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Type-aware Python JSON serialization and validation.

Project description

Welcome to the typing_json library

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The typing_json library offers type-aware JSON encoding and decoding functionalities, on top of those offered by the builtin json library. The functions dump, dumps, load and loads mirror the functionality of their json counterparts, adding type-aware encoding/decoding and runtime type-checking of decoded objects.

Supported types include JSON basic types, Decimal, typed collections from the typing library, literal types, union types, optional types and typed namedtuples. For a full list of types, see below. The function is_instance extends the functionality of the builtin isinstance to include all the additional types supported by this library.

The documentation for this library was generated with pdoc.

You can install the typing_json library with pip:

pip install typing_json

Main goals

There are two main drivers behind the development of the typing_json library:

  1. Type-aware serialisation of data using JSON.
  2. Runtime validation of JSON data for use with static typing.

The first goal of the typing_json library is to automate the serialisation of statically typed data in Python. In a statically typed Python application (e.g. one validated using mypy), data is often structured using simple static types. The typing_json library uses these types to automate the process of JSON serialisation and de-serialisation, ensuring that the serialised data can subsequently be de-serialised into a valid instance of the original static type, equivalent to the instance that was originally serialised.

The second goal of the typing_json library is to automate the validation of JSON data against existing static types. When JSON data is loaded dynamically into a statically typed Python application, it needs to be validated to ensure that it conforms to whatever static types are being used as its specification. The typing_json library uses these types to automate the validation process, i.e. to perform runtime type-checking of the JSON data against the static types. This guarantees that data successfully de-serialised from JSON using the load/loads functions of the typing_json library conforms to the static type provided.

Types supported

The following types are currently supported by the typing_json library:

  • the JSON basic types bool, int, float, str and NoneType (technically type(None), but None can be used as an alias);
  • the type Decimal from the decimal builtin (cf. below for the handling of numerical types);
  • the following typed collections from the typing builtin library, as long as all generic type arguments are themselves supported: List, Tuple, Deque, Set, FrozenSet;
  • typed namedtuples constructed using NamedTuple from the builtin typing library, as long as all fields are of supported type;
  • the following typed collections from the typing builtin library, as long as the generic key/value generic type arguments are themselves supported: Dict, Mapping and OrderedDict (see below for additional requirements on the key generic type arguments and special behaviour on JSON encoding/decoding of keys);
  • enumeration types
  • the Literal types from the typing_extensions library, as long as all literal are of one of the JSON basic types above;
  • Optional and Union types from the typing builtin library, as long as all generic type arguments are themselves supported (cf. below for a caveat about Union types).

The following function can be used at runtime to check whether t is a type supported by the typing_json library:

    def is_json_encodable(t: Any, failure_callback=None) -> bool:
        ...

The optional parameter failure_callback can be used to pass a Callable[[str], None] that will be used to log any error messages. The following provides an example of usage:

# Python 3.7.4
>>> from typing import Dict, List, Set, Tuple
>>> from typing_json import is_json_encodable
>>> error_log = []
>>> my_callback = lambda s: error_log.append(s)
>>> is_json_encodable(List[Dict[Set[int], int]], failure_callback=my_callback)
False
>>> error_log
['Type typing.Set[int] is not keyable.',
 'Type of keys in typing.Dict[typing.Set[int], int] is not keyable.',
 'Type of elements in typing.List[typing.Dict[typing.Set[int], int]] is not json-encodable.']

Overview of encoding/decoding functions

There are three pairs of encoding/decoding functions offered by the typing_json library, for use in three different circumstances.

The functions to_json_obj / from_json_obj offer runtime conversion of instances of supported types to/from JSON objects.

    def to_json_obj(obj: Any, t: Type, use_decimal: bool = False) -> Any:
        ...

    def from_json_obj(obj: Any, t: Type, cast_decimal: bool = True) -> Any:
        ...

The functions dumps / loads offer (de-)serialisation of instances of supported types to/from JSON formatted strings.

def dumps(obj: Any, encoded_type: Type, **kwargs) -> str:
    ...

def loads(s: str, decoded_type: Type, cast_decimal: bool = True, **kwargs) -> Any:
    ...

The functions dump / load offer (de-)serialisation of instances of supported types to/from JSON formatted IO streams.

def dump(obj: Any, encoded_type: Type, fp, **kwargs) -> None:
    ...

def load(fp, decoded_type: Type, cast_decimal: bool = True, **kwargs) -> Any:
    ...

The calls dump(obj, t, fp) / dumps(obj, t) first use to_json_obj(obj, t) to encode an instance obj of a supported type t into a JSON object obj_json, then call json.dump(obj_json) / json.dumps(obj_json) to serialise obj_json to a file object fp or string.

Conversely, the calls load(fp, t) / loads(s, t) first call json.load(fp) / json.loads(s) to deserialise a JSON object obj_json from a file object fp or string s, then call from_json_obj(obj_json, t) to decode an instance obj of a supported type t from obj_json.

In all functions above, TypeError is raised if the object passed does not match the type specified. This runtime type-checking is performed by the function is_instance:

def is_instance(obj: Any, t: Type, failure_callback=None, cast_decimal: bool = True) -> bool:
    ...

The function is_instance extends the behaviour of the builtin isinstance to type-checking of instances obj of all types t supported by the typing_json library. Most importantly, this includes the generic typed collections of the typing library, and features a slight alteration of behaviour on booleans and numerical types.

Using dump, dumps, load and loads

The functions dump, dumps, load and loads in the typing_json library mirror their builtin json counterparts, with a couple of exceptions:

  • an additional parameter encoded_type (resp. decoded_type) is used in dump / dumps (resp. in load / loads) to specify the type to be used in the JSON encoding (resp. decoding);
  • an additional optional parameter cast_decimal (default: True) is used in load / loads to specify whether instances of Decimal (used by default to parse float literals) should be silently cast to int and float wherever the type requires them to.

Aside from the additional type parameter, the usage of dump, dumps, load and loads is the same as that of their json counterparts:

# Python 3.7.4
>>> from typing import Dict
>>> from typing_json import load
# myexpenses.json:
#
# {
#   "home": 150.25,
#   "travel": 78.90,
#   "entertainment": 52.00
# }
#
>>> with open("myexpenses.json", "r") as fp:
...     load(fp, Dict[str, float])
...
{"home": 150.25, "travel": 78.90, "entertainment": 52.00}
# Python 3.7.4
>>> from typing import Dict
>>> from typing_json import loads
>>> s = '{"home": 150.25, "travel": 78.9, "entertainment": 52.0}'
>>> loads(s, Dict[str, float])
{"home": 150.25, "travel": 78.90, "entertainment": 52.00}
# Python 3.7.4
>>> from typing import Dict
>>> from typing_json import dump
>>> myexpenses = {"home": 150.25, "travel": 78.90, "entertainment": 52.00}
>>> with open("myexpenses.json", "w") as fp:
...      dump(myexpenses, Dict[str, float], fp)
...
# myexpenses.json:
#
# {
#   "home": 150.25,
#   "travel": 78.90,
#   "entertainment": 52.00
# }
#
>>> from typing import Dict
>>> from typing_json import loads
>>> myexpenses = {"home": 150.25, "travel": 78.90, "entertainment": 52.00}
>>> dumps(myexpenses, Dict[str, float])
'{"home": 150.25, "travel": 78.9, "entertainment": 52.0}'

Basic types

On JSON basic types, the to_json_obj and from_json_obj functions return their argument unchanged:

# Python 3.7.4
>>> from typing_json import to_json_obj
>>> to_json_obj(True, bool)
True
>>> to_json_obj(1, int)
1
>>> to_json_obj(1.5, float)
1.5
>>> to_json_obj("hello", str)
"hello"
>>> to_json_obj(None, type(None))
None
>>> to_json_obj(None, None) # `None` is alias for `type(None)`
None

The exact same outcomes above are obtained if to_json_obj is replaced with from_json_obj.

The behaviour of is_instance on JSON basic types features two slight alterations from the behaviour of the builtin isinstance. Firstly, the bool literals True and False are not deemed to be of type int by is_instance, but they are by the builtin isinstance:

# Python 3.7.4
>>> from typing_json import is_instance
>>> isinstance(False, int) # builtin
True
>>> isinstance(True, int) # builtin
True
>>> is_instance(False, int) # typing_json
False
>>> is_instance(True, int) # typing_json
False

Secondly, instances of int are deemed to be of type float by is_instance, but they are not by the builtin isinstance:

# Python 3.7.4
>>> from typing_json import is_instance
>>> isinstance(1, int) # builtin
True
>>> isinstance(1, float) # builtin
False
>>> is_instance(1, int) # typing_json
True
>>> is_instance(1, float) # typing_json
True

Number types

When parsing JSON strings, from file object using load or from string instances using loads, the default behaviour is to use the constructor of class Decimal from the builtin decimal library to parse floating point literals. This informs the following handling of number types in the to_json_obj / from_json_obj functions.

The default behaviour in from_json_obj is to silently decode instances of Decimal to instances of int and float, according to the type specified:

# Python 3.7.4
>>> from decimal import Decimal
>>> from typing_json import from_json_obj
>>> from_json_obj(Decimal("1.2"), Decimal)
Decimal("1.2")
>>> from_json_obj(Decimal("1.2"), float)
1.2
>>> from_json_obj(Decimal("1.0"), Decimal)
Decimal("1.0")
>>> from_json_obj(Decimal("1.0"), float)
1.0
>>> from_json_obj(Decimal("1.0"), int)
1

The optional parameter cast_decimal of from_json_obj (default: True) can be set to False to disable the silent conversion of Decimal to float and int:

# Python 3.7.4
>>> from decimal import Decimal
>>> from typing_json import from_json_obj
>>> from_json_obj(Decimal("1.2"), Decimal, cast_decimal=False)
Decimal("1.2")
>>> from_json_obj(Decimal("1.2"), float, cast_decimal=False)
# TypeError: Object Decimal('1.2') is not of json basic type t=<class 'float'>.
>>> from_json_obj(Decimal("1.0"), Decimal, cast_decimal=False)
Decimal("1.0")
>>> from_json_obj(Decimal("1.0"), float, cast_decimal=False)
# TypeError: Object Decimal('1.0') is not of json basic type t=<class 'float'>.
>>> from_json_obj(Decimal("1.0"), int, cast_decimal=False)
# TypeError: Object Decimal('1.0') is not of json basic type t=<class 'int'>.

To ensure that decimal precision is maintained, instances Decimal are ordinarily encoded into JSON as strings:

# Python 3.7.4
>>> from decimal import Decimal
>>> from typing_json import to_json_obj
>>> to_json_obj(Decimal("1.2"), Decimal)
"1.2"
>>> to_json_obj(Decimal("-16"), Decimal)
"-16"

The optional parameter use_decimal of to_json_obj (default: True) can be set to True to instead allow instances of Decimal to be used directly in JSON objects:

# Python 3.7.4
>>> from decimal import Decimal
>>> from typing_json import to_json_obj
>>> to_json_obj(Decimal("1.2"), Decimal, use_decimal=True)
Decimal("1.2")
>>> to_json_obj(Decimal("-16"), Decimal, use_decimal=True)
Decimal("-16")

Finally, integers are always silently converted to floating point numbers in from_json_obj, but trying to convert floating point numbers to integers will always raise an error, regardless of whether the encoded number is an integer:

# Python 3.7.4
>>> from decimal import Decimal
>>> from typing_json import from_json_obj
>>> from_json_obj(1, int)
1
>>> from_json_obj(1, float)
1.0
>>> from_json_obj(1.0, float)
1.0
>>> from_json_obj(1.0, int)
# TypeError: Object 1.0 is not of json basic type t=<class 'int'>.

Sequences

Instances of List, Tuple and Deque are encoded by to_json_obj as JSON lists, with their elements recursively encoded:

# Python 3.7.4
>>> from collections import deque
>>> from decimal import Decimal
>>> from typing import Deque, List, Tuple
>>> from typing_json import to_json_obj
>>> to_json_obj([1, 2, 3], List[int])
[1, 2, 3]
>>> to_json_obj((1, 2.5, Decimal("3.5")), Tuple[int, float, Decimal])
[1, 2.5, "3.5"]
>>> to_json_obj(deque(["a", "b", "c"]), Deque[str])
["a", "b", "c"]
>>> to_json_obj(((0, Decimal("0.5")), (1, Decimal("3"))), Tuple[Tuple[int, Decimal], ...])
[[0, "0.5"], [1, "3"]]

JSON lists are are decoded by from_json_obj to instances of List, Tuple and Deque depending on the specified type, with elements recursively decoded from the elements of the JSON list:

# Python 3.7.4
>>> from collections import deque
>>> from decimal import Decimal
>>> from typing import Deque, List, Tuple
>>> from typing_json import from_json_obj
>>> from_json_obj([1, 2, 3], List[int])
[1, 2, 3]
>>> from_json_obj([1, 2.5, '3.5'], Tuple[int, float, Decimal])
(1, 2.5, Decimal("3.5"))
>>> from_json_obj(["a", "b", "c"], Deque[str])
deque(["a", "b", "c"])
>>> from_json_obj([[0, "0.5"], [1, "3"]], Tuple[Tuple[int, Decimal], ...])
((0, Decimal("0.5")), (1, Decimal("3")))

Sets

Instances of Set and FrozenSet are encoded by to_json_obj as JSON lists, with their elements recursively encoded:

# Python 3.7.4
>>> from decimal import Decimal
>>> from typing import FrozenSet, Set
>>> from typing_json import to_json_obj
>>> to_json_obj({1, 2, 3}, Set[int])
[1, 2, 3]
>>> to_json_obj(frozenset({Decimal("1.5"), Decimal("2.5")}), FrozenSet[Decimal])
["1.5", "2.5"]

JSON lists are are decoded by from_json_obj to instances of Set and FrozenSet depending on the specified type, with elements recursively decoded from the elements of the JSON list:

# Python 3.7.4
>>> from decimal import Decimal
>>> from typing import FrozenSet, Set
>>> from typing_json import from_json_obj
>>> from_json_obj([1, 2, 3], Set[int])
{1, 2, 3}
>>> from_json_obj(["1.5", "2.5"], FrozenSet[Decimal])
frozenset({Decimal("1.5"), Decimal("2.5")})

NamedTuples

Instances of typed namedtuples constructed with NamedTuple are encoded by to_json_obj as JSON dictionaries (ordered), with the field names as their keys and the field values recursively encoded:

# Python 3.7.4
>>> from collections import OrderedDict
>>> from typing import NamedTuple, Set, Tuple
>>> from typing_json import to_json_obj
>>> class Network(NamedTuple):
...     nodes: Set[int]
...     edges: Set[Tuple[int, int]]
...
>>> network = Network({0, 1, 2}, {(0, 1), (1, 2), (0, 2)})
>>> to_json_obj(network, Network)
OrderedDict([('nodes', [0, 1, 2]), ('edges', [[0, 1], [0, 2], [1, 2]])])
>>> dict(to_json_obj(network, Network))
{'nodes': [0, 1, 2], 'edges': [[0, 1], [0, 2], [1, 2]]}

JSON dictionaries are decoded by from_json_obj to instances of typed namedtuples depending on the specified type, with fields values recursively decoded from the values of the dictionary:

# Python 3.7.4
>>> from typing import NamedTuple, Set, Tuple
>>> from typing_json import from_json_obj
>>> class Network(NamedTuple):
...     nodes: Set[int]
...     edges: Set[Tuple[int, int]]
...
>>> from_json_obj({'nodes': [0, 1, 2], 'edges': [[0, 1], [0, 2], [1, 2]]}, Network)
Network(nodes={0, 1, 2}, edges={(0, 1), (0, 2), (1, 2)})

While collections.OrderedDict is always used by to_json_obj when encoding typed namedtuples, but from_json_obj will also accept ordinary dictionaries (because the order of fields is already determined by the namedtuple type). If the namedtuple has fields with default values, from_json_obj will use the default value for any field not appearing in the dictionary:

# Python 3.7.4
>>> from typing import NamedTuple, Set, Tuple
>>> from typing_json import from_json_obj
>>> class Employee(NamedTuple):
...     name: str
...     id: int = 3
...
>>> from_json_obj({"name": "Gill", "id": 2}, Employee)
Employee(name='Gill', id=2) # "id" value from dictionary
>>> from_json_obj({"name": "John"}, Employee)
Employee(name='John', id=3) # default "id" value from `Employee`
>>> from_json_obj({"id": 0}, Employee)
# TypeError: Object {'id': 0} does not have the required keys:
# t=<class '__main__.Employee'>, missing keys {'name'}.

If a field in the namedtuple does not have a default value and does not appear in the dictionary, from_json_obj will raise TypeError.

Dictionaries

Instances of Mapping and Dict are encoded by to_json_obj as JSON dictionaries, with their keys and values recursively encoded and their keys stringified if necessary (cf. below). Instances of OrderedDict follow the exact same rules, but are encoded as instances of collections.OrderedDict rather than instances of dict.

# Python 3.7.4
>>> import collections
>>> from decimal import Decimal
>>> import typing
>>> from typing import Dict, Mapping, Tuple
>>> from typing_json import to_json_obj
>>> vect = {"x": (Decimal("1.0"), Decimal("0.0")), "y": (Decimal("0.0"), Decimal("1.0"))}
>>> to_json_obj(vect, Dict[str, Tuple[Decimal, Decimal]])
{"x": ["1.0", "0.0"], "y": ["0.0", "1.0"]}
>>> to_json_obj(vect, Mapping[str, Tuple[Decimal, Decimal]])
{"x": ["1.0", "0.0"], "y": ["0.0", "1.0"]}
>>> to_json_obj(collections.OrderedDict(vect),
...             typing.OrderedDict[str, Tuple[Decimal, Decimal]])
OrderedDict([("x", ["1.0", "0.0"]), ("y", ["0.0", "1.0"])])

Keys are either encoded or encoded and then stringified, depending on the key type:

  • JSON basic types (bool, int, float, str and type(None)) are encoded but not stringified;
  • literal types are encoded but not stringified (because only JSON basic types are allowed as literals);
  • enumeration types are encoded but not stringified (because they are already encoded as strings);
  • all other types are first encoded and then stringified.

For example, dictionaries using tuples as keys have their keys first encoded into lists and then stringified to form the keys of the final JSON dictionary:

# Python 3.7.4
>>> from typing import Dict, Tuple
>>> from typing_json import to_json_obj
>>> to_json_obj({(0,1): "yes", (2,3): "no"}, Dict[Tuple[int, int], str])
{'[0, 1]': 'yes', '[2, 3]': 'no'}

JSON dictionaries are decoded by from_json_obj to instances of Dict and Mapping depending on the specified type. JSON ordered dictionaries (collections.OrderedDict) are decoded by from_json_obj to instances of Dict, Mapping and OrderedDict depending on the specified type. Values and keys are recursively decoded, and keys are first de-serialised from strings (using json.loads) if they were stringified as part of the encoding.

# Python 3.7.4
>>> import collections
>>> from decimal import Decimal
>>> import typing
>>> from typing import Dict, Mapping, Tuple
>>> from typing_json import from_json_obj
>>> vect_json = {"x": ["1.0", "0.0"], "y": ["0.0", "1.0"]}
>>> from_json_obj(vect_json, Dict[str, Tuple[Decimal, Decimal]])
{"x": (Decimal("1.0"), Decimal("0.0")), "y": (Decimal("0.0"), Decimal("1.0"))}
>>> from_json_obj(vect_json, Mapping[str, Tuple[Decimal, Decimal]])
{"x": (Decimal("1.0"), Decimal("0.0")), "y": (Decimal("0.0"), Decimal("1.0"))}
>>> from_json_obj(collections.OrderedDict(vect_json),
...               typing.OrderedDict[str, Tuple[Decimal, Decimal]])
OrderedDict([("x", (Decimal("1.0"), Decimal("0.0"))), ("y", (Decimal("0.0"), Decimal("1.0")))])
>>> from_json_obj({'[0, 1]': 'yes', '[2, 3]': 'no'}, Dict[Tuple[int, int], str])
{(0,1): "yes", (2,3): "no"}

Enumerations

Enumerations members are encoded by using the corresponding member names and decoded by associating the number to the member of corresponding name:

# Python 3.7.4
>>> from enum import Enum
>>> from typing_json import to_json_obj, from_json_obj
>>> class Color(Enum):
...     RED = (1.0, 0.0, 0.0)
...     GREEN = (0.0, 1.0, 0.0)
...     BLUE = (0.0, 0.0, 1.0)
...
>>> to_json_obj(Color.RED, Color)
"RED"
>>> from_json_obj("RED", Color)
<Color.RED: (1.0, 0.0, 0.0)>
>>> to_json_obj({Color.RED: (255, 0, 0),
...              Color.GREEN: (0, 255, 0),
...              Color.BLUE: (0, 0, 255)},
...             Dict[Color, Tuple[int, int, int]])
{'RED': [255, 0, 0], 'GREEN': [0, 255, 0], 'BLUE': [0, 0, 255]}

Literal types

Literal types can be constructed using typing_extensions.Literal, as long as the literals are all of JSON basic type. Literal types are encoded/decoded exactly like JSON basic types would, i.e. nothing is done to them.

Optional types

When encoding instances of an Optional type, it is first checked whether the instance can be encoded using the given generic type argument. If not, it is checked that the instance is None, in which case None is returned as the encoding (following the procedure for the JSON basic type type(None)).

# Python 3.7.4
>>> from typing import Dict, Optional, Set
>>> from typing_json import to_json_obj
>>> to_json_obj({"set": {1, 2, 3}}, Dict[str, Optional[Set[int]]])
{"set": [1, 2, 3]}
>>> to_json_obj({"set": None}, Dict[str, Optional[Set[int]]])
{"set": None}

Similarly, when decoding instances of an Optional type, it is first checked whether the JSON object can be decoded using the given generic type argument. If not, it is checked that the instance is None, in which case None is returned as the decoding.

# Python 3.7.4
>>> from typing import Dict, Optional, Set
>>> from typing_json import from_json_obj
>>> from_json_obj({"set": [1, 2, 3]}, Dict[str, Optional[Set[int]]])
{"set": {1, 2, 3}}
>>> from_json_obj({"set": None}, Dict[str, Optional[Set[int]]])
{"set": None}

Union types

When serialising instances obj of a Union type, the generic type arguments of Union are tried in sequence until a type T is found of which obj is an instance (accoring to the is_instance function). The serialisation then proceeds using T as the static type:

# Python 3.7.4
>>> from typing import Union
>>> from typing_json import dumps
>>> dumps(1, Union[int, str, float]) # same as `dumps(1, int)`
'1'
>>> dumps("hello", Union[int, str, float]) # same as `dumps("hello", str)`
'"hello"'
>>> dumps(2.5, Union[int, str, float]) # same as `dumps(2.5, float)`
'2.5'

When the JSON data obj_json is de-serialised, the generic type arugments of Union ara again tried in sequence until a type T is found which results in correct de-serialisation of obj_json:

# Python 3.7.4
>>> from typing import Union
>>> from typing_json import loads
>>> loads('1', Union[int, str, float]) # same as `loads(1, int)`
1
>>> loads('"hello"', Union[int, str, float]) # same as `loads("hello", str)`
"hello"
>>> loads('2.5', Union[int, str, float]) # same as `loads(2.5, float)`
2.5

This works well as long as the JSON encodings of the types are disjoint, as is the case for all JSON basic types. Unfortunately, some issues arise with overlapping union types, explained more in detail below. In short: if two types in a Union have overlapping JSON encodings (e.g. List and Set are both encoded into JSON using lists), they may be deserialised to the incorrect runtime type (though the static Union type will still be respected).

# Python 3.7.4
>>> from typing import List, Set, Union
>>> from typing_json import dumps, loads
>>> UnionT = Union[List[int], Set[int]]
>>> dumps([1, 2, 3], UnionT)
'[1, 2, 3]'
>>> dumps({1, 2, 3}, UnionT)
'[1, 2, 3]'
>>> loads(dumps([1, 2, 3], UnionT), UnionT)
[1, 2, 3]
>>> loads(dumps({1, 2, 3}, UnionT), UnionT)
[1, 2, 3]

Tagged unions can be used to mitigate this issue. Currently, tagged unions need to be defined manually (cf. below), but an automated way to construct them is a planned feature for future versions.

Overlapping union types

However, this may create some issues when the following conditions are met:

  1. the JSON encodings for two type in the Union overlap, as is the case for the collections List, Tuple, Deque, Set and FrozenSet;
  2. the application depends on the runtime type of the Union instances in a way which results in incompatible behaviour on the overlaps.

The de-serialised object is still going to be a valid instance of the Union type, but its runtime type may not be the expected one. To see a concrete example of this, imagine that we have a network with nodes labelled by int, featuring both directed and undirected edges. The directed edges are encoded as 2-tuples, while the undirected edges are encoded as frozensets with two elements. Let's look at what happens when we serialise and de-serialise such a network:

# Python 3.7.4
>>> from typing import FrozenSet, NamedTuple, Set, Tuple, Union
>>> from typing_json import dumps, loads
>>> class Network(NamedTuple):
...     nodes: Set[int]
...     edges: Set[Union[Tuple[int, int], FrozenSet[int]]]
...
>>> nodes = {1, 2, 3}
>>> edges = {(1, 2), frozenset({2, 3}), frozenset({1, 3})}
>>> network = Network(nodes, edges)
>>> print(network)
Network(nodes={1, 2, 3}, edges={(1, 2), frozenset({1, 3}), frozenset({2, 3})})
>>> network_serialised = dumps(network, Network)
>>> print(network_serialised)
{"nodes": [1, 2, 3], "edges": [[1, 2], [1, 3], [2, 3]]}
>>> network_deserialised = loads(network_serialised, Network)
>>> print(network_deserialised)
Network(nodes={1, 2, 3}, edges={(1, 2), (1, 3), (2, 3)})

Both directed edges (instances of Tuple[int, int]) and undirected edges (instances of FrozenSet[int] with two elements) in our Network data structure are encoded as lists with two elements. For example, [1, 3] is the encoding of both the undirected edge frozenset({1, 3}) (which is in our network) and a directed edge (1, 3) (which is not in our network). Because of this, [1, 3] can be deserialised using both Tuple[int, int] and FrozenSet[int]: since Tuple[int, int] appears first in the list of generic type arguments to Union, [1, 3] will be deserialised to a directed edge (1, 3), even though it was serialised from an undirected edge frozenset({1, 3}). Indeed, you can see from the prints of the example above that our original network had one directed edge (1, 2) and two undirected edges frozenset({1, 3}) and frozenset({2, 3}), while the network we de-serialised has three directed edges (1, 2), (1, 2) and (2, 3) and no undirected edges.

A way to solve this issue is to use a tagged union instead of the original union:

# Python 3.7.4
>>> from typing import FrozenSet, NamedTuple, Set, Tuple, Union
>>> from typing_extensions import Literal
>>> from typing_json import dumps, loads
>>> DirEdgeT = Tuple[Literal["d"], Tuple[int, int]]
>>> UndirEdgeT = Tuple[Literal["u"], FrozenSet[int]]
>>> class Network(NamedTuple):
...     nodes: Set[int]
...     edges: Set[Union[DirEdgeT, UndirEdgeT]]
...     @staticmethod
...     def from_untagged(nodes: Set[int],
...                       edges: Set[Union[Tuple[int, int], FrozenSet[int]]]) -> Network:
...         tagged_edges = {("d", e) if isinstance(e, tuple) else ("u", e) for e in edges}
...         return Network(nodes, tagged_edges)
...

The factory method from_untagged is there to allow automated tagging of edges as directed/undirected based on their runtime type: it is not used when de-serialising the network objects from JSON. Because the union is tagged, the edges are now de-serialised to the correct runtime type:

# Python 3.7.4
>>> nodes = {1, 2, 3}
>>> edges = {(1, 2), frozenset({2, 3}), frozenset({1, 3})}
>>> network = Network.from_untagged(nodes, edges)
>>> print(network)
Network(nodes={1, 2, 3},
        edges={('u', frozenset({1, 3})), ('u', frozenset({2, 3})), ('d', (1, 2))})
>>> network_serialised = dumps(network, Network)
>>> print(network_serialised)
{"nodes": [1, 2, 3], "edges": [["u", [1, 3]], ["u", [2, 3]], ["d", [1, 2]]]}
>>> network_deserialised = loads(network_serialised, Network)
>>> print(network_deserialised)
Network(nodes={1, 2, 3},
        edges={('u', frozenset({1, 3})), ('u', frozenset({2, 3})), ('d', (1, 2))})

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