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# This file is part of verify. 

# 

# Developed for the LSST Data Management System. 

# This product includes software developed by the LSST Project 

# (https://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 <https://www.gnu.org/licenses/>. 

 

 

__all__ = ["make_test_butler", "make_dataset_type", "ref_from_connection", 

"run_quantum"] 

 

 

from lsst.daf.butler import Butler, DatasetType, DataCoordinate, DatasetRef 

from lsst.pipe.base import ButlerQuantumContext 

 

 

# TODO: factor this out into a pipeline testing library 

def make_test_butler(root, data_ids): 

"""Create an empty repository with default configuration. 

 

Parameters 

---------- 

root : `str` 

The location of the root directory for the repository. 

data_ids : `dict` [`str`, `iterable` [`dict`]] 

A dictionary keyed by the dimensions used in the test. Each value 

is a dictionary of fields and values for that dimension. See 

:file:`daf/butler/config/dimensions.yaml` for required fields, 

listed as "keys" and "requires" under each dimension's entry. 

 

Returns 

------- 

butler : `lsst.daf.butler.Butler` 

A Butler referring to the new repository. 

""" 

# TODO: takes 5 seconds to run; split up into class-level Butler 

# with test-level runs after DM-21246 

Butler.makeRepo(root) 

butler = Butler(root, run="test") 

for dimension, values in data_ids.items(): 

butler.registry.insertDimensionData(dimension, *values) 

return butler 

 

 

def make_dataset_type(butler, name, dimensions, storageClass): 

"""Create a dataset type in a particular repository. 

 

Parameters 

---------- 

butler : `lsst.daf.butler.Butler` 

The repository to update. 

name : `str` 

The name of the dataset type. 

dimensions : `set` [`str`] 

The dimensions of the new dataset type. 

storageClass : `str` 

The storage class the dataset will use. 

 

Returns 

------- 

dataset_type : `lsst.daf.butler.DatasetType` 

The new type. 

 

Raises 

------ 

ValueError 

Raised if the dimensions or storage class are invalid. 

ConflictingDefinitionError 

Raised if another dataset type with the same name already exists. 

""" 

dataset_type = DatasetType(name, dimensions, storageClass, 

universe=butler.registry.dimensions) 

butler.registry.registerDatasetType(dataset_type) 

return dataset_type 

 

 

def ref_from_connection(butler, connection, data_id): 

"""Create a DatasetRef for a connection in a collection. 

 

Parameters 

---------- 

butler : `lsst.daf.butler.Butler` 

The collection to point to. 

connection : `lsst.pipe.base.connectionTypes.DimensionedConnection` 

The connection defining the dataset type to point to. 

data_id : `Mapping` [`str`] or `lsst.daf.butler.DataCoordinate` 

The data ID for the dataset to point to. 

 

Returns 

------- 

ref : `lsst.daf.butler.DatasetRef` 

A reference to a dataset compatible with ``connection``, with ID 

``data_id``, in the collection pointed to by ``butler``. 

""" 

universe = butler.registry.dimensions 

data_id = DataCoordinate.standardize(data_id, universe=universe) 

return DatasetRef( 

datasetType=connection.makeDatasetType(universe), 

dataId=data_id, 

) 

 

 

def run_quantum(task, butler, quantum): 

"""Run a PipelineTask on a Quantum. 

 

Parameters 

---------- 

task : `lsst.pipe.base.PipelineTask` 

The task to run on the quantum. 

butler : `lsst.daf.butler.Butler` 

The collection to run on. 

quantum : `lsst.daf.butler.Quantum` 

The quantum to run. 

""" 

butler_qc = ButlerQuantumContext(butler, quantum) 

connections = task.config.ConnectionsClass(config=task.config) 

input_refs, output_refs = connections.buildDatasetRefs(quantum) 

task.runQuantum(butler_qc, input_refs, output_refs)