pytojsonschema 1.0.4

A package to convert Python type annotations into JSON schemas

pytojsonschema

Package that uses static analysis - ast - to convert Python 3 function type annotations to JSON schemas.

• https://docs.python.org/3/library/typing.html
• https://json-schema.org/

This allows you to auto-generate the validation schemas for JSON-RPC backend functions written in Python.

Current support is for Python 3.8+ and JSON schema draft 7+.

Getting started

After installing the package, you can open a python terminal from the root of the repo and run:

import os
import pprint

from pytojsonschema.functions import process_package

pprint.pprint(process_package(os.path.join("test", "example")))

The example package will be scanned and JSON schemas will be generated for all the top level functions it can find.

Include and exclude unix-like patterns can be used to filter function names we want to allow/disallow for scanning.

See the difference when you run this instead:

pprint.pprint(process_package(os.path.join("test", "example"), exclude_patterns=["_*"]))

Exclude pattern matching overwrite include ones.

You can also target specific files, which won't include the package namespacing in the result value:

from pytojsonschema.functions import process_file

pprint.pprint(process_file(os.path.join("test", "example", "__init__.py")))

Type annotation rules

Fitting Python's typing model to JSON means not everything is allowed in your function signatures. This is a natural restriction that comes with data we want to be able to serialize and validate using JSON.

Hopefully, most of the useful stuff is allowed.

Allowed types

Base types

Basic types bool, int, float, str, None and typing.Any are allowed. Also, you can build more complex, nested structures with the usage of typing.Union, typing.Optional, typing.Dict (Only str keys are allowed) and typing.List.

Custom types

Your functions can also use custom types like the ones defined using an assignment of typing.Union, typing.List, typing.Dict and typing.Optional, as in:

ServicePort = typing.Union[int, float]
ServiceConfig = typing.Dict[str, typing.Any]

You can also use one of the new Python 3.8 features, typing.TypedDict, to build stronger validation on dict-like objects (Only class-based syntax). As you can see, you can chain these types with no restrictions:

class Service(typing.TypedDict):
    address: str
    port: ServicePort
    config: ServiceConfig
    tags: typing.List[str]
    debug: bool

Importing types from other files

You can import these types within your package and they will be picked up. However, due to the static nature of the scan, custom types coming from external packages can't be followed and hence not supported. In other words, you can only share these types within your package, using relative imports.

Other static analysis tools like mypy use a repository with stub files to solve this issue, see https://mypy.readthedocs.io/en/stable/stubs.html.

This is out of the scope for a tiny project like this, at least for now.

Rules

1. The functions you want to scan need to be type annotated. Kind of obvious requirement, right?

2. Only the types defined in the previous section can be used. They are the types that can be safely serialised as JSON.

3. About *args, **kwargs, positional-only and keyword-only arguments:

   Function arguments are meant to be passed in key-value format as a json object, which puts a couple of restrictions:

   • def func(*args): pass syntax is not allowed.
   • def func(a, /): pass (positional-only arguments, new in Python 3.8) syntax is not allowed either.
   • def func(**kwargs): pass is fine to use.
   • def func(*, a): pass (keyword-only arguments) is fine to use as well.