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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 abc import ABCMeta, abstractmethod 

from collections.abc import Mapping 

import contextlib 

import functools 

 

from lsst.utils import doImport 

from lsst.sphgeom import ConvexPolygon 

from .config import Config, ConfigSubset 

from .dimensions import DimensionConfig, DimensionUniverse, DataId, DimensionKeyDict 

from .schema import SchemaConfig 

from .utils import transactional 

from .dataIdPacker import DataIdPackerFactory 

 

__all__ = ("RegistryConfig", "Registry", "disableWhenLimited", 

"AmbiguousDatasetError", "ConflictingDefinitionError", "OrphanedRecordError") 

 

 

class AmbiguousDatasetError(Exception): 

"""Exception raised when a `DatasetRef` has no ID and a `Registry` 

operation requires one. 

""" 

 

 

class ConflictingDefinitionError(Exception): 

"""Exception raised when trying to insert a database record when a 

conflicting record already exists. 

""" 

 

 

class OrphanedRecordError(Exception): 

"""Exception raised when trying to remove or modify a database record 

that is still being used in some other table. 

""" 

 

 

def disableWhenLimited(func): 

"""Decorator that indicates that a method should raise NotImplementedError 

on Registries whose ``limited`` attribute is `True`. 

 

This implements that check and raise for all subclasses. 

""" 

@functools.wraps(func) 

def inner(self, *args, **kwargs): 

if self.limited: 

raise NotImplementedError( 

"Operation not implemented for limited Registry; note that data IDs may need to be expanded " 

"by a full Registry before being used for some operations on a limited Registry." 

) 

return func(self, *args, **kwargs) 

return inner 

 

 

class RegistryConfig(ConfigSubset): 

component = "registry" 

requiredKeys = ("cls",) 

defaultConfigFile = "registry.yaml" 

 

 

class Registry(metaclass=ABCMeta): 

"""Registry interface. 

 

Parameters 

---------- 

registryConfig : `RegistryConfig` 

Registry configuration. 

schemaConfig : `SchemaConfig`, optional 

Schema configuration. 

dimensionConfig : `DimensionConfig` or `Config` or 

`DimensionGraph` configuration. 

""" 

 

defaultConfigFile = None 

"""Path to configuration defaults. Relative to $DAF_BUTLER_DIR/config or 

absolute path. Can be None if no defaults specified. 

""" 

 

@classmethod 

@abstractmethod 

def setConfigRoot(cls, root, config, full): 

"""Set any filesystem-dependent config options for this Registry to 

be appropriate for a new empty repository with the given root. 

 

Parameters 

---------- 

root : `str` 

Filesystem path to the root of the data repository. 

config : `Config` 

A `Config` to update. Only the subset understood by 

this component will be updated. Will not expand 

defaults. 

full : `Config` 

A complete config with all defaults expanded that can be 

converted to a `RegistryConfig`. Read-only and will not be 

modified by this method. 

Repository-specific options that should not be obtained 

from defaults when Butler instances are constructed 

should be copied from `full` to `Config`. 

""" 

Config.overrideParameters(RegistryConfig, config, full, 

toCopy=(("skypix", "cls"), ("skypix", "level"))) 

 

@staticmethod 

def fromConfig(registryConfig, schemaConfig=None, dimensionConfig=None, create=False): 

"""Create `Registry` subclass instance from `config`. 

 

Uses ``registry.cls`` from `config` to determine which subclass to 

instantiate. 

 

Parameters 

---------- 

registryConfig : `ButlerConfig`, `RegistryConfig`, `Config` or `str` 

Registry configuration 

schemaConfig : `SchemaConfig`, `Config` or `str`, optional. 

Schema configuration. Can be read from supplied registryConfig 

if the relevant component is defined and ``schemaConfig`` is 

`None`. 

dimensionConfig : `DimensionConfig` or `Config` or 

`str`, optional. `DimensionGraph` configuration. Can be read 

from supplied registryConfig if the relevant component is 

defined and ``dimensionConfig`` is `None`. 

create : `bool` 

Assume empty Registry and create a new one. 

 

Returns 

------- 

registry : `Registry` (subclass) 

A new `Registry` subclass instance. 

""" 

if schemaConfig is None: 

# Try to instantiate a schema configuration from the supplied 

# registry configuration. 

schemaConfig = SchemaConfig(registryConfig) 

elif not isinstance(schemaConfig, SchemaConfig): 

if isinstance(schemaConfig, str) or isinstance(schemaConfig, Config): 

schemaConfig = SchemaConfig(schemaConfig) 

else: 

raise ValueError("Incompatible Schema configuration: {}".format(schemaConfig)) 

 

if dimensionConfig is None: 

# Try to instantiate a schema configuration from the supplied 

# registry configuration. 

dimensionConfig = DimensionConfig(registryConfig) 

elif not isinstance(dimensionConfig, DimensionConfig): 

if isinstance(dimensionConfig, str) or isinstance(dimensionConfig, Config): 

dimensionConfig = DimensionConfig(dimensionConfig) 

else: 

raise ValueError("Incompatible Dimension configuration: {}".format(dimensionConfig)) 

 

if not isinstance(registryConfig, RegistryConfig): 

if isinstance(registryConfig, str) or isinstance(registryConfig, Config): 

registryConfig = RegistryConfig(registryConfig) 

else: 

raise ValueError("Incompatible Registry configuration: {}".format(registryConfig)) 

 

cls = doImport(registryConfig["cls"]) 

return cls(registryConfig, schemaConfig, dimensionConfig, create=create) 

 

def __init__(self, registryConfig, schemaConfig=None, dimensionConfig=None, create=False): 

assert isinstance(registryConfig, RegistryConfig) 

self.config = registryConfig 

self._pixelization = None 

self.dimensions = DimensionUniverse.fromConfig(dimensionConfig) 

self._dataIdPackerFactories = { 

name: DataIdPackerFactory.fromConfig(self.dimensions, subconfig) 

for name, subconfig in registryConfig.get("dataIdPackers", {}).items() 

} 

self._fieldsToAlwaysGet = DimensionKeyDict(keys=self.dimensions.elements, factory=set) 

for packerFactory in self._dataIdPackerFactories.values(): 

packerFactory.updateFieldsToGet(self._fieldsToAlwaysGet) 

 

def __str__(self): 

return "None" 

 

@property 

def limited(self): 

"""If True, this Registry does not maintain Dimension metadata or 

relationships (`bool`).""" 

return self.config.get("limited", False) 

 

@contextlib.contextmanager 

def transaction(self): 

"""Optionally implemented in `Registry` subclasses to provide exception 

safety guarantees in case an exception is raised in the enclosed block. 

 

This context manager may be nested (e.g. any implementation by a 

`Registry` subclass must nest properly). 

 

.. warning:: 

 

The level of exception safety is not guaranteed by this API. 

It may implement stong exception safety and roll back any changes 

leaving the state unchanged, or it may do nothing leaving the 

underlying `Registry` corrupted. Depending on the implementation 

in the subclass. 

 

.. todo:: 

 

Investigate if we may want to provide a `TransactionalRegistry` 

subclass that guarantees a particular level of exception safety. 

""" 

yield 

 

@property 

def pixelization(self): 

"""Object that interprets SkyPix Dimension values 

(`lsst.sphgeom.Pixelization`). 

 

`None` for limited registries. 

""" 

if self.limited: 

return None 

if self._pixelization is None: 

pixelizationCls = doImport(self.config["skypix", "cls"]) 

self._pixelization = pixelizationCls(level=self.config["skypix", "level"]) 

return self._pixelization 

 

@abstractmethod 

def makeDatabaseDict(self, table, types, key, value): 

"""Construct a DatabaseDict backed by a table in the same database as 

this Registry. 

 

Parameters 

---------- 

table : `table` 

Name of the table that backs the returned DatabaseDict. If this 

table already exists, its schema must include at least everything 

in `types`. 

types : `dict` 

A dictionary mapping `str` field names to type objects, containing 

all fields to be held in the database. 

key : `str` 

The name of the field to be used as the dictionary key. Must not 

be present in ``value._fields``. 

value : `type` 

The type used for the dictionary's values, typically a 

`~collections.namedtuple`. Must have a ``_fields`` class 

attribute that is a tuple of field names (i.e. as defined by 

`~collections.namedtuple`); these field names must also appear 

in the ``types`` arg, and a `_make` attribute to construct it 

from a sequence of values (again, as defined by 

`~collections.namedtuple`). 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

def find(self, collection, datasetType, dataId=None, **kwds): 

"""Lookup a dataset. 

 

This can be used to obtain a `DatasetRef` that permits the dataset to 

be read from a `Datastore`. 

 

Parameters 

---------- 

collection : `str` 

Identifies the collection to search. 

datasetType : `DatasetType` or `str` 

A `DatasetType` or the name of one. 

dataId : `dict` or `DataId`, optional 

A `dict`-like object containing the `Dimension` links that identify 

the dataset within a collection. 

kwds 

Additional keyword arguments passed to the `DataId` constructor 

to convert ``dataId`` to a true `DataId` or augment an existing 

one. 

 

Returns 

------- 

ref : `DatasetRef` 

A ref to the Dataset, or `None` if no matching Dataset 

was found. 

 

Raises 

------ 

LookupError 

If one or more data ID keys are missing. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@transactional 

def registerDatasetType(self, datasetType): 

""" 

Add a new `DatasetType` to the Registry. 

 

It is not an error to register the same `DatasetType` twice. 

 

Parameters 

---------- 

datasetType : `DatasetType` 

The `DatasetType` to be added. 

 

Raises 

------ 

ValueError 

Raised if the dimensions or storage class are invalid. 

ConflictingDefinitionError 

Raised if this DatasetType is already registered with a different 

definition. 

 

Returns 

------- 

inserted : `bool` 

`True` if ``datasetType`` was inserted, `False` if an identical 

existing `DatsetType` was found. Note that in either case the 

DatasetType is guaranteed to be defined in the Registry 

consistently with the given definition. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

def getDatasetType(self, name): 

"""Get the `DatasetType`. 

 

Parameters 

---------- 

name : `str` 

Name of the type. 

 

Returns 

------- 

type : `DatasetType` 

The `DatasetType` associated with the given name. 

 

Raises 

------ 

KeyError 

Requested named DatasetType could not be found in registry. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@transactional 

def addDataset(self, datasetType, dataId, run, producer=None, recursive=False, **kwds): 

"""Adds a Dataset entry to the `Registry` 

 

This always adds a new Dataset; to associate an existing Dataset with 

a new collection, use ``associate``. 

 

Parameters 

---------- 

datasetType : `DatasetType` or `str` 

A `DatasetType` or the name of one. 

dataId : `dict` or `DataId` 

A `dict`-like object containing the `Dimension` links that identify 

the dataset within a collection. 

run : `Run` 

The `Run` instance that produced the Dataset. Ignored if 

``producer`` is passed (`producer.run` is then used instead). 

A Run must be provided by one of the two arguments. 

producer : `Quantum` 

Unit of work that produced the Dataset. May be `None` to store 

no provenance information, but if present the `Quantum` must 

already have been added to the Registry. 

recursive : `bool` 

If True, recursively add Dataset and attach entries for component 

Datasets as well. 

kwds 

Additional keyword arguments passed to the `DataId` constructor 

to convert ``dataId`` to a true `DataId` or augment an existing 

one. 

 

Returns 

------- 

ref : `DatasetRef` 

A newly-created `DatasetRef` instance. 

 

Raises 

------ 

ConflictingDefinitionError 

If a Dataset with the given `DatasetRef` already exists in the 

given collection. 

 

Exception 

If ``dataId`` contains unknown or invalid `Dimension` entries. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

def getDataset(self, id, datasetType=None, dataId=None): 

"""Retrieve a Dataset entry. 

 

Parameters 

---------- 

id : `int` 

The unique identifier for the Dataset. 

datasetType : `DatasetType`, optional 

The `DatasetType` of the dataset to retrieve. This is used to 

short-circuit retrieving the `DatasetType`, so if provided, the 

caller is guaranteeing that it is what would have been retrieved. 

dataId : `DataId`, optional 

A `Dimension`-based identifier for the dataset within a 

collection, possibly containing additional metadata. This is used 

to short-circuit retrieving the `DataId`, so if provided, the 

caller is guaranteeing that it is what would have been retrieved. 

 

Returns 

------- 

ref : `DatasetRef` 

A ref to the Dataset, or `None` if no matching Dataset 

was found. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@transactional 

def removeDataset(self, ref): 

"""Remove a dataset from the Registry. 

 

The dataset and all components will be removed unconditionally from 

all collections, and any associated `Quantum` records will also be 

removed. `Datastore` records will *not* be deleted; the caller is 

responsible for ensuring that the dataset has already been removed 

from all Datastores. 

 

Parameters 

---------- 

ref : `DatasetRef` 

Reference to the dataset to be removed. Must include a valid 

``id`` attribute, and should be considered invalidated upon return. 

 

Raises 

------ 

AmbiguousDatasetError 

Raised if ``ref.id`` is `None`. 

OrphanedRecordError 

Raised if the dataset is still present in any `Datastore`. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@transactional 

def attachComponent(self, name, parent, component): 

"""Attach a component to a dataset. 

 

Parameters 

---------- 

name : `str` 

Name of the component. 

parent : `DatasetRef` 

A reference to the parent dataset. Will be updated to reference 

the component. 

component : `DatasetRef` 

A reference to the component dataset. 

 

Raises 

------ 

AmbiguousDatasetError 

Raised if ``parent.id`` or ``component.id`` is `None`. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@transactional 

def associate(self, collection, refs): 

"""Add existing Datasets to a collection, implicitly creating the 

collection if it does not already exist. 

 

If a DatasetRef with the same exact ``dataset_id`` is already in a 

collection nothing is changed. If a `DatasetRef` with the same 

`DatasetType1` and dimension values but with different ``dataset_id`` 

exists in the collection, `ValueError` is raised. 

 

Parameters 

---------- 

collection : `str` 

Indicates the collection the Datasets should be associated with. 

refs : iterable of `DatasetRef` 

An iterable of `DatasetRef` instances that already exist in this 

`Registry`. All component datasets will be associated with the 

collection as well. 

 

Raises 

------ 

ConflictingDefinitionError 

If a Dataset with the given `DatasetRef` already exists in the 

given collection. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@transactional 

def disassociate(self, collection, refs): 

r"""Remove existing Datasets from a collection. 

 

``collection`` and ``ref`` combinations that are not currently 

associated are silently ignored. 

 

Parameters 

---------- 

collection : `str` 

The collection the Datasets should no longer be associated with. 

refs : `list` of `DatasetRef` 

A `list` of `DatasetRef` instances that already exist in this 

`Registry`. All component datasets will also be removed. 

 

Raises 

------ 

AmbiguousDatasetError 

Raised if ``any(ref.id is None for ref in refs)``. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@transactional 

def addDatasetLocation(self, ref, datastoreName): 

"""Add datastore name locating a given dataset. 

 

Typically used by `Datastore`. 

 

Parameters 

---------- 

ref : `DatasetRef` 

A reference to the dataset for which to add storage information. 

datastoreName : `str` 

Name of the datastore holding this dataset. 

 

Raises 

------ 

AmbiguousDatasetError 

Raised if ``ref.id`` is `None`. 

""" 

# TODO: this requires `ref.dataset_id` to be not None, and probably 

# doesn't use anything else from `ref`. Should it just take a 

# `dataset_id`? 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

def getDatasetLocations(self, ref): 

"""Retrieve datastore locations for a given dataset. 

 

Typically used by `Datastore`. 

 

Parameters 

---------- 

ref : `DatasetRef` 

A reference to the dataset for which to retrieve storage 

information. 

 

Returns 

------- 

datastores : `set` of `str` 

All the matching datastores holding this dataset. Empty set 

if the dataset does not exist anywhere. 

 

Raises 

------ 

AmbiguousDatasetError 

Raised if ``ref.id`` is `None`. 

""" 

# TODO: this requires `ref.dataset_id` to be not None, and probably 

# doesn't use anything else from `ref`. Should it just take a 

# `dataset_id`? 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@transactional 

def removeDatasetLocation(self, datastoreName, ref): 

"""Remove datastore location associated with this dataset. 

 

Typically used by `Datastore` when a dataset is removed. 

 

Parameters 

---------- 

datastoreName : `str` 

Name of this `Datastore`. 

ref : `DatasetRef` 

A reference to the dataset for which information is to be removed. 

 

Raises 

------ 

AmbiguousDatasetError 

Raised if ``ref.id`` is `None`. 

""" 

# TODO: this requires `ref.dataset_id` to be not None, and probably 

# doesn't use anything else from `ref`. Should it just take a 

# `dataset_id`? 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@transactional 

def addExecution(self, execution): 

"""Add a new `Execution` to the `Registry`. 

 

If ``execution.id`` is `None` the `Registry` will update it to 

that of the newly inserted entry. 

 

Parameters 

---------- 

execution : `Execution` 

Instance to add to the `Registry`. 

The given `Execution` must not already be present in the 

`Registry`. 

 

Raises 

------ 

ConflictingDefinitionError 

If ``execution`` is already present in the `Registry`. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

def getExecution(self, id): 

"""Retrieve an Execution. 

 

Parameters 

---------- 

id : `int` 

The unique identifier for the Execution. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@transactional 

def makeRun(self, collection): 

"""Create a new `Run` in the `Registry` and return it. 

 

If a run with this collection already exists, return that instead. 

 

Parameters 

---------- 

collection : `str` 

The collection used to identify all inputs and outputs 

of the `Run`. 

 

Returns 

------- 

run : `Run` 

A new `Run` instance. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@transactional 

def ensureRun(self, run): 

"""Conditionally add a new `Run` to the `Registry`. 

 

If the ``run.id`` is `None` or a `Run` with this `id` doesn't exist 

in the `Registry` yet, add it. Otherwise, ensure the provided run is 

identical to the one already in the registry. 

 

Parameters 

---------- 

run : `Run` 

Instance to add to the `Registry`. 

 

Raises 

------ 

ConflictingDefinitionError 

If ``run`` already exists, but is not identical. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@transactional 

def addRun(self, run): 

"""Add a new `Run` to the `Registry`. 

 

Parameters 

---------- 

run : `Run` 

Instance to add to the `Registry`. 

The given `Run` must not already be present in the `Registry` 

(or any other). Therefore its `id` must be `None` and its 

`collection` must not be associated with any existing `Run`. 

 

Raises 

------ 

ConflictingDefinitionError 

If a run already exists with this collection. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

def getRun(self, id=None, collection=None): 

""" 

Get a `Run` corresponding to its collection or id 

 

Parameters 

---------- 

id : `int`, optional 

Lookup by run `id`, or: 

collection : `str` 

If given, lookup by `collection` name instead. 

 

Returns 

------- 

run : `Run` 

The `Run` instance. 

 

Raises 

------ 

ValueError 

Must supply one of ``collection`` or ``id``. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@transactional 

def addQuantum(self, quantum): 

r"""Add a new `Quantum` to the `Registry`. 

 

Parameters 

---------- 

quantum : `Quantum` 

Instance to add to the `Registry`. 

The given `Quantum` must not already be present in the 

`Registry` (or any other), therefore its: 

 

- `run` attribute must be set to an existing `Run`. 

- `predictedInputs` attribute must be fully populated with 

`DatasetRef`\ s, and its. 

- `actualInputs` and `outputs` will be ignored. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

def getQuantum(self, id): 

"""Retrieve an Quantum. 

 

Parameters 

---------- 

id : `int` 

The unique identifier for the Quantum. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@transactional 

def markInputUsed(self, quantum, ref): 

"""Record the given `DatasetRef` as an actual (not just predicted) 

input of the given `Quantum`. 

 

This updates both the `Registry`"s `Quantum` table and the Python 

`Quantum.actualInputs` attribute. 

 

Parameters 

---------- 

quantum : `Quantum` 

Producer to update. 

Will be updated in this call. 

ref : `DatasetRef` 

To set as actually used input. 

 

Raises 

------ 

KeyError 

If ``quantum`` is not a predicted consumer for ``ref``. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@disableWhenLimited 

@transactional 

def addDimensionEntry(self, dimension, dataId=None, entry=None, **kwds): 

"""Add a new `Dimension` entry. 

 

dimension : `str` or `Dimension` 

Either a `Dimension` object or the name of one. 

dataId : `dict` or `DataId`, optional 

A `dict`-like object containing the `Dimension` links that form 

the primary key of the row to insert. If this is a full `DataId` 

object, ``dataId.entries[dimension]`` will be updated with 

``entry`` and then inserted into the `Registry`. 

entry : `dict` 

Dictionary that maps column name to column value. 

kwds 

Additional keyword arguments passed to the `DataId` constructor 

to convert ``dataId`` to a true `DataId` or augment an existing 

one. 

 

If ``values`` includes a "region" key, `setDimensionRegion` will 

automatically be called to set it any associated spatial join 

tables. 

Region fields associated with a combination of Dimensions must be 

explicitly set separately. 

 

Returns 

------- 

dataId : `DataId` 

A Data ID for exactly the given dimension that includes the added 

entry. 

 

Raises 

------ 

TypeError 

If the given `Dimension` does not have explicit entries in the 

registry. 

ConflictingDefinitionError 

If an entry with the primary-key defined in `values` is already 

present. 

NotImplementedError 

Raised if `limited` is `True`. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@disableWhenLimited 

def findDimensionEntry(self, dimension, dataId=None, **kwds): 

"""Return a `Dimension` entry corresponding to a `DataId`. 

 

Parameters 

---------- 

dimension : `str` or `Dimension` 

Either a `Dimension` object or the name of one. 

dataId : `dict` or `DataId`, optional 

A `dict`-like object containing the `Dimension` links that form 

the primary key of the row to retreive. If this is a full `DataId` 

object, ``dataId.entries[dimension]`` will be updated with the 

entry obtained from the `Registry`. 

kwds 

Additional keyword arguments passed to the `DataId` constructor 

to convert ``dataId`` to a true `DataId` or augment an existing 

one. 

 

Returns 

------- 

entry : `dict` 

Dictionary with all `Dimension` values, or `None` if no matching 

entry is found. `None` if there is no entry for the given 

`DataId`. 

 

Raises 

------ 

NotImplementedError 

Raised if `limited` is `True`. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@disableWhenLimited 

@transactional 

def setDimensionRegion(self, dataId=None, *, update=True, region=None, **kwds): 

"""Set the region field for a Dimension instance or a combination 

thereof and update associated spatial join tables. 

 

Parameters 

---------- 

dataId : `dict` or `DataId` 

A `dict`-like object containing the `Dimension` links that form 

the primary key of the row to insert or update. If this is a full 

`DataId`, ``dataId.region`` will be set to ``region`` (if 

``region`` is not `None`) and then used to update or insert into 

the `Registry`. 

update : `bool` 

If True, existing region information for these Dimensions is being 

replaced. This is usually required because Dimension entries are 

assumed to be pre-inserted prior to calling this function. 

region : `lsst.sphgeom.ConvexPolygon`, optional 

The region to update or insert into the `Registry`. If not present 

``dataId.region`` must not be `None`. 

kwds 

Additional keyword arguments passed to the `DataId` constructor 

to convert ``dataId`` to a true `DataId` or augment an existing 

one. 

 

Returns 

------- 

dataId : `DataId` 

A Data ID with its ``region`` attribute set. 

 

Raises 

------ 

NotImplementedError 

Raised if `limited` is `True`. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@disableWhenLimited 

def selectDimensions(self, originInfo, expression, neededDatasetTypes, futureDatasetTypes, 

expandDataIds=True): 

"""Evaluate a filter expression and lists of 

`DatasetTypes <DatasetType>` and return a set of data unit values. 

 

Returned set consists of combinations of units participating in data 

transformation from ``neededDatasetTypes`` to ``futureDatasetTypes``, 

restricted by existing data and filter expression. 

 

Parameters 

---------- 

originInfo : `DatasetOriginInfo` 

Object which provides names of the input/output collections. 

expression : `str` 

An expression that limits the `Dimensions <Dimension>` and 

(indirectly) the Datasets returned. 

neededDatasetTypes : `list` of `DatasetType` or `str` 

The `list` of `DatasetTypes <DatasetType>` whose Dimensions will 

be included in the returned column set. Output is limited to the 

the Datasets of these DatasetTypes which already exist in the 

registry. 

futureDatasetTypes : `list` of `DatasetType` or `str` 

The `list` of `DatasetTypes <DatasetType>` whose Dimensions will 

be included in the returned column set. It is expected that 

Datasets for these DatasetTypes do not exist in the registry, 

but presently this is not checked. 

expandDataIds : `bool` 

If `True` (default), expand all data IDs when returning them. 

 

Yields 

------ 

row : `PreFlightUnitsRow` 

Single row is a unique combination of units in a transform. 

 

Raises 

------ 

NotImplementedError 

Raised if `limited` is `True`. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@disableWhenLimited 

def expandDataId(self, dataId=None, *, dimension=None, metadata=None, region=False, update=False, 

**kwds): 

"""Expand a data ID to include additional information. 

 

`expandDataId` always returns a true `DataId` and ensures that its 

`~DataId.entries` dict contains (at least) values for all implied 

dependencies. 

 

Parameters 

---------- 

dataId : `dict` or `DataId` 

A `dict`-like object containing the `Dimension` links that include 

the primary keys of the rows to query. If this is a true `DataId`, 

the object will be updated in-place. 

dimension : `Dimension` or `str` 

A dimension passed to the `DataId` constructor to create a true 

`DataId` or augment an existing one. 

metadata : `collections.abc.Mapping`, optional 

A mapping from `Dimension` or `str` name to column name, indicating 

fields to read into ``dataId.entries``. 

If ``dimension`` is provided, may instead be a sequence of column 

names for that dimension. 

region : `bool` 

If `True` and the given `DataId` is uniquely associated with a 

region on the sky, obtain that region from the `Registry` and 

attach it as ``dataId.region``. 

update : `bool` 

If `True`, assume existing entries and regions in the given 

`DataId` are out-of-date and should be updated by values in the 

database. If `False`, existing values will be assumed to be 

correct and database queries will only be executed if they are 

missing. 

kwds 

Additional keyword arguments passed to the `DataId` constructor 

to convert ``dataId`` to a true `DataId` or augment an existing 

one. 

 

Returns 

------- 

dataId : `DataId` 

A Data ID with its ``region`` attribute set. 

 

Raises 

------ 

NotImplementedError 

Raised if `limited` is `True`. 

""" 

dataId = DataId(dataId, dimension=dimension, universe=self.dimensions, **kwds) 

 

fieldsToGet = DimensionKeyDict(keys=dataId.dimensions(implied=True).elements, factory=set) 

fieldsToGet.updateValues(self._fieldsToAlwaysGet) 

 

# Interpret the 'metadata' argument and initialize the 'fieldsToGet' 

# dict, which maps each DimensionElement instance to a set of field 

# names. 

if metadata is not None: 

if dimension is not None and not isinstance(metadata, Mapping): 

# If a single dimension was passed explicitly, permit 

# 'metadata' to be a sequence corresponding to just that 

# dimension by updating our mapping-of-sets for that dimension. 

fieldsToGet[dimension].update(metadata) 

else: 

fieldsToGet.updateValues(metadata) 

 

# If 'region' was passed, add a query for that to fieldsToGet as well. 

if region and (update or dataId.region is None): 

holder = dataId.dimensions().getRegionHolder() 

if holder is not None: 

if holder.name == "SkyPix": 

# SkyPix is special; we always obtain those regions from 

# self.pixelization 

dataId.region = self.pixelization.pixel(dataId["skypix"]) 

else: 

fieldsToGet[holder].add("region") 

 

# We now process fieldsToGet with calls to _queryMetadata via a 

# depth-first traversal of the dependency graph. As we traverse, we 

# update... 

 

# A dictionary containing all link values. This starts with the given 

# dataId, but we'll update it to include links for optional 

# dependencies. 

allLinks = dict(dataId) 

 

# A set of DimensionElement names recording the vertices we've 

# processed: 

visited = set() 

 

def visit(element): 

if element.name in visited: 

return 

assert element.links() <= allLinks.keys() 

entries = dataId.entries[element] 

dependencies = element.dependencies(implied=True) 

# Get the set of fields we want to retrieve. 

fieldsToGetNow = fieldsToGet[element] 

# Note which links to dependencies we need to query for and which 

# we already know. Make sure the ones we know are in the entries 

# dict for this element. 

linksWeKnow = dependencies.links().intersection(allLinks.keys()) 

linksWeNeed = dependencies.links() - linksWeKnow 

fieldsToGetNow |= linksWeNeed 

entries.update((link, allLinks[link]) for link in linksWeKnow) 

# Remove fields that are already present in the dataId. 

if not update: 

fieldsToGetNow -= entries.keys() 

# Remove fields that are part of the primary key of this element; 

# we have to already know those if the query is going to work. 

# (and we asserted that we do know them up at the top). 

fieldsToGetNow -= element.links() 

# Actually do the query - only if there's actually anything left 

# to query. Put the results in the entries dict. 

if fieldsToGetNow: 

result = self._queryMetadata(element, allLinks, fieldsToGetNow) 

if "region" in result: 

encoded = result.pop("region") 

if encoded is None: 

dataId.region = None 

else: 

dataId.region = ConvexPolygon.decode(encoded) 

entries.update(result) 

# Update the running dictionary of link values and the marker set. 

allLinks.update((link, entries[link]) for link in dependencies.links()) 

visited.add(element.name) 

# Recurse to dependencies. Note that at this point we know that 

# allLinks has all of the links for any element we're recursing to, 

# either because we started with them in the data ID, they were 

# already in the entries dict, or we queried for them above. 

for other in dependencies: 

visit(other) 

 

# Kick off the traversal with joins, which are never dependencies of 

# any other elements. 

for join in dataId.dimensions().joins(): 

visit(join) 

 

# Now traverse over the dimensions that are not dependencies of any 

# other dependencies in this particular graph. 

for dim in dataId.dimensions().leaves: 

visit(dim) 

 

return dataId 

 

@abstractmethod 

@disableWhenLimited 

def _queryMetadata(self, element, dataId, columns): 

"""Get metadata associated with a dataId. 

 

This is conceptually a "protected" method that must be overridden by 

subclasses but should not be called directly by users, who should use 

``expandDataId(dataId, dimension=element, metadata={...})`` instead. 

 

Parameters 

---------- 

element : `DimensionElement` 

The `Dimension` or `DimensionJoin` to query for column values. 

dataId : `dict` or `DataId` 

A `dict`-like object containing the `Dimension` links that include 

the primary keys of the rows to query. May include link fields 

beyond those needed to identify ``element``. 

columns : iterable of `str` 

String column names to query values for. 

 

Returns 

------- 

metadata : `dict` 

Dictionary that maps column name to value, or `None` if there is 

no row for the given `DataId`. 

 

Raises 

------ 

LookupError 

Raised if no entry for the given data ID exists. 

NotImplementedError 

Raised if `limited` is `True`. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

def makeDataIdPacker(self, name, dataId=None, **kwds): 

"""Create an object that can pack certain data IDs into integers. 

 

Parameters 

---------- 

name : `str` 

Name of the packer, as given in the `Registry` configuration. 

dataId : `dict` or `DataId`, optional 

Data ID that identifies at least the "given" dimensions of the 

packer. 

kwds 

Addition keyword arguments used to augment or override the given 

data ID. 

 

Returns 

------- 

packer : `DataIdPacker` 

Instance of a subclass of `DataIdPacker`. 

""" 

factory = self._dataIdPackerFactories[name] 

givenDataId = self.expandDataId(dataId, dimensions=factory.dimensions.given, **kwds) 

return factory.makePacker(givenDataId) 

 

def packDataId(self, name, dataId=None, *, returnMaxBits=False, **kwds): 

"""Pack the given `DataId` into an integer. 

 

Parameters 

---------- 

name : `str` 

Name of the packer, as given in the `Registry` configuration. 

dataId : `dict` or `DataId`, optional 

Data ID that identifies at least the "required" dimensions of the 

packer. 

returnMaxBits : `bool` 

If `True`, return a tuple of ``(packed, self.maxBits)``. 

kwds 

Addition keyword arguments used to augment or override the given 

data ID. 

 

Returns 

------- 

packed : `int` 

Packed integer ID. 

maxBits : `int`, optional 

Maximum number of nonzero bits in ``packed``. Not returned unless 

``returnMaxBits`` is `True`. 

""" 

packer = self.makeDataIdPacker(name, dataId, **kwds) 

return packer.pack(dataId, returnMaxBits=returnMaxBits, **kwds)