API

@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, OSMAgent.

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

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.

GridAgent{D}

Combine with @agent to create an agent type for D-dimensional GridSpace. It attributes the fields id::Int, pos::NTuple{D,Int} to the start of the agent 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.

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.

model[id]
getindex(model::ABM, id::Integer)

Return an agent given its ID.

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.

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.

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.

nagents(model::ABM)

Return the number of agents in the model.

allagents(model)

Return an iterator over all agents of the model.

allids(model)

Return an iterator over all agent IDs of the model.

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.

GridSpace(d::NTuple{D, Int}; periodic = true, metric = :chebyshev, pathfinder = nothing)

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 ith 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.

pathfinder: Optionally provide an instance of Pathfinding.Pathfinder to enable pathfinding capabilities based on the A* algorithm, see Path-finding in the docs.

An example using GridSpace is the Forest fire model.

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.

OpenStreetMapSpace(path::AbstractString; kwargs...)

Create a space residing on the Open Street Map (OSM) file provided via path. The functionality related to Open Street Map spaces is in the submodule OSM.

This space represents the underlying map as a continuous entity choosing accuracy over performance by explicitly taking into account that every intersection is connected by a road with a finite length in meters. An example of its usage can be found in Zombie Outbreak. Nevertheless, all functions that target DiscreteSpaces apply here as well, e.g. positions. The discrete part are the underlying road intersections, that are represented by a graph.

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 OSM.TEST_MAP to use as a path for testing.

If your solution can tolerate routes to and from intersections only without caring for the continuity of the roads in between, 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 position 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.

1. Assign the value of OSM.plan_route to the .route field of an Agent. This provides :shortest and :fastest paths (with the option of a return_trip) between intersections or positions.
2. OSM.random_route!, choses a new destination an plans a new path to it; overriding the current route (if any).

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 using the constructor of the agent type of the model. Optionally provide a position to add the agent to as first argument, which must match the space position type.

This function takes care of setting the agent's id and position. The extra provided args... and kwargs... are propagated to other fields of the agent constructor (see example below).

add_agent!([pos,] A::Type, 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

add_agent_pos!(agent::AbstractAgent, model::ABM) → agent

Add the agent to the model at the agent's own position.

nextid(model::ABM) → id

Return a valid id for creating a new agent with it.

random_position(model) → pos

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

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).

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 ContinuousSpaces.

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 operation 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].

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

Move an agent by distance in meters along its planned route.

move_along_route!(agent, model_with_pathfinding)

Move agent for one step along the route toward its target set by Pathfinding.set_target! for agents on a GridSpace using a Pathfinding.Pathfinder. If the agent does not have a precalculated path or the path is empty, it remains stationary.

is_stationary(agent, model)

Return true if agent has reached the end of its route, or no route has been set for it. Used in setups where using move_along_route! is valid.

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

Remove an agent from the model.

genocide!(model::ABM)

Kill all the agents of the model.

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

Kill the agents whose IDs are larger than n.

genocide!(model::ABM, IDs)

Kill the agents with the given IDs.

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

Kill all agents where the function f(agent) returns true.

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.

Example usage in Wright-Fisher model of evolution.

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.

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_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.

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.

has_empty_positions(model::ABM{<:DiscreteSpace})

Return true if there are any positions in the model without agents.

empty_positions(model)

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

random_empty(model::ABM{<:DiscreteSpace})

Return a random position without any agents, or nothing if no such positions exist.

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, updating the agent's position to the new one.

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

move_agent_single!(agent, model::ABM{<:DiscreteSpace}) → agent

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.

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

Return true if there are no agents in position.

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.

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.

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.

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.

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!.

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.

OSM

Submodule for functionality related to OpenStreetMapSpace. See the docstring of the space for more info.

OSM.latlon(pos, model)
OSM.latlon(agent, model)

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

intersection(latlon::Tuple{Float64,Float64}, model::ABM{<:OpenStreetMapSpace})

Return the nearest intersection position to (latitude, longitude). Quicker, but less precise than OSM.road.

OSM.road(latlon::Tuple{Float64,Float64}, model::ABM{<:OpenStreetMapSpace})

Return a location on a road nearest to (latitude, longitude). Slower, but more precise than OSM.intersection.

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.

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.

OSM.random_route!(agent, model::ABM{<:OpenStreetMapSpace})

Plan a new random route for the agent, by selecting a random destination and planning a route from the agent's current position. Overwrite any current route.

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.

OSM.map_coordinates(agent, model::ABM{OpenStreetMapSpace})

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

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.

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.

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).

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.

iter_agent_groups(order::Int, model::ABM; scheduler = Schedulers.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 Schedulers.by_id scheduler by default, but can be altered with the scheduler keyword.

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.

index_mapped_groups(order::Int, model::ABM; scheduler = Schedulers.by_id)
index_mapped_groups(order::Int, model::ABM, filter::Function; scheduler = Schedulers.by_id)

Return an iterable of agent ids in the model, meeting the filter criterea if used.

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 Vectors are not expanded in the scan.

The second argument initialize is a function that creates an ABM and returns it. It must 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: by default, only the varying parameters (Vector in parameters) will be included in the output DataFrame. If true, constant parameters (non-Vector in parameteres) will also be included.
• parallel::Bool = false whether Distributed.pmap is invoked to run simulations in parallel. This must be used in conjunction with @everywhere (see Performance Tips).

All other keywords are propagated into run!. Furthermore, agent_step!, model_step!, n are also keywords here, that are given to run! as arguments. Naturally, agent_step!, model_step!, n and at least one of adata, mdata are mandatory. The adata, mdata lists shouldn't contain the parameters that are already in the parameters dictionary to avoid duplication.

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 = Schedulers.randomly)
    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(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
)

adf, _ = paramscan(parameters, initialize; adata, agent_step!, n = 3)

init_agent_dataframe(model, adata) → agent_df

Initialize a dataframe to add data later with collect_agent_data!.

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.

init_model_dataframe(model, mdata) → model_df

Initialize a dataframe to add data later with collect_model_data!.

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

Same as collect_agent_data! but for model data instead.

dataname(k) → name

Return the name of the column of the i-th collected data where k = adata[i] (or mdata[i]). dataname also accepts tuples with aggregate and conditional values.

Schedulers

Submodule containing all predefined schedulers of Agents.jl and the scheduling API. Schedulers have a very simple interface. They 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.

See also Advanced scheduling for making more advanced schedulers.

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.

Schedulers.fastest

A scheduler that activates 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.

Schedulers.by_id

A scheduler that activates all agents agents at each step according to their id.

Schedulers.randomly

A scheduler that activates all agents once per step in a random order. Different random ordering is used at each different step.

Schedulers.partially(p)

A scheduler that at each step activates only p percentage of randomly chosen agents.

Schedulers.by_property(property)

A scheduler that at each step activates the agents in an order dictated by their property, with agents with greater property acting first. property can be a Symbol, which just dictates which field of the agents to compare, or a function which inputs an agent and outputs a real number.

Schedulers.by_type(shuffle_types::Bool, shuffle_agents::Bool)

A scheduler 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 Schedulers.fastest).

Schedulers.by_type((C, B, A), shuffle_agents::Bool)

A scheduler that activates agents by type in specified order (since Unions are not order preserving). shuffle_agents = true randomizes the order of agents within each group.

ensemblerun!(models::Vector, agent_step!, model_step!, n; kwargs...)

Perform an ensemble simulation of run! for all model ∈ models. Each model should be a (different) instance of an AgentBasedModel but probably initialized with a different random seed or different initial agent distribution. All models obey the same rules agent_step!, model_step! and are evolved for n.

Similarly to run! this function will collect data. It will furthermore add one additional column to the dataframe called :ensemble, which has an integer value counting the ensemble member. The function returns agent_df, model_df, models.

The keyword parallel = false, when true, will run the simulations in parallel using Julia's Distributed.pmap (you need to have loaded Agents with @everywhere, see docs online).

All other keywords are propagated to run! as-is.

Example usage in Schelling's segregation model.

If you want to scan parameters and at the same time run multiple simulations at each parameter combination, simply use seed as a parameter, and use that parameter to tune the model's initial random seed and agent distribution.

ensemblerun!(generator, agent_step!, model_step!, n; kwargs...)

Generate many ABMs and propagate them into ensemblerun!(models, ...) using the provided generator which is a one-argument function whose input is a seed.

This method has additional keywords ensemble = 5, seeds = rand(UInt32, ensemble).

Pathfinding

Submodule containing functionality for path-finding based on the A* algorithm. Currently available only for GridSpace.

You can enable path-finding and set it's options by passing an instance of a Pathfinding.Pathfinder struct to the pathfinder parameter of the GridSpace constructor. During the simulation, call Pathfinding.set_target! to set the target destination for an agent. This triggers the algorithm to calculate a path from the agent's current position to the one specified. You can alternatively use Pathfinding.set_best_target! to choose the best target from a list. Once a target has been set, you can move an agent one step along its precalculated path using the move_along_route! function.

Refer to the Maze Solver and Mountain Runners examples using path-finding and see the available functions below as well.

Pathfinding.Pathfinder(; kwargs...)

Enable pathfinding using the A* algorithm by passing an instance of Pathfinder into GridSpace. Pathfinding works by using the functions Pathfinding.set_target! and move_along_route! see Pathfinding for more.

Keywords

• diagonal_movement = true states that agents are allowed to move diagonally. Otherwise, only orthogonal directions are possible.
• admissibility = 0 allows the algorithm to approximate paths to speed up pathfinding significantly. A value of admissibility allows paths at most (1+admissibility) times the optimal path length.
• walkable = nothing specifies (un)walkable regions of the space. If specified, it should be a BitArray array of the same size as the corresponding GridSpace. This defaults to nothing, which allows agents to walk on any position in the space. An example usage can be found in Maze Solver.
• cost_metric is an instance of a cost metric and specifies the method to use for approximating the distance between two points. This defaults to Pathfinding.DirectDistance with appropriate dimensionality.

Pathfinding.set_target!(agent, target::NTuple{D,Int}, model)

Calculate and store the shortest path to move the agent from its current position to target (a grid position e.g. (1, 5)) for models using Pathfinding.

Use this method in conjuction with move_along_route!.

Pathfinding.set_best_target!(agent, targets::Vector{NTuple{D,Int}}, model)

Calculate and store the best path to move the agent from its current position to a chosen target position taken from targets for models using Pathfinding.

The condition = :shortest keyword retuns the shortest path which is shortest (allowing for the conditions of the models pathfinder) out of the possible target positions. Alternatively, the :longest path may also be requested.

Returns the position of the chosen target.

Pathfinding.walkmap(model)

Return the walkable map of a Pathfinding.Pathfinder.

It is possible to mutate the map directly, for example Pathfinding.walkmap(model)[15, 40] = false. If this is mutated, a new path needs to be planned using Pathfinding.set_target!.

Pathfinding.heightmap(model)

Return the heightmap of a Pathfinding.Pathfinder if the Pathfinding.HeightMap metric is in use, nothing otherwise.

It is possible to mutate the map directly, for example Pathfinding.heightmap(model)[15, 40] = 115 or Pathfinding.heightmap(model) .= rand(50, 50). If this is mutated, a new path needs to be planned using Pathfinding.set_target!.

Pathfinding.DirectDistance{D}([direction_costs::Vector{Int}]) <: CostMetric{D}

Distance is approximated as the shortest path between the two points, provided the walkable property of Pathfinding.Pathfinder allows. Optionally provide a Vector{Int} that represents the cost of going from a tile to the neighboring tile on the i dimensional diagonal (default is 10√i).

If diagonal_movement=false in Pathfinding.Pathfinder, neighbors in diagonal positions will be excluded. Cost defaults to the first value of the provided vector.

Pathfinding.MaxDistance{D}() <: CostMetric{D}

Distance between two tiles is approximated as the maximum of absolute difference in coordinates between them.

Pathfinding.HeightMap(hmap::Array{Int,D} [, base_metric::CostMetric]) <: CostMetric{D}

Distance between two positions is the sum of the shortest distance between them and the absolute difference in height. A heightmap of the same size as the corresponding GridSpace{D} is required. Distance is calculated using Pathfinding.DirectDistance by default, and can be changed by specifying base_metric. An example usage can be found in Mountain Runners.