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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 

import contextlib 

import functools 

 

from lsst.utils import doImport 

from .config import Config, ConfigSubset 

from .dataUnit import DataUnitConfig, DataUnitRegistry 

from .schema import SchemaConfig 

from .utils import transactional 

 

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

 

 

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("Not implemented for 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. 

""" 

 

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, dataUnitConfig=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`. 

dataUnitConfig : `DataUnitConfig` or `Config` or 

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

from supplied registryConfig if the relevant component is 

defined and ``dataUnitConfig`` 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 dataUnitConfig is None: 

# Try to instantiate a schema configuration from the supplied 

# registry configuration. 

dataUnitConfig = DataUnitConfig(registryConfig) 

elif not isinstance(dataUnitConfig, DataUnitConfig): 

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

dataUnitConfig = DataUnitConfig(dataUnitConfig) 

else: 

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

 

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, dataUnitConfig, create=create) 

 

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

assert isinstance(registryConfig, RegistryConfig) 

self.config = registryConfig 

self._pixelization = None 

self._dataUnits = DataUnitRegistry.fromConfig(dataUnitConfig) 

 

def __str__(self): 

return "None" 

 

@property 

def limited(self): 

"""If True, this Registry does not maintain DataUnit 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 DataUnit 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): 

"""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` 

The `DatasetType`. 

dataId : `dict` 

A `dict` of `DataUnit` link name, value pairs that label the 

`DatasetRef` within a collection. 

 

Returns 

------- 

ref : `DatasetRef` 

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

was found. 

 

Raises 

------ 

ValueError 

If dataId is invalid. 

""" 

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 

DatasetType is not valid for this registry or is already registered 

but not identical. 

 

Returns 

------- 

inserted : `bool` 

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

existing `DatsetType` was found. 

""" 

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): 

"""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 : `str` 

Name of a `DatasetType`. 

dataId : `dict` 

A `dict` of `DataUnit` link name, value pairs that label the 

`DatasetRef` 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. 

 

Returns 

------- 

ref : `DatasetRef` 

A newly-created `DatasetRef` instance. 

 

Raises 

------ 

ValueError 

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

given collection. 

 

Exception 

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

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

def getDataset(self, id): 

"""Retrieve a Dataset entry. 

 

Parameters 

---------- 

id : `int` 

The unique identifier for the Dataset. 

 

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 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. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@transactional 

def associate(self, collection, refs): 

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

collection in the process. 

 

Parameters 

---------- 

collection : `str` 

Indicates the collection the Datasets should be associated with. 

refs : `list` of `DatasetRef` 

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

`Registry`. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@transactional 

def disassociate(self, collection, refs, remove=True): 

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`. 

remove : `bool` 

If `True`, remove Datasets from the `Registry` if they are not 

associated with any collection (including via any composites). 

 

Returns 

------- 

removed : `list` of `DatasetRef` 

If `remove` is `True`, the `list` of `DatasetRef`\ s that were 

removed. 

""" 

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. 

""" 

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. 

""" 

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. 

""" 

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 

------ 

Exception 

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 

------ 

ValueError 

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 

------ 

ValueError 

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 addDataUnitEntry(self, dataUnitName, values): 

"""Add a new `DataUnit` entry. 

 

dataUnitName : `str` 

Name of the `DataUnit` (e.g. ``"Instrument"``). 

values : `dict` 

Dictionary of ``columnName, columnValue`` pairs. 

 

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

automatically be called to set it any associated spatial join 

tables. 

Region fields associated with a combination of DataUnits must be 

explicitly set separately. 

 

Raises 

------ 

TypeError 

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

registry. 

ValueError 

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 findDataUnitEntry(self, dataUnitName, value): 

"""Return a `DataUnit` entry corresponding to a `value`. 

 

Parameters 

---------- 

dataUnitName : `str` 

Name of a `DataUnit` 

value : `dict` 

A dictionary of values that uniquely identify the `DataUnit`. 

 

Returns 

------- 

dataUnitEntry : `dict` 

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

entry is found. 

 

Raises 

------ 

NotImplementedError 

Raised if `limited` is `True`. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@disableWhenLimited 

@transactional 

def setDataUnitRegion(self, dataUnitNames, value, region, update=True): 

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

thereof and update associated spatial join tables. 

 

Parameters 

---------- 

dataUnitNames : sequence 

A sequence of DataUnit names whose instances are jointly associated 

with a region on the sky. 

value : `dict` 

A dictionary of values that uniquely identify the DataUnits. 

region : `sphgeom.ConvexPolygon` 

Region on the sky. 

update : `bool` 

If True, existing region information for these DataUnits is being 

replaced. This is usually required because DataUnit entries are 

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

 

Raises 

------ 

NotImplementedError 

Raised if `limited` is `True`. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@disableWhenLimited 

def getRegion(self, dataId): 

"""Get region associated with a dataId. 

 

Parameters 

---------- 

dataId : `dict` 

A `dict` of `DataUnit` link name, value pairs that label the 

`DatasetRef` within a collection. 

 

Returns 

------- 

region : `lsst.sphgeom.ConvexPolygon` 

The region associated with a ``dataId`` or `None` if not present. 

 

Raises 

------ 

KeyError 

If the set of dataunits for the ``dataId`` does not correspond to 

a unique spatial lookup. 

NotImplementedError 

Raised if `limited` is `True`. 

""" 

raise NotImplementedError("Must be implemented by subclass") 

 

@abstractmethod 

@disableWhenLimited 

def selectDataUnits(self, originInfo, expression, neededDatasetTypes, futureDatasetTypes): 

"""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 `DataUnits <DataUnit>` and 

(indirectly) the Datasets returned. 

neededDatasetTypes : `list` of `DatasetType` 

The `list` of `DatasetTypes <DatasetType>` whose DataUnits 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` 

The `list` of `DatasetTypes <DatasetType>` whose DataUnits 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. 

 

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")