def makeMeasurement(self, values):
"""Compute the number of non-updated DIAObjects.
Parameters
----------
values : sequence [`dict` [`str`, `int` or `None`]]
A list where each element corresponds to a metadata object passed
to `run`. Each `dict` has the following keys:
``"updatedObjects"``
The number of DIAObjects updated for this image (`int` or
`None`). May be `None` if the image was not
successfully associated.
``"unassociatedObjects"``
The number of DIAObjects not associated with a DiaSource in
this image (`int` or `None`). May be `None` if the image was
not successfully associated.
Returns
-------
measurement : `lsst.verify.Measurement` or `None`
The total number of unassociated objects.
"""
nUpdated = 0
nUnassociated = 0
associated = False
for value in values:
if value["updatedObjects"] is not None \
and value["unassociatedObjects"] is not None:
try:
nUpdated += value["updatedObjects"]
nUnassociated += value["unassociatedObjects"]
except TypeError as e:
raise MetricComputationError(
"Corrupted value of numUpdatedDiaObjects "
"or numUnassociatedDiaObjects") from e
associated = True
if associated:
if nUpdated <= 0 and nUnassociated <= 0:
raise MetricComputationError(
"No pre-existing DIAObjects; can't compute updated fraction."
)
else:
fraction = nUpdated / (nUpdated + nUnassociated)
return Measurement(self.getOutputMetricName(self.config),
fraction * u.dimensionless_unscaled)
else:
self.log.info("Nothing to do: no association results found.")
return None
def makeMeasurement(self, values):
"""Compute the number of new DIAObjects.
Parameters
----------
values : `dict` [`str`, `int` or `None`]
A `dict` representation of the metadata. Each `dict` has the
following key:
``"newObjects"``
The number of new objects created for this image (`int`
or `None`). May be `None` if the image was not successfully
associated.
Returns
-------
measurement : `lsst.verify.Measurement` or `None`
The total number of new objects.
"""
if values["newObjects"] is not None:
try:
nNew = int(values["newObjects"])
except (ValueError, TypeError) as e:
raise MetricComputationError(
"Corrupted value of numNewDiaObjects") from e
else:
return Measurement(self.config.metricName, nNew * u.count)
else:
self.log.info("Nothing to do: no association results found.")
return None
def makeMeasurement(self, values):
"""Compute the total number of SolarSystemObjects within a
detectorVisit.
Parameters
----------
values : `dict` [`str`, `int` or `None`]
A `dict` representation of the metadata. Each `dict` has the
following key:
``"numTotalSolarSystemObjects"``
The number of SolarSystemObjects within the observable detector
area (`int` or `None`). May be `None` if solar system
association was not attempted or the image was not
successfully associated.
Returns
-------
measurement : `lsst.verify.Measurement` or `None`
The total number of Solar System objects.
"""
if values["numTotalSolarSystemObjects"] is not None:
try:
nNew = int(values["numTotalSolarSystemObjects"])
except (ValueError, TypeError) as e:
raise MetricComputationError(
"Corrupted value of numTotalSolarSystemObjects") from e
else:
return Measurement(self.config.metricName, nNew * u.count)
else:
self.log.info("Nothing to do: no solar system results found.")
return None
def run(self, matchedFakes, band):
"""Compute the completeness of recovered fakes within a magnitude
range.
Parameters
----------
matchedFakes : `lsst.afw.table.SourceCatalog` or `None`
Catalog of fakes that were inserted into the ccdExposure matched
to their detected counterparts.
Returns
-------
result : `lsst.pipe.base.Struct`
A `~lsst.pipe.base.Struct` containing the following component:
``measurement``
the ratio (`lsst.verify.Measurement` or `None`)
"""
if matchedFakes is not None:
magnitudes = matchedFakes[f"{self.config.magVar}" % band]
magCutFakes = matchedFakes[np.logical_and(magnitudes > self.config.magMin,
magnitudes < self.config.magMax)]
if len(magCutFakes) <= 0.0:
raise MetricComputationError(
"No matched fakes catalog sources found; Completeness is "
"ill defined.")
else:
meas = Measurement(
self.config.metricName,
((magCutFakes["diaSourceId"] > 0).sum() / len(magCutFakes))
* u.dimensionless_unscaled)
else:
self.log.info("Nothing to do: no matched catalog found.")
meas = None
return Struct(measurement=meas)
def run(self, sciSources, diaSources):
"""Compute the ratio of DIASources to science sources.
Parameters
----------
sciSources : `lsst.afw.table.SourceCatalog` or `None`
A science source catalog, which may be empty or `None`.
diaSources : `lsst.afw.table.SourceCatalog` or `None`
A DIASource catalog for the same unit of processing
as ``sciSources``.
Returns
-------
result : `lsst.pipe.base.Struct`
A `~lsst.pipe.base.Struct` containing the following component:
``measurement``
the ratio (`lsst.verify.Measurement` or `None`)
"""
if diaSources is not None and sciSources is not None:
nSciSources = len(sciSources)
nDiaSources = len(diaSources)
metricName = self.config.metricName
if nSciSources <= 0.0:
raise MetricComputationError(
"No science sources found; ratio of DIASources to science sources ill-defined."
)
else:
meas = Measurement(
metricName,
nDiaSources / nSciSources * u.dimensionless_unscaled)
else:
self.log.info("Nothing to do: no catalogs found.")
meas = None
return Struct(measurement=meas)
def makeMeasurement(self, timings):
"""Compute a wall-clock measurement from metadata provided by
`lsst.pipe.base.timeMethod`.
Parameters
----------
timings : sequence [`dict` [`str`, any]]
A list where each element corresponds to a metadata object passed
to `run`. Each `dict` has the following keys:
``"StartTime"``
The time the target method started (`float` or `None`).
``"EndTime"``
The time the target method ended (`float` or `None`).
Returns
-------
measurement : `lsst.verify.Measurement` or `None`
The the total running time of the target method across all
elements of ``metadata``.
Raises
------
MetricComputationError
Raised if any of the timing metadata are invalid.
Notes
-----
This method does not return a measurement if no timing information was
provided by any of the metadata.
"""
# some timings indistinguishable from 0, so don't test totalTime > 0
timingFound = False
totalTime = 0.0
for singleRun in timings:
if singleRun["StartTime"] is not None \
or singleRun["EndTime"] is not None:
try:
totalTime += singleRun["EndTime"] - singleRun["StartTime"]
timingFound = True
except TypeError:
raise MetricComputationError("Invalid metadata")
# If both are None, assume the method was not run that time
if timingFound:
meas = Measurement(self.getOutputMetricName(self.config),
totalTime * u.second)
meas.notes['estimator'] = 'pipe.base.timeMethod'
return meas
else:
self.log.info("Nothing to do: no timing information for %s found.",
self.config.target)
return None
def run(self, sources):
"""Count the number of science sources created by deblending.
Parameters
----------
sources : `lsst.afw.table.SourceCatalog` or `None`
A science source catalog, which may be empty or `None`.
Returns
-------
result : `lsst.pipe.base.Struct`
A `~lsst.pipe.base.Struct` containing the following component:
``measurement``
the total number of science sources from deblending
(`lsst.verify.Measurement`). If no deblending information is
available in ``sources``, this is `None`.
Raises
------
MetricComputationError
Raised if ``sources`` is missing mandatory keys for
source catalogs.
"""
if sources is None:
self.log.info("Nothing to do: no catalogs found.")
meas = None
# Use deblend_parentNChild rather than detect_fromBlend because the
# latter need not be defined in post-deblending catalogs.
elif "deblend_parentNChild" not in sources.schema or "deblend_nChild" not in sources.schema:
self.log.info("Nothing to do: no deblending performed.")
meas = None
else:
try:
children = ((sources["deblend_parentNChild"] > 1) # deblend child
& (sources["deblend_nChild"] == 0) # not deblended
)
children = _filterSkySources(sources, children)
except LookupError as e:
# Probably "parent"; all other columns already checked
raise MetricComputationError("Invalid input catalog") from e
else:
nChildren = np.count_nonzero(children)
meas = Measurement(self.config.metricName, nChildren * u.dimensionless_unscaled)
return Struct(measurement=meas)
def makeMeasurement(self, timings):
"""Compute a wall-clock measurement from metadata provided by
`lsst.utils.timer.timeMethod`.
Parameters
----------
timings : `dict` [`str`, any]
A representation of the metadata passed to `run`. The `dict` has
the following keys:
``"StartTime"``
The time the target method started (`float` or `None`).
``"EndTime"``
The time the target method ended (`float` or `None`).
``"StartTimestamp"``, ``"EndTimestamp"``
The start and end timestamps, in an ISO 8601-compliant format
(`str` or `None`).
Returns
-------
measurement : `lsst.verify.Measurement` or `None`
The running time of the target method.
Raises
------
MetricComputationError
Raised if the timing metadata are invalid.
"""
if timings["StartTime"] is not None or timings["EndTime"] is not None:
try:
totalTime = timings["EndTime"] - timings["StartTime"]
except TypeError:
raise MetricComputationError("Invalid metadata")
else:
meas = Measurement(self.config.metricName,
totalTime * u.second)
meas.notes["estimator"] = "utils.timer.timeMethod"
if timings["StartTimestamp"]:
meas.extras["start"] = Datum(timings["StartTimestamp"])
if timings["EndTimestamp"]:
meas.extras["end"] = Datum(timings["EndTimestamp"])
return meas
else:
self.log.info("Nothing to do: no timing information for %s found.",
self.config.target)
return None
def makeMeasurement(self, values):
"""Compute the number of non-updated DIAObjects.
AssociationTask reports each pre-existing DIAObject as either updated
(associated with a new DIASource) or unassociated.
Parameters
----------
values : `dict` [`str`, `int` or `None`]
A `dict` representation of the metadata. Each `dict` has the
following keys:
``"updatedObjects"``
The number of DIAObjects updated for this image (`int` or
`None`). May be `None` if the image was not
successfully associated.
``"unassociatedObjects"``
The number of DIAObjects not associated with a DiaSource in
this image (`int` or `None`). May be `None` if the image was
not successfully associated.
Returns
-------
measurement : `lsst.verify.Measurement` or `None`
The total number of unassociated objects.
"""
if values["updatedObjects"] is not None \
and values["unassociatedObjects"] is not None:
try:
nUpdated = int(values["updatedObjects"])
nUnassociated = int(values["unassociatedObjects"])
except (ValueError, TypeError) as e:
raise MetricComputationError(
"Corrupted value of numUpdatedDiaObjects "
"or numUnassociatedDiaObjects") from e
else:
if nUpdated <= 0 and nUnassociated <= 0:
return None # No pre-existing DIAObjects; no fraction to compute
else:
fraction = nUpdated / (nUpdated + nUnassociated)
return Measurement(self.config.metricName,
fraction * u.dimensionless_unscaled)
else:
self.log.info("Nothing to do: no association results found.")
return None
def run(self, sources):
"""Count the number of deblended science sources.
Parameters
----------
sources : `lsst.afw.table.SourceCatalog` or `None`
A science source catalog, which may be empty or `None`.
Returns
-------
result : `lsst.pipe.base.Struct`
A `~lsst.pipe.base.Struct` containing the following component:
``measurement``
the total number of deblended science sources
(`lsst.verify.Measurement`). If no deblending information is
available in ``sources``, this is `None`.
Raises
------
MetricComputationError
Raised if ``sources`` is missing mandatory keys for
source catalogs.
"""
if sources is None:
self.log.info("Nothing to do: no catalogs found.")
meas = None
elif "deblend_nChild" not in sources.schema:
self.log.info("Nothing to do: no deblending performed.")
meas = None
else:
try:
deblended = ((sources["parent"] == 0) # top-level source
& (sources["deblend_nChild"] > 0) # deblended
)
deblended = _filterSkySources(sources, deblended)
except LookupError as e:
# Probably "parent"; all other columns already checked
raise MetricComputationError("Invalid input catalog") from e
else:
nDeblended = np.count_nonzero(deblended)
meas = Measurement(self.config.metricName, nDeblended * u.dimensionless_unscaled)
return Struct(measurement=meas)
def run(self, sciSources, diaSources):
"""Compute the ratio of DIASources to science sources.
Parameters
----------
sciSources : iterable of `lsst.afw.table.SourceCatalog`
A collection of science source catalogs, one for each unit of
processing to be incorporated into this metric. Its elements may
be `None` to represent missing data.
diaSources : iterable of `lsst.afw.table.SourceCatalog`
A collection of difference imaging catalogs similar to
``sciSources``.
Returns
-------
result : `lsst.pipe.base.Struct`
A `~lsst.pipe.base.Struct` containing the following component:
``measurement``
the ratio (`lsst.verify.Measurement` or `None`)
"""
nSciSources = 0
nDiaSources = 0
inputData = False
for sciCatalog, diaCatalog in zip(sciSources, diaSources):
if diaCatalog is not None and sciCatalog is not None:
nSciSources += len(sciCatalog)
nDiaSources += len(diaCatalog)
inputData = True
if inputData:
metricName = self.getOutputMetricName(self.config)
if nSciSources <= 0.0:
raise MetricComputationError(
"No science sources found; ratio of DIASources to science sources ill-defined.")
meas = Measurement(metricName, 0.0 * u.dimensionless_unscaled)
else:
meas = Measurement(metricName, nDiaSources / nSciSources * u.dimensionless_unscaled)
else:
self.log.info("Nothing to do: no catalogs found.")
meas = None
return Struct(measurement=meas)
def makeMeasurement(self, memory):
"""Compute a maximum resident set size measurement from metadata
provided by `lsst.utils.timer.timeMethod`.
Parameters
----------
memory : `dict` [`str`, any]
A representation of the metadata passed to `run`. Each `dict` has
the following keys:
``"EndMemory"``
The memory usage at the end of the method (`int` or `None`).
``"MetadataVersion"``
The version of the task metadata in which the value was stored
(`int` or `None`). `None` is assumed to be version 0.
Returns
-------
measurement : `lsst.verify.Measurement` or `None`
The maximum memory usage of the target method.
Raises
------
MetricComputationError
Raised if the memory metadata are invalid.
"""
if memory["EndMemory"] is not None:
try:
maxMemory = int(memory["EndMemory"])
version = memory["MetadataVersion"] \
if memory["MetadataVersion"] else 0
except (ValueError, TypeError) as e:
raise MetricComputationError("Invalid metadata") from e
else:
meas = Measurement(self.config.metricName,
self._addUnits(maxMemory, version))
meas.notes['estimator'] = 'utils.timer.timeMethod'
return meas
else:
self.log.info("Nothing to do: no memory information for %s found.",
self.config.target)
return None
def makeMeasurement(self, dbHandle, outputDataId):
"""Compute the number of unassociated DIAObjects.
Parameters
----------
dbHandle : `lsst.dax.apdb.Apdb`
A database instance.
outputDataId : any data ID type
The subset of the database to which this measurement applies.
Must be empty, as the number of unassociated sources is
ill-defined for subsets of the dataset.
Returns
-------
measurement : `lsst.verify.Measurement`
The total number of unassociated objects.
Raises
------
MetricComputationError
Raised on any failure to query the database.
ValueError
Raised if outputDataId is not empty
"""
# All data ID types define keys()
if outputDataId.keys() - {'instrument'}:
raise ValueError(
"%s must not be associated with specific data IDs (gave %s)." %
(self.config.metricName, outputDataId))
try:
nUnassociatedDiaObjects = dbHandle.countUnassociatedObjects()
except Exception as e:
raise MetricComputationError(
"Could not get unassociated objects from database") from e
meas = Measurement(self.config.metricName,
nUnassociatedDiaObjects * u.count)
return meas
def _extractMetadata(metadata, metadataKeys):
"""Read multiple keys from a metadata object.
Parameters
----------
metadata : `lsst.daf.base.PropertySet`
A metadata object, assumed not `None`.
metadataKeys : `dict` [`str`, `str`]
Keys are arbitrary labels, values are metadata keys (or their
substrings) in the format of
`lsst.pipe.base.Task.getFullMetadata()`.
Returns
-------
metadataValues : `dict` [`str`, any]
Keys are the same as for ``metadataKeys``, values are the value of
each metadata key, or `None` if no matching key was found.
Raises
------
lsst.verify.tasks.MetricComputationError
Raised if any metadata key string has more than one match
in ``metadata``.
"""
data = {}
for dataName, keyFragment in metadataKeys.items():
matchingKeys = MetadataMetricTask._searchKeys(
metadata, keyFragment)
if len(matchingKeys) == 1:
key, = matchingKeys
data[dataName] = metadata.getScalar(key)
elif not matchingKeys:
data[dataName] = None
else:
error = "String %s matches multiple metadata keys: %s" \
% (keyFragment, matchingKeys)
raise MetricComputationError(error)
return data
def makeMeasurement(self, values):
"""Compute the number of non-updated DIAObjects.
Parameters
----------
values : sequence [`dict` [`str`, `int` or `None`]]
A list where each element corresponds to a metadata object passed
to `run`. Each `dict` has the following key:
``"unassociatedObjects"``
The number of DIAObjects not associated with a DiaSource in
this image (`int` or `None`). May be `None` if the image was
not successfully associated.
Returns
-------
measurement : `lsst.verify.Measurement` or `None`
The total number of unassociated objects.
"""
nNew = 0
associated = False
for value in values:
if value["unassociatedObjects"] is not None:
try:
nNew += value["unassociatedObjects"]
except TypeError as e:
raise MetricComputationError(
"Corrupted value of numUnassociatedDiaObjects") from e
associated = True
if associated:
return Measurement(self.getOutputMetricName(self.config),
nNew * u.count)
else:
self.log.info("Nothing to do: no association results found.")
return None
def run(self, *args, **kwargs):
raise MetricComputationError()
