Coverage for python/lsst/pipe/base/pipelineTask.py: 75%
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28"""Define `PipelineTask` class and related methods.
29"""
31from __future__ import annotations
33__all__ = ["PipelineTask"] # Classes in this module
35from typing import TYPE_CHECKING, Any, ClassVar
37from .connections import InputQuantizedConnection, OutputQuantizedConnection
38from .task import Task
40if TYPE_CHECKING:
41 import logging
43 from lsst.utils.logging import LsstLogAdapter
45 from ._quantumContext import QuantumContext
46 from .config import PipelineTaskConfig
47 from .struct import Struct
50class PipelineTask(Task):
51 """Base class for all pipeline tasks.
53 This is an abstract base class for PipelineTasks which represents an
54 algorithm executed by framework(s) on data which comes from data butler,
55 resulting data is also stored in a data butler.
57 PipelineTask inherits from a `~lsst.pipe.base.Task` and uses the same
58 configuration mechanism based on :ref:`lsst.pex.config`. `PipelineTask`
59 classes also have a `PipelineTaskConnections` class associated with their
60 config which defines all of the IO a `PipelineTask` will need to do.
61 PipelineTask sub-class typically implements `run()` method which receives
62 Python-domain data objects and returns `lsst.pipe.base.Struct` object with
63 resulting data. `run()` method is not supposed to perform any I/O, it
64 operates entirely on in-memory objects. `runQuantum()` is the method (can
65 be re-implemented in sub-class) where all necessary I/O is performed, it
66 reads all input data from data butler into memory, calls `run()` method
67 with that data, examines returned `Struct` object and saves some or all of
68 that data back to data butler. `runQuantum()` method receives a
69 `QuantumContext` instance to facilitate I/O, a `InputQuantizedConnection`
70 instance which defines all input `lsst.daf.butler.DatasetRef`, and a
71 `OutputQuantizedConnection` instance which defines all the output
72 `lsst.daf.butler.DatasetRef` for a single invocation of PipelineTask.
74 Subclasses must be constructable with exactly the arguments taken by the
75 PipelineTask base class constructor, but may support other signatures as
76 well.
78 Attributes
79 ----------
80 canMultiprocess : bool, True by default (class attribute)
81 This class attribute is checked by execution framework, sub-classes
82 can set it to ``False`` in case task does not support multiprocessing.
84 Parameters
85 ----------
86 config : `~lsst.pex.config.Config`, optional
87 Configuration for this task (an instance of ``self.ConfigClass``,
88 which is a task-specific subclass of `PipelineTaskConfig`).
89 If not specified then it defaults to ``self.ConfigClass()``.
90 log : `logging.Logger`, optional
91 Logger instance whose name is used as a log name prefix, or ``None``
92 for no prefix.
93 initInputs : `dict`, optional
94 A dictionary of objects needed to construct this PipelineTask, with
95 keys matching the keys of the dictionary returned by
96 `getInitInputDatasetTypes` and values equivalent to what would be
97 obtained by calling `~lsst.daf.butler.Butler.get` with those
98 DatasetTypes and no data IDs. While it is optional for the base class,
99 subclasses are permitted to require this argument.
100 """
102 ConfigClass: ClassVar[type[PipelineTaskConfig]]
103 canMultiprocess: ClassVar[bool] = True
105 def __init__(
106 self,
107 *,
108 config: PipelineTaskConfig | None = None,
109 log: logging.Logger | LsstLogAdapter | None = None,
110 initInputs: dict[str, Any] | None = None,
111 **kwargs: Any,
112 ):
113 super().__init__(config=config, log=log, **kwargs)
115 def run(self, **kwargs: Any) -> Struct:
116 """Run task algorithm on in-memory data.
118 This method should be implemented in a subclass. This method will
119 receive keyword arguments whose names will be the same as names of
120 connection fields describing input dataset types. Argument values will
121 be data objects retrieved from data butler. If a dataset type is
122 configured with ``multiple`` field set to ``True`` then the argument
123 value will be a list of objects, otherwise it will be a single object.
125 If the task needs to know its input or output DataIds then it has to
126 override `runQuantum` method instead.
128 This method should return a `Struct` whose attributes share the same
129 name as the connection fields describing output dataset types.
131 Returns
132 -------
133 struct : `Struct`
134 Struct with attribute names corresponding to output connection
135 fields
137 Examples
138 --------
139 Typical implementation of this method may look like:
141 .. code-block:: python
143 def run(self, input, calib):
144 # "input", "calib", and "output" are the names of the config
145 # fields
147 # Assuming that input/calib datasets are `scalar` they are
148 # simple objects, do something with inputs and calibs, produce
149 # output image.
150 image = self.makeImage(input, calib)
152 # If output dataset is `scalar` then return object, not list
153 return Struct(output=image)
155 """
156 raise NotImplementedError("run() is not implemented")
158 def runQuantum(
159 self,
160 butlerQC: QuantumContext,
161 inputRefs: InputQuantizedConnection,
162 outputRefs: OutputQuantizedConnection,
163 ) -> None:
164 """Do butler IO and transform to provide in memory
165 objects for tasks `~Task.run` method.
167 Parameters
168 ----------
169 butlerQC : `QuantumContext`
170 A butler which is specialized to operate in the context of a
171 `lsst.daf.butler.Quantum`.
172 inputRefs : `InputQuantizedConnection`
173 Datastructure whose attribute names are the names that identify
174 connections defined in corresponding `PipelineTaskConnections`
175 class. The values of these attributes are the
176 `lsst.daf.butler.DatasetRef` objects associated with the defined
177 input/prerequisite connections.
178 outputRefs : `OutputQuantizedConnection`
179 Datastructure whose attribute names are the names that identify
180 connections defined in corresponding `PipelineTaskConnections`
181 class. The values of these attributes are the
182 `lsst.daf.butler.DatasetRef` objects associated with the defined
183 output connections.
184 """
185 inputs = butlerQC.get(inputRefs)
186 outputs = self.run(**inputs)
187 butlerQC.put(outputs, outputRefs)