Built-in Species
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It is possible to use in the models a set of built-in agents. These agents allow to directly use some advance features like clustering, multi-criteria analysis, etc. The creation of these agents are similar as for other kinds of agents:
create species: my_built_in_agent returns: the_agent;
So, for instance, to be able to use clustering techniques in the model:
create cluster_builder returns: clusterer;
Table of Contents​
agent, AgentDB, base_edge, experiment, graph_edge, graph_node, physical_world,
agent​
Variables​
host(agent): Returns the agent that hosts the population of the receiver agentindex(int): Returns the unique index of this agent in its population. Read-only attributelocation(point): Returns the location of the agentname(string): Returns the name of the agent (not necessarily unique in its population)peers(list): Returns the population of agents of the same species, in the same host, minus the receiver agentshape(geometry): Returns the shape of the receiver agent
Actions​
_init_​
Returned type: unknown
_step_​
Returned type: unknown
AgentDB​
AgentDB is an abstract species that can be extended to provide agents with capabilities to access databases
Variables​
agents(list): Returns the list of agents for the population(s) of which the receiver agent is a direct or undirect hostmembers(container): Returns the list of agents for the population(s) of which the receiver agent is a direct host
Actions​
close​
Close the established database connection.
Returned type: unknown : Returns null if the connection was successfully closed, otherwise, it returns an error.
connect​
Establish a database connection.
Returned type: unknown : Returns null if connection to the server was successfully established, otherwise, it returns an error.
Additional facets:​
params(map): Connection parameters
executeUpdate​
- Make a connection to DBMS - Executes the SQL statement in this PreparedStatement object, which must be an SQL INSERT, UPDATE or DELETE statement; or an SQL statement that returns nothing, such as a DDL statement.
Returned type: int : Returns the number of updated rows.
Additional facets:​
updateComm(string): SQL commands such as Create, Update, Delete, Drop with question markvalues(list): List of values that are used to replace question mark
getParameter​
Returns the list used parameters to make a connection to DBMS (dbtype, url, port, database, user and passwd).
Returned type: unknown : Returns the list of used parameters to make a connection to DBMS.
insert​
- Make a connection to DBMS - Executes the insert statement.
Returned type: int : Returns the number of updated rows.
Additional facets:​
into(string): Table namecolumns(list): List of column name of tablevalues(list): List of values that are used to insert into table. Columns and values must have same size
isConnected​
To check if connection to the server was successfully established or not.
Returned type: bool : Returns true if connection to the server was successfully established, otherwise, it returns false.
select​
Make a connection to DBMS and execute the select statement.
Returned type: list : Returns the obtained result from executing the select statement.
Additional facets:​
select(string): select stringvalues(list): List of values that are used to replace question marks
setParameter​
Sets the parameters to use in order to make a connection to the DBMS (dbtype, url, port, database, user and passwd).
Returned type: unknown : null.
Additional facets:​
params(map): Connection parameters
testConnection​
To test a database connection .
Returned type: bool : Returns true if connection to the server was successfully established, otherwise, it returns false.
Additional facets:​
params(map): Connection parameters
timeStamp​
Get the current time of the system.
Returned type: float : Current time of the system in millisecondes
base_edge​
A built-in species for agents representing the edges of a graph, from which one can inherit
Variables​
source(agent): The source agent of this edgetarget(agent): The target agent of this edge
Actions​
experiment​
GUI experiments are experiments used to visualise and interact with simulations through the regular user interface of GAMA. They can declare, parameters, used to populate the Parameters view, outputs like displays or monitors
Variables​
maximum_cycle_duration(float): The maximum duration (in seconds) a simulation cycle should last. Default is 1. Units can be used to pass values greater than a second (for instance '10 #sec')minimum_cycle_duration(float): The minimum duration (in seconds) a simulation cycle should last. Default is 0. Units can be used to pass values smaller than a second (for instance '10 #msec')model_path(string): Contains the absolute path to the folder in which the current model is locatedparameters(map): A parameters set of this experiment agentproject_path(string): Contains the absolute path to the project in which the current model is locatedrng(string): The random number generator to use. Four different ones are at the disposal of the modeler: 'mersenne' represents the default generator, based on the Mersenne-Twister algorithm. Very reliable, fast and deterministic (that is, using the same seed and the same sequence of calls, it will return the same stream of pseudo-random numbers). This algorithm is however not safe to use in simulations where agents can behave in parallel; 'threaded' is a very fast generator, based on the DotMix algorithm, that can be safely used in parallel simulations as it creates one instance per thread. However, determinism cannot be guaranteed and this algorithm does not accept a seed as each instance will compute its own;'parallel' is a version of the Mersenne-Twister algorithm that can be safely used in parallel simulations by preventing a concurrent access to its internal state. Determinism is guaranteed (in terms of generation, but not in terms of execution, as the sequence in which the threads will access it cannot be determined) and it performs a bit slower than its base version.'java' invokes the standard generator provided by the JDK, deterministic and thread-safe, albeit slower than all the other onesrng_usage(int): Returns the number of times the random number generator of the experiment has been drawnseed(float): The seed of the random number generator. Each time it is set, the random number generator is reinitialized. WARNING: Setting it to zero actually means that you let GAMA choose a random seedsimulation(agent): Contains a reference to the current simulation being run by this experimentsimulations(list): Contains the list of currently running simulationsworkspace_path(string): Contains the absolute path to the workspace of GAMA
Actions​
compact_memory​
Forces a 'garbage collect' of the unused objects in GAMA
Returned type: unknown
update_outputs​
Forces all outputs to refresh, optionally recomputing their values
Returned type: unknown
Additional facets:​
recompute(boolean): Whether or not to force the outputs to make a computation step
graph_edge​
A species that represents an edge of a graph made of agents. The source and the target of the edge should be agents
Variables​
source(agent): The source agent of this edgetarget(agent): The target agent of this edge
Actions​
graph_node​
A base species to use as a parent for species representing agents that are nodes of a graph
Variables​
my_graph(graph): A reference to the graph containing the agent
Actions​
related_to​
This operator should never be called
Returned type: bool
Additional facets:​
other(agent): The other agent
physical_world​
The base species for models that act as a 3D physical world. Can register and manage agents provided with either the 'static_body' or 'dynamic_body' skill. Inherits from 'static_body', so it can also act as a physical body itself (with a 'mass', 'friction', 'gravity'), of course without motion -- in this case, it needs to register itself as a physical agent using the 'register' action
Variables​
accurate_collision_detection(boolean): Enables or not a better (but slower) collision detectionautomated_registration(boolean): If set to true (the default), makes the world automatically register and unregister agents provided with either the 'static_body' or 'dynamic_body' skill. Otherwise, they must be registered using the 'register' action, which can be useful when only some agents need to be considered as 'physical agents'. Note that, in any case, the world needs to manually register itself if it is supposed to act as a physical body.gravity(point): Defines the value of gravity in this world. The default value is set to -9.80665 on the z-axis, that is 9.80665 m/s2 towards the 'bottom' of the world. Can be set to any direction and intensity and applies to all the bodies present in the physical worldlibrary(string): This attribute allows to manually switch between two physics library, named 'bullet' and 'box2D'. The Bullet library, which comes in two flavors (see 'use_native') and the Box2D libray in its Java version (https://github.com/jbox2d/jbox2d). Bullet is the default library but models in 2D should better use Box2Dmax_substeps(int): If equal to 0 (the default), makes the simulation engine be stepped alongside the simulation (no substeps allowed). Otherwise, sets the maximum number of physical simulation substeps that may occur within one GAMA simulation stepterrain(field): This attribute is a matrix of float that can be used to represent a 3D terrain. The shape of the world, in that case, should be a box, where thedimension on the z-axis is used to scale the z-values of the DEM. The world needs to be register itself as a physical objectuse_native(boolean): This attribute allows to manually switch between the Java version of the Bullet library (JBullet, a modified version of https://github.com/stephengold/jbullet, which corresponds to version 2.72 of the original library) and the native Bullet library (Libbulletjme, https://github.com/stephengold/Libbulletjme, which is kept up-to-date with the 3.x branch of the original library).The native version is the default one unless the libraries cannot be loaded, making JBullet the default
Actions​
register​
An action that allows to register agents in this physical world. Unregistered agents will not be governed by the physical laws of this world. If the world is to play a role in the physical world,then it needs to register itself (i.e. do register([self]);
Returned type: unknown
Additional facets:​
bodies(container): the list or container of agents to register in this physical world