AEGIS Habitat Layout Creator

An interactive web application for designing and validating space habitats based on official NASA standards. Developed for the NASA Space Apps Challenge 2024.

Live Demo: https://nsa-aegis.us/

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Table of Contents

• Key Features
• Technology Stack
• Getting Started
  • Prerequisites
  • Local Installation
  • Docker
• Usage
• Project Structure
• Contributing
• Team
• License
• Acknowledgments

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Key Features

• Dual Geometry Support: Design cylindrical or rectangular habitats.
• Real-time NASA Validation: All calculations are compliant with NASA Human Integration Design Handbook (HIDH) standards.
• Interactive Visualizations: Includes a 2D floor plan and a 3D model viewer powered by Plotly.
• Functional Zone Management: Allocate space for six key zones: Sleep, Work/Leisure, Hygiene, Kitchen, Exercise, and Storage.
• Dynamic Mission Parameters: Configure crew size, mission duration, and gravity environment.
• Automated Metrics: Instantly view total volume, Net Habitable Volume (NHV), floor area per person, and zone distribution.
• JSON Export: Save and share your complete habitat configuration.

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Technology Stack

• Core: Python 3.11+
• Web Framework: Streamlit
• Visualizations: Plotly
• Numerical Calculations: NumPy
• Deployment: Docker, Google Cloud Run

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Getting Started

Prerequisites

• Python 3.9+
• pip and venv

Local Installation

1. Clone the repository:

   git clone https://github.com/kritgarb/Habitat-Layout-Creator.git
   cd Habitat-Layout-Creator

2. Create and activate a virtual environment:

   • On Windows:

     python -m venv venv
     .\venv\Scripts\Activate.ps1

   • On Linux/macOS:

     python3 -m venv venv
     source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Run the application:

   streamlit run app.py

5. Access the application at http://localhost:8501.

Docker

Alternatively, you can use Docker and Docker Compose to run the project in a container.

1. Build and run with Docker Compose:

   docker-compose up --build

2. Or, build and run the container manually:

   docker build -t habitat-layout-creator .
   docker run -p 8501:8501 habitat-layout-creator

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Usage

1. Configure Habitat: Use the sidebar to select the habitat's shape, dimensions, and functional zones.
2. Set Mission Parameters: Adjust crew size, mission duration, and gravity.
3. Review Metrics: Check the real-time calculations for volume, NHV, and floor area against NASA standards.
4. Explore Visualizations: Switch between the 2D and 3D layout pages to view the interactive models.
5. Export Configuration: Download the complete design as a JSON file.

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Project Structure

Habitat-Layout-Creator/
├── app.py              # Main Streamlit application
├── requirements.txt    # Python dependencies
├── Dockerfile          # Docker build configuration
├── LICENSE             # MIT License
└── src/
    ├── components/     # Reusable UI components
    ├── config/         # Project constants and styles
    ├── pages/          # Application pages
    ├── utils/          # Calculation and validation functions
    └── visualizations/ # Plotly visualization generators

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Scientific References

This tool implements calculations and validations based on official NASA documentation:

• NASA Human Integration Design Handbook (HIDH): NASA/SP-2010-3407
• ISS Research Publications: Official Website
• Human Spaceflight Standards: Official Website

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Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue to report bugs or suggest features.

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Team

This project was developed by the ENTERPRISE Team for the NASA Space Apps Challenge 2024.

• Benjamin Vieira (Project Lead & Development)
• Alice Araujo (Development)
• Gabryel Batista (Design & UX)
• Vitória Ferreira (Quality Assurance)
• Caio Chagas (Research & Documentation)
• Enzo Andrade (English Review & Testing)

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License

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

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Acknowledgments

• NASA for their extensive research and public standards on space habitability.
• The Streamlit and Plotly communities for their excellent open-source frameworks.