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Scheduling callbacks and output writers ​

Callbacks and output writers in Simulation actuate on objects that are subtypes of AbstractSchedule. Schedules are small callable objects that return true when an action should fire and false otherwise. This page collects the built-in schedules, when to use them, and how to combine them for more complex behavior.

Aligned time steps

Simulation automatically shortens the next time step so that callback and output events scheduled by model time happen exactly when requested. Set align_time_step = false in the Simulation constructor to disable this.

For the examples here, we use the following simple simulation and progress:

julia
using Oceananigans
grid = RectilinearGrid(size=(1, 1, 1), extent=(1, 1, 1))
model = NonhydrostaticModel(grid)
simulation = Simulation(model, Δt=0.1, stop_time=2.5, verbose=false)
dummy(sim) = @info string("Iter: ", iteration(sim), " -- I was called at t = ", time(sim),
                          " and wall time = ", prettytime(sim.run_wall_time))
dummy (generic function with 1 method)

During the examples below, we use Oceananigans.Simulations.reset!(simulation) so that we reuse the simulation in the next example without requiring to recreate it.

Basic schedules ​

IterationInterval ​

IterationInterval actuates every interval iterations:

julia
schedule = IterationInterval(11)
add_callback!(simulation, dummy, schedule, name=:dummy)
run!(simulation)
[ Info: Iter: 0 -- I was called at t = 0.0 and wall time = 0 seconds
[ Info: Iter: 11 -- I was called at t = 1.0999999999999999 and wall time = 19.340 ms
[ Info: Iter: 22 -- I was called at t = 2.2000000000000006 and wall time = 19.499 ms

Use the offset keyword argument to shift the trigger so that, for example,

julia
Oceananigans.Simulations.reset!(simulation)
simulation.stop_time = 2.5

schedule = IterationInterval(7; offset=-2)
add_callback!(simulation, dummy, schedule, name=:dummy)
run!(simulation)
[ Info: Iter: 0 -- I was called at t = 0.0 and wall time = 0 seconds
[ Info: Iter: 5 -- I was called at t = 0.5 and wall time = 208.032 μs
[ Info: Iter: 12 -- I was called at t = 1.2 and wall time = 285.859 μs
[ Info: Iter: 19 -- I was called at t = 1.9000000000000006 and wall time = 347.101 μs

Notice that other than iteration 0, the callback is actuated on iterations 5, 12, 19, ...

Above, we have overwritten the original callback called "dummy" with the new one with the offset schedule. An alternative way is to construct the Callback manually and add it in the simulation:

julia
simulation.callbacks[:dummy] = Callback(dummy, schedule)
Callback of dummy on IterationInterval(7) with offset -2

TimeInterval ​

TimeInterval actuates every interval of model time, in units corresponding to model.clock.time. For example,

julia
Oceananigans.Simulations.reset!(simulation)
simulation.stop_time = 2.5

schedule = TimeInterval(1.11)
add_callback!(simulation, dummy, schedule, name=:dummy)
run!(simulation)
[ Info: Iter: 0 -- I was called at t = 0.0 and wall time = 0 seconds
[ Info: Iter: 12 -- I was called at t = 1.11 and wall time = 14.615 ms
[ Info: Iter: 24 -- I was called at t = 2.22 and wall time = 14.824 ms

When model.clock.time isa AbstractTime such as DateTime, then interval can be Dates.Period:

julia
using Dates

start_time = DateTime(2025, 1, 1)
clock = Clock(time = start_time)
datetime_model = NonhydrostaticModel(grid; clock)

stop_time = start_time + Dates.Minute(3)
datetime_simulation = Simulation(datetime_model; Δt=Dates.Second(25), stop_time, verbose=false)

schedule = TimeInterval(Dates.Minute(1))
add_callback!(datetime_simulation, dummy, schedule)
run!(datetime_simulation)
[ Info: Iter: 0 -- I was called at t = 2025-01-01T00:00:00 and wall time = 0 seconds
[ Info: Iter: 3 -- I was called at t = 2025-01-01T00:01:00 and wall time = 301.267 ms
[ Info: Iter: 6 -- I was called at t = 2025-01-01T00:02:00 and wall time = 301.418 ms
[ Info: Iter: 9 -- I was called at t = 2025-01-01T00:03:00 and wall time = 301.473 ms

WallTimeInterval ​

WallTimeInterval uses wall-clock seconds instead of model time. This is mostly useful for writing checkpoints to disk after consuming a fixed amount of computational resources. For example, using the previous simulation without the

julia
Oceananigans.Simulations.reset!(simulation)
simulation.stop_time = 2.5

schedule = WallTimeInterval(0.005)
add_callback!(simulation, dummy, schedule, name=:dummy)
run!(simulation)
[ Info: Iter: 0 -- I was called at t = 0.0 and wall time = 0 seconds
[ Info: Iter: 1 -- I was called at t = 0.1 and wall time = 0 seconds

SpecifiedTimes ​

SpecifiedTimes actuates when model.clock.time reaches the given values. The constructor accepts numeric times or Dates.DateTime values and sorts them automatically. This schedule is helpful for pre-planned save points or events tied to specific model times.

julia
Oceananigans.Simulations.reset!(simulation)
simulation.stop_time = 2.5

schedule = SpecifiedTimes(0.2, 1.5, 2.1)
add_callback!(simulation, dummy, schedule, name=:dummy)
run!(simulation)
[ Info: Iter: 0 -- I was called at t = 0.0 and wall time = 0 seconds
[ Info: Iter: 2 -- I was called at t = 0.2 and wall time = 20.346 ms
[ Info: Iter: 15 -- I was called at t = 1.5 and wall time = 20.552 ms
[ Info: Iter: 21 -- I was called at t = 2.1 and wall time = 20.608 ms

Arbitrary functions of model ​

Any function of model that returns a Bool can be used as a schedule:

julia
Oceananigans.Simulations.reset!(simulation)
simulation.stop_time = 2.5

after_two(model) = model.clock.time > 2
add_callback!(simulation, dummy, after_two, name=:dummy)
run!(simulation)
[ Info: Iter: 0 -- I was called at t = 0.0 and wall time = 0 seconds
[ Info: Iter: 20 -- I was called at t = 2.0000000000000004 and wall time = 10.503 ms
[ Info: Iter: 21 -- I was called at t = 2.1000000000000005 and wall time = 10.600 ms
[ Info: Iter: 22 -- I was called at t = 2.2000000000000006 and wall time = 10.637 ms
[ Info: Iter: 23 -- I was called at t = 2.3000000000000007 and wall time = 10.664 ms
[ Info: Iter: 24 -- I was called at t = 2.400000000000001 and wall time = 10.685 ms
[ Info: Iter: 25 -- I was called at t = 2.5 and wall time = 10.710 ms

Combining schedules ​

Some applications benefit from running extra steps immediately after an event or from combining multiple criteria.

ConsecutiveIterations ​

ConsecutiveIterations actuates when the parent schedule does and for the next N iterations. For example, averaging callbacks often need data at the scheduled time and immediately afterwards.

julia
Oceananigans.Simulations.reset!(simulation)
simulation.stop_time = 2.5

times = SpecifiedTimes(0.55, 1.5, 2.12)
schedule = ConsecutiveIterations(times)
add_callback!(simulation, dummy, schedule, name=:dummy)
run!(simulation)
[ Info: Iter: 0 -- I was called at t = 0.0 and wall time = 0 seconds
[ Info: Iter: 1 -- I was called at t = 0.1 and wall time = 0 seconds
[ Info: Iter: 6 -- I was called at t = 0.55 and wall time = 21.034 ms
[ Info: Iter: 7 -- I was called at t = 0.65 and wall time = 21.069 ms
[ Info: Iter: 16 -- I was called at t = 1.5 and wall time = 21.137 ms
[ Info: Iter: 17 -- I was called at t = 1.6 and wall time = 21.159 ms
[ Info: Iter: 23 -- I was called at t = 2.12 and wall time = 21.205 ms
[ Info: Iter: 24 -- I was called at t = 2.22 and wall time = 21.228 ms

TimeOffset ​

TimeOffset actuates when the parent schedule does and once more at a time offset away from it. A positive offset places the extra actuation after each parent actuation, and a negative offset places it before the next parent actuation. The time step is aligned so that the extra actuation lands exactly on the requested time. This is useful, for example, to sample a quantity a fixed time before each output is written, so that a time difference over that interval can be formed at writing time.

julia
Oceananigans.Simulations.reset!(simulation)
simulation.stop_time = 2.5

schedule = TimeOffset(TimeInterval(1), -0.2)
add_callback!(simulation, dummy, schedule, name=:dummy)
run!(simulation)
[ Info: Iter: 0 -- I was called at t = 0.0 and wall time = 0 seconds
[ Info: Iter: 9 -- I was called at t = 0.8 and wall time = 27.278 ms
[ Info: Iter: 11 -- I was called at t = 1.0 and wall time = 27.390 ms
[ Info: Iter: 19 -- I was called at t = 1.8 and wall time = 27.462 ms
[ Info: Iter: 21 -- I was called at t = 2.0 and wall time = 27.492 ms

AndSchedule and OrSchedule ​

Use AndSchedule when an action should fire only if every child schedule actuates in the same iteration. Use OrSchedule when any one of the child schedules should trigger the action. Both accept any mix of AbstractSchedules, so you can require, for example, output every hour and every 1000 iterations:

julia
Oceananigans.Simulations.reset!(simulation)
simulation.stop_time = 2.5

after_one_point_seven(model) = model.clock.time > 1.7
schedule = AndSchedule(IterationInterval(2), after_one_point_seven)
add_callback!(simulation, dummy, schedule, name=:dummy)
run!(simulation)
[ Info: Iter: 0 -- I was called at t = 0.0 and wall time = 0 seconds
[ Info: Iter: 18 -- I was called at t = 1.8000000000000005 and wall time = 33.910 ms
[ Info: Iter: 20 -- I was called at t = 2.0000000000000004 and wall time = 34.022 ms
[ Info: Iter: 22 -- I was called at t = 2.2000000000000006 and wall time = 34.057 ms
[ Info: Iter: 24 -- I was called at t = 2.400000000000001 and wall time = 34.086 ms

Stateful schedules

Stateful schedules such as TimeInterval, SpecifiedTimes, ConsecutiveIterations, and TimeOffset store their own counters, so we need to create a fresh instance (or call copy) for each callback or output writer that needs an identical pattern.

Output-specific schedules ​

Some schedules only apply to output writers because they keep extra state or require file access.

AveragedTimeInterval ​

AveragedTimeInterval asks an output writer to accumulate data over a sliding time window before writing. The window ends at each actuation time, runs for window seconds, and samples every stride iterations inside the window.

AveragedSpecifiedTimes ​

AveragedSpecifiedTimes behaves like SpecifiedTimes but with a trailing averaging window. Pass either a SpecifiedTimes instance or raw times.

FileSizeLimit ​

FileSizeLimit actuates when the target file grows beyond size_limit bytes. Output writers update the internal path automatically so you usually only pass the size limit. Combine it with OrSchedule to rotate files when either the clock reaches a value or the file becomes too large.