SaltStack VS matplotlib

In the new style, when the tag is longer than 20 characters, an end of tag string is appended to the tag given by the string constant TAGEND, that is, two line feeds '\n\n'. When the tag is less than 20 characters then the tag is padded with pipes "|" out to 20 characters as before. When the tag is exactly 20 characters no padded is done. source: https://github.com/saltstack/salt/blob/master/salt/utils/event.py

https://github.com/saltstack/salt https://github.com/chocolatey/choco https://github.com/nextcloud https://github.com/authelia/authelia https://github.com/grafana/grafana

SaltStack

One thing that really irks me about Salt, though, is that they are very slow to fix bugs. My Salt states are littered with workarounds for bugs that have been open for multiple years. Even in basic things, like ssh authorized_keys management. Other than bug velocity, though, I've been pretty pleased with Salt.

Here are several related GitHub issues: - https://github.com/saltstack/salt/issues/54791 - https://github.com/saltstack/salt/issues/57541 - https://github.com/saltstack/salt/issues/16089

For clarity, here's the issue: https://github.com/saltstack/salt/issues/64111

Nightly builds on supported branches & master running the full test suite, producing fully tested builds. https://github.com/saltstack/salt/actions/workflows/nightly.yml

It's definitely some sort of AI script. Not this exactly, but something working off Python or scripts of thar nature. https://github.com/saltstack/salt

Github: https://github.com/matplotlib/matplotlib

as suggested here.

Tool: Matplotlib Pyplot

The issue was described here: https://github.com/matplotlib/matplotlib/issues/8409, but the "solution" was just "this is fixed" which was not helpful to me.

Matplotlib: Matplotlib is a 2D plotting library that allows you to create visualizations of your data. It's a powerful tool for data analysis, but the syntax can be complex and the customization options can be overwhelming. GitHub - https://github.com/matplotlib/matplotlib

A quick google shows: this history.txt:

Let's take a small subset i.e 20 data points of our prediction and compare it with actual output using matplotlib library

I will think more about what I want to say next, but for now, I would like to say that I need the super-particles and PIC methods as I think that is the way forward for me. Are there ways to implement these methods in matplotlib, Visit or Paraview? Do I take existing code and import it into those programs to visualize it? Or can I directly program/simulate something in those visualizion tools without needing to import any code?