Signal logging and scoping for DifferentialEquations.jl simulations.
Author jonniedie
11 Stars
Updated Last
1 Year Ago
Started In
April 2021


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SimulationLogs lets you log variables from within a DifferentialEquations.jl ODE simulation.

The Basics

To log a variable, use the @log macro before an existing variable declaration in the simulation. The syntax for this looks like:

@log x = u[1]+u[3]

To log an expression to an output variable without creating that variable in the simulation use the following syntax:

@log x u[1]+u[3]

To extract logged values from a simulation, either use the logged_solve function to obtain a Logged solution or call the get_log function on an existing solution object.


using DifferentialEquations
using SimulationLogs

function lorenz!(du, u, p, t)
    @log a = u[2]-u[1]
    @log b u[3]+a
    du[1] = p[1]*a
    du[2] = u[1]*(p[2]-u[3]) - u[2]
    du[3] = u[1]*u[2] - p[3]*u[3]

p = [10.0, 28.0, 8/3]
u0 = [1.0, 0.0, 0.0]
tspan = (0.0, 100.0)

prob = ODEProblem(lorenz!, u0, tspan, p)
sol = solve(prob)

Now we can extract the simulation log with get_log.

julia> out = get_log(sol)
SimulationLog with signals:
  a :: Float64
  b :: Float64

julia> out.a
1278-element Vector{Float64}:

julia> out.b
1278-element Vector{Float64}:

We can also use scope to visually inspect signals from the simulation. This requires using the Plots.jl library. For an interactive scope (pan, zoom, etc.), use the PlotlyJS backend of Plots by calling plotlyjs().

using Plots; plotlyjs()

scope(sol, [:a, :b])


As of version 0.3.0, we can now handle cases where parameters change in DiscreteCallbacks. The callback or callback set must be passed into the get_log function through the keyword callback. Alternatively, just replace your solve with logged_solve and everything will be handled automatically. The logged variables from a logged_solve can be accessed in a solution object sol as sol.log.


How does this work with time stepping and variable caches and all that?

Despite the name, @log doesn't actually log anything while the simulation is running. The "logging" actually happens by calculating values from the stored solutions when get_log is called.

Wait, how does that work?

There is a global SimulationLog that is turned off by default. When it is off, the @log macro basically doesn't do anything. The get_log function turns on the global log and then calls your simulation function (derivative function, vector field... whatever you want to call it) for each time point (these can be supplied, but will default to the saved time points). A copy of the global simulation log is passed as an output to the user, after which the global log then gets erased and turned back off.

Will logging variables slow my simulation down?

Nope. There is no runtime overhead because no logging is actually happening during the simulation.

How does this work when the same @log gets called multiple times in the same time step (e.g. in a subfunction that gets called more than once)?

The logged solution will then be a n x m Matrix where n is the number of time steps and m is the number of times the @log macro was called in a single time step.

What if my parameters are changed during the simulation?

If you do this, you must include the callback you used to change the parameters in the get_log function as a keyword argument callback. If you changed the parameters without using a callback, the results will be incorrect (but in general you shouldn't be changing parameters without a callback anyway).

What if my simulation depends on some changing global state?

Solutions that changed global state cannot be handled (or, rather, @log will most likely give you incorrect results).


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