SequentialScheduler

class lsst.ts.scheduler.driver.SequentialScheduler(models, raw_telemetry, observing_blocks, parameters=None, log=None)

Bases: Driver

A simple scheduler driver that implements a sequential scheduler algorithm.

The driver reads from an input file of targets provided by the user and send one target at a time.

Parameters:

• models (dict`[`str, typing.Any]) – Models.

• raw_telemetry (dict`[`str, typing.Any]) – Raw telemetry

• observing_blocks (dict`[`str, observing.ObservingBlock]) – Observing blocks.

• parameters (typing.Any, optional) – Driver parameters, by default None

• log (logging.Logger | None, optional) – Logger, by default None

Methods Summary

assert_survey_observing_script(survey_name)	Assert that the input survey name has a dedicated observing script.
cold_start(observations)	Rebuilds the internal state of the scheduler from a list of observations.
configure_scheduler([config])	This method is responsible for running the scheduler configuration and returning the survey topology, which specifies the number, name and type of projects running by the scheduler.
convert_efd_observations_to_targets(...)	Convert EFD dataframe into list of driver targets.
get_state_as_file_object()	Get the current state of the scheduling algorithm as a file object.
get_stop_tracking_target()
get_survey_observing_block(survey_name)	Return the appropriate survey observing block.
get_survey_topology(config)	Get the survey topology.
load(config)	Load a new set of targets.
parse_observation_database(filename)	Parse an observation database into a list of observations.
register_observation(target)	Register observations.
register_observed_target(target)	Validates observed target and returns an observation.
reset_from_state(filename)	Load the state from a file.
save_state()	Save the current state of the scheduling algorithm to a file.
schema()	Get schema for the sequential scheduler algorithm configuration files.
select_next_target()	Picks a target and returns it as a target object.
select_next_targets()	Select a list of targets.
update_conditions()	Update driver internal conditions.

Methods Documentation

assert_survey_observing_script(survey_name)

Assert that the input survey name has a dedicated observing script.

Parameters:

survey_name (str) – Name of the survey.

Raises:

AssertError: – If survey_name is not in the list of valid survey observing scripts.

Return type:

None

cold_start(observations)

Rebuilds the internal state of the scheduler from a list of observations.

Parameters:

observations (list`[`Observation]) – List of observations.

Return type:

None

configure_scheduler(config=None)

This method is responsible for running the scheduler configuration and returning the survey topology, which specifies the number, name and type of projects running by the scheduler.

By default it will just return a test survey topology.

Parameters:

config (types.SimpleNamespace) – Configuration, as described by schema/Scheduler.yaml

Returns:

survey_topology

Return type:

lsst.ts.scheduler.kernel.SurveyTopology

convert_efd_observations_to_targets(efd_observations)

Convert EFD dataframe into list of driver targets.

Parameters:

efd_observations (pandas.DataFrame) – Data frame returned from a query to the EFD for observations.

Return type:

List[DriverTarget]

get_state_as_file_object()

Get the current state of the scheduling algorithm as a file object.

Returns:

file_object – File object with the current.

Return type:

io.BytesIO

get_stop_tracking_target()

Return type:

DriverTarget

get_survey_observing_block(survey_name)

Return the appropriate survey observing block.

Parameters:

survey_name (str) – Name of the survey.

Returns:

Survey observing block.

Return type:

observing.ObservingBlock

get_survey_topology(config)

Get the survey topology.

Parameters:

config (types.SimpleNamespace) – Configuration, as described by schema/Scheduler.yaml

Returns:

survey_topology – Survey topology.

Return type:

SurveyTopology

load(config)

Load a new set of targets.

Format of the input file must be the same as that for the configure method. The targets will be appended to the existing queue.

Raises:

RuntimeError: – If driver is not configured.

parse_observation_database(filename)

Parse an observation database into a list of observations.

Parameters:

filename (str)

Returns:

observations

Return type:

list of DriverTarget

register_observation(target)

Register observations.

This method should store the observation in a way that can be retrieved afterwards by the driver.

The default implementation is not implemented.

Parameters:

target (DriverTarget) – Observation to register.

Return type:

None

register_observed_target(target)

Validates observed target and returns an observation.

Parameters:

target (DriverTarget) – Observed target to register.

Returns:

Registered observation.

Return type:

Observation

reset_from_state(filename)

Load the state from a file.

Parameters:

filename (str) – Name of the file with the state.

save_state()

Save the current state of the scheduling algorithm to a file.

Returns:

filename – Name of the file with the state.

Return type:

str

schema()

Get schema for the sequential scheduler algorithm configuration files.

Returns:

Schema.

Return type:

dict`[str, `typing.Any]

select_next_target()

Picks a target and returns it as a target object.

Returns:

Target to observe.

Return type:

DriverTarget

select_next_targets()

Select a list of targets.

By default it will just return a dummy test target.

Return type:

list [DriverTarget]

update_conditions()

Update driver internal conditions.

When subclassing this method, make sure to call it at the start of the method, as it performs operations like running the observatory through the current targets on the queue.

Return type:

None