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modified: Project.toml DiffEqBase, RecursiveArrayTools, Unitfu only modified: README.md Short explanation modified: src/MechGlueDiffEqBase.jl Drop experiments, norm only. deleted: test/Manifest.toml deleted: test/Project.toml modified: test/runtests.jl Include 1..3 new file: test/test_1.jl First version new file: test/test_2.jl First version new file: test/test_3.jl First version
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‎.gitignore‎

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/Manifest.toml
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/.vscode

‎Project.toml‎

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name = "MechGlueDiffEqBase"
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uuid = "2532746b-52b5-4539-9431-8bb183ab067f"
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authors = ["hustf <[email protected]> and contributors"]
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version = "0.1.31"
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version = "0.1.32"
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[deps]
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DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
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MechGluePlots = "03a24088-0a82-4850-846f-2cad5d056ae1"
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MechanicalUnits = "e6be9192-89dc-11e9-36e6-5dbcb28f419e"
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RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
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SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
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RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
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Unitfu = "5ee08b94-2369-4f4a-b8c7-99333ba35fb0"
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[compat]
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julia = "1"
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[extras]
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Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
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MechanicalUnits = "e6be9192-89dc-11e9-36e6-5dbcb28f419e"
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[targets]
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test = ["Test"]

‎README.md‎

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# MechGlueDiffEqBase
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Glue code for making [DiffEqBase](https://github.com/SciML/DiffEqBase.jl) work with units.
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It also includes glue code for [RecursiveArrayTools](https://github.com/SciML/RecursiveArrayTools.jl), which enables type-stable solution of equations with mixed units.
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This defines how to calculate the vector norm when the vector is given in units compatible with [Unitfu.jl](https://github.com/hustf/Unitfu.jl), from registy [M8](https://github.com/hustf/M8). The differential equation algorithms expects the norm to be unitless, as can be seen in e.g. step size estimators:
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err_scaled = **error** / (**abstol** + norm(u) * **reltol**)
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where **bold** indicates unitful objects.
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The functions are adaptions of corresponding code from [DiffEqBase](https://github.com/SciML/DiffEqBase.jl/blob/6bb8830711e729ef513f2b1beb95853e4a691375/src/init.jl).
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‎src/MechGlueDiffEqBase.jl‎

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module MechGlueDiffEqBase
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import Unitfu: AbstractQuantity, Quantity, ustrip
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import DiffEqBase: value, ODE_DEFAULT_NORM, UNITLESS_ABS2
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import Unitfu: AbstractQuantity, Quantity, ustrip, norm
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import DiffEqBase: value, ODE_DEFAULT_NORM, UNITLESS_ABS2, zero
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import DiffEqBase: calculate_residuals, @muladd
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using RecursiveArrayTools
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export value, ODE_DEFAULT_NORM, UNITLESS_ABS2, Unitfu, AbstractQuantity, Quantity
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export norm, ArrayPartition
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import RecipesBase
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import RecipesBase.@recipe
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import SciMLBase
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import SciMLBase: AbstractTimeseriesSolution, RecipesBase
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import SciMLBase: AbstractDiscreteProblem, AbstractRODESolution, SensitivityInterpolation
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import SciMLBase: getsyms, interpret_vars, cleansyms
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import SciMLBase: diffeq_to_arrays, issymbollike, getindepsym_defaultt
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import SciMLBase: DEFAULT_PLOT_FUNC
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# This is identical to what DiffEqBase defines for Unitful
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function value(x::Type{AbstractQuantity{T,D,U}}) where {T,D,U}
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T
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end
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function value(x::AbstractQuantity)
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x.val
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end
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# This is different from what DiffEqBase defines for Unitful
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value(::Type{<:AbstractQuantity{T,D,U}}) where {T,D,U<:Core.TypeofBottom} = Base.undef_ref_str
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value(x::Q) where {Q<:AbstractQuantity} = ustrip(x)
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# This is identical to what DiffEqBase defines for Unitful
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@inline function ODE_DEFAULT_NORM(u::AbstractArray{<:AbstractQuantity,N},t) where {N}
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# Support adaptive errors should be errorless for exponentiation
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sqrt(sum(x->ODE_DEFAULT_NORM(x[1],x[2]),zip((value(x) for x in u),Iterators.repeated(t))) / length(u))
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end
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# This is identical to what DiffEqBase defines for Unitful
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@inline function ODE_DEFAULT_NORM(u::Array{<:AbstractQuantity,N},t) where {N}
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sqrt(sum(x->ODE_DEFAULT_NORM(x[1],x[2]),zip((value(x) for x in u),Iterators.repeated(t))) / length(u))
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end
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@inline function ODE_DEFAULT_NORM(u::AbstractQuantity,t)
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abs(value(u))
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# This is identical to what DiffEqBase defines for Unitful
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@inline function ODE_DEFAULT_NORM(u::AbstractQuantity, t)
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abs(ustrip(u))
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end
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# This is slightly different from what DiffEqBase defines for Unitful
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@inline function UNITLESS_ABS2(x::AbstractQuantity)
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real(abs2(x)/oneunit(x)*oneunit(x))
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real(abs2(x) / (oneunit(x)^2))
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end
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# This recipe is copied from SciMLBase, and only the signature is adopted
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# for use with unitful solutions. It can perhaps be deleted now, and the link to RecipesBase be deleted.
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@recipe function f(sol::AbstractTimeseriesSolution{T, N, A};
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plot_analytic=false,
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denseplot = (sol.dense ||
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typeof(sol.prob) <: AbstractDiscreteProblem) &&
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!(typeof(sol) <: AbstractRODESolution) &&
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!(hasfield(typeof(sol),:interp) &&
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typeof(sol.interp) <: SensitivityInterpolation),
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plotdensity = min(Int(1e5),sol.tslocation==0 ?
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(typeof(sol.prob) <: AbstractDiscreteProblem ?
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max(1000,100*length(sol)) :
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max(1000,10*length(sol))) :
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1000*sol.tslocation),
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tspan = nothing, axis_safety = 0.1,
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vars=nothing) where {T<:Quantity, N, A<:Array{<:Quantity}}
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syms = getsyms(sol)
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int_vars = interpret_vars(vars,sol,syms)
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strs = cleansyms(syms)
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tscale = get(plotattributes, :xscale, :identity)
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plot_vecs,labels = diffeq_to_arrays(sol,plot_analytic,denseplot,
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plotdensity,tspan,axis_safety,
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vars,int_vars,tscale,strs)
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tdir = sign(sol.t[end]-sol.t[1])
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xflip --> tdir < 0
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seriestype --> :path
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# Special case labels when vars = (:x,:y,:z) or (:x) or [:x,:y] ...
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if typeof(vars) <: Tuple && (issymbollike(vars[1]) && issymbollike(vars[2]))
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xguide --> issymbollike(int_vars[1][2]) ? Symbol(int_vars[1][2]) : strs[int_vars[1][2]]
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yguide --> issymbollike(int_vars[1][3]) ? Symbol(int_vars[1][3]) : strs[int_vars[1][3]]
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if length(vars) > 2
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zguide --> issymbollike(int_vars[1][4]) ? Symbol(int_vars[1][4]) : strs[int_vars[1][4]]
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end
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end
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if (!any(issymbollike,getindex.(int_vars,1)) && getindex.(int_vars,1) == zeros(length(int_vars))) ||
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(!any(issymbollike,getindex.(int_vars,2)) && getindex.(int_vars,2) == zeros(length(int_vars))) ||
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all(t->Symbol(t)==getindepsym_defaultt(sol),getindex.(int_vars,1)) || all(t->Symbol(t)==getindepsym_defaultt(sol),getindex.(int_vars,2))
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xguide --> "$(getindepsym_defaultt(sol))"
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end
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if length(int_vars[1]) >= 3 && ((!any(issymbollike,getindex.(int_vars,3)) && getindex.(int_vars,3) == zeros(length(int_vars))) ||
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all(t->Symbol(t)==getindepsym_defaultt(sol),getindex.(int_vars,3)))
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yguide --> "$(getindepsym_defaultt(sol))"
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end
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if length(int_vars[1]) >= 4 && ((!any(issymbollike,getindex.(int_vars,4)) && getindex.(int_vars,4) == zeros(length(int_vars))) ||
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all(t->Symbol(t)==getindepsym_defaultt(sol),getindex.(int_vars,4)))
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zguide --> "$(getindepsym_defaultt(sol))"
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end
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if (!any(issymbollike,getindex.(int_vars,2)) && getindex.(int_vars,2) == zeros(length(int_vars))) ||
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all(t->Symbol(t)==getindepsym_defaultt(sol),getindex.(int_vars,2))
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if tspan === nothing
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if tdir > 0
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xlims --> (sol.t[1],sol.t[end])
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else
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xlims --> (sol.t[end],sol.t[1])
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end
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else
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xlims --> (tspan[1],tspan[end])
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end
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else
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mins = minimum(sol[int_vars[1][2],:])
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maxs = maximum(sol[int_vars[1][2],:])
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for iv in int_vars
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mins = min(mins,minimum(sol[iv[2],:]))
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maxs = max(maxs,maximum(sol[iv[2],:]))
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end
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xlims --> ((1-sign(mins)*axis_safety)*mins,(1+sign(maxs)*axis_safety)*maxs)
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end
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# Analytical solutions do not save enough information to have a good idea
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# of the axis ahead of time
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# Only set axis for animations
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if sol.tslocation != 0 && !(typeof(sol) <: AbstractAnalyticalSolution)
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if all(getindex.(int_vars,1) .== DEFAULT_PLOT_FUNC)
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mins = minimum(sol[int_vars[1][3],:])
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maxs = maximum(sol[int_vars[1][3],:])
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for iv in int_vars
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mins = min(mins,minimum(sol[iv[3],:]))
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maxs = max(maxs,maximum(sol[iv[3],:]))
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end
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ylims --> ((1-sign(mins)*axis_safety)*mins,(1+sign(maxs)*axis_safety)*maxs)
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if length(int_vars[1]) >= 4
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mins = minimum(sol[int_vars[1][4],:])
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maxs = maximum(sol[int_vars[1][4],:])
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for iv in int_vars
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mins = min(mins,minimum(sol[iv[4],:]))
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maxs = max(mins,maximum(sol[iv[4],:]))
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end
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zlims --> ((1-sign(mins)*axis_safety)*mins,(1+sign(maxs)*axis_safety)*maxs)
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end
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end
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end
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label --> reshape(labels,1,length(labels))
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println("----------------------------------MechGlueDiffEqBase")
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(plot_vecs...,)
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end
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end

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