# NOT RUN {
library(oce)
data(section)
sg <- sectionGrid(section)
# 1. start of section, default fields.
plot(head(section))
# 2. Gulf Stream
GS <- subset(section, 109<=stationId&stationId<=129)
GSg <- sectionGrid(GS, p=seq(0, 2000, 100))
plot(GSg, which=c(1, 99), map.ylim=c(34, 42))
par(mfrow=c(2, 1))
plot(GS, which=1, ylim=c(2000, 0), ztype='points',
zbreaks=seq(0,30,2), pch=20, cex=3)
plot(GSg, which=1, ztype='image', zbreaks=seq(0,30,2))
par(mfrow=c(1, 1))
# 3. Image, with colored dots to indicate grid-data mismatch.
# }
# NOT RUN {
plot(GSg, which=1, ztype='image')
T <- GS[['temperature']]
col <- oceColorsViridis(100)[rescale(T, rlow=1, rhigh=100)]
points(GS[['distance']],GS[['depth']],pch=20,cex=3,col='white')
points(GS[['distance']],GS[['depth']],pch=20,cex=2.5,col=col)
# }
# NOT RUN {
# }
# NOT RUN {
# 4. Image of Absolute Salinity, with 4-minute bathymetry
# It's easy to calculate the desired area for the bathymetry,
# but for brevity we'll hard-code it. Note that download.topo()
# caches the file locally.
f <- download.topo(west=-80, east=0, south=35, north=40, resolution=4)
t <- read.topo(f)
plot(section, which="SA", xtype="longitude", ztype="image", showBottom=t)
# }
# NOT RUN {
# }
# NOT RUN {
# 5. Temperature with salinity added in red
s <- plot(section, which="temperature")
distance <- s[["distance", "byStation"]]
depth <- s[["station", 1]][["depth"]]
salinity <- matrix(s[["salinity"]], byrow=TRUE, nrow=length(s[["station"]]))
contour(distance, depth, salinity, col=2, add=TRUE)
# }
# NOT RUN {
# }
# NOT RUN {
# 6. Image with controlled colours
plot(section, which="salinity", ztype="image",
zlim=c(35, 37.5),
zbreaks=seq(35, 37.5, 0.25),
zcol=oceColorsTurbo)
# }
# NOT RUN {
# }
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