R中不規則間隔風數據的流線？

Question

我有一些車站的風力數據。數據包括csv文件中每個臺站的緯度，經度，風速和風向。這些數據不是定期分配數據。我有要求在R語言中繪製這些數據的流線。

我試過情侶套餐rasterVis爲STREAMPLOT()，TeachingDemos爲My.Symbols通過互聯網搜索，但我沒有成功。

這是我正在談論的一個示例圖。 http://wx.gmu.edu/dev/clim301/850stream.png

此處還有一些來自csv文件的示例數據，我嘗試繪製流線圖。

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 

有人可以幫我畫出流線爲不規則的風數據嗎？

Answer 1

我不認爲這將是足夠的數據做你要求什麼：

require(plotrix) 
require(maps) 
map("world",xlim=c(69,85),ylim= c(20,35)) 
with(dat, 
    vectorField(windspeed, winddirection, longitude, latitude , vecspec="deg")) 

在輸出凝視了一下之後，我覺得有可能是我如何使用功能或與問題功能本身。箭頭的方向似乎是錯誤的。同樣我覺得TeachingDemos矢量場沒有做好，但這裏是我得到：

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)) 

這情節似乎有足夠的方向變化，但箭頭似乎在大小不規則變化。無論如何，這些圖都沒有表明這些數據可以用來構建流線。這些數據在鄰近地點的方向上太稀疏且內部矛盾。

Answer 2

你一樣，我想以可視化的同類數據streamlnes的，我沒能找到會做的伎倆功能...所以我的工作我自己的原油功能：

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, ...) 
    } 
    } 

} 

的功能由interp的功能在akima包供電，並與一些擺弄，它可以產生一些半體面的視覺效果：

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)