ComponentArrays.jlArrays with arbitrarily nested named components.
ComponentArrays.jl
| Documentation | Build Status |
|---|---|
  |
  |
The main export of this package is the ComponentArray type. "Components" of ComponentArrays are really just array blocks that can be accessed through a named index. This will create a new ComponentArray whose data is a view into the original, allowing for standalone models to be composed together by simple function composition. In essence, ComponentArrays allow you to do the things you would usually need a modeling language for, but without actually needing a modeling language. The main targets are for use in DifferentialEquations.jl and Optim.jl, but anything that requires flat vectors is fair game.
Check out the NEWS for new features by minor release version.
General use
The easiest way to construct 1-dimensional ComponentArrays (aliased as ComponentVector) is as if they were NamedTuples. In fact, a good way to think about them is as arbitrarily nested, mutable NamedTuples that can be passed through a solver.
julia> c = (a=2, b=[1, 2]);
julia> x = ComponentArray(a=5, b=[(a=20., b=0), (a=33., b=0), (a=44., b=3)], c=c)
ComponentVector{Float64}(a = 5.0, b = [(a = 20.0, b = 0.0), (a = 33.0, b = 0.0), (a = 44.0, b = 3.0)], c = (a = 2.0, b = [1.0, 2.0]))
julia> x.c.a = 400; x
ComponentVector{Float64}(a = 5.0, b = [(a = 20.0, b = 0.0), (a = 33.0, b = 0.0), (a = 44.0, b = 3.0)], c = (a = 400.0, b = [1.0, 2.0]))
julia> x[8]
400.0
julia> collect(x)
10-element Array{Float64,1}:
5.0
20.0
0.0
33.0
0.0
44.0
3.0
400.0
1.0
2.0
julia> typeof(similar(x, Int32)) === typeof(ComponentVector{Int32}(a=5, b=[(a=20., b=0), (a=33., b=0), (a=44., b=3)], c=c))
true
ComponentArrays can be constructed from existing ComponentArrays (currently nested fields cannot be changed this way):
julia> x = ComponentVector(a=1, b=2, c=3);
julia> ComponentVector(x; a=11, new=42)
ComponentVector{Int64}(a = 11, b = 2, c = 3, new = 42)
Higher dimensional ComponentArrays can be created too, but it's a little messy at the moment. The nice thing for modeling is that dimension expansion through broadcasted operations can create higher-dimensional ComponentArrays automatically, so Jacobian cache arrays that are created internally with false .* x .* x' will be two-dimensional ComponentArrays (aliased as ComponentMatrix) with proper axes. Check out the ODE with Jacobian example in the examples folder to see how this looks in practice.
julia> x = ComponentArray(a=1, b=[2, 1, 4.0], c=c)
ComponentVector{Float64}(a = 1.0, b = [2.0, 1.0, 4.0], c = (a = 2.0, b = [1.0, 2.0]))
julia> x2 = x .* x'
7×7 ComponentMatrix{Float64} with axes Axis(a = 1, b = 2:4, c = ViewAxis(5:7, Axis(a = 1, b = 2:3))) × Axis(a = 1, b = 2:4, c = ViewAxis(5:7, Axis(a = 1, b = 2:3)))
1.0 2.0 1.0 4.0 2.0 1.0 2.0
2.0 4.0 2.0 8.0 4.0 2.0 4.0
1.0 2.0 1.0 4.0 2.0 1.0 2.0
4.0 8.0 4.0 16.0 8.0 4.0 8.0
2.0 4.0 2.0 8.0 4.0 2.0 4.0
1.0 2.0 1.0 4.0 2.0 1.0 2.0
2.0 4.0 2.0 8.0 4.0 2.0 4.0
julia> x2[:c,:c]
3×3 ComponentMatrix{Float64} with axes Axis(a = 1, b = 2:3) × Axis(a = 1, b = 2:3)
4.0 2.0 4.0
2.0 1.0 2.0
4.0 2.0 4.0
julia> x2[:a,:a]
1.0
julia> @view x2[:a,:c]
ComponentVector{Float64,SubArray...}(a = 2.0, b = [1.0, 2.0])
julia> x2[:b,:c]
3×3 ComponentMatrix{Float64} with axes FlatAxis() × Axis(a = 1, b = 2:3)
4.0 2.0 4.0
2.0 1.0 2.0
8.0 4.0 8.0
Examples
Differential equation example
This example uses @unpack from Parameters.jl for nice syntax. Example taken from: https://github.com/JuliaDiffEq/ModelingToolkit.jl/issues/36#issuecomment-536221300
using ComponentArrays
using DifferentialEquations
using Parameters: @unpack
tspan = (0.0, 20.0)
## Lorenz system
function lorenz!(D, u, p, t; f=0.0)
@unpack σ, ρ, β = p
@unpack x, y, z = u
D.x = σ*(y - x)
D.y = x*(ρ - z) - y - f
D.z = x*y - β*z
return nothing
end
lorenz_p = (σ=10.0, ρ=28.0, β=8/3)
lorenz_ic = ComponentArray(x=0.0, y=0.0, z=0.0)
lorenz_prob = ODEProblem(lorenz!, lorenz_ic, tspan, lorenz_p)
## Lotka-Volterra system
function lotka!(D, u, p, t; f=0.0)
@unpack α, β, γ, δ = p
@unpack x, y = u
D.x = α*x - β*x*y + f
D.y = -γ*y + δ*x*y
return nothing
end
lotka_p = (α=2/3, β=4/3, γ=1.0, δ=1.0)
lotka_ic = ComponentArray(x=1.0, y=1.0)
lotka_prob = ODEProblem(lotka!, lotka_ic, tspan, lotka_p)
## Composed Lorenz and Lotka-Volterra system
function composed!(D, u, p, t)
c = p.c #coupling parameter
@unpack lorenz, lotka = u
lorenz!(D.lorenz, lorenz, p.lorenz, t, f=c*lotka.x)
lotka!(D.lotka, lotka, p.lotka, t, f=c*lorenz.x)
return nothing
end
comp_p = (lorenz=lorenz_p, lotka=lotka_p, c=0.01)
comp_ic = ComponentArray(lorenz=lorenz_ic, lotka=lotka_ic)
comp_prob = ODEProblem(composed!, comp_ic, tspan, comp_p)
## Solve problem
# We can solve the composed system...
comp_sol = solve(comp_prob)
# ...or we can unit test one of the component systems
lotka_sol = solve(lotka_prob)
Notice how cleanly the composed! function can pass variables from one function to another with no array index juggling in sight. This is especially useful for large models as it becomes harder to keep track top-level model array position when adding new or deleting old components from the model. We could go further and compose composed! with other components ad (practically) infinitum with no mental bookkeeping.
The main benefit, however, is now our differential equations are unit testable. Both lorenz and lotka can be run as their own ODEProblem with f set to zero to see the unforced response.