如何更改R中的分辨率（或regrid）数据

3 回答

• 0

  这是一种使用 plyr::ddply() 的方法 - 可能它对于您的表大小来说有点慢，这取决于您想要重新网格化的频率 . 我将考虑使用data.table来实现它的方法，它应该更快：

  require(plyr)
  # make your data frame
  I<-data.frame(lat=seq(0.5,1000,0.5),lon=1,tmp=sample(1:100,2000,replace=T))
  
  # make an adjustment grid
  k<-expand.grid(c(0,0.25),c(0,0.25),0)
  
  # use plyr:ddply() to expand out each entry into the correponding 4 entries
  new_I<-ddply(I,.(lat,lon),function(x)as.list(x)+k)
  colnames(new_I)<-c("lat","lon","newlat","newlon","tmp")
  
  head(new_I)
  
    lat lon newlat newlon tmp
  1 0.5   1   0.50   1.00  64
  2 0.5   1   0.75   1.00  64
  3 0.5   1   0.50   1.25  64
  4 0.5   1   0.75   1.25  64
  5 1.0   1   1.00   1.00  31
  6 1.0   1   1.25   1.00  31
  

  实际上考虑一下，从时间的角度来看，这是一个更好的方法（尽管它有点像黑客，并且让你对未来可能希望做的额外数据处理的控制较少），但是2m需要6.5秒>> 8M行 .

  # make your data frame
  I<-data.frame(lat=seq(0.5,1000000,0.5),lon=1,tmp=sample(1:100,2000000,replace=T))
  
  # make an adjustment vector
  v<-rep(0.25,times=2000000)
  
  # make 3 new tables, apply the vector appropriately, and rbind
  I_latshift<-I
  I_lonshift<-I
  I_bothshift<-I
  
  I_latshift$lat<-I_latshift$lat+v
  I_lonshift$lon<-I_lonshift$lon+v
  I_bothshift$lat<-I_bothshift$lat+v
  I_bothshift$lon<-I_bothshift$lon+v
  
  I<-rbind(I,I_bothshift,I_latshift,I_lonshift)
  
  # sort it for neatness
  I<-I[with(I, order(lat, lon)), ]
  
  
  head(I)
  
           lat  lon tmp
  1       0.50 1.00   3
  6000001 0.50 1.25   3
  4000001 0.75 1.00   3
  2000001 0.75 1.25   3
  2       1.00 1.00  88
  6000002 1.00 1.25  88
  

  回复于 2024-05-11T02:59:59+08:00
• 0

  R包中存在一个解决方案 raster . 它如下

  library("ncdf4")
  library("raster")
  nc <- nc_open("my_file.nc")
  lon <- ncvar_get(nc, "lon")
  lat <- ncvar_get(nc, "lat")
  time <- ncvar_get(nc, "time")
  dname <- "pre"        ## pre for the short name of precpitation 
  nlon <- dim(lon)
  nlat <- dim(lat)
  nt <- dim(time)
  lonlat <- expand.grid(lon, lat)    # make grid of given longitude and latitude 
  pr.array <- ncvar_get(nc, dname)
  dlname <- ncatt_get(nc, dname, "long_name")
  dunits <- ncatt_get(nc, dname, "units")
  fillvalue <- ncatt_get(nc, dname, "_FillValue")
  
  pr.vec.long <- as.vector(pr.array)
  pr.mat <- matrix(pr.vec.long, nrow = nlon * nlat, ncol = nt)
  pr.df <- data.frame(cbind(lonlat, pr.mat))
  
  pr_c <- pr.df[ ,-c(1:2)]
   ### Specific region have been clipped out from global datafile by 
  ## selecting lon and lat range and extract regridded data at 1lon 1lat
   ## resolution.  
  
  x0 <- seq(67.5, 98.5, by = 1) ## choose different resolution, eg. by = 0.5 
  y0 <- seq(6.5, 37.5, by = 1)
  
  
  m <- cbind(x0, y0)
  m <- as.data.frame(m)
  s <- rasterFromXYZ(m)
  pts <- expand.grid(x0, y0)
  pos <- pr.df[ ,c(1:2)]
  l_pr <- apply(pr_c, 2, function(x) cbind(pos, x))
  colnm = c("x","y","z")
  for (j in seq_along(l_pr)){
    colnames(l_pr[[j]]) <- colnm
  }
  
  pr_rstr <- lapply(l_pr, function(x) rasterFromXYZ(x))
  ## Use resample command to regrid the data, here nearest neighbor method can also be chosen by setting method = "ngb"
  pr_bn <- lapply(pr_rstr, function(x) resample(x, s, method = "bilinear"))
  pr_extr <- lapply(pr_bn, function(x) extract(x, pts))
  df_pr <- do.call("cbind", lapply(pr_extr, data.frame))
  ## write dataframe in csv format
  write.csv(df_pr, "my_data_regridded_1.csv")
  

  我希望这能达到目的 .

  回复于 2024-05-11T02:59:59+08:00
• 0

  这不是R解决方案，只是要指出您可以使用CDO从linux / MAC OS环境中的命令行非常容易地重新编译netcdf文件 . 根据您的描述，听起来好像您想要使用最近邻插值，这对于0.25度的常规网格来说就是如此

  cdo remapnn,r1440x720 in.nc out.nc
  

  但是，您也可以使用一阶或二阶保守重映射 . 例如，对于第一个订单：

  cdo remapcon,r1440x720 in.nc out.nc
  

  然后，您可以按照与当前相同的方式将重新划分的字段读入R中 .

  回复于 2024-05-11T02:59:59+08:00