phantom-types

Phantom types for Python will help you make illegal states unrepresentable and avoid shotgun parsing by enabling you to practice "Parse, don't validate".

Checkout the complete documentation on Read the Docs →

Installation

$  python3 -m pip install phantom-types

Extras

There are a few extras available that can be used to either enable a feature or install a compatible version of a third-party library.

$  python3 -m pip install phantom-types[all]

Examples

By introducing a phantom type we can define a pre-condition for a function argument.

from phantom import Phantom
from phantom.predicates.collection import contained


class Name(str, Phantom, predicate=contained({"Jane", "Joe"})): ...


def greet(name: Name):
    print(f"Hello {name}!")

Now this will be a valid call.

greet(Name.parse("Jane"))

... and so will this.

joe = "Joe"
assert isinstance(joe, Name)
greet(joe)

But this will yield a static type checking error.

greet("bird")

To be clear, the reason the first example passes is not because the type checker somehow magically knows about our predicate, but because we provided the type checker with proof through the assert. All the type checker cares about is that runtime cannot continue executing past the assertion, unless the variable is a Name. If we move the calls around like in the example below, the type checker would give an error for the greet() call.

joe = "Joe"
greet(joe)
assert isinstance(joe, Name)

Runtime type checking

By combining phantom types with a runtime type-checker like beartype or typeguard, we can achieve the same level of security as you'd gain from using contracts.

import datetime
from beartype import beartype
from phantom.datetime import TZAware


@beartype
def soon(dt: TZAware) -> TZAware:
    return dt + datetime.timedelta(seconds=10)

The soon function will now validate that both its argument and return value is timezone aware, e.g. pre- and post conditions.

Pydantic support

Phantom types are ready to use with pydantic and have integrated support out-of-the-box. Subclasses of Phantom work with both pydantic's validation and its schema generation.

class Name(str, Phantom, predicate=contained({"Jane", "Joe"})):
    @classmethod
    def __schema__(cls) -> Schema:
        return super().__schema__() | {
            "description": "Either Jane or Joe",
            "format": "custom-name",
        }


class Person(BaseModel):
    name: Name
    created: TZAware


print(json.dumps(Person.model_json_schema(), indent=2))

The code above outputs the following JSONSchema.

{
  "properties": {
    "name": {
      "description": "Either Jane or Joe",
      "format": "custom-name",
      "title": "Name",
      "type": "string"
    },
    "created": {
      "description": "A date-time with timezone data.",
      "format": "date-time",
      "title": "TZAware",
      "type": "string"
    }
  },
  "required": ["name", "created"],
  "title": "Person",
  "type": "object"
}

Development

Install development requirements, preferably in a virtualenv:

$ python3 -m pip install .[all,test,type-check]

Run tests:

$ pytest
# or
$ make test

Run type checker:

$ mypy

Linters and formatters are set up with goose, after installing it you can run it as:

# run all checks
$ goose run --select=all
# or just a single hook
$ goose run mypy --select=all

In addition to static type checking, the project is set up with pytest-mypy-plugins to test that exposed mypy types work as expected, these checks will run together with the rest of the test suite, but you can single them out with the following command.

$ make test-typing