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# 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/>. | ||
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"""Bunch of common classes and methods for use in unit tests. | ||
""" | ||
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__all__ = ["AddTaskConfig", "AddTask", "AddTaskFactoryMock"] | ||
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import itertools | ||
import logging | ||
import numpy | ||
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from lsst.daf.butler import (Butler, Config, DatasetType, CollectionSearch) | ||
import lsst.daf.butler.tests as butlerTests | ||
import lsst.pex.config as pexConfig | ||
from ... import base as pipeBase | ||
from .. import connectionTypes as cT | ||
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_LOG = logging.getLogger(__name__) | ||
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# SimpleInstrument has an instrument-like API as needed for unit testing, but | ||
# can not explicitly depend on Instrument because pipe_base does not explicitly | ||
# depend on obs_base. | ||
class SimpleInstrument: | ||
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@staticmethod | ||
def getName(): | ||
return "SimpleInstrument" | ||
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def applyConfigOverrides(self, name, config): | ||
pass | ||
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class AddTaskConnections(pipeBase.PipelineTaskConnections, | ||
dimensions=("instrument", "detector"), | ||
defaultTemplates={"in_tmpl": "_in", "out_tmpl": "_out"}): | ||
"""Connections for AddTask, has one input and two outputs, | ||
plus one init output. | ||
""" | ||
input = cT.Input(name="add_dataset{in_tmpl}", | ||
dimensions=["instrument", "detector"], | ||
storageClass="NumpyArray", | ||
doc="Input dataset type for this task") | ||
output = cT.Output(name="add_dataset{out_tmpl}", | ||
dimensions=["instrument", "detector"], | ||
storageClass="NumpyArray", | ||
doc="Output dataset type for this task") | ||
output2 = cT.Output(name="add2_dataset{out_tmpl}", | ||
dimensions=["instrument", "detector"], | ||
storageClass="NumpyArray", | ||
doc="Output dataset type for this task") | ||
initout = cT.InitOutput(name="add_init_output{out_tmpl}", | ||
storageClass="NumpyArray", | ||
doc="Init Output dataset type for this task") | ||
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class AddTaskConfig(pipeBase.PipelineTaskConfig, | ||
pipelineConnections=AddTaskConnections): | ||
"""Config for AddTask. | ||
""" | ||
addend = pexConfig.Field(doc="amount to add", dtype=int, default=3) | ||
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class AddTask(pipeBase.PipelineTask): | ||
"""Trivial PipelineTask for testing, has some extras useful for specific | ||
unit tests. | ||
""" | ||
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ConfigClass = AddTaskConfig | ||
_DefaultName = "add_task" | ||
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initout = numpy.array([999]) | ||
"""InitOutputs for this task""" | ||
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taskFactory = None | ||
"""Factory that makes instances""" | ||
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def run(self, input): | ||
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if self.taskFactory: | ||
# do some bookkeeping | ||
if self.taskFactory.stopAt == self.taskFactory.countExec: | ||
raise RuntimeError("pretend something bad happened") | ||
self.taskFactory.countExec += 1 | ||
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self.metadata.add("add", self.config.addend) | ||
output = input + self.config.addend | ||
output2 = output + self.config.addend | ||
_LOG.info("input = %s, output = %s, output2 = %s", input, output, output2) | ||
return pipeBase.Struct(output=output, output2=output2) | ||
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class AddTaskFactoryMock(pipeBase.TaskFactory): | ||
"""Special task factory that instantiates AddTask. | ||
It also defines some bookkeeping variables used by AddTask to report | ||
progress to unit tests. | ||
""" | ||
def __init__(self, stopAt=-1): | ||
self.countExec = 0 # incremented by AddTask | ||
self.stopAt = stopAt # AddTask raises exception at this call to run() | ||
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def loadTaskClass(self, taskName): | ||
if taskName == "AddTask": | ||
return AddTask, "AddTask" | ||
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def makeTask(self, taskClass, config, overrides, butler): | ||
if config is None: | ||
config = taskClass.ConfigClass() | ||
if overrides: | ||
overrides.applyTo(config) | ||
task = taskClass(config=config, initInputs=None) | ||
task.taskFactory = self | ||
return task | ||
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def registerDatasetTypes(registry, pipeline): | ||
"""Register all dataset types used by tasks in a registry. | ||
Copied and modified from `PreExecInit.initializeDatasetTypes`. | ||
Parameters | ||
---------- | ||
registry : `~lsst.daf.butler.Registry` | ||
Registry instance. | ||
pipeline : `typing.Iterable` of `TaskDef` | ||
Iterable of TaskDef instances, likely the output of the method | ||
toExpandedPipeline on a `~lsst.pipe.base.Pipeline` object | ||
""" | ||
for taskDef in pipeline: | ||
configDatasetType = DatasetType(taskDef.configDatasetName, {}, | ||
storageClass="Config", | ||
universe=registry.dimensions) | ||
packagesDatasetType = DatasetType("packages", {}, | ||
storageClass="Packages", | ||
universe=registry.dimensions) | ||
datasetTypes = pipeBase.TaskDatasetTypes.fromTaskDef(taskDef, registry=registry) | ||
for datasetType in itertools.chain(datasetTypes.initInputs, datasetTypes.initOutputs, | ||
datasetTypes.inputs, datasetTypes.outputs, | ||
datasetTypes.prerequisites, | ||
[configDatasetType, packagesDatasetType]): | ||
_LOG.info("Registering %s with registry", datasetType) | ||
# this is a no-op if it already exists and is consistent, | ||
# and it raises if it is inconsistent. But components must be | ||
# skipped | ||
if not datasetType.isComponent(): | ||
registry.registerDatasetType(datasetType) | ||
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def makeSimplePipeline(nQuanta, instrument=None): | ||
"""Make a simple Pipeline for tests. | ||
This is called by ``makeSimpleQGraph`` if no pipeline is passed to that | ||
function. It can also be used to customize the pipeline used by | ||
``makeSimpleQGraph`` function by calling this first and passing the result | ||
to it. | ||
Parameters | ||
---------- | ||
nQuanta : `int` | ||
The number of quanta to add to the pipeline. | ||
instrument : `str` or `None`, optional | ||
The importable name of an instrument to be added to the pipeline or | ||
if no instrument should be added then an empty string or `None`, by | ||
default None | ||
Returns | ||
------- | ||
pipeline : `~lsst.pipe.base.Pipeline` | ||
The created pipeline object. | ||
""" | ||
pipeline = pipeBase.Pipeline("test pipeline") | ||
# make a bunch of tasks that execute in well defined order (via data | ||
# dependencies) | ||
for lvl in range(nQuanta): | ||
pipeline.addTask(AddTask, f"task{lvl}") | ||
pipeline.addConfigOverride(f"task{lvl}", "connections.in_tmpl", f"{lvl}") | ||
pipeline.addConfigOverride(f"task{lvl}", "connections.out_tmpl", f"{lvl+1}") | ||
if instrument: | ||
pipeline.addInstrument(instrument) | ||
return pipeline | ||
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def makeSimpleQGraph(nQuanta=5, pipeline=None, butler=None, root=None, skipExisting=False, inMemory=True, | ||
userQuery=""): | ||
"""Make simple QuantumGraph for tests. | ||
Makes simple one-task pipeline with AddTask, sets up in-memory | ||
registry and butler, fills them with minimal data, and generates | ||
QuantumGraph with all of that. | ||
Parameters | ||
---------- | ||
nQuanta : `int` | ||
Number of quanta in a graph. | ||
pipeline : `~lsst.pipe.base.Pipeline` | ||
If `None` then one-task pipeline is made with `AddTask` and | ||
default `AddTaskConfig`. | ||
butler : `~lsst.daf.butler.Butler`, optional | ||
Data butler instance, this should be an instance returned from a | ||
previous call to this method. | ||
root : `str` | ||
Path or URI to the root location of the new repository. Only used if | ||
``butler`` is None. | ||
skipExisting : `bool`, optional | ||
If `True` (default), a Quantum is not created if all its outputs | ||
already exist. | ||
inMemory : `bool`, optional | ||
If true make in-memory repository. | ||
userQuery : `str`, optional | ||
The user query to pass to ``makeGraph``, by default an empty string. | ||
Returns | ||
------- | ||
butler : `~lsst.daf.butler.Butler` | ||
Butler instance | ||
qgraph : `~lsst.pipe.base.QuantumGraph` | ||
Quantum graph instance | ||
""" | ||
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if pipeline is None: | ||
pipeline = makeSimplePipeline(nQuanta=nQuanta) | ||
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if butler is None: | ||
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if root is None: | ||
raise ValueError("Must provide `root` when `butler` is None") | ||
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config = Config() | ||
if not inMemory: | ||
config["registry", "db"] = f"sqlite:///{root}/gen3.sqlite" | ||
config["datastore", "cls"] = "lsst.daf.butler.datastores.posixDatastore.PosixDatastore" | ||
repo = butlerTests.makeTestRepo(root, {}, config=config) | ||
collection = "test" | ||
butler = Butler(butler=repo, run=collection) | ||
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# Add dataset types to registry | ||
registerDatasetTypes(butler.registry, pipeline.toExpandedPipeline()) | ||
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# Add all needed dimensions to registry | ||
butler.registry.insertDimensionData("instrument", dict(name="INSTR")) | ||
butler.registry.insertDimensionData("detector", dict(instrument="INSTR", id=0, full_name="det0")) | ||
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# Add inputs to butler | ||
data = numpy.array([0., 1., 2., 5.]) | ||
butler.put(data, "add_dataset0", instrument="INSTR", detector=0) | ||
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# Make the graph | ||
builder = pipeBase.GraphBuilder(registry=butler.registry, skipExisting=skipExisting) | ||
qgraph = builder.makeGraph( | ||
pipeline, | ||
collections=CollectionSearch.fromExpression(butler.run), | ||
run=butler.run, | ||
userQuery=userQuery | ||
) | ||
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return butler, qgraph |
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