Named dimensions and indexing for julia arrays and other data
Author rafaqz
42 Stars
Updated Last
10 Months Ago
Started In
August 2019


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DimensionalData.jl provides tools and abstractions for working with datasets that have named dimensions. It's a pluggable, generalised version of AxisArrays.jl with a cleaner syntax, and additional functionality found in NamedDimensions.jl. It has similar goals to pythons xarray, and is primarily written for use with spatial data in GeoData.jl.

!!! info "Status" This is a work in progress under active development, it may be a while before the interface stabilises and things are fully documented.


Dimensions are just wrapper types. They store the dimension index and define details about the grid and other metadata, and are also used to index into the array, wrapping a value or a Selector. X, Y, Z and Ti are the exported defaults.

A generalised Dim type is available to use arbitrary symbols to name dimensions. Custom dimensions can be defined using the @dim macro.

We can use dim wrappers for indexing, so that the dimension order in the underlying array does not need to be known:

a[X(1:10), Y(1:4)]

The core component is the AbstractDimension, and types that inherit from it, such as Time, X, Y, Z, the generic Dim{:x} or others you define manually using the @dim macro.

Dims can be used for indexing and views without knowing dimension order: a[X(20)], view(a, X(1:20), Y(30:40)) and for indicating dimesions to reduce mean(a, dims=Time), or permute permutedims(a, [X, Y, Z, Time]) in julia Base and Statistics functions that have dims arguments.


Selectors find indices in the dimension based on values At, Near, or Between the index value(s). They can be used in getindex, setindex! and view to select indices matching the passed in value(s)

  • At(x): get indices exactly matching the passed in value(s)
  • Near(x): get the closest indices to the passed in value(s)
  • Where(f::Function): filter the array axis by dimension value and a function
  • Between(a, b): get all indices between two values (inclusive)
  • Contains(x): the value x falls in the interval covered by the index. Only for Sampled [Intervals].

We can use selectors with dim wrappers:

a[X(Between(1, 10)), Y(At(25.7))]

Without dim wrappers selectors must be in the right order:

usin Unitful
a[Near(23u"s"), Between(10.5u"m", 50.5u"m")]

It's easy to write your own custom Selector if your need a different behaviour.

Example usage:

using Dates, DimensionalData
timespan = DateTime(2001,1):Month(1):DateTime(2001,12)
A = DimensionalArray(rand(12,10), (Ti(timespan), X(10:10:100)))

julia> A[X(Near(35)), Ti(At(DateTime(2001,5)))]

julia> A[Near(DateTime(2001, 5, 4)), Between(20, 50)]
DimensionalArray with dimensions:
 X: 20:10:50
and referenced dimensions:
 Time (type Ti): 2001-05-01T00:00:00
and data: 4-element Array{Float64,1}
[0.456175, 0.737336, 0.658405, 0.520152]

Dim types or objects can be used instead of a dimension number in many Base and Statistics methods:

Methods where dims can be used containing indices or Selectors

getindex, setindex! view

Methods where dims can be used

  • size, axes, firstindex, lastindex
  • cat
  • reverse
  • dropdims
  • reduce, mapreduce
  • sum, prod, maximum, minimum,
  • mean, median, extrema, std, var, cor, cov
  • permutedims, adjoint, transpose, Transpose
  • mapslices, eachslice

Example usage:

A = DimensionalArray(rand(20,10), (X, Y))
size(A, Y)
mean(A, dims=X)
std(A; dims=Y())

Alternate Packages

There are a lot of similar julia packages in this space. AxisArrays.jl, NamedDims.jl, NamedArrays.jl are registered alternative that each cover some of the functionality provided by DimensionalData.jl. DimensionalData.jl should be able to replicate any of their functionality, although with slightly more verbose syntax and less polish in some cases. If there is anything it doesn't do that these packages can do, put in an issue with the feature requrest.

AxisRanges.jl and AbstractIndices.jl are some other interesting developments. For more detail on why there are so many similar options and where things are headed, read this thread