TransformDiaSourceCatalogTask¶
- class lsst.ap.association.TransformDiaSourceCatalogTask(initInputs, **kwargs)¶
Bases:
TransformCatalogBaseTask
Transform a DiaSource catalog by calibrating and renaming columns to produce a table ready to insert into the Apdb.
- Parameters:
- initInputs
dict
Must contain
diaSourceSchema
as the schema for the input catalog.
- initInputs
Attributes Summary
Methods Summary
Add Column functor for each of the flags to the internal functor dictionary.
bitPackFlags
(df)Pack requested flag columns in inputRecord into single columns in outputRecord.
computeBBoxSizes
(inputCatalog)Compute the size of a square bbox that fully contains the detection footprint.
Empty (clear) the metadata for this Task and all sub-Tasks.
getAnalysis
(handles[, funcs, band])Get metadata for all tasks.
Get the task name as a hierarchical name including parent task names.
getName
()Get the name of the task.
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
(diaSourceCat, diffIm, band, ccdVisitId)Convert input catalog to ParquetTable/Pandas and run functors.
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.
transform
(band, handles, funcs, dataId)Attributes Documentation
- inputDataset = 'deepDiff_diaSrc'¶
- outputDataset = 'deepDiff_diaSrcTable'¶
Methods Documentation
- addUnpackedFlagFunctors()¶
Add Column functor for each of the flags to the internal functor dictionary.
- bitPackFlags(df)¶
Pack requested flag columns in inputRecord into single columns in outputRecord.
- Parameters:
- df
pandas.DataFrame
DataFrame to read bits from and pack them into.
- df
- computeBBoxSizes(inputCatalog)¶
Compute the size of a square bbox that fully contains the detection footprint.
- Parameters:
- inputCatalog
lsst.afw.table.SourceCatalog
Catalog containing detected footprints.
- inputCatalog
- Returns:
- outputBBoxSizes
np.ndarray
, (N,) Array of bbox sizes.
- outputBBoxSizes
- getAnalysis(handles, funcs=None, band=None)¶
- 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.
- metadata
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”.
- fullName
- getFunctors()¶
- getName() str ¶
Get the name of the task.
- Returns:
- taskName
str
Name of the task.
- taskName
See also
getFullName
Get the full name of the task.
- 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.
- doc
- Returns:
- configurableField
lsst.pex.config.ConfigurableField
A
ConfigurableField
for this task.
- configurableField
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")
- 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
.
- name
Notes
The subtask must be defined by
Task.config.name
, an instance ofConfigurableField
orRegistryField
.
- run(diaSourceCat, diffIm, band, ccdVisitId, reliability=None)¶
Convert input catalog to ParquetTable/Pandas and run functors.
Additionally, add new columns for stripping information from the exposure and into the DiaSource catalog.
- Parameters:
- diaSourceCat
lsst.afw.table.SourceCatalog
Catalog of sources measured on the difference image.
- diffIm
lsst.afw.image.Exposure
Result of subtracting template and science images.
- band
str
Filter band of the science image.
- ccdVisitId
int
Identifier for this detector+visit.
- reliability
lsst.afw.table.SourceCatalog
Reliability (e.g. real/bogus) scores, row-matched to
diaSourceCat
.
- diaSourceCat
- Returns:
- results
lsst.pipe.base.Struct
Results struct with components.
diaSourceTable
: Catalog of DiaSources with calibrated values and renamed columns. (lsst.pipe.tasks.ParquetTable
orpandas.DataFrame
)
- results
- runQuantum(butlerQC, inputRefs, outputRefs)¶
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:
See also
lsst.utils.timer.logInfo
Implementation function.
Examples
Creating a timer context:
with self.timer("someCodeToTime"): pass # code to time
- transform(band, handles, funcs, dataId)¶