# Event Handling and Callback Functions

DifferentialEquations.jl allows for using callback functions to inject user code into the solver algorithms. It allows for safely and accurately applying events and discontinuities. Multiple callbacks can be chained together, and these callback types can be used to build libraries of extension behavior.

## The Callback Types

The callback types are defined as follows. There are three primitive callback types: the `ContinuousCallback`

, `DiscreteCallback`

and the `VectorContinuousCallback`

:

- The
`ContinuousCallback`

is applied when a given continuous*condition function*hits zero. This hitting can happen even within an integration step and the solver must be able to detect it and adjust the integration step accordingly. This type of callback implements what is known in other problem solving environments as an*Event*. - The
`DiscreteCallback`

is applied when its*condition function*is`true`

, but the condition is only evaluated at the end of every integration step. - The
`VectorContinuousCallback`

works like a vector of`ContinuousCallbacks`

and lets the user specify which callback is called when.

### ContinuousCallback

`DiffEqBase.ContinuousCallback`

— Type```
ContinuousCallback(condition,affect!,affect_neg!;
initialize = INITIALIZE_DEFAULT,
finalize = FINALIZE_DEFAULT,
idxs = nothing,
rootfind=LeftRootFind,
save_positions=(true,true),
interp_points=10,
abstol=10eps(),reltol=0,repeat_nudge=1//100)
```

```
function ContinuousCallback(condition,affect!;
initialize = INITIALIZE_DEFAULT,
finalize = FINALIZE_DEFAULT,
idxs = nothing,
rootfind=LeftRootFind,
save_positions=(true,true),
affect_neg! = affect!,
interp_points=10,
abstol=10eps(),reltol=0,repeat_nudge=1//100)
```

Contains a single callback whose `condition`

is a continuous function. The callback is triggered when this function evaluates to 0.

**Arguments**

`condition`

: This is a function`condition(u,t,integrator)`

for declaring when the callback should be used. A callback is initiated if the condition hits`0`

within the time interval. See the Integrator Interface documentation for information about`integrator`

.`affect!`

: This is the function`affect!(integrator)`

where one is allowed to modify the current state of the integrator. If you do not pass an`affect_neg!`

function, it is called when`condition`

is found to be`0`

(at a root) and the cross is either an upcrossing (from negative to positive) or a downcrossing (from positive to negative). You need to explicitly pass`nothing`

as the`affect_neg!`

argument if it should only be called at upcrossings, e.g.`ContinuousCallback(condition, affect!, nothing)`

. For more information on what can be done, see the Integrator Interface manual page. Modifications to`u`

are safe in this function.`affect_neg!=affect!`

: This is the function`affect_neg!(integrator)`

where one is allowed to modify the current state of the integrator. This is called when`condition`

is found to be`0`

(at a root) and the cross is an downcrossing (from positive to negative). For more information on what can be done, see the Integrator Interface manual page. Modifications to`u`

are safe in this function.`rootfind=LeftRootFind`

: This is a flag to specify the type of rootfinding to do for finding event location. If this is set to`LeftRootfind`

, the solution will be backtracked to the point where`condition==0`

and if the solution isn't exact, the left limit of root is used. If set to`RightRootFind`

, the solution would be set to the right limit of the root. Otherwise the systems and the`affect!`

will occur at`t+dt`

.`interp_points=10`

: The number of interpolated points to check the condition. The condition is found by checking whether any interpolation point / endpoint has a different sign. If`interp_points=0`

, then conditions will only be noticed if the sign of`condition`

is different at`t`

than at`t+dt`

. This behavior is not robust when the solution is oscillatory, and thus it's recommended that one use some interpolation points (they're cheap to compute!).`0`

within the time interval.`save_positions=(true,true)`

: Boolean tuple for whether to save before and after the`affect!`

. This saving will occur just before and after the event, only at event times, and does not depend on options like`saveat`

,`save_everystep`

, etc. (i.e. if`saveat=[1.0,2.0,3.0]`

, this can still add a save point at`2.1`

if true). For discontinuous changes like a modification to`u`

to be handled correctly (without error), one should set`save_positions=(true,true)`

.`idxs=nothing`

: The components which will be interpolated into the condition. Defaults to`nothing`

which means`u`

will be all components.`initialize`

: This is a function`(c,u,t,integrator)`

which can be used to initialize the state of the callback`c`

. It should modify the argument`c`

and the return is ignored.`finalize`

: This is a function`(c,u,t,integrator)`

which can be used to finalize the state of the callback`c`

. It can modify the argument`c`

and the return is ignored.`abstol=1e-14`

&`reltol=0`

: These are used to specify a tolerance from zero for the rootfinder: if the starting condition is less than the tolerance from zero, then no root will be detected. This is to stop repeat events happening just after a previously rootfound event.`repeat_nudge = 1//100`

: This is used to set the next testing point after a previously found zero. Defaults to 1//100, which means after a callback the next sign check will take place at t + dt*1//100 instead of at t to avoid repeats.

### DiscreteCallback

`DiffEqBase.DiscreteCallback`

— Type```
DiscreteCallback(condition,affect!;
initialize = INITIALIZE_DEFAULT,
finalize = FINALIZE_DEFAULT,
save_positions=(true,true))
```

**Arguments**

`condition`

: This is a function`condition(u,t,integrator)`

for declaring when the callback should be used. A callback is initiated if the condition evaluates to`true`

. See the Integrator Interface documentation for information about`integrator`

.`affect!`

: This is the function`affect!(integrator)`

where one is allowed to

`save_positions`

: Boolean tuple for whether to save before and after the`affect!`

. This saving will occur just before and after the event, only at event times, and does not depend on options like`saveat`

,`save_everystep`

, etc. (i.e. if`saveat=[1.0,2.0,3.0]`

, this can still add a save point at`2.1`

if true). For discontinuous changes like a modification to`u`

to be handled correctly (without error), one should set`save_positions=(true,true)`

.`initialize`

: This is a function`(c,u,t,integrator)`

which can be used to initialize the state of the callback`c`

. It should modify the argument`c`

and the return is ignored.`finalize`

: This is a function`(c,u,t,integrator)`

which can be used to finalize the state of the callback`c`

. It should can the argument`c`

and the return is ignored.

### CallbackSet

`DiffEqBase.CallbackSet`

— Type`struct CallbackSet{T1<:Tuple, T2<:Tuple} <: SciMLBase.DECallback`

Multiple callbacks can be chained together to form a `CallbackSet`

. A `CallbackSet`

is constructed by passing the constructor `ContinuousCallback`

, `DiscreteCallback`

, `VectorContinuousCallback`

or other `CallbackSet`

instances:

`CallbackSet(cb1,cb2,cb3)`

You can pass as many callbacks as you like. When the solvers encounter multiple callbacks, the following rules apply:

`ContinuousCallback`

s and`VectorContinuousCallback`

s are applied before`DiscreteCallback`

s. (This is because they often implement event-finding that will backtrack the timestep to smaller than`dt`

).- For
`ContinuousCallback`

s and`VectorContinuousCallback`

s, the event times are found by rootfinding and only the first`ContinuousCallback`

or`VectorContinuousCallback`

affect is applied. - The
`DiscreteCallback`

s are then applied in order. Note that the ordering only matters for the conditions: if a previous callback modifies`u`

in such a way that the next callback no longer evaluates condition to`true`

, its`affect`

will not be applied.

### VectorContinuousCallback

`DiffEqBase.VectorContinuousCallback`

— Type```
VectorContinuousCallback(condition,affect!,affect_neg!,len;
initialize = INITIALIZE_DEFAULT,
finalize = FINALIZE_DEFAULT,
idxs = nothing,
rootfind=LeftRootFind,
save_positions=(true,true),
interp_points=10,
abstol=10eps(),reltol=0,repeat_nudge = 1//100)
```

```
VectorContinuousCallback(condition,affect!,len;
initialize = INITIALIZE_DEFAULT,
finalize = FINALIZE_DEFAULT,
idxs = nothing,
rootfind=LeftRootFind,
save_positions=(true,true),
affect_neg! = affect!,
interp_points=10,
abstol=10eps(),reltol=0,repeat_nudge=1//100)
```

This is also a subtype of `AbstractContinuousCallback`

. `CallbackSet`

is not feasible when you have a large number of callbacks, as it doesn't scale well. For this reason, we have `VectorContinuousCallback`

- it allows you to have a single callback for multiple events.

**Arguments**

`condition`

: This is a function`condition(out, u, t, integrator)`

which should save the condition value in the array`out`

at the right index. Maximum index of`out`

should be specified in the`len`

property of callback. So this way you can have a chain of`len`

events, which would cause the`i`

th event to trigger when`out[i] = 0`

.`affect!`

: This is a function`affect!(integrator, event_index)`

which lets you modify`integrator`

and it tells you about which event occured using`event_idx`

i.e. gives you index`i`

for which`out[i]`

came out to be zero.`len`

: Number of callbacks chained. This is compulsory to be specified.

Rest of the arguments have the same meaning as in `ContinuousCallback`

.

## Using Callbacks

The callback type is then sent to the solver (or the integrator) via the `callback`

keyword argument:

`sol = solve(prob,alg,callback=cb)`

You can supply `nothing`

, a single `DiscreteCallback`

or `ContinuousCallback`

or `VectorContinuousCallback`

, or a `CallbackSet`

.

### Note About Saving

When a callback is supplied, the default saving behavior is turned off. This is because otherwise events would "double save" one of the values. To re-enable the standard saving behavior, one must have the first `save_positions`

value be true for at least one callback.

### Modifying the Stepping Within A Callback

A common issue with callbacks is that they cause a large discontinuous change, and so it may be wise to pull down `dt`

after such a change. To control the timestepping from a callback, please see the timestepping controls in the integrator interface. Specifically, `set_proposed_dt!`

is used to set the next stepsize, and `terminate!`

can be used to cause the simulation to stop.

## DiscreteCallback Examples

### 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:

```
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`

:

```
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:

```
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:

```
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:

```
dosetimes = [4.0,8.0]
condition(u,t,integrator) = t ∈ dosetimes
affect!(integrator) = integrator.u[1] += 10
cb = DiscreteCallback(condition,affect!)
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:

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

#### PresetTimeCallback

Because events at preset times is a very common occurrence, DifferentialEquations.jl provides a pre-built callback in the Callback Library. The `PresetTimeCallback(tstops,affect!)`

takes an array of times and an `affect!`

function to apply. Thus to do the simple 2 dose example with this callback, we could do the following:

```
dosetimes = [4.0,8.0]
affect!(integrator) = integrator.u[1] += 10
cb = PresetTimeCallback(dosetimes,affect!)
sol = solve(prob,Tsit5(),callback=cb)
plot(sol)
```

Notice that this version will automatically set the `tstops`

for you.

### Example 2: A Control Problem

Another example of a `DiscreteCallback`

is a control problem switching parameters. Our parameterized ODE system is as follows:

Our ODE function will use this field as follows:

```
function f(du,u,p,t)
du[1] = -0.5*u[1] + p
du[2] = -0.5*u[2]
end
```

Now we will setup our control mechanism. It will be a simple setup which uses set timepoints at which we will change `p`

. At `t=5.0`

we will want to increase the value of `p`

, and at `t=8.0`

we will want to decrease the value of `p`

. Using the `DiscreteCallback`

interface, we code these conditions as follows:

```
const tstop1 = [5.]
const tstop2 = [8.]
function condition(u,t,integrator)
t in tstop1
end
function condition2(u,t,integrator)
t in tstop2
end
```

Now we have to apply an effect when these conditions are reached. When `condition`

is hit (at `t=5.0`

), we will increase `p`

to 1.5. When `condition2`

is reached, we will decrease `p`

to `-1.5`

. This is done via the functions:

```
function affect!(integrator)
integrator.p = 1.5
end
function affect2!(integrator)
integrator.p = -1.5
end
```

With these functions we can build our callbacks:

```
save_positions = (true,true)
cb = DiscreteCallback(condition, affect!, save_positions=save_positions)
save_positions = (false,true)
cb2 = DiscreteCallback(condition2, affect2!, save_positions=save_positions)
cbs = CallbackSet(cb,cb2)
```

Now we define our initial condition. We will start at `[10.0;10.0]`

with `p=0.0`

.

```
u0 = [10.0;10.0]
p = 0.0
prob = ODEProblem(f,u0,(0.0,10.0),p)
```

Lastly we solve the problem. Note that we must pass `tstop`

values of `5.0`

and `8.0`

to ensure the solver hits those timepoints exactly:

```
const tstop = [5.;8.]
sol = solve(prob,Tsit5(),callback = cbs, tstops=tstop)
```

It's clear from the plot how the controls affected the outcome.

### Example 3: AutoAbstol

MATLAB's Simulink has the option for an automatic absolute tolerance. In this example we will implement a callback which will add this behavior to any JuliaDiffEq solver which implements the `integrator`

and callback interface.

The algorithm is as follows. The default value is set to start at `1e-6`

, though we will give the user an option for this choice. Then as the simulation progresses, at each step the absolute tolerance is set to the maximum value that has been reached so far times the relative tolerance. This is the behavior that we will implement in `affect!`

.

Since the effect is supposed to occur every timestep, we use the trivial condition:

```
condition = function (u,t,integrator)
true
end
```

which always returns true. For our effect we will overload the call on a type. This type will have a value for the current maximum. By doing it this way, we can store the current state for the running maximum. The code is as follows:

```
mutable struct AutoAbstolAffect{T}
curmax::T
end
# Now make `affect!` for this:
function (p::AutoAbstolAffect)(integrator)
p.curmax = max(p.curmax,integrator.u)
integrator.opts.abstol = p.curmax * integrator.opts.reltol
u_modified!(integrator,false)
end
```

This makes `affect!(integrator)`

use an internal mutating value `curmax`

to update the absolute tolerance of the integrator as the algorithm states.

Lastly, we can wrap it in a nice little constructor:

```
function AutoAbstol(save=true;init_curmax=1e-6)
affect! = AutoAbstolAffect(init_curmax)
condtion = (u,t,integrator) -> true
save_positions = (save,false)
DiscreteCallback(condtion,affect!,save_positions=save_positions)
end
```

This creates the `DiscreteCallback`

from the `affect!`

and `condition`

functions that we implemented. Now

`cb = AutoAbstol(save=true;init_curmax=1e-6)`

returns the callback that we created. We can then solve an equation using this by simply passing it with the `callback`

keyword argument. Using the integrator interface rather than the solve interface, we can step through one by one to watch the absolute tolerance increase:

```
integrator = init(prob,BS3(),callback=cb)
at1 = integrator.opts.abstol
step!(integrator)
at2 = integrator.opts.abstol
@test at1 < at2
step!(integrator)
at3 = integrator.opts.abstol
@test at2 < at3
```

Note that this example is contained in the Callback Library, a library of useful callbacks for JuliaDiffEq solvers.

## ContinuousCallback Examples

### Example 1: Bouncing Ball

Let's look at the bouncing ball. Let the first variable `y`

is the height which changes by `v`

the velocity, where the velocity is always changing at `-g`

which is the gravitational constant. This is the equation:

```
function f(du,u,p,t)
du[1] = u[2]
du[2] = -p
end
```

All we have to do in order to specify the event is to have a function which should always be positive with an event occurring at 0. For now at least that's how it's specified. If a generalization is needed we can talk about this (but it needs to be "root-findable"). For here it's clear that we just want to check if the ball's height ever hits zero:

```
function condition(u,t,integrator) # Event when event_f(u,t) == 0
u[1]
end
```

Notice that here we used the values `u`

instead of the value from the `integrator`

. This is because the values `u,t`

will be appropriately modified at the interpolation points, allowing for the rootfinding behavior to occur.

Now we have to say what to do when the event occurs. In this case we just flip the velocity (the second variable)

```
function affect!(integrator)
integrator.u[2] = -integrator.u[2]
end
```

The callback is thus specified by:

`cb = ContinuousCallback(condition,affect!)`

Then you can solve and plot:

```
u0 = [50.0,0.0]
tspan = (0.0,15.0)
p = 9.8
prob = ODEProblem(f,u0,tspan,p)
sol = solve(prob,Tsit5(),callback=cb)
plot(sol)
```

As you can see from the resulting image, DifferentialEquations.jl is smart enough to use the interpolation to hone in on the time of the event and apply the event back at the correct time. Thus one does not have to worry about the adaptive timestepping "overshooting" the event as this is handled for you. Notice that the event macro will save the value(s) at the discontinuity.

The callback is robust to having multiple discontinuities occur. For example, we can integrate for long time periods and get the desired behavior:

```
u0 = [50.0,0.0]
tspan = (0.0,100.0)
prob = ODEProblem(f,u0,tspan,p)
sol = solve(prob,Tsit5(),callback=cb)
plot(sol,plotdensity=10000)
```

#### Handling Changing Dynamics and Exactness

Let's make a version of the bouncing ball where the ball sticks to the ground. We can do this by introducing a parameter `p`

to send the velocity to zero on the bounce. This looks as follows:

```
function dynamics!(du, u, p, t)
du[1] = u[2]
du[2] = p[1] * -9.8
end
function floor_aff!(int)
int.p[1] = 0
int.u[2] = 0
@show int.u[1], int.t
end
floor_event = ContinuousCallback(floor_cond, floor_aff!)
u0 = [1.0,0.0]
p = [1.0]
prob = ODEProblem{true}(dynamics!, u0, (0., 1.75), p)
sol = solve(prob, Tsit5(), callback=floor_event)
plot(sol)
@show sol[end] # [4.329177480185359e-16, 0.0]
```

Notice that at the end, the ball is not at `0.0`

like the condition would let you believe, but instead it's at `4.329177480185359e-16`

. From the printing inside of the affect function, we can see that this is the value it had at the event time `t=0.4517539514526232`

. Why did the event handling not make it exactly zero? If you instead would have run the simulation to `nextfloat(0.4517539514526232) = 0.45175395145262326`

, we would see that the value of `u[1] = -1.2647055847076505e-15`

. You can see this by changing the `rootfind`

argument of the callback:

```
floor_event = ContinuousCallback(floor_cond, floor_aff!,rootfind=DiffEqBase.RightRootFind)
u0 = [1.0,0.0]
p = [1.0]
prob = ODEProblem{true}(dynamics!, u0, (0., 1.75), p)
sol = solve(prob, Tsit5(), callback=floor_event)
@show sol[end] # [-1.2647055847076505e-15, 0.0]
```

What this means is that there is not 64-bit floating point number `t`

such that the condition is zero! By default, if there is no `t`

such that `condition=0`

, then rootfinder defaults to choosing the floating point number exactly before the exactly before the event (`LeftRootFind`

). This way manifold constraints are preserved by default (i.e. the ball never goes below the floor). However, if you require that the condition is exactly satisfied after the event, you will want to add such a change to the `affect!`

function. For example, the error correction in this case is to add `int.u[1] = 0`

to the `affect!`

, i.e.:

```
function floor_aff!(int)
int.p[1] = 0
int.u[1] = 0
int.u[2] = 0
@show int.u[1], int.t
end
floor_event = ContinuousCallback(floor_cond, floor_aff!)
u0 = [1.0,0.0]
p = [1.0]
prob = ODEProblem{true}(dynamics!, u0, (0., 1.75), p)
sol = solve(prob, Tsit5(), callback=floor_event)
@show sol[end] # [0.0,0.0]
```

and now the sticky behavior is perfect to the floating point.

#### Handling Accumulation Points

Now let's take a look at the bouncing ball with friction. After the bounce, we will send the velocity to `-v/2`

. Since the velocity is halving each time, we should have Zeno-like behavior and see an accumulation point of bounces. We will use some extra parameters to count the number of bounces (to infinity) and find the accumulation point. Let's watch!

```
function dynamics!(du, u, p, t)
du[1] = u[2]
du[2] = p[1] * -9.8
end
floor_cond(u, t, int) = u[1]
function floor_aff!(int)
int.u[2] *= -0.5
int.p[1] += 1
int.p[2] = int.t
end
floor_event = ContinuousCallback(floor_cond, floor_aff!)
u0 = [1.0,0.0]
p = [0.0,0.0]
prob = ODEProblem{true}(dynamics!, u0, (0., 2.), p)
sol = solve(prob, Tsit5(), callback=floor_event)
plot(sol)
@show p # [8.0, 1.3482031881786312]
```

From the readout we can see the ball only bounced 8 times before it went below the floor, what happened? What happened is floating point error. Because one cannot guarantee that floating point numbers exist to make the `condition=0`

, a heuristic is used to ensure that a zero is not accidentally detected at `nextfloat(t)`

after the simulation restarts (otherwise it would repeatly find the same event!). However, sooner or later the ability to detect minute floating point differences will crash, and what should be infinitely many bounces finally misses a bounce.

This leads to two questions:

- How can you improve the accuracy of an accumulation calculation?
- How can you make it gracefully continue?

For (1), note that floating point accuracy is dependent on the current `dt`

. If you know that an accumulation point is coming, one can use `set_proposed_dt!`

to shrink the `dt`

value and help find the next bounce point. You can use `t - tprev`

to know the length of the previous interval for this calculation. For this example, we can set the proposed `dt`

to `(t - tprev)/10`

to ensure an ever increasing accuracy of the check.

However, at some point we will hit machine epsilon, the value where `t + eps(t) == t`

, so we cannot measure infinitely many bounces and instead will be limited by the floating point accuracy of our number representation. Using alternative number types like ArbFloats.jl can allow for this to be done at very high accuracy, but still not infinite. Thus what we need to do is determine a tolerance after which we assume the accumulation has been reached and define the exit behavior. In this case we will say when the `dt<1e-12`

, we are almost at the edge of Float64 accuracy (`eps(1.0) = 2.220446049250313e-16`

), so we will change the position and velocity to exactly zero.

With these floating point corrections in mind, the accumulation calculations looks as follows:

```
function dynamics!(du, u, p, t)
du[1] = u[2]
du[2] = p[1] * -9.8
end
floor_cond(u, t, int) = u[1]
function floor_aff!(int)
int.u[2] *= -0.5
if int.dt > 1e-12
set_proposed_dt!(int,(int.t-int.tprev)/100)
else
int.u[1] = 0
int.u[2] = 0
int.p[1] = 0
end
int.p[2] += 1
int.p[3] = int.t
end
floor_event = ContinuousCallback(floor_cond, floor_aff!)
u0 = [1.0,0.0]
p = [1.0,0.0,0.0]
prob = ODEProblem{true}(dynamics!, u0, (0., 2.), p)
sol = solve(prob, Tsit5(), callback=floor_event)
plot(sol)
@show p # [0.0, 41.0, 1.355261854357056]
```

With this corrected version, we see that after 41 bounces the accumulation point is reached at `t = 1.355261854357056`

. To really see the accumulation, let's zoom in:

```
p1 = plot(sol,vars=1,tspan=(1.25,1.40))
p2 = plot(sol,vars=1,tspan=(1.35,1.36))
p3 = plot(sol,vars=1,tspan=(1.354,1.35526))
p4 = plot(sol,vars=1,tspan=(1.35526,1.35526185))
plot(p1,p2,p3,p4)
```

I think Zeno would be proud of our solution.

### Example 2: Terminating an Integration

In many cases you might want to terminate an integration when some condition is satisfied. To terminate an integration, use `terminate!(integrator)`

as the `affect!`

in a callback.

In this example we will solve the differential equation:

```
u0 = [1.,0.]
function fun2(du,u,p,t)
du[2] = -u[1]
du[1] = u[2]
end
tspan = (0.0,10.0)
prob = ODEProblem(fun2,u0,tspan)
```

which has cosine and -sine as the solutions respectively. We wish to solve until the sine part, `u[2]`

becomes positive. There are two things we may be looking for.

A `DiscreteCallback`

will cause this to halt at the first step such that the condition is satisfied. For example, we could use:

```
condition(u,t,integrator) = u[2]>0
affect!(integrator) = terminate!(integrator)
cb = DiscreteCallback(condition,affect!)
sol = solve(prob,Tsit5(),callback=cb)
```

However, in many cases we wish to halt exactly at the point of time that the condition is satisfied. To do that, we use a continuous callback. The condition must thus be a function which is zero at the point we want to halt. Thus we use the following:

```
condition(u,t,integrator) = u[2]
affect!(integrator) = terminate!(integrator)
cb = ContinuousCallback(condition,affect!)
sol = solve(prob,Tsit5(),callback=cb)
```

Note that this uses rootfinding to approximate the "exact" moment of the crossing. Analytically we know the value is `pi`

, and here the integration terminates at

`sol.t[end] # 3.1415902502224307`

Using a more accurate integration increases the accuracy of this prediction:

```
sol = solve(prob,Vern8(),callback=cb,reltol=1e-12,abstol=1e-12)
sol.t[end] # 3.1415926535896035
#π = 3.141592653589703...
```

Now say we wish to find the when the first period is over, i.e. we want to ignore the upcrossing and only stop on the downcrossing. We do this by ignoring the `affect!`

and only passing an `affect!`

for the second:

```
condition(u,t,integrator) = u[2]
affect!(integrator) = terminate!(integrator)
cb = ContinuousCallback(condition,nothing,affect!)
sol = solve(prob,Tsit5(),callback=cb)
```

Notice that passing only one `affect!`

is the same as `ContinuousCallback(condition,affect!,affect!)`

, i.e. both upcrossings and downcrossings will activate the event. Using `ContinuousCallback(condition,affect!,nothing)`

will thus be the same as above because the first event is an upcrossing.

### Example 3: Growing Cell Population

Another interesting issue is with models of changing sizes. The ability to handle such events is a unique feature of DifferentialEquations.jl! The problem we would like to tackle here is a cell population. We start with 1 cell with a protein `X`

which increases linearly with time with rate parameter `α`

. Since we are going to be changing the size of the population, we write the model in the general form:

```
const α = 0.3
function f(du,u,p,t)
for i in 1:length(u)
du[i] = α*u[i]
end
end
```

Our model is that, whenever the protein `X`

gets to a concentration of 1, it triggers a cell division. So we check to see if any concentrations hit 1:

```
function condition(u,t,integrator) # Event when event_f(u,t) == 0
1-maximum(u)
end
```

Again, recall that this function finds events as when `condition==0`

, so `1-maximum(u)`

is positive until a cell has a concentration of `X`

which is 1, which then triggers the event. At the event, we have that the cell splits into two cells, giving a random amount of protein to each one. We can do this by resizing the cache (adding 1 to the length of all of the caches) and setting the values of these two cells at the time of the event:

```
function affect!(integrator)
u = integrator.u
resize!(integrator,length(u)+1)
maxidx = findmax(u)[2]
Θ = rand()
u[maxidx] = Θ
u[end] = 1-Θ
nothing
end
```

As noted in the Integrator Interface, `resize!(integrator,length(integrator.u)+1)`

is used to change the length of all of the internal caches (which includes `u`

) to be their current length + 1, growing the ODE system. Then the following code sets the new protein concentrations. Now we can solve:

```
callback = ContinuousCallback(condition,affect!)
u0 = [0.2]
tspan = (0.0,10.0)
prob = ODEProblem(f,u0,tspan)
sol = solve(prob,callback=callback)
```

The plot recipes do not have a way of handling the changing size, but we can plot from the solution object directly. For example, let's make a plot of how many cells there are at each time. Since these are discrete values, we calculate and plot them directly:

```
plot(sol.t,map((x)->length(x),sol[:]),lw=3,
ylabel="Number of Cells",xlabel="Time")
```

Now let's check-in on a cell. We can still use the interpolation to get a nice plot of the concentration of cell 1 over time. This is done with the command:

```
ts = range(0, stop=10, length=100)
plot(ts,map((x)->x[1],sol.(ts)),lw=3,
ylabel="Amount of X in Cell 1",xlabel="Time")
```

Notice that every time it hits 1 the cell divides, giving cell 1 a random amount of `X`

which then grows until the next division.

Note that one macro which was not shown in this example is `deleteat!`

on the caches. For example, to delete the second cell, we could use:

`deleteat!(integrator,2)`

This allows you to build sophisticated models of populations with births and deaths.

## VectorContinuousCallback Example

### Example 1: Bouncing Ball with multiple walls

This is similar to the above Bouncing Ball example, but now we have two more vertical walls, at `x = 0`

and `x = 10.0`

. We have our ODEFunction as -

```
function f(du,u,p,t)
du[1] = u[2]
du[2] = -p
du[3] = u[4]
du[4] = 0.0
end
```

where `u[1]`

denotes `y`

-coordinate, `u[2]`

denotes velocity in `y`

-direction, `u[3]`

denotes `x`

-coordinate and `u[4]`

denotes velocity in `x`

-direction. We will make a `VectorContinuousCallback`

of length 2 - one for `x`

axis collision, one for walls parallel to `y`

axis.

```
function condition(out,u,t,integrator) # Event when event_f(u,t) == 0
out[1] = u[1]
out[2] = (u[3] - 10.0)u[3]
end
function affect!(integrator, idx)
if idx == 1
integrator.u[2] = -0.9integrator.u[2]
elseif idx == 2
integrator.u[4] = -0.9integrator.u[4]
end
end
cb = VectorContinuousCallback(condition,affect!,2)
```

It is evident that `out[2]`

will be zero when `u[3]`

(x-coordinate) is either `0.0`

or `10.0`

. And when that happens, we flip the velocity with some coefficient of restitution (`0.9`

).

Completeting rest of the code-

```
u0 = [50.0,0.0,0.0,2.0]
tspan = (0.0,15.0)
p = 9.8
prob = ODEProblem(f,u0,tspan,p)
sol = solve(prob,Tsit5(),callback=cb,dt=1e-3,adaptive=false)
plot(sol,vars=(1,3))
```

And you get the following output: