Interactive trajectory evolution

Without timeseries

InteractiveDynamics.interactive_evolution — Function

interactive_evolution(ds::DynamicalSystem, u0s; kwargs...)

Launch an interactive application that can evolve the initial conditions u0s (vector of vectors) of the given dynamical system. All initial conditions are evolved in parallel and at exactly the same time. Two controls allow you to pause/resume the evolution and to adjust the speed. The application can run forever (trajectories are computed on demand).

The function returns figure, obs. figure is the overarching figure (the entire GUI) and can be recorded. obs is a vector of observables, each containing the current state of the trajectory.

Keywords

transform = identity : Transformation applied to the state of the dynamical system before plotting. Can even return a vector that is of higher dimension than ds.
idxs = 1:min(length(transform(ds.u0)), 3) : Which variables to plot (up to three can be chosen). Variables are selected after transform has been applied.
colors : The color for each trajectory. Random colors are chosen by default.
lims : A tuple of tuples (min, max) for the axis limits. If not given, they are automatically deduced by evolving each of u0s 1000 units and picking most extreme values (limits cannot be adjust after application is launched).
m = 1.0 : The trajectory endpoints have a marker. A heuristic is done to choose appropriate marker size given the trajectory size. m is a multiplier that scales the marker size.
plotkwargs = NamedTuple() : A named tuple of keyword arguments propagated to the plotting function (lines for continuous, scatter for discrete systems). plotkwargs can also be a vector of named tuples, in which case each initial condition gets different arguments.
tail = 1000 : Length of plotted trajectory (in step units).
diffeq = DynamicalSystems.CDS_KWARGS : Named tuple of keyword arguments propagated to the solvers of DifferentialEquations.jl (for continuous systems). Because trajectories are not pre-computed and interpolated, it is recommended to use a combination of arguments that limit maximum stepsize, to ensure smooth curves. For example:
```
using OrdinaryDiffEq
diffeq = (alg = Tsit5(), dtmax = 0.01)
```

source

To generate the video at the start of this page run

using InteractiveDynamics
using DynamicalSystems, GLMakie
using OrdinaryDiffEq

ds = Systems.henonheiles()  # 4D chaotic/regular continuous system

u0s = [[0.0, -0.25, 0.42081, 0.0],
[0.0, 0.1, 0.5, 0.0],
[0.0, -0.31596, 0.354461, 0.0591255]]

diffeq = (alg = Vern9(), dtmax = 0.01)
idxs = (1, 2, 4)
colors = ["#233B43", "#499cbf", "#E84646"]

figure, obs = interactive_evolution(
    ds, u0s; idxs, tail = 10000, diffeq, colors
)

And here is another version for a discrete system:

using InteractiveDynamics
using DynamicalSystems, GLMakie

ds = Systems.towel() # 3D chaotic discrete system
u0s = [0.1ones(3) .+ 1e-3rand(3) for _ in 1:3]

figure, obs = interactive_evolution(
    ds, u0s; idxs = SVector(1, 2, 3), tail = 100000,
)

With timeseries

InteractiveDynamics.interactive_evolution_timeseries — Function

interactive_evolution_timeseries(args...; kwargs...)

Exactly like interactive_evolution, but in addition to the state space plot a panel with the timeseries is also plotted and animated in real time.

The following additional keywords apply:

total_span : How much the x-axis of the timeseries plots should span (in real time units)
linekwargs = NamedTuple() : Extra keywords propagated to the timeseries plots.

source