Object schemas

Declaration

Object schemas are defined with Object class by passing it a dictionary mapping field names to Type instances.

So given an object

class Person(object):
    def __init__(self, name, age):
        self.name = name
        self.age = age

You can define it’s type like this:

from lollipop.types import Object, String, Integer

PersonType = Object({
    'name': String(),
    'age': Integer(),
})

It will allow serializing Person types to Python’s basic types (that you can use to serialize to JSON) or validate that basic Python data:

PersonType.dump(Person('John', 38))
# => {"name": "John", "age": 38}

PersonType.validate({"name": "John"})
# => {"age": "Value is required"}

PersonType.load({"name": "John", "age": 38})
# => {"name": "John", "age": 38}

Yet it loads to same basic type dict instead of real object. To fix that, you need to provide a data constructor to type object:

PersonType = Object({
    'name': String(),
    'age': Integer(),
}, constructor=Person)

PersonType.load({"name": "John", "age": 38})
# => Person(name="John", age=38)

Constructor function should take field values as keyword arguments and return constructed object.

Value extraction

When you serialize (dump) objects, field values are expected to be object attributes. But library actually allows controlling that. This is done with Field class instances. When you define your object and pass types for it’s fields, what really happens is those types are wrapped with a Field subclass objects. The actual object fields are defined like this:

PersonType = Object({
    'name': AttributeField(String()),
    'age': AttributeField(Integer()),
})

Passing just a Type instances for field types is just a shortcut to wrap them all with a default field type which is AttributeField. You can change default field type with Object.default_field_type argument:

PersonType = Object({
    'name': String(),
    'age': Integer(),
}, default_field_type=AttributeField)

And you can actually mix fields defined with just Type with fields defined with Field. The first ones will be wrapped with default field type while the later ones will be used as is.

AttributeField is probably the one that would be used most of the time. It extracts value for serialization from object attribute with the same name as the field name. You can change the name of attribute to extract value from:

Person = namedtuple('Person', ['full_name'])

PersonType = Object({'name': AttributeField(String(), attribute='full_name')})

PersonType.dump(Person('John Doe'))  # => {'name': 'John Doe'}

Other useful instances are MethodField which calls given method on the object to get value instead of getting attribute, FunctionField which uses given function on a serialized object to get value, ConstantField which always serializes to given constant value. For last one there is another shortcut: if you provide a value for a field which is not Type and not Field then it will be wrapped with a ConstantField.

# Following lines are equivalent
Object({'answer': ConstantField(Any(), 42)}).dump(object())  # => {'answer': 42}
Object({'answer': 42}).dump(object())  # => {'answer': 42}

Object Schema Inheritance

To be able to allow reusing parts of schema, you can supply a base Object:

BaseType = Object({'base': String()})
InheritedType = Object(BaseType, {'foo': Integer()})

# is the same as
InheritedType = Object({'base': String(), 'foo': Integer()})

You can actually supply multple base types which allows using them as mixins:

TimeStamped = Object({'created_at': DateTime(), 'updated_at': DateTime()})

BaseType = Object({'base': String()})
InheritedType = Object([BaseType, TimeStamped], {'foo': Integer()})

Polymorphic types

Sometimes you need a way to serialize and deserialize values of different types put in the same list. Or maybe you value can be of either one of given types. E.g. you have a graphical application which operates with objects of different shapes:

class Point(object):
    def __init__(self, x, y):
        self.x = x
        self.y = y

class Shape(object):
    pass

class Circle(Shape):
    def __init__(self, center, radius):
        self.center = center
        self.radius = radius

class Rectangle(Shape):
    def __init__(self, left_top, right_bottom):
        self.left_top = left_top
        self.right_bottom = right_bottom

PointType = Object({'x': Integer(), 'y': Integer()}, constructor=Point)

CircleType = Object({
    'center': PointType,
    'radius': Integer
}, constructor=Circle)

RectangleType = Object({
    'left_top': PointType,
    'right_bottom': PointType,
}, constructor=Rectangle)

To support that library provides a special type - OneOf:

def with_type_annotation(subject_type, type_name):
    return Object(subject_type, {'type': type_name},
                  constructor=subject_type.constructor)

AnyShapeType = OneOf(
    {
        'circle': with_type_annotation(CircleType, 'circle'),
        'rectangle': with_type_annotation(RectangleType, 'rectangle'),
    },
    dump_hint=lambda obj: obj.__class__.__name__.lower(),
    load_hint=dict_value_hint('type'),
)

dumped = List(AnyShapeType).dump([
    Circle(Point(5, 8), 4), Rectangle(Point(1, 10), Point(10, 1))
])
# => [
#   {'type': 'circle',
#    'center': {'x': 5, 'y': 8},
#    'radius': 4},
#   {'type': 'rectangle',
#    'left_top': {'x': 1, 'y': 10},
#    'right_bottom': {'x': 10, 'y': 1}}]

List(AnyShapeType).load(dumped)
# => [Circle(Point(5, 8), 4), Rectangle(Point(1, 10), Point(10, 1))]

OneOf uses user supplied functions to determine which particular type to use during serialization/deserialization. It helps returning proper error messages. If you’re not interested in providing detailed error message, you can just supply all types as a list. OneOf will try to use each of them in given order returning first successfull result. If all types return errors it will provide generic error message. Here is example of library’s error messages schema:

ErrorMessagesType = OneOf([
    String(), List(String()), Dict('ErrorMessages')
], name='ErrorMessages')