Package: convclasses
convclassesconvclassesis an open source Python library for structuring and unstructuring
data.convclassesworks best withdataclassesclasses and the usual Python
collections, but other kinds of classes are supported by manually registering
converters.Python has a rich set of powerful, easy to use, built-in data types like
dictionaries, lists and tuples. These data types are also the lingua franca
of most data serialization libraries, for formats like json, msgpack, yaml or
toml.Data types like this, and mappings likedicts in particular, represent
unstructured data. Your data is, in all likelihood, structured: not all
combinations of field names are values are valid inputs to your programs. In
Python, structured data is better represented with classes and enumerations.dataclassesis an excellent library for declaratively describing the structure of
your data, and validating it.When you’re handed unstructured data (by your network, file system, database…),convclasseshelps to convert this data into structured data. When you have to
convert your structured data into data types other libraries can handle,convclassesturns your classes and enumerations into dictionaries, integers and
strings.Here’s a simple taste. The list containing a float, an int and a string
gets converted into a tuple of three ints.>>>importconvclasses>>>fromtypingimportTuple>>>>>>convclasses.structure([1.0,2,"3"],Tuple[int,int,int])(1,2,3)convclassesworks well withdataclassesclasses out of the box.>>>importconvclasses>>>fromdataclassesimportdataclass>>>fromtypingimportAny>>>@dataclass(frozen=True)# It works with normal classes too....classC:...a:Any...b:Any...>>>instance=C(1,'a')>>>convclasses.unstructure(instance){'a':1,'b':'a'}>>>convclasses.structure({'a':1,'b':'a'},C)C(a=1,b='a')Here’s a much more complex example, involvingdataclassesclasses with type
metadata.>>>fromenumimportunique,Enum>>>fromtypingimportAny,List,Optional,Sequence,Union>>>fromconvclassesimportstructure,unstructure>>>fromdataclassesimportdataclass>>>>>>@unique...classCatBreed(Enum):...SIAMESE="siamese"...MAINE_COON="maine_coon"...SACRED_BIRMAN="birman"...>>>@dataclass...classCat:...breed:CatBreed...names:Sequence[str]...>>>@dataclass...classDogMicrochip:...chip_id:Any...time_chipped:float...>>>@dataclass...classDog:...cuteness:int...chip:Optional[DogMicrochip]...>>>p=unstructure([Dog(cuteness=1,chip=DogMicrochip(chip_id=1,time_chipped=10.0)),...Cat(breed=CatBreed.MAINE_COON,names=('Fluffly','Fluffer'))])...>>>print(p)[{'cuteness':1,'chip':{'chip_id':1,'time_chipped':10.0}},{'breed':'maine_coon','names':('Fluffly','Fluffer')}]>>>print(structure(p,List[Union[Dog,Cat]]))[Dog(cuteness=1,chip=DogMicrochip(chip_id=1,time_chipped=10.0)),Cat(breed=<CatBreed.MAINE_COON:'maine_coon'>,names=['Fluffly','Fluffer'])]Consider unstructured data a low-level representation that needs to be converted
to structured data to be handled, and usestructure. When you’re done,unstructurethe data to its unstructured form and pass it along to another
library or module. Usedataclasses type metadatato add type metadata to attributes, soconvclasseswill know how to structure and
destructure them.Free software: MIT licenseDocumentation:https://convclasses.readthedocs.io.Python versions supported: 3.7 and up.FeaturesConverts structured data into unstructured data, recursively:dataclassesclasses are converted into dictionaries in a way similar todataclasses.asdict,
or into tuples in a way similar todataclasses.astuple.Enumeration instances are converted to their values.Other types are let through without conversion. This includes types such as
integers, dictionaries, lists and instances of non-dataclassesclasses.Custom converters for any type can be registered usingregister_unstructure_hook.Converts unstructured data into structured data, recursively, according to
your specification given as a type. The following types are supported:typing.Optional[T].typing.List[T],typing.MutableSequence[T],typing.Sequence[T](converts to a list).typing.Tuple(both variants,Tuple[T,...]andTuple[X, Y, Z]).typing.MutableSet[T],typing.Set[T](converts to a set).typing.FrozenSet[T](converts to a frozenset).typing.Dict[K, V],typing.MutableMapping[K, V],typing.Mapping[K, V](converts to a dict).dataclassesclasses with simple attributes and the usual__init__.Simple attributes are attributes that can be assigned unstructured data,
like numbers, strings, and collections of unstructured data.Alldataclassesclasses with the usual__init__, if their complex attributes
have type metadata.typing.Unions of supporteddataclassesclasses, given that all of the classes
have a unique field.typing.Unions of anything, given that you provide a disambiguation
function for it.Custom converters for any type can be registered usingregister_structure_hook.CreditsMajor credits and best wishes for the original creator of this concept -Tinche,
he developedcattrswhich this project is fork of.Major credits to Hynek Schlawack for creatingattrsand its predecessor,characteristic.convclassesis tested withHypothesis, by David R. MacIver.convclassesis benchmarked usingperf, by Victor Stinner.History2.0.0Add support for modifiersAdd dataclass field name modifierAdd support for generic types (ported from cattrs)1.1.0Removed Python 3.6 supportAdded Python 3.9 support1.0.0RenamecattrsintoconclassesMove convclasses fromattrsusage ontodataclassesFix incorrect structuring/unstructuring of private fieldsChangependulumin docs ontoarrowcattrs history0.9.1 (2019-10-26)Python 3.8 support.0.9.0 (2018-07-22)Python 3.7 support.0.8.1 (2018-06-19)The disambiguation function generator now supports unions ofattrsclasses and NoneType.0.8.0 (2018-04-14)Distribution fix.0.7.0 (2018-04-12)Removed the undocumentedConverter.unstruct_stratproperty setter.Removed the ability to set theConverter.structure_attrsinstance field.
As an alternative, create a newConverter:>>>converter=cattr.Converter(unstruct_strat=cattr.UnstructureStrategy.AS_TUPLE)Some micro-optimizations were applied; astructure(unstructure(obj))roundtrip
is now up to 2 times faster.0.6.0 (2017-12-25)Packaging fixes.
(#17)0.5.0 (2017-12-11)structure/unstructure now supports using functions as well as classes for deciding the appropriate function.addedConverter.register_structure_hook_func, to register a function instead of a class for determining handler func.addedConverter.register_unstructure_hook_func, to register a function instead of a class for determining handler func.vendored typing is no longer needed, nor provided.Attributes with default values can now be structured if they are missing in the input.
(#15)Optionalattributes can no longer be structured if they are missing in the input.In other words, this no longer works:@attr.sclassA:a:Optional[int]=attr.ib()>>>cattr.structure({},A)cattr.typedremoved since the functionality is now present inattrsitself.
Replace instances ofcattr.typed(type)withattr.ib(type=type).0.4.0 (2017-07-17)Converter.loadsis nowConverter.structure, andConverter.dumpsis nowConverter.unstructure.Python 2.7 is supported.Movedcattr.typingtocattr.vendor.typingto support different vendored versions of typing.py for Python 2 and Python 3.Type metadata can be added toattrsclasses usingcattr.typed.0.3.0 (2017-03-18)Python 3.4 is no longer supported.Introducedcattr.typingfor use with Python versions 3.5.2 and 3.6.0.Minor changes to work with newer versions oftyping.Bare Optionals are not supported any more (useOptional[Any]).Attempting to load unrecognized classes will result in a ValueError, and a helpful message to register a loads hook.Loadingattrsclasses is now documented.The global converter is now documented.cattr.loads_attrs_fromtupleandcattr.loads_attrs_fromdictare now exposed.0.2.0 (2016-10-02)Tests and documentation.0.1.0 (2016-08-13)First release on PyPI.