【发布时间】：2013-01-29 16:54:54
【问题描述】：
总结：我可以在lattice::layerplot 上显示经纬度图，我可以在spam::image 上显示兰伯特保角圆锥 (LCC) 地图。如何在lattice::layerplot 上显示 LCC 地图？以下是如何不执行此操作的示例——感谢您提供修复帮助（甚至只是调试）。
详情：
我一直在使用格子图形（通过latticeExtra 和rasterVis）成功地显示未投影的经纬度全球大气数据，效果很好（尽管我肯定对ggplot/ggmap 很感兴趣） .特别是，我可以用地图覆盖这些地块，这对于我正在做的工作来说是必不可少的。但是，我目前无法将 lattice::layerplot 用于某些 区域 数据投影 LCC：数据绘图，但我无法获得要叠加的地图。我可以通过更粗略的方式做到这一点，但更愿意知道如何在lattice/rasterVis 或类似的方式（例如ggplot/ggmap）中做到这一点。下面是两个几乎独立的示例……但是如果您已经知道如何执行此操作，请告诉我，我将跳过调试。 （我在一个项目上落后了。）
netCDF 数据 ozone_lcc.nc 附带 CRAN package=M3 ... 除了 M3 提供它
system.file("extdata/ozone_lcc.ncf", package="M3")
并且该文件扩展名 (.ncf) 当前会导致 CRAN package=raster 出现问题（请参阅 this post）。您可以重命名该文件（并将其放入当前工作目录），或者您可以下载just that file (270 kB)，或者您可以从M3 tarball 获取它（但记得重命名它！）。
然后，您可以运行以下任何示例（前提是 (IIRC) 您没有运行 Windows，package=M3 不会在其上构建（但 ICBW）），根据需要更改常量以适应您的系统。示例 1 生成我知道（根据以前的经验）将与 levelplot 中的 raster 一起使用的类型的映射；但是，在这种情况下，地图和数据/栅格的坐标值不匹配。示例 2 使用旧式基本图形，实际上绘制了数据和地图；不幸的是，我不知道如何制作它在levelplot 上生成的那种地图。因为我想让这段代码与使用raster 和levelplot 的其他代码一起工作，所以这是个问题。
示例 1： 产生类似
的输出##### start example 1 #####
library("M3") # http://cran.r-project.org/web/packages/M3/
library("rasterVis") # http://cran.r-project.org/web/packages/rasterVis/
## Use an example file with projection=Lambert conformal conic.
# lcc.file <- system.file("extdata/ozone_lcc.ncf", package="M3")
lcc.file <- "./ozone_lcc.nc" # unfortunate problem with raster::raster
lcc.proj4 <- M3::get.proj.info.M3(lcc.file)
lcc.proj4 # debugging
# [1] "+proj=lcc +lat_1=33 +lat_2=45 +lat_0=40 +lon_0=-97 +a=6370000 +b=6370000"
# Note +lat_0=40 +lat_1=33 +lat_2=45 for maps::map@projection (below)
lcc.crs <- sp::CRS(lcc.proj4)
lcc.pdf <- "./ozone_lcc.example1.pdf" # for output
## Read in variable with daily ozone.
# o3.raster <- raster::raster(x=lcc.file, varname="O3", crs=lcc.crs)
# ozone_lcc.nc has 4 timesteps, choose 1 at random
o3.raster <- raster::raster(x=lcc.file, varname="O3", crs=lcc.crs, level=1)
o3.raster@crs <- lcc.crs # why does the above assignment not take?
# start debugging
o3.raster
summary(coordinates(o3.raster)) # thanks, Felix Andrews!
M3::get.grid.info.M3(lcc.file)
# end debugging
# > o3.raster
# class : RasterLayer
# band : 1
# dimensions : 112, 148, 16576 (nrow, ncol, ncell)
# resolution : 1, 1 (x, y)
# extent : 0.5, 148.5, 0.5, 112.5 (xmin, xmax, ymin, ymax)
# coord. ref. : +proj=lcc +lat_1=33 +lat_2=45 +lat_0=40 +lon_0=-97 +a=6370000 +b=6370000
# data source : .../ozone_lcc.nc
# names : O3
# z-value : 1
# zvar : O3
# level : 1
# > summary(coordinates(o3.raster))
# x y
# Min. : 1.00 Min. : 1.00
# 1st Qu.: 37.75 1st Qu.: 28.75
# Median : 74.50 Median : 56.50
# Mean : 74.50 Mean : 56.50
# 3rd Qu.:111.25 3rd Qu.: 84.25
# Max. :148.00 Max. :112.00
# > M3::get.grid.info.M3(lcc.file)
# $x.orig
# [1] -2736000
# $y.orig
# [1] -2088000
# $x.cell.width
# [1] 36000
# $y.cell.width
# [1] 36000
# $hz.units
# [1] "m"
# $ncols
# [1] 148
# $nrows
# [1] 112
# $nlays
# [1] 1
# The grid (`coordinates(o3.raster)`) is integers, because this
# data is stored using the IOAPI convention. IOAPI makes the grid
# Cartesian by using an (approximately) equiareal projection (LCC)
# and abstracting grid positioning to metadata stored in netCDF
# global attributes. Some of this spatial metadata can be accessed
# by `M3::get.grid.info.M3` (above), and some can be accessed via
# the coordinate reference system (`CRS`, see `lcc.proj4` above)
## Get US state boundaries in projection units.
state.map <- maps::map(
database="state", projection="lambert", par=c(33,45), plot=FALSE)
# parameters to lambert: ^^^^^^^^^^^^
# see mapproj::mapproject
state.map.shp <-
maptools::map2SpatialLines(state.map, proj4string=lcc.crs)
# start debugging
# thanks, Felix Andrews!
class(state.map.shp)
summary(do.call("rbind",
unlist(coordinates(state.map.shp), recursive=FALSE)))
# end debugging
# > class(state.map.shp)
# [1] "SpatialLines"
# attr(,"package")
# [1] "sp"
# > summary(do.call("rbind",
# + unlist(coordinates(state.map.shp), recursive=FALSE)))
# V1 V2
# Min. :-0.234093 Min. :-0.9169
# 1st Qu.:-0.000333 1st Qu.:-0.8289
# Median : 0.080378 Median :-0.7660
# Mean : 0.058492 Mean :-0.7711
# 3rd Qu.: 0.162993 3rd Qu.:-0.7116
# Max. : 0.221294 Max. :-0.6343
# I can't see how to relate these coordinates to `coordinates(o3.raster)`
pdf(file=lcc.pdf)
rasterVis::levelplot(o3.raster, margin=FALSE
) + latticeExtra::layer(
sp::sp.lines(state.map.shp, lwd=0.8, col='darkgray'))
dev.off()
# change this for viewing PDF on your system
system(sprintf("xpdf %s", lcc.pdf))
# data looks correct, map invisible
## Try again, with lambert(40,33)
state.map <- maps::map(
database="state", projection="lambert", par=c(40,33), plot=FALSE)
# parameters to lambert: ^^^^^^^^^^^^
# see mapproj::mapproject
state.map.shp <-
maptools::map2SpatialLines(state.map, proj4string=lcc.crs)
# start debugging
summary(do.call("rbind",
unlist(coordinates(state.map.shp), recursive=FALSE)))
# end debugging
# > summary(do.call("rbind",
# + unlist(coordinates(state.map.shp), recursive=FALSE)))
# V1 V2
# Min. :-0.2226344 Min. :-0.9124
# 1st Qu.:-0.0003149 1st Qu.:-0.8295
# Median : 0.0763322 Median :-0.7706
# Mean : 0.0553948 Mean :-0.7752
# 3rd Qu.: 0.1546465 3rd Qu.:-0.7190
# Max. : 0.2112617 Max. :-0.6458
# no real change from previous `coordinates(state.map.shp)`
pdf(file=lcc.pdf)
rasterVis::levelplot(o3.raster, margin=FALSE
) + latticeExtra::layer(
sp::sp.lines(state.map.shp, lwd=0.8, col='darkgray'))
dev.off()
system(sprintf("xpdf %s", lcc.pdf))
# as expected, same as before: data looks correct, map invisible
##### end example 1 #####
示例 2： 产生类似
的输出##### start example 2 #####
# Following adapted from what is installed in my
# .../R/x86_64-pc-linux-gnu-library/2.14/m3AqfigExampleScript.r
# (probably by my sysadmin), which also greatly resembles
# https://wiki.epa.gov/amad/index.php/R_packages_developed_in_AMAD
# which is behind a firewall :-(
## EXAMPLE WITH LAMBERT CONIC CONFORMAL FILE.
library("M3")
library("aqfig") # http://cran.r-project.org/web/packages/aqfig/
## Use an example file with LCC projection: either local download ...
lcc.file <- "./ozone_lcc.nc"
## ... or as installed by package=M3:
# lcc.file <- system.file("extdata/ozone_lcc.ncf", package="M3")
## Choose the one that works for you.
lcc.pdf <- "./ozone_lcc.example2.pdf" # for output
## READ AND PLOT OZONE FROM FILE WITH LCC PROJECTION.
## Read in variable with daily ozone. Note that we can give dates
## rather than date-times, and that will include all time steps
## anytime during those days. Or, we can give lower and upper bounds
## and all time steps between these will be taken.
o3 <- M3::get.M3.var(
file=lcc.file, var="O3", hz.units="m",
ldatetime=as.Date("2001-07-01"), udatetime=as.Date("2001-07-04"))
# start debugging
class(o3)
summary(o3)
summary(o3$x.cell.ctr)
# end debugging
# > class(o3)
# [1] "list"
# > summary(o3)
# Length Class Mode
# data 66304 -none- numeric
# data.units 1 -none- character
# x.cell.ctr 148 -none- numeric
# y.cell.ctr 112 -none- numeric
# hz.units 1 -none- character
# rows 112 -none- numeric
# cols 148 -none- numeric
# layers 1 -none- numeric
# datetime 4 POSIXct numeric
# > summary(o3$x.cell.ctr)
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# -2718000 -1395000 -72000 -72000 1251000 2574000
# Note how these grid coordinates relate to the IOAPI metadata above:
# min(o3$x.cell.ctr) == -2718000
# == -2736000 + (36000/2) == x.orig + (x.cell.width/2)
## Get colors and map boundaries for plot.
library("fields")
col.rng <- tim.colors(20)
detach("package:fields")
## Get state boundaries in projection units.
map.lines <- M3::get.map.lines.M3.proj(
file=lcc.file, database="state", units="m")
# start debugging
class(map.lines)
summary(map.lines)
summary(map.lines$coords)
# end debugging
# > class(map.lines)
# [1] "list"
# > summary(map.lines)
# Length Class Mode
# coords 23374 -none- numeric
# units 1 -none- character
# > summary(map.lines$coords)
# x y
# Min. :-2272238 Min. :-1567156
# 1st Qu.: 94244 1st Qu.: -673953
# Median : 913204 Median : -26948
# Mean : 689997 Mean : -84644
# 3rd Qu.: 1744969 3rd Qu.: 524531
# Max. : 2322260 Max. : 1265778
# NA's :168 NA's :168
## Set color boundaries to encompass the complete data range.
z.rng <- range(as.vector(o3$data))
## Make a color tile plot of the ozone for the first day (2001-07-01).
pdf(file=lcc.pdf)
image(o3$x.cell.ctr, o3$y.cell.ctr, o3$data[,,1,1],
col=col.rng, zlim=z.rng,
xlab="x-coord (m)", ylab="y-coord (m)")
## Put date-time string and chemical name (O3) into a format I can use
## to label the actual figure.
date.str <- format(o3$datetime[1], "%Y-%m-%d")
title(main=bquote(paste(O[3], " on ", .(date.str), sep="")))
## Put the state boundaries on the plot.
lines(map.lines$coords)
## Add legend to right of plot. NOTE: YOU CANNOT ADD TO THE PLOT
## AFTER USING vertical.image.legend(). Before making a new plot,
## open a new device or turn this device off.
vertical.image.legend(zlim=z.rng, col=col.rng)
dev.off() # close the plot if you haven't already, ...
# ... and change the following to display PDFs on your system.
system(sprintf("xpdf %s", lcc.pdf))
# data displays with state map
##### end example 2 #####
但我看不出如何从M3::get.map.lines.M3.proj 返回的简单矩阵（等，但不多）到sp::sp.lines 想要的SpatialLines，更不用说latticeExtra::layer 想要的任何东西。 （我是一个新手，发现格子文档相当难以理解。）此外，我更愿意避免手动进行上面的 IOAPI 转换（尽管我当然更愿意这样做而不是跳过旧式图形的箍)。
【问题讨论】：
标签： r plot maps lattice netcdf