mlb-led-scoreboard VS sysidentpy

So this is the instructions for the scoreboard: https://github.com/MLB-LED-Scoreboard/mlb-led-scoreboard/wiki

First and foremost, thank you to Tommy Farley’s MLB LED Scoreboard Project for the inspiration + most of the code base. My brother and I have been wanting a CFB equivalent of this forever, so we decided to build our own using a Raspberry Pi Zero and a 32 * 64 LED Matrix. Here is a link to the video demo. If you just so happen to have the same equipment, great! Here is our GitHub repository with the code + instructions on making your own. We’re also looking to sell these in the future (once the semiconductor shortage has hopefully settled down)— if you’re interested please let us know! As always, feedback from you all is great appreciated on how we can make this better.

About $200 in parts, runs out of a Pi.

I help maintain the MLB LED scoreboard project and we don't recommend anything less than 5A for a single matrix, and you're running 6 of them. In fact, the tutorial you linked suggests that you are way underpowering your matrices: https://learn.adafruit.com/32x16-32x32-rgb-led-matrix/powering

quick paste of an old how to file i made however heres the github link gh

I bought the RGB matrix and Raspberry Pi and assembled it. Just followed the process here to get the software running - https://github.com/MLB-LED-Scoreboard/mlb-led-scoreboard

You can look at the code/follow steps to build one yourself here: https://github.com/MLB-LED-Scoreboard/mlb-led-scoreboard. It's pretty straight forward if you have a technical background but if not it might take you a bit to get up to speed with the raspberry pi.

It supports a bunch of different matrix sizes, requires no assembly, and is free, so developers of software like the MLB LED scoreboard don't have to set up and maintain physical hardware to maintain the project. Consumers of those types of projects get the same benefits so you can run the emulated version without needing to pick up a soldering iron.

Link para o site da documentação: SysIdentPy - SysIdentPy

I'm the only maintainer, but I keep including new features (some exclusives, like the algorithm I've developed in my thesis to create NARMAX models), improving the code and documentation, and fixing bugs.

benchmark codes: Welcome to SysIdentPy’s documentation! — NARMAX models

github: wilsonrljr/sysidentpy: A Python Package For System Identification Using NARMAX Models (github.com)

I built a package where you can build polynomial NARMAX models using the most used method for model selection of this class of models in the beckend. In addition, you can built NARX Neural Networks for forecasting problems (this is built on top of Pytorch) and you can use any model that have a fit/predict method (Catboost, any estimator from sklearn) in a NARX configuration to perform infinity-steps-ahead prediction. Maybe its worth a try. Here is the link of the package: GitHub - wilsonrljr/sysidentpy: A Python Package For System Identification Using NARMAX Models