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v6 - New Major release!

Potentially BREAKING changes

We tried to deprecate every major change, resulting in practically no breakage from v5 to v6. However, in version v6.2 we will remove all deprecations (and hence un-updated code will break)

  • The @agent macro has been rewritten to support fields with default and const values. It has a new usage syntax now that parallelizes more Julia's native struct declaration. The old macro version still works but it's deprecated. Since now the macro supports these features, using @agent is the only supported way to create agent types for Agents.jl.
  • Manually setting or altering the ids of agents is no longer allowed. The agent id is now considered a read-only field, and is set internally by Agents.jl to enable hidden optimizations in the future. Due to this, the nextid function is no longer public API. As a consequence, new constructor of agents which accept the model as first argument have been created with the agent macro e.g. A(model, pos; kwargs...), so that to handle the id assignment automatically.
    • We anyways recommend using exclusively the API function add_agent! to create new model agents. This means you never have to care about the id!
  • Agent types in ContinuousSpace now use SVector for their pos and vel fields rather than NTuple. NTuple usage in ContinuousSpace is officially deprecated, but backward compatibility is mostly maintained. Known breakages include the comparison of agent position and/or velocity with user-defined tuples, e.g., doing agent.pos == (0.5, 0.5). This will always be false in v6 as agent.pos is an SVector. The rest of the functionality should all work without problems, such as moving agents to tuple-based positions etc.
  • The :step column name of the dataframes resulting from run! has been renamed to :time, to accommodate for the fact that now both discrete time and continuous time models are possible in Agents.jl.

New features

  • AgentBasedModel defines an API that new model types may extend. This opens the door for making new types of models as well as better integration of other agent based modelling frameworks with Agents.jl.
  • Every aspect of Agents.jl is orthogonal to AgentBasedModel: movement and neighbor searching in any space, data collection, visualizations, etc., are independent of the specific type of AgentBasedModel and work out of the box with any model.
  • Logic of when to collect data in run! has been improved to accommodate both discrete and continuous time models. This is reflected in the new options for the keyword when.
    • A new keyword init is now available for run! to data collect from the model before evolving it. Whether data were collected at time 0 or not was not really obvious in the original version of run! due to the ambiguity of the previous handling of when.
  • A new @multiagent macro allows to run multi-agent simulations much more efficiently. It has two version: In :opt_speed the created agents are optimized such as there is virtually no performance difference between having 1 agent type at the cost of each agent occupying more memory that in the Union case. In :opt_memory each agent is optimized to occupy practically the same memory as the Union case, however this comes at a cost of performance versus having 1 type.
  • A new experimental model type EventQueueABM has been implemented. It operates in continuous time through the scheduling of events at arbitrary time points, in contrast with the discrete time nature of a StandardABM.
  • Both the visualization and the model abstract interface have been refactored to improve the user experience to conform to the Agents.jl API when creating a new model type and its visualizations.
  • Grid and continuous spaces support boundaries with mixed periodicity, specified by tuples with a Bool value for each dimension, e.g. GridSpace((5,5); periodic=(true,false)) is periodic along the first dimension but not along the second.
  • Arrow backend in offline_run! is now supported also for Windows users.
  • The model time is now tracked automatically, accessible through abmtime(model). This is part of the new AgentBasedModel API.
  • Two new functions random_id_in_position and random_agent_in_position can be used to select a random id/agent in a position in discrete spaces (even with filtering).
  • A new function swap_agents can be used to swap the positions of a pair of agents, which works even in spaces which allow 1 agent per position.
  • New function hasid to check if a model has an agent with a given id.

Performance Improvements

  • A new argument alloc can be used to select a more performant version in relation to the expensiveness of the filtering for all random methods selecting ids/agents/positions.
  • The random_agent function is now much faster than before. The functions random_nearby_position, random_nearby_id and random_nearby_agent are much faster thanks to a faster sampling function.
  • The nearby_agents function for ContinuousSpace and GridSpace is now 1.5x faster than before.
  • The sample! function is much faster than before.

Deprecations

  • The way the evolution rule (agent_step!, model_step!) is handled has changed. Now, the stepping functions must be given to the agent based model during construction of the model instead of given to step!, run!, abmplot, .... This change is important and allows us to:
    • Have Agents.jl have the same mental model as DifferentialEquations.jl, DynamicalSystems.jl, and other dynamical modelling packages, where the evolution rules are part of the central simulation struct.
    • Allows us to develop new types of models that may have rules that are defined differently, without being based on e.g., two particular functions.
    • Allows us to develop (in the future) a new model type that is optimized for multi-agent simulations.
    • Allows other developers of agent based modelling packages to integrate with Agents.jl by extending the established API.
  • The UnremovableABM type is deprecated, instead container = Vector should be passed when creating the model.
  • Passing the step argument to collect_model_data!, collect_agent_data! is deprecated since the time of the model is used automatically.
  • add_agent_pos! has been deprecated in favor of the more descriptive add_agent_own_pos!.
  • schedule(model, scheduler) is deprecated. Use scheduler(model) together with hasid(model).
  • Deprecations that were in place in v5 (see section # v5 of this CHANGELOG) have been removed.
  • Keyword spf is deprecated in favor of dt in abmvideo.

v5.17

  • New function replicate! allows to create a new instance of a given agent at the same position with the possibility to specify some fields with new values.
  • The sample! function is 3x faster than before.

v5.16

  • New function offline_run! allows writing data to file at predefined intervals during run! instead of storing it in memory. Currently supports CSV and Arrow files.

v5.15

  • Agents.jl moved to Julia 1.9+, and now exports visualization and interactive applications automatically once Makie (or Makie backends such as GLMakie) come into scope, using the new package extension system. The only downside of this is that now to visualize ABMs on open street maps, the package OSMMakie.jl must be explicitly loaded as well.
  • Nearby look-ups with nearby_positions, nearby_ids and derivatives are now incrementally faster than before more the radius increases.
  • The randomwalk! function is now supported for any number of dimensions in ContinuousSpace when used to create isotropic/uniform random walks. For all type of AbstractGridSpace, the randomwalk! function supports a new keyword force_motion, which is false by default. See the docs to be informed on the effect of setting this keyword. Besides, in the continuous space default case random walks are up to 2 times faster than before.
  • Adding agents through properties... is an order of magnitude faster, now matching the performance of the other versions.
  • The ByProperty scheduler can now accept any type of (ordered) properties, while before it was restricted to only floats. The ByID scheduler of an UnremovableABM is now as fast as the Fastest scheduler since in this case they are actually equivalent.

v5.14

  • New optional filtering functionality to restrict the sampling added to random_nearby_position, random_nearby_id and random_nearby_agent.

v5.13

  • random_nearby_position, random_nearby_id and random_nearby_agent are up to 2 times faster thanks to a faster sampling function.

v5.12

  • The random_nearby_position function is now much faster for GridSpaces.

v5.11

  • Iterating in neighborhood searches (with nearby_ids and derivative functions) in GridSpace and ContinuousSpace is now about 2 times faster than before.

v5.10

  • FixedMassABM is now deprecated and will be removed in future versions. Turns out, there is no performance benefit of using it over UnremovableABM. In fact, there is a performance deficit in doing so.

v5.9

  • sample! is now much faster than before when the size of the sample is big, with a size of 1 million agents the function is now 1000x faster.
  • A memory bug about offsets calculation has been solved; besides, the calculate_offsets function has been sped-up by a significant amount.
  • The following renames have been done (with deprecations):
    • genocide! -> remove_all!
    • kill_agent! -> remove_agent!
    • UnkillableABM -> UnremovableABM
  • New function random_nearby_position that returns a random neighbouring position.
  • New function empty_nearby_positions that returns an iterable of all empty neighboring positions.

v5.8

  • random_agent is now faster and has two options on how to find a random agent, each of which can offer a different performance benefit depending on the density of agents that satisfy the clause.
  • New function randomwalk! replaces walk!(agent, rand, model) (now deprecated), allowing easier creation of random walks in both discrete and continuous spaces. Random walks in continuous space also allow users to specify the reorientation distributions: polar in 2D; polar and azimuthal in 3D. This way, correlated random walks can be produced.
  • Thanks to the use of a new algorithm, the nearby_positions function for graphspaces is now much faster.
  • Huge improvement of performance of the get_direction function in the periodic case.
  • normalize_position is now 50x faster for the case of a non-periodic grid.

v5.7

  • Internals of AgentBasedModel got reworked. It is now an abstract type, defining an abstract interface that concrete implementations may satisfy. This paves the way for flexibly defining new variants of AgentBasedModel that are more specialized in their applications.
  • The old AgentBasedModel is now StandardABM.
  • Two new variants of agent based models: UnkillableABM and FixedMassABM: they yield huge performance benefits (up to twice the speed!!!) on iterating over agents if the agents can't get killed, or even added, during model evolution!
  • Huge memory performance increase in continuous space by fixing a memory leak bug.
  • multi_agents_type! has been updated to handle edge case where agents of one (or more) type are absent at the beginning of the simulation.
  • New function npositions that returns the number of positions of a model with a discrete space.

v5.6

  • add_node! and rem_node! have been renamed to add_vertex! and rem_vertex! extending Graphs.jl homonymous methods to help standardise names across ecosystems. Therefore add_node! and rem_node! have been deprecated.
  • The signature of add_edge! has been generalised with args... and kwargs... to be compatible with all the implementations the underlying graph supports.
  • New function rem_edge! that removes an edge from the graph.

v5.5

  • The @agent macro has been re-written and is now more general and more safe. It now also allows inheriting fields from any other type.
  • The @agent macro is now THE way to create agent types for Agents.jl simulations. Directly creating structs by hand is no longer mentioned in the documentation at all. This will allow us in the future to utilize additional fields that the user does not have to know about, which may bring new features or performance gains by being part of the agent structures.
    • EDIT: This has been retracted in future versions. @agent is the recommended way, but manual creation is also valid.
  • The minimal agent types like GraphAgent can be used normally as standard agent types that only have the mandatory fields. This is now clear in the docs. (this was possible also before v5.4, just not clear)
  • In the future, making agent types manually (without @agent) may be completely disallowed, resulting in error. Therefore, making agent types manually is considered deprecated.
  • New function normalize_position that normalizes a position according to the model space.
  • New function spacesize that returns the size of the space.

v5.4

This is a huge release!

Performance improvements

  • Internal representation of grid spaces has been completely overhauled. For GridSpace this lead to about 30% performance increase in nearby_stuff and 100% decrease in memory allocations.
  • Significant performance increase for nearest_neighbor in ContinuousSpace.
  • Because of the new grid spaces internals, nearby_stuff searches in ContinuousSpace are 2-5 times faster.
  • Much more efficient distributed computing in ensemblerun! and paramscan functions, like 5x performance gain. Thanks to user Matt Turner mt-digital. #624

New space

  • New space GridSpaceSingle that is the same as GridSpace but only allows for one agent per position only. It utilizes this knowledge for massive performance benefits over GridSpace, being about 3x faster than the new GridSpace, all across the board. ID = 0 is a reserved ID for this space and cannot be used by users.

Additions to existing API

  • New keyword showprogress in run! function that displays a progress bar.
  • New keyword showprogress in ensemblerun! and paramscan that displays a progress bar over total amount of simulations done.
  • New function OSM.route_length.
  • New :manhattan metric for GridSpace models.
  • New manhattan_distance utility function.
  • New keyword nearby_f = nearby_ids_exact in interacting_pairs which decides whether to use the exact or approximate algorithm for nearest neighbors.

Breaking or Deprecated

  • [Will be breaking] In the near future, agent ID = 0 will be a reserved ID by Agents.jl. This means that users should not use ID = 0 for any agent. They can use all the negative and positive integers as usual. If you were adding agents with any of the default ways that Agents.jl provides, such as add_agents!(pos, model, agent_properties...), then you were already using only the positive integers.
  • [Maybe breaking?] In ContinuousSpace spacing was documented to be a keyword but in code it was specified as a positional argument. Now it is also a keyword in code as intended.
  • [Maybe breaking?] Keyword spacing in ContinuousSpace is now minimum(extent)/20 from /10 by default, increasing accuracy of nearby_ids (which is the fastest way to iterate over neighbors). This decreases a bit the performance of move_agent!, but in the typical scenario a neighbor search is much more costly than moving an agent.
  • [Maybe breaking?] There was an ambiguity in the function move_agent!(agent, model). It typically means to move an agent to a random position. However, in ContinuousSpace this function was overwritten by the signature move_agent(agent, model, dt::Real = 1). To resolve the ambiguity, now move_agent!(agent, model) always moves the agent to a random position even in ContinuousSpace. To use the continuous space version that moves an agent using its velocity, users must explicitly provide the third argument dt.
  • [Will be breaking] Keyword exact in nearby_ids for ContinuousSpace is deprecated, because now the exact version returns different type than the non-exact, hence leading to type instabilities. Use nearby_ids_exact instead. Same for nearby_agents.

v5.3

  • Rework schedulers to prefer returning iterators over arrays, resulting in fewer allocations and improved performance. Most scheduler names are now types instead of functions:
    • Schedulers.by_id is now Schedulers.ByID
    • Schedulers.randomly is now Schedulers.Randomly
    • Schedulers.partially is now Schedulers.Partially
    • Schedulers.by_property is now Schedulers.ByProperty
    • Schedulers.by_type is now Schedulers.ByType

v5.2

  • Add random_nearby_id and random_nearby_agent for efficient random agent access
  • Stop condition for step! allows using Integers

v5

  • Agents.jl + InteractiveDynamics.jl now support native plotting for open street map spaces, which is integrated in all interactive apps as well!
  • Most examples have been moved to AgentsExampleZoo.jl. Additional examples will now be added there.

BREAKING

  • Plotting, animating, and interacting GUIs based on InteractiveDynamics.jl have changed. Please see online docs for the new format.
  • LightGraphs.jl dependency is now replaced by Graphs.jl
  • OpenStreetMapX.jl dependency now replaced by LightOSM.jl. This mean initializing the space is different, and some API methods have changed. Check documentation for more details. Note that this also means checkpoints using the old OpenStreetMapSpace cannot be read in this version.
  • Functions for planning and moving along routes have had their names unified across Pathfinding and OpenStreetMap modules. The names now are plan_route! and move_along_route! and are accessible from the top level scope.
  • OSM.intersection is renamed to OSM.nearest_node
  • OSM.road is renamed to OSM.nearest_road
  • latlon is removed in favor of OSM.lonlat

v4.5.4

  • Previously nearby_ids with r=0 for GraphSpace was undefined. Now it returns ids only in the same position as given.

v4.5.3

  • Performance enhancements for random_empty.

v4.5

New features and fixes

  • Add get_spatial_property and get_spatial_index for easier usage of spatially distributed properties in ContinuousSpace.
  • Rework the pathfinding system to be more streamlined and offer greater control over the its details.
  • Add support for pathfinding in ContinuousSpace.
  • New utility functions nearby_walkable and random_walkable for use in models with pathfinding.
  • Fixed bug where there was no differentiation between empty paths and paths to unreachable nodes.

BREAKING

  • The old pathfinding system is now deprecated. Pathfinding structs are not saved as part of the space, and instead are stored by the user.

v4.4

New features and fixes

  • Provide a generator function to collect mdata in run! and ensemblerun!.
  • Save/load entire models using save_checkpoint and load_checkpoint
  • New functions get_spatial_property and get_spatial_index that allows better handling of spatial fields present in ContinuousSpace that are represented via the forms of discretization over the space.

v4.3

New features and fixes

  • Save and load agent information from CSV files.

v4.2

New features and fixes

  • Self-contained features of Agents.jl will from now own exist in their own submodules. This will make the public API less cluttered and functionality more contained. Currently the new submodules are Schedulers, Pathfinding, OSM.
  • Pathfinding using the A* algorithm is now possible! Available for GridSpace.
  • Extend dataname (formerly aggname) to provide unique column names in collection dataframes when using anonymous functions
  • Fixed omission which did not enable updating properties of a model when model.properties is a struct.
  • New function ensemblerun! for running ensemble model simulations.
  • Scheduler Schedulers.by_property (previously property_activation) now allows as input arbitrary functions besides symbols.

Deprecated

  • Deprecate aggname in favor of dataname for naming of columns in collection dataframes
  • Keyword replicates of run! is deprecated in favor of ensemblerun!.
  • paramscan with replicates is deprecated. If you want to parameter scan and at the same time run multiple simulations at each parameter combination, simply use seed as a parameter, which tunes the model's initial random seed.
  • All the scheduler names have been deprecated in favor of a Schedulers module: fastest to Schedulers.fastest, by_id to Schedulers.by_id, random_activation to Schedulers.randomly, partial_activation to Schedulers.partially, property_activation to Schedulers.by_property, by_type to Schedulers.by_type.

v4.1.2

  • Plotting with Plots.jl and plotabm is deprecated in favor of InteractiveDynamics.jl, Makie.jl and abm_plot.

v4.1

  • A new example: Fractal Growth, explores ContinuousSpace and interactive plotting.
  • Models now supply a random number generator pool that is used in all random-related functions like random_position. Access it with model.rng and seed it with seed!(model, seed).
  • Higher-order agent grouping utilities to facilitate complex interactions, see e.g. iter_agent_groups.
  • Several documentation improvements targeting newcomers.

v4.0, Major new release!

This new release brings not only a lot of new features but also a lot of performance improvements and quality of life improvements. Worth seeing is also the new Comparison section of our docs, which compares Agents.jl with other existing software, showing that Agents.jl outmatches all current standards.

New features:

  • GridSpace has been re-written from scratch! It now supports any dimensionality and is about a full order of magnitude faster than the previous version!
  • ContinuousSpace has been re-written from scratch! It is now at least 3 times faster!
  • A new, continuous OpenStreetMapSpace which lets agents traverse real world locations via planned routes based on the Open Street Map initiative.
  • GraphSpace now allows to dynamically mutate the underlying graph via add_node!, rem_node!.
  • Agents.jl now defines a clear API for new spaces types. To create a fundamentally different type of space you have to define the space structure and extend only 5 methods.
  • GraphSpace and GridSpace are completely separated entities, reducing complexity of source code dramatically, and removing unnecessary functions like vertex2coord and coord2vertex.
  • Many things have been renamed to have clearer name that indicates their meaning (see Breaking changes).
  • Performance increase of finding neighbors in GraphSpace with r > 1.
  • New wrapping function nearby_agents that returns an iterable of neighboring agents.
  • Positions and neighbors on GridSpace can now be searched in each direction separately by accepting r as a tuple.
  • Neighbors on non-periodic chebyshev spaces can also be searched per dimension over a specific range.
  • New public schedule function for writing custom loops.
  • Mixed models are supported in data collection methods.
  • random_agent(model, condition) allows obtaining random agents that satisfy given condition.
  • New walk! utility function for GridSpace and ContinuousSpaces, providing turtle-like agent movement and random walks.
  • The Battle Royal example explores using categorical neighbor searching in a high dimensional GridSpace.
  • An @agent macro provides a quick way of creating agent structs for any space.

Breaking changes

Most changes in this section (besides changes to default values) are deprecated and therefore are not "truly breaking".

  • New ContinuousSpace now only supports Euclidean metric.
  • Keyword moore of GridSpace doesn't exist anymore. Use metric instead.
  • Default arguments for GridSpace are now periodic = true, metric = :chebyshev.
  • Internal structure of the fundamental types like ABM, GraphSpace, etc. is now explicitly not part of the public API, and the provided functions like getindex and getproperty have to be used. This will allow performance updates in the future that may change internals but not lead to breaking changes.
  • vertex2coord, coord2vertex do not exist anymore because they are unnecessary in the new design.
  • API simplification and renaming:
    • space_neighbors -> nearby_ids
    • node_neighbors -> nearby_positions
    • get_node_contents -> ids_in_position
    • get_node_agents -> agents_in_position
    • pick_empty -> random_empty
    • find_empty_nodes -> empty_positions
    • has_empty_nodes -> has_empty_positions
    • nodes -> positions

Non-breaking changes

  • GridSpace agents now use Dims rather than Tuple{N,Int} for their position in all examples and pre-defined models.

v3.7

  • Add the ability to decide whether the agent step or the model step should be performed first using the agents_first argument.

v3.6

  • Add ability to customise run! such that mutation on containers and nested structures does not affect data collection.

v3.5

  • Aggregation data for agents is now possible to do conditionally.
  • Example on how to integrate Agents.jl with BlackBoxOptim.jl.

v3.4

  • Added interactivity examples for Schelling and Daisyworld.
  • Example on how to integrate Agents.jl with DifferentialEquations.jl.
  • Dropped support for Julia 1.0, will be targeting LTS for v1.6 in the future.

v3.3

  • New fill_space! function for discrete spaces.
  • The Daisyworld example now uses multi-agent approach (surface is agent).
  • New allids function.

v3.2

  • New Models submodule, that conveniently allows loading a model from the examples.

v3.1

  • Extend interacting_pairs to allow interactions of disparate types when using mixed models.

v3.0

Additions

  • Added ContinuousSpace as a space option. Supports Euclidean and Cityblock metrics. Several new API functions were added for continuous space.
  • Universal plotting function plotabm that works for models with any kind of space.
  • new function space_neighbors, which works for any space. It always and consistently returns the IDs of neighbors irrespectively of the spatial structure.
  • AgentBasedModel now allows you to pass in an AbstractAgent type, or an instance of your agent.
  • New convenience function allagents.
  • New continuous space functions nearest_neighbor and elastic_collision!.
  • New iterator interacting_pairs.
  • Agents can be accessed from the model directly. model[id] is equivalent with model.agents[id] and replaces id2agent.
  • If model.properties is a dictionary with key type Symbol, then the convenience syntax model.prop returns model.properties[:prop].
  • Version of add_agent! now has keyword propagation as well (in case you make your types with @kwdef or Parameters.jl).
  • New function nextid
  • Cool new logo.
  • node_neighbors now accepts a neighbor_type keyword for working with directed graphs.
  • Added examples of flocking birds and bacterial growth in ContinuousSpace, daisyworld and predator-prey in GridSpace.
  • Collection of model and agent data simultaneously is now possible using the mdata and adata keywords (respectively) used in conjunction with the revamped data collection scheme (see below).
  • Better support for mixed-ABMs and a new by_type scheduler.

Breaking Changes

  • Deprecated Space in favor of the individual spaces: Nothing, GridSpace, GraphSpace, ContinuousSpace.
  • Reworked the public API of GridSpace to be simpler: position must be NTuple{Int}. As a result vertex2coord and stuff no longer exported, since they are obsolete.
  • Data collection has been completely overhauled. The main function to evolve an ABM and collect data is now run!. This function serves most situations, however multiple low level functions are exposed via the API for power users. See the Data Collection section in the documentation for full details.
  • AgentBasedModel checks the construction of your agent and will return errors when it is malformed (no id or pos when required, incorrect types). Warnings when possible problems may occur (immutable agents, types which are not concrete, vel not of the correct type when using ContinuousSpace).
  • id2agent is deprecated in favor of getindex(model, id) == model[id].
  • Function plot2D doesn't exist any more in favor of plotabm.

v2.1

  • Renamed the old scheduler as_added to by_id, to reflect reality.
  • Added a scheduler public API.
  • Added two new schedulers: partial_activation, property_activation.
  • It is now possible to step! until a boolean condition is met.

v2.0

Changelog is kept with respect to version 2.0.