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plot_regional_time_series_TE_e_ratio.R
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plot_regional_time_series_TE_e_ratio.R
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plot_regional_ts_TE_e_ratio <- function(region, DH) {
#colors for time series
color = c("violet","brown2" ,"goldenrod2", "aquamarine3", "darkorchid3", "darkorange2", "indianred4", "royalblue2")
#color = c("violet","goldenrod2", "aquamarine3", "darkorchid3", "darkorange2", "indianred4", "royalblue2")
#Southern Ocean S of 60
if(region == "SO_60") {
plot.subtitle = "Southern Ocean (South of 60S)"
#Southern Ocean S of 50
} else if(region == "SO_50"){
plot.subtitle = "Southern Ocean (South of 50S)"
#low latitudes
} else if(region == "30_low_lats"){
plot.subtitle = "Low Latitudes (30S-30N)"
#Equatorial Pacific
} else if(region == "EQ_Pacific") {
plot.subtitle = "Equatorial Pacific (15S to 15N, 160E to 75W)"
#low latitudes without Equatorial Pacific
} else if (region == "low_lats_no_EQ_Pacific") {
plot.subtitle = "Low Latitudes (30S-30N) without Equatorial Pacific"
#North Atlantic - NOTE: in calc_region_area this is called North_Atlantic_no_Arctic
} else if (region == "North_Atlantic") {
plot.subtitle = "North Atlantic (40N to 65N, 70W to 0W)"
} else {
#North Atlantic with the Arctic Ocean
plot.subtitle = "Low Latitudes (15S-15N)"
}
if(DH == "POC_100") {
DH.name = "100m to 1000m"
} else if(DH == "POC_MLDmax"){
DH.name = "MLDmax to 1000m"
} else if(DH == "POC_PCD"){
DH.name = "PCD to 1000m"
} else {
DH.name = "EZ depth to 1000m"
}
if(DH == "POC_1000") {
print("skip TE plots")
} else {
#Transfer efficiency ---------
title = paste0("Change in Transfer Efficiency - ", DH.name)
TE <- read_csv(paste0("~/regional_time_series_analysis/files/all_models/total_flux/",region, "_TE_time_series_", DH,"_POC_1000.csv"))
TE <- data.table::melt(TE, id.vars = 'Year', value.name = 'TE', variable.name = "Model")
TE_plot <- ggplot(data = TE, aes(x = Year, y = TE, color = Model)) +
geom_line() +
geom_smooth(size = 0.5, se = FALSE) +
theme_bw() +
labs(title = title,
subtitle = plot.subtitle) +
xlab(NULL) +
ylab("% Transferred") +
scale_y_continuous(n.breaks = 6) +
scale_x_continuous(expand = c(0,0)) +
scale_color_manual(values = color) +
theme(plot.title = element_text(size = 14),
plot.subtitle = element_text(size = 14, face = "bold"),
axis.text = element_text(size = 12),
axis.title = element_text(size = 14),
legend.key.size = unit(1, 'cm'),
legend.key.height = unit(1, 'cm'),
legend.key.width = unit(1, 'cm'),
legend.title = element_text(size=14),
legend.text = element_text(size=12))
TE_plot
#save figure
ggsave(filename = paste0(region, "_TE_time_series_",DH,"_POC_1000.png"), plot = TE_plot, path = "~/regional_time_series_analysis/figures/TE/", width = 20, height = 12, units = "cm", dpi = 400)
#normalized TE
normalized.TE <- read_csv(paste0("~/regional_time_series_analysis/files/all_models/total_flux/normalized_", region, "_TE_time_series_", DH,"_POC_1000.csv"))
normalized.TE <- data.table::melt(normalized.TE, id.vars = 'Year', value.name = 'TE', variable.name = "Model")
normalized.title = paste0("Normalized Change in Transfer Efficiency - ", DH.name)
TE_norm <- ggplot(data = normalized.TE, aes(x = Year, y = TE, color = Model)) +
geom_line() +
geom_smooth(size = 0.5, se = FALSE) +
theme_bw() +
labs(title = normalized.title,
subtitle = plot.subtitle) +
xlab(NULL) +
ylab("% Transferred") +
scale_y_continuous(n.breaks = 6) +
scale_x_continuous(expand = c(0,0)) +
scale_color_manual(values = color) +
theme(plot.title = element_text(size = 14),
plot.subtitle = element_text(size = 14, face = "bold"),
axis.text = element_text(size = 12),
axis.title = element_text(size = 14),
legend.key.size = unit(1, 'cm'),
legend.key.height = unit(1, 'cm'),
legend.key.width = unit(1, 'cm'),
legend.title = element_text(size=14),
legend.text = element_text(size=12))
TE_norm
#save figure
ggsave(filename = paste0("normalized_",region, "_TE_time_series_",DH,"_POC_1000.png"), plot = TE_norm, path = "~/regional_time_series_analysis/figures/TE/", width = 20, height = 12, units = "cm", dpi = 400)
}
#e-ratio ----------
if(DH == "POC_100") {
DH.name2 = "100m"
} else if(DH == "POC_MLDmax"){
DH.name2 = "the MLDmax"
} else if(DH == "POC_PCD"){
DH.name2 = "the PCD"
} else if(DH == "POC_EZ") {
DH.name2 = "the EZ depth"
} else {
DH.name2 = "1000m"
}
title2 = paste0("Change in E-ratio at ", DH.name2)
e_ratio <- read_csv(paste0("~/regional_time_series_analysis/files/all_models/total_flux/",region, "_e_ratio_time_series_", DH,".csv"))
#melt and add column for model key
e_ratio <- data.table::melt(e_ratio, id.vars = 'Year', value.name = 'e_ratio', variable.name = "Model")
e_ratio_plot <- ggplot(data = e_ratio, aes(x = Year, y = e_ratio, color = Model)) +
geom_line() +
geom_smooth(size = 0.5, se = FALSE) +
theme_bw() +
labs(title = title2,
subtitle = plot.subtitle) +
xlab(NULL) +
ylab("E-ratio") +
scale_y_continuous(n.breaks = 6) +
scale_x_continuous(expand = c(0,0)) +
scale_color_manual(values = color) +
theme(plot.title = element_text(size = 14),
plot.subtitle = element_text(size = 14, face = "bold"),
axis.text = element_text(size = 12),
axis.title = element_text(size = 14),
legend.key.size = unit(1, 'cm'),
legend.key.height = unit(1, 'cm'),
legend.key.width = unit(1, 'cm'),
legend.title = element_text(size=14),
legend.text = element_text(size=12))
e_ratio_plot
#save figure
ggsave(filename = paste0(region, "_e_ratio_time_series_",DH,".png"), plot = e_ratio_plot, path = "~/regional_time_series_analysis/figures/e_ratio/", width = 20, height = 12, units = "cm", dpi = 400)
#normalized e-ratio
normalized_e_ratio <- read_csv(paste0("~/regional_time_series_analysis/files/all_models/total_flux/normalized_", region, "_e_ratio_time_series_", DH,".csv"))
normalized_e_ratio <- data.table::melt(normalized_e_ratio, id.vars = 'Year', value.name = 'e_ratio', variable.name = "Model")
normalized.title2 = paste0("Normalized Change in E-ratio at ", DH.name2)
norm_e_ratio_plot <- ggplot(data = normalized_e_ratio, aes(x = Year, y = e_ratio, color = Model)) +
geom_line() +
geom_smooth(size = 0.5, se = FALSE) +
theme_bw() +
labs(title = title2,
subtitle = plot.subtitle) +
xlab(NULL) +
ylab("E-ratio") +
scale_y_continuous(n.breaks = 6) +
scale_x_continuous(expand = c(0,0)) +
scale_color_manual(values = color) +
theme(plot.title = element_text(size = 14),
plot.subtitle = element_text(size = 14, face = "bold"),
axis.text = element_text(size = 12),
axis.title = element_text(size = 14),
legend.key.size = unit(1, 'cm'),
legend.key.height = unit(1, 'cm'),
legend.key.width = unit(1, 'cm'),
legend.title = element_text(size=14),
legend.text = element_text(size=12))
norm_e_ratio_plot
#save figure
ggsave(filename = paste0("normalized_",region, "_e_ratio_time_series_",DH,".png"), plot = norm_e_ratio_plot, path = "~/regional_time_series_analysis/figures/e_ratio/", width = 20, height = 12, units = "cm", dpi = 400)
}
combine_regional_TE <- function(DH) {
setwd("~/regional_time_series_analysis/figures/TE/")
if(DH == "POC_1000") {
print("skip plotting")
} else {
A <- readPNG(paste0("15_low_lats_TE_time_series_",DH,"_POC_1000.png"))
B <- readPNG(paste0("30_low_lats_TE_time_series_",DH,"_POC_1000.png"))
C <- readPNG(paste0("low_lats_no_EQ_Pacific_TE_time_series_",DH,"_POC_1000.png"))
D <- readPNG(paste0("EQ_Pacific_TE_time_series_",DH,"_POC_1000.png"))
E <- readPNG(paste0("North_Atlantic_TE_time_series_",DH,"_POC_1000.png"))
f <- readPNG(paste0("SO_50_TE_time_series_",DH,"_POC_1000.png"))
G <- readPNG(paste0("SO_60_TE_time_series_",DH,"_POC_1000.png"))
setwd("~/time_series_analysis/figures/")
if(DH == "POC_100") {
H <- readPNG(paste0("time_series_TE_100_1000.png"))
} else if (DH == "POC_PCD") {
H <- readPNG(paste0("time_series_TE_PCD_1000.png"))
} else if (DH == "POC_EZ") {
H <- readPNG(paste0("time_series_TE_ez_1000.png"))
} else {
H <- readPNG(paste0("time_series_TE_MLDmax_1000.png"))
}
grid.combine <- grid.arrange(rasterGrob(H),rasterGrob(A), rasterGrob(B), rasterGrob(C),
rasterGrob(D),rasterGrob(E),rasterGrob(f),rasterGrob(G),ncol=4, nrow=2,
top= DH)
ggsave(paste0("~/regional_time_series_analysis/figures/faceted/TE_regional_time_series_",DH,"_POC_1000.png"), plot = grid.combine, width = 30, height = 12, units = "cm", dpi = 400)
setwd("~/regional_time_series_analysis/figures/TE/")
#repeat for normalized
A <- readPNG(paste0("normalized_15_low_lats_TE_time_series_",DH,"_POC_1000.png"))
B <- readPNG(paste0("normalized_30_low_lats_TE_time_series_",DH,"_POC_1000.png"))
C <- readPNG(paste0("normalized_low_lats_no_EQ_Pacific_TE_time_series_",DH,"_POC_1000.png"))
D <- readPNG(paste0("normalized_EQ_Pacific_TE_time_series_",DH,"_POC_1000.png"))
E <- readPNG(paste0("normalized_North_Atlantic_TE_time_series_",DH,"_POC_1000.png"))
f <- readPNG(paste0("normalized_SO_50_TE_time_series_",DH,"_POC_1000.png"))
G <- readPNG(paste0("normalized_SO_60_TE_time_series_",DH,"_POC_1000.png"))
setwd("~/time_series_analysis/figures/")
if(DH == "POC_100") {
H <- readPNG(paste0("normalized_time_series_100_1000_TE.png"))
} else if (DH == "POC_PCD") {
H <- readPNG(paste0("normalized_time_series_PCD_1000_TE.png"))
} else if (DH == "POC_EZ") {
H <- readPNG(paste0("normalized_time_series_ez_1000_TE.png"))
} else {
H <- readPNG(paste0("normalized_time_series_MLDmax_1000_TE.png"))
}
grid.combine2 <- grid.arrange(rasterGrob(H),rasterGrob(A), rasterGrob(B), rasterGrob(C),
rasterGrob(D),rasterGrob(E),rasterGrob(f),rasterGrob(G),ncol=4, nrow=2,
top= DH)
ggsave(paste0("~/regional_time_series_analysis/figures/faceted/normalized_TE_regional_time_series_",DH,"_POC_1000.png"), plot = grid.combine2, width = 30, height = 12, units = "cm", dpi = 400)
#free memory space
gc()
}
}
combine_regional_e_ratio <- function(DH) {
setwd("~/regional_time_series_analysis/figures/e_ratio/")
A <- readPNG(paste0("15_low_lats_e_ratio_time_series_",DH,".png"))
B <- readPNG(paste0("30_low_lats_e_ratio_time_series_",DH,".png"))
C <- readPNG(paste0("low_lats_no_EQ_Pacific_e_ratio_time_series_",DH,".png"))
D <- readPNG(paste0("EQ_Pacific_e_ratio_time_series_",DH,".png"))
E <- readPNG(paste0("North_Atlantic_e_ratio_time_series_",DH,".png"))
f <- readPNG(paste0("SO_50_e_ratio_time_series_",DH,".png"))
G <- readPNG(paste0("SO_60_e_ratio_time_series_",DH,".png"))
setwd("~/time_series_analysis/figures/")
if(DH == "POC_100") {
H <- readPNG(paste0("time_series_e_ratio_100.png"))
} else if (DH == "POC_PCD") {
H <- readPNG(paste0("time_series_e_ratio_PCD.png"))
} else if (DH == "POC_EZ") {
H <- readPNG(paste0("time_series_e_ratio_EZ.png"))
} else if (DH == "POC_MLDmax"){
H <- readPNG(paste0("time_series_e_ratio_MLDmax.png"))
} else {
H <- readPNG(paste0("time_series_e_ratio_1000.png"))
}
grid.combine <- grid.arrange(rasterGrob(H),rasterGrob(A), rasterGrob(B), rasterGrob(C),
rasterGrob(D),rasterGrob(E),rasterGrob(f),rasterGrob(G),ncol=4, nrow=2,
top= DH)
ggsave(paste0("~/regional_time_series_analysis/figures/faceted/e_ratio_regional_time_series_",DH,".png"), plot = grid.combine, width = 30, height = 12, units = "cm", dpi = 400)
#repeat for normalized
setwd("~/regional_time_series_analysis/figures/e_ratio/")
A <- readPNG(paste0("normalized_15_low_lats_e_ratio_time_series_",DH,".png"))
B <- readPNG(paste0("normalized_30_low_lats_e_ratio_time_series_",DH,".png"))
C <- readPNG(paste0("normalized_low_lats_no_EQ_Pacific_e_ratio_time_series_",DH,".png"))
D <- readPNG(paste0("normalized_EQ_Pacific_e_ratio_time_series_",DH,".png"))
E <- readPNG(paste0("normalized_North_Atlantic_e_ratio_time_series_",DH,".png"))
f <- readPNG(paste0("normalized_SO_50_e_ratio_time_series_",DH,".png"))
G <- readPNG(paste0("normalized_SO_60_e_ratio_time_series_",DH,".png"))
setwd("~/time_series_analysis/figures/")
if(DH == "POC_100") {
H <- readPNG(paste0("normalized_time_series_e_ratio_100.png"))
} else if (DH == "POC_PCD") {
H <- readPNG(paste0("normalized_time_series_e_ratio_PCD.png"))
} else if (DH == "POC_EZ") {
H <- readPNG(paste0("normalized_time_series_e_ratio_EZ.png"))
} else if (DH == "POC_MLDmax"){
H <- readPNG(paste0("normalized_time_series_e_ratio_MLDmax.png"))
} else {
H <- readPNG(paste0("normalized_time_series_e_ratio_1000.png"))
}
grid.combine <- grid.arrange(rasterGrob(H),rasterGrob(A), rasterGrob(B), rasterGrob(C),
rasterGrob(D),rasterGrob(E),rasterGrob(f),rasterGrob(G),ncol=4, nrow=2,
top= DH)
ggsave(paste0("~/regional_time_series_analysis/figures/faceted/normalized_e_ratio_regional_time_series_",DH,".png"), plot = grid.combine, width = 30, height = 12, units = "cm", dpi = 400)
#free memory space
gc()
}