AstroverseAI

 

Overview

This project combines advanced machine learning techniques with astronomical calculations to provide accurate predictions for:

• The Hijri (Islamic) calendar
• Solar and lunar eclipses on Earth
• Planetary eclipses and transits in the solar system
• Meteor shower timing and intensity

It aims to bridge traditional Islamic calendar methodologies with modern data science approaches while extending its capabilities to broader astronomical predictions.

Features

• Hijri date prediction based on Gregorian dates
• Solar and lunar eclipse prediction for Earth
• Planetary eclipse and transit predictions for Mercury, Venus, Mars, Jupiter, and Saturn
• Meteor shower predictions, including timing and intensity
• Integration of machine learning models with astronomical calculations
• Geographical analysis of calendar and eclipse observations
• Time series analysis for long-term predictions
• Hyperparameter optimization for model fine-tuning
• Comprehensive evaluation metrics for prediction accuracy

Project Structure

AstroverseAI/
│
├── data/
│   ├── hijri_gregorian_correspondence.csv
│   ├── historical_eclipses.csv
│   ├── planetary_eclipse_data/
│   │   ├── mercury_eclipses.csv
│   │   ├── venus_eclipses.csv
│   │   └── ...
│   └── meteor_shower_data.csv
│
├── src/
│   ├── data_processing/
│   │   ├── __init__.py
│   │   ├── data_loader.py
│   │   └── preprocessor.py
│   │
│   ├── models/
│   │   ├── __init__.py
│   │   ├── hijri_model.py
│   │   ├── eclipse_predictor.py
│   │   ├── planetary_eclipse_predictor.py
│   │   ├── meteor_shower_predictor.py
│   │   └── gb_meteor_shower_predictor.py
│   │
│   ├── optimization/
│   │   ├── __init__.py
│   │   ├── hyperparameter_optimizer.py
│   │   ├── meteor_shower_optimizers.py
│   │   └── planetary_eclipse_optimizer.py
│   │
│   ├── analysis/
│   │   ├── __init__.py
│   │   ├── time_series_model.py
│   │   └── geographical_analysis.py
│   │
│   ├── evaluation/
│   │   ├── __init__.py
│   │   └── prediction_evaluator.py
│   │
│   └── utils/
│       ├── __init__.py
│       └── astronomical_calculations.py
│
├── tests/
│   ├── __init__.py
│   ├── test_hijri_model.py
│   ├── test_eclipse_predictor.py
│   ├── test_planetary_eclipse_predictor.py
│   └── test_meteor_shower_predictor.py
│
├── notebooks/
│   ├── hijri_calendar_analysis.ipynb
│   ├── eclipse_prediction_analysis.ipynb
│   ├── planetary_phenomena_analysis.ipynb
│   └── meteor_shower_analysis.ipynb
│
├── main.py
├── main_advanced.py
├── requirements.txt
├── CONTRIBUTING.txt
└── README.md

Docker Image

The latest version of this project is available as a Docker image on DockerHub:

You can pull the image using:

docker pull al0olo/astroverseai:latest

Running with Docker

To run the application using the pre-built Docker image:

1. Pull the image (if you haven't already):

   docker pull al0olo/astroverseai:latest

2. Run the container:

   docker run -it al0olo/astroverseai:latest

This will start the application in interactive mode, allowing you to use the command-line interface.

Installation

1. Clone the repository:

   git clone https://github.com/al0olo/AstroverseAI.git
   cd AstroverseAI
   

2. Install required packages:

   pip install -r requirements.txt
   

3. Run the main script:

   python main.py
   

   For advanced features and analysis:

   python main_advanced.py
   

4. To run tests:

   python -m unittest discover tests
   

5. Explore the Jupyter notebooks in the notebooks/ directory for detailed analysis and visualizations.

Contributing

Contributions to this project are welcome! Please refer to the CONTRIBUTING.md file for guidelines.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Usage

Basic Usage

Run the main script for basic astronomical predictions:

python main.py

Advanced Analysis

For advanced features including meteor shower predictions, model optimization, and geographical analysis:

python main_advanced.py

Jupyter Notebooks

Explore detailed analyses and visualizations, including meteor shower patterns:

1. Start Jupyter Notebook:

   jupyter notebook
   

2. Open and run the notebooks in the notebooks/ directory.

Continuous Integration and Deployment

This project uses GitHub Actions for continuous integration and deployment.

Automated Testing

On each pull request and push to the main branch, the following steps are automatically performed:

1. The code is checked out.
2. The project is set up with Python 3.7, 3.8, and 3.9.
3. All dependencies are installed.
4. All tests in the tests directory are run.

This ensures that new changes do not break existing functionality and that the code works across different Python versions.

Automated Deployment

When changes are merged into the main branch:

1. A new Docker image is built.
2. The image is pushed to DockerHub with the tag latest.

This allows for easy deployment of the most recent version of the application.

Data

• Place your Hijri-Gregorian correspondence data in data/hijri_gregorian_correspondence.csv
• Historical Earth eclipse data should be in data/historical_eclipses.csv
• Planetary eclipse and transit data for each planet should be in separate files in the data/ directory
• Meteor shower data and historical observations should be in data/meteor_showers.csv and data/meteor_observations.csv respectively

Ensure your data files follow the required format as described in the data processing modules.

Model Evaluation

Evaluation metrics for all predictions (Hijri dates, Earth eclipses, planetary phenomena, and meteor showers) are output by the main scripts. For a detailed breakdown of the evaluation process, refer to the respective evaluation modules in src/evaluation/.