logan VS xact

I had an idea for a library that follows log files and triggers some events that can be defined through python code. I used it for one task at work and gave up on figuring out how to structure it since.

The idea is that it allows you to verify and test logging, which is an odd concept but helps ensure that things are predictable and consistent.

https://github.com/arvind-iyer/logan/

https://github.com/wtpayne/xact - Model Based Systems/Software Engineering tool with support for machine learning and synthetic data.

MBSE is a vastly underappreciated technique, and one that deserves much more exposure in the mainstream tech community.

Sadly, I have found myself exceedingly disappointed at the tooling available to support MBSE, having mainly used Simulink, and also experimented with a few others like Rhapsody, Capella and EA.

Constant mouse usage with Simulink gave me really bad RSI, and merging models was a pain, due to the way that layout and structural information were mixed together in the xml-based .mdl file format.

So (naturally) I made a text-based alternative. It's a bit like TensorFlow in that you create a compute graph (computational model), and then run it.

The model itself can be generated dynamically in a script, using JQuery-like syntax to add or change nodes, or alternatively it can be stored and version controlled in one or more text files, using YAML, XML, JSON or TOML (or some mixture of those) to serialise the structure in an easy-to-diff-and-merge form.

https://github.com/wtpayne/xact