def determineCalibClass(cls, metadata, message):
"""Attempt to find calibration class in metadata.
Parameters
----------
metadata : `dict` or `lsst.daf.base.PropertyList`
Metadata possibly containing a calibration class entry.
message : `str`
Message to include in any errors.
Returns
-------
calibClass : `object`
The class to use to read the file contents. Should be an
`lsst.ip.isr.IsrCalib` subclass.
Raises
------
ValueError :
Raised if the resulting calibClass is the base
`lsst.ip.isr.IsrClass` (which does not implement the
content methods).
"""
calibClassName = metadata.get("CALIBCLS")
calibClass = doImport(
calibClassName) if calibClassName is not None else cls
if calibClass is IsrCalib:
raise ValueError(
f"Cannot use base class to read calibration data: {message}")
return calibClass
def makeStorage(
self,
db: Database,
*,
context: Optional[StaticTablesContext] = None,
) -> GovernorDimensionRecordStorage:
"""Make storage record.
Constructs the `DimensionRecordStorage` instance that should
be used to back this element in a registry.
Parameters
----------
db : `Database`
Interface to the underlying database engine and namespace.
context : `StaticTablesContext`, optional
If provided, an object to use to create any new tables. If not
provided, ``db.ensureTableExists`` should be used instead.
Returns
-------
storage : `GovernorDimensionRecordStorage`
Storage object that should back this element in a registry.
"""
from ...registry.interfaces import GovernorDimensionRecordStorage
cls = doImport(self._storage["cls"])
assert issubclass(cls, GovernorDimensionRecordStorage)
return cls.initialize(db, self, context=context, config=self._storage)
def _importPlugin(pluginName):
"""Import a plugin that contains Click commands.
Parameters
----------
pluginName : `str`
An importable module whose __all__ parameter contains the commands
that can be called.
Returns
-------
An imported module or None
The imported module, or None if the module could not be imported.
Notes
-----
A cache is used in order to prevent repeated reports of failure
to import a module that can be triggered by ``butler --help``.
"""
try:
return doImport(pluginName)
except Exception as err:
log.warning("Could not import plugin from %s, skipping.", pluginName)
log.debug("Plugin import exception: %s\nTraceback:\n%s", err,
"".join(traceback.format_tb(err.__traceback__)))
return None
def readFitsWithOptions(cls, filename, options):
"""Build an instance of this class with options.
Parameters
----------
filename : `str`
Name of the file to read
options : `PropertyList`
Collection of metadata parameters
"""
# To avoid problems since this is no longer an abstract method
if cls is not StampsBase:
raise NotImplementedError(
f"Please implement specific FITS reader for class {cls}")
# Load metadata to get class
metadata = afwFits.readMetadata(filename, hdu=0)
type_name = metadata.get("STAMPCLS")
if type_name is None:
raise RuntimeError(
f"No class name in file {filename}. Unable to instantiate correct"
" stamps subclass. Is this an old version format Stamps file?")
# Import class and override `cls`
stamp_type = doImport(type_name)
cls = stamp_type
return cls.readFitsWithOptions(filename, options)
def addTask(self, task: Union[PipelineTask, str], label: str):
"""Add a new task to the pipeline, or replace a task that is already
associated with the supplied label.
Parameters
----------
task: `PipelineTask` or `str`
Either a derived class object of a `PipelineTask` or a string
corresponding to a fully qualified `PipelineTask` name.
label: `str`
A label that is used to identify the `PipelineTask` being added
"""
if isinstance(task, str):
taskName = task
elif issubclass(task, PipelineTask):
taskName = f"{task.__module__}.{task.__qualname__}"
else:
raise ValueError(
"task must be either a child class of PipelineTask or a string containing"
" a fully qualified name to one")
if not label:
# in some cases (with command line-generated pipeline) tasks can
# be defined without label which is not acceptable, use task
# _DefaultName in that case
if isinstance(task, str):
task = doImport(task)
label = task._DefaultName
self._pipelineIR.tasks[label] = pipelineIR.TaskIR(label, taskName)
def getClassOf(typeOrName: Union[Type, str]) -> Type:
"""Given the type name or a type, return the python type.
If a type name is given, an attempt will be made to import the type.
Parameters
----------
typeOrName : `str` or Python class
A string describing the Python class to load or a Python type.
Returns
-------
type_ : `type`
Directly returns the Python type if a type was provided, else
tries to import the given string and returns the resulting type.
Notes
-----
This is a thin wrapper around `~lsst.utils.doImport`.
"""
if isinstance(typeOrName, str):
cls = doImport(typeOrName)
else:
cls = typeOrName
return cls
def _importGraphFixup(self, args):
"""Import/instantiate graph fixup object.
Parameters
----------
args : `argparse.Namespace`
Parsed command line.
Returns
-------
fixup : `ExecutionGraphFixup` or `None`
Raises
------
ValueError
Raised if import fails, method call raises exception, or returned
instance has unexpected type.
"""
if args.graph_fixup:
try:
factory = doImport(args.graph_fixup)
except Exception as exc:
raise ValueError("Failed to import graph fixup class/method") from exc
try:
fixup = factory()
except Exception as exc:
raise ValueError("Failed to make instance of graph fixup") from exc
if not isinstance(fixup, ExecutionGraphFixup):
raise ValueError("Graph fixup is not an instance of ExecutionGraphFixup class")
return fixup
def makeStorage(
self,
db: Database, *,
context: Optional[StaticTablesContext] = None,
governors: NamedKeyMapping[GovernorDimension, GovernorDimensionRecordStorage],
) -> DatabaseDimensionRecordStorage:
"""Construct the `DimensionRecordStorage` instance that should
be used to back this element in a registry.
Parameters
----------
db : `Database`
Interface to the underlying database engine and namespace.
context : `StaticTablesContext`, optional
If provided, an object to use to create any new tables. If not
provided, ``db.ensureTableExists`` should be used instead.
governors : `NamedKeyMapping`
Mapping from `GovernorDimension` to the record storage backend for
that dimension, containing all governor dimensions.
Returns
-------
storage : `DatabaseDimensionRecordStorage`
Storage object that should back this element in a registry.
"""
from ...registry.interfaces import DatabaseDimensionRecordStorage
cls = doImport(self._storage["cls"])
assert issubclass(cls, DatabaseDimensionRecordStorage)
return cls.initialize(db, self, context=context, config=self._storage, governors=governors)
def standardize(self, mapper, item, dataId):
"""Default standardization function for calibration datasets.
If the item is of a type that should be standardized, the base class
``standardizeExposure`` method is called, otherwise the item is returned
unmodified.
Parameters
----------
mapping : `lsst.obs.base.Mapping`
Mapping object to pass through.
item : object
Will be standardized if of type lsst.afw.image.Exposure,
lsst.afw.image.DecoratedImage, lsst.afw.image.Image
or lsst.afw.image.MaskedImage
dataId : `dict`
Dataset identifier
Returns
-------
`lsst.afw.image.Exposure` or item
The standardized object.
"""
if issubclass(doImport(self.python),
(Exposure, MaskedImage, Image, DecoratedImage)):
return mapper._standardizeExposure(self,
item,
dataId,
filter=self.setFilter)
return item
def assemblerClass(self):
"""Class to use to (dis)assemble an object from components."""
if self._assembler is not None:
return self._assembler
if self._assemblerClassName is None:
return None
self._assembler = doImport(self._assemblerClassName)
return self._assembler
def delegateClass(self) -> Optional[Type]:
"""Class to use to delegate type-specific actions."""
if self._delegate is not None:
return self._delegate
if self._delegateClassName is None:
return None
self._delegate = doImport(self._delegateClassName)
return self._delegate
def toExpandedPipeline(self) -> Generator[TaskDef]:
"""Returns a generator of TaskDefs which can be used to create quantum
graphs.
Returns
-------
generator : generator of `TaskDef`
The generator returned will be the sorted iterator of tasks which
are to be used in constructing a quantum graph.
Raises
------
NotImplementedError
If a dataId is supplied in a config block. This is in place for
future use
"""
taskDefs = []
for label, taskIR in self._pipelineIR.tasks.items():
taskClass = doImport(taskIR.klass)
taskName = taskClass.__qualname__
config = taskClass.ConfigClass()
overrides = ConfigOverrides()
if self._pipelineIR.instrument is not None:
overrides.addInstrumentOverride(self._pipelineIR.instrument, taskClass._DefaultName)
if taskIR.config is not None:
for configIR in taskIR.config:
if configIR.dataId is not None:
raise NotImplementedError("Specializing a config on a partial data id is not yet "
"supported in Pipeline definition")
# only apply override if it applies to everything
if configIR.dataId is None:
if configIR.file:
for configFile in configIR.file:
overrides.addFileOverride(configFile)
if configIR.python is not None:
overrides.addPythonOverride(configIR.python)
for key, value in configIR.rest.items():
overrides.addValueOverride(key, value)
overrides.applyTo(config)
# This may need to be revisited
config.validate()
taskDefs.append(TaskDef(taskName=taskName, config=config, taskClass=taskClass, label=label))
# lets evaluate the contracts
if self._pipelineIR.contracts is not None:
label_to_config = {x.label: x.config for x in taskDefs}
for contract in self._pipelineIR.contracts:
# execute this in its own line so it can raise a good error message if there was problems
# with the eval
success = eval(contract.contract, None, label_to_config)
if not success:
extra_info = f": {contract.msg}" if contract.msg is not None else ""
raise pipelineIR.ContractError(f"Contract(s) '{contract.contract}' were not "
f"satisfied{extra_info}")
yield from pipeTools.orderPipeline(taskDefs)
def fromConfig(config, registry, butlerRoot=None):
"""Create datastore from type specified in config file.
Parameters
----------
config : `Config`
Configuration instance.
"""
cls = doImport(config["datastore", "cls"])
return cls(config=config, registry=registry, butlerRoot=butlerRoot)
def pytype(self) -> Type:
"""Python type associated with this `StorageClass`."""
if self._pytype is not None:
return self._pytype
if hasattr(builtins, self._pytypeName):
pytype = getattr(builtins, self._pytypeName)
else:
pytype = doImport(self._pytypeName)
self._pytype = pytype
return self._pytype
def __init__(self,
config: Union[Config, str],
bridgeManager: DatastoreRegistryBridgeManager,
butlerRoot: str = None):
super().__init__(config, bridgeManager)
# Scan for child datastores and instantiate them with the same registry
self.datastores = []
for c in self.config["datastores"]:
c = DatastoreConfig(c)
datastoreType = doImport(c["cls"])
datastore = datastoreType(c, bridgeManager, butlerRoot=butlerRoot)
log.debug("Creating child datastore %s", datastore.name)
self.datastores.append(datastore)
# Name ourself based on our children
if self.datastores:
# We must set the names explicitly
self._names = [d.name for d in self.datastores]
childNames = ",".join(self.names)
else:
childNames = "(empty@{})".format(time.time())
self._names = [childNames]
self.name = "{}[{}]".format(type(self).__qualname__, childNames)
# We declare we are ephemeral if all our child datastores declare
# they are ephemeral
isEphemeral = True
for d in self.datastores:
if not d.isEphemeral:
isEphemeral = False
break
self.isEphemeral = isEphemeral
# per-datastore override constraints
if "datastore_constraints" in self.config:
overrides = self.config["datastore_constraints"]
if len(overrides) != len(self.datastores):
raise DatastoreValidationError(
f"Number of registered datastores ({len(self.datastores)})"
" differs from number of constraints overrides"
f" {len(overrides)}")
self.datastoreConstraints = [
Constraints(c.get("constraints"),
universe=bridgeManager.universe) for c in overrides
]
else:
self.datastoreConstraints = (None, ) * len(self.datastores)
log.debug("Created %s (%s)", self.name,
("ephemeral" if self.isEphemeral else "permanent"))
def __init__(self, config: Optional[RawIngestConfig] = None, *, butler: Butler, **kwds: Any):
super().__init__(config, **kwds)
self.butler = butler
self.universe = self.butler.registry.dimensions
self.instrument = doImport(self.config.instrument)()
# For now, we get a nominal Camera from the Instrument.
# In the future, we may want to load one from a Butler calibration
# collection that's appropriate for the observation timestamp of
# the exposure.
self.camera = self.instrument.getCamera()
self.datasetType = self.getDatasetType()
def setUpClass(cls):
# Storage Classes are fixed for all datastores in these tests
scConfigFile = os.path.join(TESTDIR, "config/basic/storageClasses.yaml")
cls.storageClassFactory = StorageClassFactory()
cls.storageClassFactory.addFromConfig(scConfigFile)
# Read the Datastore config so we can get the class
# information (since we should not assume the constructor
# name here, but rely on the configuration file itself)
datastoreConfig = DatastoreConfig(cls.configFile)
cls.datastoreType = doImport(datastoreConfig["cls"])
cls.universe = DimensionUniverse.fromConfig()
def getDatabaseClass(self) -> Type[Database]:
"""Returns the `Database` class targeted by configuration values.
The appropriate class is determined by parsing the `db` key to extract
the dialect, and then looking that up under the `engines` key of the
registry config.
"""
dialect = self.getDialect()
if dialect not in self["engines"]:
raise ValueError(f"Connection string dialect has no known aliases. Received: {dialect}")
databaseClass = self["engines", dialect]
return doImport(databaseClass)
def ingestRaws(repo,
locations,
regex,
output_run,
config=None,
config_file=None,
transfer="auto",
processes=1,
ingest_task="lsst.obs.base.RawIngestTask"):
"""Ingests raw frames into the butler registry
Parameters
----------
repo : `str`
URI to the repository.
locations : `list` [`str`]
Files to ingest and directories to search for files that match
``regex`` to ingest.
regex : `str`
Regex string used to find files in directories listed in locations.
output_run : `str`
The path to the location, the run, where datasets should be put.
config : `dict` [`str`, `str`] or `None`
Key-value pairs to apply as overrides to the ingest config.
config_file : `str` or `None`
Path to a config file that contains overrides to the ingest config.
transfer : `str` or None
The external data transfer type, by default "auto".
processess : `int`
Number of processes to use for ingest.
ingest_task : `str`
The fully qualified class name of the ingest task to use by default
lsst.obs.base.RawIngestTask.
Raises
------
Exception
Raised if operations on configuration object fail.
"""
butler = Butler(repo, writeable=True)
TaskClass = doImport(ingest_task)
ingestConfig = TaskClass.ConfigClass()
ingestConfig.transfer = transfer
configOverrides = ConfigOverrides()
if config_file is not None:
configOverrides.addFileOverride(config_file)
if config is not None:
for name, value in config.items():
configOverrides.addValueOverride(name, value)
configOverrides.applyTo(ingestConfig)
ingester = TaskClass(config=ingestConfig, butler=butler)
files = findFileResources(locations, regex)
ingester.run(files, run=output_run, processes=processes)
def makeRegistry(self) -> Registry:
prefix = f"test_{secrets.token_hex(8).lower()}_"
self._prefixes.append(prefix)
config = self.makeRegistryConfig()
# Can't use Registry.fromConfig for these tests because we don't want
# to reconnect to the server every single time. But we at least use
# OracleDatabase.fromConnection rather than the constructor so
# we can try to pass a prefix through via "+" in a namespace.
database = OracleDatabase.fromConnection(connection=self._connection,
origin=0,
namespace=f"+{prefix}")
attributes = doImport(config["managers", "attributes"])
opaque = doImport(config["managers", "opaque"])
dimensions = doImport(config["managers", "dimensions"])
collections = doImport(config["managers", "collections"])
datasets = doImport(config["managers", "datasets"])
datastoreBridges = doImport(config["managers", "datastores"])
return Registry(database=database,
attributes=attributes,
opaque=opaque,
dimensions=dimensions,
collections=collections,
datasets=datasets,
datastoreBridges=datastoreBridges,
universe=DimensionUniverse(config),
create=True)
def pixelization(self):
"""Object that interprets skypix Dimension values
(`lsst.sphgeom.Pixelization`).
`None` for limited registries.
"""
if self.limited:
return None
if self._pixelization is None:
pixelizationCls = doImport(self.config["skypix", "cls"])
self._pixelization = pixelizationCls(level=self.config["skypix",
"level"])
return self._pixelization
def makeGraph(self, pipeline, collections, run, userQuery):
"""Create execution graph for a pipeline.
Parameters
----------
pipeline : `Pipeline`
Pipeline definition, task names/classes and their configs.
collections
Expressions representing the collections to search for input
datasets. May be any of the types accepted by
`lsst.daf.butler.CollectionSearch.fromExpression`.
run : `str`, optional
Name of the `~lsst.daf.butler.CollectionType.RUN` collection for
output datasets, if it already exists.
userQuery : `str`
String which defines user-defined selection for registry, should be
empty or `None` if there is no restrictions on data selection.
Returns
-------
graph : `QuantumGraph`
Raises
------
UserExpressionError
Raised when user expression cannot be parsed.
OutputExistsError
Raised when output datasets already exist.
Exception
Other exceptions types may be raised by underlying registry
classes.
"""
scaffolding = _PipelineScaffolding(pipeline, registry=self.registry)
instrument = pipeline.getInstrument()
if isinstance(instrument, str):
instrument = doImport(instrument)
if instrument is not None:
dataId = DataCoordinate.standardize(
instrument=instrument.getName(),
universe=self.registry.dimensions)
else:
dataId = DataCoordinate.makeEmpty(self.registry.dimensions)
with scaffolding.connectDataIds(self.registry, collections, userQuery,
dataId) as commonDataIds:
scaffolding.resolveDatasetRefs(self.registry,
collections,
run,
commonDataIds,
skipExisting=self.skipExisting)
return scaffolding.makeQuantumGraph()
def addInstrumentOverride(self, instrument: str, task_name: str):
"""Apply any overrides that an instrument has for a task
Parameters
----------
instrument: str
A string containing the fully qualified name of an instrument from
which configs should be loaded and applied
task_name: str
The _DefaultName of a task associated with a config, used to look
up overrides from the instrument.
"""
instrument_lib = doImport(instrument)()
self._overrides.append((OverrideTypes.Instrument, (instrument_lib, task_name)))
def visit(self, builder: DimensionConstructionBuilder) -> None:
# Docstring inherited from DimensionConstructionVisitor.
PixelizationClass = doImport(self._pixelizationClassName)
maxLevel = self._maxLevel if self._maxLevel is not None else PixelizationClass.MAX_LEVEL
system = SkyPixSystem(
self.name,
maxLevel=maxLevel,
PixelizationClass=PixelizationClass,
)
builder.topology[TopologicalSpace.SPATIAL].add(system)
for level in range(maxLevel + 1):
dimension = system[level]
builder.dimensions.add(dimension)
builder.elements.add(dimension)
def __call__(self, fixed: DataCoordinate) -> DimensionPacker:
"""Construct a `DimensionPacker` instance for the given fixed data ID.
Parameters
----------
fixed : `DataCoordinate`
Data ID that provides values for the "fixed" dimensions of the
packer. Must be expanded with all metadata known to the
`Registry`. ``fixed.hasRecords()`` must return `True`.
"""
assert fixed.graph.issuperset(self.fixed)
if self._cls is None:
self._cls = doImport(self._clsName)
return self._cls(fixed, self.dimensions)
def pytype(self):
"""Python type associated with this `StorageClass`."""
if self._pytype is not None:
return self._pytype
# Handle case where we did get a python type not string
if not isinstance(self._pytypeName, str):
pytype = self._pytypeName
self._pytypeName = self._pytypeName.__name__
elif hasattr(builtins, self._pytypeName):
pytype = getattr(builtins, self._pytypeName)
else:
pytype = doImport(self._pytypeName)
self._pytype = pytype
return self._pytype
def fromConfig(config: Config, registry: Registry, butlerRoot: Optional[str] = None) -> 'Datastore':
"""Create datastore from type specified in config file.
Parameters
----------
config : `Config`
Configuration instance.
registry : `Registry`
Registry to be used by the Datastore for internal data.
butlerRoot : `str`, optional
Butler root directory.
"""
cls = doImport(config["datastore", "cls"])
return cls(config=config, registry=registry, butlerRoot=butlerRoot)
def getFullTypeName(cls: Any) -> str:
"""Return full type name of the supplied entity.
Parameters
----------
cls : `type` or `object`
Entity from which to obtain the full name. Can be an instance
or a `type`.
Returns
-------
name : `str`
Full name of type.
Notes
-----
Builtins are returned without the ``builtins`` specifier included. This
allows `str` to be returned as "str" rather than "builtins.str". Any
parts of the path that start with a leading underscore are removed
on the assumption that they are an implementation detail and the
entity will be hoisted into the parent namespace.
"""
# If we have an instance we need to convert to a type
if not hasattr(cls, "__qualname__"):
cls = type(cls)
if hasattr(builtins, cls.__qualname__):
# Special case builtins such as str and dict
return cls.__qualname__
real_name = cls.__module__ + "." + cls.__qualname__
# Remove components with leading underscores
cleaned_name = ".".join(c for c in real_name.split(".")
if not c.startswith("_"))
# Consistency check
if real_name != cleaned_name:
try:
test = doImport(cleaned_name)
except Exception:
# Could not import anything so return the real name
return real_name
# The thing we imported should match the class we started with
# despite the clean up. If it does not we return the real name
if test is not cls:
return real_name
return cleaned_name
def getInstanceOf(typeOrName):
"""Given the type name or a type, instantiate an object of that type.
If a type name is given, an attempt will be made to import the type.
Parameters
----------
typeOrName : `str` or Python class
A string describing the Python class to load or a Python type.
"""
if isinstance(typeOrName, str):
cls = doImport(typeOrName)
else:
cls = typeOrName
return cls()
def fromConfig(config, types, key, value, lengths=None, registry=None):
"""Create a `DatabaseDict` subclass instance from `config`.
If ``config`` contains a class ``cls`` key, this will be assumed to
be the fully-qualified name of a DatabaseDict subclass to construct.
If not, ``registry.makeDatabaseDict`` will be called instead, and
``config`` must contain a ``table`` key with the name of the table
to use.
Parameters
----------
config : `Config`
Configuration used to identify and construct a subclass.
types : `dict`
A dictionary mapping `str` field names to Python type objects,
containing all fields to be held in the database.
key : `str`
The name of the field to be used as the dictionary key. Must not
be present in ``value._fields``.
value : `type`
The type used for the dictionary's values, typically a
`namedtuple`. Must have a ``_fields`` class attribute that is a
tuple of field names (i.e., as defined by
`~collections.namedtuple`); these field names must also appear
in the ``types`` arg, and a ``_make`` attribute to construct it
from a sequence of values (again, as defined by
`~collections.namedtuple`).
lengths : `dict`, optional
Specific lengths of string fields. Defaults will be used if not
specified.
registry : `Registry`, optional
A registry instance from which a `DatabaseDict` subclass can be
obtained. Ignored if ``config["cls"]`` exists; may be None if
it does.
Returns
-------
dictionary : `DatabaseDict` (subclass)
A new `DatabaseDict` subclass instance.
"""
if "cls" in config:
cls = doImport(config["cls"])
return cls(config=config, types=types, key=key, value=value, lengths=lengths)
else:
table = config["table"]
if registry is None:
raise ValueError("Either config['cls'] or registry must be provided.")
return registry.makeDatabaseDict(table, types=types, key=key, value=value, lengths=lengths)
def getPupilFactory(self, visitInfo, pupilSize, npix, **kwargs):
"""Construct a PupilFactory.
Parameters
----------
visitInfo : `~lsst.afw.image.VisitInfo`
VisitInfo object for a particular exposure.
pupilSize : `float`
Size in meters of constructed Pupil array. Note that this may be
larger than the actual diameter of the illuminated pupil to
accommodate zero-padding.
npix : `int`
Constructed Pupils will be npix x npix.
**kwargs : `dict`
Other keyword arguments forwarded to the PupilFactoryClass
constructor.
"""
cls = doImport(self.getPupilFactoryName())
return cls(visitInfo, pupilSize, npix, **kwargs)
def telescopeDiameter(self):
cls = doImport(self.getPupilFactoryName())
return cls.telescopeDiameter
def testDoImport(self):
c = doImport("lsst.utils.tests.TestCase")
self.assertEqual(c, lsst.utils.tests.TestCase)
c = doImport("lsst.utils.doImport")
self.assertEqual(type(c), type(doImport))
self.assertTrue(inspect.isfunction(c))
c = doImport("lsst.utils")
self.assertTrue(inspect.ismodule(c))
with self.assertRaises(ImportError):
doImport("lsst.utils.tests.TestCase.xyprint")
with self.assertRaises(ImportError):
doImport("lsst.utils.nothere")
with self.assertRaises(ModuleNotFoundError):
doImport("missing module")
with self.assertRaises(ModuleNotFoundError):
doImport("lsstdummy.import.fail")
with self.assertRaises(ImportError):
doImport("lsst.import.fail")
with self.assertRaises(ImportError):
doImport("lsst.utils.x")
with self.assertRaises(TypeError):
doImport([])
