22 """This module defines PipelineTask class and related methods. 25 __all__ = [
"PipelineTask"]
27 from .task
import Task
28 from .butlerQuantumContext
import ButlerQuantumContext
29 from .connections
import InputQuantizedConnection, OutputQuantizedConnection
33 """Base class for all pipeline tasks. 35 This is an abstract base class for PipelineTasks which represents an 36 algorithm executed by framework(s) on data which comes from data butler, 37 resulting data is also stored in a data butler. 39 PipelineTask inherits from a `pipe.base.Task` and uses the same 40 configuration mechanism based on `pex.config`. `PipelineTask` classes also 41 have a `PipelineTaskConnections` class associated with their config which 42 defines all of the IO a `PipelineTask` will need to do. PipelineTask 43 sub-class typically implements `run()` method which receives Python-domain 44 data objects and returns `pipe.base.Struct` object with resulting data. 45 `run()` method is not supposed to perform any I/O, it operates entirely on 46 in-memory objects. `runQuantum()` is the method (can be re-implemented in 47 sub-class) where all necessary I/O is performed, it reads all input data 48 from data butler into memory, calls `run()` method with that data, examines 49 returned `Struct` object and saves some or all of that data back to data 50 butler. `runQuantum()` method receives a `ButlerQuantumContext` instance to 51 facilitate I/O, a `InputQuantizedConnection` instance which defines all 52 input `lsst.daf.butler.DatasetRef`, and a `OutputQuantizedConnection` 53 instance which defines all the output `lsst.daf.butler.DatasetRef` for a 54 single invocation of PipelineTask. 56 Subclasses must be constructable with exactly the arguments taken by the 57 PipelineTask base class constructor, but may support other signatures as 62 canMultiprocess : bool, True by default (class attribute) 63 This class attribute is checked by execution framework, sub-classes 64 can set it to ``False`` in case task does not support multiprocessing. 68 config : `pex.config.Config`, optional 69 Configuration for this task (an instance of ``self.ConfigClass``, 70 which is a task-specific subclass of `PipelineTaskConfig`). 71 If not specified then it defaults to `self.ConfigClass()`. 72 log : `lsst.log.Log`, optional 73 Logger instance whose name is used as a log name prefix, or ``None`` 75 initInputs : `dict`, optional 76 A dictionary of objects needed to construct this PipelineTask, with 77 keys matching the keys of the dictionary returned by 78 `getInitInputDatasetTypes` and values equivalent to what would be 79 obtained by calling `Butler.get` with those DatasetTypes and no data 80 IDs. While it is optional for the base class, subclasses are 81 permitted to require this argument. 83 canMultiprocess =
True 85 def __init__(self, *, config=None, log=None, initInputs=None, **kwargs):
86 super().
__init__(config=config, log=log, **kwargs)
88 def run(self, **kwargs):
89 """Run task algorithm on in-memory data. 91 This method should be implemented in a subclass. This method will 92 receive keyword arguments whose names will be the same as names of 93 connection fields describing input dataset types. Argument values will 94 be data objects retrieved from data butler. If a dataset type is 95 configured with ``multiple`` field set to ``True`` then the argument 96 value will be a list of objects, otherwise it will be a single object. 98 If the task needs to know its input or output DataIds then it has to 99 override `runQuantum` method instead. 101 This method should return a `Struct` whose attributes share the same 102 name as the connection fields describing output dataset types. 107 Struct with attribute names corresponding to output connection 112 Typical implementation of this method may look like:: 114 def run(self, input, calib): 115 # "input", "calib", and "output" are the names of the config fields 117 # Assuming that input/calib datasets are `scalar` they are simple objects, 118 # do something with inputs and calibs, produce output image. 119 image = self.makeImage(input, calib) 121 # If output dataset is `scalar` then return object, not list 122 return Struct(output=image) 125 raise NotImplementedError(
"run() is not implemented")
127 def runQuantum(self, butlerQC: ButlerQuantumContext, inputRefs: InputQuantizedConnection,
128 outputRefs: OutputQuantizedConnection):
129 """Method to do butler IO and or transforms to provide in memory objects for tasks run method 133 butlerQC : `ButlerQuantumContext` 134 A butler which is specialized to operate in the context of a `lsst.daf.butler.Quantum`. 135 inputRefs : `InputQuantizedConnection` 136 Datastructure whose attribute names are the names that identify connections defined in 137 corresponding `PipelineTaskConnections` class. The values of these attributes are the 138 `lsst.daf.butler.DatasetRef` objects associated with the defined input/prerequisite connections. 139 outputRefs : `OutputQuantizedConnection` 140 Datastructure whose attribute names are the names that identify connections defined in 141 corresponding `PipelineTaskConnections` class. The values of these attributes are the 142 `lsst.daf.butler.DatasetRef` objects associated with the defined output connections. 144 inputs = butlerQC.get(inputRefs)
145 outputs = self.
run(**inputs)
146 butlerQC.put(outputs, outputRefs)
149 """Return resource configuration for this task. 153 Object of type `~config.ResourceConfig` or ``None`` if resource 154 configuration is not defined for this task. 156 return getattr(self.
config,
"resources",
None)
def __init__(self, config=None, log=None, initInputs=None, kwargs)
def getResourceConfig(self)