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ChrisRackauckasChrisRackauckas
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Merge pull request #26 from rafaqz/loose_typed_nodes
More types for nodes
2 parents 8f2a994 + b14833b commit c7bb8fe

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-18
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5 files changed

+117
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‎README.md

+9
Original file line numberDiff line numberDiff line change
@@ -74,6 +74,15 @@ tissue2 = construct(Tissue, deepcopy([population2, population]))
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embryo = construct(Embryo, deepcopy([tissue1, tissue2])) # Make an embryo from Tissues
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```
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Note that tuples can be used as well. This allows for type-stable indexing with
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heterogeneous nodes. For example:
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```julia
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tissue1 = construct(Tissue, deepcopy((population, cell3)))
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```
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(of course at the cost of mutability).
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The head node then acts as the king. It is designed to have functionality which
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mimics a vector in order for usage in DifferentialEquations or Optim. So for example
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‎src/indexing.jl

+5-2
Original file line numberDiff line numberDiff line change
@@ -1,3 +1,6 @@
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nodeselect(ns, i, I...) = ns[i][I...]
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nodechild(ns, i, j) = ns[i].nodes[j]
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function bisect_search(a, i)
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first(searchsorted(a,i))
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end
@@ -7,7 +10,7 @@ Base.IndexStyle(::Type{<:AbstractMultiScaleArray}) = IndexLinear()
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@inline function getindex(m::AbstractMultiScaleArray, i::Int)
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idx = bisect_search(m.end_idxs, i)
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idx > 1 && (i -= m.end_idxs[idx-1]) # also works with values
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(isempty(m.values) || idx < length(m.end_idxs)) ? m.nodes[idx][i] : m.values[i]
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(isempty(m.values) || idx < length(m.end_idxs)) ? nodeselect(m.nodes, idx, i) : m.values[i]
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end
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@inline function setindex!(m::AbstractMultiScaleArray, nodes, i::Int)
@@ -25,7 +28,7 @@ end
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@inline function getindex(m::AbstractMultiScaleArray, i, I...)
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if isempty(m.values) || i < length(m.end_idxs)
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length(I) == 1 ? m.nodes[i].nodes[I[1]] : m.nodes[i][I...]
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length(I) == 1 ? nodechild(m.nodes, i, I[1]) : nodeselect(m.nodes, i, I...)
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else
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m.values[I...]
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end

‎src/shape_construction.jl

+14-16
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@@ -24,25 +24,11 @@ end
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end
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recursive_similar(x,T) = [similar(y, T) for y in x]
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recursive_similar(x::Tuple,T) = tuple((similar(y, T) for y in x)...)
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construct(::Type{T}, args...) where {T<:AbstractMultiScaleArrayLeaf} = T(args...)
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function __construct(nodes::Vector{<:AbstractMultiScaleArray})
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end_idxs = Vector{Int}(length(nodes))
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off = 0
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@inbounds for i in 1:length(nodes)
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end_idxs[i] = (off += length(nodes[i]))
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end
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end_idxs
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end
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function (construct(::Type{T}, nodes::Vector{<:AbstractMultiScaleArray},args...)
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where {T<:AbstractMultiScaleArray})
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T(nodes, eltype(T)[], __construct(nodes),args...)
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end
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function (construct(::Type{T}, nodes::Vector{<:AbstractMultiScaleArray}, values, args...)
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where {T<:AbstractMultiScaleArray})
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function __construct(T, nodes, values, args...)
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vallen = length(values)
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end_idxs = Vector{Int}(length(nodes) + ifelse(vallen == 0, 0, 1))
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off = 0
@@ -53,6 +39,18 @@ function (construct(::Type{T}, nodes::Vector{<:AbstractMultiScaleArray}, values,
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T(nodes, values, end_idxs, args...)
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end
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(construct(::Type{T}, nodes::AbstractVector{<:AbstractMultiScaleArray}, args...)
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where {T<:AbstractMultiScaleArray}) = __construct(T, nodes, eltype(T)[], args...)
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(construct(::Type{T}, nodes::AbstractVector{<:AbstractMultiScaleArray}, values, args...)
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where {T<:AbstractMultiScaleArray}) = __construct(T, nodes, values, args...)
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(construct(::Type{T}, nodes::Tuple{Vararg{<:AbstractMultiScaleArray}}, args...)
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where {T<:AbstractMultiScaleArray}) = __construct(T, nodes, eltype(T)[], args...)
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(construct(::Type{T}, nodes::Tuple{Vararg{<:AbstractMultiScaleArray}}, values, args...)
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where {T<:AbstractMultiScaleArray}) = __construct(T, nodes, values, args...)
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vcat(m1::AbstractMultiScaleArray, m2::AbstractMultiScaleArray) =
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error("AbstractMultiScaleArrays cannot be concatenated")
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‎test/runtests.jl

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Original file line numberDiff line numberDiff line change
@@ -1,6 +1,7 @@
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using MultiScaleArrays, OrdinaryDiffEq, DiffEqBase, StochasticDiffEq
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using Base.Test
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@time @testset "Tuple Nodes" begin include("tuple_nodes.jl") end
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@time @testset "Bisect Search Tests" begin include("bisect_search_tests.jl") end
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@time @testset "Indexing and Creation Tests" begin include("indexing_and_creation_tests.jl") end
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@time @testset "Values Indexing" begin include("values_indexing.jl") end

‎test/tuple_nodes.jl

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,88 @@
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using MultiScaleArrays, OrdinaryDiffEq, DiffEqBase, StochasticDiffEq
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using Base.Test
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struct PlantSettings{T} x::T end
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struct OrganParams{T} y::T end
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struct Organ{B<:Number,P} <: AbstractMultiScaleArrayLeaf{B}
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values::Vector{B}
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name::Symbol
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params::P
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end
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struct Plant{B,S,N<:Tuple{Vararg{<:Organ{<:Number}}}} <: AbstractMultiScaleArray{B}
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nodes::N
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values::Vector{B}
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end_idxs::Vector{Int}
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settings::S
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end
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struct Community{B,N<:Tuple{Vararg{<:Plant{<:Number}}}} <: AbstractMultiScaleArray{B}
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nodes::N
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values::Vector{B}
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end_idxs::Vector{Int}
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end
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mutable struct Scenario{B,N<:Tuple{Vararg{<:Community{<:Number}}}} <: AbstractMultiScaleArrayHead{B}
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nodes::N
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values::Vector{B}
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end_idxs::Vector{Int}
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end
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organ1 = Organ([1.1,2.1,3.1], :Shoot, OrganParams(:grows_up))
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organ2 = Organ([4.1,5.1,6.1], :Root, OrganParams("grows down"))
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organ3 = Organ([1.2,2.2,3.2], :Shoot, OrganParams(true))
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organ4 = Organ([4.2,5.2,6.2], :Root, OrganParams(1//3))
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plant1 = construct(Plant, (deepcopy(organ1), deepcopy(organ2)), Float64[], PlantSettings(1))
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plant2 = construct(Plant, (deepcopy(organ3), deepcopy(organ4)), Float64[], PlantSettings(1.0))
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community = construct(Community, (deepcopy(plant1), deepcopy(plant2), ))
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scenario = construct(Scenario, (deepcopy(community),))
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@inferred getindex(organ1, 1)
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@inferred getindex(plant1, 3)
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@inferred getindex(community, 4)
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@inferred getindex(scenario, 8)
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@test scenario[1] == 1.1
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@test scenario[2] == 2.1
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@test scenario[3] == 3.1
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@test scenario[4] == 4.1
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@test scenario[5] == 5.1
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@test scenario[6] == 6.1
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@test scenario[7] == 1.2
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@test scenario[8] == 2.2
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@test scenario[9] == 3.2
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@test scenario[10] == 4.2
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@test scenario[11] == 5.2
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@test scenario[12] == 6.2
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@test getindices(scenario, 1) == 1:12
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@test getindices(scenario, 1, 1) == 1:6
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@test getindices(scenario, 1, 2) == 7:12
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@test getindices(scenario, 1, 1, 1) == 1:3
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@test getindices(scenario, 1, 1, 2) == 4:6
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@test getindices(scenario, 1, 2, 1) == 7:9
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@test getindices(scenario, 1, 2, 2) == 10:12
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organ_ode = function (dorgan,organ,p,t)
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m = mean(organ)
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for i in eachindex(organ)
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dorgan[i] = -m*organ[i]
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end
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end
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f = function (dscenario,scenario,p,t)
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for (organ, y, z) in LevelIterIdx(scenario, 2)
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organ_ode(@view(dscenario[y:z]),organ,p,t)
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end
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end
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affect! = function (integrator)
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add_node!(integrator, integrator.u[1, 1, 1], 1, 1)
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end
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println("ODE with tuple nodes")
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prob = ODEProblem(f, scenario, (0.0, 1.0))
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sol = solve(prob, Tsit5())
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@test length(sol[end]) == 12

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