pytoschema 1.0.0

A package to convert Python type annotations into JSON schemas

pytoschema

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. For example, we can take this snippet:

from typing import List, Literal, TypedDict

class UserProperties(TypedDict, total=False):
   username: str
   is_superadmin: bool
   groups: List[str]
   status: Literal["ACTIVE", "DISABLED"]


def update_user(user_id: str, user_properties: UserProperties):
    pass  # Your function code

It would produce this JSON schema to be used by other layers of your application, usually the front-end:

{
    "update_user": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "type": "object",
        "properties": {
            "user_id": {
                "type": "string"
            },
            "user_properties": {
                "type": "object",
                "properties": {
                    "username": {
                        "type": "string"
                    },
                    "is_superadmin": {
                        "type": "boolean"
                    },
                    "groups": {
                        "type": "array",
                        "items": {
                            "type": "string"
                        }
                    },
                    "status": {
                        "enum": [
                            "ACTIVE",
                            "DISABLED"
                        ]
                    }
                },
                "required": [],
                "additionalProperties": false
            }
        },
        "required": [
            "user_id",
            "user_properties"
        ],
        "additionalProperties": false
    }
}

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

Table of contents

1. Getting started

   1. Installation
   2. Scan a package
   3. Scan a file
   4. Include and exclude patterns

2. Type annotation rules

   1. Rules

   2. Allowed types

      1. Base types
      2. Custom types
      3. Importing types from other files

Getting started

Installation

From a Python 3.8+ environment, run pip install pytoschema.

Scan a package

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

import os
import json

from pytoschema.functions import process_package

print(json.dumps(process_package(os.path.join("test", "example")), indent=4))

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

Scan a file

You can also target specific files, which won't include the package namespacing in the result value. Following on the same terminal:

from pytoschema.functions import process_file

print(json.dumps(process_file(os.path.join("test", "example", "service.py")), indent=4))

Include and exclude patterns

Include and exclude unix-like patterns can be used to filter function and module names we want to allow/disallow for scanning. See the difference when you now run this instead:

print(json.dumps(process_package(os.path.join("test", "example"), exclude_patterns=["_*"]), indent=4))

Similarly, but applied to specific files:

print(json.dumps(process_file(os.path.join("test", "example", "service.py"), include_patterns=["_*"]), indent=4))

Things to take into account:

• Exclude pattern matching overwrite include matches.
• __init__.py files are not affected by pattern rules and are always scanned. However, you can still filter its internal functions.

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 JSON data serialization. Hopefully, most of the useful stuff you need is allowed, provided you follow these simple rules.

Rules

1. The functions you want to scan need to be type annotated.

2. Function arguments are meant to be passed in key-value format, like a json object. This puts a couple of restrictions regarding *args, **kwargs, positional-only and keyword-only arguments:

   The following is allowed:

   • **kwargs: def func(**kwargs): pass
   • keyword-only arguments: def func(*, a): pass

   The following is not allowed:

   • *args: def func(*args): pass
   • positional-only arguments: def func(a, /): pass

3. Only certain JSON-safe type annotations can be used. They are explained in the next section.

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), typing.List and typing.Literal. All these types have a direct, non-ambiguous representation in both JSON and JSON schema.

Custom types

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

ServicePort = typing.Union[int, float]
ServiceConfig = typing.Dict[str, typing.Any]
ServiceState = typing.Literal["RUNNING", "STOPPED", "UNKNOWN"]

You can use typing.TypedDict, to build stronger validation on dict-like objects (Only class-based syntax). See how you can chain types with no restrictions:

class Service(typing.TypedDict, total=False):
    address: str
    port: ServicePort
    config: ServiceConfig
    state: ServiceState
    tags: typing.List[str]
    debug: bool

The flag total=False is there to indicate that not all properties are required, default is True.

Importing types from other files

You can import these custom 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 young project like this, at least for now.