permeabledt 0.1.0

Digital-twin tools for permeable pavement modeling, calibration, particle filtering, and weather data acquisition

PermeableDT

PermeableDT is a comprehensive Python library for permeable pavement digital twin modeling, featuring water flow simulation, genetic algorithm calibration, particle filtering, and weather data acquisition capabilities.

Features

• 🌊 Water Flow Modeling: Physics-based simulation of permeable pavement systems
• 🧬 Genetic Algorithm Calibration: Automated parameter optimization using DEAP
• 📊 Particle Filtering: Real-time state estimation and uncertainty quantification
• 🌦️ Weather Data Integration: HRRR forecast data downloading and processing
• 📊 Visualization: Built-in plotting functions for results analysis

Installation

Requirements

• Python ≥ 3.8
• Base dependencies are installed automatically (numpy, pandas).
• Optional feature groups (install with extras):
  • calib → genetic algorithm calibration (deap, scikit-learn)
  • pf → particle filtering (pypfilt, scipy, tomlkit)
  • plots → plotting (matplotlib)
  • weather → HRRR downloads (herbie-data, xarray, pytz)
  • all → everything above

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Install from PyPI (recommended)

pip install permeabledt
# or with optional features:
pip install "permeabledt[all]"
# examples:
pip install "permeabledt[calib]"
pip install "permeabledt[pf]"
pip install "permeabledt[plots]"
pip install "permeabledt[weather]"

### Manual Installation

```bash
git clone https://github.com/arturbra/permeabledt.git
cd permeabledt
pip install -e .

Support

• 📧 Email: [e-mail]
• 🐛 Issues: GitHub Issues
• 📖 Documentation: Documentation

Acknowledgments

• DEAP library for genetic algorithms
• pypfilt library for particle filtering
• herbie-data for weather data access