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Description

lupin helps in serializing python objects (user classes) to native Python types (dict, list, etc.) and loading data to python complex objects.

You can use your existing models (and don't need to modify them) in order to make them work with lupin. You just have to define a schema and bind it to your class in order to make it work.

It handles polymorphic lists and associations. For example if in one list there are a User and an Admin object, then it will be able to load and dump those objects to native python datatypes or load the list with one instance of the User class and the other one with the Admin class.

It can also be used to validate incoming data. Complex validators combinations can be made in order to make the data validation process easier For example if a field is defined with this validators combination : `Equal("Lupin") | Equal("Andrésy")` then the validation stage will only pass if the data is Andrésy or Lupin.

Programming language: Python
License: MIT License
Tags: Serialization     JSON     Schema     Mapper     Validation    

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README

lupin is a Python JSON object mapper

Build Status

lupin is meant to help in serializing python objects to JSON and unserializing JSON data to python objects.

Installation

pip install lupin

Usage

lupin uses schemas to create a representation of a python object.

A schema is composed of fields which represents the way to load and dump an attribute of an object.

Define schemas

from datetime import datetime
from lupin import Mapper, Schema, fields as f


# 1) Define your models
class Thief(object):
    def __init__(self, name, stolen_items):
        self.name = name
        self.stolen_items = stolen_items


class Painting(object):
    def __init__(self, name, author):
        self.name = name
        self.author = author


class Artist(object):
    def __init__(self, name, birth_date):
        self.name = name
        self.birth_date = birth_date


# 2) Create schemas
artist_schema = Schema({
    "name": f.String(),
    "birthDate": f.DateTime(binding="birth_date", format="%Y-%m-%d")
}, name="artist")

painting_schema = Schema({
    "name": f.String(),
    "author": f.Object(artist_schema)
}, name="painting")

thief_schema = Schema({
    "name": f.String(),
    "stolenItems": f.List(painting_schema, binding="stolen_items")
}, name="thief")

# 3) Create a mapper and register a schema for each of your models you want to map to JSON objects
mapper = Mapper()

mapper.register(Artist, artist_schema)
mapper.register(Painting, painting_schema)
mapper.register(Thief, thief_schema)


# 4) Create some sample data
leonardo = Artist(name="Leonardo da Vinci", birth_date=datetime(1452, 4, 15))
mona_lisa = Painting(name="Mona Lisa", author=leonardo)
arsene = Thief(name="Arsène Lupin", stolen_items=[mona_lisa])

Dump objects

# use mapper to dump python objects
assert mapper.dump(leonardo) == {
    "name": "Leonardo da Vinci",
    "birthDate": "1452-04-15"
}

assert mapper.dump(mona_lisa) == {
    "name": "Mona Lisa",
    "author": {
        "name": "Leonardo da Vinci",
        "birthDate": "1452-04-15"
    }
}

assert mapper.dump(arsene) == {
    "name": "Arsène Lupin",
    "stolenItems": [
        {
            "name": "Mona Lisa",
            "author": {
                "name": "Leonardo da Vinci",
                "birthDate": "1452-04-15"
            }
        }
    ]
}

Load objects

# use mapper to load JSON data
data = {
    "name": "Mona Lisa",
    "author": {
        "name": "Leonardo da Vinci",
        "birthDate": "1452-04-15"
    }
}
painting = mapper.load(data, "painting")  # "painting" is the name of the schame you want to use
artist = painting.author

assert isinstance(painting, Painting)
assert painting.name == "Mona Lisa"

assert isinstance(artist, Artist)
assert artist.name == "Leonardo da Vinci"
assert artist.birth_date == datetime(1452, 4, 15)

Polymorphic lists

Sometimes a list can contain multiple type of objects. In such cases you will have to use a PolymorphicList, you will also need to add a key in the items schema to store the type of the object (you can use a Constant field).

Say that our thief has level up and has stolen a diamond.

class Diamond(object):
    def __init__(self, carat):
        self.carat = carat


mapper = Mapper()

# Register a schema for diamonds
diamond_schema = Schema({
    "carat": f.Field(),
    "type": f.Constant("diamond")  # this will be used to know which schema to used while loading JSON
}, name="diamond")
mapper.register(Diamond, diamond_schema)

# Change our painting schema in order to include a `type` field
painting_schema = Schema({
    "name": f.String(),
    "type": f.Constant("painting"),
    "author": f.Object(artist_schema)
}, name="painting")
mapper.register(Painting, painting_schema)

# Use `PolymorphicList` for `stolen_items`
thief_schema = Schema({
    "name": f.String(),
    "stolenItems": f.PolymorphicList(on="type",  # JSON key to lookup for the polymorphic type
                                     binding="stolen_items",
                                     schemas={
                                         "painting": painting_schema,  # if `type == "painting"` then use painting_schema
                                         "diamond": diamond_schema  # if `type == "diamond"` then use diamond_schema
                                     })
}, name="thief")
mapper.register(Thief, thief_schema)


diamond = Diamond(carat=20)
arsene.stolen_items.append(diamond)

# Dump object
data = mapper.dump(arsene)
assert data == {
    "name": "Arsène Lupin",
    "stolenItems": [
        {
            "name": "Mona Lisa",
            "type": "painting",
            "author": {
                "name": "Leonardo da Vinci",
                "birthDate": "1452-04-15"
            }
        },
        {
            "carat": 20,
            "type": "diamond"
        }
    ]
}

# Load data
thief = mapper.load(data, "thief")
assert isinstance(thief.stolen_items[0], Painting)
assert isinstance(thief.stolen_items[1], Diamond)

Validation

Lupin provides a set of builtin validators, you can find them in the lupin/validators folder.

While creating your schemas you can assign validators to the fields. Before loading a document lupin will validate its format. If one field is invalid, an InvalidDocument is raised with all the error detected in the data.

Example :

from lupin import Mapper, Schema, fields as f, validators as v
from lupin.errors import InvalidDocument, InvalidLength
from models import Artist

mapper = Mapper()

artist_schema = Schema({
    "name": f.String(validators=v.Length(max=10)),
}, name="artist")
mapper.register(Artist, artist_schema)

data = {
    "name": "Leonardo da Vinci"
}

try:
    mapper.load(data, artist_schema, allow_partial=True)
except InvalidDocument as errors:
    error = errors[0]
    assert isinstance(error, InvalidLength)
    assert error.path == ["name"]

Current validators are :

  • DateTimeFormat (validate that value is a valid datetime format)
  • Equal (validate that value is equal to a predefined one)
  • In (validate that a value is contained in a set of value)
  • Length (validate the length of a value)
  • Match (validate the format of a value with a regex)
  • Type (validate the type of a value, this validator is already included in all fields to match the field type)
  • URL (validate an URL string format)
  • IsNone (validate that value is None)
  • Between (validate that value belongs to a range)

Combination

You can build validators combinations using the & and | operator.

Example :

from lupin import validators as v
from lupin.errors import ValidationError

validators = v.Equal("Lupin") | v.Equal("Andrésy")
# validators passes only if value is "Lupin" or "Andrésy"

validators("Lupin", [])

try:
    validators("Holmes", [])
except ValidationError:
    print("Validation error")