mosaik.scheduler
— Coordinate and execute simulators¶
This module is responsible for performing the simulation of a scenario.
- async mosaik.scheduler.run(world: World, until: int, rt_factor: Optional[float] = None, rt_strict: bool = False, lazy_stepping: bool = True)¶
Run the simulation for a
World
until the simulation time until has been reached.Return the final simulation time.
See
mosaik.scenario.World.run()
for a detailed description of the rt_factor and rt_strict arguments.
- async mosaik.scheduler.sim_process(world: World, sim: SimRunner, until: int, rt_factor: Optional[float], rt_strict: bool, lazy_stepping: bool)¶
Coroutine running the simulator sim.
- async mosaik.scheduler.wait_for_dependencies(world: World, sim: SimRunner, lazy_stepping: bool) → None¶
Wait until all simulators that can provide input for this simulator have run for this step.
Also notify any simulator that is already waiting to perform its next step.
world is a mosaik
World
.
- mosaik.scheduler.get_input_data(world: World, sim: SimRunner) → InputData¶
Return a dictionary with the input data for sim.
The dict will look like:
{ 'eid': { 'attrname': {'src_eid_0': val_0, ... 'src_eid_n': val_n}, ... }, ... }
For every entity, there is an entry in the dict and each entry is itself a dict with attributes and a list of values. This is, because we may have inputs from multiple simulators (e.g., different consumers that provide loads for a node in a power grid) and cannot know how to aggregate that data (sum, max, …?).
world is a mosaik
World
.
- async mosaik.scheduler.step(world: World, sim: SimRunner, inputs: InputData, max_advance: int) → int¶
Advance (step) a simulator sim with the given inputs. Return an event that is triggered when the step was performed.
inputs is a dictionary, that maps entity IDs to data dictionaries which map attribute names to lists of values (see
get_input_data()
).max_advance is the simulation time until the simulator can safely advance it’s internal time without causing any causality errors.