nrel.hive.state.simulation_state.update.step_simulation_ops

Module Contents

Classes

UserProvidedUpdateAccumulator

Typed version of namedtuple.

Functions

_instruction_to_report(...)

log_instructions(instructions, env, sim_time)

step_vehicle(...)

perform_driver_state_updates(...)

helper function for StepSimulation which runs the update function for all driver states

perform_vehicle_state_updates(...)

helper function for StepSimulation which applies a vehicle state update to each vehicle

apply_instructions(...)

this helper function takes a map with one instruction per agent at most, and attempts to apply each

instruction_generator_update_fn(...)

applies a user-provided function designed to inject an external update to InstructionGenerators

Attributes

log

InstructionApplicationResult

nrel.hive.state.simulation_state.update.step_simulation_ops.log[source]
nrel.hive.state.simulation_state.update.step_simulation_ops._instruction_to_report(i: nrel.hive.dispatcher.instruction.instruction.Instruction, sim_time: nrel.hive.model.sim_time.SimTime) → nrel.hive.reporting.reporter.Report[source]
nrel.hive.state.simulation_state.update.step_simulation_ops.log_instructions(instructions: Tuple[nrel.hive.dispatcher.instruction.instruction.Instruction, Ellipsis], env: nrel.hive.runner.environment.Environment, sim_time: nrel.hive.model.sim_time.SimTime)[source]
nrel.hive.state.simulation_state.update.step_simulation_ops.step_vehicle(s: nrel.hive.state.simulation_state.simulation_state.SimulationState, env: nrel.hive.runner.environment.Environment, vehicle: nrel.hive.model.vehicle.vehicle.Vehicle) → nrel.hive.state.simulation_state.simulation_state.SimulationState[source]
nrel.hive.state.simulation_state.update.step_simulation_ops.perform_driver_state_updates(simulation_state: nrel.hive.state.simulation_state.simulation_state.SimulationState, env: nrel.hive.runner.environment.Environment) → nrel.hive.state.simulation_state.simulation_state.SimulationState[source]

helper function for StepSimulation which runs the update function for all driver states

Parameters:

  • simulation_state – the simulation state to update

  • env – the simulation environment

Returns:

the sim after all vehicle update functions have been called

nrel.hive.state.simulation_state.update.step_simulation_ops.perform_vehicle_state_updates(simulation_state: nrel.hive.state.simulation_state.simulation_state.SimulationState, env: nrel.hive.runner.environment.Environment) → nrel.hive.state.simulation_state.simulation_state.SimulationState[source]

helper function for StepSimulation which applies a vehicle state update to each vehicle

Parameters:

  • simulation_state – the simulation state to update

  • env – the simulation environment

Returns:

the sim after all vehicle update functions have been called

nrel.hive.state.simulation_state.update.step_simulation_ops.InstructionApplicationResult
nrel.hive.state.simulation_state.update.step_simulation_ops.apply_instructions(sim: nrel.hive.state.simulation_state.simulation_state.SimulationState, env: nrel.hive.runner.environment.Environment, instructions: Tuple[nrel.hive.dispatcher.instruction.instruction.Instruction, Ellipsis]) → nrel.hive.state.simulation_state.simulation_state.SimulationState[source]

this helper function takes a map with one instruction per agent at most, and attempts to apply each instruction to the simulation, managing the instruction’s externalities, and managing failure.

Parameters:

  • sim – the current simulation state

  • env – the sim environment

  • instructions – all instructions to add at this time step

Returns:

the simulation state modified by all successful Instructions

class nrel.hive.state.simulation_state.update.step_simulation_ops.UserProvidedUpdateAccumulator[source]

Bases: NamedTuple

Typed version of namedtuple.

Usage in Python versions >= 3.6:

class Employee(NamedTuple):
    name: str
    id: int

This is equivalent to:

Employee = collections.namedtuple('Employee', ['name', 'id'])

The resulting class has an extra __annotations__ attribute, giving a dict that maps field names to types. (The field names are also in the _fields attribute, which is part of the namedtuple API.) Alternative equivalent keyword syntax is also accepted:

Employee = NamedTuple('Employee', name=str, id=int)

In Python versions <= 3.5 use:

Employee = NamedTuple('Employee', [('name', str), ('id', int)])
updated_fns: Tuple[nrel.hive.dispatcher.instruction_generator.instruction_generator.InstructionGenerator, Ellipsis] = ()
apply_updated_instruction_generator(i: nrel.hive.dispatcher.instruction_generator.instruction_generator.InstructionGenerator)[source]
nrel.hive.state.simulation_state.update.step_simulation_ops.instruction_generator_update_fn(fn: Callable[[nrel.hive.dispatcher.instruction_generator.instruction_generator.InstructionGenerator, nrel.hive.state.simulation_state.simulation_state.SimulationState], nrel.hive.dispatcher.instruction_generator.instruction_generator.InstructionGenerator | None], sim: nrel.hive.state.simulation_state.simulation_state.SimulationState) → Callable[[UserProvidedUpdateAccumulator, nrel.hive.dispatcher.instruction_generator.instruction_generator.InstructionGenerator], UserProvidedUpdateAccumulator][source]

applies a user-provided function designed to inject an external update to InstructionGenerators

Parameters:

  • fn – the function which applies an update or returns None for no update

  • sim – the simulation state, which will not be modified but available to the update function

Returns:

the updated list of InstructionGenerators