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# This file is part of daf_butler. 

# 

# 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/>. 

from __future__ import annotations 

 

__all__ = ["RegistryTests"] 

 

from abc import ABC, abstractmethod 

from datetime import datetime 

 

import sqlalchemy 

 

from ...core import ( 

DataCoordinate, 

DatasetType, 

DimensionGraph, 

StorageClass, 

ddl, 

) 

from .._registry import Registry, ConflictingDefinitionError, OrphanedRecordError 

 

 

class RegistryTests(ABC): 

"""Generic tests for the `Registry` class that can be subclassed to 

generate tests for different configurations. 

""" 

 

@abstractmethod 

def makeRegistry(self) -> Registry: 

raise NotImplementedError() 

 

def assertRowCount(self, registry: Registry, table: str, count: int): 

"""Check the number of rows in table. 

""" 

# TODO: all tests that rely on this method should be rewritten, as it 

# needs to depend on Registry implementation details to have any chance 

# of working. 

sql = sqlalchemy.sql.select( 

[sqlalchemy.sql.func.count()] 

).select_from( 

getattr(registry._tables, table) 

) 

self.assertEqual(registry._db.query(sql).scalar(), count) 

 

def testOpaque(self): 

"""Tests for `Registry.registerOpaqueTable`, 

`Registry.insertOpaqueData`, `Registry.fetchOpaqueData`, and 

`Registry.deleteOpaqueData`. 

""" 

registry = self.makeRegistry() 

table = "opaque_table_for_testing" 

registry.registerOpaqueTable( 

table, 

spec=ddl.TableSpec( 

fields=[ 

ddl.FieldSpec("id", dtype=sqlalchemy.BigInteger, primaryKey=True), 

ddl.FieldSpec("name", dtype=sqlalchemy.String, length=16, nullable=False), 

ddl.FieldSpec("count", dtype=sqlalchemy.SmallInteger, nullable=True), 

], 

) 

) 

rows = [ 

{"id": 1, "name": "one", "count": None}, 

{"id": 2, "name": "two", "count": 5}, 

{"id": 3, "name": "three", "count": 6}, 

] 

registry.insertOpaqueData(table, *rows) 

self.assertCountEqual(rows, list(registry.fetchOpaqueData(table))) 

self.assertEqual(rows[0:1], list(registry.fetchOpaqueData(table, id=1))) 

self.assertEqual(rows[1:2], list(registry.fetchOpaqueData(table, name="two"))) 

self.assertEqual([], list(registry.fetchOpaqueData(table, id=1, name="two"))) 

registry.deleteOpaqueData(table, id=3) 

self.assertCountEqual(rows[:2], list(registry.fetchOpaqueData(table))) 

registry.deleteOpaqueData(table) 

self.assertEqual([], list(registry.fetchOpaqueData(table))) 

 

def testDatasetType(self): 

"""Tests for `Registry.registerDatasetType` and 

`Registry.getDatasetType`. 

""" 

registry = self.makeRegistry() 

# Check valid insert 

datasetTypeName = "test" 

storageClass = StorageClass("testDatasetType") 

registry.storageClasses.registerStorageClass(storageClass) 

dimensions = registry.dimensions.extract(("instrument", "visit")) 

differentDimensions = registry.dimensions.extract(("instrument", "patch")) 

inDatasetType = DatasetType(datasetTypeName, dimensions, storageClass) 

# Inserting for the first time should return True 

self.assertTrue(registry.registerDatasetType(inDatasetType)) 

outDatasetType1 = registry.getDatasetType(datasetTypeName) 

self.assertEqual(outDatasetType1, inDatasetType) 

 

# Re-inserting should work 

self.assertFalse(registry.registerDatasetType(inDatasetType)) 

# Except when they are not identical 

with self.assertRaises(ConflictingDefinitionError): 

nonIdenticalDatasetType = DatasetType(datasetTypeName, differentDimensions, storageClass) 

registry.registerDatasetType(nonIdenticalDatasetType) 

 

# Template can be None 

datasetTypeName = "testNoneTemplate" 

storageClass = StorageClass("testDatasetType2") 

registry.storageClasses.registerStorageClass(storageClass) 

dimensions = registry.dimensions.extract(("instrument", "visit")) 

inDatasetType = DatasetType(datasetTypeName, dimensions, storageClass) 

registry.registerDatasetType(inDatasetType) 

outDatasetType2 = registry.getDatasetType(datasetTypeName) 

self.assertEqual(outDatasetType2, inDatasetType) 

 

allTypes = registry.getAllDatasetTypes() 

self.assertEqual(allTypes, {outDatasetType1, outDatasetType2}) 

 

def testDimensions(self): 

"""Tests for `Registry.insertDimensionData` and 

`Registry.expandDataId`. 

""" 

registry = self.makeRegistry() 

dimensionName = "instrument" 

dimension = registry.dimensions[dimensionName] 

dimensionValue = {"name": "DummyCam", "visit_max": 10, "exposure_max": 10, "detector_max": 2} 

registry.insertDimensionData(dimensionName, dimensionValue) 

# Inserting the same value twice should fail 

with self.assertRaises(sqlalchemy.exc.IntegrityError): 

registry.insertDimensionData(dimensionName, dimensionValue) 

# expandDataId should retrieve the record we just inserted 

self.assertEqual( 

registry.expandDataId( 

instrument="DummyCam", 

graph=dimension.graph 

).records[dimensionName].toDict(), 

dimensionValue 

) 

# expandDataId should raise if there is no record with the given ID. 

with self.assertRaises(LookupError): 

registry.expandDataId({"instrument": "Unknown"}, graph=dimension.graph) 

# abstract_filter doesn't have a table; insert should fail. 

with self.assertRaises(TypeError): 

registry.insertDimensionData("abstract_filter", {"abstract_filter": "i"}) 

dimensionName2 = "physical_filter" 

dimension2 = registry.dimensions[dimensionName2] 

dimensionValue2 = {"name": "DummyCam_i", "abstract_filter": "i"} 

# Missing required dependency ("instrument") should fail 

with self.assertRaises(sqlalchemy.exc.IntegrityError): 

registry.insertDimensionData(dimensionName2, dimensionValue2) 

# Adding required dependency should fix the failure 

dimensionValue2["instrument"] = "DummyCam" 

registry.insertDimensionData(dimensionName2, dimensionValue2) 

# expandDataId should retrieve the record we just inserted. 

self.assertEqual( 

registry.expandDataId( 

instrument="DummyCam", physical_filter="DummyCam_i", 

graph=dimension2.graph 

).records[dimensionName2].toDict(), 

dimensionValue2 

) 

 

def testDataset(self): 

"""Basic tests for `Registry.addDataset`, `Registry.getDataset`, and 

`Registry.removeDataset`. 

""" 

registry = self.makeRegistry() 

run = "test" 

registry.registerRun(run) 

storageClass = StorageClass("testDataset") 

registry.storageClasses.registerStorageClass(storageClass) 

datasetType = DatasetType(name="testtype", dimensions=registry.dimensions.extract(("instrument",)), 

storageClass=storageClass) 

registry.registerDatasetType(datasetType) 

dataId = {"instrument": "DummyCam"} 

registry.insertDimensionData("instrument", dataId) 

ref = registry.addDataset(datasetType, dataId=dataId, run=run) 

outRef = registry.getDataset(ref.id) 

self.assertIsNotNone(ref.id) 

self.assertEqual(ref, outRef) 

with self.assertRaises(ConflictingDefinitionError): 

ref = registry.addDataset(datasetType, dataId=dataId, run=run) 

registry.removeDataset(ref) 

self.assertIsNone(registry.find(run, datasetType, dataId)) 

 

def testComponents(self): 

"""Tests for `Registry.attachComponent` and other dataset operations 

on composite datasets. 

""" 

registry = self.makeRegistry() 

childStorageClass = StorageClass("testComponentsChild") 

registry.storageClasses.registerStorageClass(childStorageClass) 

parentStorageClass = StorageClass("testComponentsParent", 

components={"child1": childStorageClass, 

"child2": childStorageClass}) 

registry.storageClasses.registerStorageClass(parentStorageClass) 

parentDatasetType = DatasetType(name="parent", 

dimensions=registry.dimensions.extract(("instrument",)), 

storageClass=parentStorageClass) 

childDatasetType1 = DatasetType(name="parent.child1", 

dimensions=registry.dimensions.extract(("instrument",)), 

storageClass=childStorageClass) 

childDatasetType2 = DatasetType(name="parent.child2", 

dimensions=registry.dimensions.extract(("instrument",)), 

storageClass=childStorageClass) 

registry.registerDatasetType(parentDatasetType) 

registry.registerDatasetType(childDatasetType1) 

registry.registerDatasetType(childDatasetType2) 

dataId = {"instrument": "DummyCam"} 

registry.insertDimensionData("instrument", dataId) 

run = "test" 

registry.registerRun(run) 

parent = registry.addDataset(parentDatasetType, dataId=dataId, run=run) 

children = {"child1": registry.addDataset(childDatasetType1, dataId=dataId, run=run), 

"child2": registry.addDataset(childDatasetType2, dataId=dataId, run=run)} 

for name, child in children.items(): 

registry.attachComponent(name, parent, child) 

self.assertEqual(parent.components, children) 

outParent = registry.getDataset(parent.id) 

self.assertEqual(outParent.components, children) 

# Remove the parent; this should remove both children. 

registry.removeDataset(parent) 

self.assertIsNone(registry.find(run, parentDatasetType, dataId)) 

self.assertIsNone(registry.find(run, childDatasetType1, dataId)) 

self.assertIsNone(registry.find(run, childDatasetType2, dataId)) 

 

def testFind(self): 

"""Tests for `Registry.find`. 

""" 

registry = self.makeRegistry() 

storageClass = StorageClass("testFind") 

registry.storageClasses.registerStorageClass(storageClass) 

datasetType = DatasetType(name="dummytype", 

dimensions=registry.dimensions.extract(("instrument", "visit")), 

storageClass=storageClass) 

registry.registerDatasetType(datasetType) 

registry.insertDimensionData("instrument", 

{"instrument": "DummyCam"}, 

{"instrument": "MyCam"}) 

registry.insertDimensionData("physical_filter", 

{"instrument": "DummyCam", "physical_filter": "d-r", 

"abstract_filter": "r"}, 

{"instrument": "MyCam", "physical_filter": "m-r", 

"abstract_filter": "r"}) 

registry.insertDimensionData("visit", 

{"instrument": "DummyCam", "id": 0, "name": "zero", 

"physical_filter": "d-r"}, 

{"instrument": "DummyCam", "id": 1, "name": "one", 

"physical_filter": "d-r"}, 

{"instrument": "DummyCam", "id": 2, "name": "two", 

"physical_filter": "d-r"}, 

{"instrument": "MyCam", "id": 2, "name": "two", 

"physical_filter": "m-r"}) 

run = "test" 

dataId = {"instrument": "DummyCam", "visit": 0, "physical_filter": "d-r", "abstract_filter": None} 

registry.registerRun(run) 

inputRef = registry.addDataset(datasetType, dataId=dataId, run=run) 

outputRef = registry.find(run, datasetType, dataId) 

self.assertEqual(outputRef, inputRef) 

# Check that retrieval with invalid dataId raises 

with self.assertRaises(LookupError): 

dataId = {"instrument": "DummyCam", "abstract_filter": "g"} # should be visit 

registry.find(run, datasetType, dataId) 

# Check that different dataIds match to different datasets 

dataId1 = {"instrument": "DummyCam", "visit": 1, "physical_filter": "d-r", "abstract_filter": None} 

inputRef1 = registry.addDataset(datasetType, dataId=dataId1, run=run) 

dataId2 = {"instrument": "DummyCam", "visit": 2, "physical_filter": "d-r", "abstract_filter": None} 

inputRef2 = registry.addDataset(datasetType, dataId=dataId2, run=run) 

dataId3 = {"instrument": "MyCam", "visit": 2, "physical_filter": "m-r", "abstract_filter": None} 

inputRef3 = registry.addDataset(datasetType, dataId=dataId3, run=run) 

self.assertEqual(registry.find(run, datasetType, dataId1), inputRef1) 

self.assertEqual(registry.find(run, datasetType, dataId2), inputRef2) 

self.assertEqual(registry.find(run, datasetType, dataId3), inputRef3) 

self.assertNotEqual(registry.find(run, datasetType, dataId1), inputRef2) 

self.assertNotEqual(registry.find(run, datasetType, dataId2), inputRef1) 

self.assertNotEqual(registry.find(run, datasetType, dataId3), inputRef1) 

# Check that requesting a non-existing dataId returns None 

nonExistingDataId = {"instrument": "DummyCam", "visit": 42} 

self.assertIsNone(registry.find(run, datasetType, nonExistingDataId)) 

 

def testCollections(self): 

"""Tests for `Registry.getAllCollections`, `Registry.registerRun`, 

`Registry.disassociate`, and interactions between collections and 

`Registry.find`. 

""" 

registry = self.makeRegistry() 

storageClass = StorageClass("testCollections") 

registry.storageClasses.registerStorageClass(storageClass) 

datasetType = DatasetType(name="dummytype", 

dimensions=registry.dimensions.extract(("instrument", "visit")), 

storageClass=storageClass) 

registry.registerDatasetType(datasetType) 

registry.insertDimensionData("instrument", {"instrument": "DummyCam"}) 

registry.insertDimensionData("physical_filter", {"instrument": "DummyCam", "physical_filter": "d-r", 

"abstract_filter": "R"}) 

registry.insertDimensionData("visit", {"instrument": "DummyCam", "id": 0, "name": "zero", 

"physical_filter": "d-r"}) 

registry.insertDimensionData("visit", {"instrument": "DummyCam", "id": 1, "name": "one", 

"physical_filter": "d-r"}) 

run = "ingest" 

registry.registerRun(run) 

# Dataset.physical_filter should be populated as well here from the 

# visit Dimension values. 

dataId1 = {"instrument": "DummyCam", "visit": 0} 

inputRef1 = registry.addDataset(datasetType, dataId=dataId1, run=run) 

dataId2 = {"instrument": "DummyCam", "visit": 1} 

inputRef2 = registry.addDataset(datasetType, dataId=dataId2, run=run) 

# We should be able to find both datasets in their run 

outputRef = registry.find(run, datasetType, dataId1) 

self.assertEqual(outputRef, inputRef1) 

outputRef = registry.find(run, datasetType, dataId2) 

self.assertEqual(outputRef, inputRef2) 

# and with the associated collection 

newCollection = "something" 

registry.associate(newCollection, [inputRef1, inputRef2]) 

outputRef = registry.find(newCollection, datasetType, dataId1) 

self.assertEqual(outputRef, inputRef1) 

outputRef = registry.find(newCollection, datasetType, dataId2) 

self.assertEqual(outputRef, inputRef2) 

# but no more after disassociation 

registry.disassociate(newCollection, [inputRef1, ]) 

self.assertIsNone(registry.find(newCollection, datasetType, dataId1)) 

outputRef = registry.find(newCollection, datasetType, dataId2) 

self.assertEqual(outputRef, inputRef2) 

collections = registry.getAllCollections() 

self.assertEqual(collections, {"something", "ingest"}) 

 

def testAssociate(self): 

"""Tests for `Registry.associate`. 

""" 

registry = self.makeRegistry() 

storageClass = StorageClass("testAssociate") 

registry.storageClasses.registerStorageClass(storageClass) 

dimensions = registry.dimensions.extract(("instrument", "visit")) 

datasetType1 = DatasetType(name="dummytype", dimensions=dimensions, storageClass=storageClass) 

registry.registerDatasetType(datasetType1) 

datasetType2 = DatasetType(name="smartytype", dimensions=dimensions, storageClass=storageClass) 

registry.registerDatasetType(datasetType2) 

registry.insertDimensionData("instrument", {"instrument": "DummyCam"}) 

registry.insertDimensionData("physical_filter", {"instrument": "DummyCam", "physical_filter": "d-r", 

"abstract_filter": "R"}) 

registry.insertDimensionData("visit", {"instrument": "DummyCam", "id": 0, "name": "zero", 

"physical_filter": "d-r"}) 

registry.insertDimensionData("visit", {"instrument": "DummyCam", "id": 1, "name": "one", 

"physical_filter": "d-r"}) 

run1 = "ingest1" 

registry.registerRun(run1) 

run2 = "ingest2" 

registry.registerRun(run2) 

run3 = "ingest3" 

registry.registerRun(run3) 

# Dataset.physical_filter should be populated as well here 

# from the visit Dimension values. 

dataId1 = {"instrument": "DummyCam", "visit": 0} 

dataId2 = {"instrument": "DummyCam", "visit": 1} 

ref1_run1 = registry.addDataset(datasetType1, dataId=dataId1, run=run1) 

ref2_run1 = registry.addDataset(datasetType1, dataId=dataId2, run=run1) 

ref1_run2 = registry.addDataset(datasetType2, dataId=dataId1, run=run2) 

ref2_run2 = registry.addDataset(datasetType2, dataId=dataId2, run=run2) 

ref1_run3 = registry.addDataset(datasetType2, dataId=dataId1, run=run3) 

ref2_run3 = registry.addDataset(datasetType2, dataId=dataId2, run=run3) 

for ref in (ref1_run1, ref2_run1, ref1_run2, ref2_run2, ref1_run3, ref2_run3): 

self.assertEqual(ref.dataId.records["visit"].physical_filter, "d-r") 

self.assertEqual(ref.dataId.records["physical_filter"].abstract_filter, "R") 

# should have exactly 4 rows in Dataset 

self.assertRowCount(registry, "dataset", 6) 

self.assertRowCount(registry, "dataset_collection", 6) 

# adding same DatasetRef to the same run is an error 

with self.assertRaises(ConflictingDefinitionError): 

registry.addDataset(datasetType1, dataId=dataId2, run=run1) 

# above exception must rollback and not add anything to Dataset 

self.assertRowCount(registry, "dataset", 6) 

self.assertRowCount(registry, "dataset_collection", 6) 

# associated refs from run1 with some other collection 

newCollection = "something" 

registry.associate(newCollection, [ref1_run1, ref2_run1]) 

self.assertRowCount(registry, "dataset_collection", 8) 

# associating same exact DatasetRef is OK (not doing anything), 

# two cases to test - single-ref and many-refs 

registry.associate(newCollection, [ref1_run1]) 

registry.associate(newCollection, [ref1_run1, ref2_run1]) 

self.assertRowCount(registry, "dataset_collection", 8) 

# associated refs from run2 with same other collection, this should 

# be OK because thy have different dataset type 

registry.associate(newCollection, [ref1_run2, ref2_run2]) 

self.assertRowCount(registry, "dataset_collection", 10) 

# associating DatasetRef with the same units but different ID is not OK 

with self.assertRaises(ConflictingDefinitionError): 

registry.associate(newCollection, [ref1_run3]) 

with self.assertRaises(ConflictingDefinitionError): 

registry.associate(newCollection, [ref1_run3, ref2_run3]) 

 

def testDatasetLocations(self): 

"""Tests for `Registry.addDatasetLocation`, 

`Registry.getDatasetLocations`, and `Registry.removeDatasetLocations`. 

""" 

registry = self.makeRegistry() 

storageClass = StorageClass("testStorageInfo") 

registry.storageClasses.registerStorageClass(storageClass) 

datasetType = DatasetType(name="test", dimensions=registry.dimensions.extract(("instrument",)), 

storageClass=storageClass) 

datasetType2 = DatasetType(name="test2", dimensions=registry.dimensions.extract(("instrument",)), 

storageClass=storageClass) 

registry.registerDatasetType(datasetType) 

registry.registerDatasetType(datasetType2) 

registry.insertDimensionData("instrument", {"instrument": "DummyCam"}) 

run = "test" 

registry.registerRun(run) 

ref = registry.addDataset(datasetType, dataId={"instrument": "DummyCam"}, run=run) 

ref2 = registry.addDataset(datasetType2, dataId={"instrument": "DummyCam"}, run=run) 

datastoreName = "dummystore" 

datastoreName2 = "dummystore2" 

# Test adding information about a new dataset 

registry.addDatasetLocation(ref, datastoreName) 

addresses = registry.getDatasetLocations(ref) 

self.assertIn(datastoreName, addresses) 

self.assertEqual(len(addresses), 1) 

registry.addDatasetLocation(ref, datastoreName2) 

registry.addDatasetLocation(ref2, datastoreName2) 

addresses = registry.getDatasetLocations(ref) 

self.assertEqual(len(addresses), 2) 

self.assertIn(datastoreName, addresses) 

self.assertIn(datastoreName2, addresses) 

registry.removeDatasetLocation(datastoreName, ref) 

addresses = registry.getDatasetLocations(ref) 

self.assertEqual(len(addresses), 1) 

self.assertNotIn(datastoreName, addresses) 

self.assertIn(datastoreName2, addresses) 

with self.assertRaises(OrphanedRecordError): 

registry.removeDataset(ref) 

registry.removeDatasetLocation(datastoreName2, ref) 

addresses = registry.getDatasetLocations(ref) 

self.assertEqual(len(addresses), 0) 

self.assertNotIn(datastoreName2, addresses) 

registry.removeDataset(ref) # should not raise 

addresses = registry.getDatasetLocations(ref2) 

self.assertEqual(len(addresses), 1) 

self.assertIn(datastoreName2, addresses) 

 

def testBasicTransaction(self): 

"""Test that all operations within a single transaction block are 

rolled back if an exception propagates out of the block. 

""" 

registry = self.makeRegistry() 

storageClass = StorageClass("testDatasetType") 

registry.storageClasses.registerStorageClass(storageClass) 

dimensions = registry.dimensions.extract(("instrument",)) 

dataId = {"instrument": "DummyCam"} 

datasetTypeA = DatasetType(name="A", 

dimensions=dimensions, 

storageClass=storageClass) 

datasetTypeB = DatasetType(name="B", 

dimensions=dimensions, 

storageClass=storageClass) 

datasetTypeC = DatasetType(name="C", 

dimensions=dimensions, 

storageClass=storageClass) 

run = "test" 

registry.registerRun(run) 

refId = None 

with registry.transaction(): 

registry.registerDatasetType(datasetTypeA) 

with self.assertRaises(ValueError): 

with registry.transaction(): 

registry.registerDatasetType(datasetTypeB) 

registry.registerDatasetType(datasetTypeC) 

registry.insertDimensionData("instrument", {"instrument": "DummyCam"}) 

ref = registry.addDataset(datasetTypeA, dataId=dataId, run=run) 

refId = ref.id 

raise ValueError("Oops, something went wrong") 

# A should exist 

self.assertEqual(registry.getDatasetType("A"), datasetTypeA) 

# But B and C should both not exist 

with self.assertRaises(KeyError): 

registry.getDatasetType("B") 

with self.assertRaises(KeyError): 

registry.getDatasetType("C") 

# And neither should the dataset 

self.assertIsNotNone(refId) 

self.assertIsNone(registry.getDataset(refId)) 

# Or the Dimension entries 

with self.assertRaises(LookupError): 

registry.expandDataId({"instrument": "DummyCam"}) 

 

def testNestedTransaction(self): 

"""Test that operations within a transaction block are not rolled back 

if an exception propagates out of an inner transaction block and is 

then caught. 

""" 

registry = self.makeRegistry() 

dimension = registry.dimensions["instrument"] 

dataId1 = {"instrument": "DummyCam"} 

dataId2 = {"instrument": "DummyCam2"} 

checkpointReached = False 

with registry.transaction(): 

# This should be added and (ultimately) committed. 

registry.insertDimensionData(dimension, dataId1) 

with self.assertRaises(sqlalchemy.exc.IntegrityError): 

with registry.transaction(): 

# This does not conflict, and should succeed (but not 

# be committed). 

registry.insertDimensionData(dimension, dataId2) 

checkpointReached = True 

# This should conflict and raise, triggerring a rollback 

# of the previous insertion within the same transaction 

# context, but not the original insertion in the outer 

# block. 

registry.insertDimensionData(dimension, dataId1) 

self.assertTrue(checkpointReached) 

self.assertIsNotNone(registry.expandDataId(dataId1, graph=dimension.graph)) 

with self.assertRaises(LookupError): 

registry.expandDataId(dataId2, graph=dimension.graph) 

 

def testInstrumentDimensions(self): 

"""Test queries involving only instrument dimensions, with no joins to 

skymap.""" 

registry = self.makeRegistry() 

 

# need a bunch of dimensions and datasets for test 

registry.insertDimensionData( 

"instrument", 

dict(name="DummyCam", visit_max=25, exposure_max=300, detector_max=6) 

) 

registry.insertDimensionData( 

"physical_filter", 

dict(instrument="DummyCam", name="dummy_r", abstract_filter="r"), 

dict(instrument="DummyCam", name="dummy_i", abstract_filter="i"), 

) 

registry.insertDimensionData( 

"detector", 

*[dict(instrument="DummyCam", id=i, full_name=str(i)) for i in range(1, 6)] 

) 

registry.insertDimensionData( 

"visit", 

dict(instrument="DummyCam", id=10, name="ten", physical_filter="dummy_i"), 

dict(instrument="DummyCam", id=11, name="eleven", physical_filter="dummy_r"), 

dict(instrument="DummyCam", id=20, name="twelve", physical_filter="dummy_r"), 

) 

registry.insertDimensionData( 

"exposure", 

dict(instrument="DummyCam", id=100, name="100", visit=10, physical_filter="dummy_i"), 

dict(instrument="DummyCam", id=101, name="101", visit=10, physical_filter="dummy_i"), 

dict(instrument="DummyCam", id=110, name="110", visit=11, physical_filter="dummy_r"), 

dict(instrument="DummyCam", id=111, name="111", visit=11, physical_filter="dummy_r"), 

dict(instrument="DummyCam", id=200, name="200", visit=20, physical_filter="dummy_r"), 

dict(instrument="DummyCam", id=201, name="201", visit=20, physical_filter="dummy_r"), 

) 

# dataset types 

run1 = "test" 

run2 = "test2" 

registry.registerRun(run1) 

registry.registerRun(run2) 

storageClass = StorageClass("testDataset") 

registry.storageClasses.registerStorageClass(storageClass) 

rawType = DatasetType(name="RAW", 

dimensions=registry.dimensions.extract(("instrument", "exposure", "detector")), 

storageClass=storageClass) 

registry.registerDatasetType(rawType) 

calexpType = DatasetType(name="CALEXP", 

dimensions=registry.dimensions.extract(("instrument", "visit", "detector")), 

storageClass=storageClass) 

registry.registerDatasetType(calexpType) 

 

# add pre-existing datasets 

for exposure in (100, 101, 110, 111): 

for detector in (1, 2, 3): 

# note that only 3 of 5 detectors have datasets 

dataId = dict(instrument="DummyCam", exposure=exposure, detector=detector) 

ref = registry.addDataset(rawType, dataId=dataId, run=run1) 

# exposures 100 and 101 appear in both collections, 100 has 

# different dataset_id in different collections, for 101 only 

# single dataset_id exists 

if exposure == 100: 

registry.addDataset(rawType, dataId=dataId, run=run2) 

if exposure == 101: 

registry.associate(run2, [ref]) 

# Add pre-existing datasets to second collection. 

for exposure in (200, 201): 

for detector in (3, 4, 5): 

# note that only 3 of 5 detectors have datasets 

dataId = dict(instrument="DummyCam", exposure=exposure, detector=detector) 

registry.addDataset(rawType, dataId=dataId, run=run2) 

 

dimensions = DimensionGraph( 

registry.dimensions, 

dimensions=(rawType.dimensions.required | calexpType.dimensions.required) 

) 

# Test that single dim string works as well as list of str 

rows = list(registry.queryDimensions("visit", datasets={rawType: [run1]}, expand=True)) 

rowsI = list(registry.queryDimensions(["visit"], datasets={rawType: [run1]}, expand=True)) 

self.assertEqual(rows, rowsI) 

# with empty expression 

rows = list(registry.queryDimensions(dimensions, datasets={rawType: [run1]}, expand=True)) 

self.assertEqual(len(rows), 4*3) # 4 exposures times 3 detectors 

for dataId in rows: 

self.assertCountEqual(dataId.keys(), ("instrument", "detector", "exposure")) 

packer1 = registry.dimensions.makePacker("visit_detector", dataId) 

packer2 = registry.dimensions.makePacker("exposure_detector", dataId) 

self.assertEqual(packer1.unpack(packer1.pack(dataId)), 

DataCoordinate.standardize(dataId, graph=packer1.dimensions)) 

self.assertEqual(packer2.unpack(packer2.pack(dataId)), 

DataCoordinate.standardize(dataId, graph=packer2.dimensions)) 

self.assertNotEqual(packer1.pack(dataId), packer2.pack(dataId)) 

self.assertCountEqual(set(dataId["exposure"] for dataId in rows), 

(100, 101, 110, 111)) 

self.assertCountEqual(set(dataId["visit"] for dataId in rows), (10, 11)) 

self.assertCountEqual(set(dataId["detector"] for dataId in rows), (1, 2, 3)) 

 

# second collection 

rows = list(registry.queryDimensions(dimensions, datasets={rawType: [run2]})) 

self.assertEqual(len(rows), 4*3) # 4 exposures times 3 detectors 

for dataId in rows: 

self.assertCountEqual(dataId.keys(), ("instrument", "detector", "exposure")) 

self.assertCountEqual(set(dataId["exposure"] for dataId in rows), 

(100, 101, 200, 201)) 

self.assertCountEqual(set(dataId["visit"] for dataId in rows), (10, 20)) 

self.assertCountEqual(set(dataId["detector"] for dataId in rows), (1, 2, 3, 4, 5)) 

 

# with two input datasets 

rows = list(registry.queryDimensions(dimensions, datasets={rawType: [run1, run2]})) 

self.assertEqual(len(set(rows)), 6*3) # 6 exposures times 3 detectors; set needed to de-dupe 

for dataId in rows: 

self.assertCountEqual(dataId.keys(), ("instrument", "detector", "exposure")) 

self.assertCountEqual(set(dataId["exposure"] for dataId in rows), 

(100, 101, 110, 111, 200, 201)) 

self.assertCountEqual(set(dataId["visit"] for dataId in rows), (10, 11, 20)) 

self.assertCountEqual(set(dataId["detector"] for dataId in rows), (1, 2, 3, 4, 5)) 

 

# limit to single visit 

rows = list(registry.queryDimensions(dimensions, datasets={rawType: [run1]}, 

where="visit = 10")) 

self.assertEqual(len(rows), 2*3) # 2 exposures times 3 detectors 

self.assertCountEqual(set(dataId["exposure"] for dataId in rows), (100, 101)) 

self.assertCountEqual(set(dataId["visit"] for dataId in rows), (10,)) 

self.assertCountEqual(set(dataId["detector"] for dataId in rows), (1, 2, 3)) 

 

# more limiting expression, using link names instead of Table.column 

rows = list(registry.queryDimensions(dimensions, datasets={rawType: [run1]}, 

where="visit = 10 and detector > 1")) 

self.assertEqual(len(rows), 2*2) # 2 exposures times 2 detectors 

self.assertCountEqual(set(dataId["exposure"] for dataId in rows), (100, 101)) 

self.assertCountEqual(set(dataId["visit"] for dataId in rows), (10,)) 

self.assertCountEqual(set(dataId["detector"] for dataId in rows), (2, 3)) 

 

# expression excludes everything 

rows = list(registry.queryDimensions(dimensions, datasets={rawType: [run1]}, 

where="visit > 1000")) 

self.assertEqual(len(rows), 0) 

 

# Selecting by physical_filter, this is not in the dimensions, but it 

# is a part of the full expression so it should work too. 

rows = list(registry.queryDimensions(dimensions, datasets={rawType: [run1]}, 

where="physical_filter = 'dummy_r'")) 

self.assertEqual(len(rows), 2*3) # 2 exposures times 3 detectors 

self.assertCountEqual(set(dataId["exposure"] for dataId in rows), (110, 111)) 

self.assertCountEqual(set(dataId["visit"] for dataId in rows), (11,)) 

self.assertCountEqual(set(dataId["detector"] for dataId in rows), (1, 2, 3)) 

 

def testSkyMapDimensions(self): 

"""Tests involving only skymap dimensions, no joins to instrument.""" 

registry = self.makeRegistry() 

 

# need a bunch of dimensions and datasets for test, we want 

# "abstract_filter" in the test so also have to add physical_filter 

# dimensions 

registry.insertDimensionData( 

"instrument", 

dict(instrument="DummyCam") 

) 

registry.insertDimensionData( 

"physical_filter", 

dict(instrument="DummyCam", name="dummy_r", abstract_filter="r"), 

dict(instrument="DummyCam", name="dummy_i", abstract_filter="i"), 

) 

registry.insertDimensionData( 

"skymap", 

dict(name="DummyMap", hash="sha!".encode("utf8")) 

) 

for tract in range(10): 

registry.insertDimensionData("tract", dict(skymap="DummyMap", id=tract)) 

registry.insertDimensionData( 

"patch", 

*[dict(skymap="DummyMap", tract=tract, id=patch, cell_x=0, cell_y=0) 

for patch in range(10)] 

) 

 

# dataset types 

run = "test" 

registry.registerRun(run) 

storageClass = StorageClass("testDataset") 

registry.storageClasses.registerStorageClass(storageClass) 

calexpType = DatasetType(name="deepCoadd_calexp", 

dimensions=registry.dimensions.extract(("skymap", "tract", "patch", 

"abstract_filter")), 

storageClass=storageClass) 

registry.registerDatasetType(calexpType) 

mergeType = DatasetType(name="deepCoadd_mergeDet", 

dimensions=registry.dimensions.extract(("skymap", "tract", "patch")), 

storageClass=storageClass) 

registry.registerDatasetType(mergeType) 

measType = DatasetType(name="deepCoadd_meas", 

dimensions=registry.dimensions.extract(("skymap", "tract", "patch", 

"abstract_filter")), 

storageClass=storageClass) 

registry.registerDatasetType(measType) 

 

dimensions = DimensionGraph( 

registry.dimensions, 

dimensions=(calexpType.dimensions.required | mergeType.dimensions.required | 

measType.dimensions.required) 

) 

 

# add pre-existing datasets 

for tract in (1, 3, 5): 

for patch in (2, 4, 6, 7): 

dataId = dict(skymap="DummyMap", tract=tract, patch=patch) 

registry.addDataset(mergeType, dataId=dataId, run=run) 

for aFilter in ("i", "r"): 

dataId = dict(skymap="DummyMap", tract=tract, patch=patch, abstract_filter=aFilter) 

registry.addDataset(calexpType, dataId=dataId, run=run) 

 

# with empty expression 

rows = list(registry.queryDimensions(dimensions, 

datasets={calexpType: [run], mergeType: [run]})) 

self.assertEqual(len(rows), 3*4*2) # 4 tracts x 4 patches x 2 filters 

for dataId in rows: 

self.assertCountEqual(dataId.keys(), ("skymap", "tract", "patch", "abstract_filter")) 

self.assertCountEqual(set(dataId["tract"] for dataId in rows), (1, 3, 5)) 

self.assertCountEqual(set(dataId["patch"] for dataId in rows), (2, 4, 6, 7)) 

self.assertCountEqual(set(dataId["abstract_filter"] for dataId in rows), ("i", "r")) 

 

# limit to 2 tracts and 2 patches 

rows = list(registry.queryDimensions(dimensions, 

datasets={calexpType: [run], mergeType: [run]}, 

where="tract IN (1, 5) AND patch IN (2, 7)")) 

self.assertEqual(len(rows), 2*2*2) # 2 tracts x 2 patches x 2 filters 

self.assertCountEqual(set(dataId["tract"] for dataId in rows), (1, 5)) 

self.assertCountEqual(set(dataId["patch"] for dataId in rows), (2, 7)) 

self.assertCountEqual(set(dataId["abstract_filter"] for dataId in rows), ("i", "r")) 

 

# limit to single filter 

rows = list(registry.queryDimensions(dimensions, 

datasets={calexpType: [run], mergeType: [run]}, 

where="abstract_filter = 'i'")) 

self.assertEqual(len(rows), 3*4*1) # 4 tracts x 4 patches x 2 filters 

self.assertCountEqual(set(dataId["tract"] for dataId in rows), (1, 3, 5)) 

self.assertCountEqual(set(dataId["patch"] for dataId in rows), (2, 4, 6, 7)) 

self.assertCountEqual(set(dataId["abstract_filter"] for dataId in rows), ("i",)) 

 

# expression excludes everything, specifying non-existing skymap is 

# not a fatal error, it's operator error 

rows = list(registry.queryDimensions(dimensions, 

datasets={calexpType: [run], mergeType: [run]}, 

where="skymap = 'Mars'")) 

self.assertEqual(len(rows), 0) 

 

def testSpatialMatch(self): 

"""Test involving spatial match using join tables. 

 

Note that realistic test needs a reasonably-defined skypix and regions 

in registry tables which is hard to implement in this simple test. 

So we do not actually fill registry with any data and all queries will 

return empty result, but this is still useful for coverage of the code 

that generates query. 

""" 

registry = self.makeRegistry() 

 

# dataset types 

collection = "test" 

registry.registerRun(name=collection) 

storageClass = StorageClass("testDataset") 

registry.storageClasses.registerStorageClass(storageClass) 

 

calexpType = DatasetType(name="CALEXP", 

dimensions=registry.dimensions.extract(("instrument", "visit", "detector")), 

storageClass=storageClass) 

registry.registerDatasetType(calexpType) 

 

coaddType = DatasetType(name="deepCoadd_calexp", 

dimensions=registry.dimensions.extract(("skymap", "tract", "patch", 

"abstract_filter")), 

storageClass=storageClass) 

registry.registerDatasetType(coaddType) 

 

dimensions = DimensionGraph( 

registry.dimensions, 

dimensions=(calexpType.dimensions.required | coaddType.dimensions.required) 

) 

 

# without data this should run OK but return empty set 

rows = list(registry.queryDimensions(dimensions, datasets={calexpType: [collection]})) 

self.assertEqual(len(rows), 0) 

 

def testCalibrationLabelIndirection(self): 

"""Test that we can look up datasets with calibration_label dimensions 

from a data ID with exposure dimensions. 

""" 

registry = self.makeRegistry() 

 

flat = DatasetType( 

"flat", 

registry.dimensions.extract( 

["instrument", "detector", "physical_filter", "calibration_label"] 

), 

"ImageU" 

) 

registry.registerDatasetType(flat) 

registry.insertDimensionData("instrument", dict(name="DummyCam")) 

registry.insertDimensionData( 

"physical_filter", 

dict(instrument="DummyCam", name="dummy_i", abstract_filter="i"), 

) 

registry.insertDimensionData( 

"detector", 

*[dict(instrument="DummyCam", id=i, full_name=str(i)) for i in (1, 2, 3, 4, 5)] 

) 

registry.insertDimensionData( 

"visit", 

dict(instrument="DummyCam", id=10, name="ten", physical_filter="dummy_i"), 

dict(instrument="DummyCam", id=11, name="eleven", physical_filter="dummy_i"), 

) 

registry.insertDimensionData( 

"exposure", 

dict(instrument="DummyCam", id=100, name="100", visit=10, physical_filter="dummy_i", 

datetime_begin=datetime(2005, 12, 15, 2), datetime_end=datetime(2005, 12, 15, 3)), 

dict(instrument="DummyCam", id=101, name="101", visit=11, physical_filter="dummy_i", 

datetime_begin=datetime(2005, 12, 16, 2), datetime_end=datetime(2005, 12, 16, 3)), 

) 

registry.insertDimensionData( 

"calibration_label", 

dict(instrument="DummyCam", name="first_night", 

datetime_begin=datetime(2005, 12, 15, 1), datetime_end=datetime(2005, 12, 15, 4)), 

dict(instrument="DummyCam", name="second_night", 

datetime_begin=datetime(2005, 12, 16, 1), datetime_end=datetime(2005, 12, 16, 4)), 

dict(instrument="DummyCam", name="both_nights", 

datetime_begin=datetime(2005, 12, 15, 1), datetime_end=datetime(2005, 12, 16, 4)), 

) 

# Different flats for different nights for detectors 1-3 in first 

# collection. 

run1 = "calibs1" 

registry.registerRun(run1) 

for detector in (1, 2, 3): 

registry.addDataset(flat, dict(instrument="DummyCam", calibration_label="first_night", 

physical_filter="dummy_i", detector=detector), 

run=run1) 

registry.addDataset(flat, dict(instrument="DummyCam", calibration_label="second_night", 

physical_filter="dummy_i", detector=detector), 

run=run1) 

# The same flat for both nights for detectors 3-5 (so detector 3 has 

# multiple valid flats) in second collection. 

run2 = "calib2" 

registry.registerRun(run2) 

for detector in (3, 4, 5): 

registry.addDataset(flat, dict(instrument="DummyCam", calibration_label="both_nights", 

physical_filter="dummy_i", detector=detector), 

run=run2) 

# Perform queries for individual exposure+detector combinations, which 

# should always return exactly one flat. 

for exposure in (100, 101): 

for detector in (1, 2, 3): 

with self.subTest(exposure=exposure, detector=detector): 

rows = registry.queryDatasets("flat", collections=[run1], 

instrument="DummyCam", 

exposure=exposure, 

detector=detector) 

self.assertEqual(len(list(rows)), 1) 

for detector in (3, 4, 5): 

with self.subTest(exposure=exposure, detector=detector): 

rows = registry.queryDatasets("flat", collections=[run2], 

instrument="DummyCam", 

exposure=exposure, 

detector=detector) 

self.assertEqual(len(list(rows)), 1) 

for detector in (1, 2, 4, 5): 

with self.subTest(exposure=exposure, detector=detector): 

rows = registry.queryDatasets("flat", collections=[run1, run2], 

instrument="DummyCam", 

exposure=exposure, 

detector=detector) 

self.assertEqual(len(list(rows)), 1) 

for detector in (3,): 

with self.subTest(exposure=exposure, detector=detector): 

rows = registry.queryDatasets("flat", collections=[run1, run2], 

instrument="DummyCam", 

exposure=exposure, 

detector=detector) 

self.assertEqual(len(list(rows)), 2)