Getters

DimensionalData.jl defines consistent methods to retreive information from objects like DimArray, DimStack, Tuples of Dimension, Dimension and Lookup.

First we will define an example DimArray.

using DimensionalData
using DimensionalData.Lookups
x, y = X(10:-1:1), Y(100.0:10:200.0)
A = rand(x, y)

╭───────────────────────────╮
│ 10×11 DimArray{Float64,2} │
├───────────────────────────┴─────────────────────────────────── dims ┐
  ↓ X Sampled{Int64} 10:-1:1 ReverseOrdered Regular Points,
  → Y Sampled{Float64} 100.0:10.0:200.0 ForwardOrdered Regular Points
└─────────────────────────────────────────────────────────────────────┘
  ↓ →  100.0       110.0         120.0        …  190.0        200.0
 10      0.60582     0.141215      0.21304         0.030899     0.225936
  9      0.579684    0.194032      0.0925107       0.625407     0.864552
  8      0.147835    0.320466      0.0218371       0.736047     0.273577
  7      0.852385    0.410331      0.554825        0.0816985    0.558087
  6      0.476084    0.398895      0.924338   …    0.342836     0.408025
  5      0.737617    0.736993      0.262798        0.874753     0.367589
  4      0.9423      0.0612503     0.51853         0.863122     0.0773346
  3      0.402457    0.257745      0.692264        0.232609     0.872355
  2      0.694731    0.721035      0.0105931       0.572112     0.416011
  1      0.82384     0.00198649    0.928308   …    0.793683     0.717434

dims retreives dimensions from any object that has them.

What makes it so useful is you can filter which dimensions you want in what order, using any Dimension, Type{Dimension} or Symbol.

julia> dims(A)

↓ X Sampled{Int64} 10:-1:1 ReverseOrdered Regular Points,
→ Y Sampled{Float64} 100.0:10.0:200.0 ForwardOrdered Regular Points

julia> dims(A, Y)

Y Sampled{Float64} ForwardOrdered Regular Points
wrapping: 100.0:10.0:200.0

julia> dims(A, Y())

Y Sampled{Float64} ForwardOrdered Regular Points
wrapping: 100.0:10.0:200.0

julia> dims(A, :Y)

Y Sampled{Float64} ForwardOrdered Regular Points
wrapping: 100.0:10.0:200.0

julia> dims(A, (X,))

↓ X Sampled{Int64} 10:-1:1 ReverseOrdered Regular Points

julia> dims(A, (Y, X))

↓ Y Sampled{Float64} 100.0:10.0:200.0 ForwardOrdered Regular Points,
→ X Sampled{Int64} 10:-1:1 ReverseOrdered Regular Points

julia> dims(A, reverse(dims(A)))

↓ Y Sampled{Float64} 100.0:10.0:200.0 ForwardOrdered Regular Points,
→ X Sampled{Int64} 10:-1:1 ReverseOrdered Regular Points

julia> dims(A, isregular)

↓ X Sampled{Int64} 10:-1:1 ReverseOrdered Regular Points,
→ Y Sampled{Float64} 100.0:10.0:200.0 ForwardOrdered Regular Points

Predicates

These always return true or false. With multiple dimensions, fale means !all and true means all.

dims and all other methods listed above can use predicates to filter the returned dimensions.

julia> issampled(A)

true

julia> issampled(dims(A))

true

julia> issampled(A, Y)

true

julia> issampled(lookup(A, Y))

true

julia> dims(A, issampled)

↓ X Sampled{Int64} 10:-1:1 ReverseOrdered Regular Points,
→ Y Sampled{Float64} 100.0:10.0:200.0 ForwardOrdered Regular Points

julia> otherdims(A, issampled)

()

julia> lookup(A, issampled)

Sampled{Int64} 10:-1:1 ReverseOrdered Regular Points,
Sampled{Float64} 100.0:10.0:200.0 ForwardOrdered Regular Points