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Rasters.jl is a powerful Julia package for handling geospatial raster data. It provides a unified interface for reading, writing, and manipulating rasters, allowing users to work with various source formats like GeoTIFF and NetCDF using identical syntax.

• Standardized Interface: The package abstracts away the complexities of different file formats. Users can treat Raster, RasterStack, and RasterSeries objects consistently, regardless of whether the data is in a file, in memory, or on a GPU.
• Intuitive Spatial Indexing: By extending DimensionalData.jl, Rasters.jl enables intuitive spatial indexing with named dimensions (e.g., X, Y, Time). This allows for selecting data by specific values (e.g., ras[Ti=At(DateTime(2001))]) rather than just numerical indices.
• Performance and Optimization: The package is optimized for high-performance operations on spatial data and includes features like lazy loading for handling large datasets efficiently.
• Comprehensive Support: It supports a wide range of raster formats, multi-layered stacks, and time series. It also includes built-in support for Coordinate Reference Systems (CRS), with automatic projection conversions via reproject.
• Extensible Functionality: While the core package is lightweight, it has extensions on other Julia packages (e.g., ArchGDAL for GDAL backend, CairoMakie for plotting) that add more functionality. For example, to load a raster, you can simply import ArchGDAL or import NCDatasets (for .nc files), and then call Raster("myfile.tiff").

Install the package by typing:

]
add Rasters

Then to use it:

using Rasters

Using 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 given

6×ばつ6×ばつ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年01月01日T00:00:00"):Month(1):DateTime("2002年01月01日T00:00:00") ForwardOrdered Regular Points
extent: Extent(X = (25, 30), Y = (25, 30), Ti = (DateTime("2001年01月01日T00:00:00"), DateTime("2002年01月01日T00:00:00")))
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...]

Rasters reduces its dependencies to keep the using time low. This means you need to load additional packages for expanded functionality.

For example, to use the GDAL backend, and download RasterDataSources files, you need to do:

using Rasters, ArchGDAL, RasterDataSources

Sources and packages needed:

• :gdal: using ArchGDAL
• :netcdf: using NCDatasets
• :grd: built-in.
• :grib: using GRIBDatasets.
• :zarr: using ZarrDatasets.

Other functionality in extensions:

• Raster data downloads, like Worldclim{Climate}: using RasterDataSources
• Makie plots: using GLMakie (opengl interactive) or using CairoMakie (print) etc.
• Coordinate transformations for GDAL rasters: using CoordinateTransformations

lon = lookup(ras, X) # if X is longitude
lat = lookup(ras, Y) # if Y is latitude

Sampled{Int64} ForwardOrdered Regular Points
wrapping: 25:1:30

Selecting a time slice by index is done via

ras[Ti(1)]

6×ばつ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年01月01日T00:00:00"):Month(1):DateTime("2001年01月01日T00:00:00") 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 0.461862

ras[Ti=1]

6×ばつ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年01月01日T00:00:00"):Month(1):DateTime("2001年01月01日T00:00:00") 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 0.461862

or and interval of indices using the syntax =a:b or (a:b)

ras[Ti(1:10)]

6×ばつ6×ばつ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年01月01日T00:00:00"):Month(1):DateTime("2001年10月01日T00:00:00") ForwardOrdered Regular Points
extent: Extent(X = (25, 30), Y = (25, 30), Ti = (DateTime("2001年01月01日T00:00:00"), DateTime("2001年10月01日T00:00:00")))
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...]

ras[Ti=At(DateTime(2001))]

6×ばつ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年01月01日T00:00:00"):Month(1):DateTime("2001年01月01日T00:00:00") 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 0.461862

More 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.

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, and RasterSeries as black boxes.
  • The data could hold GeoTiff or NetCDF files, Arrays in memory or CuArrays on the GPU - they will all behave in the same way.
  • RasterStack can be backed by a Netcdf or HDF5 file, or a NamedTuple of Raster holding .tif files, or all Raster in memory.
  • Users do not have to deal with the specifics of spatial file types.
• Projected lookups with Cylindrical projections can by indexed using other Cylindrical projections by setting the mappedcrs keyword on construction. You don't need to know the underlying projection, the conversion is handled automatically. This means lat/lon EPSG(4326) can be used seamlessly if you need that.

Rasters should be the fastest tool available for most tasks. If you find something is faster in another package, it's likely a bug - so make an issue!

image

Figure based on raster benchmarks

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, that's great! but its not always possible).

To make an issue we can fix quickly (or at all) there are three key steps:

1. Use a RasterDataSources.jl file if you can, so there are no download hassles. Otherwise store a file in an accessible place on web without authentication and preferably where you can use dowload directly, so we just run the script can spend our time finding your bug.
2. Add a minimum working example to the issue template that first downloads the file with download, then runs the function that triggers the bug.
3. Paste the complete stack trace of the error it produces, right to the bottom, into the issue template. Then we can be sure we have 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.