GitExtensions VS sysidentpy

I agree that git is almost asking you to juggle commits.

My preference is to use temporary branches and cherry-picking instead of stashing; I mostly use a gui* to work with git so it is easy to select the two or three commits to cherry-picking or see visually if an interactive rebase would work.

* https://gitextensions.github.io/

I use Git Extensions myself as I find the git interface very straight forward, however they still have this fucking insane and frustrating issue: In the mergetool "Theirs" and "Mine" are swapped

Git Extensions is a more-intuitive way to manage your Git repositories in Windows. Its standalone interface serves as an effective, CLI-free means to control Git. Preferred by namtab00, because "SourceTree hides and shortcuts too much git functionality."

I presume this is where one goes to make a feature request? https://github.com/gitextensions/gitextensions/issues

If you're on Windows, I'd start with installing official Git. It comes with a Git Bash CLI and what not. There are also third party apps like GitExtensions and TortoiseGit if you want more UI/shell integration.

Everyone's going to downvote this, but I prefer the GUI over the command-line. I use http://gitextensions.github.io/

https://github.com/gitextensions/gitextensions/releases/tag/v2.51.05 - nice little ui for working with git. unfortunately, v2.51.05 is the last version that I can confirm works under mono (it was the last 2.x version and they completely rewrote the code from scratch in the 3.x series. My understanding was that it lost Linux compatibility at that point).

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