pipelineTask.py

Go to the documentation of this file.

# This file is part of pipe_base.
#
# Developed for the LSST Data Management System.
# This product includes software developed by the LSST Project
# (http://www.lsst.org).
# See the COPYRIGHT file at the top-level directory of this distribution
# for details of code ownership.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

"""This module defines PipelineTask class and related methods.
"""

__all__ = ["PipelineTask"]  # Classes in this module

from .task import Task
from .butlerQuantumContext import ButlerQuantumContext
from .connections import InputQuantizedConnection, OutputQuantizedConnection


class PipelineTask(Task):
    """Base class for all pipeline tasks.

    This is an abstract base class for PipelineTasks which represents an
    algorithm executed by framework(s) on data which comes from data butler,
    resulting data is also stored in a data butler.

    PipelineTask inherits from a `pipe.base.Task` and uses the same
    configuration mechanism based on `pex.config`. `PipelineTask` classes also
    have a `PipelineTaskConnections` class associated with their config which
    defines all of the IO a `PipelineTask` will need to do. PipelineTask
    sub-class typically implements `run()` method which receives Python-domain
    data objects and returns `pipe.base.Struct` object with resulting data.
    `run()` method is not supposed to perform any I/O, it operates entirely on
    in-memory objects. `runQuantum()` is the method (can be re-implemented in
    sub-class) where all necessary I/O is performed, it reads all input data
    from data butler into memory, calls `run()` method with that data, examines
    returned `Struct` object and saves some or all of that data back to data
    butler. `runQuantum()` method receives a `ButlerQuantumContext` instance to
    facilitate I/O, a `InputQuantizedConnection` instance which defines all
    input `lsst.daf.butler.DatasetRef`, and a `OutputQuantizedConnection`
    instance which defines all the output `lsst.daf.butler.DatasetRef` for a
    single invocation of PipelineTask.

    Subclasses must be constructable with exactly the arguments taken by the
    PipelineTask base class constructor, but may support other signatures as
    well.

    Attributes
    ----------
    canMultiprocess : bool, True by default (class attribute)
        This class attribute is checked by execution framework, sub-classes
        can set it to ``False`` in case task does not support multiprocessing.

    Parameters
    ----------
    config : `pex.config.Config`, optional
        Configuration for this task (an instance of ``self.ConfigClass``,
        which is a task-specific subclass of `PipelineTaskConfig`).
        If not specified then it defaults to `self.ConfigClass()`.
    log : `lsst.log.Log`, optional
        Logger instance whose name is used as a log name prefix, or ``None``
        for no prefix.
    initInputs : `dict`, optional
        A dictionary of objects needed to construct this PipelineTask, with
        keys matching the keys of the dictionary returned by
        `getInitInputDatasetTypes` and values equivalent to what would be
        obtained by calling `Butler.get` with those DatasetTypes and no data
        IDs.  While it is optional for the base class, subclasses are
        permitted to require this argument.
    """
    canMultiprocess = True

    def __init__(self, *, config=None, log=None, initInputs=None, **kwargs):
        super().__init__(config=config, log=log, **kwargs)

    def run(self, **kwargs):
        """Run task algorithm on in-memory data.

        This method should be implemented in a subclass. This method will
        receive keyword arguments whose names will be the same as names of
        connection fields describing input dataset types. Argument values will
        be data objects retrieved from data butler. If a dataset type is
        configured with ``multiple`` field set to ``True`` then the argument
        value will be a list of objects, otherwise it will be a single object.

        If the task needs to know its input or output DataIds then it has to
        override `runQuantum` method instead.

        This method should return a `Struct` whose attributes share the same
        name as the connection fields describing output dataset types.

        Returns
        -------
        struct : `Struct`
            Struct with attribute names corresponding to output connection
            fields

        Examples
        --------
        Typical implementation of this method may look like::

            def run(self, input, calib):
                # "input", "calib", and "output" are the names of the config fields

                # Assuming that input/calib datasets are `scalar` they are simple objects,
                # do something with inputs and calibs, produce output image.
                image = self.makeImage(input, calib)

                # If output dataset is `scalar` then return object, not list
                return Struct(output=image)

        """
        raise NotImplementedError("run() is not implemented")

    def runQuantum(self, butlerQC: ButlerQuantumContext, inputRefs: InputQuantizedConnection,
                   outputRefs: OutputQuantizedConnection):
        """Method to do butler IO and or transforms to provide in memory objects for tasks run method

        Parameters
        ----------
        butlerQC : `ButlerQuantumContext`
            A butler which is specialized to operate in the context of a `lsst.daf.butler.Quantum`.
        inputRefs : `InputQuantizedConnection`
            Datastructure whose attribute names are the names that identify connections defined in
            corresponding `PipelineTaskConnections` class. The values of these attributes are the
            `lsst.daf.butler.DatasetRef` objects associated with the defined input/prerequisite connections.
        outputRefs : `OutputQuantizedConnection`
            Datastructure whose attribute names are the names that identify connections defined in
            corresponding `PipelineTaskConnections` class. The values of these attributes are the
            `lsst.daf.butler.DatasetRef` objects associated with the defined output connections.
        """
        inputs = butlerQC.get(inputRefs)
        outputs = self.run(**inputs)
        butlerQC.put(outputs, outputRefs)

    def getResourceConfig(self):
        """Return resource configuration for this task.

        Returns
        -------
        Object of type `~config.ResourceConfig` or ``None`` if resource
        configuration is not defined for this task.
        """
        return getattr(self.config, "resources", None)