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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/>. 

 

__all__ = ("Formatter", "FormatterFactory") 

 

from abc import ABCMeta, abstractmethod 

import logging 

import copy 

from typing import ClassVar, Set, FrozenSet, Union, Optional, Dict, Any, Tuple, Type 

 

from .configSupport import processLookupConfigs, LookupKey 

from .mappingFactory import MappingFactory 

from .utils import getFullTypeName 

from .fileDescriptor import FileDescriptor 

from .location import Location 

from .config import Config 

from .dimensions import DimensionUniverse 

from .storageClass import StorageClass 

from .datasets import DatasetType, DatasetRef 

 

log = logging.getLogger(__name__) 

 

# Define a new special type for functions that take "entity" 

Entity = Union[DatasetType, DatasetRef, StorageClass, str] 

 

 

class Formatter(metaclass=ABCMeta): 

"""Interface for reading and writing Datasets with a particular 

`StorageClass`. 

 

Parameters 

---------- 

fileDescriptor : `FileDescriptor`, optional 

Identifies the file to read or write, and the associated storage 

classes and parameter information. Its value can be `None` if the 

caller will never call `Formatter.read` or `Formatter.write`. 

""" 

 

unsupportedParameters: ClassVar[Optional[Union[FrozenSet[str], Set[str]]]] = frozenset() 

"""Set of parameters not understood by this `Formatter`. An empty set means 

all parameters are supported. `None` indicates that no parameters 

are supported (`frozenset`). 

""" 

 

def __init__(self, fileDescriptor: FileDescriptor): 

if not isinstance(fileDescriptor, FileDescriptor): 

raise TypeError("File descriptor must be a FileDescriptor") 

self._fileDescriptor = fileDescriptor 

 

def __str__(self): 

return f"{self.name()}@{self.fileDescriptor.location.path}" 

 

def __repr__(self): 

return f"{self.name()}({self.fileDescriptor!r})" 

 

@property 

def fileDescriptor(self) -> FileDescriptor: 

"""FileDescriptor associated with this formatter 

(`FileDescriptor`, read-only)""" 

return self._fileDescriptor 

 

@classmethod 

def name(cls) -> str: 

"""Returns the fully qualified name of the formatter. 

 

Returns 

------- 

name : `str` 

Fully-qualified name of formatter class. 

""" 

return getFullTypeName(cls) 

 

@abstractmethod 

def read(self, component: Optional[str] = None) -> object: 

"""Read a Dataset. 

 

Parameters 

---------- 

component : `str`, optional 

Component to read from the file. Only used if the `StorageClass` 

for reading differed from the `StorageClass` used to write the 

file. 

 

Returns 

------- 

inMemoryDataset : `object` 

The requested Dataset. 

""" 

raise NotImplementedError("Type does not support reading") 

 

@abstractmethod 

def write(self, inMemoryDataset: Any) -> str: 

"""Write a Dataset. 

 

Parameters 

---------- 

inMemoryDataset : `object` 

The Dataset to store. 

 

Returns 

------- 

path : `str` 

The path to where the Dataset was stored within the datastore. 

""" 

raise NotImplementedError("Type does not support writing") 

 

def fromBytes(self, serializedDataset: bytes, 

component: Optional[str] = None) -> object: 

"""Reads serialized data into a Dataset or its component. 

 

Parameters 

---------- 

serializedDataset : `bytes` 

Bytes object to unserialize. 

component : `str`, optional 

Component to read from the Dataset. Only used if the `StorageClass` 

for reading differed from the `StorageClass` used to write the 

file. 

 

Returns 

------- 

inMemoryDataset : `object` 

The requested data as a Python object. The type of object 

is controlled by the specific formatter. 

""" 

raise NotImplementedError("Type does not support reading from bytes.") 

 

def toBytes(self, inMemoryDataset: Any) -> bytes: 

"""Serialize the Dataset to bytes based on formatter. 

 

Parameters 

---------- 

inMemoryDataset : `object` 

The Python object to serialize. 

 

Returns 

------- 

serializedDataset : `bytes` 

Bytes representing the serialized dataset. 

""" 

raise NotImplementedError("Type does not support writing to bytes.") 

 

@classmethod 

def makeUpdatedLocation(cls, location: Location) -> Location: 

"""Return a new `Location` instance updated with this formatter's 

extension. 

 

Parameters 

---------- 

location : `Location` 

The location to update. 

 

Returns 

------- 

updated : `Location` 

The updated location with a new file extension applied. 

 

Raises 

------ 

NotImplementedError 

Raised if there is no ``extension`` attribute associated with 

this formatter. 

""" 

location = copy.deepcopy(location) 

try: 

location.updateExtension(cls.extension) 

except AttributeError: 

raise NotImplementedError("No file extension registered with this formatter") from None 

return location 

 

@classmethod 

def predictPathFromLocation(cls, location: Location) -> str: 

"""Return the path that would be returned by write, without actually 

writing. 

 

Parameters 

---------- 

location : `Location` 

Location of file for which path prediction is required. 

 

Returns 

------- 

path : `str` 

Path within datastore that would be associated with this location. 

""" 

return cls.makeUpdatedLocation(location).pathInStore 

 

def predictPath(self) -> str: 

"""Return the path that would be returned by write, without actually 

writing. 

 

Uses the `FileDescriptor` associated with the instance. 

 

Returns 

------- 

path : `str` 

Path within datastore that would be associated with the location 

stored in this `Formatter`. 

""" 

return self.predictPathFromLocation(self.fileDescriptor.location) 

 

def segregateParameters(self, parameters: Optional[Dict[str, Any]] = None) -> Tuple[Dict, Dict]: 

"""Segregate the supplied parameters into those understood by the 

formatter and those not understood by the formatter. 

 

Any unsupported parameters are assumed to be usable by associated 

assemblers. 

 

Parameters 

---------- 

parameters : `dict`, optional 

Parameters with values that have been supplied by the caller 

and which might be relevant for the formatter. If `None` 

parameters will be read from the registered `FileDescriptor`. 

 

Returns 

------- 

supported : `dict` 

Those parameters supported by this formatter. 

unsupported : `dict` 

Those parameters not supported by this formatter. 

""" 

 

if parameters is None: 

parameters = self.fileDescriptor.parameters 

 

if parameters is None: 

return {}, {} 

 

if self.unsupportedParameters is None: 

# Support none of the parameters 

return {}, parameters.copy() 

 

# Start by assuming all are supported 

supported = parameters.copy() 

unsupported = {} 

 

# And remove any we know are not supported 

for p in set(supported): 

if p in self.unsupportedParameters: 

unsupported[p] = supported.pop(p) 

 

return supported, unsupported 

 

 

class FormatterFactory: 

"""Factory for `Formatter` instances. 

""" 

 

def __init__(self): 

self._mappingFactory = MappingFactory(Formatter) 

 

def __contains__(self, key): 

"""Indicates whether the supplied key is present in the factory. 

 

Parameters 

---------- 

key : `LookupKey`, `str` or objects with ``name`` attribute 

Key to use to lookup in the factory whether a corresponding 

formatter is present. 

 

Returns 

------- 

in : `bool` 

`True` if the supplied key is present in the factory. 

""" 

return key in self._mappingFactory 

 

def registerFormatters(self, config: Config, *, universe: DimensionUniverse) -> None: 

"""Bulk register formatters from a config. 

 

Parameters 

---------- 

config : `Config` 

``formatters`` section of a configuration. 

universe : `DimensionUniverse`, optional 

Set of all known dimensions, used to expand and validate any used 

in lookup keys. 

 

Notes 

----- 

The configuration can include one level of hierarchy where an 

instrument-specific section can be defined to override more general 

template specifications. This is represented in YAML using a 

key of form ``instrument<name>`` which can then define templates 

that will be returned if a `DatasetRef` contains a matching instrument 

name in the data ID. 

 

The config is parsed using the function 

`~lsst.daf.butler.configSubset.processLookupConfigs`. 

""" 

contents = processLookupConfigs(config, universe=universe) 

for key, f in contents.items(): 

self.registerFormatter(key, f) 

 

def getLookupKeys(self) -> Set[LookupKey]: 

"""Retrieve the look up keys for all the registry entries. 

 

Returns 

------- 

keys : `set` of `LookupKey` 

The keys available for matching in the registry. 

""" 

return self._mappingFactory.getLookupKeys() 

 

def getFormatterClassWithMatch(self, entity: Entity) -> Tuple[LookupKey, Type]: 

"""Get the matching formatter class along with the matching registry 

key. 

 

Parameters 

---------- 

entity : `DatasetRef`, `DatasetType`, `StorageClass`, or `str` 

Entity to use to determine the formatter to return. 

`StorageClass` will be used as a last resort if `DatasetRef` 

or `DatasetType` instance is provided. Supports instrument 

override if a `DatasetRef` is provided configured with an 

``instrument`` value for the data ID. 

 

Returns 

------- 

matchKey : `LookupKey` 

The key that resulted in the successful match. 

formatter : `type` 

The class of the registered formatter. 

""" 

if isinstance(entity, str): 

names = (entity,) 

else: 

names = entity._lookupNames() 

matchKey, formatter = self._mappingFactory.getClassFromRegistryWithMatch(names) 

log.debug("Retrieved formatter %s from key '%s' for entity '%s'", getFullTypeName(formatter), 

matchKey, entity) 

 

return matchKey, formatter 

 

def getFormatterClass(self, entity: Entity) -> Type: 

"""Get the matching formatter class. 

 

Parameters 

---------- 

entity : `DatasetRef`, `DatasetType`, `StorageClass`, or `str` 

Entity to use to determine the formatter to return. 

`StorageClass` will be used as a last resort if `DatasetRef` 

or `DatasetType` instance is provided. Supports instrument 

override if a `DatasetRef` is provided configured with an 

``instrument`` value for the data ID. 

 

Returns 

------- 

formatter : `type` 

The class of the registered formatter. 

""" 

_, formatter = self.getFormatterClassWithMatch(entity) 

return formatter 

 

def getFormatterWithMatch(self, entity: Entity, *args, **kwargs) -> Tuple[LookupKey, Formatter]: 

"""Get a new formatter instance along with the matching registry 

key. 

 

Parameters 

---------- 

entity : `DatasetRef`, `DatasetType`, `StorageClass`, or `str` 

Entity to use to determine the formatter to return. 

`StorageClass` will be used as a last resort if `DatasetRef` 

or `DatasetType` instance is provided. Supports instrument 

override if a `DatasetRef` is provided configured with an 

``instrument`` value for the data ID. 

args : `tuple` 

Positional arguments to use pass to the object constructor. 

kwargs : `dict` 

Keyword arguments to pass to object constructor. 

 

Returns 

------- 

matchKey : `LookupKey` 

The key that resulted in the successful match. 

formatter : `Formatter` 

An instance of the registered formatter. 

""" 

if isinstance(entity, str): 

names = (entity,) 

else: 

names = entity._lookupNames() 

matchKey, formatter = self._mappingFactory.getFromRegistryWithMatch(names, *args, **kwargs) 

log.debug("Retrieved formatter %s from key '%s' for entity '%s'", getFullTypeName(formatter), 

matchKey, entity) 

 

return matchKey, formatter 

 

def getFormatter(self, entity: Entity, *args, **kwargs) -> Formatter: 

"""Get a new formatter instance. 

 

Parameters 

---------- 

entity : `DatasetRef`, `DatasetType`, `StorageClass`, or `str` 

Entity to use to determine the formatter to return. 

`StorageClass` will be used as a last resort if `DatasetRef` 

or `DatasetType` instance is provided. Supports instrument 

override if a `DatasetRef` is provided configured with an 

``instrument`` value for the data ID. 

args : `tuple` 

Positional arguments to use pass to the object constructor. 

kwargs : `dict` 

Keyword arguments to pass to object constructor. 

 

Returns 

------- 

formatter : `Formatter` 

An instance of the registered formatter. 

""" 

_, formatter = self.getFormatterWithMatch(entity, *args, **kwargs) 

return formatter 

 

def registerFormatter(self, type_: Union[LookupKey, str, StorageClass, DatasetType], 

formatter: str, overwrite: bool = False) -> None: 

"""Register a `Formatter`. 

 

Parameters 

---------- 

type_ : `LookupKey`, `str`, `StorageClass` or `DatasetType` 

Type for which this formatter is to be used. If a `LookupKey` 

is not provided, one will be constructed from the supplied string 

or by using the ``name`` property of the supplied entity. 

formatter : `str` or class of type `Formatter` 

Identifies a `Formatter` subclass to use for reading and writing 

Datasets of this type. Can be a `Formatter` class. 

overwrite : `bool`, optional 

If `True` an existing entry will be replaced by the new value. 

Default is `False`. 

 

Raises 

------ 

ValueError 

Raised if the formatter does not name a valid formatter type and 

``overwrite`` is `False`. 

""" 

self._mappingFactory.placeInRegistry(type_, formatter, overwrite=overwrite)