A Butler-like class specialized for a single quantum
A ButlerQuantumContext class wraps a standard butler interface and
specializes it to the context of a given quantum. What this means
in practice is that the only gets and puts that this class allows
are DatasetRefs that are contained in the quantum.
In the future this class will also be used to record provenance on
what was actually get and put. This is in contrast to what the
preflight expects to be get and put by looking at the graph before
execution.
Parameters
----------
butler : `lsst.daf.butler.Butler`
Butler object from/to which datasets will be get/put
quantum : `lsst.daf.butler.core.Quantum`
Quantum object that describes the datasets which will be get/put by a
single execution of this node in the pipeline graph. All input
dataset references must be resolved (i.e. satisfy
``DatasetRef.id is not None``) prior to constructing the
`ButlerQuantumContext`.
Notes
-----
Most quanta in any non-trivial graph will not start with resolved dataset
references, because they represent processing steps that can only run
after some other quanta have produced their inputs. At present, it is the
responsibility of ``lsst.ctrl.mpexec.SingleQuantumExecutor`` to resolve all
datasets prior to constructing `ButlerQuantumContext` and calling
`runQuantum`, and the fact that this precondition is satisfied by code in
a downstream package is considered a problem with the
``pipe_base/ctrl_mpexec`` separation of concerns that will be addressed in
the future.
Definition at line 35 of file butlerQuantumContext.py.
def lsst.pipe.base.butlerQuantumContext.ButlerQuantumContext.put |
( |
|
self, |
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|
typing.Union[Struct, typing.List[typing.Any], object] |
values, |
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|
typing.Union[OutputQuantizedConnection, typing.List[DatasetRef], DatasetRef] |
dataset |
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) |
| |
Puts data into the butler
Parameters
----------
values : `Struct` or `list` of `object` or `object`
The data that should be put with the butler. If the type of the
dataset is `OutputQuantizedConnection` then this argument should be
a `Struct` with corresponding attribute names. Each attribute
should then correspond to either a list of object or a single
object depending of the type of the corresponding attribute on
dataset. I.e. if ``dataset.calexp`` is
``[datasetRef1, datasetRef2]`` then ``values.calexp`` should be
``[calexp1, calexp2]``. Like wise if there is a single ref, then
only a single object need be passed. The same restriction applies
if dataset is directly a `list` of `DatasetRef` or a single
`DatasetRef`.
dataset
This argument may either be an `InputQuantizedConnection` which
describes all the inputs of a quantum, a list of
`lsst.daf.butler.DatasetRef`, or a single
`lsst.daf.butler.DatasetRef`. The function will get and return
the corresponding datasets from the butler.
Raises
------
ValueError
Raised if a `DatasetRef` is passed to put that is not defined in
the quantum object, or the type of values does not match what is
expected from the type of dataset.
Definition at line 154 of file butlerQuantumContext.py.