TractMeasurementTask¶
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class
lsst.faro.measurement.
TractMeasurementTask
(config, *args, **kwargs)¶ Bases:
lsst.faro.base.CatalogMeasurementBaseTask
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.makeSubtask
(name, **keyArgs)Create a subtask as a new instance as the name
attribute of this task.run
(catalogs, photoCalibs, astromCalibs, dataIds)Run the MetricTask on in-memory data. 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
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canMultiprocess
= True¶
Methods Documentation
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emptyMetadata
() → None¶ Empty (clear) the metadata for this Task and all sub-Tasks.
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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.- schemacatalogs :
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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.- metadata :
-
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”.
- fullName :
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getResourceConfig
() → Optional[ResourceConfig]¶ Return resource configuration for this task.
Returns: - Object of type
ResourceConfig
orNone
if resource - configuration is not defined for this task.
- Object of type
-
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.
- schemaCatalogs :
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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.
- taskDict :
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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")
- doc :
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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 ofConfigurableField
orRegistryField
.- name :
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run
(catalogs: List[lsst.afw.table.SourceCatalog], photoCalibs: List[lsst.afw.image.PhotoCalib], astromCalibs: List[lsst.afw.geom.SkyWcs], dataIds)¶ Run the MetricTask on in-memory data.
Parameters: - **kwargs
Keyword arguments matching the inputs given in the class config; see
lsst.pipe.base.PipelineTask.run
for more details.
Returns: - struct :
lsst.pipe.base.Struct
A
Struct
containing at least the following component:measurement
: the value of the metric (lsst.verify.Measurement
orNone
). This method is not responsible for adding mandatory metadata (e.g., the data ID); this is handled by the caller.None
may be used to indicate that a metric is undefined or irrelevant instead of raisingNoWorkFound
.
Raises: - lsst.verify.tasks.MetricComputationError
Raised if an algorithmic or system error prevents calculation of the metric. Examples include corrupted input data or unavoidable exceptions raised by analysis code. The
MetricComputationError
should be chained to a more specific exception describing the root cause.Not having enough data for a metric to be applicable is not an error, and should raise
NoWorkFound
(see below) instead of this exception.- lsst.pipe.base.NoWorkFound
Raised if the metric is ill-defined or otherwise inapplicable to the data. Typically this means that the pipeline step or option being measured was not run.
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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.
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timer
(name: str, logLevel: int = 10) → Iterator[None]¶ Context manager to log performance data for an arbitrary block of code.
Parameters: See also
timer.logInfo
Examples
Creating a timer context:
with self.timer("someCodeToTime"): pass # code to time
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