dtyper 2.0.2

⌨️dtyper: Call typer commands or make dataclasses from them ⌨

⌨️dtyper: a tiny library to improve typer ⌨️

What is dtyper?

typer is a famously clear and useful system for writing Python CLIs but it has two issues that people seem to run into a lot:

1. You can't call the typer.command functions it creates directly because they have the wrong defaults.

2. As you add more arguments to your CLI, there is no easy way to break up the code sitting in one file without passing around long, verbose parameter lists.

dtyper is a single-file library that adds to an existing installation of typer that solves these two problems without changing existing code at all.

• dtyper.function decorates a typer.command to make it callable.

• dtyper.dataclass automatically makes a dataclass from a typer.command.

How to use dtyper?

Install as usual with poetry add dtyper, pip install dtyper, or your favorite package manager.

dtyper is a drop-in replacement for typer - it copies all typers properties - so you can even write

import dtyper as typer

to experiment with it before deciding.

dtyper has two new functions that typer doesn't, and overrides a typer class:

• @dtyper.function is a decorator that takes a typer command and returns a callable function with the correct defaults. It is unncessary if you use dtyper.Typer (below)

• @dtyper.dataclass is a decorator that takes an existing typer or dtyper command and makes a dataclass from it.

• dtyper.Typeris a class identical to typer.Typer, except it fixes Typer.command functions so you can call them directly.

None of the typer functionality is changed to the slightest degree - adding dtyper will not affect how your command line program runs at all.

Examples

All the examples will assume this block of code exists somewhere:

from typer import Typer

app = Typer()
command = app.command

Example: using dtyper.function

Very little to type, it fixes the problem:

import dtyper


@dtyper.function  #  <--- add this one line
@command(help='test')
def get_keys(
    bucket: str = Argument(
        'buck, help='The bucket to use'
    ),

    keys: bool = Option(
        False, help='The keys to download'
    ),
):
    print(bucket, keys)


# Elsewhere in the code

get_keys()  # correctly prints 'buck False'

Example: a simple dtyper.dataclass

Here's a simple CLI in one Python file with two arguments bucket, keys and one option pid:

@command(help='test')
def get_keys(
    bucket: str = Argument(
        ..., help='The bucket to use'
    ),

    keys: str = Argument(
        'keys', help='The keys to download'
    ),

    pid: Optional[int] = Option(
        None, '--pid', '-p', help='process id, or None for this process'
    ),
):
    get_keys = GetKeys(**locals())
    print(get_keys.run())


@dtyper.dataclass(get_keys)
class GetKeys:
    site = 'https://www.some-websijt.nl'

    def run(self):
        return self.url, self.keys, self.pid

    def __post_init__(self):
        self.pid = self.pid or os.getpid()

    def url(self):
       return f'{self.site}/{self.url}/{self.pid}'

Example: splitting a large typer.command into multiple files

Real world CLIs frequently have dozens if not hundreds of commands, with hundreds if not thousands of options, arguments, settings or command line flags.

The natural structure for this is the "big ball of mud", a popular anti-pattern known to cause misery and suffering to maintainers.

dtyper.dataclass can split the user-facing definition of the API from its implementation and then split that implementation over multiple files in a natural and convenient way.

The example has three Python files.

interface.py contains the Typer CLI definitions for this command.

@command(help='test')
def big_calc(
    bucket: str = Argument(
        ..., help='The bucket to use'
    ),
    more: str = Argument(
        '', help='More information'
    ),
    enable_something: boolean = Option(
        False, help='Turn on one of many important parameters'
    ),
    # [dozens of parameters here]
):
    d = dict(locals())  # Capture all the command line arguments as a dict

    from .big_calc import BigCalc  # Lazy import to avoid a cycle

    bc = BigCalc(**d)
    bc.run()

big_calc.py contains the dtyper.dataclass implementation

from .interface import big_calc
from . import helper
import dtyper


@dtyper.dataclass(big_calc)
class BigCalc:
    def run(self):
       # Each argument in `big_calc` becomes a dataclass field
       print(self.bucket, self.more)
       print(self)  # dataclass gives you a nice output of all fields

       if helper.huge_thing(self) and self._etc():
          self.stuff()
          helper.more_stuff(self)
          ...

    def _etc(self):
       ...
       # Dozens more methods here perhaps!

Some of the code is offloaded to helper files like helper.py:

def huge_thing(big_calc):
    if has_hole(big_calc.bucket):
       fix_it(big_calc.bucket, big_calc.more)

def more_stuff(big_calc):
    # even more code