Juliacon 2024

DimensionalData.jl is not the first or only package to define arrays and datasets with named dimensions and lookups in julia - AxisArrays.jl, AxisKeys.jl, NamedDims.jl all succeed in this space.

But DD is unique in its focus on extensibility, and has spawned many objects inheriting its behaviors, largely across spatial sciences, such as in Rasters.jl, YAXArrays.jl and ClimateBase.jl. It's also generic enough to find uses in statistics, machine learning and image processing.

Another focus driven by its spatial use-cases is on being able to accurately represent a wide rang of lookup values and behaviors:
- Points or Intervals
- Regular or Irregular
- Ordered or Unordered
- Sampled or Categorical
- Linear or Cyclic
- grid-aligned or with transformed coordinates

This means it can represent the wide variety of CF compliant netcdf files, as well as affine transformed coordinates and projections of GDAL.

DD also shines in ease of plotting, with extensive Plots.jl and Makie.jl recipes that automatically put the right values and labels in the right places.

This talk will demonstrate the basics of both how to use DimensionalData.jl, and for package developers - how to extend it.