RawIngestTask¶

class lsst.obs.base.RawIngestTask(config: Optional[lsst.obs.base.ingest.RawIngestConfig] = None, *, butler: lsst.daf.butler._butler.Butler, **kwargs)¶

Bases: lsst.pipe.base.Task

Driver Task for ingesting raw data into Gen3 Butler repositories.

Parameters:	config : `RawIngestConfig` Configuration for the task. butler : `Butler` Writeable butler instance, with `butler.run` set to the appropriate `RUN` collection for these raw datasets. **kwargs Additional keyword arguments are forwarded to the `lsst.pipe.base.Task` constructor.

Notes

Each instance of RawIngestTask writes to the same Butler. Each invocation of RawIngestTask.run ingests a list of files.

Methods Summary

`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`expandDataIds`(data)	Expand the data IDs associated with a raw exposure to include additional metadata records.
`extractMetadata`(filename)	Extract and process metadata from a single raw file.
`getAllSchemaCatalogs`()	Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.
`getDatasetType`()	Return the DatasetType of the datasets ingested by this Task.
`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.
`getSchemaCatalogs`()	Get the schemas generated by this task.
`getTaskDict`()	Get a dictionary of all tasks as a shallow copy.
`groupByExposure`(files)	Group an iterable of `RawFileData` by exposure.
`ingestExposureDatasets`(exposure, *, run)	Ingest all raw files in one exposure.
`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.
`prep`(files, *, pool, processes)	Perform all ingest preprocessing steps that do not involve actually modifying the database.
`run`(files, *, pool, processes, run)	Ingest files into a Butler data repository.
`timer`(name[, logLevel])	Context manager to log performance data for an arbitrary block of code.

Methods Documentation

emptyMetadata()¶: Empty (clear) the metadata for this Task and all sub-Tasks.

expandDataIds(data: lsst.obs.base.ingest.RawExposureData) → lsst.obs.base.ingest.RawExposureData¶

Expand the data IDs associated with a raw exposure to include additional metadata records.

Parameters:	exposure : `RawExposureData` A structure containing information about the exposure to be ingested. Must have `RawExposureData.records` populated. Should be considered consumed upon return.
Returns:	exposure : `RawExposureData` An updated version of the input structure, with `RawExposureData.dataId` and nested `RawFileData.dataId` attributes updated to data IDs for which `DataCoordinate.hasRecords` returns `True`.

extractMetadata(filename: str) → lsst.obs.base.ingest.RawFileData¶

Extract and process metadata from a single raw file.

Parameters:	filename : `str` Path to the file.
Returns:	data : `RawFileData` A structure containing the metadata extracted from the file, as well as the original filename. All fields will be populated, but the `RawFileData.dataId` attribute will be a minimal (unexpanded) `DataCoordinate` instance.

Notes

Assumes that there is a single dataset associated with the given file. Instruments using a single file to store multiple datasets must implement their own version of this method.

getAllSchemaCatalogs()¶

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.

getDatasetType()¶: Return the DatasetType of the datasets ingested by this Task.

getFullMetadata()¶

Get metadata for all tasks.

Returns:	metadata : `lsst.daf.base.PropertySet` The `PropertySet` 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()¶

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”.

getName()¶

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()¶

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.

groupByExposure(files: Iterable[lsst.obs.base.ingest.RawFileData]) → List[lsst.obs.base.ingest.RawExposureData]¶

Group an iterable of RawFileData by exposure.

Parameters:	files : iterable of `RawFileData` File-level information to group.
Returns:	exposures : `list` of `RawExposureData` A list of structures that group the file-level information by exposure. All fields will be populated. The `RawExposureData.dataId` attributes will be minimal (unexpanded) `DataCoordinate` instances.

ingestExposureDatasets(exposure: lsst.obs.base.ingest.RawExposureData, *, run: Optional[str] = None) → List[lsst.daf.butler.core.datasets.ref.DatasetRef]¶

Ingest all raw files in one exposure.

Parameters:	exposure : `RawExposureData` A structure containing information about the exposure to be ingested. Must have `RawExposureData.records` populated and all data ID attributes expanded. run : `str`, optional Name of a RUN-type collection to write to, overriding `self.butler.run`.
Returns:	refs : `list` of `lsst.daf.butler.DatasetRef` Dataset references for ingested raws.

classmethod makeField(doc)¶

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, **keyArgs)¶

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.

prep(files, *, pool: Optional[multiprocessing.context.BaseContext.Pool] = None, processes: int = 1) → Iterator[lsst.obs.base.ingest.RawExposureData]¶

Perform all ingest preprocessing steps that do not involve actually modifying the database.

Parameters:	files : iterable over `str` or path-like objects Paths to the files to be ingested. Will be made absolute if they are not already. pool : `multiprocessing.Pool`, optional If not `None`, a process pool with which to parallelize some operations. processes : `int`, optional The number of processes to use. Ignored if `pool` is not `None`.
Yields:	exposure : `RawExposureData` Data structures containing dimension records, filenames, and data IDs to be ingested (one structure for each exposure). bad_files : `list` of `str` List of all the files that could not have metadata extracted.

run(files, *, pool: Optional[multiprocessing.context.BaseContext.Pool] = None, processes: int = 1, run: Optional[str] = None)¶

Ingest files into a Butler data repository.

This creates any new exposure or visit Dimension entries needed to identify the ingested files, creates new Dataset entries in the Registry and finally ingests the files themselves into the Datastore. Any needed instrument, detector, and physical_filter Dimension entries must exist in the Registry before run is called.

Parameters:

files : iterable over str or path-like objects: Paths to the files to be ingested. Will be made absolute if they are not already.
pool : multiprocessing.Pool, optional: If not None, a process pool with which to parallelize some operations.
processes : int, optional: The number of processes to use. Ignored if pool is not None.
run : str, optional: Name of a RUN-type collection to write to, overriding the default derived from the instrument name.

Returns:

refs : list of lsst.daf.butler.DatasetRef: Dataset references for ingested raws.

Notes

This method inserts all datasets for an exposure within a transaction, guaranteeing that partial exposures are never ingested. The exposure dimension record is inserted with Registry.syncDimensionData first (in its own transaction), which inserts only if a record with the same primary key does not already exist. This allows different files within the same exposure to be incremented in different runs.

timer(name, logLevel=10000)¶

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 `lsst.log` level constant.

Navigation

RawIngestTask¶