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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 copy import deepcopy 

 

from types import MappingProxyType 

from .utils import slotValuesAreEqual, slotValuesToHash 

from .storageClass import StorageClass 

 

__all__ = ("DatasetType", "DatasetRef") 

 

 

def _safeMakeMappingProxyType(data): 

if data is None: 

data = {} 

return MappingProxyType(data) 

 

 

class DatasetType: 

r"""A named category of Datasets that defines how they are organized, 

related, and stored. 

 

A concrete, final class whose instances represent `DatasetType`\ s. 

`DatasetType` instances may be constructed without a `Registry`, 

but they must be registered 

via `Registry.registerDatasetType()` before corresponding Datasets 

may be added. 

`DatasetType` instances are immutable. 

 

Parameters 

---------- 

name : `str` 

A string name for the Dataset; must correspond to the same 

`DatasetType` across all Registries. 

dataUnits : `iterable` of `str` 

`DataUnit` names that defines the `DatasetRef`\ s corresponding to 

this `DatasetType`. The input iterable is copied into a `frozenset`. 

storageClass : `StorageClass` 

Instance of a `StorageClass` that defines how this `DatasetType` 

is persisted. 

""" 

 

__slots__ = ("_name", "_dataUnits", "_storageClass") 

__eq__ = slotValuesAreEqual 

__hash__ = slotValuesToHash 

 

def __str__(self): 

return "DatasetType({}, {}, {})".format(self.name, self.storageClass.name, self.dataUnits) 

 

@staticmethod 

def nameWithComponent(datasetTypeName, componentName): 

"""Form a valid DatasetTypeName from a parent and component. 

 

No validation is performed. 

 

Parameters 

---------- 

datasetTypeName : `str` 

Base type name. 

componentName : `str` 

Name of component. 

 

Returns 

------- 

compTypeName : `str` 

Name to use for component DatasetType. 

""" 

return "{}.{}".format(datasetTypeName, componentName) 

 

@property 

def name(self): 

"""A string name for the Dataset; must correspond to the same 

`DatasetType` across all Registries. 

""" 

return self._name 

 

@property 

def dataUnits(self): 

"""A `frozenset` of `DataUnit` names that defines the `DatasetRef`\ s 

corresponding to this `DatasetType`. 

""" 

return self._dataUnits 

 

@property 

def storageClass(self): 

"""`StorageClass` instance that defines how this `DatasetType` 

is persisted. 

""" 

return self._storageClass 

 

def __init__(self, name, dataUnits, storageClass): 

self._name = name 

self._dataUnits = frozenset(dataUnits) 

assert isinstance(storageClass, StorageClass) 

self._storageClass = storageClass 

 

def component(self): 

"""Component name (if defined) 

 

Returns 

------- 

comp : `str` 

Name of component part of DatasetType name. `None` if this 

`DatasetType` is not associated with a component. 

""" 

comp = None 

if "." in self.name: 

_, comp = self.name.split(".", maxsplit=1) 

return comp 

 

def componentTypeName(self, component): 

"""Given a component name, derive the datasetTypeName of that component 

 

Parameters 

---------- 

component : `str` 

Name of component 

 

Returns 

------- 

derived : `str` 

Compound name of this `DatasetType` and the component. 

 

Raises 

------ 

KeyError 

Requested component is not supported by this `DatasetType`. 

""" 

if component in self.storageClass.components: 

return self.nameWithComponent(self.name, component) 

raise KeyError("Requested component ({}) not understood by this DatasetType".format(component)) 

 

def isComposite(self): 

"""Boolean indicating whether this `DatasetType` is a composite type. 

 

Returns 

------- 

isComposite : `bool` 

`True` if this `DatasetType` is a composite type, `False` 

otherwise. 

""" 

return self.storageClass.isComposite() 

 

def _lookupNames(self): 

"""Names to use when looking up this datasetType in a configuration. 

 

The names are returned in order of priority. 

 

Returns 

------- 

names : `tuple` of `str` 

Tuple of the `DatasetType` name and the `StorageClass` name. 

""" 

return (self.name, *self.storageClass._lookupNames()) 

 

 

class DatasetRef: 

"""Reference to a Dataset in a `Registry`. 

 

A `DatasetRef` may point to a Dataset that currently does not yet exist 

(e.g., because it is a predicted input for provenance). 

 

Parameters 

---------- 

datasetType : `DatasetType` 

The `DatasetType` for this Dataset. 

dataId : `dict` 

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

`DatasetRef` within a Collection. 

id : `int`, optional 

A unique identifier. 

Normally set to `None` and assigned by `Registry` 

""" 

 

__slots__ = ("_id", "_datasetType", "_dataId", "_producer", "_run", 

"_predictedConsumers", "_actualConsumers", "_components") 

 

__eq__ = slotValuesAreEqual 

 

def __init__(self, datasetType, dataId, id=None, run=None): 

assert isinstance(datasetType, DatasetType) 

self._id = id 

self._datasetType = datasetType 

self._dataId = dataId 

self._producer = None 

self._predictedConsumers = dict() 

self._actualConsumers = dict() 

self._components = dict() 

self._run = run 

 

@property 

def id(self): 

"""Primary key of the dataset (`int`) 

 

Typically assigned by `Registry`. 

""" 

return self._id 

 

@property 

def datasetType(self): 

"""The `DatasetType` associated with the Dataset the `DatasetRef` 

points to. 

""" 

return self._datasetType 

 

@property 

def dataId(self): 

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

within a Collection. 

""" 

return self._dataId 

 

@property 

def producer(self): 

"""The `Quantum` instance that produced (or will produce) the 

Dataset. 

 

Read-only; update via `Registry.addDataset()`, 

`QuantumGraph.addDataset()`, or `Butler.put()`. 

May be `None` if no provenance information is available. 

""" 

return self._producer 

 

@property 

def run(self): 

"""The `Run` instance that produced (or will produce) the 

Dataset. 

 

Read-only; update via `Registry.addDataset()` or `Butler.put()`. 

""" 

return self._run 

 

@property 

def predictedConsumers(self): 

"""A sequence of `Quantum` instances that list this Dataset in their 

`predictedInputs` attributes. 

 

Read-only; update via `Quantum.addPredictedInput()`. 

May be an empty list if no provenance information is available. 

""" 

return _safeMakeMappingProxyType(self._predictedConsumers) 

 

@property 

def actualConsumers(self): 

"""A sequence of `Quantum` instances that list this Dataset in their 

`actualInputs` attributes. 

 

Read-only; update via `Registry.markInputUsed()`. 

May be an empty list if no provenance information is available. 

""" 

return _safeMakeMappingProxyType(self._actualConsumers) 

 

@property 

def components(self): 

"""Named `DatasetRef` components. 

 

Read-only; update via `Registry.attachComponent()`. 

""" 

return _safeMakeMappingProxyType(self._components) 

 

def __str__(self): 

components = "" 

if self.components: 

components = ", components=[" + ", ".join(self.components) + "]" 

return "DatasetRef({}, id={}, dataId={} {})".format(self.datasetType.name, 

self.id, self.dataId, components) 

 

def detach(self): 

"""Obtain a new DatasetRef that is detached from the registry. 

 

Its ``id`` property will be `None`. This can be used for transfers 

and similar operations. 

""" 

ref = deepcopy(self) 

ref._id = None 

return ref 

 

def isComposite(self): 

"""Boolean indicating whether this `DatasetRef` is a composite type. 

 

Returns 

------- 

isComposite : `bool` 

`True` if this `DatasetRef` is a composite type, `False` 

otherwise. 

""" 

return self.datasetType.isComposite() 

 

def _lookupNames(self): 

"""Names to use when looking up this DatasetRef in a configuration. 

 

The names are returned in order of priority. 

 

Returns 

------- 

names : `tuple` of `str` 

Tuple of the `DatasetType` name and the `StorageClass` name. 

""" 

return self.datasetType._lookupNames()