Rasters.jl
Rasters — ModuleRasters
Rasters.jl defines common types and methods for reading, writing and manipulating rasterized spatial data.
These currently include raster arrays like GeoTIFF and NetCDF, R grd files, multi-layered stacks, and multi-file series of arrays and stacks.
A RasterStack of EarthEnv HabitatHeterogeneity layers, trimmed to Australia and plotted with Plots.jl
:warning: Packages extensions and Rasters 0.8 and onwards
On Julia 1.9 we can put additional packages in extensions, so the code only loads when you load a specific package. Rasters.jl was always intended to work like this, and its finally possible. This reduced package using time from many seconds to well under a second.
But, it means you have to manually load packages you need for each backend or additional functionality.
For example, to use the GDAL backend, and download files, you now need to do:
using Rasters, ArchGDAL, RasterDataSourceswhere previously it was just using Rasters.
Sources and packages needed:
:gdal:using ArchGDAL:netcdf:using NCDatasets:grd: built-in.:smap:using HDF5:grib: not yet finished.
Other functionality in extensions:
- Raster data downloads, like
Worldclim{Climate}:using RasterDataSources - Makie plots:
using Makie - Coordinate transformations for gdal rasters:
using CoordinateTransformations
Quick start
Install the package by typing:
]
+add Rastersusing RastersUsing Rasters to read GeoTiff or NetCDF files will output something similar to the following toy examples. This is possible because Rasters.jl extends DimensionalData.jl so that spatial data can be indexed using named dimensions like X, Y and Ti (time) and e.g. spatial coordinates.
using Rasters, Dates
+lon, lat = X(25:1:30), Y(25:1:30)
+ti = Ti(DateTime(2001):Month(1):DateTime(2002))
+ras = Raster(rand(lon, lat, ti)) # this generates random numbers with the dimensions given6×6×13 Raster{Float64,3} with dimensions:
+ X Sampled{Int64} 25:1:30 ForwardOrdered Regular Points,
+ Y Sampled{Int64} 25:1:30 ForwardOrdered Regular Points,
+ Ti Sampled{DateTime} DateTime("2001-01-01T00:00:00"):Month(1):DateTime("2002-01-01T00:00:00") ForwardOrdered Regular Points
+extent: Extent(X = (25, 30), Y = (25, 30), Ti = (DateTime("2001-01-01T00:00:00"), DateTime("2002-01-01T00:00:00")))
+missingval: missing
+values: [:, :, 1]
+ 25 26 27 28 29 30
+ 25 0.9063 0.427328 0.0320967 0.297023 0.0571002 0.891377
+ 26 0.443494 0.867547 0.350546 0.150155 0.24565 0.711039
+ 27 0.745673 0.0991336 0.930332 0.893537 0.805931 0.360583
+ 28 0.512083 0.125287 0.959434 0.354868 0.337824 0.259563
+ 29 0.253849 0.692209 0.774092 0.131798 0.823656 0.390013
+ 30 0.334152 0.136551 0.183555 0.941133 0.450484 0.461862
+[and 12 more slices...]Getting the lookup array from dimensions
lon = lookup(ras, X) # if X is longitude
+lat = lookup(ras, Y) # if Y is latitudeSampled{Int64} ForwardOrdered Regular Points
+wrapping: 25:1:30Select by index
Selecting a time slice by index is done via
ras[Ti(1)]6×6 Raster{Float64,2} with dimensions:
+ X Sampled{Int64} 25:1:30 ForwardOrdered Regular Points,
+ Y Sampled{Int64} 25:1:30 ForwardOrdered Regular Points
+and reference dimensions:
+ Ti Sampled{DateTime} DateTime("2001-01-01T00:00:00"):Month(1):DateTime("2001-01-01T00:00:00") ForwardOrdered Regular Points
+extent: Extent(X = (25, 30), Y = (25, 30))
+missingval: missing
+values: 25 26 27 28 29 30
+ 25 0.9063 0.427328 0.0320967 0.297023 0.0571002 0.891377
+ 26 0.443494 0.867547 0.350546 0.150155 0.24565 0.711039
+ 27 0.745673 0.0991336 0.930332 0.893537 0.805931 0.360583
+ 28 0.512083 0.125287 0.959434 0.354868 0.337824 0.259563
+ 29 0.253849 0.692209 0.774092 0.131798 0.823656 0.390013
+ 30 0.334152 0.136551 0.183555 0.941133 0.450484 0.461862ras[Ti=1]6×6 Raster{Float64,2} with dimensions:
+ X Sampled{Int64} 25:1:30 ForwardOrdered Regular Points,
+ Y Sampled{Int64} 25:1:30 ForwardOrdered Regular Points
+and reference dimensions:
+ Ti Sampled{DateTime} DateTime("2001-01-01T00:00:00"):Month(1):DateTime("2001-01-01T00:00:00") ForwardOrdered Regular Points
+extent: Extent(X = (25, 30), Y = (25, 30))
+missingval: missing
+values: 25 26 27 28 29 30
+ 25 0.9063 0.427328 0.0320967 0.297023 0.0571002 0.891377
+ 26 0.443494 0.867547 0.350546 0.150155 0.24565 0.711039
+ 27 0.745673 0.0991336 0.930332 0.893537 0.805931 0.360583
+ 28 0.512083 0.125287 0.959434 0.354868 0.337824 0.259563
+ 29 0.253849 0.692209 0.774092 0.131798 0.823656 0.390013
+ 30 0.334152 0.136551 0.183555 0.941133 0.450484 0.461862or and interval of indices using the syntax =a:b or (a:b)
ras[Ti(1:10)]6×6×10 Raster{Float64,3} with dimensions:
+ X Sampled{Int64} 25:1:30 ForwardOrdered Regular Points,
+ Y Sampled{Int64} 25:1:30 ForwardOrdered Regular Points,
+ Ti Sampled{DateTime} DateTime("2001-01-01T00:00:00"):Month(1):DateTime("2001-10-01T00:00:00") ForwardOrdered Regular Points
+extent: Extent(X = (25, 30), Y = (25, 30), Ti = (DateTime("2001-01-01T00:00:00"), DateTime("2001-10-01T00:00:00")))
+missingval: missing
+values: [:, :, 1]
+ 25 26 27 28 29 30
+ 25 0.9063 0.427328 0.0320967 0.297023 0.0571002 0.891377
+ 26 0.443494 0.867547 0.350546 0.150155 0.24565 0.711039
+ 27 0.745673 0.0991336 0.930332 0.893537 0.805931 0.360583
+ 28 0.512083 0.125287 0.959434 0.354868 0.337824 0.259563
+ 29 0.253849 0.692209 0.774092 0.131798 0.823656 0.390013
+ 30 0.334152 0.136551 0.183555 0.941133 0.450484 0.461862
+[and 9 more slices...]Select by value
ras[Ti=At(DateTime(2001))]6×6 Raster{Float64,2} with dimensions:
+ X Sampled{Int64} 25:1:30 ForwardOrdered Regular Points,
+ Y Sampled{Int64} 25:1:30 ForwardOrdered Regular Points
+and reference dimensions:
+ Ti Sampled{DateTime} DateTime("2001-01-01T00:00:00"):Month(1):DateTime("2001-01-01T00:00:00") ForwardOrdered Regular Points
+extent: Extent(X = (25, 30), Y = (25, 30))
+missingval: missing
+values: 25 26 27 28 29 30
+ 25 0.9063 0.427328 0.0320967 0.297023 0.0571002 0.891377
+ 26 0.443494 0.867547 0.350546 0.150155 0.24565 0.711039
+ 27 0.745673 0.0991336 0.930332 0.893537 0.805931 0.360583
+ 28 0.512083 0.125287 0.959434 0.354868 0.337824 0.259563
+ 29 0.253849 0.692209 0.774092 0.131798 0.823656 0.390013
+ 30 0.334152 0.136551 0.183555 0.941133 0.450484 0.461862More options are available, like Near, Contains and Where. For more details go here.
Dimensions can also be used in most Base and Statistics methods like mean and reduce where dims arguments are required. Much of the behaviour is covered in the DimensionalData docs.
See the docs for more details and examples for Rasters.jl.
Data-source abstraction
Rasters provides a standardised interface that allows many source data types to be used with identical syntax.
- Scripts and packages building on Rasters.jl can treat
Raster,RasterStack, andRasterSeriesas black boxes.- The data could hold GeoTiff or NetCDF files,
Arrays in memory orCuArrays on the GPU - they will all behave in the same way. RasterStackcan be backed by a Netcdf or HDF5 file, or aNamedTupleofRasterholding.tiffiles, or allRasterin memory.- Users do not have to deal with the specifics of spatial file types.
- The data could hold GeoTiff or NetCDF files,
Projectedlookups with Cylindrical projections can by indexed using other Cylindrical projections by setting themappedcrskeyword on construction. You don't need to know the underlying projection, the conversion is handled automatically. This means lat/lonEPSG(4326)can be used seamlessly if you need that.
Bugs, errors and making issues for Rasters.jl
Raster data is complicated and there are many places for subtle or not-so-subtle bugs to creep in.
We need bug reports to reduce how often they occur over time. But also, we need issues that are easy to reproduce or it isn't practically possible to fix them.
Because there are so many raster file types and variations of them, most of the time we need the exact file that caused your problem to know how to fix it, and be sure that we have actually fixed it when we are done. So fixing a Rasters.jl bug nearly always involves downloading some file and running some code that breaks with it (if you can trigger the bug without a file, thats great! but its not always possible).
To make an issue we can fix quickly (or at all) there are three key steps:
- Include the file in an accessible place on web without autentication or any other work on our part, so we can just get it and find your bug. You can put it on a file hosting platform (e.g. google drive, drop box, whatever you use) and share the url.
- Add a minimum working example to the issue template that first downloads the file, then runs the function that triggers the bug.
- Paste the complete stack trace of the error it produces, right to the bottom, into the issue template. Then we can be sure we reproduced the same problem.
Good issues are really appreciated, but they do take just a little extra effort with Rasters.jl because of this need for files.
Common Applications
Subsetting an object
Regular getindex (e.g. A[1:100, :]) and view work on all objects just as with an Array. view is always lazy, and reads from disk are deferred until getindex is used. DimensionalData.jl Dimensions and Selectors are the other way to subset an object, making use of the objects index to find values at e.g. certain X/Y coordinates. The available selectors are listed here:
At(x) | get the index exactly matching the passed in value(s). |
Near(x) | get the closest index to the passed in value(s). |
Where(f::Function) | filter the array axis by a function of the dimension index values. |
a..b/Between(a, b) | get all indices between two values, excluding the high value. |
Contains(x) | get indices where the value x falls within an interval. |
Use the .. selector to take a view of madagascar:
using Rasters, RasterDataSources, ArchGDAL, Plots
+A = Raster(WorldClim{BioClim}, 5)
+madagascar = view(A, X(43.25 .. 50.48), Y(-25.61 .. -12.04)) # Note the space between .. -12
+plot(madagascar)Methods that change the reslolution or extent of an object
Click through to the function documentation for more in-depth descriptions and examples.
aggregate | aggregate data by the same or different amounts for each axis. |
disaggregate | similarly disaggregate data. |
mosaic | join rasters covering different extents into a single array or file. |
crop | shrink objects to specific dimension sizes or the extent of another object. |
extend | extend objects to specific dimension sizes or the extent of another object. |
trim | trims areas of missing values for arrays and across stack layers. |
resample | resample data to a different size and projection, or snap to another object. |
warp | use gdalwarp on any object, e.g. a multidimensional NetCDF stack. |
Methods that change an objects values:
Note that most regular Julia methods, such as replace, work as for a standard Array. These additional methods are commonly required in GIS applications.
classify | classify values into categories. |
mask | mask an object by a polygon or Raster along X/Y, or other dimensions. |
replace_missing | replace all missing values in an object and update missingval. |
Point, polygon and table operation
rasterize | rasterize points and geometries. |
coverage | get the fraction of each pixel covered by geometries. |
extract | extract values from points or geometries. |
zonal | calculate zonal statistics for an object masked by geometries. |
Methods to load, write and modify data sources:
modify | replace the data in objects. Useful to e.g. move objects to/from a GPU. |
read | read data to memory if it is on disk. |
read! | read data to predefined memory. |
open | open the underlying data for manually reading or writing. |
write | write objects to file. |
Altering and summarising arrays and stacks with regular julia methods
Most base methods work as for regular julia Arrays, such as reverse and rotations like rotl90. Base, statistics and linear algebra methods like mean that take a dims argument can also use the dimension name. To take the mean over the time dimension:
mean(A, dims=Ti)broadcast works lazily from disk when lazy=true, and is only applied when data is directly indexed. Adding a dot to any function will use broadcast over a Raster just like an Array.
Broadcasting
For a disk-based array A, this will only be applied when indexing occurs or when we read the array.
A .*= 2To broadcast directly to disk, we need to open the file in write mode:
open(Raster(filename); write=true) do O
+ O .*= 2
+endTo broadcast over a RasterStack use map, which applies a function to the raster layers of the stack.
newstack = map(stack) do raster
+ raster .* 2
+endModifying object properties
rebuild can be used to modify the fields of an object, generating a new object (but possibly holding the same arrays or files).
If you know that a file had an incorrectly specified missing value, you can do:
rebuild(A; missingval=-9999)(replace_missing will actualy replace the current values)
Or if you need to change the name of the layer:
rebuild(A; name=:temperature)set can be used to modify the nested properties of an objects dimensions, that are more difficult to change with rebuild. set works on the principal that dimension properties can only be in one specific field, so we generally don't have to specify which one it is. set will also try to update anything affected by a change you make.
This will set the X axis to specify points, instead of intervals:
set(A, X => Points)We can also reassign dimensions, here X becomes Z:
set(A, X => Z)setcrs(A, crs) and setmappedcrs(A, crs) will set the crs value/s of an object to any GeoFormat from GeoFormatTypes.jl.
Examples and Plotting
Plots.jl and Makie.jl are fully supported by Rasters.jl, with recipes for plotting Raster and RasterStack provided. plot will plot a heatmap with axes matching dimension values. If mappedcrs is used, converted values will be shown on axes instead of the underlying crs values. contourf will similarly plot a filled contour plot.
Pixel resolution is limited to allow loading very large files quickly. max_res specifies the maximum pixel resolution to show on the longest axis of the array. It can be set manually to change the resolution (e.g. for large or high-quality plots):
using Rasters, RasterDataSources, ArchGDAL, Plots
+A = Raster(WorldClim{BioClim}, 5)
+plot(A; max_res=3000)For Makie, plot functions in a similar way. plot will only accept two-dimensional rasters. You can invoke contour, contourf, heatmap, surface or any Makie plotting function which supports surface-like data on a 2D raster.
To obtain tiled plots for 3D rasters and RasterStacks, use the function Rasters.rplot([gridposition], raster; kw_args...). This is an unexported function, since we're not sure how the API will change going forward.
using Rasters, CairoMakie, RasterDataSources, ArchGDAL
+A = Raster(WorldClim{BioClim}, 5)
+Makie.plot(A)
Loading and plotting data
Our first example simply loads a file from disk and plots it.
This netcdf file only has one layer, if it has more we could use RasterStack instead.
using Rasters, NCDatasets, Plots
+url = "https://www.unidata.ucar.edu/software/netcdf/examples/tos_O1_2001-2002.nc";
+filename = download(url, "tos_O1_2001-2002.nc");
+A = Raster(filename)180×170×24 Raster{Union{Missing, Float32},3} tos with dimensions:
+ X Mapped{Float64} Float64[1.0, 3.0, …, 357.0, 359.0] ForwardOrdered Explicit Intervals crs: EPSG mappedcrs: EPSG,
+ Y Mapped{Float64} Float64[-79.5, -78.5, …, 88.5, 89.5] ForwardOrdered Explicit Intervals crs: EPSG mappedcrs: EPSG,
+ Ti Sampled{DateTime360Day} DateTime360Day[DateTime360Day(2001-01-16T00:00:00), …, DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Explicit Intervals
+extent: Extent(X = (-0.0, 360.0), Y = (-80.0, 90.0), Ti = (DateTime360Day(2001-01-01T00:00:00), DateTime360Day(2003-01-01T00:00:00)))missingval: missingcrs: EPSG:4326
+mappedcrs: EPSG:4326
+parent:
+[:, :, 1]
+ -79.5 -78.5 … 86.5 87.5 88.5 89.5
+ 1.0 missing missing 271.43 271.437 271.445 271.459
+ 3.0 missing missing 271.431 271.438 271.445 271.459
+ 5.0 missing missing 271.431 271.438 271.445 271.459
+ 7.0 missing missing 271.431 271.439 271.446 271.459
+ ⋮ ⋱ ⋮
+ 351.0 missing missing 271.43 271.435 271.445 271.459
+ 353.0 missing missing 271.43 271.436 271.445 271.459
+ 355.0 missing missing 271.43 271.436 271.445 271.459
+ 357.0 missing missing 271.43 271.437 271.445 271.459
+ 359.0 missing missing … 271.43 271.437 271.445 271.459
+[and 23 more slices...]Objects with Dimensions other than X and Y will produce multi-pane plots. Here we plot every third month in the first year in one plot:
A[Ti=1:3:12] |> plotNow plot the ocean temperatures around the Americas in the first month of 2001. Notice we are using lat/lon coordinates and date/time instead of regular indices. The time dimension uses DateTime360Day, so we need to load CFTime.jl to index it with Near.
using CFTime
+A[Ti(Near(DateTime360Day(2001, 01, 17))), Y(-60.0 .. 90.0), X(45.0 .. 190.0)] |> plotNow get the mean over the timespan, then save it to disk, and plot it as a filled contour:
Other plot functions and sliced objects that have only one X/Y/Z dimension fall back to generic DimensionalData.jl plotting, which will still correctly label plot axes.
using Statistics
+# Take the mean
+mean_tos = mean(A; dims=Ti)180×170×1 Raster{Union{Missing, Float32},3} tos with dimensions:
+ X Mapped{Float64} Float64[1.0, 3.0, …, 357.0, 359.0] ForwardOrdered Explicit Intervals crs: EPSG mappedcrs: EPSG,
+ Y Mapped{Float64} Float64[-79.5, -78.5, …, 88.5, 89.5] ForwardOrdered Explicit Intervals crs: EPSG mappedcrs: EPSG,
+ Ti Sampled{DateTime360Day} DateTime360Day(2002-01-16T00:00:00):Millisecond(2592000000):DateTime360Day(2002-01-16T00:00:00) ForwardOrdered Explicit Intervals
+extent: Extent(X = (-0.0, 360.0), Y = (-80.0, 90.0), Ti = (DateTime360Day(2001-01-01T00:00:00), DateTime360Day(2003-01-01T00:00:00)))missingval: missingcrs: EPSG:4326
+mappedcrs: EPSG:4326
+parent:
+[:, :, 1]
+ -79.5 -78.5 … 86.5 87.5 88.5 89.5
+ 1.0 missing missing 271.427 271.434 271.443 271.454
+ 3.0 missing missing 271.427 271.434 271.443 271.454
+ 5.0 missing missing 271.427 271.434 271.443 271.454
+ 7.0 missing missing 271.427 271.435 271.444 271.454
+ ⋮ ⋱ ⋮
+ 351.0 missing missing 271.427 271.433 271.443 271.454
+ 353.0 missing missing 271.427 271.433 271.443 271.454
+ 355.0 missing missing 271.427 271.433 271.443 271.454
+ 357.0 missing missing 271.427 271.433 271.443 271.454
+ 359.0 missing missing … 271.427 271.433 271.443 271.454Plot a contour plot
contourf(mean_tos; dpi=300, size=(800, 400))Write the mean values to disk
write("mean_tos.nc", mean_tos)"mean_tos.nc"Plotting recipes in DimensionalData.jl are the fallback for Rasters.jl when the object doesn't have 2 X/Y/Z dimensions, or a non-spatial plot command is used. So (as a random example) we could plot a transect of ocean surface temperature at 20 degree latitude :
A[Y(Near(20.0)), Ti(1)] |> plotA basic species distribution modelling workflow
Load occurrences for the Mountain Pygmy Possum using GBIF.jl
using Rasters, RasterDataSources, ArchGDAL, GBIF2, Plots
+records = GBIF2.occurrence_search("Burramys parvus"; limit=300)300-element GBIF2.Table{GBIF2.Occurrence, JSON3.Array{JSON3.Object, Vector{UInt8}, SubArray{UInt64, 1, Vector{UInt64}, Tuple{UnitRange{Int64}}, true}}}┌──────────────────┬─────────────────┬─────────┬─────────┬─────────┬────────────
+│ decimalLongitude │ decimalLatitude │ year │ month │ day │ kingdom ⋯
+│ Float64? │ Float64? │ Int64? │ Int64? │ Int64? │ String? ⋯
+├──────────────────┼─────────────────┼─────────┼─────────┼─────────┼────────────
+│ missing │ missing │ missing │ missing │ missing │ Animalia ⋯
+│ missing │ missing │ missing │ missing │ missing │ Animalia ⋯
+│ missing │ missing │ 2021 │ 1 │ 6 │ Animalia ⋯
+│ missing │ missing │ missing │ missing │ missing │ Animalia ⋯
+│ missing │ missing │ missing │ missing │ missing │ Animalia ⋯
+│ missing │ missing │ missing │ missing │ missing │ Animalia ⋯
+│ 148.391 │ -36.3036 │ 2015 │ 11 │ 15 │ Animalia ⋯
+│ 148.333 │ -36.4333 │ 2011 │ 11 │ 21 │ Animalia ⋯
+│ 148.396 │ -36.3818 │ 2016 │ 11 │ 15 │ Animalia ⋯
+│ missing │ missing │ 1975 │ 3 │ 26 │ Animalia ⋯
+│ 147.096 │ -36.9357 │ 2020 │ 2 │ 10 │ Animalia ⋯
+│ 148.329 │ -36.4317 │ 2016 │ 1 │ 3 │ Animalia ⋯
+│ 148.236 │ -36.5249 │ 2012 │ 11 │ 23 │ Animalia ⋯
+│ missing │ missing │ missing │ missing │ missing │ Animalia ⋯
+│ missing │ missing │ missing │ missing │ missing │ Animalia ⋯
+│ ⋮ │ ⋮ │ ⋮ │ ⋮ │ ⋮ │ ⋮ ⋱
+└──────────────────┴─────────────────┴─────────┴─────────┴─────────┴────────────
+ 77 columns and 285 rows omitted
+Extract the longitude/latitude value to a Vector of points (a Tuple counts as a (x, y) point in GeoInterface.jl):
coords = [(r.decimalLongitude, r.decimalLatitude) for r in records if !ismissing(r.decimalLatitude)]253-element Vector{Tuple{Float64, Float64}}:
+ (148.391097, -36.30362)
+ (148.332969, -36.433349)
+ (148.396453, -36.381847)
+ (147.096394, -36.935687)
+ (148.328896, -36.431684)
+ (148.235596, -36.524924)
+ (148.240881, -36.400058)
+ (148.347186, -36.504673)
+ (151.250866, -33.831883)
+ (147.1, -37.0)
+ ⋮
+ (148.248473, -36.424117)
+ (148.368407, -36.413914)
+ (148.256519, -36.442973)
+ (148.467196, -36.000859)
+ (148.40889, -36.006805)
+ (148.40889, -36.006805)
+ (148.40889, -36.006805)
+ (148.467196, -36.000859)
+ (148.392241, -35.951583)Get BioClim layers and subset to south-east Australia
A = RasterStack(WorldClim{BioClim}, (1, 3, 7, 12))
+se_aus = A[X(138 .. 155), Y(-40 .. -25)]RasterStack with dimensions:
+ X Projected{Float64} LinRange{Float64}(138.0, 154.833, 102) ForwardOrdered Regular Intervals crs: WellKnownText,
+ Y Projected{Float64} LinRange{Float64}(-25.1667, -39.8333, 89) ReverseOrdered Regular Intervals crs: WellKnownText
+and 4 layers:
+ :bio1 Float32 dims: X, Y (102×89)
+ :bio3 Float32 dims: X, Y (102×89)
+ :bio7 Float32 dims: X, Y (102×89)
+ :bio12 Float32 dims: X, Y (102×89)
+Plot BioClim predictors and scatter occurrence points on all subplots
p = plot(se_aus);
+kw = (legend=:none, opacity=0.5, markershape=:cross, markercolor=:black)
+foreach(i -> scatter!(p, coords; subplot=i, kw...), 1:4)
+display(p)Then extract predictor variables and write to CSV.
using CSV
+predictors = collect(extract(se_aus, coords))
+CSV.write("burramys_parvus_predictors.csv", predictors)"burramys_parvus_predictors.csv"Or convert them to a DataFrame.
using DataFrames
+df = DataFrame(predictors)
+df[1:5, :]| Row | geometry | bio1 | bio3 | bio7 | bio12 |
|---|---|---|---|---|---|
| Tuple… | Float32 | Float32 | Float32 | Float32 | |
| 1 | (148.391, -36.3036) | 6.1707 | 41.1198 | 23.4645 | 1692.0 |
| 2 | (148.333, -36.4333) | 7.83572 | 41.5975 | 23.5028 | 1500.0 |
| 3 | (148.396, -36.3818) | 6.88158 | 42.2681 | 23.133 | 1544.0 |
| 4 | (147.096, -36.9357) | 9.40835 | 40.7905 | 23.0895 | 1292.0 |
| 5 | (148.329, -36.4317) | 7.83572 | 41.5975 | 23.5028 | 1500.0 |
Polygon masking, mosaic and plot
In this example we will mask the Scandinavian countries with border polygons, then mosaic together to make a single plot.
First, get the country boundary shape files using GADM.jl.
using Rasters, RasterDataSources, ArchGDAL, Shapefile, Plots, Dates, Downloads, NCDatasets
+
+# Download the shapefile
+shapefile_url = "https://github.com/nvkelso/natural-earth-vector/raw/master/10m_cultural/ne_10m_admin_0_countries.shp"
+shapefile_name = "boundary_lines.shp"
+Downloads.download(shapefile_url, shapefile_name)
+
+# Load using Shapefile.jl
+shapes = Shapefile.Handle(shapefile_name)
+denmark_border = shapes.shapes[71]
+norway_border = shapes.shapes[53]
+sweden_border = shapes.shapes[54]Polygon(4665 Points)Then load raster data. We load some worldclim layers using RasterDataSources via Rasters.jl:
climate = RasterStack(WorldClim{Climate}, (:tmin, :tmax, :prec, :wind); month=July)RasterStack with dimensions:
+ X Projected{Float64} LinRange{Float64}(-180.0, 179.833, 2160) ForwardOrdered Regular Intervals crs: WellKnownText,
+ Y Projected{Float64} LinRange{Float64}(89.8333, -90.0, 1080) ReverseOrdered Regular Intervals crs: WellKnownText
+and 4 layers:
+ :tmin Float32 dims: X, Y (2160×1080)
+ :tmax Float32 dims: X, Y (2160×1080)
+ :prec Int16 dims: X, Y (2160×1080)
+ :wind Float32 dims: X, Y (2160×1080)
+mask Denmark, Norway and Sweden from the global dataset using their border polygon, then trim the missing values. We pad trim with a 10 pixel margin.
mask_trim(climate, poly) = trim(mask(climate; with=poly); pad=10)
+
+denmark = mask_trim(climate, denmark_border)
+norway = mask_trim(climate, norway_border)
+sweden = mask_trim(climate, sweden_border)RasterStack with dimensions:
+ X Projected{Float64} LinRange{Float64}(9.5, 25.6667, 98) ForwardOrdered Regular Intervals crs: WellKnownText,
+ Y Projected{Float64} LinRange{Float64}(70.5, 53.6667, 102) ReverseOrdered Regular Intervals crs: WellKnownText
+and 4 layers:
+ :tmin Float32 dims: X, Y (98×102)
+ :tmax Float32 dims: X, Y (98×102)
+ :prec Int16 dims: X, Y (98×102)
+ :wind Float32 dims: X, Y (98×102)
+Plotting
First define a function to add borders to all subplots.
function borders!(p, poly)
+ for i in 1:length(p)
+ plot!(p, poly; subplot=i, fillalpha=0, linewidth=0.6)
+ end
+ return p
+endborders! (generic function with 1 method)Now we can plot the individual countries.
dp = plot(denmark)
+borders!(dp, denmark_border)sp = plot(sweden)
+borders!(sp, sweden_border)np = plot(norway)
+borders!(np, norway_border)The Norway shape includes a lot of islands. Lets crop them out using .. intervals:
norway_region = climate[X(0..40), Y(55..73)]
+plot(norway_region)And mask it with the border again:
norway = mask_trim(norway_region, norway_border)
+np = plot(norway)
+borders!(np, norway_border)Now we can combine the countries into a single raster using mosaic. first will take the first value if/when there is an overlap.
scandinavia = mosaic(first, denmark, norway, sweden)RasterStack with dimensions:
+ X Projected{Float64} 3.1666666666666443:0.16666666666666666:32.49999999999998 ForwardOrdered Regular Intervals crs: WellKnownText,
+ Y Projected{Float64} 72.66666666666666:-0.16666666666666666:52.99999999999999 ReverseOrdered Regular Intervals crs: WellKnownText
+and 4 layers:
+ :tmin Float32 dims: X, Y (177×119)
+ :tmax Float32 dims: X, Y (177×119)
+ :prec Int16 dims: X, Y (177×119)
+ :wind Float32 dims: X, Y (177×119)
+And plot scandinavia, with all borders included:
p = plot(scandinavia)
+borders!(p, denmark_border)
+borders!(p, norway_border)
+borders!(p, sweden_border)And save to netcdf - a single multi-layered file, and tif, which will write a file for each stack layer.
write("scandinavia.nc", scandinavia)
+write("scandinavia.tif", scandinavia)(tmin = "scandinavia_tmin.tif", tmax = "scandinavia_tmax.tif", prec = "scandinavia_prec.tif", wind = "scandinavia_wind.tif")Rasters.jl provides a range of other methods that are being added to over time. Where applicable these methods read and write lazily to and from disk-based arrays of common raster file types. These methods also work for entire RasterStacks and RasterSeries using the same syntax.
Plotting in Makie
2-D rasters in Makie
Plotting in Makie works somewhat differently than Plots, since the recipe system is different. You can pass a 2-D raster to any surface-like function (heatmap, contour, contourf, or even surface for a 3D plot) with ease.
using CairoMakie, Makie
+using Rasters, RasterDataSources, ArchGDAL
+A = Raster(WorldClim{BioClim}, 5) # this is a 3D raster, so is not accepted.
+B = A[:, :, 1] # this converts to a 2D raster which Makie accepts!
+figure = Figure()
+plot(figure[1, 1], B)
+contour(figure[1, 2], B)
+ax = Axis(figure[2, 1]; aspect = DataAspect())
+contourf!(ax, B)
+surface(figure[2, 2], B) # even a 3D plot works!
+figure
3-D rasters and RasterStacks in Makie
This interface is experimental, and unexported for that reason. It may break at any time!
Just as in Plots, 3D rasters are treated as a series of 2D rasters, which are tiled and plotted.
You can use Rasters.rplot to visualize 3D rasters or RasterStacks in this way. An example is below:
stack = RasterStack(WorldClim{Climate}; month = 1)
+Rasters.rplot(stack; Axis = (aspect = DataAspect(),))
You can pass any theming keywords in, which are interpreted by Makie appropriately.
The plots seem a little squished here. We provide a Makie theme which makes text a little smaller and has some other space-efficient attributes:
CairoMakie.set_theme!(Rasters.theme_rasters())
+Rasters.rplot(stack)
Plotting with Observables
Rasters.rplot should support Observable input out of the box, but the dimensions of that input must remain the same - i.e., the element names of a RasterStack must remain the same.
stack_obs = Observable(stack)
+fig = Rasters.rplot(stack_obs) # `stack` is the WorldClim climate data for January
+record(fig, "rplot.mp4", 1:12; framerate = 3) do i
+ stack_obs[] = RasterStack(WorldClim{Climate}; month = i)
+end"rplot.mp4"Rasters.rplot — FunctionRasters.rplot([position::GridPosition], raster; kw...)raster may be a Raster (of 2 or 3 dimensions) or a RasterStack whose underlying rasters are 2 dimensional, or 3-dimensional with a singleton (length-1) third dimension.
Keywords
plottype = Makie.Heatmap: The type of plot. Can be any Makie plot type which accepts aRaster; in practice,Heatmap,Contour,ContourfandSurfaceare the best bets.axistype = Makie.Axis: The type of axis. This can be anAxis,Axis3,LScene, or even aGeoAxisfrom GeoMakie.jl.X=X: The X dimension of the raster.Y=Y: The Y dimension of the raster.draw_colorbar = true: Whether to draw a colorbar for the axis or not.colorbar_position = Makie.Right(): Indicates which side of the axis the colorbar should be placed on. Can beMakie.Top(),Makie.Bottom(),Makie.Left(), orMakie.Right().colorbar_padding = Makie.automatic: The amound of padding between the colorbar and its axis. Ifautomatic, then this is set to the width of the colorbar.title = Makie.automatic: The titles of each plot. Ifautomatic, these are set to the name of the band.xlabel = Makie.automatic: The x-label for the axis. Ifautomatic, set to the dimension name of the X-dimension of the raster.ylabel = Makie.automatic: The y-label for the axis. Ifautomatic, set to the dimension name of the Y-dimension of the raster.colorbarlabel = "": Usually nothing, but here if you need it. Sets the label on the colorbar.colormap = nothing: The colormap for the heatmap. This can be set to a vector of colormaps (symbols, strings,cgrads) if plotting a 3D raster or RasterStack.colorrange = Makie.automatic: The colormap for the heatmap. This can be set to a vector of(low, high)if plotting a 3D raster or RasterStack.nan_color = :transparent: The color whichNaNvalues should take. Default to transparent.
Objects
Raster
Spatial raster data is essentially just an Array. But Raster wrappers allow treating them as an array that maintains its spatial index, crs and other metadata through all transformations. This means they can always be plotted and written to disk after applying most base Julia methods, and most broadcasts.
Rasters.AbstractRaster — TypeAbstractRaster <: DimensionalData.AbstractDimArrayAbstract supertype for objects that wrap an array (or location of an array) and metadata about its contents. It may be memory or hold a FileArray, which holds the filename, and is only opened when required.
AbstractRasters inherit from AbstractDimArray from DimensionalData.jl. They can be indexed as regular Julia arrays or with DimensionalData.jl Dimensions. They will plot as a heatmap in Plots.jl with correct coordinates and labels, even after slicing with getindex or view. getindex on a AbstractRaster will always return a memory-backed Raster.
Rasters.Raster — TypeRaster <: AbsractRaster
+
+Raster(filepath::AbstractString, dims; kw...)
+Raster(A::AbstractArray{T,N}, dims; kw...)
+Raster(A::AbstractRaster; kw...)A generic AbstractRaster for spatial/raster array data. It may hold memory-backed arrays or FileArray, that simply holds the String path to an unopened file. This will only be opened lazily when it is indexed with getindex or when read(A) is called. Broadcasting, taking a view, reversing and most other methods do not load data from disk: they are applied later, lazily.
Keywords
dims:TupleofDimensions for the array.lazy: ABoolspecifying if to load the stack lazily from disk.falseby default.name:Symbolname for the array, which will also retreive named layers ifRasteris used on a multi-layered file like a NetCDF.missingval: value reprsenting missing data, normally detected form the file. Set manually when you know the value is not specified or is incorrect. This will not change any values in the raster, it simply assigns which value is treated as missing. To replace all of the missing values in the raster, usereplace_missing.metadata:ArrayMetadataobject for the array, orNoMetadata().crs: the coordinate reference system of the objectsXDim/YDimdimensions. Only set this if you know the detected crs is incrorrect, or it is not present in the file. Thecrsis expected to be a GeoFormatTypes.jlCRSorMixedGeoFormattype.mappedcrs: the mapped coordinate reference system of the objectsXDim/YDimdimensions. forMappedlookups these are the actual values of the index. ForProjectedlookups this can be used to index in eg.EPSG(4326)lat/lon values, having it converted automatically. Only set this if the detectedmappedcrsin incorrect, or the file does not have amappedcrs, e.g. a tiff. Themappedcrsis expected to be a GeoFormatTypes.jlCRSorMixedGeoFormattype.dropband: drop single band dimensions.trueby default.
Internal Keywords
In some cases it is possible to set these keywords as well.
data: can replace the data in anAbstractRasterrefdims:Tuple ofpositionDimensions the array was sliced from, defaulting to().
Rasters.Raster — MethodRaster(T::Type{<:RasterDataSource}, [layer]; kw...) => RasterLoad a RasterDataSource as an Raster. T and layers are are passed to RasterDataSources.getraster, while kw args are for both getraster and Raster.
Keywords
month: anIntbetween1and12, usually forClimatedatasetsdate: aDateTimeobject, usually forWeatherdatasets.res: aStringresolution, for datasets with multiple resolutions.
MODIS datasets require a specific set of keyword arguments:
latandlon: Coordinates in decimal degrees (Floats ) of the center of the rasterkm_abandkm_lr: Kilometers above/below and left/right (Integers up to 100) of the center of the raster
Other Raster keywords are passed to the Raster constructor.
See the docs for RasterDatasources.getraster for more specific details about data sources, layers and keyword arguments.
RasterStack
Spatial data often comes as a bundle of multiple named arrays, as in netcdf. RasterStack can represent this, or multiple files organised in a similar way.
Rasters.AbstractRasterStack — TypeAbstractRasterStackAbstract supertype for objects that hold multiple AbstractRasters that share spatial dimensions.
They are NamedTuple-like structures that may either contain NamedTuple of AbstractRasters, string paths that will load AbstractRasters, or a single path that points to a file containing multiple layers, like NetCDF or HDF5. Use and syntax is similar or identical for all cases.
AbstractRasterStack can hold layers that share some or all of their dimensions. They cannot have the same dimension with different length or spatial extent as another layer.
getindex on an AbstractRasterStack generally returns a memory backed standard Raster. raster[:somelayer] |> plot plots the layers array, while raster[:somelayer, X(1:100), Band(2)] |> plot will plot the subset without loading the whole array.
getindex on an AbstractRasterStack with a key returns another stack with getindex applied to all the arrays in the stack.
Rasters.RasterStack — TypeRasterStack <: AbstrackRasterStack
+
+RasterStack(data...; name, kw...)
+RasterStack(data::Union{Vector,Tuple}; name, kw...)
+RasterStack(data::NamedTuple; kw...))
+RasterStack(s::AbstractRasterStack; kw...)
+RasterStack(s::AbstractRaster; layersfrom=Band, kw...)
+RasterStack(filename::AbstractString; kw...)Load a file path or a NamedTuple of paths as a RasterStack, or convert arguments, a Vector or NamedTuple of Rasters to RasterStack.
Arguments
data: ANamedTupleofRasters, or aVector,Tupleor splatted arguments ofRaster. The latter options must pass anamekeyword argument.filename: A file (such as netcdf or tif) to be loaded as a stack, or a directory path containing multiple files.
Keywords
name: Used as stack layer names when aTuple,Vectoror splat ofRasteris passed in. Has no effect whenNameTupleis used - theNamedTuplekeys are the layer names.metadata: ADictorDimensionalData.Metadataobject.refdims:TupleofDimensionthat the stack was sliced from.layersfrom:Dimensionto source stack layers from if the file is not already multi-layered.nothingis default, so that a singleRasterStack(raster)is a single layered stack.RasterStack(raster; layersfrom=Band)will use the bands as layers.lazy: ABoolspecifying whether to load the stack lazily from disk.falseby default.dropband: drop single band dimensions when creating stacks from filenames.trueby default.
files = (temp="temp.tif", pressure="pressure.tif", relhum="relhum.tif")
+stack = RasterStack(files; mappedcrs=EPSG(4326))
+stack[:relhum][Lat(Contains(-37), Lon(Contains(144))Rasters.RasterStack — MethodRasterStack(T::Type{<:RasterDataSource}, [layers::Union{Symbol,AbstractArray,Tuple}]; kw...) => RasterStackLoad a RasterDataSource as an RasterStack. T and layers are passed to RasterDataSources.getraster, while kw args are for both getraster and RasterStack.
Keywords
month: anIntbetween1and12, usually forClimatedatasets.date: aDateTimeobject, usually forWeatherdatasets.res: aStringresolution, for datasets with multiple resolutions.
MODIS datasets require a specific set of keyword arguments:
latandlon: Coordinates in decimal degrees (Floats ) of the center of the rasterkm_abandkm_lr: Kilometers above/below and left/right (Integers up to 100) of the center of the raster
Other RasterStack keywords are passed to the RasterStack constructor.
See the docs for RasterDatasources.getraster for more specific details about data sources, layers and keyword arguments.
RasterSeries
A series is a meta-array that holds other files/data that is distributed over some dimension, often time. These files/data can be Rasters or RasterStacks.
Rasters.AbstractRasterSeries — TypeAbstractRasterSeries <: DimensionalData.AbstractDimensionalArrayAbstract supertype for high-level DimensionalArray that hold RasterStacks, Rasters, or the paths they can be loaded from. RasterSeries are indexed with dimensions as with a AbstractRaster. This is useful when you have multiple files containing rasters or stacks of rasters spread over dimensions like time and elevation.
As much as possible, implementations should facilitate loading entire directories and detecting the dimensions from metadata.
This allows syntax like below for a series of stacks of arrays:
RasterSeries[Time(Near(DateTime(2001, 1))][:temp][Y(Between(70, 150)), X(Between(-20,20))] |> plot`RasterSeries is the concrete implementation.
Rasters.RasterSeries — TypeRasterSeries <: AbstractRasterSeries
+
+RasterSeries(rasters::AbstractArray{<:AbstractRaster}, dims; [refdims])
+RasterSeries(stacks::AbstractArray{<:AbstractRasterStack}, dims; [refdims])
+
+RasterSeries(paths::AbstractArray{<:AbstractString}, dims; child, duplicate_first, kw...)
+RasterSeries(path:::AbstractString, dims; ext, separator, child, duplicate_first, kw...)Concrete implementation of AbstractRasterSeries.
A RasterSeries is an array of Rasters or RasterStacks, along some dimension(s).
Existing Raster RasterStack can be wrapped in a RasterSeries, or new files can be loaded from an array of String or from a single String.
A single String can refer to a whole directory, or the name of a series of files in a directory, sharing a common stem. The differnce between the filenames can be used as the lookup for the series.
For example, with some tifs at these paths :
"series_dir/myseries_2001-01-01T00:00:00.tif"
+"series_dir/myseries_2002-01-01T00:00:00.tif"We can load a RasterSeries with a DateTime lookup:
julia> ser = RasterSeries("series_dir/myseries.tif", Ti(DateTime))
+2-element RasterSeries{Raster,1} with dimensions:
+ Ti Sampled{DateTime} DateTime[DateTime("2001-01-01T00:00:00"), DateTime("2002-01-01T00:00:00")] ForwardOrdered Irregular PointsThe DateTime suffix is parsed from the filenames. Using Ti(Int) would try to parse integers intead.
Just using the directory will also work, unless there are other files mixed in it:
julia> ser = RasterSeries("series_dir", Ti(DateTime))
+2-element RasterSeries{Raster,1} with dimensions:
+ Ti Sampled{DateTime} DateTime[DateTime("2001-01-01T00:00:00"), DateTime("2002-01-01T00:00:00")] ForwardOrdered Irregular PointsArguments
dims: series dimension/s.
Keywords
When loading a series from a Vector of String paths or a single String path:
child: constructor of child objects for use when filenames are passed in, can beRasterorRasterStack. Defaults toRaster.duplicate_first::Bool: wether to duplicate the dimensions and metadata of the first file with all other files. This can save load time with a large series where dimensions are identical.falseby default.lazy: load files lazily,falseby default.kw: keywords passed to the child constructorRasterorRasterStack.
When loading a series from a single String path:
ext: filename extension such as ".tiff" or ".nc". Use to specify a subset of files if only a directory path is passed in.separator: separator used to split lookup elements from the rest of a filename. '_' by default.
Others:
refdims: existing reference dimension/s, normally not required.
Rasters.RasterSeries — MethodRasterSeries(T::Type{<:RasterDataSource}, [layers::Union{Symbol,AbstractArray,Tuple}]; kw...) => AbstractRasterSeriesLoad a RasterDataSource as an AbstractRasterSeries. T, args are are passed to RasterDataSource.getraster, while kw args are for both getraster and RasterSeries.
Keywords
month: aVectoror range ofIntbetween1and12, usually forClimatedatasets.date: aVectorofDateTimeobjects, usually forWeatherdatasets.res: aStringresolution, for datasets with multiple resolutions.
MODIS datasets require a specific set of keyword arguments:
latandlon: Coordinates in decimal degrees (Floats ) of the center of the rasterkm_abandkm_lr: Kilometers above/below and left/right (Integers up to 100) of the center of the raster
Other RasterSeries keywords are passed to the RasterSeries constructor.
See the docs for RasterDatasources.getraster for more specific details about data sources, layers and keyword arguments.
Dimensions
Rasters uses X, Y, and Z dimensions from DimensionalData.jl to represent spatial directions like longitude, latitude and the vertical dimension, and subset data with them. Ti is used for time, and Band represent bands. Other dimensions can have arbitrary names, but will be treated generically. See DimensionalData.jl for more details on how they work.
Rasters.Band — TypeBand <: Dimension
+
+Band(val=:)Band Dimension for multi-band rasters.
Example:
banddim = Band(10:10:100)
+# Or
+val = A[Band(1)]
+# Or
+mean(A; dims=Band)Lookup Arrays
These specify properties of the index associated with e.g. the X and Y dimension. Rasters.jl defines additional lookup arrays: Projected to handle dimensions with projections, and Mapped where the projection is mapped to another projection like EPSG(4326). Mapped is largely designed to handle NetCDF dimensions, especially with Explicit spans.
Rasters.AbstractProjected — TypeAbstractProjected <: AbstractSampledAbstract supertype for projected index lookups.
Rasters.Projected — TypeProjected <: AbstractProjected
+
+Projected(order, span, sampling, crs, mappedcrs)
+Projected(; order=AutoOrder(), span=AutoSpan(), sampling=AutoSampling(), crs, mappedcrs=nothing)An AbstractSampled LookupArray with projections attached.
Fields and behaviours are identical to Sampled with the addition of crs and mappedcrs fields.
If both crs and mappedcrs fields contain CRS data (in a GeoFormat wrapper from GeoFormatTypes.jl) the selector inputs and plot axes will be converted from and to the specified mappedcrs projection automatically. A common use case would be to pass mappedcrs=EPSG(4326) to the constructor when loading eg. a GDALarray:
GDALarray(filename; mappedcrs=EPSG(4326))The underlying crs will be detected by GDAL.
If mappedcrs is not supplied (ie. mappedcrs=nothing), the base index will be shown on plots, and selectors will need to use whatever format it is in.
Rasters.Mapped — TypeMapped <: AbstractProjected
+
+Mapped(order, span, sampling, crs, mappedcrs)
+Mapped(; order=AutoOrder(), span=AutoSpan(), sampling=AutoSampling(), crs=nothing, mappedcrs)An AbstractSampled LookupArray, where the dimension index has been mapped to another projection, usually lat/lon or EPSG(4326). Mapped matches the dimension format commonly used in netcdf files.
Fields and behaviours are identical to Sampled with the addition of crs and mappedcrs fields.
The mapped dimension index will be used as for Sampled, but to save in another format the underlying crs may be used to convert it.
Data sources
Rasters.jl uses a number of backends to load raster data. Raster, RasterStack and RasterSeries will detect which backend to use for you, automatically.
GRD
R GRD files can be loaded natively, using Julias MMap - which means they are very fast, but are not compressed. They are always 3 dimensional, and have Y, X and Band dimensions.
NetCDF
NetCDF .nc files are loaded using NCDatasets.jl. Layers from files can be loaded as Raster("filename.nc"; key=:layername). Without key the first layer is used. RasterStack("filename.nc") will use all netcdf variables in the file that are not dimensions as layers.
NetCDF layers can have arbitrary dimensions. Known, common dimension names are converted to X, Y Z, and Ti, otherwise Dim{:layername} is used. Layers in the same file may also have different dimensions.
NetCDF files still have issues loading directly from disk for some operations. Using read(ncstack) may help.
GDAL
All files GDAL can access, such as .tiff and .asc files, can be loaded, using ArchGDAL.jl. These are generally best loaded as Raster("filename.tif"), but can be loaded as RasterStack("filename.tif"; layersfrom=Band), taking layers from the Band dimension, which is also the default.
SMAP
The Soil Moisture Active-Passive dataset provides global layers of soil moisture, temperature and other related data, in a custom HDF5 format. Layers are always 2 dimensional, with Y and X dimensions.
These can be loaded as multi-layered RasterStack("filename.h5"). Individual layers can be loaded as Raster("filename.h5"; key=:layerkey), without key the first layer is used.
Rasters.smapseries — Functionsmapseries(filenames::AbstractString; kw...)
+smapseries(filenames::Vector{<:AbstractString}, dims=nothing; kw...)RasterSeries loader for SMAP files and whole folders of files, organised along the time dimension. Returns a RasterSeries.
Arguments
filenames: AStringpath to a directory of SMAP files, or a vector ofStringpaths to specific files.dims:TuplecontainingTidimension for the series. Automatically generated formfilenamesunless passed in.
Keywords
kw: Passed toRasterSeries.
Writing file formats to disk
Files can be written to disk in all formats other than SMAP HDF5 using write("filename.ext", A). See the docs for write. They can (with some caveats) be written to different formats than they were loaded in as, providing file-type conversion for spatial data.
Some metadata may be lost in formats that store little metadata, or where metadata conversion has not been completely implemented.
RasterDataSources.jl integration
RasterDataSources.jl standardises the download of common raster data sources, with a focus on datasets used in ecology and the environmental sciences. RasterDataSources.jl is tightly integrated into Rasters.jl, so that datsets and keywords can be used directly to download and load data as a Raster, RasterStack, or RasterSeries.
using Rasters, RasterDataSources, ArchGDAL, Plots, Dates
+A = Raster(WorldClim{Climate}, :tavg; month=June)
+plot(A)See the docs for Raster, RasterStack and RasterSeries, and the docs for RasterDataSources.getraster for syntax to specify various data sources.
Exported functions
Rasters.jl is a direct extension of DimensionalData.jl. See DimensionalData.jl docs for the majority of types and functions that can be used in Rasters.jl.
Functions more specific to geospatial data are included in Rasters.jl, and listed below.
Rasters.aggregate — Functionaggregate(method, object, scale; filename, progress, skipmissing)Aggregate a Raster, or all arrays in a RasterStack or RasterSeries, by scale using method.
Arguments
method: a function such asmeanorsumthat can combine the value of multiple cells to generate the aggregated cell, or aLocuslikeStart()orCenter()that specifies where to sample from in the interval.object: Object to aggregate, likeAbstractRasterSeries,AbstractStack,AbstractRasterorDimension.scale: the aggregation factor, which can be an integer, a tuple of integers for each dimension, or anyDimension,SelectororIntcombination you can usually use ingetindex. Using aSelectorwill determine the scale by the distance from the start of the index.
When the aggregation scale of is larger than the array axis, the length of the axis is used.
Keywords
skipmissingval: iftrue, anymissingvalwill be skipped during aggregation, so that only areas of all missing values will be aggregated tomissingval. Iffalse, any aggegrated area containing amissingvalwill be assignedmissingval.filename: a filename to write to directly, useful for large files.suffix: a string or value to append to the filename. A tuple ofsuffixwill be applied to stack layers.keys(stack)are the default.progress: show a progress bar,trueby default,falseto hide.
Example
using Rasters, RasterDataSources, Statistics, Plots
+using Rasters: Center
+st = read(RasterStack(WorldClim{Climate}; month=1))
+ag = aggregate(Center(), st, (Y(20), X(20)); skipmissingval=true, progress=false)
+plot(ag)
+savefig("build/aggregate_example.png"); nothing
+# output
+
Note: currently it is faster to aggregate over memory-backed arrays. Use read on src before use where required.
Rasters.aggregate! — Functionaggregate!(method, dst::AbstractRaster, src::AbstractRaster, scale; skipmissingval=false)Aggregate array src to array dst by scale, using method.
Arguments
method: a function such asmeanorsumthat can combine the value of multiple cells to generate the aggregated cell, or aLocuslikeStart()orCenter()that species where to sample from in the interval.scale: the aggregation factor, which can be an integer, a tuple of integers for each dimension, or anyDimension,SelectororIntcombination you can usually use ingetindex. Using aSelectorwill determine the scale by the distance from the start of the index in thesrcarray.
When the aggregation scale of is larger than the array axis, the length of the axis is used.
Keywords
progress: show a progress bar.skipmissingval: iftrue, anymissingvalwill be skipped during aggregation, so that only areas of all missing values will be aggregated tomissingval. Iffalse, any aggegrated area containing amissingvalwill be assignedmissingval.
Note: currently it is much faster to aggregate over memory-backed arrays. Use read on src before use where required.
Rasters.boolmask — Functionboolmask(obj::Raster; [missingval])
+boolmask(obj; [to, res, size])Create a mask array of Bool values, from another Raster. An AbstractRasterStack or AbstractRasterSeries are also accepted, but a mask is taken of the first layer or object not all of them.
The array returned from calling boolmask on a AbstractRaster is a Raster with the same dimensions as the original array and a missingval of false.
Arguments
obj: aRaster, a GeoInterface.jl geometry, or a vector or table of geometries.
Raster / RasterStack Keywords
missingval: The missing value of the source array, with defaultmissingval(raster).
Geometry keywords
to: aRaster,RasterStack,TupleofDimensionorExtents.Extent. If notoobject is provided the extent will be calculated from the geometries, Additionally, when notoobject or anExtentis passed forto, thesizeorreskeyword must also be used.res: the resolution of the dimensions, aRealorTuple{<:Real,<:Real}. Only required whentois not used or is anExtents.Extent, andsizeis not used.size: the size of the output array, as aTuple{Int,Int}or singleIntfor a square. Only required whentois not used or is anExtents.Extent, andresis not used.crs: acrswhich will be attached to the resulting raster whentonot passed or is anExtent. Otherwise the crs fromtois used.shape: Forcedatato be treated as:polygon,:lineor:pointgeometries. using points or lines as polygons may have unexpected results.boundary: for polygons, include pixels where the:centeris inside the polygon, where the polygon:touchesthe pixel, or that are completely:insidethe polygon. The default is:center.
And specifically for shape=:polygon:
boundary: include pixels where the:centeris inside the polygon, where the line:touchesthe pixel, or that are completely:insideinside the polygon. The default is:center.
For tabular data, feature collections and other iterables
collapse: iftrue, collapse all geometry masks into a single mask. Otherwise return a Raster with an additionalgeometrydimension, so that each slice along this axis is the mask of thegeometryopbject of each row of the table, feature in the feature collection, or just each geometry in the iterable.
Example
using Rasters, RasterDataSources, ArchGDAL, Plots, Dates
+wc = Raster(WorldClim{Climate}, :prec; month=1)
+boolmask(wc) |> plot
+
+savefig("build/boolmask_example.png"); nothing
+
+# output
WARNING: This feature is experimental. It may change in future versions, and may not be 100% reliable in all cases. Please file github issues if problems occur.
Rasters.classify — Functionclassify(x, pairs; lower=(>=), upper=(<), others=nothing)
+classify(x, pairs...; lower, upper, others)Create a new array with values in x classified by the values in pairs.
pairs can hold tuples fo values (2, 3), a Fix2 function e.g. <=(1), a Tuple of Fix2 e.g. (>=(4), <(7)), or an IntervalSets.jl interval, e.g. 3..9 or OpenInterval(10, 12). pairs can also be a n * 3 matrix where each row is lower bounds, upper bounds, replacement.
If tuples or a Matrix are used, the lower and upper keywords define how the lower and upper boundaries are chosen.
If others is set other values not covered in pairs will be set to that values.
Arguments
x: aRasterorRasterStackpairs: each pair contains a value and a replacement, a tuple of lower and upper range and a replacement, or a Tuple ofFix2like(>(x), <(y).
Keywords
lower: Which comparison (<or<=) to use for lower values, ifFix2are not used.upper: Which comparison (>or>=) to use for upper values, ifFix2are not used.others: A value to assign to all values not included inpairs. Passingnothing(the default) will leave them unchanged.
Example
using Rasters, RasterDataSources, ArchGDAL, Plots
+A = Raster(WorldClim{Climate}, :tavg; month=1)
+classes = <=(15) => 10,
+ 15..25 => 20,
+ 25..35 => 30,
+ >(35) => 40
+classified = classify(A, classes; others=0, missingval=0)
+plot(classified; c=:magma)
+
+savefig("build/classify_example.png"); nothing
+
+# output
WARNING: This feature is experimental. It may change in future versions, and may not be 100% reliable in all cases. Please file github issues if problems occur.
Rasters.classify! — Functionclassify!(x, pairs...; lower, upper, others)
+classify!(x, pairs; lower, upper, others)Classify the values of x in-place, by the values in pairs.
If Fix2 is not used, the lower and upper keywords
If others is set other values not covered in pairs will be set to that values.
Arguments
x: aRasterorRasterStackpairs: each pair contains a value and a replacement, a tuple of lower and upper range and a replacement, or a Tuple ofFix2like(>(x), <(y).
Keywords
lower: Which comparison (<or<=) to use for lower values, ifFix2are not used.upper: Which comparison (>or>=) to use for upper values, ifFix2are not used.others: A value to assign to all values not included inpairs. Passingnothing(the default) will leave them unchanged.
Example
classify! to disk, with key steps:
- copying a tempory file so we don't write over the RasterDataSources.jl version.
- use
openwithwrite=trueto open the file with disk-write permissions. - use
Float32like10.0f0for all our replacement values andother, because the file is stored asFloat32. Attempting to write some other type will fail.
using Rasters, RasterDataSources, ArchGDAL, Plots
+# Download and copy the file
+filename = getraster(WorldClim{Climate}, :tavg; month=6)
+tempfile = tempname() * ".tif"
+cp(filename, tempfile)
+# Define classes
+classes = (5, 15) => 10,
+ (15, 25) => 20,
+ (25, 35) => 30,
+ >=(35) => 40
+# Open the file with write permission
+open(Raster(tempfile); write=true) do A
+ classify!(A, classes; others=0)
+end
+# Open it again to plot the changes
+plot(Raster(tempfile); c=:magma)
+
+savefig("build/classify_bang_example.png"); nothing
+
+# output
WARNING: This feature is experimental. It may change in future versions, and may not be 100% reliable in all cases. Please file github issues if problems occur.
Rasters.coverage — Functioncoverage(mode, geom; [to, res, size, scale, verbose, progress])
+coverage(geom; [to, mode, res, size, scale, verbose, progress])Calculate the area of a raster covered by GeoInterface.jl compatible geomtry geom, as a fraction.
Each pixel is assigned a grid of points (by default 10 x 10) that are each checked to be inside the geometry. The sum divided by the number of points to give coverage.
In pracice, most pixel coverage is not calculated this way - shortcuts that produce the same result are taken wherever possible.
If geom is an AbstractVector or table, the mode keyword will determine how coverage is combined.
Keywords
mode: method for combining multiple geometries -unionorsum.union(the default) gives the areas covered by all geometries. Usefull in spatial coverage where overlapping regions should not be counted twice. The returned raster will containFloat64values between0.0and1.0.sumgives the summed total of the areas covered by all geometries, as in taking the sum of runningcoverageseparately on all geometries. The returned values are positiveFloat64.
For a single geometry, the
modekeyword has no effect - the result is the same.scale:Integerscale of pixel subdivision. The default of10means each pixel has 10 x 10 or 100 points that contribute to coverage. Using100means 10,000 points contribute. Performance will decline asscaleincreases. Memory use will grow byscale^2whenmode=:union.progress: show a progress bar,trueby default,falseto hide.vebose: whether to print messages about potential problems.trueby default.
to: aRaster,RasterStack,TupleofDimensionorExtents.Extent. If notoobject is provided the extent will be calculated from the geometries, Additionally, when notoobject or anExtentis passed forto, thesizeorreskeyword must also be used.size: the size of the output array, as aTuple{Int,Int}or singleIntfor a square. Only required whentois not used or is anExtents.Extent, andresis not used.res: the resolution of the dimensions, aRealorTuple{<:Real,<:Real}. Only required whentois not used or is anExtents.Extent, andsizeis not used.
Rasters.coverage! — Functioncoverage!(A, geom; [mode, scale])Calculate the area of a raster covered by GeoInterface.jl compatible geomtry geom, as a fraction.
Each pixel is assigned a grid of points (by default 10 x 10) that are each checked to be inside the geometry. The sum divided by the number of points to give coverage.
In pracice, most pixel coverage is not calculated this way - shortcuts that produce the same result are taken wherever possible.
If geom is an AbstractVector or table, the mode keyword will determine how coverage is combined.
Keywords
mode: method for combining multiple geometries -unionorsum.union(the default) gives the areas covered by all geometries. Usefull in spatial coverage where overlapping regions should not be counted twice. The returned raster will containFloat64values between0.0and1.0.sumgives the summed total of the areas covered by all geometries, as in taking the sum of runningcoverageseparately on all geometries. The returned values are positiveFloat64.
For a single geometry, the
modekeyword has no effect - the result is the same.scale:Integerscale of pixel subdivision. The default of10means each pixel has 10 x 10 or 100 points that contribute to coverage. Using100means 10,000 points contribute. Performance will decline asscaleincreases. Memory use will grow byscale^2whenmode=:union.progress: show a progress bar,trueby default,falseto hide.vebose: whether to print messages about potential problems.trueby default.
Rasters.convertlookup — Functionconvertlookup(dstlookup::Type{<:LookupArray}, x)Convert the dimension lookup between Projected and Mapped. Other dimension lookups pass through unchanged.
This is used to e.g. save a netcdf file to GeoTiff.
Rasters.crop — Functioncrop(x; to)
+crop(xs...; to)Crop one or multiple AbstractRaster or AbstractRasterStack x to match the size of the object to, or smallest of any dimensions that are shared.
crop is lazy, using a view into the object rather than alocating new memory.
Keywords
to: the object to crop to. Iftokeyword is passed, the smallest shared area of allxsis used.touches:trueorfalse. Whether to useToucheswraper on the object extent. When lines need to be included in e.g. zonal statistics,trueshoudle be used.
As crop is lazy, filename and suffix keywords are not used.
Example
Crop to another raster:
using Rasters, RasterDataSources, Plots
+evenness = Raster(EarthEnv{HabitatHeterogeneity}, :evenness)
+rnge = Raster(EarthEnv{HabitatHeterogeneity}, :range)
+
+# Roughly cut out New Zealand from the evenness raster
+nz_bounds = X(165 .. 180), Y(-50 .. -32)
+nz_evenness = evenness[nz_bounds...]
+
+# Crop range to match evenness
+nz_range = crop(rnge; to=nz_evenness)
+plot(nz_range)
+
+savefig("build/nz_crop_example.png")
+nothing
+
+# output
Crop to a polygon:
using Rasters, RasterDataSources, Plots, Dates, Shapefile, Downloads
+
+# Download a borders shapefile
+shapefile_url = "https://github.com/nvkelso/natural-earth-vector/raw/master/10m_cultural/ne_10m_admin_0_countries.shp"
+shapefile_name = "boundary.shp"
+isfile(shapefile_name) || Downloads.download(shapefile_url, shapefile_name)
+shp = Shapefile.Handle(shapefile_name).shapes[6]
+
+evenness = Raster(EarthEnv{HabitatHeterogeneity}, :evenness)
+argentina_evenness = crop(evenness; to=shp)
+plot(argentina_evenness)
+
+savefig("build/argentina_crop_example.png"); nothing
+
+# output
WARNING: This feature is experimental. It may change in future versions, and may not be 100% reliable in all cases. Please file github issues if problems occur.
Rasters.crs — Functioncrs(x)Get the projected coordinate reference system of a Y or X Dimension, or of the Y/X dims of an AbstractRaster.
For Mapped lookup this may be nothing as there may be no projected coordinate reference system at all. See setcrs to set it manually.
Rasters.disaggregate — Functiondisaggregate(method, object, scale; filename, progress, keys)Disaggregate array, or all arrays in a stack or series, by some scale.
Arguments
method: a function such asmeanorsumthat can combine the value of multiple cells to generate the aggregated cell, or aLocuslikeStart()orCenter()that species where to sample from in the interval.object: Object to aggregate, likeAbstractRasterSeries,AbstractStack,AbstractRasteror aDimension.scale: the aggregation factor, which can be an integer, a tuple of integers for each dimension, or anyDimension,SelectororIntcombination you can usually use ingetindex. Using aSelectorwill determine the scale by the distance from the start of the index.
Keywords
progress: show a progress bar.
Note: currently it is faster to aggregate over memory-backed arrays. Use read on src before use where required.
Rasters.disaggregate! — Functiondisaggregate!(method, dst::AbstractRaster, src::AbstractRaster, filename, scale)Disaggregate array src to array dst by some scale, using method.
method: a function such asmeanorsumthat can combine the value of multiple cells to generate the aggregated cell, or aLocuslikeStart()orCenter()that species where to sample from in the interval.scale: the aggregation factor, which can be an integer, a tuple of integers for each dimension, or anyDimension,SelectororIntcombination you can usually use ingetindex. Using aSelectorwill determine the scale by the distance from the start of the index in thesrcarray.
Note: currently it is faster to aggregate over memory-backed arrays. Use read on src before use where required.
Rasters.extend — Functionextend(xs...; [to])
+extend(xs; [to])
+extend(x::Union{AbstractRaster,AbstractRasterStack}; to, kw...)Extend one or multiple AbstractRaster to match the area covered by all xs, or by the keyword argument to.
Keywords
to: the Raster or dims to extend to. If notokeyword is passed, the largest shared area of allxsis used.touches:trueorfalse. Whether to useToucheswraper on the object extent. When lines need to be included in e.g. zonal statistics,trueshoudle be used.filename: a filename to write to directly, useful for large files.suffix: a string or value to append to the filename. A tuple ofsuffixwill be applied to stack layers.keys(stack)are the default.
using Rasters, RasterDataSources, Plots
+evenness = Raster(EarthEnv{HabitatHeterogeneity}, :evenness)
+rnge = Raster(EarthEnv{HabitatHeterogeneity}, :range)
+
+# Roughly cut out South America
+sa_bounds = X(-88 .. -32), Y(-57 .. 13)
+sa_evenness = evenness[sa_bounds...]
+
+# Extend range to match the whole-world raster
+sa_range = extend(sa_evenness; to=rnge)
+plot(sa_range)
+
+savefig("build/extend_example.png")
+nothing
+# output
WARNING: This feature is experimental. It may change in future versions, and may not be 100% reliable in all cases. Please file github issues if problems occur.
Rasters.extract — Functionextract(x, geoms; atol)Extracts the value of Raster or RasterStack at given points, returning an iterable of NamedTuple with properties for :geometry and raster or stack layer values.
Note that if objects have more dimensions than the length of the point tuples, sliced arrays or stacks will be returned instead of single values.
Arguments
x: aRasterorRasterStackto extract values from.geoms: GeoInterface.jl compatible geometries, or tables or iterables of geometries.
Keywords
atol: a tolorerance for floating point lookup values for when theLookupArraycontainsPoints.atolis ignored forIntervals.
Example
Here we extact points matching the occurrence of the Mountain Pygmy Possum, Burramis parvus. This could be used to fit a species distribution model.
using Rasters, RasterDataSources, ArchGDAL, GBIF2, CSV
+
+# Get a stack of BioClim layers, and replace missing values with `missing`
+st = RasterStack(WorldClim{BioClim}, (1, 3, 5, 7, 12)) |> replace_missing
+
+# Download some occurrence data
+obs = GBIF2.occurrence_search("Burramys parvus"; limit=5, year="2009")
+
+# Convert observations to points
+pnts = collect((o.decimalLongitude, o.decimalLatitude) for o in obs if !ismissing(o.decimalLongitude))
+
+# use `extract` to get values for all layers at each observation point.
+# We `collect` to get a `Vector` from the lazy iterator.
+collect(extract(st, pnts))
+
+# output
+5-element Vector{NamedTuple{(:geometry, :bio1, :bio3, :bio5, :bio7, :bio12)}}:
+ (geometry = (0.21, 40.07), bio1 = 17.077084f0, bio3 = 41.20417f0, bio5 = 30.1f0, bio7 = 24.775f0, bio12 = 446.0f0)
+ (geometry = (0.03, 39.97), bio1 = 17.076923f0, bio3 = 39.7983f0, bio5 = 29.638462f0, bio7 = 24.153847f0, bio12 = 441.0f0)
+ (geometry = (0.03, 39.97), bio1 = 17.076923f0, bio3 = 39.7983f0, bio5 = 29.638462f0, bio7 = 24.153847f0, bio12 = 441.0f0)
+ (geometry = (0.52, 40.37), bio1 = missing, bio3 = missing, bio5 = missing, bio7 = missing, bio12 = missing)
+ (geometry = (0.32, 40.24), bio1 = 16.321388f0, bio3 = 41.659454f0, bio5 = 30.029825f0, bio7 = 25.544561f0, bio12 = 480.0f0)Rasters.mappedcrs — Functionmappedcrs(x)Get the mapped coordinate reference system for the Y/X dims of an array.
In Projected lookup this is used to convert Selector values form the mappedcrs defined projection to the underlying projection, and to show plot axes in the mapped projection.
In Mapped lookup this is the coordinate reference system of the index values. See setmappedcrs to set it manually.
Rasters.mappedbounds — Functionmappedbounds(x)Get the bounds converted to the mappedcrs value.
Whithout ArchGDAL loaded, this is just the regular bounds.
Rasters.mappedindex — Functionmappedindex(x)Get the index value of a dimension converted to the mappedcrs value.
Whithout ArchGDAL loaded, this is just the regular dim value.
Rasters.mask — Functionmask(A:AbstractRaster; with, missingval=missingval(A))
+mask(x; with)Return a new array with values of A masked by the missing values of with, or by the shape of with, if with is a geometric object.
Arguments
x: aRasterorRasterStack
Keywords
with: anAbstractRaster, or any GeoInterface.jl compatible objects or table. The coordinate reference system of the point must matchcrs(A).missingval: the missing value to use in the returned file.filename: a filename to write to directly, useful for large files.suffix: a string or value to append to the filename. A tuple ofsuffixwill be applied to stack layers.keys(stack)are the default.
Geometry keywords
These can be used when with is a GeoInterface.jl compatible object:
shape: Forcedatato be treated as:polygon,:lineor:pointgeometries. using points or lines as polygons may have unexpected results.boundary: for polygons, include pixels where the:centeris inside the polygon, where the polygon:touchesthe pixel, or that are completely:insidethe polygon. The default is:center.
Example
Mask an unmasked AWAP layer with a masked WorldClim layer, by first resampling the mask.
using Rasters, RasterDataSources, ArchGDAL, Plots, Dates
+
+# Load and plot the file
+awap = read(Raster(AWAP, :tmax; date=DateTime(2001, 1, 1)))
+a = plot(awap; clims=(10, 45))
+
+# Create a mask my resampling a worldclim file
+wc = Raster(WorldClim{Climate}, :prec; month=1)
+wc_mask = resample(wc; to=awap)
+
+# Mask
+awap_masked = mask(awap; with=wc_mask)
+b = plot(awap_masked; clims=(10, 45))
+
+savefig(a, "build/mask_example_before.png");
+savefig(b, "build/mask_example_after.png"); nothing
+# output
+Before mask:
After mask:
WARNING: This feature is experimental. It may change in future versions, and may not be 100% reliable in all cases. Please file github issues if problems occur.
Rasters.mask! — Functionmask!(x; with, missingval=missingval(A))Mask A by the missing values of with, or by all values outside with if it is a polygon.
If with is a polygon, creates a new array where points falling outside the polygon have been replaced by missingval(A).
Return a new array with values of A masked by the missing values of with, or by a polygon.
Arguments
x: aRasterorRasterStack.
Keywords
with: anotherAbstractRaster, aAbstractVectorofTuplepoints, or any GeoInterface.jlAbstractGeometry. The coordinate reference system of the point must matchcrs(A).missingval: the missing value to write to A in masked areas, by defaultmissingval(A).
Example
Mask an unmasked AWAP layer with a masked WorldClim layer, by first resampling the mask to match the size and projection.
using Rasters, RasterDataSources, ArchGDAL, Plots, Dates
+
+# Load and plot the file
+awap = read(RasterStack(AWAP, (:tmin, :tmax); date=DateTime(2001, 1, 1)))
+a = plot(awap; clims=(10, 45), c=:imola)
+
+# Create a mask my resampling a worldclim file
+wc = Raster(WorldClim{Climate}, :prec; month=1)
+wc_mask = resample(wc; to=awap)
+
+# Mask
+mask!(awap; with=wc_mask)
+b = plot(awap; clims=(10, 45))
+
+savefig(a, "build/mask_bang_example_before.png");
+savefig(b, "build/mask_bang_example_after.png"); nothing
+
+# output
+Before mask!:
After mask!:
WARNING: This feature is experimental. It may change in future versions, and may not be 100% reliable in all cases. Please file github issues if problems occur.
Rasters.missingval — Functionmissingval(x)Returns the value representing missing data in the dataset
Rasters.missingmask — Functionmissingmask(obj::Raster; kw...)
+missingmask(obj; [to, res, size, collapse])Create a mask array of missing and true values, from another Raster. AbstractRasterStack or AbstractRasterSeries are also accepted, but a mask is taken of the first layer or object not all of them.
For AbstractRaster the default missingval is missingval(A), but others can be chosen manually.
The array returned from calling missingmask on a AbstractRaster is a Raster with the same size and fields as the original array.
Keywords
to: aRaster,RasterStack,TupleofDimensionorExtents.Extent. If notoobject is provided the extent will be calculated from the geometries, Additionally, when notoobject or anExtentis passed forto, thesizeorreskeyword must also be used.res: the resolution of the dimensions, aRealorTuple{<:Real,<:Real}. Only required whentois not used or is anExtents.Extent, andsizeis not used.size: the size of the output array, as aTuple{Int,Int}or singleIntfor a square. Only required whentois not used or is anExtents.Extent, andresis not used.crs: acrswhich will be attached to the resulting raster whentonot passed or is anExtent. Otherwise the crs fromtois used.shape: Forcedatato be treated as:polygon,:lineor:pointgeometries. using points or lines as polygons may have unexpected results.boundary: for polygons, include pixels where the:centeris inside the polygon, where the polygon:touchesthe pixel, or that are completely:insidethe polygon. The default is:center.
Example
using Rasters, RasterDataSources, ArchGDAL, Plots, Dates
+wc = Raster(WorldClim{Climate}, :prec; month=1)
+missingmask(wc) |> plot
+
+savefig("build/missingmask_example.png"); nothing
+
+# output
WARNING: This feature is experimental. It may change in future versions, and may not be 100% reliable in all cases. Please file github issues if problems occur.
Rasters.mosaic — Functionmosaic(f, regions...; missingval, atol)
+mosaic(f, regions; missingval, atol)Combine regions into a single raster.
Arguments
f: A reducing function (mean,sum,first,lastetc.) for values whereregionsoverlap.regions: Iterable or splattedRasterorRasterStack.
Keywords
missingval: Fills empty areas, and defualts to themissingvalof the first region.atol: Absolute tolerance for comparison between index values. This is often required due to minor differences in range values due to floating point error. It is not applied to non-float dimensions. A tuple of tolerances may be passed, matching the dimension order.filename: a filename to write to directly, useful for large files.suffix: a string or value to append to the filename. A tuple ofsuffixwill be applied to stack layers.keys(stack)are the default.
If your mosaic has has apparent line errors, increase the atol value.
Example
Here we cut out Australia and Africa from a stack, and join them with mosaic.
using Rasters, RasterDataSources, ArchGDAL, Plots
+st = RasterStack(WorldClim{Climate}; month=1);
+
+africa = st[X(-20.0 .. 60.0), Y(-40.0 .. 35.0)]
+a = plot(africa)
+
+aus = st[X(100.0 .. 160.0), Y(-50.0 .. -10.0)]
+b = plot(aus)
+
+# Combine with mosaic
+mos = mosaic(first, aus, africa)
+c = plot(mos)
+
+savefig(a, "build/mosaic_example_africa.png")
+savefig(b, "build/mosaic_example_aus.png")
+savefig(c, "build/mosaic_example_combined.png")
+nothing
+# output
+Individual continents
Mosaic of continents
WARNING: This feature is experimental. It may change in future versions, and may not be 100% reliable in all cases. Please file github issues if problems occur.
Rasters.mosaic! — Functionmosaic!(f, x, regions...; missingval, atol)
+mosaic!(f, x, regions::Tuple; missingval, atol)Combine regions in x using the function f.
Arguments
fa function (e.g.mean,sum,firstorlast) that is applied to values whereregionsoverlap.x: ARasterorRasterStack. May be a an opened disk-basedRaster, the result will be written to disk. With the current algorithm, the read speed is slow.regions: source objects to be joined. These should be memory-backed (usereadfirst), or may experience poor performance. If all objects have the same extent,mosaicis simply a merge.
Keywords
missingval: Fills empty areas, and defualts to the `missingval/ of the first layer.atol: Absolute tolerance for comparison between index values. This is often required due to minor differences in range values due to floating point error. It is not applied to non-float dimensions. A tuple of tolerances may be passed, matching the dimension order.
Example
Cut out Australia and Africa stacks, then combined them into a single stack.
using Rasters, RasterDataSources, ArchGDAL, Statistics, Plots
+st = read(RasterStack(WorldClim{Climate}; month=1))
+aus = st[X=100.0 .. 160.0, Y=-50.0 .. -10.0]
+africa = st[X=-20.0 .. 60.0, Y=-40.0 .. 35.0]
+mosaic!(first, st, aus, africa)
+plot(st)
+savefig("build/mosaic_bang_example.png")
+nothing
+# output
+
WARNING: This feature is experimental. It may change in future versions, and may not be 100% reliable in all cases. Please file github issues if problems occur.
Rasters.points — Functionpoints(A::AbstractRaster; dims=(YDim, XDim), ignore_missing) => Array{Tuple}Returns a generator of the points in A for dimensions in dims, where points are a tuple of the values in each specified dimension index.
Keywords
dimsthe dimensions to return points from. The first slice of other layers will be used.ignore_missing: wether to ignore missing values in the array when considering points. Iftrue, all points in the dimensions will be returned, iffalseonly the points that are not=== missingval(A)will be returned.
The order of dims determines the order of the points.
WARNING: This feature is experimental. It may change in future versions, and may not be 100% reliable in all cases. Please file github issues if problems occur.
Rasters.rasterize — Functionrasterize([reducer], data; kw...)Rasterize a GeoInterface.jl compatable geometry or feature, or a Tables.jl table with a :geometry column of GeoInterface.jl objects, or X, Y points columns.
Arguments
reducer: a reducing function to reduce the fill value for all geometries that cover or touch a pixel down to a single value. The default islast. Any that takes an iterable and returns a single value will work, including custom functions. However, there are optimisations for built-in methods includingsum,first,last,minimum,maximum,extremaandStatistics.mean. These may be an order of magnitude or more faster thancountis a special-cased as it does not need a fill value.data: a GeoInterface.jlAbstractGeometry, or a nestedVectorofAbstractGeometry, or a Tables.jl compatible object containing a:geometrycolumn or points and values columns.
Keywords
These are detected automatically from data where possible.
to: aRaster,RasterStack,TupleofDimensionorExtents.Extent. If notoobject is provided the extent will be calculated from the geometries, Additionally, when notoobject or anExtentis passed forto, thesizeorreskeyword must also be used.res: the resolution of the dimensions, aRealorTuple{<:Real,<:Real}. Only required whentois not used or is anExtents.Extent, andsizeis not used.size: the size of the output array, as aTuple{Int,Int}or singleIntfor a square. Only required whentois not used or is anExtents.Extent, andresis not used.crs: acrswhich will be attached to the resulting raster whentonot passed or is anExtent. Otherwise the crs fromtois used.shape: Forcedatato be treated as:polygon,:lineor:pointgeometries. using points or lines as polygons may have unexpected results.boundary: for polygons, include pixels where the:centeris inside the polygon, where the polygon:touchesthe pixel, or that are completely:insidethe polygon. The default is:center.
fill: the value or values to fill a polygon with. ASymbolor tuple ofSymbolwill be used to retrieve properties from features or column values from table rows. An array or other iterable will be used for each geometry, in order.fillcan also be a function of the current value, e.g.x -> x + 1.op: A reducing function that accepts two values and returns one, likemintominimum. For common methods this will be assigned for you, or is not required. But you can use it instead of areduceras it will usually be faster.shape: forcedatato be treated as:polygon,:lineor:point, where possible Points can't be treated as lines or polygons, and lines may not work as polygons, but an attempt will be made.geometrycolumn:Symbolto manually select the column the geometries are in whendatais a Tables.jl compatible table, or a tuple ofSymbolfor columns of point coordinates.progress: show a progress bar,trueby default,falseto hide..verbose: print information and warnings whne there are problems with the rasterisation.trueby default.threaded: run operations in parallel.trueby default.filename: a filename to write to directly, useful for large files.suffix: a string or value to append to the filename. A tuple ofsuffixwill be applied to stack layers.keys(stack)are the default.
Example
Rasterize a shapefile for China and plot, with a border.
using Rasters, RasterDataSources, ArchGDAL, Plots, Dates, Shapefile, Downloads
+using Rasters.LookupArrays
+
+# Download a borders shapefile
+shapefile_url = "https://github.com/nvkelso/natural-earth-vector/raw/master/10m_cultural/ne_10m_admin_0_countries.shp"
+shapefile_name = "country_borders.shp"
+isfile(shapefile_name) || Downloads.download(shapefile_url, shapefile_name)
+
+# Load the shapes for china
+china_border = Shapefile.Handle(shapefile_name).shapes[10]
+
+# Rasterize the border polygon
+china = rasterize(last, china_border; res=0.1, missingval=0, fill=1, boundary=:touches, progress=false)
+
+# And plot
+p = plot(china; color=:spring, legend=false)
+plot!(p, china_border; fillalpha=0, linewidth=0.6)
+
+savefig("build/china_rasterized.png"); nothing
+
+# output
+
WARNING: This feature is experimental. It may change in future versions, and may not be 100% reliable in all cases. Please file github issues if problems occur.
Rasters.rasterize! — Functionrasterize!([reducer], dest, data; kw...)Rasterize the geometries in data into the Raster or RasterStack dest, using the values specified by fill.
Arguments
dest: aRasterorRasterStackto rasterize into.reducer: a reducing function to reduce the fill value for all geometries that cover or touch a pixel down to a single value. The default islast. Any that takes an iterable and returns a single value will work, including custom functions. However, there are optimisations for built-in methods includingsum,first,last,minimum,maximum,extremaandStatistics.mean. These may be an order of magnitude or more faster thancountis a special-cased as it does not need a fill value.data: a GeoInterface.jlAbstractGeometry, or a nestedVectorofAbstractGeometry, or a Tables.jl compatible object containing a:geometrycolumn or points and values columns.
Keywords
These are detected automatically from A and data where possible.
fill: the value or values to fill a polygon with. ASymbolor tuple ofSymbolwill be used to retrieve properties from features or column values from table rows. An array or other iterable will be used for each geometry, in order.fillcan also be a function of the current value, e.g.x -> x + 1.op: A reducing function that accepts two values and returns one, likemintominimum. For common methods this will be assigned for you, or is not required. But you can use it instead of areduceras it will usually be faster.shape: forcedatato be treated as:polygon,:lineor:point, where possible Points can't be treated as lines or polygons, and lines may not work as polygons, but an attempt will be made.geometrycolumn:Symbolto manually select the column the geometries are in whendatais a Tables.jl compatible table, or a tuple ofSymbolfor columns of point coordinates.progress: show a progress bar,trueby default,falseto hide..verbose: print information and warnings whne there are problems with the rasterisation.trueby default.threaded: run operations in parallel.trueby default.to: aRaster,RasterStack,TupleofDimensionorExtents.Extent. If notoobject is provided the extent will be calculated from the geometries, Additionally, when notoobject or anExtentis passed forto, thesizeorreskeyword must also be used.res: the resolution of the dimensions, aRealorTuple{<:Real,<:Real}. Only required whentois not used or is anExtents.Extent, andsizeis not used.size: the size of the output array, as aTuple{Int,Int}or singleIntfor a square. Only required whentois not used or is anExtents.Extent, andresis not used.crs: acrswhich will be attached to the resulting raster whentonot passed or is anExtent. Otherwise the crs fromtois used.shape: Forcedatato be treated as:polygon,:lineor:pointgeometries. using points or lines as polygons may have unexpected results.boundary: for polygons, include pixels where the:centeris inside the polygon, where the polygon:touchesthe pixel, or that are completely:insidethe polygon. The default is:center.
Example
using Rasters, RasterDataSources, ArchGDAL, Plots, Dates, Shapefile, GeoInterface, Downloads
+using Rasters.LookupArrays
+
+# Download a borders shapefile
+shapefile_url = "https://github.com/nvkelso/natural-earth-vector/raw/master/10m_cultural/ne_10m_admin_0_countries.shp"
+shapefile_name = "country_borders.shp"
+isfile(shapefile_name) || Downloads.download(shapefile_url, shapefile_name)
+
+# Load the shapes for indonesia
+indonesia_border = Shapefile.Handle(shapefile_name).shapes[1]
+
+# Make an empty EPSG 4326 projected Raster of the area of Indonesia
+dimz = X(Projected(90.0:0.1:145; sampling=Intervals(), crs=EPSG(4326))),
+ Y(Projected(-15.0:0.1:10.9; sampling=Intervals(), crs=EPSG(4326)))
+
+A = zeros(UInt32, dimz; missingval=UInt32(0))
+
+# Rasterize each indonesian island with a different number. The islands are
+# rings of a multi-polygon, so we use `GI.getring` to get them all separately.
+islands = collect(GeoInterface.getring(indonesia_border))
+rasterize!(last, A, islands; fill=1:length(islands), progress=false)
+
+# And plot
+p = plot(Rasters.trim(A); color=:spring)
+plot!(p, indonesia_border; fillalpha=0, linewidth=0.7)
+
+savefig("build/indonesia_rasterized.png"); nothing
+
+# output
+
WARNING: This feature is experimental. It may change in future versions, and may not be 100% reliable in all cases. Please file github issues if problems occur.
Rasters.resample — Functionresample(x; kw...)
+resample(xs...; to=first(xs), kw...)resample uses ArchGDAL.gdalwarp to resample a Raster or RasterStack to a new resolution and optionally new crs, or to snap to the bounds, resolution and crs of the object to.
Arguments
x: the object/s to resample.
Keywords
to: aRaster,RasterStack,TupleofDimensionorExtents.Extent. If notoobject is provided the extent will be calculated fromx,res: the resolution of the dimensions, aRealorTuple{<:Real,<:Real}. Only required whentois not used or is anExtents.Extent, andsizeis not used.size: the size of the output array, as aTuple{Int,Int}or singleIntfor a square. Only required whentois not used or is anExtents.Extent, andresis not used.crs: acrswhich will be attached to the resulting raster whentonot passed or is anExtent. Otherwise the crs fromtois used.method: ASymbolorStringspecifying the method to use for resampling. From the docs forgdalwarp::near: nearest neighbour resampling (default, fastest algorithm, worst interpolation quality).:bilinear: bilinear resampling.:cubic: cubic resampling.:cubicspline: cubic spline resampling.:lanczos: Lanczos windowed sinc resampling.:average: average resampling, computes the weighted average of all non-NODATA contributing pixels. rms root mean square / quadratic mean of all non-NODATA contributing pixels (GDAL >= 3.3):mode: mode resampling, selects the value which appears most often of all the sampled points.:max: maximum resampling, selects the maximum value from all non-NODATA contributing pixels.:min: minimum resampling, selects the minimum value from all non-NODATA contributing pixels.:med: median resampling, selects the median value of all non-NODATA contributing pixels.:q1: first quartile resampling, selects the first quartile value of all non-NODATA contributing pixels.:q3: third quartile resampling, selects the third quartile value of all non-NODATA contributing pixels.:sum: compute the weighted sum of all non-NODATA contributing pixels (since GDAL 3.1)
Where NODATA values are set to
missingval.filename: a filename to write to directly, useful for large files.suffix: a string or value to append to the filename. A tuple ofsuffixwill be applied to stack layers.keys(stack)are the default.
Note:
- GDAL may cause some unexpected changes in the data, such as returning a reversed Y dimension or changing the
crstype fromEPSGtoWellKnownText(it will represent the same CRS).
Example
Resample a WorldClim layer to match an EarthEnv layer:
using Rasters, RasterDataSources, ArchGDAL, Plots
+A = Raster(WorldClim{Climate}, :prec; month=1)
+B = Raster(EarthEnv{HabitatHeterogeneity}, :evenness)
+
+a = plot(A)
+b = plot(resample(A; to=B))
+
+savefig(a, "build/resample_example_before.png");
+savefig(b, "build/resample_example_after.png"); nothing
+# outputBefore resample:
After resample:
WARNING: This feature is experimental. It may change in future versions, and may not be 100% reliable in all cases. Please file github issues if problems occur.
Rasters.replace_missing — Functionreplace_missing(a::AbstractRaster, newmissingval)
+replace_missing(a::AbstractRasterStack, newmissingval)Replace missing values in the array or stack with a new missing value, also updating the missingval field/s.
Keywords
filename: a filename to write to directly, useful for large files.suffix: a string or value to append to the filename. A tuple ofsuffixwill be applied to stack layers.keys(stack)are the default.
Example
using Rasters, RasterDataSources, ArchGDAL
+A = Raster(WorldClim{Climate}, :prec; month=1) |> replace_missing
+missingval(A)
+# output
+missingRasters.reproject — Functionreproject(target::GeoFormat, x)Reproject the dimensions of x to a different crs.
Arguments
target: any crs in a GeoFormatTypes.jl wrapper, e.g.EPSG,WellKnownText,ProjString.x: aDimension,TupleofDimension,RasterorRasterStack.
Dimensions without an AbstractProjected lookup (such as a Ti dimension) are silently returned without modification.
reproject(source::GeoFormat, target::GeoFormat, dim::Dimension, val)reproject uses ArchGDAL.reproject, but implemented for a reprojecting a value array of values, a single dimension at a time.
Rasters.setcrs — Functionsetcrs(x, crs)Set the crs of a Raster, RasterStack, Tuple of Dimension, or a Dimension. The crs is expected to be a GeoFormatTypes.jl CRS or Mixed GeoFormat type
Rasters.setmappedcrs — Functionsetmappedcrs(x, crs)Set the mapped crs of a Raster, a RasterStack, a Tuple of Dimension, or a Dimension. The crs is expected to be a GeoFormatTypes.jl CRS or Mixed GeoFormat type
Base.skipmissing — Functionskipmissing(itr::Raster)Returns an iterable over the elements in a Raster object, skipping any values equal to either the missingval or missing.
Rasters.trim — Functiontrim(x; dims::Tuple, pad::Int)Trim missingval(x) from x for axes in dims, returning a view of x.
Arguments
x: ARasterorRasterStack. For stacks, all layers must having missing values for a pixel for it to be trimmed.
Keywords
dims: By defaultdims=(XDim, YDim), so that trimming keeps the area ofXandYthat contains non-missing values along all other dimensions.pad: The trimmed size will be padded bypadon all sides, although padding will not be added beyond the original extent of the array.
As trim is lazy, filename and suffix keywords are not used.
Example
Create trimmed layers of Australian habitat heterogeneity.
using Rasters, RasterDataSources, Plots
+layers = (:evenness, :range, :contrast, :correlation)
+st = RasterStack(EarthEnv{HabitatHeterogeneity}, layers)
+
+# Roughly cut out australia
+ausbounds = X(100 .. 160), Y(-50 .. -10)
+aus = st[ausbounds...]
+a = plot(aus)
+
+# Trim missing values and plot
+b = plot(trim(aus))
+
+savefig(a, "build/trim_example_before.png");
+savefig(b, "build/trim_example_after.png"); nothing
+
+# output
+Before trim:
After trim:
WARNING: This feature is experimental. It may change in future versions, and may not be 100% reliable in all cases. Please file github issues if problems occur.
Rasters.warp — Functionwarp(A::AbstractRaster, flags::Dict; kw...)Gives access to the GDALs gdalwarp method given a Dict of flag => value arguments that can be converted to strings, or vectors where multiple space-separated arguments are required.
Arrays with additional dimensions not handled by GDAL (other than X, Y, Band) are sliced, warped, and then combined to match the original array dimensions. These slices will not be written to disk and loaded lazyily at this stage - you will need to do that manually if required.
See the gdalwarp docs for a list of arguments.
Keywords
filename: a filename to write to directly, useful for large files.suffix: a string or value to append to the filename. A tuple ofsuffixwill be applied to stack layers.keys(stack)are the default.
Any additional keywords are passed to ArchGDAL.Dataset.
Example
This simply resamples the array with the :tr (output file resolution) and :r flags, giving us a pixelated version:
using Rasters, RasterDataSources, Plots
+A = Raster(WorldClim{Climate}, :prec; month=1)
+a = plot(A)
+
+flags = Dict(
+ :tr => [2.0, 2.0],
+ :r => :near,
+)
+b = plot(warp(A, flags))
+
+savefig(a, "build/warp_example_before.png");
+savefig(b, "build/warp_example_after.png"); nothing
+
+# output
+Before warp:
After warp:
In practise, prefer resample for this. But warp may be more flexible.
WARNING: This feature is experimental. It may change in future versions, and may not be 100% reliable in all cases. Please file github issues if problems occur.
Rasters.zonal — Functionzonal(f, x::Union{Raster,RasterStack}; of, kw...)Calculate zonal statistics for the the zone of a Raster or RasterStack covered by the of object/s.
Arguments
f: any function that reduces an iterable to a single value, such assumorStatistics.meanx: ARasterorRasterStackof: ARaster,RasterStack, dim tuple, extent, GeoInterface.jl compatible geometry, Tables.jl compatible table of a:geometrycolumn, or anAbstractVectorof any of these objects..
Keywords
These can be used when of is a GeoInterface.jl compatible object:
shape: Forcedatato be treated as:polygon,:lineor:point, where possible.boundary: for polygons, include pixels where the:centeris inside the polygon, where the line:touchesthe pixel, or that are completely:insideinside the polygon. The default is:center.progress: show a progress bar,trueby default,falseto hide..
Example
``jldoctest using Rasters, RasterDataSources, ArchGDAL, Shapefile, DataFrames, Downloads, Statistics, Dates
Download a borders shapefile
neurl = "https://github.com/nvkelso/natural-earth-vector/raw/master/10mcultural/ne10madmin0countries" shpurl, dbfurl = neurl * ".shp", neurl * ".dbf" shpname, dbfname = "countryborders.shp", "countryborders.dbf" isfile(shpname) || Downloads.download(shpurl, shpname) isfile(dbfurl) || Downloads.download(dbfurl, dbfname)
Download and read a raster stack from WorldClim
st = RasterStack(WorldClim{Climate}; month=Jan, lazy=false)
Load the shapes for world countries
countries = Shapefile.Table(shp_name) |> DataFrame
Calculate the january mean of all climate variables for all countries
january_stats = zonal(mean, st; of=countries, boundary=:touches, progress=false) |> DataFrame
Add the country name column (natural earth has some string errors it seems)
insertcols!(january_stats, 1, :country => first.(split.(countries.ADMIN, r"[^A-Za-z ]")))
output
258×8 DataFrame Row │ country tmin tmax tavg prec ⋯ │ SubStrin… Float32 Float32 Float32 Float64 ⋯ ─────┼────────────────────────────────────────────────────────────────────────── 1 │ Indonesia 21.5447 29.1864 25.3656 271.063 ⋯ 2 │ Malaysia 21.3087 28.4291 24.8688 273.381 3 │ Chile 7.24534 17.9263 12.5858 78.1287 4 │ Bolivia 17.2065 27.7454 22.4759 192.542 5 │ Peru 15.0273 25.5504 20.2888 180.007 ⋯ 6 │ Argentina 13.6751 27.6715 20.6732 67.1837 7 │ Dhekelia Sovereign Base Area 5.87126 15.8991 10.8868 76.25 8 │ Cyprus 5.65921 14.6665 10.1622 97.4474 ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱ 252 │ Spratly Islands 25.0 29.2 27.05 70.5 ⋯ 253 │ Clipperton Island 21.5 33.2727 27.4 6.0 254 │ Macao S 11.6694 17.7288 14.6988 28.0 255 │ Ashmore and Cartier Islands NaN NaN NaN NaN 256 │ Bajo Nuevo Bank NaN NaN NaN NaN ⋯ 257 │ Serranilla Bank NaN NaN NaN NaN 258 │ Scarborough Reef NaN NaN NaN NaN 3 columns and 243 rows omitted ```
Slice and combine to and from RasterSeries
Rasters.slice — Functionslice(A::Union{AbstractRaster,AbstractRasterStack,AbstracRasterSeries}, dims) => RasterSeriesSlice views along some dimension/s to obtain a RasterSeries of the slices.
For a Raster or RasterStack this will return a RasterSeries of Raster or RasterStack that are slices along the specified dimensions.
For a RasterSeries, the output is another series where the child objects are sliced and the series dimensions index is now of the child dimensions combined. slice on a RasterSeries with no dimensions will slice along the dimensions shared by both the series and child object.
WARNING: This feature is experimental. It may change in future versions, and may not be 100% reliable in all cases. Please file github issues if problems occur.
Rasters.combine — Functioncombine(A::Union{AbstractRaster,AbstractRasterStack,AbstracRasterSeries}, [dims]) => RasterCombine a RasterSeries along some dimension/s, creating a new Raster or RasterStack, depending on the contents of the series.
If dims are passed, only the specified dimensions will be combined with a RasterSeries returned, unless dims is all the dims in the series.
WARNING: This feature is experimental. It may change in future versions, and may not be 100% reliable in all cases. Please file github issues if problems occur.
File operations
These Base and DimensionalData methods have specific Rasters.jl versions:
DimensionalData.modify — Functionmodify(f, series::AbstractRasterSeries)Apply function f to the data of the child object. If the child is an AbstractRasterStack the function will be passed on to its child AbstractRasters.
f must return an idenically sized array.
This method triggers a complete rebuild of all objects, and disk based objects will be transferred to memory.
An example of the usefulnesss of this is for swapping out array backend for an entire series to CuArray from CUDA.jl to copy data to a GPU.
Base.open — Functionopen(f, A::AbstractRaster; write=false)open is used to open any lazy=true AbstractRaster and do multiple operations on it in a safe way. The write keyword opens the file in write lookup so that it can be altered on disk using e.g. a broadcast.
f is a method that accepts a single argument - an Raster object which is just an AbstractRaster that holds an open disk-based object. Often it will be a do block:
lazy=false (in-memory) rasters will ignore open and pass themselves to f.
# A is an `Raster` wrapping the opened disk-based object.
+open(Raster(filepath); write=true) do A
+ mask!(A; with=maskfile)
+ A[I...] .*= 2
+ # ... other things you need to do with the open file
+endBy using a do block to open files we ensure they are always closed again after we finish working with them.
Base.read — Functionread(A::AbstractRaster)
+read(A::AbstractRasterStack)
+read(A::AbstractRasterSeries)read will move a Rasters.jl object completely to memory.
Base.read! — Functionread!(src::Union{AbstractString,AbstractRaster}, dst::AbstractRaster)
+read!(src::Union{AbstractString,AbstractRasterStack}, dst::AbstractRasterStack)
+read!(scr::AbstractRasterSeries, dst::AbstractRasterSeries)read! will copy the data from src to the object dst.
src can be an object or a file-path String.
Base.write — FunctionBase.write(filename::AbstractString, A::AbstractRaster; kw...)Write an AbstractRaster to file, guessing the backend from the file extension.
Keyword arguments are passed to the write method for the backend.
Base.write(filename::AbstractString, s::AbstractRasterStack; suffix, kw...)Write any AbstractRasterStack to file, guessing the backend from the file extension.
Keywords
suffix: suffix to append to file names. By default the layer key is used.
Other keyword arguments are passed to the write method for the backend.
If the source can't be saved as a stack-like object, individual array layers will be saved.
Base.write(filepath::AbstractString, s::AbstractRasterSeries; kw...)Write any AbstractRasterSeries to file, guessing the backend from the file extension.
The lookup values of the series will be appended to the filepath (before the extension), separated by underscores.
Keywords
See other docs for write. All keywords are passed through to Raster and RasterStack methods.
Base.write(filename::AbstractString, ::Type{GRDsource}, s::AbstractRaster; force=false)Write a Raster to a .grd file with a .gri header file. The extension of filename will be ignored.
Returns filename.
Base.write(filename::AbstractString, ::Type{NCDsource}, A::AbstractRaster)Write an NCDarray to a NetCDF file using NCDatasets.jl
Returns filename.
Base.write(filename::AbstractString, ::Type{NCDsource}, s::AbstractRasterStack; kw...)Write an NCDstack to a single netcdf file, using NCDatasets.jl.
Currently Metadata is not handled for dimensions, and Metadata from other AbstractRaster @types is ignored.
Keywords
Keywords are passed to NCDatasets.defVar.
append: If true, the variable of the current Raster will be appended tofilename. Note that the variable of the current Raster should be not exist before. If not, you need to setappend = false. Rasters.jl can not overwrite a previous existing variable.fillvalue: A value filled in the NetCDF file to indicate missing data. It will be stored in the_FillValueattribute.chunksizes: Vector integers setting the chunk size. The total size of a chunk must be less than 4 GiB.deflatelevel: Compression level: 0 (default) means no compression and 9 means maximum compression. Each chunk will be compressed individually.shuffle: If true, the shuffle filter is activated which can improve the compression ratio.checksum: The checksum method can be:fletcher32or:nochecksum(checksumming is disabled, which is the default)typename(string): The name of the NetCDF type required for vlen arrays (https://web.archive.org/save/https://www.unidata.ucar.edu/software/netcdf/netcdf-4/newdocs/netcdf-c/nc005fdef005fvlen.html)
Base.write(filename::AbstractString, ::Type{GDALsource}, A::AbstractRaster; force=false, kw...)Write a Raster to file using GDAL.
Keywords
driver: A GDAL driver name or a GDAL driver retrieved viaArchGDAL.getdriver(drivername). Guessed from the filename extension by default.options::Dict{String,String}: A dictionary containing the dataset creation options passed to the driver. For example:Dict("COMPRESS"=>"DEFLATE")Valid options for the drivers can be looked up here: https://gdal.org/drivers/raster/index.html
Returns filename.
Internals
Rasters.FileArray — TypeFileArray{X} <: DiskArrays.AbstractDiskArrayFilearray is a DiskArrays.jl AbstractDiskArray. Instead of holding an open object, it just holds a filename string that is opened lazily when it needs to be read.
Rasters.FileStack — TypeFileStack{X,K}
+
+FileStack{X,K}(filename, types, sizes, eachchunk, haschunks, write)A wrapper object that holds file pointer and size/chunking metadata for a multi-layered stack stored in a single file, typically netcdf or hdf5.
X is a backend type like NCDsource, and K is a tuple of Symbol keys.
Rasters.OpenStack — TypeOpenStack{X,K}
+
+OpenStack{X,K}(dataset)A wrapper for any stack-like opened dataset that can be indexed with Symbol keys to retrieve AbstractArray layers.
OpenStack is usually hidden from users, wrapped in a regular RasterStack passed as the function argument in open(stack) when the stack is contained in a single file.
X is a backend type like NCDsource, and K is a tuple of Symbol keys.
Rasters.RasterDiskArray — TypeRasterDiskArray <: DiskArrays.AbstractDiskArrayA basic DiskArrays.jl wrapper for objects that don't have one defined yet. When we open a FileArray it is replaced with a RasterDiskArray.