lsst.jointcal.jointcal Namespace Reference

Classes
class	JointcalRunner
class	JointcalTaskConnections
class	JointcalConfig
class	JointcalInputData
class	JointcalTask

Functions
def	add_measurement (job, name, value)
def	lookupStaticCalibrations (datasetType, registry, quantumDataId, collections)
def	lookupVisitRefCats (datasetType, registry, quantumDataId, collections)
def	writeModel (model, filename, log)

Variables
	Photometry = collections.namedtuple('Photometry', ('fit', 'model'))
	Astrometry = collections.namedtuple('Astrometry', ('fit', 'model', 'sky_to_tan_projection'))

Function Documentation

add_measurement()

def lsst.jointcal.jointcal.add_measurement	(		job,
			name,
			value
	)

Definition at line 60 of file jointcal.py.

def add_measurement(job, name, value):
    meas = Measurement(job.metrics[name], value)
    job.measurements.insert(meas)
 
 

lookupStaticCalibrations()

def lsst.jointcal.jointcal.lookupStaticCalibrations	(		datasetType,
			registry,
			quantumDataId,
			collections
	)

Lookup function that asserts/hopes that a static calibration dataset
exists in a particular collection, since this task can't provide a single
date/time to use to search for one properly.

This is mostly useful for the ``camera`` dataset, in cases where the task's
quantum dimensions do *not* include something temporal, like ``exposure``
or ``visit``.

Parameters
----------
datasetType : `lsst.daf.butler.DatasetType`
    Type of dataset being searched for.
registry : `lsst.daf.butler.Registry`
    Data repository registry to search.
quantumDataId : `lsst.daf.butler.DataCoordinate`
    Data ID of the quantum this camera should match.
collections : `Iterable` [ `str` ]
    Collections that should be searched - but this lookup function works
    by ignoring this in favor of a more-or-less hard-coded value.

Returns
-------
refs : `Iterator` [ `lsst.daf.butler.DatasetRef` ]
    Iterator over dataset references; should have only one element.

Notes
-----
This implementation duplicates one in fgcmcal, and is at least quite
similar to another in cp_pipe.  This duplicate has the most documentation.
Fixing this is DM-29661.

Definition at line 142 of file jointcal.py.

def lookupStaticCalibrations(datasetType, registry, quantumDataId, collections):
    """Lookup function that asserts/hopes that a static calibration dataset
    exists in a particular collection, since this task can't provide a single
    date/time to use to search for one properly.
 
    This is mostly useful for the ``camera`` dataset, in cases where the task's
    quantum dimensions do *not* include something temporal, like ``exposure``
    or ``visit``.
 
    Parameters
    ----------
    datasetType : `lsst.daf.butler.DatasetType`
        Type of dataset being searched for.
    registry : `lsst.daf.butler.Registry`
        Data repository registry to search.
    quantumDataId : `lsst.daf.butler.DataCoordinate`
        Data ID of the quantum this camera should match.
    collections : `Iterable` [ `str` ]
        Collections that should be searched - but this lookup function works
        by ignoring this in favor of a more-or-less hard-coded value.
 
    Returns
    -------
    refs : `Iterator` [ `lsst.daf.butler.DatasetRef` ]
        Iterator over dataset references; should have only one element.
 
    Notes
    -----
    This implementation duplicates one in fgcmcal, and is at least quite
    similar to another in cp_pipe.  This duplicate has the most documentation.
    Fixing this is DM-29661.
    """
    instrument = Instrument.fromName(quantumDataId["instrument"], registry)
    unboundedCollection = instrument.makeUnboundedCalibrationRunName()
    return registry.queryDatasets(datasetType,
                                  dataId=quantumDataId,
                                  collections=[unboundedCollection],
                                  findFirst=True)
 
 

lookupVisitRefCats()

def lsst.jointcal.jointcal.lookupVisitRefCats	(		datasetType,
			registry,
			quantumDataId,
			collections
	)

Lookup function that finds all refcats for all visits that overlap a
tract, rather than just the refcats that directly overlap the tract.

Parameters
----------
datasetType : `lsst.daf.butler.DatasetType`
    Type of dataset being searched for.
registry : `lsst.daf.butler.Registry`
    Data repository registry to search.
quantumDataId : `lsst.daf.butler.DataCoordinate`
    Data ID of the quantum; expected to be something we can use as a
    constraint to query for overlapping visits.
collections : `Iterable` [ `str` ]
    Collections to search.

Returns
-------
refs : `Iterator` [ `lsst.daf.butler.DatasetRef` ]
    Iterator over refcat references.

Definition at line 182 of file jointcal.py.

def lookupVisitRefCats(datasetType, registry, quantumDataId, collections):
    """Lookup function that finds all refcats for all visits that overlap a
    tract, rather than just the refcats that directly overlap the tract.
 
    Parameters
    ----------
    datasetType : `lsst.daf.butler.DatasetType`
        Type of dataset being searched for.
    registry : `lsst.daf.butler.Registry`
        Data repository registry to search.
    quantumDataId : `lsst.daf.butler.DataCoordinate`
        Data ID of the quantum; expected to be something we can use as a
        constraint to query for overlapping visits.
    collections : `Iterable` [ `str` ]
        Collections to search.
 
    Returns
    -------
    refs : `Iterator` [ `lsst.daf.butler.DatasetRef` ]
        Iterator over refcat references.
    """
    refs = set()
    # Use .expanded() on the query methods below because we need data IDs with
    # regions, both in the outer loop over visits (queryDatasets will expand
    # any data ID we give it, but doing it up-front in bulk is much more
    # efficient) and in the data IDs of the DatasetRefs this function yields
    # (because the RefCatLoader relies on them to do some of its own
    # filtering).
    for visit_data_id in set(registry.queryDataIds("visit", dataId=quantumDataId).expanded()):
        refs.update(
            registry.queryDatasets(
                datasetType,
                collections=collections,
                dataId=visit_data_id,
                findFirst=True,
            ).expanded()
        )
    yield from refs
 
 

writeModel()

def lsst.jointcal.jointcal.writeModel	(		model,
			filename,
			log
	)

Write model to outfile.

Definition at line 626 of file jointcal.py.

def writeModel(model, filename, log):
    """Write model to outfile."""
    with open(filename, "w") as file:
        file.write(repr(model))
    log.info("Wrote %s to file: %s", model, filename)
 
 
@dataclasses.dataclass

Variable Documentation

Astrometry

lsst.jointcal.jointcal.Astrometry = collections.namedtuple('Astrometry', ('fit', 'model', 'sky_to_tan_projection'))

Definition at line 56 of file jointcal.py.

Photometry

lsst.jointcal.jointcal.Photometry = collections.namedtuple('Photometry', ('fit', 'model'))

Definition at line 55 of file jointcal.py.