问题
I have got wind data for some stations. The data includes station latitude, longitude, wind speed and wind direction for each station in a csv file. This data is not regularly spaced data. I have a requirement to draw streamlines for this data in R language.
I tried couple of packages rasterVis for STREAMPLOT(), TeachingDemos for My.Symbols by searching through internet, however I was not successful.
Here is an example plot I was talking about. http://wx.gmu.edu/dev/clim301/850stream.png
Also here is some sample data from csv file that I got for which I was trying to draw streamlines.
longitude,latitude,windspeed,winddirection
84.01,20,1.843478261,126.6521739
77.13,28.48,3.752380952,138.952381
77.2,28.68,2.413333333,140.2666667
78.16,31.32,1.994444444,185.0555556
77.112,31.531,2.492,149.96
77,28.11,7.6,103
77.09,31.5,1.752631579,214.8947368
76.57,31.43,1.28,193.6
77.02,32.34,3.881818182,264.4545455
77.15,28.7,2.444,146.12
77.35,30.55,3.663157895,131.3684211
75.5,29.52,4.175,169.75
72.43,24.17,2.095,279.3
76.19,25.1,1.816666667,170
76.517,30.975,1.284210526,125.6315789
76.13,28.8,4.995,126.7
75.04,29.54,4.09,151.85
72.3,24.32,0,359
72.13,23.86,1.961111111,284.7777778
74.95,30.19,3.032,137.32
73.16,22.36,1.37,251.8
75.84,30.78,3.604347826,125.8695652
73.52,21.86,1.816666667,228.9166667
70.44,21.5,2.076,274.08
69.75,21.36,3.81875,230
78.05,30.32,0.85625,138.5625
Can someone please help me out in drawing streamlines for the irregular wind data?
回答1:
Like you, I wanted to visualize the same kind of data as streamlnes and I failed to find a function that would do the trick...so I worked up my own crude function:
streamlines <- function(x, y, u, v, step.dist=NULL,
max.dist=NULL, col.ramp=c("white","black"),
fade.col=NULL, length=0.05, ...) {
## Function for adding smoothed vector lines to a plot.
## Interpolation powered by akima package
## step.distance - distance between interpolated locations (user coords)
## max.dist - maximum length of interpolated line (user coords)
## col.ramp - colours to be passed to colorRampPalette
## fade.col - NULL or colour to add fade effect to interpolated line
## ... - further arguments to pass to arrows
## build smoothed lines using interp function
maxiter <- max.dist/step.dist
l <- replicate(5, matrix(NA, length(x), maxiter), simplify=FALSE)
names(l) <- c("x","y","u","v","col")
l$x[,1] <- x
l$y[,1] <- y
l$u[,1] <- u
l$v[,1] <- v
for(i in seq(maxiter)[-1]) {
l$x[,i] <- l$x[,i-1]+(l$u[,i-1]*step.dist)
l$y[,i] <- l$y[,i-1]+(l$v[,i-1]*step.dist)
r <- which(l$x[,i]==l$x[,i-1] & l$y[,i]==l$y[,i-1])
l$x[r,i] <- NA
l$y[r,i] <- NA
for(j in seq(length(x))) {
if(!is.na(l$x[j,i])) {
l$u[j,i] <- c(interp(x, y, u, xo=l$x[j,i], yo=l$y[j,i])$z)
l$v[j,i] <- c(interp(x, y, v, xo=l$x[j,i], yo=l$y[j,i])$z)
}
}
}
## make colour a function of speed and fade line
spd <- sqrt(l$u^2 + l$v^2) # speed
spd <- apply(spd, 1, mean, na.rm=TRUE) # mean speed for each line
spd.int <- seq(min(spd, na.rm=TRUE), max(spd, na.rm=TRUE), length.out=maxiter)
cr <- colorRampPalette(col.ramp)
cols <- as.numeric(cut(spd, spd.int))
ncols <- max(cols, na.rm=TRUE)
cols <- cr(ncols)[cols]
if(is.null(fade.col)) {
l$col <- replicate(maxiter, cols)
} else {
nfade <- apply(!is.na(l$x), 1, sum)
for(j in seq(length(x))) {
l$col[j,seq(nfade[j])] <- colorRampPalette(c(fade.col, cols[j]))(nfade[j])
}
}
## draw arrows
for(j in seq(length(x))) {
arrows(l$x[j,], l$y[j,], c(l$x[j,-1], NA), c(l$y[j,-1], NA),
col=l$col[j,], length=0, ...)
i <- which.max(which(!is.na(l$x[j,]))) # draw arrow at end of line
if(i>1) {
arrows(l$x[j,i-1], l$y[j,i-1], l$x[j,i], l$y[j,i],
col=l$col[j,i-1], length=length, ...)
}
}
}
The function is powered by the interp function in the akima package and, with some fiddling, it can produce some half decent visuals:
dat <- "longitude,latitude,windspeed,winddirection
84.01,20,1.843478261,126.6521739
77.13,28.48,3.752380952,138.952381
77.2,28.68,2.413333333,140.2666667
78.16,31.32,1.994444444,185.0555556
77.112,31.531,2.492,149.96
77,28.11,7.6,103
77.09,31.5,1.752631579,214.8947368
76.57,31.43,1.28,193.6
77.02,32.34,3.881818182,264.4545455
77.15,28.7,2.444,146.12
77.35,30.55,3.663157895,131.3684211
75.5,29.52,4.175,169.75
72.43,24.17,2.095,279.3
76.19,25.1,1.816666667,170
76.517,30.975,1.284210526,125.6315789
76.13,28.8,4.995,126.7
75.04,29.54,4.09,151.85
72.3,24.32,0,359
72.13,23.86,1.961111111,284.7777778
74.95,30.19,3.032,137.32
73.16,22.36,1.37,251.8
75.84,30.78,3.604347826,125.8695652
73.52,21.86,1.816666667,228.9166667
70.44,21.5,2.076,274.08
69.75,21.36,3.81875,230
78.05,30.32,0.85625,138.5625"
tf <- tempfile()
writeLines(dat, tf)
dat <- read.csv(tf)
library(rgdal) # for projecting locations to utm coords
library(akima) # for interpolation
## add utm coords
xy <- as.data.frame(project(cbind(dat$longitude, dat$latitude), "+proj=utm +zone=43 +datum=NAD83"))
names(xy) <- c("easting","northing")
dat <- cbind(dat, xy)
## add u and v coords
dat$u <- -dat$windspeed*sin(dat$winddirection*pi/180)
dat$v <- -dat$windspeed*cos(dat$winddirection*pi/180)
#par(bg="black", fg="white", col.lab="white", col.axis="white")
plot(northing~easting, data=dat, type="n", xlab="Easting (m)", ylab="Northing (m)")
streamlines(dat$easting, dat$northing, dat$u, dat$v,
step.dist=1000, max.dist=50000, col.ramp=c("blue","green","yellow","red"),
fade.col="white", length=0, lwd=5)
回答2:
I do not think this would be enough data to do what you request:
require(plotrix)
require(maps)
map("world",xlim=c(69,85),ylim= c(20,35))
with(dat,
vectorField(windspeed, winddirection, longitude, latitude , vecspec="deg") )
After staring at the output a bit, I think there may be problems with how I am using that function or with the function itself. The orientations of the arrows seems wrong. Likewise I think the TeachingDemos vector field is not well done, but here is what I get:
require(TeachingDemos)
map("world",xlim=c(69,85),ylim= c(20,35))
with(dat, my.symbols(x=longitude, y=latitude,
symb= ms.arrows, length=windspeed/10, angle=2*pi*winddirection/360))
This plot seems to have sufficient variation in direction but the arrow heads seem to vary erratically in size. In any event neither of these plots suggests that this data can be used to construct streamlines. The data is both too sparse and internally contradictory as far as wid direction at adjacent locations.
来源：https://stackoverflow.com/questions/19125598/streamlines-for-irregular-spaced-wind-data-in-r