MatchProbabilisticTask

class lsst.meas.astrom.MatchProbabilisticTask(**kwargs)

Bases: lsst.pipe.base.Task

Run MatchProbabilistic on a reference and target catalog covering the same tract.

Attributes Summary

columns_in_ref
columns_in_target

Methods Summary

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(catalog_ref, catalog_target, …)	Match sources in a reference tract catalog with a target catalog.
run(catalog_ref, catalog_target, wcs, **kwargs)	Match sources in a reference tract catalog with a target catalog.
timer(name, logLevel)	Context manager to log performance data for an arbitrary block of code.

Attributes Documentation

columns_in_ref

columns_in_target

Methods Documentation

emptyMetadata() → None

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

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.

match(catalog_ref: pandas.core.frame.DataFrame, catalog_target: pandas.core.frame.DataFrame, select_ref: numpy.array = None, select_target: numpy.array = None, wcs: lsst.afw.geom.SkyWcs = None, logger: logging.Logger = None, logging_n_rows: int = None) → Tuple[pandas.core.frame.DataFrame, pandas.core.frame.DataFrame, Dict[int, str]]

Match sources in a reference tract catalog with a target catalog.

Parameters:

catalog_ref : pandas.DataFrame

A reference catalog to match objects/sources from.

catalog_target : pandas.DataFrame

A target catalog to match reference objects/sources to.

select_ref : numpy.array

A boolean array of the same length as catalog_ref selecting the sources that can be matched.

select_target : numpy.array

A boolean array of the same length as catalog_target selecting the sources that can be matched.

wcs : lsst.afw.image.SkyWcs

A coordinate system to convert catalog positions to sky coordinates. Only used if self.config.coords_ref_to_convert is set.

logger : logging.Logger

A Logger for logging.

logging_n_rows : int

Number of matches to make before outputting incremental log message.

Returns:

catalog_out_ref : pandas.DataFrame

Reference matched catalog with indices of target matches.

catalog_out_target : pandas.DataFrame

Reference matched catalog with indices of target matches.

run(catalog_ref: pandas.core.frame.DataFrame, catalog_target: pandas.core.frame.DataFrame, wcs: lsst.afw.geom.SkyWcs = None, **kwargs) → lsst.pipe.base.struct.Struct

Match sources in a reference tract catalog with a target catalog.

Parameters:

catalog_ref : pandas.DataFrame

A reference catalog to match objects/sources from.

catalog_target : pandas.DataFrame

A target catalog to match reference objects/sources to.

wcs : lsst.afw.image.SkyWcs

A coordinate system to convert catalog positions to sky coordinates. Only needed if config.coords_ref_to_convert is used to convert reference catalog sky coordinates to pixel positions.

kwargs : Additional keyword arguments to pass to match.

Returns:

retStruct : lsst.pipe.base.Struct

A struct with output_ref and output_target attribute containing the output matched catalogs, as well as a dict

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