parsimonious VS transitions

Oh sorry, I grabbed the wrong PEG parser… I’ve used this one: https://github.com/erikrose/parsimonious

A grammar provides the high level constructs you need to define the "shape" of your data, and it largely takes care of the rest. Grammar libraries exist in other language (eg. lark or Parsimonius in Python) and they weren't created just to make XML parsing easier.

I'm honestly shocked that you are being downvoted heavily for this. I was literally reading a pip module a few days ago that cites stackoverflow in the code. It may not be for code snips but it it's not wild to think that someone would do this for code they pulled from SO.

Parsimonious

Since the input comes in the form of well-formatted text with with variable-width lines, this seems a perfect fit for a PEG parser, as described in Day 04. Using a PEG parser with a node visitor also lets us process each each node as it is being parsed, saving a loop or two. As usual, I will be using the parsimonious library for Python. Importing it with from parsimonious.grammar import Grammar

Start with that you know, if you know Excel well, why not start there? I usually recommend Python if you’re new to programming, then you can pick whatever additional libraries you think would benefit the model, and the syntax is highly forgiving. Sounds to me like you’re describing a pretty big finite state machine, with 288 states. Once you encode your idea and some tests, you’ll have the vocabulary (and means) to expand to other model variations such as applying probabilities to different transition states, and opening a whole world of probabilistic models and hidden Markov models, but gotta walk before running :)

BTW, did you check this?

https://github.com/pytransitions/transitions/blob/master/exa...

Transitions

In Python's ecosystem of libraries, there's a nice one called transitions (which I've used in my robotics work). transitions (as the name suggests) lets you define your state machines more declaratively in terms of transitions between states, the rules that govern those transitions, and the triggers that cause these transitions to happen.