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change around DiscreteCallback examples

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ChrisRackauckas committed Dec 3, 2019
1 parent 8fa3f6b commit 663dc71e82e8e15485614821bae6816a34f543f9
Showing with 72 additions and 5 deletions.
  1. +72 −5 docs/src/features/callback_functions.md
@@ -173,7 +173,78 @@ and `terminate!` can be used to cause the simulation to stop.

## DiscreteCallback Examples

### Example 1: AutoAbstol
### Example 1: Interventions at Preset Times

Assume we have a patient whose internal drug concentration follows exponential decay, i.e. the linear ODE with
a negative coefficient:

```julia
using DifferentialEquations
function f(du,u,p,t)
du[1] = -u[1]
end
u0 = [10.0]
const V = 1
prob = ODEProblem(f,u0,(0.0,10.0))
sol = solve(prob,Tsit5())
using Plots; plot(sol)
```

Now assume we wish to give the patient a dose of 10 at time `t==4`. For this, we can use a `DiscreteCallback` which will
only be true at `t==4`:

```julia
condition(u,t,integrator) = t==4
affect!(integrator) = integrator.u[1] += 10
cb = DiscreteCallback(condition,affect!)
```

If we then solve with this callback enabled, we see no change:

```julia
sol = solve(prob,Tsit5(),callback=cb)
plot(sol)
```

The reason there is no change is because the `DiscreteCallback` only applies at a specific time, and the integrator never
hit that time. Thus we would like to force the ODE solver to step exactly at `t=4` so that the condition can be applied.
We can do that with the `tstops` argument:

```julia
sol = solve(prob,Tsit5(),callback=cb,tstops=[4.0])
plot(sol)
```

and thus we achieve the desired result.

Performing multiple doses then just requires that we have multiple points which are hit. For example, to dose at time `t=4`
and `t=8`, we can do the following:

```julia
dosetimes = [4.0,8.0]
condition(u,t,integrator) = t ∈ dosetimes
affect!(integrator) = integrator.u[1] += 10
sol = solve(prob,Tsit5(),callback=cb,tstops=dosetimes);
plot(sol)
```

We can then use this mechanism to make the model arbitrarily complex. For example, let's say there's now 3 dose times, but
the dose only triggers if the current concentration is below 1.0. Additionally, the dose is now `10t` instead of just `10`.
This model is implemented as simply:

```julia
dosetimes = [4.0,6.0,8.0]
condition(u,t,integrator) = t ∈ dosetimes && (u[1] < 1.0)
affect!(integrator) = integrator.u[1] += 10integrator.t
sol = solve(prob,Tsit5(),callback=cb,tstops=dosetimes);
plot(sol)
```

### Example 2: A Control Problem

Another example of a `DiscreteCallback` is the [control problem demonstrated on the DiffEq-specific arrays page](http://docs.juliadiffeq.org/dev/features/diffeq_arrays#Example:-A-Control-Problem-1).

### Example 3: AutoAbstol

MATLAB's Simulink has the option for [an automatic absolute tolerance](https://www.mathworks.com/help/simulink/gui/absolute-tolerance.html).
In this example we will implement a callback which will add this behavior to
@@ -249,10 +320,6 @@ at3 = integrator.opts.abstol
Note that this example is contained in [DiffEqCallbacks.jl](https://github.com/JuliaDiffEq/DiffEqCallbacks.jl),
a library of useful callbacks for JuliaDiffEq solvers.

### Example 2: A Control Problem

Another example of a `DiscreteCallback` is the [control problem demonstrated on the DiffEq-specific arrays page](http://docs.juliadiffeq.org/dev/features/diffeq_arrays#Example:-A-Control-Problem-1).

## ContinuousCallback Examples

### Example 1: Bouncing Ball

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