Dan Kelley is one of those heroes that creates and maintain software libraries for oceanographers. His language of choice is R, I particularly do not like R syntax. I use it only when I have really have to.
However, R does have some amazing libraries, like Dan's oce package.
This post is just a copy-and-paste of few examples from Dan's oce blog, to show how to use the oce library with the new IPython/Jupyter R kernel.
Every example has a link to the original post and I encourage you to go check it out.
library(devtools)
options(unzip = "unzip")
install_github('ocedata', 'dankelley', 'master')
install_github('oce', 'dankelley', 'develop')
In [1]:
library(oce)
daylength <- function(t, lon=-38.5, lat=-13)
{
t <- as.numeric(t)
alt <- function(t)
sunAngle(t, longitude=lon, latitude=lat)$altitude
rise <- uniroot(alt, lower=t-86400/2, upper=t)$root
set <- uniroot(alt, lower=t, upper=t+86400/2)$root
set - rise
}
t0 <- as.POSIXct("2014-01-01 12:00:00", tz="UTC")
t <- seq.POSIXt(t0, by="1 day", length.out=1*356)
dayLength <- unlist(lapply(t, daylength))
par(mfrow=c(2,1), mar=c(3, 3, 1, 1), mgp=c(2, 0.7, 0))
plot(t, dayLength/3600, type='o', pch=20,
xlab="", ylab="Day length (hours)")
grid()
solstice <- as.POSIXct("2013-12-21", tz="UTC")
plot(t[-1], diff(dayLength), type='o', pch=20,
xlab="Day in 2013", ylab="Seconds gained per day")
In [2]:
library(deSolve)
IC <- 0
parms <- list(A=1e6, gamma=10)
sperday <- 86400 # seconds per day
times <- seq(0, 10*sperday, length.out=200)
F <- function(t)
{
ifelse (t > sperday & t < 2*sperday, 1, 0)
}
DE <- function(t, y, parms)
{
h <- y[1]
dhdt <- (F(t) - parms$gamma * h) / parms$A
list(dhdt)
}
sol <- lsoda(IC, times, DE, parms)
par(mfrow=c(2,1), mar=c(3,3,1,1), mgp=c(2,0.7,0))
h <- sol[,2]
Day <- times / 86400
plot(Day, F(times), type='l', ylab="River input [m^3/s]")
plot(Day, h, type='l', ylab="Lake level [m]")
In [3]:
library(oce)
x <- 1:100
y <- 1 + x/100 + sin(x/5)
yn <- y + rnorm(100, sd=0.1)
L <- 4
calc <- runlm(x, y, L=L, deriv=0)
plot(x, y, type='l', lwd=7, col='gray')
points(x, yn, pch=20, col='blue')
lines(x, calc, lwd=2, col='red')
In [4]:
library(oce)
data(ctd)
rho <- swRho(ctd)
z <- swZ(ctd)
drhodz <- runlm(z, rho, deriv = 1)
g <- 9.81
rho0 <- mean(rho, na.rm = TRUE)
N2 <- -g * drhodz/rho0
plot(ctd, which = "N2")
lines(N2, -z, col = "blue")
legend("bottomright", lwd = 2, col = c("brown", "blue"), legend = c("spline",
"runlm"), bg = "white")
In [5]:
# Alter next three lines as desired; a and b are watermasses.
Sa <- 30
Ta <- 10
Sb <- 40
library(oce)
# Should not need to edit below this line
rho0 <- swRho(Sa, Ta, 0)
Tb <- uniroot(function(T) rho0-swRho(Sb,T,0), lower=0, upper=100)$root
Sc <- (Sa + Sb) /2
Tc <- (Ta + Tb) /2
## density change, and equiv temp change
drho <- swRho(Sc, Tc, 0) - rho0
dT <- drho / rho0 / swAlpha(Sc, Tc, 0)
plotTS(as.ctd(c(Sa, Sb, Sc), c(Ta, Tb, Tc), 0), pch=20, cex=2)
drawIsopycnals(levels=rho0, col="red", cex=0)
segments(Sa, Ta, Sb, Tb, col="blue")
text(Sb, Tb, "b", pos=4)
text(Sa, Ta, "a", pos=4)
text(Sc, Tc, "c", pos=4)
legend("topleft",
legend=sprintf("Sa=%.1f, Ta=%.1f, Sb=%.1f -> Tb=%.1f, drho=%.2f, dT=%.2f",
Sa, Ta, Sb, Tb, drho, dT),
bg="white")
In [6]:
findHalocline <- function(ctd, deltap=5, plot=TRUE)
{
S <- ctd[['salinity']]
p <- ctd[['pressure']]
n <- length(p)
## trim df to be no larger than n/2 and no smaller than 3.
N <- deltap / median(diff(p))
df <- min(n/2, max(3, n / N))
spline <- smooth.spline(S~p, df=df)
SS <- predict(spline, p)
dSSdp <- predict(spline, p, deriv=1)
H <- p[which.max(dSSdp$y)]
if (plot) {
par(mar=c(3, 3, 1, 1), mgp=c(2, 0.7, 0))
plotProfile(ctd, xtype="salinity")
lines(SS$y, SS$x, col='red')
abline(h=H, col='blue')
mtext(sprintf("%.2f m", H), side=4, at=H, cex=3/4, col='blue')
mtext(sprintf(" deltap: %.0f, N: %.0f, df: %.0f", deltap, N, df),
side=1, line=-1, adj=0, cex=3/4)
}
return(H)
}
# Plot two panels to see influence of deltap.
par(mfrow=c(1, 2))
data(ctd)
findHalocline(ctd)
findHalocline(ctd, 1)
Out[6]:
Out[6]:
In [7]:
library(oce)
library(signal)
data(ctd)
S <- ctd[['salinity']]
p <- ctd[['pressure']]
dp <- median(diff(p))
pp <- seq(min(p), max(p), dp)
S0 <- approx(p, S, pp)$y
W <- dp / 2
f1 <- butter(1, W)
f2 <- butter(2, W)
par(mfrow=c(1, 3))
plotProfile(ctd, xtype="salinity", type='l')
S0f1 <- filtfilt(f1, S0)
S0f2 <- filtfilt(f2, S0)
lines(S0f1, pp, col='red')
lines(S0f2, pp, col='blue')
mtext("(a) ", side=3, adj=1, line=-5/4, cex=3/4)
plotProfile(ctd, xtype="salinity", type='l')
Sd <- detrend(pp, S0)
S1f1 <- filtfilt(f1, Sd$Y) + Sd$a + Sd$b * pp
S1f2 <- filtfilt(f2, Sd$Y) + Sd$a + Sd$b * pp
lines(S1f1, pp, col='red')
lines(S1f2, pp, col='blue')
mtext("(b) ", side=3, adj=1, line=-5/4, cex=3/4)
spline <- smooth.spline(pp, S0, df=3/W)
S2 <- predict(spline)$y
plotProfile(ctd, xtype="salinity", type='l')
lines(S2, pp, col='red')
mtext("(c) ", side=3, adj=1, line=-5/4, cex=3/4)
There are many more interesting posts, like the Gulf Stream center and the solstice. I was unable to run them due to data and/or difficulty to install certain R packages :-(
PS: Note that, due to a bug in the R-ipython kernel, a new figure is created every time R adds something new to the plot. There is a work around here.
In [2]:
HTML(html)
Out[2]:
This post was written as an IPython notebook. It is available for download or as a static html.
python4oceanographers by Filipe Fernandes is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://ocefpaf.github.io/.