batgrl VS typer

Create with: batgrl

All my terminal visualizations are created with batgrl.

All visualizations created with my terminal graphics library, batgrl.

I'm really sorry! I forget to add the source: https://github.com/salt-die/Advent-of-Code/tree/main/2023/visuals/06_Wait_For_It My visualizations are created with my terminal graphics library: batgrl

They are the author of https://github.com/salt-die/batgrl a TUI framework. The examples are straight up magic IMO

But I may have borrowed from a previous project: https://github.com/salt-die/nurses_2/tree/main/examples/advanced/rubiks

The main tool is https://github.com/salt-die/nurses_2.

No, this is all in the terminal. The library is nurses_2. Code for the visualization is here!

Storing character and color information in numpy arrays is how https://github.com/salt-die/nurses_2 works as well. Graphic widgets use an upper-half block character ▀ with proper foreground and background colors to double the resolution. Widgets even have proper alpha compositing in the terminal!

He is probably most well know for creating FastAPI that I taught to some of my clients and Typer that I've never used.

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/