TransformCatalogBaseTask¶

class lsst.pipe.tasks.postprocess.TransformCatalogBaseTask(*args, **kwargs)¶

Bases: lsst.pipe.base.PipelineTask

Base class for transforming/standardizing a catalog

by applying functors that convert units and apply calibrations. The purpose of this task is to perform a set of computations on an input ParquetTable dataset (such as deepCoadd_obj) and write the results to a new dataset (which needs to be declared in an outputDataset attribute).

The calculations to be performed are defined in a YAML file that specifies a set of functors to be computed, provided as a --functorFile config parameter. An example of such a YAML file is the following:

funcs:

psfMag:

functor: Mag args:

base_PsfFlux

filt: HSC-G dataset: meas

cmodel_magDiff:

functor: MagDiff args:

modelfit_CModel

base_PsfFlux

filt: HSC-G

gauss_magDiff:

functor: MagDiff args:

base_GaussianFlux

base_PsfFlux

filt: HSC-G

count:

functor: Column args:

base_InputCount_value

filt: HSC-G

deconvolved_moments:

functor: DeconvolvedMoments filt: HSC-G dataset: forced_src

refFlags:

calib_psfUsed

merge_measurement_i

merge_measurement_r

merge_measurement_z

merge_measurement_y

merge_measurement_g

base_PixelFlags_flag_inexact_psfCenter

detect_isPrimary

The names for each entry under “func” will become the names of columns in the output dataset. All the functors referenced are defined in lsst.pipe.tasks.functors. Positional arguments to be passed to each functor are in the args list, and any additional entries for each column other than “functor” or “args” (e.g., 'filt', 'dataset') are treated as keyword arguments to be passed to the functor initialization.

The “flags” entry is the default shortcut for Column functors. All columns listed under “flags” will be copied to the output table untransformed. They can be of any datatype. In the special case of transforming a multi-level oject table with band and dataset indices (deepCoadd_obj), these will be taked from the meas dataset and exploded out per band.

There are two special shortcuts that only apply when transforming multi-level Object (deepCoadd_obj) tables:

The “refFlags” entry is shortcut for Column functor taken from the 'ref' dataset if transforming an ObjectTable.

The “forcedFlags” entry is shortcut for Column functors. taken from the forced_src dataset if transforming an ObjectTable. These are expanded out per band.

This task uses the lsst.pipe.tasks.postprocess.PostprocessAnalysis object to organize and excecute the calculations.

Attributes Summary

`ConfigClass`
`canMultiprocess`
`inputDataset`
`outputDataset`

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.
`getAnalysis`(parq[, funcs, band])
`getFullMetadata`()	Get metadata for all tasks.
`getFullName`()	Get the task name as a hierarchical name including parent task names.
`getFunctors`()
`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`(parq[, funcs, dataId, band])	Do postprocessing calculations
`runQuantum`(butlerQC, inputRefs, outputRefs)	Method to do butler IO and or transforms 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, parq, funcs, dataId)

Attributes Documentation

ConfigClass¶

canMultiprocess = True¶

inputDataset¶

outputDataset¶

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.

getAnalysis(parq, funcs=None, band=None)¶

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”.

getFunctors()¶

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")

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(parq, funcs=None, dataId=None, band=None)¶

Do postprocessing calculations

Takes a ParquetTable object and dataId, returns a dataframe with results of postprocessing calculations.

Parameters:	parq : `lsst.pipe.tasks.parquetTable.ParquetTable` ParquetTable from which calculations are done. funcs : `lsst.pipe.tasks.functors.Functors` Functors to apply to the table’s columns dataId : dict, optional Used to add a `patchId` column to the output dataframe. band : `str`, optional Filter band that is being processed. Returns —— df : `pandas.DataFrame`

runQuantum(butlerQC, inputRefs, outputRefs)¶

Method to do butler IO and or transforms to provide in memory objects for tasks run method

Parameters:

butlerQC : ButlerQuantumContext: 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 the lsst.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 the lsst.daf.butler.DatasetRef objects associated with the defined output connections.

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

TransformCatalogBaseTask¶