Rasters

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

Quick start

Install the package by typing:

]
add Rasters

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

6×6×13 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 latitude

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

Select 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    0.461862

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    0.461862

or 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    0.461862

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.

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, 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.