MegaPrimeRawIngestTask

class lsst.obs.cfht.MegaPrimeRawIngestTask(config: Optional[lsst.obs.base.ingest.RawIngestConfig] = None, *, butler: lsst.daf.butler._butler.Butler, on_success: Callable[[List[lsst.daf.butler.core.fileDataset.FileDataset]], Any] = <function _do_nothing>, on_metadata_failure: Callable[[str, Exception], Any] = <function _do_nothing>, on_ingest_failure: Callable[[lsst.obs.base.ingest.RawExposureData, Exception], Any] = <function _do_nothing>, **kwargs)

Bases: lsst.obs.base.RawIngestTask

Task for ingesting raw MegaPrime multi-extension FITS data into Gen3.

Deprecated since version 22.0: MegaPrime no longer requires a specialist gen3 ingest task. Please use the default. Will be removed after v23.

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.
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.
ingestFiles(files, *, pool, processes, run, …) Ingest files into a Butler data repository.
locateAndReadIndexFiles(files) Given a list of files, look for index files and read them.
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 non-database-updating ingest preprocessing steps.
processIndexEntries(index_entries) Convert index entries to RawFileData.
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.

This adds the 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 hasRecords returns True.

extractMetadata(filename: lsst.daf.butler.core._butlerUri._butlerUri.ButlerURI) → lsst.obs.base.ingest.RawFileData

Extract and process metadata from a single raw file.

Parameters:
filename : ButlerURI

URI 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. The instrument field will be None if there is a problem with metadata extraction.

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.

By default the method will catch all exceptions unless the failFast configuration item is True. If an error is encountered the _on_metadata_failure() method will be called. If no exceptions result and an error was encountered the returned object will have a null-instrument class and no datasets.

This method supports sidecar JSON files which can be used to extract metadata without having to read the data file itself. The sidecar file is always used if found.

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

getFullName

getSchemaCatalogs()

Get the schemas generated by this task.

Returns:
schemaCatalogs : dict

Keys are butler dataset type, values are an empty catalog (an instance of the appropriate lsst.afw.table Catalog type) for this 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, skip_existing_exposures: bool = False) → List[lsst.daf.butler.core.fileDataset.FileDataset]

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.

skip_existing_exposures : bool, optional

If True (False is default), skip raws that have already been ingested (i.e. raws for which we already have a dataset with the same data ID in the target collection, even if from another file). Note that this is much slower than just not passing already-ingested files as inputs, because we still need to read and process metadata to identify which exposures to search for. It also will not work reliably if multiple processes are attempting to ingest raws from the same exposure concurrently, in that different processes may still attempt to ingest the same raw and conflict, causing a failure that prevents other raws from the same exposure from being ingested.

Returns:
datasets : list of lsst.daf.butler.FileDataset

Per-file structures identifying the files ingested and their dataset representation in the data repository.

ingestFiles(files, *, pool: Optional[multiprocessing.context.BaseContext.Pool] = None, processes: int = 1, run: Optional[str] = None, skip_existing_exposures: bool = False, update_exposure_records: bool = False)

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 ButlerURI

URIs to the files to be ingested.

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.

skip_existing_exposures : bool, optional

If True (False is default), skip raws that have already been ingested (i.e. raws for which we already have a dataset with the same data ID in the target collection, even if from another file). Note that this is much slower than just not passing already-ingested files as inputs, because we still need to read and process metadata to identify which exposures to search for. It also will not work reliably if multiple processes are attempting to ingest raws from the same exposure concurrently, in that different processes may still attempt to ingest the same raw and conflict, causing a failure that prevents other raws from the same exposure from being ingested.

update_exposure_records : bool, optional

If True (False is default), update existing exposure records that conflict with the new ones instead of rejecting them. THIS IS AN ADVANCED OPTION THAT SHOULD ONLY BE USED TO FIX METADATA THAT IS KNOWN TO BE BAD. This should usually be combined with skip_existing_exposures=True.

Returns:
refs : list of lsst.daf.butler.DatasetRef

Dataset references for ingested raws.

locateAndReadIndexFiles(files)

Given a list of files, look for index files and read them.

Index files can either be explicitly in the list of files to ingest, or else located in the same directory as a file to ingest. Index entries are always used if present.

Parameters:
files : iterable over ButlerURI

URIs to the files to be ingested.

Returns:
index : dict [str, Any]

Merged contents of all relevant index files found. These can be explicitly specified index files or ones found in the directory alongside a data file to be ingested.

updated_files : iterable of str

Updated list of the input files with entries removed that were found listed in an index file. Order is not guaranteed to match the order of the files given to this routine.

bad_index_files: `set[str]`

Files that looked like index files but failed to read properly.

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) → Tuple[Iterator[lsst.obs.base.ingest.RawExposureData], List[str]]

Perform all non-database-updating ingest preprocessing steps.

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:
exposures : Iterator [ 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.

processIndexEntries(index_entries)

Convert index entries to RawFileData.

Parameters:
index_entries : dict [str, Any]

Dict indexed by name of file to ingest and with keys either raw metadata or translated ObservationInfo.

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.

run(files, *, pool: Optional[multiprocessing.context.BaseContext.Pool] = None, processes: int = 1, run: Optional[str] = None, file_filter: Union[str, re.Pattern] = '\\.fit[s]?\\b', group_files: bool = True, skip_existing_exposures: bool = False, update_exposure_records: bool = False)

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 ButlerURI, str or path-like objects

Paths to the files to be ingested. Can refer to directories. 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.

file_filter : str or re.Pattern, optional

Pattern to use to discover files to ingest within directories. The default is to search for FITS files. The regex applies to files within the directory.

group_files : bool, optional

Group files by directory if they have been discovered in directories. Will not affect files explicitly provided.

skip_existing_exposures : bool, optional

If True (False is default), skip raws that have already been ingested (i.e. raws for which we already have a dataset with the same data ID in the target collection, even if from another file). Note that this is much slower than just not passing already-ingested files as inputs, because we still need to read and process metadata to identify which exposures to search for. It also will not work reliably if multiple processes are attempting to ingest raws from the same exposure concurrently, in that different processes may still attempt to ingest the same raw and conflict, causing a failure that prevents other raws from the same exposure from being ingested.

update_exposure_records : bool, optional

If True (False is default), update existing exposure records that conflict with the new ones instead of rejecting them. THIS IS AN ADVANCED OPTION THAT SHOULD ONLY BE USED TO FIX METADATA THAT IS KNOWN TO BE BAD. This should usually be combined with skip_existing_exposures=True.

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 ingested in different runs.

timer(name, logLevel=10)

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.

See also

timer.logInfo

Examples

Creating a timer context:

with self.timer("someCodeToTime"):
    pass  # code to time