The PyOpticon project is designed to monitor and analyze system performance and network data. It aims to provide insights into system processes, hardware efficiency, and network utilization. I created this in my spare time to see why my PC is lagging sometimes. So do not take it too seriously (or maybe do 😉).
Currently, the project includes the following features:
- Docker Container Initialization: Leveraging Docker to create isolated environments for data collection.
- Data Collection: Gathering data related to system processes, hardware performance, and network statistics.
- Data Storage: Utilizing MySQL for efficient data storage and retrieval.
- Python: The core programming language for the project.
- Docker: For creating and managing containerized environments.
- MySQL: Database management system for storing collected data.
The project contains the following main components:
compose.yaml: Docker compose file for setting up the environment.src/: Source directory containing the core code.db/: Contains Dockerfile for the database setup.tracker/: Includes the application logic for data collection (app.py) and database configuration (db.yaml).
To use PyOpticon, ensure Docker and Python are installed on your system. Then, you can start the Docker container defined in compose.yaml to initiate the data collection process.
The following features and improvements are currently under development:
- Data Cleaning: Implementing procedures to clean and preprocess the collected data for analysis.
- Data Visualization and Pattern Discovery: Developing tools and methods for visualizing the collected data and uncovering meaningful patterns and insights.
Contributions to the project are welcome. Please follow standard practices for code contributions, including feature branching and pull requests.
MIT-License