plotly VS matplotlib

Github: https://github.com/plotly/plotly.py

First we need to install the Plotly. To create some very dynamic graphics, this tool helps a lot.

my good dude wake up and smell the plotly. Knowing the ins and outs of matplotlib is helpful but doing interactive stuff with jupyter I always use plotly.

Plotly documentation (https://plotly.com/python/)

I don´t know your workflow, but I use 2 markdown based presentations: obsidian advance slides and Quarto presentations. The former is a plugin for Obsidian, which is the software I use to take all my notes, write my thesis, etc., so It makes it extremely easy to make presentations since all my information is in Obsidian. In the other hand, Quarto is a publishing system (articles, presentations, websites books) that can be easily integrated with python and R. This makes it supper convenient for showing my data to my PI since I can analyze my data and at the same time make a presentation for the data. Besides this, Quarto also integrates with my Zotero library, so I can insert citations. Lastly, one thing that made my Quarto presentations infinitely better that the powerpoints, Is that I can insert interactive graphs with plotly, so when I'm showing my data, my PI is able to explore the data inside the presentation.

Plotly GitHub repository: https://github.com/plotly/plotly.py

I'm using plotly.py now. This is why I love this community.

Depending on the nature of your reporting requirements, you could output a formatted Excel document with Python and a library such as openpyxl, and shove that into your SharePoint environment. This would be less dynamic than PBI reports can be, but may be sufficient. If you want viz as well, you can use something like ggplot or Plotly. Again, less dynamic than PBI for the same effort.

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?