def testRunQuantum(self):
inputId = {
"instrument": self.CAMERA_ID,
"visit": self.VISIT_ID,
"detector": self.CHIP_ID,
}
butler = self._makeButler()
# self.task.config not persistable because it refers to a local class
# We don't actually use the persisted config, so just make a new one
butler.put(self.task.ConfigClass(), "apdb_marker", inputId)
quantum = Quantum(taskClass=self.taskClass)
quantum.addPredictedInput(
ref_from_connection(butler, self.connections.dbInfo, inputId))
quantum.addOutput(
ref_from_connection(butler, self.connections.measurement, {
"instrument": self.CAMERA_ID,
}))
run_quantum(self.task, butler, quantum)
# Did output data ID get passed to DummyTask.run?
measurement = butler.get(self.connections.measurement.name,
instrument=self.CAMERA_ID)
self.assertEqual(measurement.quantity,
len(self.CAMERA_ID) * u.dimensionless_unscaled)
def _makeQuanta(self, config):
"""Create set of Quanta
"""
run = Run(collection=1, environment=None, pipeline=None)
descriptor = pipeBase.DatasetTypeDescriptor.fromConfig(config.input)
dstype0 = descriptor.datasetType
descriptor = pipeBase.DatasetTypeDescriptor.fromConfig(config.output)
dstype1 = descriptor.datasetType
quanta = []
for visit in range(100):
quantum = Quantum(run=run, task=None)
quantum.addPredictedInput(self._makeDSRefVisit(dstype0, visit))
quantum.addOutput(self._makeDSRefVisit(dstype1, visit))
quanta.append(quantum)
return quanta
def _makeQuanta(self, config):
"""Create set of Quanta
"""
universe = DimensionUniverse()
run = Run(collection=1, environment=None, pipeline=None)
connections = config.connections.ConnectionsClass(config=config)
dstype0 = connections.input.makeDatasetType(universe)
dstype1 = connections.output.makeDatasetType(universe)
quanta = []
for visit in range(100):
quantum = Quantum(run=run)
quantum.addPredictedInput(
self._makeDSRefVisit(dstype0, visit, universe))
quantum.addOutput(self._makeDSRefVisit(dstype1, visit, universe))
quanta.append(quantum)
return quanta
def _makeQuanta(self, config):
"""Create set of Quanta
"""
universe = DimensionUniverse.fromConfig()
run = Run(collection=1, environment=None, pipeline=None)
descriptor = pipeBase.DatasetTypeDescriptor.fromConfig(config.input)
dstype0 = descriptor.makeDatasetType(universe)
descriptor = pipeBase.DatasetTypeDescriptor.fromConfig(config.output)
dstype1 = descriptor.makeDatasetType(universe)
quanta = []
for visit in range(100):
quantum = Quantum(run=run, task=None)
quantum.addPredictedInput(self._makeDSRefVisit(dstype0, visit))
quantum.addOutput(self._makeDSRefVisit(dstype1, visit))
quanta.append(quantum)
return quanta
def testAddInputsOutputs(self):
"""Test of addPredictedInput() method.
"""
quantum = Quantum(taskName="some.task.object", run=None)
# start with empty
self.assertEqual(quantum.predictedInputs, dict())
universe = DimensionUniverse()
instrument = "DummyCam"
datasetTypeName = "test_ds"
storageClass = StorageClass("testref_StructuredData")
datasetType = DatasetType(datasetTypeName,
universe.extract(("instrument", "visit")),
storageClass)
# add one ref
ref = DatasetRef(datasetType, dict(instrument=instrument, visit=42))
quantum.addPredictedInput(ref)
self.assertIn(datasetTypeName, quantum.predictedInputs)
self.assertEqual(len(quantum.predictedInputs[datasetTypeName]), 1)
# add second ref
ref = DatasetRef(datasetType, dict(instrument=instrument, visit=43))
quantum.addPredictedInput(ref)
self.assertEqual(len(quantum.predictedInputs[datasetTypeName]), 2)
# mark last ref as actually used
self.assertEqual(quantum.actualInputs, dict())
quantum._markInputUsed(ref)
self.assertIn(datasetTypeName, quantum.actualInputs)
self.assertEqual(len(quantum.actualInputs[datasetTypeName]), 1)
# add couple of outputs too
self.assertEqual(quantum.outputs, dict())
ref = DatasetRef(datasetType, dict(instrument=instrument, visit=42))
quantum.addOutput(ref)
self.assertIn(datasetTypeName, quantum.outputs)
self.assertEqual(len(quantum.outputs[datasetTypeName]), 1)
ref = DatasetRef(datasetType, dict(instrument=instrument, visit=43))
quantum.addOutput(ref)
self.assertEqual(len(quantum.outputs[datasetTypeName]), 2)
def _makeGraph(self, taskDatasets, inputs, outputs, initInputs, initOutputs, originInfo, userQuery):
"""Make QuantumGraph instance.
Parameters
----------
taskDatasets : sequence of `_TaskDatasetTypes`
Tasks with their inputs and outputs.
inputs : `set` of `DatasetType`
Datasets which should already exist in input repository
outputs : `set` of `DatasetType`
Datasets which will be created by tasks
initInputs : `set` of `DatasetType`
Datasets which should exist in input repository, and will be used
in task initialization
initOutputs : `set` of `DatasetType`
Datasets which which will be created in task initialization
originInfo : `DatasetOriginInfo`
Object which provides names of the input/output collections.
userQuery : `str`
String which defunes user-defined selection for registry, should be
empty or `None` if there is no restrictions on data selection.
Returns
-------
`QuantumGraph` instance.
"""
parsedQuery = self._parseUserQuery(userQuery or "")
expr = None if parsedQuery is None else str(parsedQuery)
rows = self.registry.selectDimensions(originInfo, expr, inputs, outputs)
# store result locally for multi-pass algorithm below
# TODO: change it to single pass
dimensionVerse = []
for row in rows:
_LOG.debug("row: %s", row)
dimensionVerse.append(row)
# Next step is to group by task quantum dimensions
qgraph = QuantumGraph()
qgraph._inputDatasetTypes = inputs
qgraph._outputDatasetTypes = outputs
for dsType in initInputs:
for collection in originInfo.getInputCollections(dsType.name):
result = self.registry.find(collection, dsType)
if result is not None:
qgraph.initInputs.append(result)
break
else:
raise GraphBuilderError(f"Could not find initInput {dsType.name} in any input"
" collection")
for dsType in initOutputs:
qgraph.initOutputs.append(DatasetRef(dsType, {}))
for taskDss in taskDatasets:
taskQuantaInputs = {} # key is the quantum dataId (as tuple)
taskQuantaOutputs = {} # key is the quantum dataId (as tuple)
qlinks = []
for dimensionName in taskDss.taskDef.config.quantum.dimensions:
dimension = self.dimensions[dimensionName]
qlinks += dimension.link
_LOG.debug("task %s qdimensions: %s", taskDss.taskDef.label, qlinks)
# some rows will be non-unique for subset of dimensions, create
# temporary structure to remove duplicates
for row in dimensionVerse:
qkey = tuple((col, row.dataId[col]) for col in qlinks)
_LOG.debug("qkey: %s", qkey)
def _dataRefKey(dataRef):
return tuple(sorted(dataRef.dataId.items()))
qinputs = taskQuantaInputs.setdefault(qkey, {})
for dsType in taskDss.inputs:
dataRefs = qinputs.setdefault(dsType, {})
dataRef = row.datasetRefs[dsType]
dataRefs[_dataRefKey(dataRef)] = dataRef
_LOG.debug("add input dataRef: %s %s", dsType.name, dataRef)
qoutputs = taskQuantaOutputs.setdefault(qkey, {})
for dsType in taskDss.outputs:
dataRefs = qoutputs.setdefault(dsType, {})
dataRef = row.datasetRefs[dsType]
dataRefs[_dataRefKey(dataRef)] = dataRef
_LOG.debug("add output dataRef: %s %s", dsType.name, dataRef)
# pre-flight does not fill dataset components, and graph users
# may need to know that, re-retrieve all input datasets to have
# their components properly filled.
for qinputs in taskQuantaInputs.values():
for dataRefs in qinputs.values():
for key in dataRefs.keys():
if dataRefs[key].id is not None:
dataRefs[key] = self.registry.getDataset(dataRefs[key].id)
# all nodes for this task
quanta = []
for qkey in taskQuantaInputs:
# taskQuantaInputs and taskQuantaOutputs have the same keys
_LOG.debug("make quantum for qkey: %s", qkey)
quantum = Quantum(run=None, task=None)
# add all outputs, but check first that outputs don't exist
outputs = list(chain.from_iterable(dataRefs.values()
for dataRefs in taskQuantaOutputs[qkey].values()))
for ref in outputs:
_LOG.debug("add output: %s", ref)
if self.skipExisting and all(ref.id is not None for ref in outputs):
_LOG.debug("all output dataRefs already exist, skip quantum")
continue
if any(ref.id is not None for ref in outputs):
# some outputs exist, can't override them
raise OutputExistsError(taskDss.taskDef.taskName, outputs)
for ref in outputs:
quantum.addOutput(ref)
# add all inputs
for dataRefs in taskQuantaInputs[qkey].values():
for ref in dataRefs.values():
quantum.addPredictedInput(ref)
_LOG.debug("add input: %s", ref)
quanta.append(quantum)
qgraph.append(QuantumGraphNodes(taskDss.taskDef, quanta))
return qgraph
def _makeGraph(self,
taskDatasets,
required,
optional,
prerequisite,
initInputs,
initOutputs,
originInfo,
userQuery,
perDatasetTypeDimensions=()):
"""Make QuantumGraph instance.
Parameters
----------
taskDatasets : sequence of `_TaskDatasetTypes`
Tasks with their inputs and outputs.
required : `set` of `~lsst.daf.butler.DatasetType`
Datasets that must exist in the repository in order to generate
a QuantumGraph node that consumes them.
optional : `set` of `~lsst.daf.butler.DatasetType`
Datasets that will be produced by the graph, but may exist in
the repository. If ``self.skipExisting`` and all outputs of a
particular node already exist, it will be skipped. Otherwise
pre-existing datasets of these types will cause
`OutputExistsError` to be raised.
prerequisite : `set` of `~lsst.daf.butler.DatasetType`
Datasets that must exist in the repository, but whose absence
should cause `PrerequisiteMissingError` to be raised if they
are needed by any graph node that would otherwise be created.
initInputs : `set` of `DatasetType`
Datasets which should exist in input repository, and will be used
in task initialization
initOutputs : `set` of `DatasetType`
Datasets which which will be created in task initialization
originInfo : `DatasetOriginInfo`
Object which provides names of the input/output collections.
userQuery : `str`
String which defines user-defined selection for registry, should be
empty or `None` if there is no restrictions on data selection.
perDatasetTypeDimensions : iterable of `Dimension` or `str`
Dimensions (or names thereof) that may have different values for
different dataset types within the same quantum.
Returns
-------
`QuantumGraph` instance.
"""
rows = self.registry.selectMultipleDatasetTypes(
originInfo,
userQuery,
required=required,
optional=optional,
prerequisite=prerequisite,
perDatasetTypeDimensions=perDatasetTypeDimensions)
# store result locally for multi-pass algorithm below
# TODO: change it to single pass
dimensionVerse = []
try:
for row in rows:
_LOG.debug("row: %s", row)
dimensionVerse.append(row)
except LookupError as err:
raise PrerequisiteMissingError(str(err)) from err
# Next step is to group by task quantum dimensions
qgraph = QuantumGraph()
qgraph._inputDatasetTypes = (required | prerequisite)
qgraph._outputDatasetTypes = optional
for dsType in initInputs:
for collection in originInfo.getInputCollections(dsType.name):
result = self.registry.find(collection, dsType)
if result is not None:
qgraph.initInputs.append(result)
break
else:
raise GraphBuilderError(
f"Could not find initInput {dsType.name} in any input"
" collection")
for dsType in initOutputs:
qgraph.initOutputs.append(DatasetRef(dsType, {}))
for taskDss in taskDatasets:
taskQuantaInputs = {} # key is the quantum dataId (as tuple)
taskQuantaOutputs = {} # key is the quantum dataId (as tuple)
qlinks = []
for dimensionName in taskDss.taskDef.config.quantum.dimensions:
dimension = self.dimensions[dimensionName]
qlinks += dimension.links()
_LOG.debug("task %s qdimensions: %s", taskDss.taskDef.label,
qlinks)
# some rows will be non-unique for subset of dimensions, create
# temporary structure to remove duplicates
for row in dimensionVerse:
qkey = tuple((col, row.dataId[col]) for col in qlinks)
_LOG.debug("qkey: %s", qkey)
def _datasetRefKey(datasetRef):
return tuple(sorted(datasetRef.dataId.items()))
qinputs = taskQuantaInputs.setdefault(qkey, {})
for dsType in taskDss.inputs:
datasetRefs = qinputs.setdefault(dsType, {})
datasetRef = row.datasetRefs[dsType]
datasetRefs[_datasetRefKey(datasetRef)] = datasetRef
_LOG.debug("add input datasetRef: %s %s", dsType.name,
datasetRef)
qoutputs = taskQuantaOutputs.setdefault(qkey, {})
for dsType in taskDss.outputs:
datasetRefs = qoutputs.setdefault(dsType, {})
datasetRef = row.datasetRefs[dsType]
datasetRefs[_datasetRefKey(datasetRef)] = datasetRef
_LOG.debug("add output datasetRef: %s %s", dsType.name,
datasetRef)
# all nodes for this task
quanta = []
for qkey in taskQuantaInputs:
# taskQuantaInputs and taskQuantaOutputs have the same keys
_LOG.debug("make quantum for qkey: %s", qkey)
quantum = Quantum(run=None, task=None)
# add all outputs, but check first that outputs don't exist
outputs = list(
chain.from_iterable(
datasetRefs.values()
for datasetRefs in taskQuantaOutputs[qkey].values()))
for ref in outputs:
_LOG.debug("add output: %s", ref)
if self.skipExisting and all(ref.id is not None
for ref in outputs):
_LOG.debug(
"all output datasetRefs already exist, skip quantum")
continue
if any(ref.id is not None for ref in outputs):
# some outputs exist, can't override them
raise OutputExistsError(taskDss.taskDef.taskName, outputs)
for ref in outputs:
quantum.addOutput(ref)
# add all inputs
for datasetRefs in taskQuantaInputs[qkey].values():
for ref in datasetRefs.values():
quantum.addPredictedInput(ref)
_LOG.debug("add input: %s", ref)
quanta.append(quantum)
qgraph.append(QuantumGraphTaskNodes(taskDss.taskDef, quanta))
return qgraph
def _makeGraph(self, taskDatasets, required, optional, prerequisite,
initInputs, initOutputs, originInfo, userQuery,
perDatasetTypeDimensions=()):
"""Make QuantumGraph instance.
Parameters
----------
taskDatasets : sequence of `_TaskDatasetTypes`
Tasks with their inputs and outputs.
required : `set` of `~lsst.daf.butler.DatasetType`
Datasets that must exist in the repository in order to generate
a QuantumGraph node that consumes them.
optional : `set` of `~lsst.daf.butler.DatasetType`
Datasets that will be produced by the graph, but may exist in
the repository. If ``self.skipExisting`` and all outputs of a
particular node already exist, it will be skipped. Otherwise
pre-existing datasets of these types will cause
`OutputExistsError` to be raised.
prerequisite : `set` of `~lsst.daf.butler.DatasetType`
Datasets that must exist in the repository, but whose absence
should cause `PrerequisiteMissingError` to be raised if they
are needed by any graph node that would otherwise be created.
initInputs : `set` of `DatasetType`
Datasets which should exist in input repository, and will be used
in task initialization
initOutputs : `set` of `DatasetType`
Datasets which which will be created in task initialization
originInfo : `DatasetOriginInfo`
Object which provides names of the input/output collections.
userQuery : `str`
String which defines user-defined selection for registry, should be
empty or `None` if there is no restrictions on data selection.
perDatasetTypeDimensions : iterable of `Dimension` or `str`
Dimensions (or names thereof) that may have different values for
different dataset types within the same quantum.
Returns
-------
`QuantumGraph` instance.
"""
rows = self.registry.selectMultipleDatasetTypes(
originInfo, userQuery,
required=required, optional=optional, prerequisite=prerequisite,
perDatasetTypeDimensions=perDatasetTypeDimensions
)
# store result locally for multi-pass algorithm below
# TODO: change it to single pass
dimensionVerse = []
try:
for row in rows:
_LOG.debug("row: %s", row)
dimensionVerse.append(row)
except LookupError as err:
raise PrerequisiteMissingError(str(err)) from err
# Next step is to group by task quantum dimensions
qgraph = QuantumGraph()
qgraph._inputDatasetTypes = (required | prerequisite)
qgraph._outputDatasetTypes = optional
for dsType in initInputs:
for collection in originInfo.getInputCollections(dsType.name):
result = self.registry.find(collection, dsType)
if result is not None:
qgraph.initInputs.append(result)
break
else:
raise GraphBuilderError(f"Could not find initInput {dsType.name} in any input"
" collection")
for dsType in initOutputs:
qgraph.initOutputs.append(DatasetRef(dsType, {}))
for taskDss in taskDatasets:
taskQuantaInputs = {} # key is the quantum dataId (as tuple)
taskQuantaOutputs = {} # key is the quantum dataId (as tuple)
qlinks = []
for dimensionName in taskDss.taskDef.config.quantum.dimensions:
dimension = self.dimensions[dimensionName]
qlinks += dimension.links()
_LOG.debug("task %s qdimensions: %s", taskDss.taskDef.label, qlinks)
# some rows will be non-unique for subset of dimensions, create
# temporary structure to remove duplicates
for row in dimensionVerse:
qkey = tuple((col, row.dataId[col]) for col in qlinks)
_LOG.debug("qkey: %s", qkey)
def _datasetRefKey(datasetRef):
return tuple(sorted(datasetRef.dataId.items()))
qinputs = taskQuantaInputs.setdefault(qkey, {})
for dsType in taskDss.inputs:
datasetRefs = qinputs.setdefault(dsType, {})
datasetRef = row.datasetRefs[dsType]
datasetRefs[_datasetRefKey(datasetRef)] = datasetRef
_LOG.debug("add input datasetRef: %s %s", dsType.name, datasetRef)
qoutputs = taskQuantaOutputs.setdefault(qkey, {})
for dsType in taskDss.outputs:
datasetRefs = qoutputs.setdefault(dsType, {})
datasetRef = row.datasetRefs[dsType]
datasetRefs[_datasetRefKey(datasetRef)] = datasetRef
_LOG.debug("add output datasetRef: %s %s", dsType.name, datasetRef)
# all nodes for this task
quanta = []
for qkey in taskQuantaInputs:
# taskQuantaInputs and taskQuantaOutputs have the same keys
_LOG.debug("make quantum for qkey: %s", qkey)
quantum = Quantum(run=None, task=None)
# add all outputs, but check first that outputs don't exist
outputs = list(chain.from_iterable(datasetRefs.values()
for datasetRefs in taskQuantaOutputs[qkey].values()))
for ref in outputs:
_LOG.debug("add output: %s", ref)
if self.skipExisting and all(ref.id is not None for ref in outputs):
_LOG.debug("all output datasetRefs already exist, skip quantum")
continue
if any(ref.id is not None for ref in outputs):
# some outputs exist, can't override them
raise OutputExistsError(taskDss.taskDef.taskName, outputs)
for ref in outputs:
quantum.addOutput(ref)
# add all inputs
for datasetRefs in taskQuantaInputs[qkey].values():
for ref in datasetRefs.values():
quantum.addPredictedInput(ref)
_LOG.debug("add input: %s", ref)
quanta.append(quantum)
qgraph.append(QuantumGraphTaskNodes(taskDss.taskDef, quanta))
return qgraph