def __init__(self, butler=None, profile_jointcal=False, **kwargs):
"""
Instantiate a JointcalTask.
Parameters
----------
butler : `lsst.daf.persistence.Butler`
The butler is passed to the refObjLoader constructor in case it is
needed. Ignored if the refObjLoader argument provides a loader directly.
Used to initialize the astrometry and photometry refObjLoaders.
profile_jointcal : `bool`
Set to True to profile different stages of this jointcal run.
"""
pipeBase.CmdLineTask.__init__(self, **kwargs)
self.profile_jointcal = profile_jointcal
self.makeSubtask("sourceSelector")
if self.config.doAstrometry:
self.makeSubtask('astrometryRefObjLoader', butler=butler)
self.makeSubtask("astrometryReferenceSelector")
else:
self.astrometryRefObjLoader = None
if self.config.doPhotometry:
self.makeSubtask('photometryRefObjLoader', butler=butler)
self.makeSubtask("photometryReferenceSelector")
else:
self.photometryRefObjLoader = None
# To hold various computed metrics for use by tests
self.job = Job.load_metrics_package(subset='jointcal')
def runDataRefs(self, datarefs, customMetadata=None):
"""Call all registered metric tasks on each dataref.
This method loads all datasets required to compute a particular
metric, and persists the metrics as one or more `lsst.verify.Job`
objects. Only metrics that successfully produce a
`~lsst.verify.Measurement` will be included in a job.
Parameters
----------
datarefs : `list` of `lsst.daf.persistence.ButlerDataRef`
The data to measure. Datarefs may be complete or partial; each
generates a measurement at the same granularity (e.g., a
dataref with only ``"visit"`` specified generates visit-level
measurements).
customMetadata : `dict`, optional
Any metadata that are needed for a specific pipeline, but that are
not needed by the ``lsst.verify`` framework or by general-purpose
measurement analysis code (these cases are handled by the
`~MetricsControllerConfig.metadataAdder` subtask). If omitted,
only generic metadata are added. Both keys and values must be valid
inputs to `~lsst.verify.Metadata`.
Returns
-------
struct : `lsst.pipe.base.Struct`
A `~lsst.pipe.base.Struct` containing the following component:
- ``jobs`` : a list of collections of measurements (`list` of
`lsst.verify.Job`). Each job in the list contains the
measurement(s) for the corresponding dataref, and each job has
at most one measurement for each element in `self.measurers`. A
particular measurement is omitted if it could not be created.
Notes
-----
Some objects may be persisted, or incorrectly persisted, in the event
of an exception.
"""
jobs = []
index = 0
for dataref in datarefs:
job = Job.load_metrics_package()
try:
self.metadataAdder.run(job, dataref=dataref)
if customMetadata:
job.meta.update(customMetadata)
for task in self.measurers:
self._computeSingleMeasurement(job, task, dataref)
finally:
jobFile = self._getJobFilePath(index, dataref.dataId)
self.log.info("Persisting metrics to %s...", jobFile)
# This call order maximizes the chance that job gets
# written, and to a unique file
index += 1
job.write(jobFile)
jobs.append(job)
return Struct(jobs=jobs)
def __init__(self, butler=None, profile_jointcal=False, **kwargs):
"""
Instantiate a JointcalTask.
Parameters
----------
butler : lsst.daf.persistence.Butler
The butler is passed to the refObjLoader constructor in case it is
needed. Ignored if the refObjLoader argument provides a loader directly.
Used to initialize the astrometry and photometry refObjLoaders.
profile_jointcal : bool
set to True to profile different stages of this jointcal run.
"""
pipeBase.CmdLineTask.__init__(self, **kwargs)
self.profile_jointcal = profile_jointcal
self.makeSubtask("sourceSelector")
if self.config.doAstrometry:
self.makeSubtask('astrometryRefObjLoader', butler=butler)
if self.config.doPhotometry:
self.makeSubtask('photometryRefObjLoader', butler=butler)
# To hold various computed metrics for use by tests
self.job = Job.load_metrics_package(subset='jointcal')
def runOneFilter(repo,
visitDataIds,
brightSnrMin=None,
brightSnrMax=None,
makeJson=True,
filterName=None,
outputPrefix='',
doApplyExternalPhotoCalib=False,
externalPhotoCalibName=None,
doApplyExternalSkyWcs=False,
externalSkyWcsName=None,
skipTEx=False,
verbose=False,
metrics_package='verify_metrics',
instrument='Unknown',
dataset_repo_url='./',
skipNonSrd=False,
**kwargs):
r"""Main executable for the case where there is just one filter.
Plot files and JSON files are generated in the local directory
prefixed with the repository name (where '_' replace path separators),
unless overriden by specifying `outputPrefix`.
E.g., Analyzing a repository ``CFHT/output``
will result in filenames that start with ``CFHT_output_``.
Parameters
----------
repo : string or Butler
A Butler 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` pixel image is needed for the photometric calibration
unless doApplyExternalPhotoCalib is True such
that the appropriate `photoCalib` dataset is used. Note that these
have data IDs that include the tract number.
brightSnrMin : float, optional
Minimum median SNR for a source to be considered bright; passed to
`lsst.validate.drp.matchreduce.build_matched_dataset`.
brightSnrMax : float, optional
Maximum median SNR for a source to be considered bright; passed to
`lsst.validate.drp.matchreduce.build_matched_dataset`.
makeJson : bool, optional
Create JSON output file for metrics. Saved to current working directory.
outputPrefix : str, optional
Specify the beginning filename for output files.
filterName : str, optional
Name of the filter (bandpass).
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).
verbose : bool, optional
Output additional information on the analysis steps.
skipNonSrd : bool, optional
Skip any metrics not defined in the LSST SRD.
Raises
------
RuntimeError:
Raised if "doApplyExternalPhotoCalib" is True and "externalPhotoCalibName"
is None, or if "doApplyExternalSkyWcs" is True and "externalSkyWcsName" is
None.
"""
if kwargs:
log.warn(
f"Extra kwargs - {kwargs}, will be ignored. Did you add extra things to your config file?"
)
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.")
# collect just the common key, value pairs to omit the keys that are aggregated over
job_metadata = dict(
set.intersection(*[set(vid.items()) for vid in visitDataIds]))
# update with metadata passed into the method
job_metadata.update({
'instrument': instrument,
'filter_name': filterName,
'dataset_repo_url': dataset_repo_url
})
job = Job.load_metrics_package(meta=job_metadata,
subset='validate_drp',
package_name_or_path=metrics_package)
matchedDataset = build_matched_dataset(
repo,
visitDataIds,
doApplyExternalPhotoCalib=doApplyExternalPhotoCalib,
externalPhotoCalibName=externalPhotoCalibName,
doApplyExternalSkyWcs=doApplyExternalSkyWcs,
externalSkyWcsName=externalSkyWcsName,
skipTEx=skipTEx,
skipNonSrd=skipNonSrd,
brightSnrMin=brightSnrMin,
brightSnrMax=brightSnrMax)
snr = matchedDataset['snr'].quantity
bright = (matchedDataset['brightSnrMin'].quantity <
snr) & (snr < matchedDataset['brightSnrMax'].quantity)
photomModel = build_photometric_error_model(matchedDataset, bright)
astromModel = build_astrometric_error_model(matchedDataset, bright)
linkedBlobs = [matchedDataset, photomModel, astromModel]
metrics = job.metrics
specs = job.specs
def add_measurement(measurement):
for blob in linkedBlobs:
measurement.link_blob(blob)
job.measurements.insert(measurement)
for x, D in zip((1, 2, 3), (5., 20., 200.)):
amxName = 'AM{0:d}'.format(x)
afxName = 'AF{0:d}'.format(x)
adxName = 'AD{0:d}'.format(x)
amx = measureAMx(metrics['validate_drp.' + amxName],
matchedDataset,
D * u.arcmin,
verbose=verbose)
add_measurement(amx)
afx_spec_set = specs.subset(required_meta={'instrument': 'HSC'},
spec_tags=[
afxName,
])
adx_spec_set = specs.subset(required_meta={'instrument': 'HSC'},
spec_tags=[
adxName,
])
for afx_spec_key, adx_spec_key in zip(afx_spec_set, adx_spec_set):
afx_spec = afx_spec_set[afx_spec_key]
adx_spec = adx_spec_set[adx_spec_key]
adx = measureADx(metrics[adx_spec.metric_name], amx, afx_spec)
add_measurement(adx)
afx = measureAFx(metrics[afx_spec.metric_name], amx, adx, adx_spec)
add_measurement(afx)
pa1 = measurePA1(metrics['validate_drp.PA1'], filterName,
matchedDataset.matchesBright, matchedDataset.magKey)
add_measurement(pa1)
pf1_spec_set = specs.subset(required_meta={
'instrument': instrument,
'filter_name': filterName
},
spec_tags=[
'PF1',
])
pa2_spec_set = specs.subset(required_meta={
'instrument': instrument,
'filter_name': filterName
},
spec_tags=[
'PA2',
])
# I worry these might not always be in the right order. Sorting...
pf1_spec_keys = list(pf1_spec_set.keys())
pa2_spec_keys = list(pa2_spec_set.keys())
pf1_spec_keys.sort()
pa2_spec_keys.sort()
for pf1_spec_key, pa2_spec_key in zip(pf1_spec_keys, pa2_spec_keys):
pf1_spec = pf1_spec_set[pf1_spec_key]
pa2_spec = pa2_spec_set[pa2_spec_key]
pa2 = measurePA2(metrics[pa2_spec.metric_name], pa1,
pf1_spec.threshold)
add_measurement(pa2)
pf1 = measurePF1(metrics[pf1_spec.metric_name], pa1, pa2_spec)
add_measurement(pf1)
if not skipTEx:
for x, D, bin_range_operator in zip((1, 2), (1.0, 5.0), ("<=", ">=")):
texName = 'TE{0:d}'.format(x)
tex = measureTEx(metrics['validate_drp.' + texName],
matchedDataset,
D * u.arcmin,
bin_range_operator,
verbose=verbose)
add_measurement(tex)
if not skipNonSrd:
model_phot_reps = measure_model_phot_rep(metrics, filterName,
matchedDataset)
for measurement in model_phot_reps:
add_measurement(measurement)
if makeJson:
job.write(outputPrefix + '.json')
return job
def merge(jobs, lastJob):
"""Combine measurements from multiple chips or visits.
Other job properties will be dictionary-merged (i.e., if multiple entries
are assigned to the same key, only one will be preserved).
Parameters
----------
jobs: iterable of `lsst.verify.Job`
The jobs containing data to combine.
lastJob:
The job corresponding to the final run of ap_verify.
Return
------
A single `lsst.verify.Job` object containing merged measurements from
`jobs`.
"""
merged = Job.load_metrics_package()
# Visible Job state:
# job.measurements
# job.meta
# job.metrics (guaranteed by load_metrics_package)
# job.specs (guaranteed by load_metrics_package)
measurementsPerMetric = defaultdict(list)
for job in jobs:
for metricName in job.measurements:
measurementsPerMetric[str(metricName)].append(
job.measurements[metricName])
for metric in measurementsPerMetric:
# Running times, object counts
if metric.endswith("Time") or metric in {
"ip_diffim.numSciSources", "association.numNewDiaObjects",
"association.totalUnassociatedDiaObjects"
}:
addIfDefined(merged.measurements,
sumMeasurements(measurementsPerMetric[metric]))
# Fractions require special handling
addIfDefined(
merged.measurements,
# Due to time constraints, no metric for total DIAObjects was implemented,
# so we have to work around its absence
mergeFractionsPartial(
measurementsPerMetric["association.fracUpdatedDiaObjects"],
measurementsPerMetric["association.numUnassociatedDiaObjects"]))
addIfDefined(
merged.measurements,
mergeFractions(
measurementsPerMetric["ip_diffim.fracDiaSourcesToSciSources"],
measurementsPerMetric["ip_diffim.numSciSources"]))
# L1 database metrics are cumulative, not per-CCD, so just copy them over
for metric in ["association.totalUnassociatedDiaObjects"]:
if metric in lastJob.measurements:
addIfDefined(merged.measurements, lastJob.measurements[metric])
for job in jobs:
merged.meta.update(job.meta)
return merged
def runOneFilter(repo,
visitDataIds,
metrics,
brightSnr=100,
makeJson=True,
filterName=None,
outputPrefix='',
useJointCal=False,
skipTEx=False,
verbose=False,
metrics_package='verify_metrics',
**kwargs):
"""Main executable for the case where there is just one filter.
Plot files and JSON files are generated in the local directory
prefixed with the repository name (where '_' replace path separators),
unless overriden by specifying `outputPrefix`.
E.g., Analyzing a repository ``CFHT/output``
will result in filenames that start with ``CFHT_output_``.
Parameters
----------
repo : string or Butler
A Butler 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` pixel image is needed for the photometric calibration
unless useJointCal is True, in which the `photoCalib` and `wcs`
datasets are used instead. Note that these have data IDs that include
the tract number.
metrics : `dict` or `collections.OrderedDict`
Dictionary of `lsst.validate.base.Metric` instances. Typically this is
data from ``validate_drp``\ 's ``metrics.yaml`` and loaded with
`lsst.validate.base.load_metrics`.
brightSnr : float, optional
Minimum SNR for a star to be considered bright
makeJson : bool, optional
Create JSON output file for metrics. Saved to current working directory.
outputPrefix : str, optional
Specify the beginning filename for output files.
filterName : str, optional
Name of the filter (bandpass).
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).
verbose : bool, optional
Output additional information on the analysis steps.
"""
matchedDataset = build_matched_dataset(repo,
visitDataIds,
useJointCal=useJointCal,
skipTEx=skipTEx)
photomModel = build_photometric_error_model(matchedDataset)
astromModel = build_astrometric_error_model(matchedDataset)
linkedBlobs = [matchedDataset, photomModel, astromModel]
try:
instrument = kwargs['instrument']
dataset_repo_url = kwargs['dataset_repo_url']
except KeyError:
raise ValueError(
"Instrument name and input dataset URL must be set in config file")
job = Job.load_metrics_package(meta={
'instrument': instrument,
'filter_name': filterName,
'dataset_repo_url': dataset_repo_url
},
subset='validate_drp',
package_name_or_path=metrics_package)
metrics = job.metrics
specs = job.specs
def add_measurement(measurement):
for blob in linkedBlobs:
measurement.link_blob(blob)
job.measurements.insert(measurement)
for x, D in zip((1, 2, 3), (5., 20., 200.)):
amxName = 'AM{0:d}'.format(x)
afxName = 'AF{0:d}'.format(x)
adxName = 'AD{0:d}'.format(x)
amx = measureAMx(metrics['validate_drp.' + amxName], matchedDataset,
D * u.arcmin)
add_measurement(amx)
afx_spec_set = specs.subset(required_meta={'instrument': 'HSC'},
spec_tags=[
afxName,
])
adx_spec_set = specs.subset(required_meta={'instrument': 'HSC'},
spec_tags=[
adxName,
])
for afx_spec_key, adx_spec_key in zip(afx_spec_set, adx_spec_set):
afx_spec = afx_spec_set[afx_spec_key]
adx_spec = adx_spec_set[adx_spec_key]
adx = measureADx(metrics[adx_spec.metric_name], amx, afx_spec)
add_measurement(adx)
afx = measureAFx(metrics[afx_spec.metric_name], amx, adx, adx_spec)
add_measurement(afx)
pa1 = measurePA1(metrics['validate_drp.PA1'], matchedDataset, filterName)
add_measurement(pa1)
pf1_spec_set = specs.subset(required_meta={
'instrument': instrument,
'filter_name': filterName
},
spec_tags=[
'PF1',
])
pa2_spec_set = specs.subset(required_meta={
'instrument': instrument,
'filter_name': filterName
},
spec_tags=[
'PA2',
])
# I worry these might not always be in the right order. Sorting...
pf1_spec_keys = list(pf1_spec_set.keys())
pa2_spec_keys = list(pa2_spec_set.keys())
pf1_spec_keys.sort()
pa2_spec_keys.sort()
for pf1_spec_key, pa2_spec_key in zip(pf1_spec_keys, pa2_spec_keys):
pf1_spec = pf1_spec_set[pf1_spec_key]
pa2_spec = pa2_spec_set[pa2_spec_key]
pa2 = measurePA2(metrics[pa2_spec.metric_name], pa1,
pf1_spec.threshold)
add_measurement(pa2)
pf1 = measurePF1(metrics[pf1_spec.metric_name], pa1, pa2_spec)
add_measurement(pf1)
if not skipTEx:
for x, D, bin_range_operator in zip((1, 2), (1.0, 5.0), ("<=", ">=")):
texName = 'TE{0:d}'.format(x)
tex = measureTEx(metrics['validate_drp.' + texName],
matchedDataset, D * u.arcmin, bin_range_operator)
add_measurement(tex)
if makeJson:
job.write(outputPrefix + '.json')
return job
