Coverage for python / lsst / pipe / base / pipelineTask.py: 78%
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28"""Define `PipelineTask` class and related methods."""
30from __future__ import annotations
32__all__ = ["PipelineTask"] # Classes in this module
34from collections.abc import Callable
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 `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 Parameters
79 ----------
80 config : `~lsst.pex.config.Config`, optional
81 Configuration for this task (an instance of ``self.ConfigClass``,
82 which is a task-specific subclass of `PipelineTaskConfig`).
83 If not specified then it defaults to ``self.ConfigClass()``.
84 log : `logging.Logger`, optional
85 Logger instance whose name is used as a log name prefix, or ``None``
86 for no prefix.
87 initInputs : `dict`, optional
88 A dictionary of objects needed to construct this PipelineTask, with
89 keys matching the keys of the dictionary returned by
90 `getInitInputDatasetTypes` and values equivalent to what would be
91 obtained by calling `~lsst.daf.butler.Butler.get` with those
92 DatasetTypes and no data IDs. While it is optional for the base class,
93 subclasses are permitted to require this argument.
94 **kwargs : `~typing.Any`
95 Arbitrary parameters, passed to base class constructor.
96 """
98 ConfigClass: ClassVar[type[PipelineTaskConfig]]
100 canMultiprocess: ClassVar[bool] = True
101 """Whether this task can be run by an executor that uses subprocesses for
102 parallelism.
103 """
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 run: Callable[..., Struct] # The 'run' method for subclasses will have a different signature
117 def run(self, **kwargs: Any) -> Struct: # type: ignore
118 """Run task algorithm on in-memory data.
120 This method should be implemented in a subclass. This method will
121 receive keyword-only arguments whose names will be the same as names of
122 connection fields describing input dataset types. Argument values will
123 be data objects retrieved from data butler. If a dataset type is
124 configured with ``multiple`` field set to ``True`` then the argument
125 value will be a list of objects, otherwise it will be a single object.
127 If the task needs to know its input or output DataIds then it also has
128 to override the `runQuantum` method.
130 This method should return a `Struct` whose attributes share the same
131 name as the connection fields describing output dataset types.
133 Parameters
134 ----------
135 **kwargs : `~typing.Any`
136 Arbitrary parameters accepted by subclasses.
138 Returns
139 -------
140 struct : `Struct`
141 Struct with attribute names corresponding to output connection
142 fields.
144 Examples
145 --------
146 Typical implementation of this method may look like:
148 .. code-block:: python
150 def run(self, *, input, calib):
151 # "input", "calib", and "output" are the names of the
152 # connection fields.
154 # Assuming that input/calib datasets are `scalar` they are
155 # simple objects, do something with inputs and calibs, produce
156 # output image.
157 image = self.makeImage(input, calib)
159 # If output dataset is `scalar` then return object, not list
160 return Struct(output=image)
161 """
162 raise NotImplementedError("run() is not implemented")
164 def runQuantum(
165 self,
166 butlerQC: QuantumContext,
167 inputRefs: InputQuantizedConnection,
168 outputRefs: OutputQuantizedConnection,
169 ) -> None:
170 """Do butler IO and transform to provide in memory
171 objects for tasks `~Task.run` method.
173 Parameters
174 ----------
175 butlerQC : `QuantumContext`
176 A butler which is specialized to operate in the context of a
177 `lsst.daf.butler.Quantum`.
178 inputRefs : `InputQuantizedConnection`
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 input/prerequisite connections.
184 outputRefs : `OutputQuantizedConnection`
185 Datastructure whose attribute names are the names that identify
186 connections defined in corresponding `PipelineTaskConnections`
187 class. The values of these attributes are the
188 `lsst.daf.butler.DatasetRef` objects associated with the defined
189 output connections.
190 """
191 inputs = butlerQC.get(inputRefs)
192 outputs = self.run(**inputs)
193 butlerQC.put(outputs, outputRefs)