The goal of lazyraster is to get raster data on demand at the right resolution. This means that you can define a graphics device and then stream just the right amount of pixels to fill it from a GDAL data source.
The package can be installed from Github.
## install.packages("remotes")
remotes::install_github("hypertidy/lazyraster")See vapour for more on the installation prerequisites.
There are functions lazyraster() to act like the raster::raster()
function and provide information but no data, and crop() to act like
raster::crop() and then as_raster to break the lazy chain and build
an actual raster object. The size of the currently open (or
latent-default) device is used as a reasonable size for the output grid,
but can be controlled by argument dim.
When the data is read lazyraster can specify the exact dimensions of
the output raster, and by default a reasonable guess at the number of
pixels required to fill the current device is used.
A variety of resampling algorithms are available (nearest neighbour is the default, see this list for more) and will be applied to reduce or increase the resolution.
We can’t utilize the RasterIO level-of-detail functionality for non-GDAL sources.
We can’t control the details of the data type. (This is WIP)
This uses a standard internal functionality of GDAL, the RasterIO
function of the
GDALRasterBand. This
is used in a lot of different software, and is obviously pretty robust
and well tested by the GDAL community, but I only really have experience
with one product (commercial, now defunct) that used it extensively for
live interactive visualization and data streaming. I haven’t found any
problems with it at all using it in R, but the support for it is very
minimal. You can access it indirectly using rgdal::readGDAL for the
underlying function, as the raster package does, and there is the
obscure ‘RasterIO’ list in the stars::read_stars(), but so far I can’t
get this to work for tile servers.
To make this work we use the GDAL package
vapour. All of the ease-of-use
code is in this package, vapour is pointedly bare-bones and provides
very little interpretation of a data source because it is designed for
use in development.
Connect lazily to a GeoTIFF, see details of what’s there, crop to a section and then read it in and plot.
This is not a huge file, but is easily accessible and demonstrates the idea.
First we connect to a source and show two versions, the first is information about the data in its native form (286 rows and 143 columns), and then an actual RasterLayer but at a very small requested size (24 rows by 12 columns).
sstfile <- system.file("extdata/sst.tif", package = "vapour")
library(lazyraster)
lazy <- lazyraster(sstfile)
lazy ## stay lazy
#> class : LazyRaster
#> dimensions : 286, 143 (nrow, ncol)
#> resolution : 0.07000000, 0.07000389 (x, y)
#> extent : 140.00000, 150.01000, -60.01833, -39.99722 (xmin, xmax, ymin, ymax)
#> crs : +proj=longlat +datum=WGS84 +no_defs
#> values : NA, NA (min, max - range from entire extent)
#> window extent : <whole extent>
#> window index : <->
#> window dimension : (full) (ncol, nrow)
## be only so lazy
as_raster(lazy, dim = c(12, 24))
#> class : RasterLayer
#> dimensions : 24, 12, 288 (nrow, ncol, ncell)
#> resolution : 0.8341667, 0.834213 (x, y)
#> extent : 140, 150.01, -60.01833, -39.99722 (xmin, xmax, ymin, ymax)
#> crs : +proj=longlat +datum=WGS84 +no_defs
#> source : memory
#> names : layer
#> values : 271.602, 289.478 (min, max)The call to as_raster read actual data from the file, hence the
difference between the range of data values reported first from the
whole extent, and then reported by raster itself for the resample data
read in.
Now let lazyraster make its own choice about the size of the output.
This will be based on the return value of dev.size("px").
## note how we actually resample up because this data is not very large
as_raster(lazy)
#> class : RasterLayer
#> dimensions : 480, 672, 322560 (nrow, ncol, ncell)
#> resolution : 0.01489583, 0.04171065 (x, y)
#> extent : 140, 150.01, -60.01833, -39.99722 (xmin, xmax, ymin, ymax)
#> crs : +proj=longlat +datum=WGS84 +no_defs
#> source : memory
#> names : layer
#> values : 271.35, 289.859 (min, max)More concretely, if we open a graphics device at a given size the raster data read in will match it. (This is not the best choice but works fine for demonstration and experimenting.)
## what do we get if we set up a bitmap device
tf <- tempfile(fileext = "png")
png(tf, height = 50, width = 40)
as_raster(lazy)
#> class : RasterLayer
#> dimensions : 50, 40, 2000 (nrow, ncol, ncell)
#> resolution : 0.25025, 0.4004222 (x, y)
#> extent : 140, 150.01, -60.01833, -39.99722 (xmin, xmax, ymin, ymax)
#> crs : +proj=longlat +datum=WGS84 +no_defs
#> source : memory
#> names : layer
#> values : 271.41, 289.815 (min, max)
#plot(as_raster(lazy))
dev.off()
#> png
#> 2
unlink(tf)This will work on really big files or online tile servers. Here are some online examples.
## a global satellite image tile server
vearth <- '<GDAL_WMS> <Service name="VirtualEarth">
<ServerUrl>http://a${server_num}.ortho.tiles.virtualearth.net/tiles/a${quadkey}.jpeg?g=90</ServerUrl></Service>
<MaxConnections>4</MaxConnections> <Cache/> </GDAL_WMS>'
## a USA topographic map service
usgs <- paste0("WMTS:https://basemap.nationalmap.gov/arcgis/rest/services/USGSTopo/",
"MapServer/WMTS/1.0.0/WMTSCapabilities.xml,layer=USGSTopo,tilematrixset=default028mm")
## a global elevation service
awselev <- "<GDAL_WMS> <Service name=\"TMS\"> <ServerUrl>https://s3.amazonaws.com/elevation-tiles-prod/geotiff/${z}/${x}/${y}.tif</ServerUrl> </Service> <DataWindow> <UpperLeftX>-20037508.34</UpperLeftX> <UpperLeftY>20037508.34</UpperLeftY> <LowerRightX>20037508.34</LowerRightX> <LowerRightY>-20037508.34</LowerRightY> <TileLevel>14</TileLevel> <TileCountX>1</TileCountX> <TileCountY>1</TileCountY> <YOrigin>top</YOrigin> </DataWindow> <Projection>EPSG:3857</Projection> <BlockSizeX>512</BlockSizeX> <BlockSizeY>512</BlockSizeY> <BandsCount>1</BandsCount> <DataType>Int16</DataType> <ZeroBlockHttpCodes>403,404</ZeroBlockHttpCodes> <DataValues> <NoData>-32768</NoData> </DataValues> <Cache/> </GDAL_WMS>"
library(raster)
ve <- lazyraster(vearth)
#plotRGB(as_raster(ve, band = 1:3, c(1024, 1024)))
#(ve1 <- as_raster(ve, band = 1:3, c(1024, 1024)))
plotRGB(as_raster(ve, band = 1:3, c(1024, 1024)))# raster::plotRGB(as_raster(crop(ve, merc(0, 51.5, wh = 1e4)), band = 1:3, c(1024, 1024)))
#
# us <- lazyraster(usgs)
# raster::plotRGB(as_raster(us, band = 1:3, c(1024, 1024)))
# raster::plotRGB(as_raster(crop(us, merc(-74, 41), wh = 150e3), dev.size("px"), band = 1:3, resample = "bilinear"))
#
# elev <- lazyraster(awselev)
# plot(elev, col = grey.colors(24))
# plot(crop(elev, merc(147, -41, wh = 150e3)), zlim = c(0, NA), col = grey.colors(24))(This example can’t work on your computer probably given use of local raadtools, but try it on your favourite big file).
This takes a fairly large grid and plots just enough detail by reading just enough detail for the plot space. That’s all that happens.
library(raadtools)
f <- raadtools::topofile("gebco_19")
lazyraster(f)
#> class : LazyRaster
#> dimensions : 43200, 86400 (nrow, ncol)
#> resolution : 0.004166667, 0.004166667 (x, y)
#> extent : -180.0000, 180.0000, -90.0000, 90.0000 (xmin, xmax, ymin, ymax)
#> crs : +proj=longlat +datum=WGS84 +no_defs
#> values : NA, NA (min, max - range from entire extent)
#> window extent : <whole extent>
#> window index : <->
#> window dimension : (full) (ncol, nrow)
library(raster)
op <- par(mar = rep(0, 4))
system.time({
rworld <- lazyraster(f)
plot(rworld, col = grey(seq(0, 1, length = 100)), axes = FALSE, xlab = "", ylab = "", asp = "", legend = FALSE)
})#> user system elapsed
#> 0.775 0.004 0.802
par(op)
Now, plot the same kind of image but zoom in on a region purposefully. The resolution provided has adapted to the context asked for.
rtas <- crop(rworld, extent(143.4, 149, -44, -39.1))
plot(rtas, col = grey(seq(0, 1, length.out = 64)), zlim = c(0, 1550))
rbath <- as_raster(rtas)
rbath[rbath > 0] <- NA
contour(rbath, add = TRUE, levels = quantile(rbath, prob = seq(0, 1, length.out = 8)))
title("Tasmania topography + bathymetric contours, from Gebco 2019", cex.main = 0.85)This is not just to plot big rasters, it’s potentially useful for streaming gridded data to a device that is resizing the view port interactively. We also use it to explore a data set for useability and general coverage, and designing sensible resampling workflows for very large data models.
## PLEASE take care with this url, it is a 11Gb GeoTIFF
## https://astrogeology.usgs.gov/search/map/Mars/Topography/HRSC_MOLA_Blend/Mars_HRSC_MOLA_BlendDEM_Global_200mp_v2
dangerurl <- "https://planetarymaps.usgs.gov/mosaic/Mars/HRSC_MOLA_Blend/Mars_HRSC_MOLA_BlendDEM_Global_200mp_v2.tif"
## note prefix with GDAL's URL indirection
mars <- lazyraster(file.path("/vsicurl", dangerurl))
mars
#> class : LazyRaster
#> dimensions : 53347, 106694 (nrow, ncol)
#> resolution : 0.003374121, 0.003374121 (x, y)
#> extent : -180.00000, 179.99845, -89.99922, 90.00000 (xmin, xmax, ymin, ymax)
#> crs : +proj=longlat +R=3396190 +no_defs
#> values : NA, NA (min, max - range from entire extent)
#> window extent : <whole extent>
#> window index : <->
#> window dimension : (full) (ncol, nrow)
plot(mars, col = grey(seq(0, 1, length = 256)))as_raster(mars)
#> class : RasterLayer
#> dimensions : 480, 672, 322560 (nrow, ncol, ncell)
#> resolution : 0.535712, 0.3749984 (x, y)
#> extent : -180, 179.9984, -89.99922, 90 (xmin, xmax, ymin, ymax)
#> crs : +proj=longlat +R=3396190 +no_defs
#> source : memory
#> names : layer
#> values : -7716, 20910 (min, max)
plot(crop(mars, raster::extent(-100, -30, -18, 5)),
col = grey(seq(0, 1, length = 256)))We’ve successfully used it to plot a DEM of Australia from a 67 Gb ESRI binary grid (ADF) supplied by GeoScience Australia in a few minutes (the grid is more than 1e5 pixels each dimension, so I’m not having this document build do the job but here’s a figure I prepared earlier).
gafile <- raadtools::topofile("ga_srtm")
ga <- lazyraster(gafile)
ga
plot(ga)# Make a TMS source and read at the desired resolution.
# More here: http://rpubs.com/cyclemumner/358029
#
library(lazyraster)
gibs_xml <- function(date, level = 3) {
date <- format(date, "%Y-%m-%d")
sprintf('<GDAL_WMS>
<Service name="TMS">
<ServerUrl>
https://gibs.earthdata.nasa.gov/wmts/epsg3413/best/MODIS_Terra_CorrectedReflectance_TrueColor/default/%s/250m/${z}/${y}/${x}.jpg</ServerUrl>
</Service>
<DataWindow>
<UpperLeftX>-4194304</UpperLeftX>
<UpperLeftY>4194304</UpperLeftY>
<LowerRightX>4194304</LowerRightX>
<LowerRightY>-4194304</LowerRightY>
<TileLevel>%i</TileLevel>
<TileCountX>2</TileCountX>
<TileCountY>2</TileCountY>
<YOrigin>top</YOrigin>
</DataWindow>
<Projection>EPSG:3413</Projection>
<BlockSizeX>512</BlockSizeX>
<BlockSizeY>512</BlockSizeY>
<BandsCount>3</BandsCount>
</GDAL_WMS>
', date, level)
}
s <- gibs_xml(Sys.Date()-10)
gibs <- lazyraster(s)
r2 <- as_raster(gibs, dim = c(150, 150))
library(raster)
## run the same simplification but with a different resampling
## method
plot(as_raster(gibs, dim = c(150, 150), resample = "CubicSpline"), col = head(palr::sstPal(64), 45))
#> Warning: 'palr::sstPal' is deprecated.
#> Use 'sst_pal' instead.
#> See help("Deprecated")## run with a different extent
e <- extent(-806000, 1080000, -3200000, -500000)
plot(as_raster(crop(gibs, e), dim = c(150, 150), resample = "CubicSpline"), col = head(palr::sstPal(64), 45))
#> Warning: 'palr::sstPal' is deprecated.
#> Use 'sst_pal' instead.
#> See help("Deprecated")Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.