ApPipeTask¶

class lsst.ap.pipe.ApPipeTask(butler, *args, **kwargs)¶

Bases: lsst.pipe.base.CmdLineTask

Command-line task representing the entire AP pipeline.

ApPipeTask processes raw DECam images from basic processing through source association. Other observatories will be supported in the future.

ApPipeTask can be run from the command line, but it can also be called from other pipeline code. It provides public methods for executing each major step of the pipeline by itself.

Parameters:	butler : `lsst.daf.persistence.Butler` A Butler providing access to the science, calibration, and (unless `config.differencer.getTemplate` is overridden) template data to be processed. Its output repository must be both readable and writable.

Attributes Summary

canMultiprocess

Methods Summary

`applyOverrides`(config)	A hook to allow a task to change the values of its config after the camera-specific overrides are loaded but before any command-line overrides are applied.
`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.
`parseAndRun`([args, config, log, doReturnResults])	Parse an argument list and run the command.
`runAssociation`(sensorRef)	Do source association.
`runDataRef`(rawRef[, templateIds, reuse])	Execute the ap_pipe pipeline on a single image.
`runDiffIm`(sensorRef[, templateIds])	Do difference imaging with a template and a science image
`runProcessCcd`(sensorRef)	Perform ISR with ingested images and calibrations via processCcd.
`timer`(name, logLevel)	Context manager to log performance data for an arbitrary block of code.
`writeConfig`(butler[, clobber, doBackup])	Write the configuration used for processing the data, or check that an existing one is equal to the new one if present.
`writeMetadata`(dataRef)	Write the metadata produced from processing the data.
`writePackageVersions`(butler[, clobber, …])	Compare and write package versions.
`writeSchemas`(butler[, clobber, doBackup])	Write the schemas returned by `lsst.pipe.base.Task.getAllSchemaCatalogs`.

Attributes Documentation

canMultiprocess = True¶

Methods Documentation

classmethod applyOverrides(config)¶

A hook to allow a task to change the values of its config after the camera-specific overrides are loaded but before any command-line overrides are applied.

Parameters:	config : instance of task’s `ConfigClass` Task configuration.

Notes

This is necessary in some cases because the camera-specific overrides may retarget subtasks, wiping out changes made in ConfigClass.setDefaults. See LSST Trac ticket #2282 for more discussion.

Warning

This is called by CmdLineTask.parseAndRun; other ways of constructing a config will not apply these overrides.

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

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]¶

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.

classmethod parseAndRun(args=None, config=None, log=None, doReturnResults=False)¶

Parse an argument list and run the command.

Parameters:

args : list, optional: List of command-line arguments; if None use sys.argv.
config : lsst.pex.config.Config-type, optional: Config for task. If None use Task.ConfigClass.
log : logging.Logger-type, optional: Log. If None use the default log.
doReturnResults : bool, optional: If True, return the results of this task. Default is False. This is only intended for unit tests and similar use. It can easily exhaust memory (if the task returns enough data and you call it enough times) and it will fail when using multiprocessing if the returned data cannot be pickled.

Returns:

struct : lsst.pipe.base.Struct

Fields are:

argumentParser: the argument parser (lsst.pipe.base.ArgumentParser).
parsedCmd: the parsed command returned by the argument parser’s parse_args method (argparse.Namespace).
taskRunner: the task runner used to run the task (an instance of Task.RunnerClass).
resultList: results returned by the task runner’s run method, one entry per invocation (list). This will typically be a list of Struct, each containing at least an exitStatus integer (0 or 1); see Task.RunnerClass (TaskRunner by default) for more details.

Notes

Calling this method with no arguments specified is the standard way to run a command-line task from the command-line. For an example see pipe_tasks bin/makeSkyMap.py or almost any other file in that directory.

If one or more of the dataIds fails then this routine will exit (with a status giving the number of failed dataIds) rather than returning this struct; this behaviour can be overridden by specifying the --noExit command-line option.

runAssociation(sensorRef)¶

Do source association.

This method writes an apdb_marker dataset once all changes related to the current exposure have been committed.

Parameters:	sensorRef : `lsst.daf.persistence.ButlerDataRef` Data reference for multiple input dataset types.
Returns:	result : `lsst.pipe.base.Struct` Result struct with components: apdb : `lsst.dax.apdb.Apdb` Initialized association database containing final association results. taskResults : output of `config.diaPipe.run` (`lsst.pipe.base.Struct`).

runDataRef(rawRef, templateIds=None, reuse=None)¶

Execute the ap_pipe pipeline on a single image.

Parameters:

rawRef : lsst.daf.persistence.ButlerDataRef: A reference to the raw data to process.
templateIds : list of dict, optional: A list of parsed data IDs for templates to use. Only used if config.differencer is configured to do so. differencer or its subtasks may restrict the allowed IDs.
reuse : list of str, optional: The names of all subtasks that may be skipped if their output is present. Defaults to skipping nothing.

Returns:

result : lsst.pipe.base.Struct

Result struct with components:

l1Database : handle for accessing the final association database, conforming to

ap_association’s DB access API
ccdProcessor : output of config.ccdProcessor.runDataRef (lsst.pipe.base.Struct or None).
differencer : output of config.differencer.runDataRef (lsst.pipe.base.Struct or None).
diaPipe : output of config.diaPipe.run (lsst.pipe.base.Struct or None).

runDiffIm(sensorRef, templateIds=None)¶

Do difference imaging with a template and a science image

The output repository associated with sensorRef will be populated with difference images and catalogs of detected sources (diaSrc, diffexp, and metadata files)

Parameters:	sensorRef : `lsst.daf.persistence.ButlerDataRef` Data reference for multiple dataset types, both input and output. templateIds : `list` of `dict`, optional A list of parsed data IDs for templates to use. Only used if `config.differencer` is configured to do so. `differencer` or its subtasks may restrict the allowed IDs.
Returns:	result : `lsst.pipe.base.Struct` Output of `config.differencer.runDataRef`.

runProcessCcd(sensorRef)¶

Perform ISR with ingested images and calibrations via processCcd.

The output repository associated with sensorRef will be populated with the usual post-ISR data (bkgd, calexp, icExp, icSrc, postISR).

Parameters:	sensorRef : `lsst.daf.persistence.ButlerDataRef` Data reference for raw data.
Returns:	result : `lsst.pipe.base.Struct` Output of `config.ccdProcessor.runDataRef`.

Notes

The input repository corresponding to sensorRef must already contain the refcats.

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.

Navigation

ApPipeTask¶