topsim.core.instrument.Instrument

class topsim.core.instrument.Instrument(env, config, planner, scheduler)[source]

The Simulation class is a wrapper for all Actors; we start the simulation through the simulation class, which in turn invokes the initial Actors and monitoring, and provides the conditions for checking if the simulation has finished.

Parameters:

  • env (simpy.Environment object) – The discrete-event simulation environment. This is the way TOpSim simulation maintains state across the different actors, and interfaces with the simpy processes.

  • config (Config instance) – Config object wrapper for the configuration file.

  • planner (Planner instance) – The Planner Actor for the current simulation

  • scheduler (Scheduler instance) – The Scheduler Actor for the current simulation

Notes

This class is a Python ABC, meaning it requires user addition to implement the metaclasses.

Recommended decisions to make in the run() method include:

Check observation and instrument are ready given current instrument demand:

>>> # Assuming self.capacity is a user-defined attribute
>>> if(observation.is_ready(self.env.now, self.capacity))

Communicate with Scheduler to determine if the Buffer and Cluster have capacity to run a new Observation (Ingest and Storage conditions):

>>> self.scheduler.check_ingest_capacity(
>>>    observation, pipelines, max_ingest
>>>)

If above conditions are reached, begin observations and request ingest allocation via scheduler:

>>> self.begin_observation(observation)
>>> self.env.process(self.scheduler.allocate_ingest(
>>> observation, pipelines, planner))

Note: allocate_ingest() generates a timeout on the SimPy discrete-event queue, which is why we call env.process.

Finalise an Observation and initiate the ‘clean-up’.

>>> observation.status = self.finish_observation(observation)

See also

Telescope

Methods

__init__(env, config, planner, scheduler)

run()

The starting point for the Instrument actor; this will make decisions per timestep and then once these decisions have been resolved, yield a timeout to indicate a single timestep has passed for the Telescope.

to_df()

Produce a pandas.DataFrame of output for the Simulation Monitor.