Awesome

DimensionalData

[!TIP] Visit the latest documentation at https://rafaqz.github.io/DimensionalData.jl/dev/

DimensionalData.jl provides tools and abstractions for working with datasets that have named dimensions, and optionally a lookup index. It provides no-cost abstractions for named indexing, and fast index lookups.

DimensionalData is a pluggable, generalised version of AxisArrays.jl with a cleaner syntax, and additional functionality found in NamedDims.jl. It has similar goals to pythons xarray, and is primarily written for use with spatial data in Rasters.jl.

Installation

julia>]
pkg> add DimensionalData

Quick start

Start using the package:

using DimensionalData

The basic syntax to create a dimensional array (DimArray) is:

A = DimArray(rand(50, 31), (X(), Y(10.0:40.0)));

Or just use rand directly, which also works for zeros, ones and fill:

A = rand(X(10), Y(10.0:20.0))

╭───────────────────────────╮
│ 10×11 DimArray{Float64,2} │
├───────────────────────────┴──────────────────────────────── dims ┐
  ↓ X,
  → Y Sampled{Float64} 10.0:1.0:20.0 ForwardOrdered Regular Points
└──────────────────────────────────────────────────────────────────┘
 10.0       11.0       12.0        13.0       14.0         …  16.0       17.0       18.0        19.0       20.0
  0.71086    0.689255   0.672889    0.766345   0.00277696      0.773863   0.252199   0.279538    0.808931   0.783528
  0.934464   0.815631   0.815715    0.890573   0.158584        0.304733   0.936321   0.499803    0.839926   0.979722
  ⋮                                                        ⋱                                                ⋮
  0.935495   0.460879   0.0218015   0.703387   0.756411    …   0.431141   0.619897   0.0536918   0.506488   0.170494
  0.800226   0.208188   0.512795    0.421171   0.492668        0.238562   0.4694     0.320596    0.934364   0.147563

[!NOTE] Subsetting by index is easy:

A[Y=1:10, X=1]

╭────────────────────────────────╮
│ 10-element DimArray{Float64,1} │
├────────────────────────────────┴─────────────────────────── dims ┐
  ↓ Y Sampled{Float64} 10.0:1.0:19.0 ForwardOrdered Regular Points
└──────────────────────────────────────────────────────────────────┘
 10.0  0.130198
 11.0  0.693343
 12.0  0.400656
  ⋮    
 17.0  0.877581
 18.0  0.866406
 19.0  0.605331

One can also subset by lookup, using a Selector, let's try At:

A[Y(At(25))]

╭────────────────────────────────╮
│ 50-element DimArray{Float64,1} │
├────────────────────────── dims ┤
  ↓ X
└────────────────────────────────┘
  1  0.5318
  2  0.212491
  3  0.99119
  4  0.373549
  5  0.0987397
  ⋮  
 46  0.503611
 47  0.225421
 48  0.293564
 49  0.976395
 50  0.622586

There is also Near (for inexact/nearest selection), Contains (for Intervals containing values), Between or .. for range selection, and Where for queries, among others.

Plotting with Makie.jl is as easy as:

using GLMakie, DimensionalData
boxplot(rand(X('a':'d'), Y(2:5:20)))

And the plot will have the right ticks and labels.

See the docs for more details

[!NOTE] Recent changes have greatly reduced the exported API.

Previously exported methods can be brought into global scope by using the sub-modules they have been moved to - Lookup and Dimensions:

using DimensionalData
using DimensionalData.Lookup, DimensionalData.Dimensions

Alternative 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 most of their syntax and functionality.

AxisKeys.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.

The main functionality is explained here, but the full list of features is listed at the API page.