mosaik.scenario — Classes related to the scenario creation

This module provides the interface for users to create simulation scenarios for mosaik.

The World holds all necessary data for the simulation and allows the user to start simulators. It provides a ModelFactory (and a ModelMock) via which the user can instantiate model instances (entities). The method World.run() finally starts the simulation.

class mosaik.scenario.World(sim_config: SimConfig, mosaik_config=None, time_resolution: float = 1.0, debug: bool = False, cache: bool = True, max_loop_iterations: int = 100, asyncio_loop: asyncio.AbstractEventLoop | None = None)

The world holds all data required to specify and run the scenario.

It provides a method to start a simulator process (start()) and manages the simulator instances.

You have to provide a sim_config which tells the world which simulators are available and how to start them. See mosaik.simmanager.start() for more details.

mosaik_config can be a dict or list of key-value pairs to set addional parameters overriding the defaults:

{
    'addr': ('127.0.0.1', 5555),
    'start_timeout': 2,  # seconds
    'stop_timeout': 2,   # seconds
}

Here, addr is the network address that mosaik will bind its socket to. start_timeout and stop_timeout specifiy a timeout (in seconds) for starting/stopping external simulator processes.

If execution_graph is set to True, an execution graph will be created during the simulation. This may be useful for debugging and testing. Note, that this increases the memory consumption and simulation time.

until: int

The time until which this simulation will run.

rt_factor: float | None

The number of real-time seconds corresponding to one mosaik step.

sim_config

The config dictionary that tells mosaik how to start a simulator.

config

The config dictionary for general mosaik settings.

time_resolution

An optional global time_resolution (in seconds) for the scenario, which tells the simulators what the integer time step means in seconds. Its default value is 1., meaning one integer step corresponds to one second simulated time.

sims: Dict[SimId, simmanager.SimRunner]

A dictionary of already started simulators instances.

df_graph

The directed data-flow graph for this scenario.

trigger_graph

The directed graph from all triggering connections for this scenario.

entity_graph

The graph of related entities. Nodes are (sid, eid) tuples. Each note has an attribute entity with an Entity.

sim_progress: float

Progress of the current simulation (in percent).

start(sim_name: str, **sim_params) → ModelFactory

Start the simulator named sim_name and return a ModelFactory for it.

connect(src: Entity, dest: Entity, *attr_pairs: str | Tuple[str, str], async_requests: bool = False, time_shifted: bool = False, initial_data: Dict[Attr, Any] = {}, weak: bool = False)

Connect the src entity to dest entity.

Establish a data-flow for each (src_attr, dest_attr) tuple in attr_pairs. If src_attr and dest_attr have the same name, you you can optionally only pass one of them as a single string.

Raise a ScenarioError if both entities share the same simulator instance, if at least one (src. or dest.) attribute in attr_pairs does not exist, or if the connection would introduce a cycle in the data-flow (e.g., A → B → C → A).

If the dest simulator may make asynchronous requests to mosaik to query data from src (or set data to it), async_requests should be set to True so that the src simulator stays in sync with dest.

An alternative to asynchronous requests are time-shifted connections. Their data flow is always resolved after normal connections so that cycles in the data-flow can be realized without introducing deadlocks. For such a connection time_shifted should be set to True and initial_data should contain a dict with input data for the first simulation step of the receiving simulator.

An alternative to using async_requests to realize cyclic data-flow is given by the time_shifted kwarg. If set to True it marks the connection as cycle-closing (e.g. C → A). It must always be used with initial_data specifying a dict with the data sent to the destination simulator at the first step (e.g. {‘src_attr’: value}).

set_initial_event(sid: SimId, time: int = 0)

Set an initial step for simulator sid at time time (default=0).

get_data(entity_set: Iterable[Entity], *attributes: Attr) → Dict[Entity, Dict[Attr, Any]]

Get and return the values of all attributes for each entity of an entity_set.

The return value is a dict mapping the entities of entity_set to dicts containing the values of each attribute in attributes:

{
    Entity(...): {
        'attr_1': 'val_1',
        'attr_2': 'val_2',
        ...
    },
    ...
}

run(until: int, rt_factor: float | None = None, rt_strict: bool = False, print_progress: bool | Literal['individual'] = True, lazy_stepping: bool = True)

Start the simulation until the simulation time until is reached.

In order to perform real-time simulations, you can set rt_factor to a number > 0. A rt-factor of 1. means that 1 second in simulated time takes 1 second in real-time. An rt-factor 0f 0.5 will let the simulation run twice as fast as real-time. For correct behavior of the rt_factor the time_resolution of the scenario has to be set adequately, which is 1. [second] by default.

If the simulators are too slow for the rt-factor you chose, mosaik prints by default only a warning. In order to raise a RuntimeError, you can set rt_strict to True.

print_progress controls whether progress bars are printed while the simulation is running. The default is to print one bar representing the global progress of the simulation. You can also set print_progress='individual' to get one bar per simulator in your simulation (in addition to the global one). ``print_progress=False` turns off the progress bars completely. The progress bars use tqdm; see their documentation on how to write to the console without interfering with the bars.

You can also set the lazy_stepping flag (default: True). If True a simulator can only run ahead one step of it’s successors. If False a simulator always steps as long all input is provided. This might decrease the simulation time but increase the memory consumption.

Before this method returns, it stops all simulators and closes mosaik’s server socket. So this method should only be called once.

detect_unresolved_cycles()

Searches for unresolved cycles, i.e. cycles that do not have a weak or time_shifted connection. Raises an error if an unresolved cycle is found.

cache_trigger_cycles()

For all simulators, if they are part of a cycle, add information about trggers in the cycle(s) to the simulator object

cache_dependencies()

Loops through all simulations and adds predecessors and successors to the simulations.

cache_related_sims()

Stores the related simulators for a simulator in the simulator object. The related simulators are all simulators in the scenario that are not the simulator itself.

cache_triggering_ancestors()

Collects the ancestors of each simulator and stores them in the respective simulator object.

create_simulator_ranking()

Deduce a simulator ranking from a topological sort of the df_graph.

shutdown()

Shut-down all simulators and close the server socket.

class mosaik.scenario.ModelFactory(world: World, sim: SimRunner)

This is a facade for a simulator sim that allows the user to create new model instances (entities) within that simulator.

For every model that a simulator publicly exposes, the ModelFactory provides a ModelMock attribute that actually creates the entities.

If you access an attribute that is not a model or if the model is not marked as public, an ScenarioError is raised.

class mosaik.scenario.ModelMock(world: World, name: str, sim: SimRunner)

Instances of this class are exposed as attributes of ModelFactory and allow the instantiation of simulator models.

You can call an instance of this class to create exactly one entity: sim.ModelName(x=23). Alternatively, you can use the create() method to create multiple entities with the same set of parameters at once: sim.ModelName.create(3, x=23).

create(num: int, **model_params)

Create num entities with the specified model_params and return a list with the entity dicts.

The returned list of entities is the same as returned by mosaik_api_v3.Simulator.create(), but the simulator is prepended to every entity ID to make them globally unique.

class mosaik.scenario.Entity(sid, eid, sim_name, type, children, sim)

An entity represents an instance of a simulation model within mosaik.

sid: SimId

The ID of the simulator this entity belongs to.

eid: EntityId

The entity’s ID.

sim_name: str

The entity’s simulator name.

type: str

The entity’s type (or class).

children: Iterable[Entity]

An entity set containing subordinate entities.

sim: simmanager.SimRunner

The SimProxy containing the entity.

property full_id: FullId

Full, globally unique entity id sid.eid.