GitLocker: The Coding Marketplace

Description:

pytsinterfaces 0.5.0

py-ts-interfaces
Python to TypeScript Interfaces

What is this?
This library provides utilities that convert Python dataclasses with type
annotations to a TypeScript interface and serializes them to a file.
Installation
python --version # requires 3.7+
pip install py-ts-interfaces

Motivation
In web applications where Python is used in the backend and TypeScript is used
in the frontend, it is often the case that the client will make calls to the
backend to request some data with some specific pre-defined "shape". On the
client-side, an interface for this data is usually defined and if the Python
backend authors use typechecking, like with mypy, the
project authors may be typing the JSON response values as well.
This results in a duplication of code. If the shape changes in the backend,
the related interface must also be reflect its changes in the frontend. At
best, this is annoying to maintain. At worst, over time the interfaces may
diverge and cause bugs.
This library aims to have a single source of truth that describes the shape of
the payload between the backend and the frontend.
Usage
In Python, py-ts-interfaces exposes a new class object called Interface.
By subclassing this object, you identify to the also-packaged script that you
want it to be serialized to an interface file.

First, hook up your dataclasses:

# views.py
from dataclasses import dataclass
from py_ts_interfaces import Interface

@dataclass
class MyComponentProps(Interface):
name: str
show: bool
value: float

@dataclass
class WillNotGetPickedUp: # this doesn't subclass Interface, so it won't be included
name: str
value: float

In your shell, run the included command and pass in the name of the file or
directory you want to use. By default it will output to a file in your
directory called interface.ts

$ py-ts-interfaces views.py
Created interface.ts!

You may also use the following arguments:

-o, --output [filepath]: where the file will be saved. default is interface.ts.
-a, --append: by default each run will overwrite the output file. this flag
allows only appends. Be warned, duplicate interfaces are not tested.

The resulting file will look like this:

// interface.ts
interface MyComponentProps {
name: string;
show: boolean;
value: number;
}

Why @dataclass?
Dataclasses were introduced in Python 3.7 and they are great. Some
alternatives that I have seen other codebases using are NamedTuple and
TypedDict. All of these objects attempt to do the same thing: group together
pieces of data that belong close together like a struct.
However, dataclass won out over the other two for the following reasons:

dataclasses are built-in to Python. As of writing, NamedTuple is also
built-in to the typing module, but TypedDict is still considered
experimental.
dataclasses cannot be declared and defined inline like you can do with
NamedTuple and TypedDict, e.g., NamedTuple can be defined using class
inheritance like class MyNamedTuple(NamedTuple): ..., but also like
MyNamedTuple = NamedTuple('MyNamedTuple', [('name', str), ('id', int)]).
This is a good thing. Dataclasses require you to use a class style
declaration, which not only looks closer to a TypeScript interface
declaration, but it avoids the complex metaclass machinery that NamedTuples
and TypedDicts use to gain all its features. Since this library uses the
AST and static analysis of the code to determine what data to serialize,
this makes the choice a no-brainer.
dataclasses can be made to be immutable (mostly) by setting frozen=True.
This library does not require it but in later versions we may provide a
partialed dataclass decorator that guarantees immutability.
Because we avoid the metaclass machinery of NamedTuples and TypedDicts, it
opens up the possibility of writing custom classes that allows mypy to
typecheck it one way, but gives the AST parser some clues in order to
generate TypeScript types that cannot easily be expressed in Python.

Why define the types in Python instead of TypeScript?
TypeScript is significantly more mature for typing syntax than Python.
Generally speaking, you can express any type that Python can do in TypeScript,
but not vice versa.
So defining the types in Python guarantee that you can also express the whole
interface in both languages.
Supported Type Mappings
Please note that usage of T U and V in the table below represent
stand-ins for actual types. They do not represent actually using generic typed
variables.

Python
Typescript

None
null

str
string

int
number

float
number

complex
number

bool
boolean

List
Array<any>

Tuple
[any]

Dict
Record<any, any>

List[T]
Array[T]

Tuple[T, U]
[T, U]

Dict[T, U]
Record<T, U>

Optional[T]
T | null

Union[T, U, V]
T | U | V

Planned Supported Mappings

String literals
Undefined type
isNaN type
ReadOnly types
Excess Properties

Unsupported/Rejected Mappings
The primary purpose of this library is to help type, first and foremost, data
moving back and forth from client to server. Many of these features, whether they be specific to TypeScript or Python, would be overkill to support.

void
callables/functions
enums
Dates, datetime, dates, times (send these over as strings and convert them to richer objects on the client)
extends
generics, TypeVars
intersection types
mapped types
conditional types
classes

Contributing
Interested in contributing? You're awesome! It's not much, but here's some notes to get you started CONTRIBUTING.md.
Author
Christopher Sabater Cordero