def build_matched_dataset(repo,
dataIds,
matchRadius=None,
safeSnr=50.,
useJointCal=False,
skipTEx=False):
blob = Blob('MatchedMultiVisitDataset')
if not matchRadius:
matchRadius = afwGeom.Angle(1, afwGeom.arcseconds)
# Extract single filter
blob['filterName'] = Datum(quantity=set([dId['filter']
for dId in dataIds]).pop(),
description='Filter name')
# Record important configuration
blob['useJointCal'] = Datum(
quantity=useJointCal,
description='Whether jointcal/meas_mosaic calibrations were used')
# Match catalogs across visits
blob._catalog, blob._matchedCatalog = \
_loadAndMatchCatalogs(repo, dataIds, matchRadius,
useJointCal=useJointCal, skipTEx=False)
blob.magKey = blob._matchedCatalog.schema.find("base_PsfFlux_mag").key
# Reduce catalogs into summary statistics.
# These are the serialiable attributes of this class.
_reduceStars(blob, blob._matchedCatalog, safeSnr)
return blob
def build_matched_dataset(repo,
dataIds,
matchRadius=None,
safeSnr=50.,
useJointCal=False,
skipTEx=False):
"""Construct a container for matched star catalogs from multple visits, with filtering,
summary statistics, and modelling.
`lsst.verify.Blob` instances are serializable to JSON.
Parameters
----------
repo : `str` or `Butler`
A Butler instance or a repository URL that can be used to construct
one.
dataIds : `list` of `dict`
List of `butler` data IDs of Image catalogs to compare to reference.
The `calexp` cpixel image is needed for the photometric calibration.
matchRadius : afwGeom.Angle(), optional
Radius for matching. Default is 1 arcsecond.
safeSnr : `float`, optional
Minimum median SNR for a match to be considered "safe".
useJointCal : `bool`, optional
Use jointcal/meas_mosaic outputs to calibrate positions and fluxes.
skipTEx : `bool`, optional
Skip TEx calculations (useful for older catalogs that don't have
PsfShape measurements).
Attributes of returned Blob
----------
filterName : `str`
Name of filter used for all observations.
mag : `astropy.units.Quantity`
Mean PSF magnitudes of stars over multiple visits (magnitudes).
magerr : `astropy.units.Quantity`
Median 1-sigma uncertainty of PSF magnitudes over multiple visits
(magnitudes).
magrms : `astropy.units.Quantity`
RMS of PSF magnitudes over multiple visits (magnitudes).
snr : `astropy.units.Quantity`
Median signal-to-noise ratio of PSF magnitudes over multiple visits
(dimensionless).
dist : `astropy.units.Quantity`
RMS of sky coordinates of stars over multiple visits (milliarcseconds).
*Not serialized.*
goodMatches
all good matches, as an afw.table.GroupView;
good matches contain only objects whose detections all have
1. a PSF Flux measurement with S/N > 1
2. a finite (non-nan) PSF magnitude. This separate check is largely
to reject failed zeropoints.
3. and do not have flags set for bad, cosmic ray, edge or saturated
*Not serialized.*
safeMatches
safe matches, as an afw.table.GroupView. Safe matches
are good matches that are sufficiently bright and sufficiently
compact.
*Not serialized.*
magKey
Key for `"base_PsfFlux_mag"` in the `goodMatches` and `safeMatches`
catalog tables.
*Not serialized.*
"""
blob = Blob('MatchedMultiVisitDataset')
if not matchRadius:
matchRadius = afwGeom.Angle(1, afwGeom.arcseconds)
# Extract single filter
blob['filterName'] = Datum(quantity=set([dId['filter']
for dId in dataIds]).pop(),
description='Filter name')
# Record important configuration
blob['useJointCal'] = Datum(
quantity=useJointCal,
description='Whether jointcal/meas_mosaic calibrations were used')
# Match catalogs across visits
blob._catalog, blob._matchedCatalog = \
_loadAndMatchCatalogs(repo, dataIds, matchRadius,
useJointCal=useJointCal, skipTEx=skipTEx)
blob.magKey = blob._matchedCatalog.schema.find("base_PsfFlux_mag").key
# Reduce catalogs into summary statistics.
# These are the serialiable attributes of this class.
_reduceStars(blob, blob._matchedCatalog, safeSnr)
return blob
def build_matched_dataset(repo,
dataIds,
matchRadius=None,
brightSnrMin=None,
brightSnrMax=None,
faintSnrMin=None,
faintSnrMax=None,
doApplyExternalPhotoCalib=False,
externalPhotoCalibName=None,
doApplyExternalSkyWcs=False,
externalSkyWcsName=None,
skipTEx=False,
skipNonSrd=False):
"""Construct a container for matched star catalogs from multple visits, with filtering,
summary statistics, and modelling.
`lsst.verify.Blob` instances are serializable to JSON.
Parameters
----------
repo : `str` or `lsst.daf.persistence.Butler`
A Butler instance or a repository URL that can be used to construct
one.
dataIds : `list` of `dict`
List of `butler` data IDs of Image catalogs to compare to reference.
The `calexp` cpixel image is needed for the photometric calibration.
matchRadius : `lsst.geom.Angle`, optional
Radius for matching. Default is 1 arcsecond.
brightSnrMin : `float`, optional
Minimum median SNR for a source to be considered bright; passed to `filterSources`.
brightSnrMax : `float`, optional
Maximum median SNR for a source to be considered bright; passed to `filterSources`.
faintSnrMin : `float`, optional
Minimum median SNR for a source to be considered faint; passed to `filterSources`.
faintSnrMax : `float`, optional
Maximum median SNR for a source to be considered faint; passed to `filterSources`.
doApplyExternalPhotoCalib : bool, optional
Apply external photoCalib to calibrate fluxes.
externalPhotoCalibName : str, optional
Type of external `PhotoCalib` to apply. Currently supported are jointcal,
fgcm, and fgcm_tract. Must be set if "doApplyExternalPhotoCalib" is True.
doApplyExternalSkyWcs : bool, optional
Apply external wcs to calibrate positions.
externalSkyWcsName : str, optional:
Type of external `wcs` to apply. Currently supported is jointcal.
Must be set if "doApplyExternalSkyWcs" is True.
skipTEx : `bool`, optional
Skip TEx calculations (useful for older catalogs that don't have
PsfShape measurements).
skipNonSrd : `bool`, optional
Skip any metrics not defined in the LSST SRD; default False.
Attributes of returned Blob
----------
filterName : `str`
Name of filter used for all observations.
mag : `astropy.units.Quantity`
Mean PSF magnitudes of stars over multiple visits (magnitudes).
magerr : `astropy.units.Quantity`
Median 1-sigma uncertainty of PSF magnitudes over multiple visits
(magnitudes).
magrms : `astropy.units.Quantity`
RMS of PSF magnitudes over multiple visits (magnitudes).
snr : `astropy.units.Quantity`
Median signal-to-noise ratio of PSF magnitudes over multiple visits
(dimensionless).
dist : `astropy.units.Quantity`
RMS of sky coordinates of stars over multiple visits (milliarcseconds).
*Not serialized.*
matchesFaint : `afw.table.GroupView`
Faint matches containing only objects that have:
1. A PSF Flux measurement with sufficient S/N.
2. A finite (non-nan) PSF magnitude. This separate check is largely
to reject failed zeropoints.
3. No flags set for bad, cosmic ray, edge or saturated.
4. Extendedness consistent with a point source.
*Not serialized.*
matchesBright : `afw.table.GroupView`
Bright matches matching a higher S/N threshold than matchesFaint.
*Not serialized.*
magKey
Key for `"base_PsfFlux_mag"` in the `matchesFaint` and `matchesBright`
catalog tables.
*Not serialized.*
Raises
------
RuntimeError:
Raised if "doApplyExternalPhotoCalib" is True and "externalPhotoCalibName"
is None, or if "doApplyExternalSkyWcs" is True and "externalSkyWcsName" is
None.
"""
if doApplyExternalPhotoCalib and externalPhotoCalibName is None:
raise RuntimeError(
"Must set externalPhotoCalibName if doApplyExternalPhotoCalib is True."
)
if doApplyExternalSkyWcs and externalSkyWcsName is None:
raise RuntimeError(
"Must set externalSkyWcsName if doApplyExternalSkyWcs is True.")
blob = Blob('MatchedMultiVisitDataset')
if not matchRadius:
matchRadius = geom.Angle(1, geom.arcseconds)
# Extract single filter
blob['filterName'] = Datum(quantity=set([dId['filter']
for dId in dataIds]).pop(),
description='Filter name')
# Record important configuration
blob['doApplyExternalPhotoCalib'] = Datum(
quantity=doApplyExternalPhotoCalib,
description=('Whether external photometric '
'calibrations were used.'))
blob['externalPhotoCalibName'] = Datum(
quantity=externalPhotoCalibName,
description='Name of external PhotoCalib dataset used.')
blob['doApplyExternalSkyWcs'] = Datum(
quantity=doApplyExternalSkyWcs,
description='Whether external wcs calibrations were used.')
blob['externalSkyWcsName'] = Datum(
quantity=externalSkyWcsName,
description='Name of external wcs dataset used.')
# Match catalogs across visits
blob._catalog, blob._matchedCatalog = \
_loadAndMatchCatalogs(repo, dataIds, matchRadius,
doApplyExternalPhotoCalib=doApplyExternalPhotoCalib,
externalPhotoCalibName=externalPhotoCalibName,
doApplyExternalSkyWcs=doApplyExternalSkyWcs,
externalSkyWcsName=externalSkyWcsName,
skipTEx=skipTEx, skipNonSrd=skipNonSrd)
blob.magKey = blob._matchedCatalog.schema.find("base_PsfFlux_mag").key
# Reduce catalogs into summary statistics.
# These are the serializable attributes of this class.
filterResult = filterSources(
blob._matchedCatalog,
brightSnrMin=brightSnrMin,
brightSnrMax=brightSnrMax,
faintSnrMin=faintSnrMin,
faintSnrMax=faintSnrMax,
)
blob['brightSnrMin'] = Datum(
quantity=filterResult.brightSnrMin * u.Unit(''),
label='Bright SNR Min',
description='Minimum median SNR for a source to be considered bright')
blob['brightSnrMax'] = Datum(
quantity=filterResult.brightSnrMax * u.Unit(''),
label='Bright SNR Max',
description='Maximum median SNR for a source to be considered bright')
summarizeSources(blob, filterResult)
return blob