zerocode VS matplotlib

Zerocode is an open-source Java test automation framework that uses a declarative style of testing. In declarative testing, you don't write code, you declare scenarios that describe each step of a test in a JSON/YAML file. The Zerocode framework will then interpret the scenario and execute the instructions that you specify via a custom DSL. Zerocode can be used for end-to-end testing of your data stream.

Zerocode

https://github.com/authorjapps/zerocode A community-developed, free, open source, API automation and load testing framework built using JUnit core runners for Http REST, SOAP, Security, Database, Kafka and much more.

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?