GenerateDonutFromRefitWcsTask¶
- class lsst.ts.wep.task.GenerateDonutFromRefitWcsTask(**kwargs)¶
Bases:
GenerateDonutCatalogWcsTask
Fit a new WCS to the image from a direct detect Donut Catalog and return the input exposure with the new WCS attached.
Attributes Summary
Methods Summary
Empty (clear) the metadata for this Task and all sub-Tasks.
formatDonutCatalog
(catalog)Create a minimal donut catalog in afwTable format from the input direct detect catalog.
Get metadata for all tasks.
Get the task name as a hierarchical name including parent task names.
getName
()Get the name of the task.
getRefObjLoader
(refCatalogList)Create a
ReferenceObjectLoader
from available reference catalogs in the repository.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
(astromRefCat, exposure, fitDonutCatalog, ...)Run task algorithm on in-memory data.
runQuantum
(butlerQC, inputRefs, outputRefs)Do butler IO and transform to provide in memory objects for tasks
run
method.timer
(name[, logLevel])Context manager to log performance data for an arbitrary block of code.
Attributes Documentation
- canMultiprocess: ClassVar[bool] = True¶
Methods Documentation
- emptyMetadata() None ¶
Empty (clear) the metadata for this Task and all sub-Tasks.
- formatDonutCatalog(catalog)¶
Create a minimal donut catalog in afwTable format from the input direct detect catalog.
Parameters¶
- catalog
astropy.table.QTable
Catalog containing donut sources already detected on the exposure.
Returns¶
lsst.afw.table.SimpleCatalog
Minimal catalog needed for astromeryTask to fit WCS.
- catalog
- getFullMetadata() 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
- getName() str ¶
Get the name of the task.
Returns¶
- taskName
str
Name of the task.
See Also¶
getFullName : Get the full name of the task.
- taskName
- getRefObjLoader(refCatalogList)¶
Create a
ReferenceObjectLoader
from available reference catalogs in the repository.Parameters¶
- refCatalogList
list
List of deferred butler references for the reference catalogs.
Returns¶
lsst.meas.algorithms.ReferenceObjectsLoader
Object to conduct spatial searches through the reference catalogs
- refCatalogList
- getTaskDict() dict[str, weakref.ReferenceType[lsst.pipe.base.task.Task]] ¶
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
- classmethod makeField(doc: str) 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
- makeSubtask(name: str, **keyArgs: Any) 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
- run(astromRefCat: List[SimpleCatalog], exposure: Exposure, fitDonutCatalog: QTable, photoRefCat: List[SimpleCatalog]) Struct ¶
Run task algorithm on in-memory data.
This method should be implemented in a subclass. This method will receive keyword arguments whose names will be the same as names of connection fields describing input dataset types. Argument values will be data objects retrieved from data butler. If a dataset type is configured with
multiple
field set toTrue
then the argument value will be a list of objects, otherwise it will be a single object.If the task needs to know its input or output DataIds then it has to override
runQuantum
method instead.This method should return a
Struct
whose attributes share the same name as the connection fields describing output dataset types.Parameters¶
- **kwargs
Any
Arbitrary parameters accepted by subclasses.
Returns¶
- struct
Struct
Struct with attribute names corresponding to output connection fields.
Examples¶
Typical implementation of this method may look like:
def run(self, input, calib): # "input", "calib", and "output" are the names of the config # fields # Assuming that input/calib datasets are `scalar` they are # simple objects, do something with inputs and calibs, produce # output image. image = self.makeImage(input, calib) # If output dataset is `scalar` then return object, not list return Struct(output=image)
- **kwargs
- runQuantum(butlerQC: QuantumContext, inputRefs: InputQuantizedConnection, outputRefs: OutputQuantizedConnection) None ¶
Do butler IO and transform to provide in memory objects for tasks
run
method.Parameters¶
- butlerQC
QuantumContext
A butler which is specialized to operate in the context of a
lsst.daf.butler.Quantum
.- inputRefs
InputQuantizedConnection
Datastructure whose attribute names are the names that identify connections defined in corresponding
PipelineTaskConnections
class. The values of these attributes are thelsst.daf.butler.DatasetRef
objects associated with the defined input/prerequisite connections.- outputRefs
OutputQuantizedConnection
Datastructure whose attribute names are the names that identify connections defined in corresponding
PipelineTaskConnections
class. The values of these attributes are thelsst.daf.butler.DatasetRef
objects associated with the defined output connections.
- butlerQC
- 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
andEnd
.- logLevel
int
A
logging
level constant.
Examples¶
Creating a timer context:
with self.timer("someCodeToTime"): pass # code to time
See Also¶
lsst.utils.timer.logInfo : Implementation function.
- name