AssociationTask

class lsst.ap.association.AssociationTask(config=None, name=None, parentTask=None, log=None)

Bases: lsst.pipe.base.Task

Associate DIAOSources into existing DIAObjects.

This task performs the association of detected DIASources in a visit with the previous DIAObjects detected over time. It also creates new DIAObjects out of DIASources that cannot be associated with previously detected DIAObjects.

Methods Summary

associate_sources(dia_objects, dia_sources) Associate the input DIASources with the catalog of DIAObjects.
check_dia_source_radec(dia_sources) Check that all DiaSources have non-NaN values for RA/DEC.
emptyMetadata() Empty (clear) the metadata for this Task and all sub-Tasks.
getAllSchemaCatalogs() Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.
getFullMetadata() Get metadata for all tasks.
getFullName() Get the task name as a hierarchical name including parent task names.
getName() Get the name of the task.
getSchemaCatalogs() Get the schemas generated by this task.
getTaskDict() Get a dictionary of all tasks as a shallow copy.
makeField(doc) Make a lsst.pex.config.ConfigurableField for this task.
makeSubtask(name, **keyArgs) Create a subtask as a new instance as the name attribute of this task.
match(dia_objects, dia_sources, score_struct) Match DIAsources to DIAObjects given a score and create new DIAObject Ids for new unassociated DIASources.
run(diaSources, diaObjects, diaSourceHistory) Associate the new DiaSources with existing or new DiaObjects, updating the DiaObjects.
score(dia_objects, dia_sources, max_dist) Compute a quality score for each dia_source/dia_object pair between this catalog of DIAObjects and the input DIASource catalog.
timer(name[, logLevel]) Context manager to log performance data for an arbitrary block of code.

Methods Documentation

associate_sources(dia_objects, dia_sources)

Associate the input DIASources with the catalog of DIAObjects.

DiaObject DataFrame must be indexed on diaObjectId.

Parameters:
dia_objects : pandas.DataFrame

Catalog of DIAObjects to attempt to associate the input DIASources into.

dia_sources : pandas.DataFrame

DIASources to associate into the DIAObjectCollection.
Returns:
result : lsst.pipeBase.Struct

Results struct with components:

  • updated_and_new_dia_object_ids : ids of new and updated dia_objects as the result of association. (list of int).
  • new_dia_objects : Newly created DiaObjects from unassociated diaSources. (pandas.DataFrame)
  • n_updated_dia_objects : Number of previously known dia_objects with newly associated DIASources. (int).
  • n_new_dia_objects : Number of newly created DIAObjects from unassociated DIASources (int).
  • n_unupdated_dia_objects : Number of previous DIAObjects that were not associated to a new DIASource (int).
check_dia_source_radec(dia_sources)

Check that all DiaSources have non-NaN values for RA/DEC.

If one or more DiaSources are found to have NaN values, throw a warning to the log with the ids of the offending sources. Drop them from the table.

Parameters:
dia_sources : pandas.DataFrame

Input DiaSources to check for NaN values.
Returns:
trimmed_sources : pandas.DataFrame

DataFrame of DiaSources trimmed of all entries with NaN values for RA/DEC.
emptyMetadata()

Empty (clear) the metadata for this Task and all sub-Tasks.

getAllSchemaCatalogs()

Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.

Returns:
schemacatalogs : dict

Keys are butler dataset type, values are a empty catalog (an instance of the appropriate lsst.afw.table Catalog type) for all tasks in the hierarchy, from the top-level task down through all subtasks.

Notes

This method may be called on any task in the hierarchy; it will return the same answer, regardless.

The default implementation should always suffice. If your subtask uses schemas the override Task.getSchemaCatalogs, not this method.

getFullMetadata()

Get metadata for all tasks.

Returns:
metadata : lsst.daf.base.PropertySet

The PropertySet keys are the full task name. Values are metadata for the top-level task and all subtasks, sub-subtasks, etc.

Notes

The returned metadata includes timing information (if @timer.timeMethod is used) and any metadata set by the task. The name of each item consists of the full task name with . replaced by :, followed by . and the name of the item, e.g.:

topLevelTaskName:subtaskName:subsubtaskName.itemName

using : in the full task name disambiguates the rare situation that a task has a subtask and a metadata item with the same name.

getFullName()

Get the task name as a hierarchical name including parent task names.

Returns:
fullName : str

The full name consists of the name of the parent task and each subtask separated by periods. For example:

  • The full name of top-level task “top” is simply “top”.
  • The full name of subtask “sub” of top-level task “top” is “top.sub”.
  • The full name of subtask “sub2” of subtask “sub” of top-level task “top” is “top.sub.sub2”.
getName()

Get the name of the task.

Returns:
taskName : str

Name of the task.

See also

getFullName

getSchemaCatalogs()

Get the schemas generated by this task.

Returns:
schemaCatalogs : dict

Keys are butler dataset type, values are an empty catalog (an instance of the appropriate lsst.afw.table Catalog type) for this task.

See also

Task.getAllSchemaCatalogs

Notes

Warning

Subclasses that use schemas must override this method. The default implementation returns an empty dict.

This method may be called at any time after the Task is constructed, which means that all task schemas should be computed at construction time, not when data is actually processed. This reflects the philosophy that the schema should not depend on the data.

Returning catalogs rather than just schemas allows us to save e.g. slots for SourceCatalog as well.

getTaskDict()

Get a dictionary of all tasks as a shallow copy.

Returns:
taskDict : dict

Dictionary containing full task name: task object for the top-level task and all subtasks, sub-subtasks, etc.
classmethod makeField(doc)

Make a lsst.pex.config.ConfigurableField for this task.

Parameters:
doc : str

Help text for the field.
Returns:
configurableField : lsst.pex.config.ConfigurableField

A ConfigurableField for this task.

Examples

Provides a convenient way to specify this task is a subtask of another task.

Here is an example of use:

class OtherTaskConfig(lsst.pex.config.Config):
    aSubtask = ATaskClass.makeField("brief description of task")
makeSubtask(name, **keyArgs)

Create a subtask as a new instance as the name attribute of this task.

Parameters:
name : str

Brief name of the subtask.

keyArgs

Extra keyword arguments used to construct the task. The following arguments are automatically provided and cannot be overridden:

  • “config”.
  • “parentTask”.

Notes

The subtask must be defined by Task.config.name, an instance of ConfigurableField or RegistryField.

match(dia_objects, dia_sources, score_struct)

Match DIAsources to DIAObjects given a score and create new DIAObject Ids for new unassociated DIASources.

Parameters:
dia_objects : pandas.DataFrame

A SourceCatalog of DIAObjects to associate to DIASources.

dia_sources : pandas.DataFrame

A contiguous catalog of dia_sources for which the set of scores has been computed on with DIAObjectCollection.score.

score_struct : lsst.pipe.base.Struct

Results struct with components:

  • scores: array of floats of match quality
    updated DIAObjects (array-like of float).
  • obj_ids: array of floats of match quality
    updated DIAObjects (array-like of int).
  • obj_idxs: indexes of the matched DIAObjects in the catalog.
    (array-like of int)

Default values for these arrays are INF, -1 and -1 respectively for unassociated sources.
Returns:
result : lsst.pipeBase.Struct

Results struct with components:

  • updated_and_new_dia_object_ids : ids of new and updated dia_objects as the result of association. (list of int).
  • new_dia_objects : Newly created DiaObjects from unassociated diaSources. (pandas.DataFrame)
  • n_updated_dia_objects : Number of previously know dia_objects with newly associated DIASources. (int).
  • n_new_dia_objects : Number of newly created DIAObjects from unassociated DIASources (int).
  • n_unupdated_dia_objects : Number of previous DIAObjects that were not associated to a new DIASource (int).
run(diaSources, diaObjects, diaSourceHistory)

Associate the new DiaSources with existing or new DiaObjects, updating the DiaObjects.

Parameters:
diaSources : pandas.DataFrame

New DIASources to be associated with existing DIAObjects.

diaObjects : pandas.DataFrame

Existing diaObjects from the Apdb.

diaSourceHistory : pandas.DataFrame

12 month DiaSource history of the loaded diaObjects.
Returns:
result : lsst.pipe.base.Struct

Results struct with components.

  • diaObjects : Complete set of dia_objects covering the input exposure. Catalog contains newly created, updated, and untouched diaObjects. (pandas.DataFrame)
  • updatedDiaObjects : Subset of DiaObjects that were updated or created during processing. (pandas.DataFrame)
  • matchedDiaObjectIds : DiaSources detected in this ccdVisit with associated diaObjectIds. (numpy.ndarray)
score(dia_objects, dia_sources, max_dist)

Compute a quality score for each dia_source/dia_object pair between this catalog of DIAObjects and the input DIASource catalog.

max_dist sets maximum separation in arcseconds to consider a dia_source a possible match to a dia_object. If the pair is beyond this distance no score is computed.

Parameters:
dia_objects : pandas.DataFrame

A contiguous catalog of DIAObjects to score against dia_sources.

dia_sources : pandas.DataFrame

A contiguous catalog of dia_sources to “score” based on distance and (in the future) other metrics.

max_dist : lsst.geom.Angle

Maximum allowed distance to compute a score for a given DIAObject DIASource pair.
Returns:
result : lsst.pipe.base.Struct

Results struct with components:

  • scores: array of floats of match quality updated DIAObjects
    (array-like of float).
  • obj_idxs: indexes of the matched DIAObjects in the catalog.
    (array-like of int)
  • obj_ids: array of floats of match quality updated DIAObjects
    (array-like of int).

Default values for these arrays are INF, -1, and -1 respectively for unassociated sources.
timer(name, logLevel=10)

Context manager to log performance data for an arbitrary block of code.

Parameters:
name : str

Name of code being timed; data will be logged using item name: Start and End.

logLevel

A logging level constant.

See also

timer.logInfo

Examples

Creating a timer context:

with self.timer("someCodeToTime"):
    pass  # code to time