Package: resoto-cattrs-snapshot-56eda37
cattrsis an open source Python library for structuring and unstructuring
data.cattrsworks best withattrsclasses, dataclasses 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 or values are valid inputs to your programs. In
Python, structured data is better represented with classes and enumerations.attrsis 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…),cattrshelps to convert this data into structured data. When you have to
convert your structured data into data types other libraries can handle,cattrsturns 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.>>>importcattrs>>>>>>cattrs.structure([1.0,2,"3"],tuple[int,int,int])(1, 2, 3)cattrsworks well withattrsclasses out of the box.>>>fromattrsimportfrozen>>>importcattrs>>>>>>@frozen# It works with non-frozen classes too....classC:...a:int...b:str...>>>instance=C(1,'a')>>>cattrs.unstructure(instance){'a': 1, 'b': 'a'}>>>cattrs.structure({'a':1,'b':'a'},C)C(a=1, b='a')Here’s a much more complex example, involvingattrsclasses with type
metadata.>>>fromenumimportunique,Enum>>>fromtypingimportOptional,Sequence,Union>>>fromcattrsimportstructure,unstructure>>>fromattrsimportdefine,field>>>>>>@unique...classCatBreed(Enum):...SIAMESE="siamese"...MAINE_COON="maine_coon"...SACRED_BIRMAN="birman"...>>>@define...classCat:...breed:CatBreed...names:Sequence[str]...>>>@define...classDogMicrochip:...chip_id=field()# Type annotations are optional, but recommended...time_chipped:float=field()...>>>@define...classDog:...cuteness:int...chip:Optional[DogMicrochip]=None...>>>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. Useattrs type metadatato add type metadata to attributes, socattrswill know how to structure and
destructure them.Free software: MIT licenseDocumentation:https://cattrs.readthedocs.io.Python versions supported: 3.7 and up. (Older Python versions, like 2.7, 3.5 and 3.6 are supported by older versions; see the changelog.)FeaturesConverts structured data into unstructured data, recursively:attrsclasses and dataclasses are converted into dictionaries in a way similar toattrs.asdict, or into tuples in a way similar toattrs.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-attrsclasses.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).attrsclasses 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.Allattrsclasses and dataclasses with the usual__init__, if their complex attributes have type metadata.typing.Unions of supportedattrsclasses, 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.cattrscomes with preconfigured converters for a number of serialization libraries, including json, msgpack, bson, yaml and toml.
For details, see thecattr.preconf package.Additional documentationOn structured and unstructured data, or the case for cattrsWhy I use attrs instead of pydanticcattrs I: un/structuring speedCreditsMajor credits to Hynek Schlawack for creatingattrsand its predecessor,characteristic.cattrsis tested withHypothesis, by David R. MacIver.cattrsis benchmarked usingperfandpytest-benchmark.This package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.