```
# ⚙️ Installation Guide - Aviation Safety AI Framework
## System Requirements
### Minimum Requirements
```
Operating Systems:
•Linux: Ubuntu 20.04+, CentOS 8+, RHEL 8+
•Windows: Windows 10/11, Windows Server 2019+
•macOS: macOS 11.0+ (Big Sur)
•RTOS: QNX 7.0+, VxWorks 7+
Hardware:
•CPU: x86-64 or ARMv8-A (4+ cores)
•RAM: 8 GB minimum (16 GB recommended)
•Storage: 10 GB available space
•GPU: Optional (NVIDIA CUDA 11.0+ for acceleration)
Python Environment:
•Python: 3.9, 3.10, 3.11 (3.12 experimental)
•pip: 21.0+
•virtualenv: 20.0+ (recommended)
```
### Aircraft Deployment Requirements
```
Certified Hardware:
•Avionics Computer: DO-254 Level B compliant
•Memory: 4 GB ECC RAM minimum
•Storage: 64 GB SSD with wear leveling
•Power: 28V DC, 20W typical consumption
Certified Software:
•OS: ARINC 653 compliant RTOS
•Middleware: DDS or equivalent
•Runtime: Python 3.9 (DO-178C qualified)
•Libraries: All with certification evidence
```
## Quick Installation
### Method 1: pip Installation (Recommended)
```bash
# Create virtual environment
python -m venv aviation_env
source aviation_env/bin/activate # Linux/macOS
# aviation_env\Scripts\activate # Windows
# Install core package
pip install aviation-safety-ai
# Install with optional dependencies
pip install aviation-safety-ai[full] # Includes visualization, GPU support
# Verify installation
python -c "import aviation_safety; print(aviation_safety.__version__)"
```
Method 2: Docker Installation
```bash
# Pull Docker image
docker pull emeraldcompass/aviation-safety:latest
# Run container with data volume
docker run -p 8080:8080 \
-v /path/to/flight_data:/data \
-v /path/to/models:/models \
emeraldcompass/aviation-safety:latest
# Run with GPU support (NVIDIA)
docker run --gpus all \
-p 8080:8080 \
emeraldcompass/aviation-safety:gpu-latest
```
Method 3: Source Installation
```bash
# Clone repository
git clone https://github.com/emerladcompass/Aviation.git
cd Aviation
# Install dependencies
pip install -r requirements.txt
# Install in development mode
pip install -e .
# Run tests
python -m pytest tests/
```
Detailed Installation
Step 1: Environment Setup
```bash
# Create dedicated environment
conda create -n aviation python=3.9
conda activate aviation
# Or using venv
python3.9 -m venv ~/aviation
source ~/aviation/bin/activate
```
Step 2: Dependency Installation
```bash
# Base dependencies
pip install numpy>=1.21.0
pip install scipy>=1.7.0
pip install pandas>=1.3.0
pip install scikit-learn>=1.0.0
# Machine learning
pip install torch>=1.10.0
pip install tensorflow>=2.7.0 # Optional
pip install xgboost>=1.5.0
# Scientific computing
pip install pysindy>=1.7.0 # Sparse Identification of Nonlinear Dynamics
pip install nolds>=0.5.0 # Nonlinear dynamics and Lyapunov exponents
pip install pyts>=0.12.0 # Time series analysis
# Aviation-specific
pip install pyavia>=0.2.0 # Aviation data handling
pip install aeropy>=1.1.0 # Aerodynamics calculations
# Visualization
pip install matplotlib>=3.5.0
pip install plotly>=5.5.0
pip install seaborn>=0.11.0
```
Step 3: Framework Installation
```bash
# Option A: From PyPI (stable)
pip install emerald-compass-aviation
# Option B: From GitHub (development)
pip install git+https://github.com/emerladcompass/Aviation.git
# Option C: Local development
git clone https://github.com/emerladcompass/Aviation.git
cd Aviation
pip install -e ".[dev,test,docs]"
```
Step 4: Verification
```bash
# Test basic functionality
python -c "
import aviation_safety as av
print(f'Version: {av.__version__}')
# Test data loading
from aviation_safety.data import FlightDataLoader
loader = FlightDataLoader()
print('Data loader initialized')
# Test modeling
from aviation_safety.modeling import VanDerPolModel
model = VanDerPolModel(mu=0.47, omega=1.23)
print('Model initialized successfully')
"
# Run test suite
pytest tests/ -v
```
Platform-Specific Instructions
Linux (Ubuntu/Debian)
```bash
# System dependencies
sudo apt update
sudo apt install -y \
python3-dev \
python3-pip \
build-essential \
libhdf5-dev \
libatlas-base-dev \
ffmpeg \
libsm6 \
libxext6
# For GPU support (NVIDIA)
sudo apt install -y nvidia-cuda-toolkit
pip install nvidia-cudnn-cu11
# Install framework
pip install aviation-safety-ai[gpu]
```
Windows
```powershell
# Install Python 3.9 from Microsoft Store or python.org
# Enable "Add Python to PATH" during installation
# Install Visual C++ Build Tools
# Download from: https://visualstudio.microsoft.com/visual-cpp-build-tools/
# Install using PowerShell
python -m venv aviation_env
.\aviation_env\Scripts\Activate.ps1
pip install aviation-safety-ai
# For GPU support
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install aviation-safety-ai[gpu]
```
macOS
```bash
# Install Homebrew if not installed
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
# Install system dependencies
brew install python@3.9
brew install hdf5
brew install ffmpeg
# For Apple Silicon (M1/M2/M3)
# Install miniforge for ARM support
brew install miniforge
conda init "$(basename "${SHELL}")"
conda create -n aviation python=3.9
conda activate aviation
# Install framework
pip install aviation-safety-ai
```
Aircraft Deployment
DO-178C Certified Installation
```bash
# 1. Prepare certified environment
cd /certified/avionics
tar -xzf aviation-safety-certified-1.0.0.tar.gz
# 2. Validate checksums
sha256sum -c checksums.txt
# 3. Install certified Python
./install_certified_python.sh
# 4. Install framework
./install_aviation_framework.sh --certification-level=B
# 5. Run certification tests
./run_certification_tests.sh
# 6. Generate certification evidence
./generate_evidence_package.sh
```
Docker for Avionics (Containerized)
```dockerfile
# Dockerfile.avionics
FROM emeraldcompass/aviation-safety:avionics-latest
# Set up ARINC 653 environment
ENV ARINC653_CONFIG=/config/partition.xml
ENV TIME_PARTITION=A
# Copy application
COPY app/ /app/
# Set entrypoint
ENTRYPOINT ["python", "/app/aviation_safety_rt.py"]
```
Development Setup
Full Development Environment
```bash
# Clone with submodules
git clone --recursive https://github.com/emerladcompass/Aviation.git
cd Aviation
# Install development dependencies
pip install -r requirements-dev.txt
pip install -r requirements-test.txt
pip install -r requirements-docs.txt
# Set up pre-commit hooks
pre-commit install
# Install in development mode
pip install -e ".[dev]"
# Set up testing database
python scripts/setup_test_db.py
# Launch Jupyter for development
jupyter lab notebooks/
```
IDE Configuration
VS Code Configuration (.vscode/settings.json):
```json
{
"python.defaultInterpreterPath": "./aviation_env/bin/python",
"python.linting.enabled": true,
"python.linting.pylintEnabled": true,
"python.formatting.provider": "black",
"python.testing.pytestEnabled": true,
"python.testing.unittestEnabled": false,
"[python]": {
"editor.formatOnSave": true,
"editor.codeActionsOnSave": {
"source.organizeImports": true
}
}
}
```
PyCharm Configuration:
1. Create new project from existing sources
2. Set interpreter to aviation_env/bin/python
3. Enable pytest as test runner
4. Configure run configurations for examples
Configuration
Basic Configuration File (config.yaml)
```yaml
# config.yaml
aviation_safety:
# Model settings
model:
type: "hybrid"
physics_weight: 0.6
ai_weight: 0.4
confidence_threshold: 0.8
# Data settings
data:
sampling_rate: 8
parameters: 127
normalize: true
impute_missing: true
# Prediction settings
prediction:
horizon: 300
update_interval: 1.0
buffer_size: 1000
# Safety settings
safety:
lambda_thresholds:
stable: 0.01
ccz_lower: 0.01
ccz_upper: 0.5
chaos: 0.5
alert_levels:
info: 0.1
warning: 0.3
critical: 0.5
# Logging settings
logging:
level: "INFO"
file: "/var/log/aviation_safety.log"
rotation: "daily"
retention: 30
```
Environment Variables
```bash
# Set in shell or .env file
export AVIATION_SAFETY_MODEL_PATH="/models/hybrid_v1.pkl"
export AVIATION_SAFETY_DATA_PATH="/data/flights"
export AVIATION_SAFETY_LOG_LEVEL="INFO"
export AVIATION_SAFETY_CERTIFICATION_LEVEL="B"
export AVIATION_SAFETY_USE_GPU="true"
export CUDA_VISIBLE_DEVICES="0"
```
Integration Examples
Integration with Flight Simulators
```python
# X-Plane integration
import aviation_safety as av
from aviation_safety.integration.xplane import XPlaneInterface
# Connect to X-Plane
xplane = XPlaneInterface(host='127.0.0.1', port=49000)
# Create safety monitor
monitor = av.RealTimeMonitor(
model_path='models/hybrid_model.pkl',
update_rate=8.0 # Hz
)
# Main loop
while True:
# Get flight data from X-Plane
data = xplane.get_flight_data()
# Analyze safety
analysis = monitor.analyze(data)
# Provide feedback
if analysis['risk_level'] == 'HIGH':
xplane.show_alert("Simplify cockpit interface")
time.sleep(0.125) # 8 Hz
```
Integration with Existing Systems
```python
# Integration with airline operations
from aviation_safety import AviationSafetyAPI
from aviation_safety.integration.acars import ACARSInterface
class AirlineSafetySystem:
def __init__(self):
self.api = AviationSafetyAPI(
certification_level='OPERATIONAL',
airline_code='QFA'
)
self.acars = ACARSInterface()
def monitor_flight(self, flight_number):
# Real-time monitoring
while flight_active:
# Get data from aircraft
data = self.acars.get_flight_data(flight_number)
# Analyze
result = self.api.analyze_flight(data)
# Send alerts if needed
if result['requires_attention']:
self.send_alert_to_crew(result)
# Update ground operations
self.update_operations_dashboard(result)
```
Maintenance and Updates
Update Procedure
```bash
# Check current version
python -c "import aviation_safety; print(aviation_safety.__version__)"
# Update via pip
pip install --upgrade aviation-safety-ai
# Update from source
cd Aviation
git pull origin main
pip install --upgrade -e .
# Verify update
python -c "import aviation_safety; print(f'Updated to: {aviation_safety.__version__}')"
```
Backup and Recovery
```bash
# Backup configuration
tar -czf aviation_backup_$(date +%Y%m%d).tar.gz \
/etc/aviation_safety \
/var/lib/aviation_safety/models \
/var/log/aviation_safety
# Restore from backup
tar -xzf aviation_backup_20251228.tar.gz -C /
systemctl restart aviation-safety
```
Uninstallation
Complete Removal
```bash
# Remove package
pip uninstall aviation-safety-ai -y
# Remove virtual environment
deactivate # If activated
rm -rf aviation_env
# Remove configuration files (optional)
sudo rm -rf /etc/aviation_safety
sudo rm -rf /var/lib/aviation_safety
# Remove Docker containers
docker stop aviation-safety-container
docker rm aviation-safety-container
docker rmi emeraldcompass/aviation-safety:latest
```
Partial Removal
```bash
# Remove only models
rm -rf ~/.aviation_safety/models
# Clear cache
rm -rf ~/.cache/aviation_safety
# Reset configuration
aviation_safety reset --all
```
Support and Troubleshooting
Getting Help
```bash
# Check documentation
aviation_safety --help
aviation_safety docs --open
# Run diagnostic
aviation_safety diagnose
# Check system compatibility
aviation_safety check-system
# Generate support report
aviation_safety support-report
```
Common Issues and Solutions
```
Issue: ImportError: libcudart.so.11.0
Solution: Install CUDA 11.0 or use CPU version
Issue: MemoryError with large datasets
Solution: Use data streaming or increase swap
Issue: Certification validation failed
Solution: Check DO-178C compliance settings
Issue: Real-time latency too high
Solution: Optimize model or reduce parameters
```
Performance Tuning
Optimization for Production
```bash
# Compile models for performance
aviation_safety compile-models --optimize=aggressive
# Quantize for embedded deployment
aviation_safety quantize --bits=8 --platform=armv8
# Generate deployment package
aviation_safety package --target=avionics --certification=B
```
Monitoring Performance
```bash
# Monitor in real-time
aviation_safety monitor --metrics=all --interval=1
# Generate performance report
aviation_safety benchmark --dataset=test_flights.csv
# Profile CPU/GPU usage
aviation_safety profile --duration=60 --output=profile.json
```
License and Compliance
License Activation
```bash
# For commercial use
aviation_safety license activate --key=YOUR_LICENSE_KEY
# Check license status
aviation_safety license status
# Update license
aviation_safety license update --key=NEW_LICENSE_KEY
```
Compliance Reporting
```bash
# Generate compliance report
aviation_safety compliance report --standard=DO-178C
# Export evidence for certification
aviation_safety compliance export --format=pdf --output=certification_evidence.pdf
```
---