NumberAssociatedSolarSystemObjectsMetricTask

class lsst.ap.association.metrics.NumberAssociatedSolarSystemObjectsMetricTask(**kwargs)

Bases: lsst.verify.tasks.MetadataMetricTask

Number of SolarSystemObjects that were associated with new DiaSources for this detectorVisit.

Attributes Summary

canMultiprocess

Methods Summary

emptyMetadata() Empty (clear) the metadata for this Task and all sub-Tasks.
extractMetadata(metadata, metadataKeys) Read multiple keys from a metadata object.
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.
getInputMetadataKeys(config) Return the metadata keys read by this task.
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(values) Compute the number of associated SolarSystemObjects.
makeSubtask(name, **keyArgs) Create a subtask as a new instance as the name attribute of this task.
run(metadata) Compute a measurement from science task metadata.
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.

static extractMetadata(metadata, metadataKeys)

Read multiple keys from a metadata object.

Parameters:
metadata : lsst.pipe.base.TaskMetadata

A metadata object, assumed not None.

metadataKeys : dict [str, str]

Keys are arbitrary labels, values are metadata keys (or their substrings) in the format of lsst.pipe.base.Task.getFullMetadata().
Returns:
metadataValues : dict [str, any]

Keys are the same as for metadataKeys, values are the value of each metadata key, or None if no matching key was found.
Raises:
lsst.verify.tasks.MetricComputationError

Raised if any metadata key string has more than one match in metadata.
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”.
classmethod getInputMetadataKeys(config)

Return the metadata keys read by this task.

Parameters:
config : cls.ConfigClass

Configuration for this task.
Returns:
keys : dict [str, str]

The keys are the (arbitrary) names of values to use in task code, the values are the metadata keys to be looked up (see the metadataKeys parameter to extractMetadata). Metadata keys are assumed to include task prefixes in the format of lsst.pipe.base.Task.getFullMetadata(). This method may return a substring of the desired (full) key, but the string must match a unique metadata key.
getName() → str

Get the name of the task.

Returns:
taskName : str

Name of the task.

See also

getFullName
getResourceConfig() → Optional[ResourceConfig]

Return resource configuration for this task.

Returns:
Object of type ResourceConfig or None if resource
configuration is not defined for this task.
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]

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(values)

Compute the number of associated SolarSystemObjects.

Parameters:
values : dict [str, int or None]

A dict representation of the metadata. Each dict has the following key:

"numAssociatedSsObjects"

The number of successfully associated SolarSystem Objects (int or None). May be None if solar system association was not attempted or the image was not successfully associated.
Returns:
measurement : lsst.verify.Measurement or None

The total number of associated SolarSystemObjects.
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(metadata)

Compute a measurement from science task metadata.

Parameters:
metadata : lsst.pipe.base.TaskMetadata or None

A metadata object for the unit of science processing to use for this metric, or a collection of such objects if this task combines many units of processing into a single metric.
Returns:
result : lsst.pipe.base.Struct

A Struct containing the following component:
Raises:
lsst.verify.tasks.MetricComputationError

Raised if the strings returned by getInputMetadataKeys match more than one key in any metadata object.

Notes

This implementation calls getInputMetadataKeys, then searches for matching keys in each metadata. It then passes the values of these keys (or None if no match) to makeMeasurement, and returns its result to the caller.

runQuantum(butlerQC, inputRefs, outputRefs)

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

This specialization of runQuantum performs error-handling specific to MetricTasks. Most or all of this functionality may be moved to activators in the future.

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