Jinja2 VS matplotlib

But, on the other hand, I can read in the jinja repository that there is not the same Switching From Other Template Engines and also exists documentation about Support for templates engines

jinja: Default templating engine for and dependency of flask

or as complex as bringing Jinja into the picture. Some other options include mustache templates, the built in Template class, and some libraries noted here.

Jinja

Hey u/GuzTech , this issue is mostly related to an old version of jinja that you might be using, here is the report: https://github.com/pallets/jinja/issues/1585

Turns out it wasn't how I was getting the variable -- registered from the api call VS lookup('file') -- it was mainly that for some reason, using 'map()' sometimes needs ' | list | to_json ' tacked on to the end to correctly output the results without the "generator object do_map at xxxx" instead (as mentioned here).

- Link: https://github.com/pallets/jinja/blob/master/jinja2/sandbox.py

Cookiecutter is a command-line utility that creates projects from templates. There's a list of templates maintained by the cookiecutter team and plenty of community awesome lists. It's built with python and uses the jinja templating framework (found in python web frameworks like flask). You can use it to make a template for pretty much anything! All you need to get started is pip install cookiecutter.

This major release of Flask is accompanied by major releases of Werkzeug, Jinja2, click, and itsdangerous which we'd also welcome and appreciate testing (their pre releases are installed with the Flask pre release).

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?