Example:
In [14]: from dataclasses import dataclass
...: from typing import Optional, Union, List
...:
Note that Author and Reviewer are isomorphic even if totally unrelated.
In [15]: @dataclass
...: class Author:
...: first: Optional[str]
...: last: str
...:
...: @dataclass
...: class Reviewer:
...: first: Optional[str]
...: last: str
...:
Here, items can be heterogenous, or of ambiguous type based only on its fields values.
In [16]: @dataclass
...: class Book:
...: author: List[Union[Author, Reviewer]]
...: title: str
...:
In [17]: obj = Book([Author("Matthias", "B"), Reviewer("Tony", "Fast")], "pyshs")
...:
...: data = serialize(obj , Book)
...:
...: deserialize(Book, Book, data)
Out[17]: Book(author=[Author(first='Matthias', last='B'), Reviewer(first='Tony', last='Fast')], title='pyshs')
^...................................^
.
.Note the conserved types.
Unlike other similar libraries that automatically serialise/deserialise it has the following properties:
object do not need to have a give baseclass, they need to have an __init__ or _deserialise class method that takes each parameter as kwargs.
Subclass or isomorphic classes are kept in the de-serialisation, in particular in Union and List of Unions. That is to say it will properly de-serialise and heterogenous list or dict, as long as those respect the type annotation.
Both Pydantic and Jetblack-serialize would have erased the types and returned either 2 Authors or 2 Reviewers.
it is also compatible with Rust Serde with adjacently tagged Unions (not
critical but nice to have)
A mini-implementation of an automatic serialiser-deserialiser for nested dataclass like class based on type annotations.
A mini-implementation of an automatic serialiser-deserialiser for nested dataclass like class based on type annotations.
Example:
In [14]: from dataclasses import dataclass
...: from typing import Optional, Union, List
...:
Note that Author and Reviewer are isomorphic even if totally unrelated.
In [15]: @dataclass
...: class Author:
...: first: Optional[str]
...: last: str
...:
...: @dataclass
...: class Reviewer:
...: first: Optional[str]
...: last: str
...:
Here, items can be heterogenous, or of ambiguous type based only on its fields values.
In [16]: @dataclass
...: class Book:
...: author: List[Union[Author, Reviewer]]
...: title: str
...:
In [17]: obj = Book([Author("Matthias", "B"), Reviewer("Tony", "Fast")], "pyshs")
...:
...: data = serialize(obj , Book)
...:
...: deserialize(Book, Book, data)
Out[17]: Book(author=[Author(first='Matthias', last='B'), Reviewer(first='Tony', last='Fast')], title='pyshs')
^...................................^
.
.Note the conserved types.
Unlike other similar libraries that automatically serialise/deserialise it has the following properties:
object do not need to have a give baseclass, they need to have an __init__ or _deserialise class method that takes each parameter as kwargs.
Subclass or isomorphic classes are kept in the de-serialisation, in particular in Union and List of Unions. That is to say it will properly de-serialise and heterogenous list or dict, as long as those respect the type annotation.
Both Pydantic and Jetblack-serialize would have erased the types and returned either 2 Authors or 2 Reviewers.
it is also compatible with Rust Serde with adjacently tagged Unions (not
critical but nice to have)
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