Custom conversion functions

It is possible to use a callable during a field mapping. That can be a function, a lambda, or a callable object.

>>> @dataclass
... class Person:
...     name: str
...     age: int
>>>
>>> @dataclass
... class Contact:
...     surname: str
...     first_name: str
>>>
>>> def compute_full_name(contact: Contact) -> str:
...     return f"{contact.first_name} {contact.surname}"
>>>
>>> create_mapper(Contact, Person, {"age": lambda: 45, "name": compute_full_name})
>>>
>>> contact = Contact(first_name="Jesse", surname="Cross")
>>> map_to(contact, Person)
Person(name='Jesse Cross', age=45)

In case the function takes no arguments, the function just behaves like setting a constant. The first function lambda: 45 has no parameters and just returns the constant 45, so the age will always be initialized with 45.

In case the function has one parameter, the source object will be passed and you can initialize the field however you want. In the second function compute_full_name takes one parameter contact: Contact, and combines the first_name and surname into a string and initialize the field name with it.

In case the function or callable object is annotated with types, the types will be checked. Teh first parameter (if specified) needs to be compatible with the type of the source class (could also be a super class, or an optional type). The return type needs to be compatible with the target field. E.g. if the function compute_full_name returns an int or takes the a parameter of type Person, it will produce an TypeError during the creation of the mapper.

Warning

Always use typed functions or typed callable objects. Untyped functions, untyped callable objects or just lambas (which cannot be types) will not by type-checked by the library. Especially a lambda can return anything, and the library will not check the type during the conversion!