Sampling

The simplest mechanism for generating discrete events is to have a clock clk execute an action 𝜸 periodically. We can generate periodic events in various ways:

sampling events with periodic!(clk, 𝜸, Δt) are executed at the clock sample rate Δt,
conditional events with event!(clk, 𝝃, 𝜸) check the condition 𝜸 at the clock's sample rate Δt until it returns true. Then 𝝃 is executed.

Sampling is useful if we want to model repeated or periodic events interacting with a DES, check conditions, trace or visualize the system periodically.

Sampling introduces a time uncertainty into simulations since it triggers changes, takes measurements or checks for conditions only at a given time interval Δt.

Stochastic Event Sequences

Independent from the clock's sample rate you can have repeating events with fixed or stochastic inter-event times:

event!(clk, 𝜸, every, Δt) where Δt is a Number,
event!(clk, 𝜸, every, Δt) where Δt is a Distribution.

Thus you can create stochastic processes like arrivals or failures easily:

using DiscreteEvents, Distributions, Plots
c = Clock()
λ = 0.5
a = [0]
t = Float64[0.0]; y = Float64[0.0]
incra(c) = (a[1]+=1; push!(t, tau(c)); push!(y, a[1]))
event!(c, fun(incra, c), every, Exponential(1/λ))
run!(c, 100)
plot(t, y, linetype=:steppost, xlabel="t", ylabel="y", title="Poisson Process", legend=false)

poisson process