andreasvarga / descriptorsystems.jl Goto Github PK

Manipulation of generalized state-space (descriptor) system representations using Julia

License: MIT License

Julia 100.00%

control-systems descriptor-systems differential-algebraic-equations julia system-analysis-and-design dae linear-systems-theory

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descriptorsystems.jl's Issues

Type piracy issues

I am facing the following type piracy issue when testing with Aqua:

julia> using DescriptorSystems

julia> using Aqua

julia> Aqua.test_piracies(DescriptorSystems)
Possible type-piracy detected:
[1] hcat(A::Union{Number, DescriptorStateSpace, LinearAlgebra.UniformScaling, AbstractVecOrMat}...) @ DescriptorSystems C:\Users\Andreas\Documents\software\Julia\DescriptorSystems.jl\src\connections.jl:220
[2] hvcat(rows::Tuple{Vararg{Int64}}, A::Union{Number, DescriptorStateSpace, LinearAlgebra.UniformScaling, AbstractVecOrMat}...) @ DescriptorSystems C:\Users\Andreas\Documents\software\Julia\DescriptorSystems.jl\src\connections.jl:260
[3] vcat(A::Union{Number, DescriptorStateSpace, LinearAlgebra.UniformScaling, AbstractVecOrMat}...) @ DescriptorSystems C:\Users\Andreas\Documents\software\Julia\DescriptorSystems.jl\src\connections.jl:220
Test Failed at C:\Users\Andreas\.julia\packages\Aqua\9p8ck\src\piracies.jl:232
  Expression: isempty(v)
   Evaluated: isempty(Method[hcat(A::Union{Number, DescriptorStateSpace, LinearAlgebra.UniformScaling, AbstractVecOrMat}...) @ DescriptorSystems C:\Users\Andreas\Documents\software\Julia\DescriptorSystems.jl\src\connections.jl:220, hvcat(rows::Tuple{Vararg{Int64}}, A::Union{Number, DescriptorStateSpace, LinearAlgebra.UniformScaling, AbstractVecOrMat}...) @ DescriptorSystems C:\Users\Andreas\Documents\software\Julia\DescriptorSystems.jl\src\connections.jl:260, vcat(A::Union{Number, DescriptorStateSpace, LinearAlgebra.UniformScaling, AbstractVecOrMat}...) @ DescriptorSystems C:\Users\Andreas\Documents\software\Julia\DescriptorSystems.jl\src\connections.jl:220])

ERROR: There was an error during testing

The implementation of hcat, vcat and hvcat operations on the DescriptorStateSpace structure should be similar to that employed in the LinearMaps package, which is apparently fully Aqua compliant. I wonder how this compliance can be enforced also for this package. Any help is welcome @dkarrasch.

Obtaining minimal realization of concatenated coprime factorization

Hello! I wonder if it's somehow possible to obtain the concatenated coprime factorization [N M] without going the route of forming the concatenation of N and M which will be non-minimal. The corresponding matlab command lncf appears to output some version of this factorization
https://se.mathworks.com/help/robust/ref/lti.lncf.html
The ultimate purpose I have in mind is model reduction of unstable models.

Type piracy in `hcat, vcat`

Hello and thanks for this package!

I've found that this package commits type piracy on the functions hcat and vcat. The offending definitions are located here

DescriptorSystems.jl/src/connections.jl

Line 205 in 2745eda

    
           function $f(A::Union{DescriptorStateSpace,AbstractNumOrArray,UniformScaling}...)

It's generally not a good idea to define a method for a function you do not own, with a signature containing only types you do not own. In this case, the functions are owned by Base, and this package defines methods with signatures containing only Base types, e.g., UniformScaling. This break functionality in completely unrelated packages as soon as DescriptorSystems is loaded.

More information on this subject is available here
https://docs.julialang.org/en/v1/manual/style-guide/#Avoid-type-piracy

In my instance, loading DescriptorSystems breaks Symbolics.jl due to the call to vcat here

function TermInterface.arguments(a::Mul)
    a.sorted_args_cache[] !== nothing && return a.sorted_args_cache[]
    args = sort!([unstable_pow(k, v) for (k,v) in a.dict], lt=<ₑ)
    a.sorted_args_cache[] = isone(a.coeff) ? args : vcat(a.coeff, args)
end

dispatches to a broken method in DescriptorSystems instead of the correct method in Base.

Multiplication between rtf and dss

In an algorithm I am working on I need to multiply a discrete-time system's model by z, so I tried

desc = DescriptorSystems.rtf(:z; Ts=sys.Ts) * DescriptorSystems.dss( sys.A, sys.B, sys.C, sys.D; Ts=sys.Ts )

But this gives the error

MethodError: no method matching *(::DescriptorSystems.RationalTransferFunction{Int64}, ::DescriptorSystems.DescriptorStateSpace{Float64})
Closest candidates are:
  *(::Any, ::Any, ::Any, ::Any...) at operators.jl:560
  *(::Union{MathOptInterface.ScalarAffineFunction{T}, MathOptInterface.ScalarQuadraticFunction{T}, MathOptInterface.VectorAffineFunction{T}, MathOptInterface.VectorQuadraticFunction{T}}, ::T) where T at /home/imcinerney/.julia/packages/MathOptInterface/YDdD3/src/Utilities/functions.jl:2995
  *(::T, ::Any) where T<:(Union{Polyhedra.Line{T, AT}, Polyhedra.Ray{T, AT}} where {T, AT}) at /home/imcinerney/.julia/packages/Polyhedra/lR2w5/src/elements.jl:191

`gbalmr` complains about stable system being unstable

Please excuse the following large system definition. The result is the error

ERROR: The system sys is unstable
Stacktrace:
 [1] error(s::String)
   @ Base ./error.jl:35
 [2] gbalmr(sys::DescriptorSystems.DescriptorStateSpace{Float64, UniformScaling{Bool}}; balance::Bool, matchdc::Bool, fast::Bool, ord::Missing, atolhsv::Float64, rtolhsv::Float64, atolmin::Float64, rtolmin::Float64, offset::Float64, atol::Float64, atol1::Float64, atol2::Float64, rtol::Float64)
   @ DescriptorSystems ~/.julia/packages/DescriptorSystems/v0fkK/src/order_reduction.jl:542

I've found the (undocumented?) keyword argument offset that can be tweaked to get around the problem, is this the intended solution?

import DescriptorSystems
sys = let
    tempA = [-0.916732482309317 17.208413001912046 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0; 0.0 -253.55888240069518 -0.40492157747833013 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8098431549566603 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0; 253.15396081311684 -0.40492157747833013 -253.55888240069518 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8098431549566603 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0; 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0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.03708791208791209 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.03708791208791209 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.08653847163846153 0.0 0.0 0.0 0.0; 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.03708791208791209 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.03708791208791209 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.08653847163846153 0.0 0.0 0.0; 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.03708791208791209 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.03708791208791209 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.08653847163846153 0.0 0.0; 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    tempB = [134.03823538533322 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.1875; 0.0 0.1875; 0.0 0.1875; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0]
    tempC = [0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0]
    tempD = [0.0 0.0]
    DescriptorSystems.dss(tempA, tempB, tempC, tempD)
end

@show maximum(real, eigvals(sys.A))
#  = -1.0099960116143138e-8

DescriptorSystems.gbalmr(sys)

Extreme compile times for some functions

I've experienced quite extreme compile times for some functions, here's an example

julia> using DescriptorSystems

julia> G = rss(2,2,4);

julia> @time gnugap(G, G)
 59.315304 seconds (185.38 M allocations: 10.129 GiB, 6.14% gc time, 99.97% compilation time)
(6.598356591423948e-16, 2.55648096966907)

This problem is likely to be related to #8, since type piracy is likely to lead to method invalidations and inference problems that in turn cause recompilation of a lot of base methods. To investigate this hypothesis, we can make use of SnoopCompile.jl

using Revise
using SnoopCompileCore
using DescriptorSystems;
G = rss(2,2,4);

tinf = @snoopi_deep gnugap(G, G)

using ProfileView, SnoopCompile
ProfileView.view(flamegraph(tinf))

This displays the following graph

The colored flames indicates stacks, and their extent in the horizontal direction indicate their run time. Notice how there is a large horizontal gap in the flame graph, followed immediately by the stack for Base.typed_hvcat (where I've held the mouse to display the method name in the screenshot. This is a clear indication that the type piracy of #8 causes huge compile times, likely due to type inference problems.

Running

@code_warntype [G G]

indicates that there are type inference problems present when concatenating systems:

julia> @code_warntype [G G]
MethodInstance for hcat(::DescriptorStateSpace{Float64}, ::DescriptorStateSpace{Float64})
  from hcat(SYS1::DescriptorStateSpace, SYS2::DescriptorStateSpace) in DescriptorSystems at /home/fredrikb/.julia/packages/DescriptorSystems/20HLg/src/connections.jl:71
Arguments
  #self#::Core.Const(hcat)
  SYS1::DescriptorStateSpace{Float64}
  SYS2::DescriptorStateSpace{Float64}
Locals
  D::Matrix{Float64}
  C::Matrix{Float64}
  B::Matrix{Float64}
  blockdims::Vector{Int64}
  E::Any
  A::Matrix{Float64}
  Ts::Float64
  T::Type{Float64}
  ny::Any
  @_13::Union{UniformScaling, Matrix{Float64}}
  @_14::Union{UniformScaling, Matrix{Float64}}

notice variables E::Any and @_13::Union{UniformScaling, Matrix{Float64}}.

The other gaps in the flame graph points to the folowing methods

function Base.hvcat(rows::Tuple{Vararg{Int}}, A::Union{DescriptorStateSpace, AbstractVecOrMat{<:Number}, Number, UniformScaling}...)

as well as https://github.com/andreasvarga/DescriptorSystems.jl/blob/main/src/dss.jl#L52

TagBot trigger issue

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Excessive compilation times

After the transition to v2.0 of Polynomials, excessive compilation times resulted, for example, 50 minutes vs. the usual 16-20 minutes for a runtest. A similar behaviour occurred for MatrixPencils, where a change from type Polynomial to Polynomial{T} in just one function (poly2pm) solved the problem!

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