ffraster allows loading a file-backed raster as an ff object, and some
wrappers to deal with the rasterfile
format
using ff’s array abstractions linked to a file on disk.
library(raster)
b <- brick("/some/huge/brick.grd")
library(ffraster)
ff_object(b)ffraster tries to solve the following problem, posed by raster’s native binary “rasterfile” format for long time series.
- a multilayer rasterfile can only be created from an existing multi-layer raster
- setting the values of a rasterfile for a given layer reads all the data into memory and cuts the link to the rasterfile
Create a brick by instantiating the file with an ff “out of memory”
array and populating the data layer by layer using ff’s array methods.
First, obtain the SST data use in rasterVis::hovmoller, we end up with
several file paths to NetCDF files in files.
td <- tempdir()
curl::curl_download('https://raw.github.com/oscarperpinan/spacetime-vis/master/data/SISmm2008_CMSAF.zip',
destfile = file.path(td, 'SISmm2008_CMSAF.zip'))
unzip(file.path(td, 'SISmm2008_CMSAF.zip'), exdir = td)
files <- file.path(td, list.files(td, pattern = "SISmm.*UD.nc$"))Now set up the .grd metadata file and a ff-array as an interface to the raw binary data in the .gri file.
library(raster)
#> Loading required package: sp
## we'll create a brick layer by layer in this file (.grd/.gri)
r_brickfile <- rasterTmpFile()
## we need the first layer to initiate the target as a template
r0 <- raster(files[1])
#> Loading required namespace: ncdf4
library(ffraster)
ffraster:::.writeGRD(r0,
dataType = "FLT4S",
filename = r_brickfile,
nbands = length(files),
## if dates are known, input them here for each file
dates = NULL)
#> [1] TRUE
## use mode "single", float32 (or double, float64)
ff_array <- ffrarr(c(nrow(r0), ncol(r0),
length(files)),
mode = "single",
filename = r_brickfile,
readonly = FALSE)
## now loop and populate
for (i in seq_along(files)) {
## in the loop we read each layer from file (or whatever source of layers we have)
r <- raster(files[i])
## the ff array can be written to by treating like a slice in a 3D array
## the default is transpose, but note other layouts are possible for rasterfile and in ff
ff_array[,,i] <- values(t(r))
}
plot(brick(r_brickfile), zlim = c(36, 365))
## show that we get the same result
plot(stack(files), zlim = c(36, 365))
See related work in library mmap, in GDAL virtualmem, VRT-linked binary, GDAL driver for R-raster, R packages ff, matter, spatial.tools, mmap, bigmemory, rasterfaster, Manifold’s Raw Binary surface.
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