SimpleAssociationTask

class lsst.pipe.tasks.simpleAssociation.SimpleAssociationTask(config: Optional[Config] = None, name: Optional[str] = None, parentTask: Optional[Task] = None, log: Optional[Union[logging.Logger, lsst.utils.logging.LsstLogAdapter]] = None)

Bases: lsst.pipe.base.Task

Construct DiaObjects from a DataFrame of DIASources by spatially associating the sources.

Represents a simple, brute force algorithm, 2-way matching of DiaSources into. DiaObjects. Algorithm picks the nearest, first match within the matching radius of a DiaObject to associate a source to for simplicity.

Methods Summary

addNewDiaObject(diaSrc, diaSources, …) Create a new DiaObject and append its data.
createDiaObject(objId, ra, decl) Create a simple empty DiaObject with location and id information.
emptyMetadata() Empty (clear) the metadata for this Task and all sub-Tasks.
findMatches(src_ra, src_dec, tol, hpIndices, …) Search healPixels around DiaSource locations for DiaObjects.
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.
run(diaSources, tractPatchId, skymapBits) Associate DiaSources into a collection of DiaObjects using a brute force matching algorithm.
timer(name, logLevel) Context manager to log performance data for an arbitrary block of code.
updateCatalogs(matchIndex, diaSrc, …) Update DiaObject and DiaSource values after an association.

Methods Documentation

addNewDiaObject(diaSrc, diaSources, ccdVisit, diaSourceId, diaObjCat, idCat, diaObjCoords, healPixIndices)

Create a new DiaObject and append its data.

Parameters:
diaSrc : pandas.Series

Full unassociated DiaSource to create a DiaObject from.

diaSources : pandas.DataFrame

DiaSource catalog to update information in. The catalog is modified in place.

ccdVisit : int

Unique identifier of the ccdVisit where diaSrc was observed.

diaSourceId : int

Unique identifier of the DiaSource.

diaObjectCat : list of `dict`s

Catalog of diaObjects to append the new object o.

idCat : lsst.afw.table.SourceCatalog

Catalog with the IdFactory used to generate unique DiaObject identifiers.

diaObjectCoords : list of `list`s of `lsst.geom.SpherePoint`s

Set of coordinates of DiaSource locations that make up the DiaObject average coordinate.

healPixIndices : list of `int`s

HealPix indices representing the locations of each currently existing DiaObject.

createDiaObject(objId, ra, decl)

Create a simple empty DiaObject with location and id information.

Parameters:
objId : int

Unique ID for this new DiaObject.

ra : float

RA location of this DiaObject.

decl : float

Dec location of this DiaObject

Returns:
DiaObject : dict

Dictionary of values representing a DiaObject.

emptyMetadata() → None

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

findMatches(src_ra, src_dec, tol, hpIndices, diaObjs)

Search healPixels around DiaSource locations for DiaObjects.

Parameters:
src_ra : float

DiaSource RA location.

src_dec : float

DiaSource Dec location.

tol : float

Size of annulus to convert to covering healPixels and search for DiaObjects.

hpIndices : list of `int`s

List of heal pix indices containing the DiaObjects in diaObjs.

diaObjs : list of `dict`s

Catalog diaObjects to with full location information for comparing to DiaSources.

Returns:
results : lsst.pipe.base.Struct

Results struct containing

dists

Array of distances between the current DiaSource diaObjects. (numpy.ndarray or None)

matches

Array of array indices of diaObjects this DiaSource matches to. (numpy.ndarray or None)

getAllSchemaCatalogs() → Dict[str, Any]

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() → lsst.pipe.base._task_metadata.TaskMetadata

Get metadata for all tasks.

Returns:
metadata : TaskMetadata

The 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() → str

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() → str

Get the name of the task.

Returns:
taskName : str

Name of the task.

See also

getFullName
getSchemaCatalogs() → Dict[str, Any]

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() → Dict[str, weakref.ReferenceType[lsst.pipe.base.task.Task]]

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: str) → lsst.pex.config.configurableField.ConfigurableField

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: str, **keyArgs) → None

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.

run(diaSources, tractPatchId, skymapBits)

Associate DiaSources into a collection of DiaObjects using a brute force matching algorithm.

Reproducible is for the same input data is assured by ordering the DiaSource data by ccdVisit ordering.

Parameters:
diaSources : pandas.DataFrame

DiaSources grouped by CcdVisitId to spatially associate into DiaObjects.

tractPatchId : int

Unique identifier for the tract patch.

skymapBits : int

Maximum number of bits used the tractPatchId integer identifier.

Returns:
results : lsst.pipe.base.Struct

Results struct with attributes:

assocDiaSources

Table of DiaSources with updated values for the DiaObjects they are spatially associated to (pandas.DataFrame).

diaObjects

Table of DiaObjects from matching DiaSources (pandas.DataFrame).

timer(name: str, logLevel: int = 10) → Iterator[None]

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
updateCatalogs(matchIndex, diaSrc, diaSources, ccdVisit, diaSourceId, diaObjCat, diaObjCoords, healPixIndices)

Update DiaObject and DiaSource values after an association.

Parameters:
matchIndex : int

Array index location of the DiaObject that diaSrc was associated to.

diaSrc : pandas.Series

Full unassociated DiaSource to create a DiaObject from.

diaSources : pandas.DataFrame

DiaSource catalog to update information in. The catalog is modified in place.

ccdVisit : int

Unique identifier of the ccdVisit where diaSrc was observed.

diaSourceId : int

Unique identifier of the DiaSource.

diaObjectCat : list of `dict`s

Catalog of diaObjects to append the new object o.

diaObjectCoords : list of `list`s of `lsst.geom.SpherePoint`s

Set of coordinates of DiaSource locations that make up the DiaObject average coordinate.

healPixIndices : list of `int`s

HealPix indices representing the locations of each currently existing DiaObject.