MatchedVisitMetricsRunner¶

class lsst.validate.drp.matchedVisitMetricsTask.MatchedVisitMetricsRunner(TaskClass, parsedCmd, doReturnResults=False)¶

Bases: lsst.pipe.base.TaskRunner

Subclass of TaskRunner for MatchedVisitMetrics

This class transforms the processed arguments generated by the ArgumentParser into the arguments expected by MatchedVisitMetricsTask.run().

Attributes Summary

TIMEOUT

Methods Summary

`__call__`(args)	Run the Task on a single target.
`getTargetList`(parsedCmd, **kwargs)	Get a list of (dataRef, kwargs) for `TaskRunner.__call__`.
`makeTask`([parsedCmd, args])	Create a Task instance.
`precall`(parsedCmd)	Hook for code that should run exactly once, before multiprocessing.
`prepareForMultiProcessing`()	Prepare this instance for multiprocessing
`run`(parsedCmd)	Run the task on all targets.
`runTask`(task, dataRef, kwargs)	Make the actual call to `runDataRef` for this task.

Attributes Documentation

TIMEOUT = 2592000¶

Methods Documentation

__call__(args)¶

Run the Task on a single target.

Parameters:

args: Arguments for Task.runDataRef()

Returns:

struct : lsst.pipe.base.Struct

Contains these fields if doReturnResults is True:

dataRef: the provided data reference.
metadata: task metadata after execution of run.
result: result returned by task run, or None if the task fails.
exitStatus: 0 if the task completed successfully, 1 otherwise.

If doReturnResults is False the struct contains:

exitStatus: 0 if the task completed successfully, 1 otherwise.

Notes

This default implementation assumes that the args is a tuple containing a data reference and a dict of keyword arguments.

Warning

If you override this method and wish to return something when doReturnResults is False, then it must be picklable to support multiprocessing and it should be small enough that pickling and unpickling do not add excessive overhead.

static getTargetList(parsedCmd, **kwargs)¶

Get a list of (dataRef, kwargs) for TaskRunner.__call__.

Parameters:	parsedCmd : `argparse.Namespace` The parsed command object returned by `lsst.pipe.base.ArgumentParser.parse_args`. kwargs Any additional keyword arguments. In the default `TaskRunner` this is an empty dict, but having it simplifies overriding `TaskRunner` for tasks whose runDataRef method takes additional arguments (see case (1) below).

Notes

The default implementation of TaskRunner.getTargetList and TaskRunner.__call__ works for any command-line task whose runDataRef method takes exactly one argument: a data reference. Otherwise you must provide a variant of TaskRunner that overrides TaskRunner.getTargetList and possibly TaskRunner.__call__. There are two cases.

Case 1

If your command-line task has a runDataRef method that takes one data reference followed by additional arguments, then you need only override TaskRunner.getTargetList to return the additional arguments as an argument dict. To make this easier, your overridden version of getTargetList may call TaskRunner.getTargetList with the extra arguments as keyword arguments. For example, the following adds an argument dict containing a single key: “calExpList”, whose value is the list of data IDs for the calexp ID argument:

def getTargetList(parsedCmd):
    return TaskRunner.getTargetList(
        parsedCmd,
        calExpList=parsedCmd.calexp.idList
    )

It is equivalent to this slightly longer version:

@staticmethod
def getTargetList(parsedCmd):
    argDict = dict(calExpList=parsedCmd.calexp.idList)
    return [(dataId, argDict) for dataId in parsedCmd.id.idList]

Case 2

If your task does not meet condition (1) then you must override both TaskRunner.getTargetList and TaskRunner.__call__. You may do this however you see fit, so long as TaskRunner.getTargetList returns a list, each of whose elements is sent to TaskRunner.__call__, which runs your task.

makeTask(parsedCmd=None, args=None)¶

Create a Task instance.

Parameters:	parsedCmd Parsed command-line options (used for extra task args by some task runners). args Args tuple passed to `TaskRunner.__call__` (used for extra task arguments by some task runners).

Notes

makeTask can be called with either the parsedCmd argument or args argument set to None, but it must construct identical Task instances in either case.

Subclasses may ignore this method entirely if they reimplement both TaskRunner.precall and TaskRunner.__call__.

precall(parsedCmd)¶

Hook for code that should run exactly once, before multiprocessing.

Notes

Must return True if TaskRunner.__call__ should subsequently be called.

Warning

Implementations must take care to ensure that no unpicklable attributes are added to the TaskRunner itself, for compatibility with multiprocessing.

The default implementation writes package versions, schemas and configs, or compares them to existing files on disk if present.

prepareForMultiProcessing()¶

Prepare this instance for multiprocessing

Optional non-picklable elements are removed.

This is only called if the task is run under multiprocessing.

run(parsedCmd)¶

Run the task on all targets.

Parameters:	parsedCmd : `argparse.Namespace` Parsed command `argparse.Namespace`.
Returns:	resultList : `list` A list of results returned by `TaskRunner.__call__`, or an empty list if `TaskRunner.__call__` is not called (e.g. if `TaskRunner.precall` returns `False`). See `TaskRunner.__call__` for details.

Notes

The task is run under multiprocessing if TaskRunner.numProcesses is more than 1; otherwise processing is serial.

runTask(task, dataRef, kwargs)¶

Make the actual call to runDataRef for this task.

Parameters:	task : `lsst.pipe.base.CmdLineTask` class The class of the task to run. dataRef Butler data reference that contains the data the task will process. kwargs Any additional keyword arguments. See `TaskRunner.getTargetList` above.

Notes

The default implementation of TaskRunner.runTask works for any command-line task which has a runDataRef method that takes a data reference and an optional set of additional keyword arguments. This method returns the results generated by the task’s runDataRef method.

Navigation

MatchedVisitMetricsRunner¶