Coverage for python/lsst/pipe/base/pipelineTask.py: 68%
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1# This file is part of pipe_base.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (http://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <http://www.gnu.org/licenses/>.
22"""This module defines PipelineTask class and related methods.
23"""
25__all__ = ["PipelineTask"] # Classes in this module
27from .butlerQuantumContext import ButlerQuantumContext
28from .connections import InputQuantizedConnection, OutputQuantizedConnection
29from .task import Task
32class PipelineTask(Task):
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
58 well.
60 Attributes
61 ----------
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.
66 Parameters
67 ----------
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 : `logging.Logger`, optional
73 Logger instance whose name is used as a log name prefix, or ``None``
74 for no prefix.
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.
82 """
84 canMultiprocess = True
86 def __init__(self, *, config=None, log=None, initInputs=None, **kwargs):
87 super().__init__(config=config, log=log, **kwargs)
89 def run(self, **kwargs):
90 """Run task algorithm on in-memory data.
92 This method should be implemented in a subclass. This method will
93 receive keyword arguments whose names will be the same as names of
94 connection fields describing input dataset types. Argument values will
95 be data objects retrieved from data butler. If a dataset type is
96 configured with ``multiple`` field set to ``True`` then the argument
97 value will be a list of objects, otherwise it will be a single object.
99 If the task needs to know its input or output DataIds then it has to
100 override `runQuantum` method instead.
102 This method should return a `Struct` whose attributes share the same
103 name as the connection fields describing output dataset types.
105 Returns
106 -------
107 struct : `Struct`
108 Struct with attribute names corresponding to output connection
109 fields
111 Examples
112 --------
113 Typical implementation of this method may look like:
115 .. code-block:: python
117 def run(self, input, calib):
118 # "input", "calib", and "output" are the names of the config
119 # fields
121 # Assuming that input/calib datasets are `scalar` they are
122 # simple objects, do something with inputs and calibs, produce
123 # output image.
124 image = self.makeImage(input, calib)
126 # If output dataset is `scalar` then return object, not list
127 return Struct(output=image)
129 """
130 raise NotImplementedError("run() is not implemented")
132 def runQuantum(
133 self,
134 butlerQC: ButlerQuantumContext,
135 inputRefs: InputQuantizedConnection,
136 outputRefs: OutputQuantizedConnection,
137 ):
138 """Method to do butler IO and or transforms to provide in memory
139 objects for tasks run method
141 Parameters
142 ----------
143 butlerQC : `ButlerQuantumContext`
144 A butler which is specialized to operate in the context of a
145 `lsst.daf.butler.Quantum`.
146 inputRefs : `InputQuantizedConnection`
147 Datastructure whose attribute names are the names that identify
148 connections defined in corresponding `PipelineTaskConnections`
149 class. The values of these attributes are the
150 `lsst.daf.butler.DatasetRef` objects associated with the defined
151 input/prerequisite connections.
152 outputRefs : `OutputQuantizedConnection`
153 Datastructure whose attribute names are the names that identify
154 connections defined in corresponding `PipelineTaskConnections`
155 class. The values of these attributes are the
156 `lsst.daf.butler.DatasetRef` objects associated with the defined
157 output connections.
158 """
159 inputs = butlerQC.get(inputRefs)
160 outputs = self.run(**inputs)
161 butlerQC.put(outputs, outputRefs)
163 def getResourceConfig(self):
164 """Return resource configuration for this task.
166 Returns
167 -------
168 Object of type `~config.ResourceConfig` or ``None`` if resource
169 configuration is not defined for this task.
170 """
171 return getattr(self.config, "resources", None)