A lightweight, object-oriented finite state machine implementation in Python with many extensions
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Updated
May 28, 2024 - Python
A lightweight, object-oriented finite state machine implementation in Python with many extensions
Django friendly finite state machine support
A serverless architecture for orchestrating ETL jobs in arbitrarily-complex workflows using AWS Step Functions and AWS Lambda.
Django workflow library that supports on the fly changes ⛵
Python Finite State Machines made easy.
This tool automates restoration of RDS database instances from snapshots into any dev, staging or production environments. It supports individual RDS Snapshot as well as cluster snapshot restore operations.
Python library to control Chinese USB HID 125Khz RFID Reader/Writer
Temporal Logic Planning toolbox
RAFCON (RMC advanced flow control) uses hierarchical state machines, featuring concurrent state execution, to represent robot programs. It ships with a graphical user interface supporting the creation of state machines and contains IDE like debugging mechanisms. Alternatively, state machines can programmatically be generated using RAFCON's API.
Build applications that make decisions (chatbots, agents, simulations, etc...). Monitor, persist, and execute on your own infrastructure.
An implementation of the LSTAR Grammatical Inference Algorithm
BitDust project source codes development cycle, official Development Git repository (mirror on GitHub) : https://bitdust.io
This project implements a functional motion planning stack for autonomous vehicles to avoid both static and dynamic obstacles while tracking the center line of a lane, while also handling stop signs.
Python State Machine
SDK for developing smart-contracts (distributed state-machines) in Self-sovereign-identity environments.
Logic Minimization in Python
A Pythonic API for Amazon's States Language for defining AWS Step Functions
Framework for state machines with run-to-completion concurrency using asyncio. Python 3.4 or later
This repository contains the code for the paper "Image Generation for Efficient Neural Network Training in Autonomous Drone Racing" of the WCCI 2020 congress.
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