asciimatics VS typer

For small apps, I may choose a TUI (Terminal User Interface). Curses for Python is very lightweight, but quite low level and difficult to use for anything more than a very simple interface (if your app tries to draw outside of the drawing area, the app crashes, so you have to carefully manage every detail). Textual and asciimatics are both mature TUI frameworks that provide a higher level and more Pythonic way to create TUI's.

If you are looking for something more flexible that just printing to a terminal, take a look at Asciimatics and textual. Both are available from Pypi.

Scroll down to the TUI example in the README, just above the documentation link: https://github.com/peterbrittain/asciimatics

Download fireworks.py

Source code: https://github.com/peterbrittain/asciimatics/blob/master/samples/fireworks.py

I have made yet another ssh connection manager with TUI, using asciimatics and tinyDB.

Personally I dig the aesthetics of asciimatics, it's quite handy and cross-platform.

I've used asciimatics in the past to good effect.

Take a look at rich (kinda new, but pretty neat), asciimatics, or urwid

I have been using Typer on every one of my CLI projects which uses Click under the hood. The documentation is fantastic, the CLI app it produces looks great and lets you create things quickly. I high recommend it.

https://typer.tiangolo.com/

Adding CLI capabilities. My preferred library here is typer.

I just heard about this but it seems to be pretty much the type of thing you want and want fast.

Docstrings are for documenting how a function/ class/ method/ module works. Often you don't need to add a docstring to your main function because no one will be importing it to use elsewhere. And if you want it to run as a CLI, then there are better ways to document the available options. For example, typer does most of it for you, or in click you add the help text to the decorator.

Most computing tasks in airflow DAGs are KubernetesPodOperator containing a CLI (Python Typer). It allows us to pass arguments easily to run DAG manually if needed (the new UI to pass arguments to DAG in airflow 2.6 is really nice). Arguments allow us to replay DAG easily (change start / end dates for instance).

So it's basically the main .gitlab-ci.yml file plus a separate Python CI app using Typer for the AWS instrumentation.

Similarly for Typer, which is literally "the FastAPI of CLIs"[1]. Handy to type your `main` parameters and have CLI argument parsing. For more complicated cases, it's a wrapper around Click.

[1] https://typer.tiangolo.com/

interesting that you hate python, but love Click. Did you try Typer which uses Click underneath?