ApdbMetricTask¶

class lsst.verify.tasks.ApdbMetricTask(**kwargs)¶

Bases: lsst.verify.tasks.MetricTask

A base class for tasks that compute metrics from an alert production database.

Parameters:	**kwargs Constructor parameters are the same as for `lsst.pipe.base.PipelineTask`.

Notes

This class should be customized by overriding makeMeasurement. You should not need to override run.

Attributes Summary

canMultiprocess

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.
`getResourceConfig`()	Return resource configuration for this 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.
`makeMeasurement`(dbHandle, outputDataId)	Compute the metric from database data.
`makeSubtask`(name, **keyArgs)	Create a subtask as a new instance as the `name` attribute of this task.
`run`(dbInfo[, outputDataId])	Compute a measurement from a database.
`runQuantum`(butlerQC, inputRefs, outputRefs)	Do Butler I/O to provide in-memory objects for run.
`timer`(name, logLevel)	Context manager to log performance data for an arbitrary block of code.

Attributes Documentation

canMultiprocess = True¶

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

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")

makeMeasurement(dbHandle, outputDataId)¶

Compute the metric from database data.

Parameters:	dbHandle : `lsst.dax.apdb.Apdb` A database instance. outputDataId : any data ID type The subset of the database to which this measurement applies. May be empty to represent the entire dataset.
Returns:	measurement : `lsst.verify.Measurement` or `None` The measurement corresponding to the input data.
Raises:	lsst.verify.tasks.MetricComputationError Raised if an algorithmic or system error prevents calculation of the metric. See `run` for expected behavior. lsst.pipe.base.NoWorkFound Raised if the metric is ill-defined or otherwise inapplicable to the database state. Typically this means that the pipeline step or option being measured was not run.

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(dbInfo, outputDataId={})¶

Compute a measurement from a database.

Parameters:	dbInfo : `list` The datasets (of the type indicated by the config) from which to load the database. If more than one dataset is provided (as may be the case if DB writes are fine-grained), all are assumed identical. outputDataId: any data ID type, optional The output data ID for the metric value. Defaults to the empty ID, representing a value that covers the entire dataset.
Returns:	result : `lsst.pipe.base.Struct` Result struct with component: `measurement` the value of the metric (`lsst.verify.Measurement` or `None`)
Raises:	lsst.verify.tasks.MetricComputationError Raised if an algorithmic or system error prevents calculation of the metric. lsst.pipe.base.NoWorkFound Raised if the metric is ill-defined or otherwise inapplicable to the database state. Typically this means that the pipeline step or option being measured was not run.

Notes

This implementation calls dbLoader to acquire a database handle, then passes it and the value of outputDataId to makeMeasurement. The result of makeMeasurement is returned to the caller.

runQuantum(butlerQC, inputRefs, outputRefs)¶

Do Butler I/O to provide in-memory objects for run.

This specialization of runQuantum passes the output data ID to run.

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

ApdbMetricTask¶