Problem when using Rodas5 as the differential equation solver
Hi, I encountered a MethodError, when using Rodas5 to compute the timeseries of a 3D dynamical system. It works fine with other solvers such as Vern9. The example code reads
using GLMakie
using OrdinaryDiffEq
using DynamicalSystems
using InteractiveDynamics
function my_system(x, p, t)
@unpack a, b, c = p
dx = exp(x[2] - 0.5) * (1.0 - x[1]) - a * tanh(10.0 * (x[1] - 0.5)) - a
dy = a * tanh(10.0 * (x[1] - 0.5)) + a - exp(x[2] - 0.5) + (x[3] - x[2]) * 2.0
dz = 3.0 - exp(x[3] - 0.5) - (x[3] - x[2]) * 0.5
return SVector(dx, dy, dz)
end
p = Dict(
:a => 2.0,
:b => 70.0,
:c => 4.0e8,
)
ps = Dict(
:a => 1.9:0.01:2.1
)
x0 = [0.3, 0.5, 0.4]
ds = ContinuousDynamicalSystem(my_system, x0, p)
diffeq = (alg = Rodas5(), adaptive = false, dt = 0.001, reltol = 1e-8, abstol = 1e-8)
tr = trajectory(ds, 100; diffeq...)
u0s = [ds.u0]
fig, obs = interactive_evolution_timeseries(
ds, u0s, ps; tail = 10000, diffeq, idxs = (1, 2, 3),
lims =((0.0,1.0), (0.0, 2.0), (0.0, 2.0))
)
The error message reads:
ERROR: MethodError: Cannot `convert` an object of type Bool to an object of type SVector{3, Float64}
Closest candidates are:
convert(::Type{T}, ::Intervals.AnchoredInterval{P, T, L, R} where {L<:Intervals.Bounded, R<:Intervals.Bounded}) where {P, T} at /Users/luca/.julia/packages/Intervals/ua9cq/src/anchoredinterval.jl:181
convert(::Type{T}, ::Intervals.Interval{T, L, R} where {L<:Intervals.Bound, R<:Intervals.Bound}) where T at /Users/luca/.julia/packages/Intervals/ua9cq/src/interval.jl:253
convert(::Type{SVector{N, T}}, ::CartesianIndex{N}) where {N, T} at /Users/luca/.julia/packages/StaticArrays/OWJK7/src/SVector.jl:46
I don't think, DynamicalSystems.jl is the problem because the trajectory (tr) is computed correctly. The programme only fails when interactive_evolution_timeseries() is used.
I can provide the stacktrace on request.
quick comment on MWE: When making a MWE try to minimize everything else that does not have a connection with the code. E.g., do not use unecessary packages like DataFrames and use the minimal packages that make the code run, e.g., replace DifferentialEquations with OrdinaryDiffEq and replace GLMakie with Makie.