CmdLineTask

class lsst.pipe.base.CmdLineTask(config=None, name=None, parentTask=None, log=None)

Bases: lsst.pipe.base.Task

Base class for command-line tasks: tasks that may be executed from the command-line.

Notes

See Overview of the task framework to learn what tasks are and Creating a command-line task for more information about writing command-line tasks.

Subclasses must specify the following class variables:

Subclasses may also specify the following class variables:

  • RunnerClass: a task runner class. The default is TaskRunner, which works for any task with a runDataRef method that takes exactly one argument: a data reference. If your task does not meet this requirement then you must supply a variant of TaskRunner; see TaskRunner for more information.
  • canMultiprocess: the default is True; set False if your task does not support multiprocessing.

Subclasses must specify a method named runDataRef:

  • By default runDataRef accepts a single butler data reference, but you can specify an alternate task runner (subclass of TaskRunner) as the value of class variable RunnerClass if your run method needs something else.
  • runDataRef is expected to return its data in a lsst.pipe.base.Struct. This provides safety for evolution of the task since new values may be added without harming existing code.
  • The data returned by runDataRef must be picklable if your task is to support multiprocessing.

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.
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()

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.

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 : lsst.log.Log-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.

timer(name, logLevel=10000)

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 lsst.log level constant.

See also

timer.logInfo

Examples

Creating a timer context:

with self.timer("someCodeToTime"):
    pass  # code to time
writeConfig(butler, clobber=False, doBackup=True)

Write the configuration used for processing the data, or check that an existing one is equal to the new one if present.

Parameters:
butler : lsst.daf.persistence.Butler

Data butler used to write the config. The config is written to dataset type CmdLineTask._getConfigName.

clobber : bool, optional

A boolean flag that controls what happens if a config already has been saved:

  • True: overwrite or rename the existing config, depending on doBackup.
  • False: raise TaskError if this config does not match the existing config.
doBackup : bool, optional

Set to True to backup the config files if clobbering.
writeMetadata(dataRef)

Write the metadata produced from processing the data.

Parameters:
dataRef

Butler data reference used to write the metadata. The metadata is written to dataset type CmdLineTask._getMetadataName.
writePackageVersions(butler, clobber=False, doBackup=True, dataset='packages')

Compare and write package versions.

Parameters:
butler : lsst.daf.persistence.Butler

Data butler used to read/write the package versions.

clobber : bool, optional

A boolean flag that controls what happens if versions already have been saved:

  • True: overwrite or rename the existing version info, depending on doBackup.
  • False: raise TaskError if this version info does not match the existing.
doBackup : bool, optional

If True and clobbering, old package version files are backed up.

dataset : str, optional

Name of dataset to read/write.
Raises:
TaskError

Raised if there is a version mismatch with current and persisted lists of package versions.

Notes

Note that this operation is subject to a race condition.

writeSchemas(butler, clobber=False, doBackup=True)

Write the schemas returned by lsst.pipe.base.Task.getAllSchemaCatalogs.

Parameters:
butler : lsst.daf.persistence.Butler

Data butler used to write the schema. Each schema is written to the dataset type specified as the key in the dict returned by getAllSchemaCatalogs.

clobber : bool, optional

A boolean flag that controls what happens if a schema already has been saved:

  • True: overwrite or rename the existing schema, depending on doBackup.
  • False: raise TaskError if this schema does not match the existing schema.
doBackup : bool, optional

Set to True to backup the schema files if clobbering.

Notes

If clobber is False and an existing schema does not match a current schema, then some schemas may have been saved successfully and others may not, and there is no easy way to tell which is which.