c(1:4,6)]) +
facet_wrap(~factor(stage,
levels = c("split", "median_filter", "lowpass_filter", "align", "ctmcorrect", "slowdown"),
labels = c("Split", "Median Filter", "Low Pass Filter", "Align T", "CTM Corr.", "Slowdown"))) +
theme_bw() +
theme(legend.position = "bottom",
legend.title = element_blank(),
strip.background = element_blank(),
strip.text = element_text(size = 8.5),
axis.text = element_text(size = 8, color = "black"),
axis.title = element_text(size = 9, color = "black"),
legend.text = element_text(size = 8),
legend.margin=margin())
plot_S <- ggplot() +
geom_path(data = sel_dat,
mapping = aes(y = depth,
x = salinity + range_salinity*shift_var,
color = factor(processing_method, levels = c("All", "Typical", "Typ. CTM", "TSA", "MSG")),
linetype = cast_direction),
size = rel(0.3)) +
scale_x_continuous(name = "Salnity (PSS-78)",
guide = guide_axis(check.overlap = TRUE)) +
scale_linetype(guide = "none") +
scale_y_reverse(name = "Depth (m)") +
scale_color_manual(values = ggthemes::colorblind_pal()(6)[c(1:4,6)]) +
facet_wrap(~factor(stage,
levels = c("split", "median_filter", "lowpass_filter", "align", "ctmcorrect", "slowdown"),
labels = c("Split", "Median Filter", "Low Pass Filter", "Align T", "CTM Corr.", "Slowdown"))) +
theme_bw() +
guides(color=guide_legend(nrow=2)) +
theme(legend.position = "bottom",
legend.title = element_blank(),
strip.background = element_blank(),
strip.text = element_text(size = 8.5),
axis.text = element_text(size = 8, color = "black"),
axis.title = element_text(size = 9, color = "black"),
legend.text = element_text(size = 8),
legend.margin=margin(),
legend.box = "vertical")
plot_density <- ggplot() +
geom_path(data = sel_dat |>
dplyr::filter(stage == "slowdown"),
mapping = aes(y = depth,
x = density-1000 + range_density*shift_var,
color = factor(processing_method, levels = c("All", "Typical", "Typ. CTM", "TSA", "MSG")),
linetype = cast_direction),
size = rel(0.3)) +
scale_x_continuous(name = expression(sigma~'('*kg~m^-3*')'),
guide = guide_axis(check.overlap = TRUE)) +
scale_y_reverse(name = "Depth (m)") +
scale_linetype(name = "Cast") +
scale_color_manual(name = "Method", values = ggthemes::colorblind_pal()(6)[c(1:4,6)], drop = FALSE) +
facet_wrap(~factor(stage,
levels = c("split", "median_filter", "lowpass_filter", "align", "ctmcorrect", "slowdown"),
labels = c("Split", "Median Filter", "Low Pass Filter", "Align T", "CTM Corr.", "Slowdown"))) +
theme_bw() +
theme(legend.position = "none",
legend.title = element_blank(),
strip.background = element_blank(),
strip.text = element_text(size = 8.5),
axis.text = element_text(size = 8, color = "black"),
axis.title = element_text(size = 9, color = "black"),
legend.text = element_text(size = 8),
legend.margin=margin())
plot_direction_legend <- cowplot::get_legend(plot_T)
plot_method_legend <- cowplot::get_legend(plot_S)
plot_sigmat <- ggplot() +
geom_path(data = sel_dat |>
dplyr::filter(stage == "slowdown"),
mapping = aes(y = depth,
x = sigmat + range_sigmat*shift_var,
color = factor(processing_method, levels = c("All", "Typical", "Typ. CTM", "TSA", "MSG")),
linetype = cast_direction),
size = rel(0.3)) +
scale_x_continuous(name = expression(sigma[t]~'('*kg~m^-3*')'),
guide = guide_axis(check.overlap = TRUE)) +
scale_y_reverse(name = "Depth (m)") +
scale_linetype(name = "Cast") +
scale_color_manual(name = "Method", values = ggthemes::colorblind_pal()(6)[c(1:4,6)], drop = FALSE) +
facet_wrap(~factor(stage,
levels = c("split", "median_filter", "lowpass_filter", "align", "ctmcorrect", "slowdown"),
labels = c("Split", "Median Filter", "Low Pass Filter", "Align T", "CTM Corr.", "Slowdown"))) +
theme_bw() +
theme(legend.position = "none",
legend.title = element_blank(),
strip.background = element_blank(),
strip.text = element_text(size = 8.5),
axis.text = element_text(size = 8, color = "black"),
axis.title = element_text(size = 9, color = "black"),
legend.text = element_text(size = 8),
legend.margin=margin())
plot_direction_legend <- cowplot::get_legend(plot_T)
plot_method_legend <- cowplot::get_legend(plot_S)
plot_sigmatheta <- ggplot() +
geom_path(data = sel_dat |>
dplyr::filter(stage == "slowdown"),
mapping = aes(y = depth,
x = sigmatheta + range_sigmatheta*shift_var,
color = factor(processing_method, levels = c("All", "Typical", "Typ. CTM", "TSA", "MSG")),
linetype = cast_direction),
size = rel(0.3)) +
scale_x_continuous(name = expression(sigma[theta]~'('*kg~m^-3*')'),
guide = guide_axis(check.overlap = TRUE)) +
scale_y_reverse(name = "Depth (m)") +
scale_linetype(name = "Cast") +
scale_color_manual(name = "Method", values = ggthemes::colorblind_pal()(6)[c(1:4,6)], drop = FALSE) +
facet_wrap(~factor(stage,
levels = c("split", "median_filter", "lowpass_filter", "align", "ctmcorrect", "slowdown"),
labels = c("Split", "Median Filter", "Low Pass Filter", "Align T", "CTM Corr.", "Slowdown"))) +
theme_bw() +
theme(legend.position = "none",
legend.title = element_blank(),
strip.background = element_blank(),
strip.text = element_text(size = 8.5),
axis.text = element_text(size = 8, color = "black"),
axis.title = element_text(size = 9, color = "black"),
legend.text = element_text(size = 8),
legend.margin=margin())
plot_direction_legend <- cowplot::get_legend(plot_T)
plot_method_legend <- cowplot::get_legend(plot_S)
# Sigma-theta
ragg::agg_png(file = here::here("paper", "plots",
paste0("process_sigmatheta_",
unique_deployments$VESSEL[jj], "_",
unique_deployments$CRUISE[jj], "_",
unique_deployments$HAUL[jj], ".png")),
width = 220,
height = 110,
res = 600,
units = "mm")
print(
cowplot::plot_grid(
cowplot::plot_grid(
plot_T + theme(legend.position = "none"),
plot_S + theme(legend.position = "none"),
align = "hv", labels = c("A", "B")),
plot_sigmatheta + theme(legend.position = "right",
legend.title = element_text(size = 8, hjust = 0)),
rel_widths = c(2,0.8),
labels = c(NA, "C")
)
)
dev.off()
# Sigma-t
ragg::agg_png(file = here::here("paper", "plots",
paste0("process_sigmat",
unique_deployments$VESSEL[jj], "_",
unique_deployments$CRUISE[jj], "_",
unique_deployments$HAUL[jj], ".png")),
width = 220,
height = 110,
res = 600,
units = "mm")
print(
cowplot::plot_grid(
cowplot::plot_grid(
plot_T + theme(legend.position = "none"),
plot_S + theme(legend.position = "none"),
align = "hv", labels = c("A", "B")),
plot_sigmat + theme(legend.position = "right",
legend.title = element_text(size = 8, hjust = 0)),
rel_widths = c(2,0.8),
labels = c(NA, "C")
)
)
dev.off()
# Sigma
ragg::agg_png(file = here::here("paper", "plots",
paste0("process_sigma_",
unique_deployments$VESSEL[jj], "_",
unique_deployments$CRUISE[jj], "_",
unique_deployments$HAUL[jj], ".png")),
width = 220,
height = 110,
res = 600,
units = "mm")
print(
cowplot::plot_grid(
cowplot::plot_grid(
plot_T + theme(legend.position = "none"),
plot_S + theme(legend.position = "none"),
align = "hv", labels = c("A", "B")),
plot_density + theme(legend.position = "right",
legend.title = element_text(size = 8, hjust = 0)),
rel_widths = c(2,0.8),
labels = c(NA, "C")
)
)
dev.off()
}
# Inspect plots ------------------------------------------------------------------------------------
library(plotly)
sel_dat <- process_df |>
dplyr::filter(VESSEL == 148,
CRUISE == 202201,
HAUL == 186,
!(stage %in% c("slowdown", "bin_average")))
sel_dat$range_temperature <- diff(range(sel_dat$temperature, na.rm = TRUE))
sel_dat$range_salinity <- diff(range(sel_dat$salinity, na.rm = TRUE))
sel_dat <- dplyr::inner_join(sel_dat,
offset_curves,
by = "processing_method")
names(sel_dat) <- tolower(names(sel_dat))
plotly::ggplotly(
ggplot() +
geom_path(data = sel_dat,
mapping = aes(y = depth,
x = temperature + range_temperature*shift_var,
color = factor(processing_method, levels = c("All", "Typical", "Typ. CTM", "TSA", "MSG")),
linetype = cast_direction),
size = rel(0.3)) +
scale_x_continuous(name = expression("Temperature,"~degree*C),
guide = guide_axis(check.overlap = TRUE)) +
scale_y_reverse(name = "Depth (m)") +
scale_color_manual(guide = "none", values = ggthemes::colorblind_pal()(6)[c(1:4,6)]) +
facet_wrap(~factor(stage,
levels = c("split", "median_filter", "lowpass_filter", "align", "ctmcorrect", "slowdown"),
labels = c("Split", "Median Filter", "Low Pass Filter", "Align T", "CTM Corr.", "Slowdown"))) +
theme_bw() +
theme(legend.position = "bottom",
legend.title = element_blank(),
strip.background = element_blank(),
axis.text = element_text(size = 8, color = "black"),
axis.title = element_text(size = 9, color = "black"),
legend.text = element_text(size = 8),
legend.margin=margin())
)
plotly::ggplotly(
ggplot() +
geom_path(data = sel_dat,
mapping = aes(y = depth,
x = salinity + range_salinity*shift_var,
color = factor(processing_method, levels = c("All", "Typical", "Typ. CTM", "TSA", "MSG")),
linetype = cast_direction),
size = rel(0.3)) +
scale_x_continuous(name = "Salnity, PSS-78",
guide = guide_axis(check.overlap = TRUE)) +
scale_linetype(guide = "none") +
scale_y_reverse(name = "Depth (m)") +
scale_color_manual(values = ggthemes::colorblind_pal()(6)[c(1:4,6)]) +
facet_wrap(~factor(stage,
levels = c("split", "median_filter", "lowpass_filter", "align", "ctmcorrect", "slowdown"),
labels = c("Split", "Median Filter", "Low Pass Filter", "Align T", "CTM Corr.", "Slowdown"))) +
theme_bw() +
guides(color=guide_legend(nrow=2)) +
theme(legend.position = "bottom",
legend.title = element_blank(),
strip.background = element_blank(),
axis.text = element_text(size = 8, color = "black"),
axis.title = element_text(size = 9, color = "black"),
legend.text = element_text(size = 8),
legend.margin=margin(),
legend.box = "vertical")
)
plotly::ggplotly(
ggplot() +
geom_path(data = sel_dat,
mapping = aes(y = depth,
x = density,
color = factor(processing_method, levels = c("All", "Typical", "Typ. CTM", "TSA", "MSG")),
linetype = cast_direction),
size = rel(0.3)) +
scale_x_continuous(name = "Density",
guide = guide_axis(check.overlap = TRUE)) +
scale_linetype(guide = "none") +
scale_y_reverse(name = "Depth (m)") +
scale_color_manual(values = ggthemes::colorblind_pal()(6)[c(1:4,6)]) +
facet_wrap(~factor(stage,
levels = c("split", "median_filter", "lowpass_filter", "align", "ctmcorrect", "slowdown"),
labels = c("Split", "Median Filter", "Low Pass Filter", "Align T", "CTM Corr.", "Slowdown"))) +
theme_bw() +
guides(color=guide_legend(nrow=2)) +
theme(legend.position = "bottom",
legend.title = element_blank(),
strip.background = element_blank(),
axis.text = element_text(size = 8, color = "black"),
axis.title = element_text(size = 9, color = "black"),
legend.text = element_text(size = 8),
legend.margin=margin(),
legend.box = "vertical")
)
afsc-gap-products/gapctd documentation built on March 5, 2025, 3:42 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# Process and plot deployment examples
# Sean Rohan, November 2022
#
# Processes example deployments using the gapctd package with the following methods:
# - Typical: Manfacturer recommendend temperature offset (-0.5) and CTM parameters (alpha_C = 0.04, beta_C = 1/8)
# - Typ. CTM: Estimate temperature offset and manufacturer recommended CTM parameters (alpha_C = 0.04, beta_C = 1/8)
# - Temperature-Salinity Curve Area (TSA): Estimated temperature offset and CTM parameters. CTM optimization based on area between temperature-salinity curves.
# - Minimum Salinity Gradient (MSG): Estimated temperature offset and CTM parameters. CTM optimization based on path distance of salinity curves.
#
# Prepare data -------------------------------------------------------------------------------------
library(gapctd)
# Get haul data
haul_files <- list.files(here::here("paper", "data", "haul_data"), full.names = TRUE)
ex_deployments <- read.csv(file = here::here("paper", "data", "example_deployments.csv"))
deployment_dat <- data.frame()
for(ii in 1:length(haul_files)) {
deployment_dat <- deployment_dat |>
dplyr::bind_rows(
readRDS(file = haul_files[ii]) |>
janitor::clean_names() |>
dplyr::inner_join(ex_deployments)
)
}
names(deployment_dat) <- toupper(names(deployment_dat))
saveRDS(deployment_dat,
here::here("paper", "data", "ex_hauls.rds"))
# Functions for plotting examples ------------------------------------------------------------------
derive_and_bin <- function(x, bin_width = NULL) {
x$downcast <- gapctd::derive_eos(x$downcast)
x$upcast <- gapctd::derive_eos(x$upcast)
if(!is.null(bin_width)) {
x$downcast <- gapctd:::bin_average(x$downcast,
by = "depth",
bin_width = bin_width)
x$upcast <- gapctd:::bin_average(x$upcast,
by = "depth",
bin_width = bin_width)
}
return(x)
}
make_stage_df <- function(x, stage, method) {
out <- data.frame()
if("downcast" %in% names(x)) {
out <- dplyr::bind_rows(out,
as.data.frame(x$downcast@data)|>
dplyr::mutate(cast_direction = x$downcast@metadata$cast_direction,
processing_method = method) |>
dplyr::bind_cols(x$downcast@metadata$race_metadata |>
dplyr::select(VESSEL, CRUISE, HAUL, REGION))
)
}
if("upcast" %in% names(x)) {
out <- dplyr::bind_rows(out,
as.data.frame(x$upcast@data) |>
dplyr::mutate(cast_direction = x$upcast@metadata$cast_direction,
processing_method = method) |>
dplyr::bind_cols(x$upcast@metadata$race_metadata |>
dplyr::select(VESSEL, CRUISE, HAUL, REGION)))
}
out$stage <- stage
return(out)
}
# Get names of example deployment files
ex_files <- list.files(here::here("paper", "data", "cnv"),
pattern = ".cnv",
full.names = TRUE)
haul_df <- readRDS(here::here("paper", "data", "ex_hauls.rds"))
# Make table of representative stations
ts_gradient <- readRDS(here::here("paper", "output", "TS_gradient_by_cast.rds")) |>
dplyr::group_by(vessel, cruise, haul) |>
dplyr::summarise(temperature_range = round(mean(temperature_range), 2),
salinity_range = round(mean(salinity_range), 2),
temperature_max_gradient = round(mean(temperature_max_gradient_z), 3),
salinity_max_gradient = round(mean(salinity_max_gradient_s), 3))
names(ts_gradient) <- toupper(names(ts_gradient))
representative_station_df <- haul_df |>
dplyr::mutate(LATITUDE = (START_LATITUDE+END_LATITUDE)/2,
LONGITUDE = (START_LONGITUDE+END_LONGITUDE)/2) |>
dplyr::inner_join(ts_gradient,
by = c("VESSEL", "CRUISE", "HAUL")) |>
dplyr::inner_join(
data.frame(VESSEL = c(148,
176,
148,
176,
162,
94,
162,
94,
162,
94,
162,
94),
CRUISE = c(202201,
202201,
202101,
202101,
202102,
202102,
202202,
202202,
202201,
202201,
202101,
202101),
FULL_REGION = c("Aleutian Islands",
"Aleutian Islands",
"Gulf of Alaska",
"Gulf of Alaska",
"Northern Bering Sea",
"Northern Bering Sea",
"Northern Bering Sea",
"Northern Bering Sea",
"Eastern Bering Sea",
"Eastern Bering Sea",
"Eastern Bering Sea",
"Eastern Bering Sea"
)),
by = c("VESSEL", "CRUISE")
) |>
dplyr::mutate(DATE = paste0(lubridate::month(START_TIME, label = TRUE, abbr = TRUE), " ",
lubridate::day(START_TIME), ", ",
lubridate::year(START_TIME)),
LATITUDE = format(round(LATITUDE, 2), nsmall = 2),
LONGITUDE = format(round(LONGITUDE, 2), nsmall = 2)) |>
dplyr::select(EXAMPLE, REGION, DATE, LATITUDE, LONGITUDE,
TEMPERATURE_RANGE, SALINITY_RANGE, TEMPERATURE_MAX_GRADIENT, SALINITY_MAX_GRADIENT,
DESCRIPTION) |>
dplyr::arrange(EXAMPLE)
write.csv(representative_station_df,
here::here("paper", "plots", "representative_station_table.csv"),
row.names = FALSE)
# Run data processing methods on each example deployment -------------------------------------------
process_df <- data.frame()
param_df <- data.frame()
for(ii in 1:length(ex_files)) {
sel_ctd <- read.oce(file = ex_files[ii])
# Steps that are the same across methods
stage_1 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "split") |>
derive_and_bin(bin_width = 1)
stage_2 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "median_filter") |>
derive_and_bin(bin_width = 1)
stage_3 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "lowpass_filter") |>
derive_and_bin(bin_width = 1)
# Typical method
stage_4_typical <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "align",
align_pars = list(temperature = -0.5),
ctm_pars = list(alpha_C = 0.04, beta_C = 1/8),
cor_var = "conductivity") |>
derive_and_bin(bin_width = 1)
stage_5_typical <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "ctmcorrect",
align_pars = list(temperature = -0.5),
ctm_pars = list(alpha_C = 0.04, beta_C = 1/8),
cor_var = "conductivity") |>
derive_and_bin(bin_width = 1)
stage_6_typical <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "slowdown",
align_pars = list(temperature = -0.5),
ctm_pars = list(alpha_C = 0.04, beta_C = 1/8),
cor_var = "conductivity") |>
derive_and_bin(bin_width = 1)
stage_7_typical <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "bin_average",
align_pars = list(temperature = -0.5),
ctm_pars = list(alpha_C = 0.04, beta_C = 1/8),
cor_var = "conductivity")
stage_8_typical <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "full", # w/ Density inversion check and completeness check
align_pars = list(temperature = -0.5),
ctm_pars = list(alpha_C = 0.04, beta_C = 1/8),
cor_var = "conductivity")
# Estimated temperature alignment, typical CTM parameters
round1_4 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "align",
align_pars = list(),
ctm_pars = list(alpha_C = 0.04, beta_C = 1/8)) |>
derive_and_bin(bin_width = 1)
round1_5 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
align_pars = list(),
ctm_pars = list(alpha_C = 0.04, beta_C = 1/8),
return_stage = "ctmcorrect") |>
derive_and_bin(bin_width = 1)
round1_6 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "slowdown",
ctm_pars = round1_5$downcast@metadata$ctm$both) |>
derive_and_bin(bin_width = 1)
round1_7 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "bin_average",
ctm_pars = round1_5$downcast@metadata$ctm$both)
round1_8 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "full", # w/ Density inversion check and completeness check
ctm_pars = round1_5$downcast@metadata$ctm$both)
# Estimated alignment and CTM parameters, with optimization based on T-S Area (TSA)
round2_4 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
align_pars = list(),
ctm_pars = list(),
return_stage = "align") |>
derive_and_bin(bin_width = 1)
round2_5 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
align_pars = list(),
ctm_pars = list(),
return_stage = "ctmcorrect") |>
derive_and_bin(bin_width = 1)
round2_6 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
align_pars = list(),
ctm_pars = round2_5$downcast@metadata$ctm$both,
return_stage = "slowdown") |>
derive_and_bin(bin_width = 1)
round2_7 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
align_pars = list(),
ctm_pars = round2_5$downcast@metadata$ctm$both,
return_stage = "bin_average")
round2_8 <- run_gapctd(x = sel_ctd,
haul_df = haul_df,
align_pars = list(),
ctm_pars = round2_5$downcast@metadata$ctm$both,
ctd_tz = "America/Anchorage",
return_stage = "full")
# Estimated alignment and CTM parameters, with optimization based on Minimum Salinity Gradient (MSG)
sel_downcast <- oce::ctdTrim(x = sel_ctd,
method = "range",
parameters = list(item = "timeS",
from = 0,
to = max(stage_1$downcast@data$timeS + 0.25, na.rm = TRUE)))
round3_4 <- run_gapctd(x = sel_downcast,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "align") |>
derive_and_bin(bin_width = 1)
round3_5 <- run_gapctd(x = sel_downcast,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "ctmcorrect") |>
derive_and_bin(bin_width = 1)
round3_6 <- run_gapctd(x = sel_downcast,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "slowdown",
ctm_pars = round3_5$downcast@metadata$ctm$both) |>
derive_and_bin(bin_width = 1)
round3_7 <- run_gapctd(x = sel_downcast,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "bin_average",
ctm_pars = round3_5$downcast@metadata$ctm$both)
round3_8 <- run_gapctd(x = sel_downcast,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "full", # w/ Density inversion check and completeness check
ctm_pars = round3_5$downcast@metadata$ctm$both)
sel_upcast <- oce::ctdTrim(x = sel_ctd,
method = "range",
parameters = list(item = "timeS",
from = min(stage_1$upcast@data$timeS - 0.25, na.rm = TRUE),
to = 5e6))
round3_4_uc <- run_gapctd(x = sel_upcast,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "align") |>
derive_and_bin(bin_width = 1)
round3_5_uc <- run_gapctd(x = sel_upcast,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "ctmcorrect") |>
derive_and_bin(bin_width = 1)
round3_6_uc <- run_gapctd(x = sel_upcast,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "slowdown",
ctm_pars = round3_5_uc$upcast@metadata$ctm$both) |>
derive_and_bin(bin_width = 1)
round3_7_uc <- run_gapctd(x = sel_upcast,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "bin_average",
ctm_pars = round3_5_uc$upcast@metadata$ctm$both)
round3_8_uc <- run_gapctd(x = sel_upcast,
haul_df = haul_df,
ctd_tz = "America/Anchorage",
return_stage = "full", # w/ Density inversion check and completeness check
ctm_pars = round3_5_uc$upcast@metadata$ctm$both)
# Make data frame with all outputs
all_steps <- dplyr::bind_rows(make_stage_df(stage_1, stage = "split", method = "All"),
make_stage_df(stage_2, stage = "median_filter", method = "All"),
make_stage_df(stage_3, stage = "lowpass_filter", method = "All"),
make_stage_df(round1_4, stage = "align", method = "Typ. CTM"),
make_stage_df(round1_5, stage = "ctmcorrect", method = "Typ. CTM"),
make_stage_df(round1_6, stage = "slowdown", method = "Typ. CTM"),
make_stage_df(round1_7, stage = "bin_average", method = "Typ. CTM"),
make_stage_df(round1_8, stage = "slowdown", method = "Typ. CTM"),
make_stage_df(round2_4, stage = "align", method = "TSA"),
make_stage_df(round2_5, stage = "ctmcorrect", method = "TSA"),
make_stage_df(round2_6, stage = "slowdown", method = "TSA"),
make_stage_df(round2_7, stage = "bin_average", method = "TSA"),
make_stage_df(round2_8, stage = "slowdown", method = "TSA"),
make_stage_df(round3_4, stage = "align", method = "MSG"),
make_stage_df(round3_5, stage = "ctmcorrect", method = "MSG"),
make_stage_df(round3_6, stage = "slowdown", method = "MSG"),
make_stage_df(round3_7, stage = "bin_average", method = "MSG"),
make_stage_df(round3_8, stage = "slowdown", method = "MSG"),
make_stage_df(round3_4_uc, stage = "align", method = "MSG"),
make_stage_df(round3_5_uc, stage = "ctmcorrect", method = "MSG"),
make_stage_df(round3_6_uc, stage = "slowdown", method = "MSG"),
make_stage_df(round3_7_uc, stage = "bin_average", method = "MSG"),
make_stage_df(round3_8_uc, stage = "slowdown", method = "MSG"),
make_stage_df(stage_4_typical, stage = "align", method = "Typical"),
make_stage_df(stage_5_typical, stage = "ctmcorrect", method = "Typical"),
make_stage_df(stage_6_typical, stage = "slowdown", method = "Typical"),
make_stage_df(stage_7_typical, stage = "bin_average", method = "Typical"),
make_stage_df(stage_8_typical, stage = "slowdown", method = "Typical")
)
process_df <- dplyr::bind_rows(process_df, all_steps)
# Data frame with parameters used for each method
param_df <- dplyr::bind_rows(param_df,
dplyr::bind_rows(
data.frame(
T_offset_dc = round1_4$downcast@metadata$align[["temperature"]]['offset'],
TC_corr_dc = round1_4$downcast@metadata$align[["temperature"]]['corr'],
T_offset_uc = round1_4$upcast@metadata$align[["temperature"]]['offset'],
TC_corr_uc = round1_4$upcast@metadata$align[["temperature"]]['corr'],
alpha_C = round1_5$downcast@metadata$ctm['alpha_C'],
beta_C = round1_5$downcast@metadata$ctm['beta_C'],
round = 1)|>
dplyr::bind_cols(round1_8$downcast@metadata$race_metadata),
data.frame(
T_offset_dc = round2_4$downcast@metadata$align[["temperature"]]['offset'],
TC_corr_dc = round2_4$downcast@metadata$align[["temperature"]]['corr'],
T_offset_uc = round2_4$upcast@metadata$align[["temperature"]]['offset'],
TC_corr_uc = round2_4$upcast@metadata$align[["temperature"]]['corr'],
alpha_C = round2_5$downcast@metadata$ctm$both['alpha_C'],
beta_C = round2_5$downcast@metadata$ctm$both['beta_C'],
round = 2)|>
dplyr::bind_cols(round1_8$downcast@metadata$race_metadata),
data.frame(
T_offset_dc = round3_4$downcast@metadata$align[["temperature"]]['offset'],
TC_corr_dc = round3_4$downcast@metadata$align[["temperature"]]['corr'],
T_offset_uc = NA,
TC_corr_uc = NA,
alpha_C = round3_5$downcast@metadata$ctm$both['alpha_C'],
beta_C = round3_5$downcast@metadata$ctm$both['beta_C'],
round = 3)|>
dplyr::bind_cols(round1_8$downcast@metadata$race_metadata)
)
)
}
# Save outputs
saveRDS(process_df, file = here::here("paper", "data", "ex_process_df.rds"))
saveRDS(param_df, file = here::here("paper", "data", "param_df.rds"))
# Plot results -------------------------------------------------------------------------------------
library(gapctd)
process_df <- readRDS(here::here("paper", "data", "ex_process_df.rds"))
param_df <- readRDS(here::here("paper", "data", "param_df.rds"))
# profile_df <- readRDS(here::here("paper", "data", "ex_profiles.rds"))
representative_station_df <- read.csv(here::here("paper", "plots", "representative_station_table.csv"))
unique_deployments <- process_df |>
dplyr::select(VESSEL, CRUISE, HAUL) |>
unique()
process_df <- dplyr::filter(process_df, depth > 1)
offset_curves <- data.frame(processing_method = c("All", NA, "Typ. CTM", "TSA", "MSG", "Typical"),
shift_var = c(0, 0, 0.125, 0.25, 0.375, 0))
for(jj in 1:nrow(unique_deployments)) {
sel_dat <- process_df |>
dplyr::filter(VESSEL == unique_deployments$VESSEL[jj],
CRUISE == unique_deployments$CRUISE[jj],
HAUL == unique_deployments$HAUL[jj],
!(stage %in% c("bin_average"))) |>
dplyr::arrange(depth)
sel_dat$sigmat <- oce::swSigmaT(salinity = sel_dat$salinity,
temperature = sel_dat$temperature,
pressure = sel_dat$pressure,
longitude = representative_station_df$LONGITUDE[jj],
latitude = representative_station_df$LATITUDE[jj])
sel_dat$sigmatheta <- oce::swSigmaTheta(salinity = sel_dat$salinity,
temperature = sel_dat$temperature,
pressure = sel_dat$pressure,
longitude = representative_station_df$LONGITUDE[jj],
latitude = representative_station_df$LATITUDE[jj])
sel_dat$range_temperature <- diff(range(sel_dat$temperature, na.rm = TRUE))
sel_dat$range_salinity <- diff(range(sel_dat$salinity, na.rm = TRUE))
sel_dat$range_sigmat <- diff(range(sel_dat$sigmat, na.rm = TRUE))
sel_dat$range_sigmatheta <- diff(range(sel_dat$sigmat, na.rm = TRUE))
sel_dat$range_density <- diff(range(sel_dat$density-1000, na.rm = TRUE))
sel_dat <- dplyr::inner_join(sel_dat,
offset_curves,
by = "processing_method")
names(sel_dat) <- tolower(names(sel_dat))
plot_T <- ggplot() +
geom_path(data = sel_dat,
mapping = aes(y = depth,
x = temperature + range_temperature*shift_var,
color = factor(processing_method, levels = c("All", "Typical", "Typ. CTM", "TSA", "MSG")),
linetype = cast_direction),
size = rel(0.3)) +
scale_x_continuous(name = expression("Temperature ("*degree*C*')'),
guide = guide_axis(check.overlap = TRUE)) +
scale_y_reverse(name = "Depth (m)") +
scale_color_manual(guide = "none", values = ggthemes::colorblind_pal()(6)[c(1:4,6)]) +
facet_wrap(~factor(stage,
levels = c("split", "median_filter", "lowpass_filter", "align", "ctmcorrect", "slowdown"),
labels = c("Split", "Median Filter", "Low Pass Filter", "Align T", "CTM Corr.", "Slowdown"))) +
theme_bw() +
theme(legend.position = "bottom",
legend.title = element_blank(),
strip.background = element_blank(),
strip.text = element_text(size = 8.5),
axis.text = element_text(size = 8, color = "black"),
axis.title = element_text(size = 9, color = "black"),
legend.text = element_text(size = 8),
legend.margin=margin())
plot_S <- ggplot() +
geom_path(data = sel_dat,
mapping = aes(y = depth,
x = salinity + range_salinity*shift_var,
color = factor(processing_method, levels = c("All", "Typical", "Typ. CTM", "TSA", "MSG")),
linetype = cast_direction),
size = rel(0.3)) +
scale_x_continuous(name = "Salnity (PSS-78)",
guide = guide_axis(check.overlap = TRUE)) +
scale_linetype(guide = "none") +
scale_y_reverse(name = "Depth (m)") +
scale_color_manual(values = ggthemes::colorblind_pal()(6)[c(1:4,6)]) +
facet_wrap(~factor(stage,
levels = c("split", "median_filter", "lowpass_filter", "align", "ctmcorrect", "slowdown"),
labels = c("Split", "Median Filter", "Low Pass Filter", "Align T", "CTM Corr.", "Slowdown"))) +
theme_bw() +
guides(color=guide_legend(nrow=2)) +
theme(legend.position = "bottom",
legend.title = element_blank(),
strip.background = element_blank(),
strip.text = element_text(size = 8.5),
axis.text = element_text(size = 8, color = "black"),
axis.title = element_text(size = 9, color = "black"),
legend.text = element_text(size = 8),
legend.margin=margin(),
legend.box = "vertical")
plot_density <- ggplot() +
geom_path(data = sel_dat |>
dplyr::filter(stage == "slowdown"),
mapping = aes(y = depth,
x = density-1000 + range_density*shift_var,
color = factor(processing_method, levels = c("All", "Typical", "Typ. CTM", "TSA", "MSG")),
linetype = cast_direction),
size = rel(0.3)) +
scale_x_continuous(name = expression(sigma~'('*kg~m^-3*')'),
guide = guide_axis(check.overlap = TRUE)) +
scale_y_reverse(name = "Depth (m)") +
scale_linetype(name = "Cast") +
scale_color_manual(name = "Method", values = ggthemes::colorblind_pal()(6)[c(1:4,6)], drop = FALSE) +
facet_wrap(~factor(stage,
levels = c("split", "median_filter", "lowpass_filter", "align", "ctmcorrect", "slowdown"),
labels = c("Split", "Median Filter", "Low Pass Filter", "Align T", "CTM Corr.", "Slowdown"))) +
theme_bw() +
theme(legend.position = "none",
legend.title = element_blank(),
strip.background = element_blank(),
strip.text = element_text(size = 8.5),
axis.text = element_text(size = 8, color = "black"),
axis.title = element_text(size = 9, color = "black"),
legend.text = element_text(size = 8),
legend.margin=margin())
plot_direction_legend <- cowplot::get_legend(plot_T)
plot_method_legend <- cowplot::get_legend(plot_S)
plot_sigmat <- ggplot() +
geom_path(data = sel_dat |>
dplyr::filter(stage == "slowdown"),
mapping = aes(y = depth,
x = sigmat + range_sigmat*shift_var,
color = factor(processing_method, levels = c("All", "Typical", "Typ. CTM", "TSA", "MSG")),
linetype = cast_direction),
size = rel(0.3)) +
scale_x_continuous(name = expression(sigma[t]~'('*kg~m^-3*')'),
guide = guide_axis(check.overlap = TRUE)) +
scale_y_reverse(name = "Depth (m)") +
scale_linetype(name = "Cast") +
scale_color_manual(name = "Method", values = ggthemes::colorblind_pal()(6)[c(1:4,6)], drop = FALSE) +
facet_wrap(~factor(stage,
levels = c("split", "median_filter", "lowpass_filter", "align", "ctmcorrect", "slowdown"),
labels = c("Split", "Median Filter", "Low Pass Filter", "Align T", "CTM Corr.", "Slowdown"))) +
theme_bw() +
theme(legend.position = "none",
legend.title = element_blank(),
strip.background = element_blank(),
strip.text = element_text(size = 8.5),
axis.text = element_text(size = 8, color = "black"),
axis.title = element_text(size = 9, color = "black"),
legend.text = element_text(size = 8),
legend.margin=margin())
plot_direction_legend <- cowplot::get_legend(plot_T)
plot_method_legend <- cowplot::get_legend(plot_S)
plot_sigmatheta <- ggplot() +
geom_path(data = sel_dat |>
dplyr::filter(stage == "slowdown"),
mapping = aes(y = depth,
x = sigmatheta + range_sigmatheta*shift_var,
color = factor(processing_method, levels = c("All", "Typical", "Typ. CTM", "TSA", "MSG")),
linetype = cast_direction),
size = rel(0.3)) +
scale_x_continuous(name = expression(sigma[theta]~'('*kg~m^-3*')'),
guide = guide_axis(check.overlap = TRUE)) +
scale_y_reverse(name = "Depth (m)") +
scale_linetype(name = "Cast") +
scale_color_manual(name = "Method", values = ggthemes::colorblind_pal()(6)[c(1:4,6)], drop = FALSE) +
facet_wrap(~factor(stage,
levels = c("split", "median_filter", "lowpass_filter", "align", "ctmcorrect", "slowdown"),
labels = c("Split", "Median Filter", "Low Pass Filter", "Align T", "CTM Corr.", "Slowdown"))) +
theme_bw() +
theme(legend.position = "none",
legend.title = element_blank(),
strip.background = element_blank(),
strip.text = element_text(size = 8.5),
axis.text = element_text(size = 8, color = "black"),
axis.title = element_text(size = 9, color = "black"),
legend.text = element_text(size = 8),
legend.margin=margin())
plot_direction_legend <- cowplot::get_legend(plot_T)
plot_method_legend <- cowplot::get_legend(plot_S)
# Sigma-theta
ragg::agg_png(file = here::here("paper", "plots",
paste0("process_sigmatheta_",
unique_deployments$VESSEL[jj], "_",
unique_deployments$CRUISE[jj], "_",
unique_deployments$HAUL[jj], ".png")),
width = 220,
height = 110,
res = 600,
units = "mm")
print(
cowplot::plot_grid(
cowplot::plot_grid(
plot_T + theme(legend.position = "none"),
plot_S + theme(legend.position = "none"),
align = "hv", labels = c("A", "B")),
plot_sigmatheta + theme(legend.position = "right",
legend.title = element_text(size = 8, hjust = 0)),
rel_widths = c(2,0.8),
labels = c(NA, "C")
)
)
dev.off()
# Sigma-t
ragg::agg_png(file = here::here("paper", "plots",
paste0("process_sigmat",
unique_deployments$VESSEL[jj], "_",
unique_deployments$CRUISE[jj], "_",
unique_deployments$HAUL[jj], ".png")),
width = 220,
height = 110,
res = 600,
units = "mm")
print(
cowplot::plot_grid(
cowplot::plot_grid(
plot_T + theme(legend.position = "none"),
plot_S + theme(legend.position = "none"),
align = "hv", labels = c("A", "B")),
plot_sigmat + theme(legend.position = "right",
legend.title = element_text(size = 8, hjust = 0)),
rel_widths = c(2,0.8),
labels = c(NA, "C")
)
)
dev.off()
# Sigma
ragg::agg_png(file = here::here("paper", "plots",
paste0("process_sigma_",
unique_deployments$VESSEL[jj], "_",
unique_deployments$CRUISE[jj], "_",
unique_deployments$HAUL[jj], ".png")),
width = 220,
height = 110,
res = 600,
units = "mm")
print(
cowplot::plot_grid(
cowplot::plot_grid(
plot_T + theme(legend.position = "none"),
plot_S + theme(legend.position = "none"),
align = "hv", labels = c("A", "B")),
plot_density + theme(legend.position = "right",
legend.title = element_text(size = 8, hjust = 0)),
rel_widths = c(2,0.8),
labels = c(NA, "C")
)
)
dev.off()
}
# Inspect plots ------------------------------------------------------------------------------------
library(plotly)
sel_dat <- process_df |>
dplyr::filter(VESSEL == 148,
CRUISE == 202201,
HAUL == 186,
!(stage %in% c("slowdown", "bin_average")))
sel_dat$range_temperature <- diff(range(sel_dat$temperature, na.rm = TRUE))
sel_dat$range_salinity <- diff(range(sel_dat$salinity, na.rm = TRUE))
sel_dat <- dplyr::inner_join(sel_dat,
offset_curves,
by = "processing_method")
names(sel_dat) <- tolower(names(sel_dat))
plotly::ggplotly(
ggplot() +
geom_path(data = sel_dat,
mapping = aes(y = depth,
x = temperature + range_temperature*shift_var,
color = factor(processing_method, levels = c("All", "Typical", "Typ. CTM", "TSA", "MSG")),
linetype = cast_direction),
size = rel(0.3)) +
scale_x_continuous(name = expression("Temperature,"~degree*C),
guide = guide_axis(check.overlap = TRUE)) +
scale_y_reverse(name = "Depth (m)") +
scale_color_manual(guide = "none", values = ggthemes::colorblind_pal()(6)[c(1:4,6)]) +
facet_wrap(~factor(stage,
levels = c("split", "median_filter", "lowpass_filter", "align", "ctmcorrect", "slowdown"),
labels = c("Split", "Median Filter", "Low Pass Filter", "Align T", "CTM Corr.", "Slowdown"))) +
theme_bw() +
theme(legend.position = "bottom",
legend.title = element_blank(),
strip.background = element_blank(),
axis.text = element_text(size = 8, color = "black"),
axis.title = element_text(size = 9, color = "black"),
legend.text = element_text(size = 8),
legend.margin=margin())
)
plotly::ggplotly(
ggplot() +
geom_path(data = sel_dat,
mapping = aes(y = depth,
x = salinity + range_salinity*shift_var,
color = factor(processing_method, levels = c("All", "Typical", "Typ. CTM", "TSA", "MSG")),
linetype = cast_direction),
size = rel(0.3)) +
scale_x_continuous(name = "Salnity, PSS-78",
guide = guide_axis(check.overlap = TRUE)) +
scale_linetype(guide = "none") +
scale_y_reverse(name = "Depth (m)") +
scale_color_manual(values = ggthemes::colorblind_pal()(6)[c(1:4,6)]) +
facet_wrap(~factor(stage,
levels = c("split", "median_filter", "lowpass_filter", "align", "ctmcorrect", "slowdown"),
labels = c("Split", "Median Filter", "Low Pass Filter", "Align T", "CTM Corr.", "Slowdown"))) +
theme_bw() +
guides(color=guide_legend(nrow=2)) +
theme(legend.position = "bottom",
legend.title = element_blank(),
strip.background = element_blank(),
axis.text = element_text(size = 8, color = "black"),
axis.title = element_text(size = 9, color = "black"),
legend.text = element_text(size = 8),
legend.margin=margin(),
legend.box = "vertical")
)
plotly::ggplotly(
ggplot() +
geom_path(data = sel_dat,
mapping = aes(y = depth,
x = density,
color = factor(processing_method, levels = c("All", "Typical", "Typ. CTM", "TSA", "MSG")),
linetype = cast_direction),
size = rel(0.3)) +
scale_x_continuous(name = "Density",
guide = guide_axis(check.overlap = TRUE)) +
scale_linetype(guide = "none") +
scale_y_reverse(name = "Depth (m)") +
scale_color_manual(values = ggthemes::colorblind_pal()(6)[c(1:4,6)]) +
facet_wrap(~factor(stage,
levels = c("split", "median_filter", "lowpass_filter", "align", "ctmcorrect", "slowdown"),
labels = c("Split", "Median Filter", "Low Pass Filter", "Align T", "CTM Corr.", "Slowdown"))) +
theme_bw() +
guides(color=guide_legend(nrow=2)) +
theme(legend.position = "bottom",
legend.title = element_blank(),
strip.background = element_blank(),
axis.text = element_text(size = 8, color = "black"),
axis.title = element_text(size = 9, color = "black"),
legend.text = element_text(size = 8),
legend.margin=margin(),
legend.box = "vertical")
)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.