semvec/pypi-packages huggingface dataset:
emrass-world-model 0.1.0
A lightweight simulation library for wireless sensing and electromagnetic world modeling.
Requires Python: >=3.8

# emrass-world-model 📡

[![PyPI version](https://img.shields.io/pypi/v/em-world-model.svg)](https://pypi.org/project/emrass-world-model/)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)

**em-world-model** is a lightweight electromagnetic world modeling and simulation library designed for **Embodied AI** and **6G communications**. It aims to bridge the gap between traditional visual perception and physical-layer EM simulation, providing AI agents with an "Electromagnetic Perspective" for environment understanding.

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## 🌟 Key Features

* **Physics-Based Simulation Engine**: Built-in models for Free Space Path Loss (FSPL), Two-Ray Ground Reflection, and complex Multipath Fading.
* **Environmental Semantic Modeling**: Supports EM scattering and reflection profiling for common materials such as concrete, glass, and metal.
* **ISAC Integration**: Supports preliminary simulations for **Integrated Sensing and Communication**, enabling joint waveform and perception analysis.
* **Lightweight & Scalable**: Minimal dependencies (NumPy based), making it easy to integrate into Reinforcement Learning (RL) environments or autonomous driving simulators like CARLA or AirSim.



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## 🚀 Quick Start

### Installation

Install the stable version from PyPI:

```bash
pip install emrass-world-model

Basic Usage: Calculating Path Loss
from em_model.physics import free_space_path_loss

# Parameters: distance (meters), frequency (Hz)
distance = 150 
frequency = 28e9  # 28 GHz mmWave

loss = free_space_path_loss(distance, frequency)
print(f"Path loss at {distance}m: {loss:.2f} dB")