raster

With your help in another thread I have managed to plot some global maps. First I convert meteorological GRIB2 data to Netcdf and then plot the global maps. Now I want to plot just a subregion of the map. I have tried crop command and succesfully extracted the subregion of the global nc file. But when plotting I can't find how to control axis limits. It plots a map bigger than data region so big white spaces appear on both sides. This is the script I'm using to plot maps library("ncdf") library("raster") library("maptools") DIA=format(Sys.time(), "%Y%m%d00") # Data d'avui url=sprintf("ftp:/

Clarification: I somehow left out the key aspect: not using os.system or subprocess - just the python API. I'm trying to convert a section of a NOAA GTX offset grid for vertical datum transformations and not totally following how to do this in GDAL with python. I'd like to take a grid (in this case a Bathymetry Attributed Grid, but it could be a geotif) and use it as the template that I'd like to do to. If I can do this right, I have a feeling that it will greatly help people make use of this type of data. Here is what I have that is definitely not working. When I run gdalinfo on the resulting

I have recently started to work with netcdf in R. Sample data is here: http://www.earthstat.org/data-download/ > Harvested area and yield for 175 crops > individual crops > soybean_HarvAreaYield2000_NetCDF In this folder, there is a netcdf file called soybean_AreaYieldProduction.nc This is how I open the netcdf library(ncdf4) dat <- nc_open("soybean_AreaYieldProduction.nc") print(soy) 1 variables (excluding dimension variables): float soybeanData[longitude,latitude,level,time] LayerDescriptions: struct(5).Data(:,:,1/2/3/4/5/6) to access data layer: 1=Harvested Area fraction, 2=Yield 3

In R (Win64), I'm trying to plot a combination of raster images and histograms in a single plot window using the layout() command with a matrix defining the layout. Here's some sample code with simplified data: library(raster) r <- raster(ncols=5, nrows=5, xmn=1, xmx=5, ymn=1, ymx=5) rast1 <- rasterize(expand.grid(1:5,1:5), r, rnorm(25)) rast2 <- rasterize(expand.grid(1:5,1:5), r, rnorm(25)) rast3 <- rasterize(expand.grid(1:5,1:5), r, rnorm(25)) layout(matrix(c(1,2,3,4,1,2,3,5,1,2,3,6), 3, 4, byrow=T)) layout.show(6) plot(rast1, axes=F, ann=F, legend=F, box=F, useRaster=T) plot(rast2, axes=F,

In R I can easily compute the max/min value of each cell in a georeferenced raster stack using the max/min commands. set.seed(42) require(raster) r1 <- raster(nrows=10, ncols=10) r2=r3=r4=r1 r1[]= runif(ncell(r1)) r2[]= runif(ncell(r1))+0.2 r3[]= runif(ncell(r1))-0.2 r4[]= runif(ncell(r1)) rs=stack(r1,r2,r3,r4) plot(rs) max(rs) min(rs) However, I have been trying to find a way to find the second highest values across a stack. In my case, each raster on the stack denotes performance of a particular model across space. I would like to compare the first vs second best values to determine how much