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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 

 

from .configSupport import processLookupConfigs 

from .mappingFactory import MappingFactory 

from .utils import getFullTypeName 

 

log = logging.getLogger(__name__) 

 

 

class Formatter(metaclass=ABCMeta): 

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

`StorageClass`. 

""" 

 

unsupportedParameters = frozenset() 

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

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

are supported. 

""" 

 

@classmethod 

def name(cls): 

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

""" 

return getFullTypeName(cls) 

 

@abstractmethod 

def read(self, fileDescriptor, component=None): 

"""Read a Dataset. 

 

Parameters 

---------- 

fileDescriptor : `FileDescriptor` 

Identifies the file to read, type to read it into and parameters 

to be used for reading. 

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 : `InMemoryDataset` 

The requested Dataset. 

""" 

raise NotImplementedError("Type does not support reading") 

 

@abstractmethod 

def write(self, inMemoryDataset, fileDescriptor): 

"""Write a Dataset. 

 

Parameters 

---------- 

inMemoryDataset : `InMemoryDataset` 

The Dataset to store. 

fileDescriptor : `FileDescriptor` 

Identifies the file to write. 

 

Returns 

------- 

path : `str` 

The path to where the Dataset was stored. 

""" 

raise NotImplementedError("Type does not support writing") 

 

@abstractmethod 

def predictPath(self, location): 

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

writing. 

 

location : `Location` 

The location to simulate writing to. 

""" 

raise NotImplementedError("Type does not support writing") 

 

def segregateParameters(self, parameters): 

"""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` 

Parameters with values that have been supplied by the caller 

and which might be relevant for the formatter. 

 

Returns 

------- 

supported : `dict` 

Those parameters supported by this formatter. 

unsupported : `dict` 

Those parameters not supported by this formatter. 

""" 

 

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, *, universe): 

"""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): 

"""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 getFormatterWithMatch(self, entity): 

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

key. 

 

Parameters 

---------- 

entity : `DatasetRef`, `DatasetType` or `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 : `Formatter` 

An instance of the registered formatter. 

""" 

if isinstance(entity, str): 

names = (entity,) 

else: 

names = entity._lookupNames() 

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

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

 

return matchKey, formatter 

 

def getFormatter(self, entity): 

"""Get a new formatter instance. 

 

Parameters 

---------- 

entity : `DatasetRef`, `DatasetType` or `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 : `Formatter` 

An instance of the registered formatter. 

""" 

_, formatter = self.getFormatterWithMatch(entity) 

return formatter 

 

def registerFormatter(self, type_, formatter): 

"""Register a `Formatter`. 

 

Parameters 

---------- 

type_ : `LookupKey`, `str` or `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` 

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

Datasets of this type. 

 

Raises 

------ 

ValueError 

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

""" 

self._mappingFactory.placeInRegistry(type_, formatter)