DecamRawIngestTask¶

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

Bases: lsst.obs.base.RawIngestTask

Task for ingesting raw DECam data into a Gen3 Butler repository.

Methods Summary

`collectDimensionRecords`(exposure)	Collect the `DimensionRecord` instances that must be inserted into the `Registry` before an exposure’s raw files may be.
`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, butler)	Ingest all raw files in one exposure.
`insertDimensionData`(records, …)	Insert dimension records for one or more exposures.
`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)	Ingest files into a Butler data repository.
`timer`(name[, logLevel])	Context manager to log performance data for an arbitrary block of code.

Methods Documentation

collectDimensionRecords(exposure: lsst.obs.base.ingest.RawExposureData) → lsst.obs.base.ingest.RawExposureData¶

Collect the DimensionRecord instances that must be inserted into the Registry before an exposure’s raw files may be.

Parameters:	exposure : `RawExposureData` A structure containing information about the exposure to be ingested. Should be considered consumed upon return.
Returns:	exposure : `RawExposureData` An updated version of the input structure, with `RawExposureData.records` populated.

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 containing `ExpandedDataCoordinate` instances.

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 implemenation 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. The `RawExposureData.records` attributes of elements will be `None`, but all other fields will be populated. The `RawExposureData.dataId` attributes will be minimal (unexpanded) `DataCoordinate` instances.

ingestExposureDatasets(exposure: lsst.obs.base.ingest.RawExposureData, butler: Optional[lsst.daf.butler._butler.Butler] = 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. butler : `lsst.daf.butler.Butler`, optional Butler to use for ingest. If not provided, `self.butler` will be used.
Returns:	refs : `list` of `lsst.daf.butler.DatasetRef` Dataset references for ingested raws.

insertDimensionData(records: Mapping[str, List[lsst.daf.butler.core.dimensions.records.DimensionRecord]])¶

Insert dimension records for one or more exposures.

Parameters:	records : `dict` mapping `str` to `list` Dimension records to be inserted, organized as a mapping from dimension name to a list of records for that dimension. This may be a single `RawExposureData.records` dict, or an aggregate for multiple exposures created by concatenating the value lists of those dictionaries.
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("a brief description of what this task does")

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 pex_config 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).

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

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

Notes

This method inserts all records (dimensions and datasets) for an exposure within a transaction, guaranteeing that partial exposures are never ingested.

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

DecamRawIngestTask¶