deserialize

A library to make deserialization easy. To get started, just run pip install deserialize

How it used to be

Without the library, if you want to convert:

{
    "a": 1,
    "b": 2
}

into a dedicated class, you had to do something like this:

class MyThing:

    def __init__(self, a, b):
        self.a = a
        self.b = b

    @staticmethod
    def from_json(json_data):
        a_value = json_data.get("a")
        b_value = json_data.get("b")

        if a_value is None:
            raise Exception("'a' was None")
        elif b_value is None:
            raise Exception("'b' was None")
        elif type(a_value) != int:
            raise Exception("'a' was not an int")
        elif type(b_value) != int:
            raise Exception("'b' was not an int")

        return MyThing(a_value, b_value)

my_instance = MyThing.from_json(json_data)

How it is now

With deserialize all you need to do is this:

import deserialize

class MyThing:
    a: int
    b: int

my_instance = deserialize.deserialize(MyThing, json_data)

That's it. It will pull out all the data and set it for you type checking and even checking for null values.

If you want null values to be allowed though, that's easy too:

from typing import Optional

class MyThing:
    a: Optional[int]
    b: Optional[int]

Now None is a valid value for these.

Types can be nested as deep as you like. For example, this is perfectly valid:

class Actor:
    name: str
    age: int

class Episode:
    title: str
    identifier: st
    actors: List[Actor]

class Season:
    episodes: List[Episode]
    completed: bool

class TVShow:
    seasons: List[Season]
    creator: str

Advanced Usage

Custom Keys

It may be that you want to name your properties in your object something different to what is in the data. This can be for readability reasons, or because you have to (such as if your data item is named __class__). This can be handled too. Simply use the key annotation as follows:

@deserialize.key("identifier", "id")
class MyClass:
    value: int
    identifier: str

This will now assign the data with the key id to the field identifier. You can have multiple annotations to override multiple keys.

Ignored Keys

You may want some properties in your object that aren't loaded from disk, but instead created some other way. To do this, use the ignore decorator. Here's an example:

@deserialize.ignore("identifier")
class MyClass:
    value: int
    identifier: str

When deserializing, the library will now ignore the identifier property.

Parsers

Sometimes you'll want something in your object in a format that the data isn't in. For example, if you get the data:

{
    "successful": True,
    "timestamp": 1543770752
}

You may want that to be represented as:

class Result:
    successful: bool
    timestamp: datetime.datetime

By default, it will fail on this deserialization as the value in the data is not a timestamp. To correct this, use the parser decorator to tell it a function to use to parse the data. E.g.

@deserialize.parser("timestamp", datetime.datetime.fromtimestamp)
class Result:
    successful: bool
    timestamp: datetime.datetime

This will now detect when handling the data for the key timestamp and run it through the parser function supplied before assigning it to your new class instance.

The parser is run before type checking is done. This means that if you had something like Optional[datetime.datetime], you should ensure your parser can handle the value being None. Your parser will obviously need to return the type that you have declared on the property in order to work.

Subclassing

Subclassing is supported. If you have a type Shape for example, which has a subclass Rectangle, any properties on Shape are supported if you try and decode some data into a `rectangle object.