FeatureScheduler

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

Bases: Driver

Feature scheduler driver.

Attributes Summary

default_observation_database_name

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 Targets.
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.
fbs_observation_named_parameter_map()	Return the mapping between feature scheduler observation parameter keywords and the Observation parameter name keywords
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 observation.
register_observed_target(target)	Validates observation and returns a list of successfully completed observations.
reset_from_state(filename)	Load the state from a file.
save_state()	Save the current state of the scheduling algorithm to a file.
select_next_target()	Pick a target and return it as a target object.
select_next_targets()	Pick a target and return it as a list of target objects.
update_conditions()	Update conditions on the scheduler.

Attributes Documentation

default_observation_database_name = PosixPath('/home/saluser/fbs_observation_database.sql')

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 Targets.

Parameters:

observations (list of DriverTarget)

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

Raises:

• RuntimeError: – If config does not have a scheduler_config attribute or if it points to a non-existing file.

• NoSchedulerError: – If scheduler configuration does not define scheduler attribute.

• NoNsideError: – If scheduler configuration does not define nside attribute.

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]

static fbs_observation_named_parameter_map()

Return the mapping between feature scheduler observation parameter keywords and the Observation parameter name keywords

Returns:

Mapping between fbs observations keywords and Observation keywords.

Return type:

dict

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.

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 observation.

Write the target observation into the observation database.

Parameters:

target (FeatureSchedulerTarget) – Target to register.

Return type:

None

register_observed_target(target)

Validates observation and returns a list of successfully completed observations.

Add target observation to the feature based scheduler.

Parameters:

• observation (list of Target) – Python list of Targets observed.

• target (DriverTarget)

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

select_next_target()

Pick a target and return it as a target object.

Return type:

Target

select_next_targets()

Pick a target and return it as a list of target objects.

Instead of requesting a single target like select_next_target, this method will inquire the scheduling algorithm for a list of potential next targets.

Returns:

desired_observations – List of desired observations.

Return type:

list`[`FeatureSchedulerTarget]

update_conditions()

Update conditions on the scheduler.