# API

The API of Agents.jl is defined on top of the fundamental structures `AgentBasedModel`

, Space, `AbstractAgent`

which are described in the Tutorial page. In this page we list the remaining API functions, which constitute the bulk of Agents.jl functionality.

`@agents`

macro

The `@agent`

macro makes defining agent types within Agents.jl simple.

`Agents.@agent`

— Macro```
@agent YourAgentType{X, Y} AgentSupertype begin
some_property::X
other_extra_property::Y
# etc...
end
```

Create a struct for your agents which includes the mandatory fields required to operate in a particular space. Depending on the space of your model, the `AgentSupertype`

is chosen appropriately from `GraphAgent`

, `GridAgent`

, `ContinuousAgent`

.

**Example**

Using

```
@agent Person{T} GridAgent{2} begin
age::Int
moneyz::T
end
```

will in fact create an agent appropriate for using with 2-dimensional `GridSpace`

```
mutable struct Person{T} <: AbstractAgent
id::Int
pos::NTuple{2, Int}
age::Int
moneyz::T
end
```

`Agents.GraphAgent`

— Type`GraphAgent`

Combine with `@agent`

to create an agent type for `GraphSpace`

. It attributes the fields `id::Int, pos::Int`

to the start of the agent type.

`Agents.GridAgent`

— Type`GridAgent{D}`

Combine with `@agent`

to create an agent type for `D`

-dimensional `GraphSpace`

. It attributes the fields `id::Int, pos::NTuple{D,Int}`

to the start of the agent type.

`Agents.ContinuousAgent`

— Type`ContinuousAgent{D}`

Combine with `@agent`

to create an agent type for `D`

-dimensional `ContinuousSpace`

. It attributes the fields `id::Int, pos::NTuple{D,Float64}, vel::NTuple{D,Float64}`

to the start of the agent type.

`Agents.OSMAgent`

— Type`OSMAgent`

Combine with `@agent`

to create an agent type for `OpenStreetMapSpace`

. It attributes the fields `id::Int, pos::Tuple{Int,Int,Float64}, route::Vector{Int}, destination::Tuple{Int,Int,Float64}`

to the start of the agent type.

## Agent/model retrieval and access

`Base.getindex`

— Method```
model[id]
getindex(model::ABM, id::Integer)
```

Return an agent given its ID.

`Base.getproperty`

— Method```
model.prop
getproperty(model::ABM, :prop)
```

Return a property with name `:prop`

from the current `model`

, assuming the model `properties`

are either a dictionary with key type `Symbol`

or a Julia struct. For example, if a model has the set of properties `Dict(:weight => 5, :current => false)`

, retrieving these values can be obtained via `model.weight`

.

The property names `:agents, :space, :scheduler, :properties, :maxid`

are internals and **should not be accessed by the user**.

`Agents.seed!`

— Function`seed!(model [, seed])`

Reseed the random number pool of the model with the given seed or a random one, when using a pseudo-random number generator like `MersenneTwister`

.

`Agents.random_agent`

— Function`random_agent(model) → agent`

Return a random agent from the model.

`random_agent(model, condition) → agent`

Return a random agent from the model that satisfies `condition(agent) == true`

. The function generates a random permutation of agent IDs and iterates through them. If no agent satisfies the condition, `nothing`

is returned instead.

`Agents.nagents`

— Function`nagents(model::ABM)`

Return the number of agents in the `model`

.

`Agents.allagents`

— Function`allagents(model)`

Return an iterator over all agents of the model.

`Agents.allids`

— Function`allids(model)`

Return an iterator over all agent IDs of the model.

## Available spaces

Here we list the spaces that are available "out of the box" from Agents.jl. To create your own, see Creating a new space type.

### Discrete spaces

`Agents.GraphSpace`

— Type`GraphSpace(graph::AbstractGraph)`

Create a `GraphSpace`

instance that is underlined by an arbitrary graph from LightGraphs.jl. The position type for this space is `Int`

, use `GraphAgent`

for convenience. The underlying graph can be altered using `add_node!`

and `rem_node!`

.

`GraphSpace`

represents a space where each node (i.e. position) of a graph can hold an arbitrary amount of agents, and each agent can move between the nodes of the graph. An example of its usage can be found in SIR model for the spread of COVID-19. If you want to model social networks, where each agent is equivalent with a node of a graph, you're better of using `nothing`

(or other spaces) as the model space, and using a graph from LightGraphs.jl directly in the model parameters, as shown in the Social networks with LightGraphs.jl integration example.

`Agents.GridSpace`

— Type`GridSpace(d::NTuple{D, Int}; periodic = true, metric = :chebyshev)`

Create a `GridSpace`

that has size given by the tuple `d`

, having `D ≥ 1`

dimensions. Optionally decide whether the space will be periodic and what will be the distance metric used, which decides the behavior of e.g. `nearby_ids`

. The position type for this space is `NTuple{D, Int}`

, use `GridAgent`

for convenience. In our examples we typically use `Dims{D}`

instead of `NTuple{D, Int}`

(they are equivalent). Valid positions have indices in the range `1:d[i]`

for the `i`

th dimension.

`:chebyshev`

metric means that the `r`

-neighborhood of a position are all positions within the hypercube having side length of `2*floor(r)`

and being centered in the origin position.

`:euclidean`

metric means that the `r`

-neighborhood of a position are all positions whose cartesian indices have Euclidean distance `≤ r`

from the cartesian index of the given position.

An example using `GridSpace`

is the Forest fire model.

### Continuous spaces

`Agents.ContinuousSpace`

— Type`ContinuousSpace(extent::NTuple{D, <:Real}, spacing = min(extent...)/10; kwargs...)`

Create a `D`

-dimensional `ContinuousSpace`

in range 0 to (but not including) `extent`

. `spacing`

configures the compartment spacing that the space is divided in, in order to accelerate nearest neighbor functions like `nearby_ids`

. All dimensions in `extent`

must be completely divisible by `spacing`

(i.e. no fractional remainder). Your agent positions (field `pos`

) must be of type `NTuple{D, <:Real}`

, use `ContinuousAgent`

for convenience. In addition it is useful for agents to have a field `vel::NTuple{D, <:Real}`

to use in conjunction with `move_agent!`

.

The keyword `periodic = true`

configures whether the space is periodic or not. If set to `false`

an error will occur if an agent's position exceeds the boundary.

The keyword argument `update_vel!`

is a **function**, `update_vel!(agent, model)`

that updates the agent's velocity **before** the agent has been moved, see `move_agent!`

. You can of course change the agents' velocities during the agent interaction, the `update_vel!`

functionality targets spatial force fields acting on the agents individually (e.g. some magnetic field). By default no update is done this way. If you use `update_vel!`

, the agent type must have a field `vel::NTuple{D, <:Real}`

.

There is no "best" choice for the value of `spacing`

. If you need optimal performance it's advised to set up a benchmark over a range of choices. The value matters most when searching for neighbors. In `Models.flocking`

for example, an optimal value for `spacing`

is 66% of the search distance.

`Agents.OpenStreetMapSpace`

— Type`OpenStreetMapSpace(path::AbstractString; kwargs...)`

Create a space residing on the Open Street Map (OSM) file provided via `path`

.

The abbreviation `OSMSpace`

may be used interchangeably.

Much of the functionality of this space is provided by interfacing with OpenStreetMapX.jl, for example the two keyword arguments `use_cache = false`

and `trim_to_connected_graph = true`

can be passed into the `OpenStreetMapX.get_map_data`

function.

For details on how to obtain an OSM file for your use case, consult the OpenStreetMapX.jl README. We provide a variable `TEST_MAP`

to use as a `path`

for testing.

This space represents the underlying map as a *continuous* entity choosing accuracy over performance. An example of its usage can be found in Zombie Outbreak.

If your solution can tolerate routes to and from intersections only, a faster implementation can be achieved by using the graph representation of your map provided by OpenStreetMapX.jl. For tips on how to implement this, see our integration example: Social networks with LightGraphs.jl.

**The OSMAgent**

The base properties for an agent residing on an `OSMSpace`

are as follows:

```
mutable struct OSMAgent <: AbstractAgent
id::Int
pos::Tuple{Int,Int,Float64}
route::Vector{Int}
destination::Tuple{Int,Int,Float64}
end
```

Current `pos`

ition and `destination`

tuples are represented as `(start intersection index, finish intersection index, distance travelled in meters)`

. The `route`

is an ordered list of intersections, providing a path to reach `destination`

.

Further details can be found in `OSMAgent`

.

**Routing**

There are two ways to generate a route, depending on the situation.

`osm_plan_route`

, which provides`:shortest`

and`:fastest`

paths (with the option of a`return_trip`

) between intersections or positions.`osm_random_route!`

, choses a new`destination`

an plans a new path to it; overriding the current route (if any).

## Model-agent interaction

The following API is mostly universal across all types of Space. Only some specific methods are exclusive to a specific type of space, but these are described further below in this page.

### Adding agents

`Agents.add_agent!`

— Function`add_agent!(agent::AbstractAgent [, pos], model::ABM) → agent`

Add the `agent`

to the model in the given position. If `pos`

is not given, the `agent`

is added to a random position. The `agent`

's position is always updated to match `position`

, and therefore for `add_agent!`

the position of the `agent`

is meaningless. Use `add_agent_pos!`

to use the `agent`

's position.

The type of `pos`

must match the underlying space position type.

`add_agent!([pos,] model::ABM, args...; kwargs...) → newagent`

Create and add a new agent to the model by constructing an agent of the type of the `model`

. Propagate all *extra* positional arguments and keyword arguemts to the agent constructor. Optionally provide a position to add the agent to as *first argument*, which must match the space position type.

Notice that this function takes care of setting the agent's id *and* position and thus `args...`

and `kwargs...`

are propagated to other fields the agent has (see example below).

`add_agent!([pos,] A, model::ABM, args...; kwargs...) → newagent`

Use this version for mixed agent models, with `A`

the agent type you wish to create (to be called as `A(id, pos, args...; kwargs...)`

), because it is otherwise not possible to deduce a constructor for `A`

.

**Example**

```
using Agents
mutable struct Agent <: AbstractAgent
id::Int
pos::Int
w::Float64
k::Bool
end
Agent(id, pos; w=0.5, k=false) = Agent(id, pos, w, k) # keyword constructor
model = ABM(Agent, GraphSpace(complete_digraph(5)))
add_agent!(model, 1, 0.5, true) # incorrect: id/pos is set internally
add_agent!(model, 0.5, true) # correct: w becomes 0.5
add_agent!(5, model, 0.5, true) # add at position 5, w becomes 0.5
add_agent!(model; w = 0.5) # use keywords: w becomes 0.5, k becomes false
```

`Agents.add_agent_pos!`

— Function`add_agent_pos!(agent::AbstractAgent, model::ABM) → agent`

Add the agent to the `model`

at the agent's own position.

`Agents.nextid`

— Function`nextid(model::ABM) → id`

Return a valid `id`

for creating a new agent with it.

`Agents.random_position`

— Function`random_position(model) → pos`

Return a random position in the model's space (always with appropriate Type).

### Moving agents

`Agents.move_agent!`

— Function`move_agent!(agent [, pos], model::ABM) → agent`

Move agent to the given position, or to a random one if a position is not given. `pos`

must have the appropriate position type depending on the space type.

The agent's position is updated to match `pos`

after the move.

`move_agent!(agent::A, model::ABM{<:ContinuousSpace,A}, dt::Real = 1.0)`

Propagate the agent forwards one step according to its velocity, *after* updating the agent's velocity (if configured, see `ContinuousSpace`

). Also take care of periodic boundary conditions.

For this continuous space version of `move_agent!`

, the "evolution algorithm" is a trivial Euler scheme with `dt`

the step size, i.e. the agent position is updated as `agent.pos += agent.vel * dt`

. If you want to move the agent to a specified position, do `move_agent!(agent, pos, model)`

.

`move_agent!(agent, model::ABM{<:OpenStreetMapSpace}, distance::Real)`

Move an agent by `distance`

in meters along its planned route.

`Agents.walk!`

— Function`walk!(agent, direction::NTuple, model; ifempty = false)`

Move agent in the given `direction`

respecting periodic boundary conditions. If `periodic = false`

, agents will walk to, but not exceed the boundary value. Possible on both `GridSpace`

and `ContinuousSpace`

s.

The dimensionality of `direction`

must be the same as the space. `GridSpace`

asks for `Int`

, and `ContinuousSpace`

for `Float64`

vectors, describing the walk distance in each direction. `direction = (2, -3)`

is an example of a valid direction on a `GridSpace`

, which moves the agent to the right 2 positions and down 3 positions. Velocity is ignored for this opreation in `ContinuousSpace`

.

**Keywords**

`ifempty`

will check that the target position is unnocupied and only move if that's true. Available only on`GridSpace`

.

Example usage in Battle Royale.

`walk!(agent, rand, model)`

Invoke a random walk by providing the `rand`

function in place of `distance`

. For `GridSpace`

, the walk will cover ±1 positions in all directions, `ContinuousSpace`

will reside within [-1, 1].

### Removing agents

`Agents.kill_agent!`

— Function```
kill_agent!(agent::AbstractAgent, model::ABM)
kill_agent!(id::Int, model::ABM)
```

Remove an agent from the model.

`Agents.genocide!`

— Function`genocide!(model::ABM)`

Kill all the agents of the model.

`genocide!(model::ABM, n::Int)`

Kill the agents of the model whose IDs are larger than n.

`genocide!(model::ABM, f::Function)`

Kill all agents where the function `f(agent)`

returns `true`

.

`Agents.sample!`

— Function`sample!(model::ABM, n [, weight]; kwargs...)`

Replace the agents of the `model`

with a random sample of the current agents with size `n`

.

Optionally, provide a `weight`

: Symbol (agent field) or function (input agent out put number) to weight the sampling. This means that the higher the `weight`

of the agent, the higher the probability that this agent will be chosen in the new sampling.

**Keywords**

`replace = true`

: whether sampling is performed with replacement, i.e. all agents can

be chosen more than once.

`rng = GLOBAL_RNG`

: a random number generator to perform the sampling with.

Example usage in Wright-Fisher model of evolution.

## Discrete space exclusives

`Agents.positions`

— Function`positions(model::ABM{<:DiscreteSpace}) → ns`

Return an iterator over all positions of a model with a discrete space.

`positions(model::ABM{<:DiscreteSpace}, by::Symbol) → ns`

Return all positions of a model with a discrete space, sorting them using the argument `by`

which can be:

`:random`

- randomly sorted`:population`

- positions are sorted depending on how many agents they accommodate. The more populated positions are first.

`Agents.ids_in_position`

— Function```
ids_in_position(position, model::ABM{<:DiscreteSpace})
ids_in_position(agent, model::ABM{<:DiscreteSpace})
```

Return the ids of agents in the position corresponding to `position`

or position of `agent`

.

`Agents.agents_in_position`

— Function```
agents_in_position(position, model::ABM{<:DiscreteSpace})
agents_in_position(agent, model::ABM{<:DiscreteSpace})
```

Return the agents in the position corresponding to `position`

or position of `agent`

.

`Agents.fill_space!`

— Function```
fill_space!([A ,] model::ABM{<:DiscreteSpace,A}, args...; kwargs...)
fill_space!([A ,] model::ABM{<:DiscreteSpace,A}, f::Function; kwargs...)
```

Add one agent to each position in the model's space. Similarly with `add_agent!`

, the function creates the necessary agents and the `args...; kwargs...`

are propagated into agent creation. If instead of `args...`

a function `f`

is provided, then `args = f(pos)`

is the result of applying `f`

where `pos`

is each position (tuple for grid, index for graph).

An optional first argument is an agent **type** to be created, and targets mixed agent models where the agent constructor cannot be deduced (since it is a union).

Example usage in Daisyworld.

`Agents.has_empty_positions`

— Function`has_empty_positions(model::ABM{<:DiscreteSpace})`

Return `true`

if there are any positions in the model without agents.

`Agents.empty_positions`

— Function`empty_positions(model)`

Return a list of positions that currently have no agents on them.

`Agents.random_empty`

— Function`random_empty(model::ABM{<:DiscreteSpace})`

Return a random position without any agents, or `nothing`

if no such positions exist.

`Agents.add_agent_single!`

— Function`add_agent_single!(agent, model::ABM{<:DiscreteSpace}) → agent`

Add the `agent`

to a random position in the space while respecting a maximum of one agent per position. This function does nothing if there aren't any empty positions.

`add_agent_single!(model::ABM{<:DiscreteSpace}, properties...; kwargs...)`

Same as `add_agent!(model, properties...)`

but ensures that it adds an agent into a position with no other agents (does nothing if no such position exists).

`Agents.move_agent_single!`

— Function`move_agent_single!(agent, model::ABM{<:DiscreteSpace}) → agentt`

Move agent to a random position while respecting a maximum of one agent per position. If there are no empty positions, the agent won't move.

`Base.isempty`

— Method`isempty(position, model::ABM{<:DiscreteSpace})`

Return `true`

if there are no agents in `position`

.

## Continuous space exclusives

`Agents.interacting_pairs`

— Function`interacting_pairs(model, r, method; scheduler = model.scheduler)`

Return an iterator that yields unique pairs of agents `(a1, a2)`

that are close neighbors to each other, within some interaction radius `r`

.

This function is usefully combined with `model_step!`

, when one wants to perform some pairwise interaction across all pairs of close agents once (and does not want to trigger the event twice, both with `a1`

and with `a2`

, which is unavoidable when using `agent_step!`

).

The argument `method`

provides three pairing scenarios

`:all`

: return every pair of agents that are within radius`r`

of each other, not only the nearest ones.`:nearest`

: agents are only paired with their true nearest neighbor (existing within radius`r`

). Each agent can only belong to one pair, therefore if two agents share the same nearest neighbor only one of them (sorted by distance, then by next id in`scheduler`

) will be paired.`:types`

: For mixed agent models only. Return every pair of agents within radius`r`

(similar to`:all`

), only capturing pairs of differing types. For example, a model of`Union{Sheep,Wolf}`

will only return pairs of`(Sheep, Wolf)`

. In the case of multiple agent types,*e.g.*`Union{Sheep, Wolf, Grass}`

, skipping pairings that involve`Grass`

, can be achived by a`scheduler`

that doesn't schedule`Grass`

types,*i.e.*:`scheduler(model) = (a.id for a in allagents(model) if !(a isa Grass))`

.

Example usage in Bacterial Growth.

`Agents.nearest_neighbor`

— Function`nearest_neighbor(agent, model::ABM{<:ContinuousSpace}, r) → nearest`

Return the agent that has the closest distance to given `agent`

. Return `nothing`

if no agent is within distance `r`

.

`Agents.elastic_collision!`

— Function`elastic_collision!(a, b, f = nothing)`

Resolve a (hypothetical) elastic collision between the two agents `a, b`

. They are assumed to be disks of equal size touching tangentially. Their velocities (field `vel`

) are adjusted for an elastic collision happening between them. This function works only for two dimensions. Notice that collision only happens if both disks face each other, to avoid collision-after-collision.

If `f`

is a `Symbol`

, then the agent property `f`

, e.g. `:mass`

, is taken as a mass to weight the two agents for the collision. By default no weighting happens.

One of the two agents can have infinite "mass", and then acts as an immovable object that specularly reflects the other agent. In this case of course momentum is not conserved, but kinetic energy is still conserved.

Example usage in Continuous space social distancing for COVID-19.

## OpenStreetMap space exclusives

`Agents.osm_latlon`

— Function```
osm_latlon(pos, model)
osm_latlon(agent, model)
```

Return (latitude, longitude) of current road or intersection position.

`Agents.osm_intersection`

— Function`osm_intersection(latlon::Tuple{Float64,Float64}, model::ABM{<:OpenStreetMapSpace})`

Returns the nearest intersection position to (latitude, longitude). Quicker, but less precise than `osm_road`

.

`Agents.osm_road`

— Function`osm_road(latlon::Tuple{Float64,Float64}, model::ABM{<:OpenStreetMapSpace})`

Returns a location on a road nearest to (latitude, longitude). Slower, but more precise than `osm_intersection`

.

`Agents.osm_random_road_position`

— Function`osm_random_road_position(model::ABM{OpenStreetMapSpace})`

Similar to `random_position`

, but rather than providing only intersections, this method returns a location somewhere on a road heading in a random direction.

`Agents.osm_plan_route`

— Function```
osm_plan_route(start, finish, model::ABM{<:OpenStreetMapSpace};
by = :shortest, return_trip = false, kwargs...)
```

Generate a list of intersections between `start`

and `finish`

points on the map. `start`

and `finish`

can either be intersections (`Int`

) or positions (`Tuple{Int,Int,Float64}`

).

When either point is a position, the associated intersection index will be removed from the route to avoid double counting.

Route is planned via the shortest path by default (`by = :shortest`

), but can also be planned `by = :fastest`

. Road speeds are needed for this method which can be passed in via extra keyword arguments. Consult the OpenStreetMapX documentation for more details.

If `return_trip = true`

, a route will be planned from start -> finish -> start.

`Agents.osm_random_route!`

— Function`osm_random_route!(agent, model::ABM{<:OpenStreetMapSpace})`

Selects a random destination and plans a route from the agent's current position. Will overwrite any current route.

`Agents.osm_road_length`

— Function```
osm_road_length(start::Int, finish::Int, model)
osm_road_length(pos::Tuple{Int,Int,Float64}, model)
```

Return the road length (in meters) between two intersections given by intersection ids.

`Agents.osm_is_stationary`

— Function`osm_is_stationary(agent)`

Return `true`

if agent has no route left to follow and is therefore standing still.

`Agents.osm_map_coordinates`

— Function`osm_map_coordinates(agent, model::ABM{OpenStreetMapSpace})`

Return a set of coordinates for an agent on the underlying map. Useful for plotting.

## Graph space exclusives

`LightGraphs.SimpleGraphs.add_edge!`

— Function`add_edge!(model::ABM{<: GraphSpace}, n::Int, m::Int)`

Add a new edge (relationship between two positions) to the graph. Returns a boolean, true if the operation was succesful.

`Agents.add_node!`

— Function`add_node!(model::ABM{<: GraphSpace})`

Add a new node (i.e. possible position) to the model's graph and return it. You can connect this new node with existing ones using `add_edge!`

.

`Agents.rem_node!`

— Function`rem_node!(model::ABM{<: GraphSpace}, n::Int)`

Remove node (i.e. position) `n`

from the model's graph. All agents in that node are killed.

**Warning:** LightGraphs.jl (and thus Agents.jl) swaps the index of the last node with that of the one to be removed, while every other node remains as is. This means that when doing `rem_node!(n, model)`

the last node becomes the `n`

-th node while the previous `n`

-th node (and all its edges and agents) are deleted.

## Local area

`Agents.nearby_ids`

— Function`nearby_ids(position, model::ABM, r; kwargs...) → ids`

Return an iterable of the ids of the agents within "radius" `r`

of the given `position`

(which must match type with the spatial structure of the `model`

).

What the "radius" means depends on the space type:

`GraphSpace`

: the degree of neighbors in the graph (thus`r`

is always an integer). For example, for`r=2`

include first and second degree neighbors.`GridSpace, ContinuousSpace`

: Either Chebyshev (also called Moore) or Euclidean distance, in the space of cartesian indices.`GridSpace`

can also take a tuple argument, e.g.`r = (5, 2)`

for a 2D space, which

extends 5 positions in the x direction and 2 in the y. Only possible with Chebyshev spaces.

`OpenStreetMapSpace`

:`r`

is equivalent with distance (in meters) neeeded to be travelled according to existing roads in order to reach given`position`

.

**Keywords**

Keyword arguments are space-specific. For `GraphSpace`

the keyword `neighbor_type=:default`

can be used to select differing neighbors depending on the underlying graph directionality type.

`:default`

returns neighbors of a vertex (position). If graph is directed, this is equivalent to`:out`

. For undirected graphs, all options are equivalent to`:out`

.`:all`

returns both`:in`

and`:out`

neighbors.`:in`

returns incoming vertex neighbors.`:out`

returns outgoing vertex neighbors.

For `ContinuousSpace`

, the keyword `exact=false`

controls whether the found neighbors are exactly accurate or approximate (with approximate always being a strict over-estimation), see `ContinuousSpace`

.

`nearby_ids(agent::AbstractAgent, model::ABM, r=1)`

Same as `nearby_ids(agent.pos, model, r)`

but the iterable *excludes* the given `agent`

's id.

`nearby_ids(pos, model::ABM{<:GridSpace}, r::Vector{Tuple{Int,UnitRange{Int}}})`

Return an iterable of ids over specified dimensions of `space`

with fine grained control of distances from `pos`

using each value of `r`

via the (dimension, range) pattern.

**Note:** Only available for use with non-periodic chebyshev grids.

Example, with a `GridSpace((100, 100, 10))`

: `r = [(1, -1:1), (3, 1:2)]`

searches dimension 1 one step either side of the current position (as well as the current position) and the third dimension searches two positions above current.

For a complete tutorial on how to use this method, see Battle Royale.

`Agents.nearby_agents`

— Function`nearby_agents(agent, model::ABM, args...; kwargs...) -> agent`

Return an iterable of the agents near the position of the given `agent`

.

The value of the argument `r`

and possible keywords operate identically to `nearby_ids`

.

`Agents.nearby_positions`

— Function`nearby_positions(position, model::ABM, r=1; kwargs...) → positions`

Return an iterable of all positions within "radius" `r`

of the given `position`

(which excludes given `position`

). The `position`

must match type with the spatial structure of the `model`

.

The value of `r`

and possible keywords operate identically to `nearby_ids`

.

This function only makes sense for discrete spaces with a finite amount of positions.

`nearby_positions(position, model::ABM{<:OpenStreetMapSpace}; kwargs...) → positions`

For `OpenStreetMapSpace`

this means "nearby intersections" and operates directly on the underlying graph of the OSM, providing the intersection nodes nearest to the given position.

`nearby_positions(agent::AbstractAgent, model::ABM, r=1)`

Same as `nearby_positions(agent.pos, model, r)`

.

`Agents.edistance`

— Function`edistance(a, b, model::ABM)`

Return the euclidean distance between `a`

and `b`

(either agents or agent positions), respecting periodic boundary conditions (if in use). Works with any space where it makes sense: currently `GridSpace`

and `ContinuousSpace`

.

Example usage in the Flock model.

## A note on iteration

Most iteration in Agents.jl is **dynamic** and **lazy**, when possible, for performance reasons.

**Dynamic** means that when iterating over the result of e.g. the `ids_in_position`

function, the iterator will be affected by actions that would alter its contents. Specifically, imagine the scenario

```
using Agents
mutable struct Agent <: AbstractAgent
id::Int
pos::NTuple{4, Int}
end
model = ABM(Agent, GridSpace((5, 5, 5, 5)))
add_agent!((1, 1, 1, 1), model)
add_agent!((1, 1, 1, 1), model)
add_agent!((2, 1, 1, 1), model)
for id in ids_in_position((1, 1, 1, 1), model)
kill_agent!(id, model)
end
collect(allids(model))
```

2-element Array{Int64,1}: 2 3

You will notice that only 1 agent got killed. This is simply because the final state of the iteration of `ids_in_position`

was reached unnaturally, because the length of its output was reduced by 1 *during* iteration. To avoid problems like these, you need to `collect`

the iterator to have a non dynamic version.

**Lazy** means that when possible the outputs of the iteration are not collected and instead are generated on the fly. A good example to illustrate this is `nearby_ids`

, where doing something like

```
a = random_agent(model)
sort!(nearby_ids(random_agent(model), model))
```

leads to error, since you cannot `sort!`

the returned iterator. This can be easily solved by adding a `collect`

in between:

```
a = random_agent(model)
sort!(collect(nearby_agents(a, model)))
```

1-element Array{Main.ex-docs.Agent,1}: Main.ex-docs.Agent(2, (1, 1, 1, 1))

## Higher-order interactions

There may be times when pair-wise, triplet-wise or higher interactions need to be accounted for across most or all of the model's agent population. The following methods provide an interface for such calculation.

`Agents.iter_agent_groups`

— Function`iter_agent_groups(order::Int, model::ABM; scheduler = by_id)`

Return an iterator over all agents of the model, grouped by order. When `order = 2`

, the iterator returns agent pairs, e.g `(agent1, agent2)`

and when `order = 3`

: agent triples, e.g. `(agent1, agent7, agent8)`

. `order`

must be larger than `1`

but has no upper bound.

Index order is provided by the `by_id`

scheduler by default, but can be altered with the `scheduler`

keyword.

`Agents.map_agent_groups`

— Function```
map_agent_groups(order::Int, f::Function, model::ABM; kwargs...)
map_agent_groups(order::Int, f::Function, model::ABM, filter::Function; kwargs...)
```

Applies function `f`

to all grouped agents of an `iter_agent_groups`

iterator. `kwargs`

are passed to the iterator method. `f`

must take the form `f(NTuple{O,AgentType})`

, where the dimension `O`

is equal to `order`

.

Optionally, a `filter`

function that accepts an iterable and returns a `Bool`

can be applied to remove unwanted matches from the results. **Note:** This option cannot keep matrix order, so should be used in conjuction with `index_mapped_groups`

to associate agent ids with the resultant data.

`Agents.index_mapped_groups`

— Function```
index_mapped_groups(order::Int, model::ABM; scheduler = by_id)
index_mapped_groups(order::Int, model::ABM, filter::Function; scheduler = by_id)
```

Return an iterable of agent ids in the model, meeting the `filter`

criterea if used.

## Parameter scanning

`Agents.paramscan`

— Function`paramscan(parameters, initialize; kwargs...) → adf, mdf`

Perform a parameter scan of a ABM simulation output by collecting data from all parameter combinations into dataframes (one for agent data, one for model data). The dataframes columns are both the collected data (as in `run!`

) but also the input parameter values used.

`parameters`

is a dictionary with key type `Symbol`

which contains various parameters that will be scanned over (as well as other parameters that remain constant). This function uses `DrWatson`

's `dict_list`

convention. This means that every entry of `parameters`

that is a `Vector`

contains many parameters and thus is scanned. All other entries of `parameters`

that are not `Vector`

s are not expanded in the scan.

The second argument `initialize`

is a function that creates an ABM and returns it. It should accept keyword arguments which are the *keys* of the `parameters`

dictionary. Since the user decides how to use input arguments to make an ABM, `parameters`

can be used to affect model properties, space type and creation as well as agent properties, see the example below.

**Keywords**

The following keywords modify the `paramscan`

function:

`include_constants::Bool=false`

determines whether constant parameters should be included in the output`DataFrame`

.`progress::Bool = true`

whether to show the progress of simulations.

The following keywords are propagated into `run!`

:

`agent_step!, model_step!, n, when, step0, parallel, replicates, adata, mdata`

`agent_step!, model_step!, n`

and at least one of `adata, mdata`

are mandatory.

**Example**

A runnable example that uses `paramscan`

is shown in Schelling's segregation model. There we define

```
function initialize(; numagents = 320, griddims = (20, 20), min_to_be_happy = 3)
space = GridSpace(griddims, moore = true)
properties = Dict(:min_to_be_happy => min_to_be_happy)
model = ABM(SchellingAgent, space;
properties = properties, scheduler = random_activation)
for n in 1:numagents
agent = SchellingAgent(n, (1, 1), false, n < numagents / 2 ? 1 : 2)
add_agent_single!(agent, model)
end
return model
end
```

and do a parameter scan by doing:

```
happyperc(moods) = count(x -> x == true, moods) / length(moods)
adata = [(:mood, happyperc)]
parameters = Dict(
:min_to_be_happy => collect(2:5), # expanded
:numagents => [200, 300], # expanded
:griddims => (20, 20), # not Vector = not expanded
)
data, _ = paramscan(parameters, initialize; adata = adata, n = 3, agent_step! = agent_step!)
```

## Data collection

The central simulation function is `run!`

, which is mentioned in our Tutorial. But there are other functions that are related to simulations listed here. Specifically, these functions aid in making custom data collection loops, instead of using the `run!`

function.

For example, the core loop of `run!`

is just

```
df_agent = init_agent_dataframe(model, adata)
df_model = init_model_dataframe(model, mdata)
s = 0
while until(s, n, model)
if should_we_collect(s, model, when)
collect_agent_data!(df_agent, model, adata, s)
end
if should_we_collect(s, model, when_model)
collect_model_data!(df_model, model, mdata, s)
end
step!(model, agent_step!, model_step!, 1)
s += 1
end
return df_agent, df_model
```

(here `until`

and `should_we_collect`

are internal functions)

`run!`

uses the following functions:

`Agents.init_agent_dataframe`

— Function`init_agent_dataframe(model, adata) → agent_df`

Initialize a dataframe to add data later with `collect_agent_data!`

.

`Agents.collect_agent_data!`

— Function`collect_agent_data!(df, model, properties, step = 0; obtainer = identity)`

Collect and add agent data into `df`

(see `run!`

for the dispatch rules of `properties`

and `obtainer`

). `step`

is given because the step number information is not known.

`Agents.init_model_dataframe`

— Function`init_model_dataframe(model, mdata) → model_df`

Initialize a dataframe to add data later with `collect_model_data!`

.

`Agents.collect_model_data!`

— Function`collect_model_data!(df, model, properties, step = 0, obtainer = identity)`

Same as `collect_agent_data!`

but for model data instead.

`Agents.aggname`

— Function```
aggname(k) → name
aggname(k, agg) → name
aggname(k, agg, condition) → name
```

Return the name of the column of the `i`

-th collected data where `k = adata[i]`

(or `mdata[i]`

). `aggname`

also accepts tuples with aggregate and conditional values.

## Schedulers

The schedulers of Agents.jl have a very simple interface. All schedulers are functions, that take as an input the ABM and return an iterator over agent IDs. Notice that this iterator can be a "true" iterator (non-allocated) or can be just a standard vector of IDs. You can define your own scheduler according to this API and use it when making an `AgentBasedModel`

. You can also use the function `schedule(model)`

to obtain the scheduled ID list, if you prefer to write your own `step!`

-like loop.

Notice that schedulers can be given directly to model creation, and thus become the "default" scheduler a model uses, but they can just as easily be incorporated in a `model_step!`

function as shown in Advanced stepping.

### Predefined schedulers

Some useful schedulers are available below as part of the Agents.jl public API:

`Agents.fastest`

— Function`fastest`

Activate all agents once per step in the order dictated by the agent's container, which is arbitrary (the keys sequence of a dictionary). This is the fastest way to activate all agents once per step.

`Agents.by_id`

— Function`by_id`

Activate agents at each step according to their id.

`Agents.random_activation`

— Function`random_activation`

Activate agents once per step in a random order. Different random ordering is used at each different step.

`Agents.partial_activation`

— Function`partial_activation(p)`

At each step, activate only `p`

percentage of randomly chosen agents.

`Agents.property_activation`

— Function`property_activation(property)`

At each step, activate the agents in an order dictated by their `property`

, with agents with greater `property`

acting first. `property`

is a `Symbol`

, which just dictates which field the agents to compare.

`Agents.by_type`

— Function`by_type(shuffle_types::Bool, shuffle_agents::Bool)`

Useful only for mixed agent models using `Union`

types.

- Setting
`shuffle_types = true`

groups by agent type, but randomizes the type order.

Otherwise returns agents grouped in order of appearance in the `Union`

.

`shuffle_agents = true`

randomizes the order of agents within each group,`false`

returns

the default order of the container (equivalent to `fastest`

).

`by_type((C, B, A), shuffle_agents::Bool)`

Activate agents by type in specified order (since `Union`

s are not order preserving). `shuffle_agents = true`

randomizes the order of agents within each group.

### Advanced scheduling

You can use Function-like-objects to make your scheduling possible of arbitrary events. For example, imagine that after the `n`

-th step of your simulation you want to fundamentally change the order of agents. To achieve this you can define

```
mutable struct MyScheduler
n::Int # step number
w::Float64
end
```

and then define a calling method for it like so

```
function (ms::MyScheduler)(model::ABM)
ms.n += 1 # increment internal counter by 1 each time its called
# be careful to use a *new* instance of this scheduler when plotting!
if ms.n < 10
return allids(model) # order doesn't matter in this case
else
ids = collect(allids(model))
# filter all ids whose agents have `w` less than some amount
filter!(id -> model[id].w < ms.w, ids)
return ids
end
end
```

and pass it to e.g. `step!`

by initializing it

```
ms = MyScheduler(100, 0.5)
step!(model, agentstep, modelstep, 100; scheduler = ms)
```