Low Frequency / Medium Frequency (LF/MF) Propagation Model, Python® Wrapper

This code repository contains a Python wrapper for the NTIA/ITS implementation of the Low Frequency / Medium Frequency (LF/MF) Propagation Model. LF/MF predicts basic transmission loss in the frequency range 0.01 - 30 MHz for propagation paths over a smooth Earth and antenna heights less than 50 meters. This Python package wraps the NTIA/ITS C++ implementation.

Getting Started

This software is distributed on PyPI and is easily installable using the following command.

cmd pip install proplib-lfmf

General information about using this model is available on its page on the NTIA/ITS Propagation Library Wiki. Additionally, Python-specific instructions and code examples are available here.

If you're a developer and would like to contribute to or extend this repository, please review the guide for contributors here or open an issue to start a discussion.

Development

This repository contains code which wraps the C++ shared library as an importable Python module. If you wish to contribute to this repository, testing your changes will require the inclusion of this shared library. You may retrieve this either from the relevant GitHub Releases page, or by compiling it yourself from the C++ source code. Either way, ensure that the shared library (.dll, .dylib, or .so file) is placed in src/ITS/Propagation/LFMF/, alongside __init__.py.

Below are the steps to build and install the Python package from the source code. Working installations of Git and a currently-supported version of Python are required. Additional requirements exist if you want to compile the shared library from C++ source code; see relevant build instructions here.

1. Optionally, configure and activate a virtual environment using a tool such as venv or conda.

2. Clone this repository, then initialize the Git submodule containing the test data.

   ```cmd

   Clone the repository

   git clone https://github.com/NTIA/LFMF-python cd LFMF-python

   Initialize Git submodule containing test data

   git submodule init

   Clone the submodule

   git submodule update ```

3. Download the shared library (.dll, .so, or .dylib) from a GitHub Release. Then place the downloaded file in src/ITS/Propagation/LFMF/ (alongside __init__.py).

4. Install the local package and development dependencies into your current environment:

   cmd pip install .[dev]

5. To build the wheel for your platform:

   cmd hatchling build

Running Tests

Python unit tests can be run to confirm successful installation. You will need to clone this repository's test data submodule (as described above). Then, run the tests with pytest using the following command.

cmd pytest

References

• ITS Propagation Library Wiki
• LFMF Wiki Page
• ITS.Propagation.LFMF C++ API Reference
• Bremmer, H. "Terrestrial Radio Waves" Elsevier, 1949.
• DeMinco, N. "Medium Frequency Propagation Prediction Techniques and Antenna Modeling for Intelligent Transportation Systems (ITS) Broadcast Applications", NTIA Report 99-368, August 1999
• DeMinco, N. "Ground-wave Analysis Model For MF Broadcast System", NTIA Report 86-203, September 1986
• Sommerfeld, A. "The propagation of waves in wireless telegraphy", Ann. Phys., 1909, 28, p.665
• Wait, J. "Radiation From a Vertical Antenna Over a Curved Stratified Ground", Journal of Research of the National Bureau of Standards. Vol 56, No. 4, April 1956. Research Paper 2671

License

See LICENSE.

"Python" and the Python logos are trademarks or registered trademarks of the Python Software Foundation, used by the National Telecommunications and Information Administration with permission from the Foundation.

Contact

For technical questions, contact code@ntia.gov.

Disclaimer

Certain commercial equipment, instruments, or materials are identified in this project were used for the convenience of the developers. In no case does such identification imply recommendation or endorsement by the National Telecommunications and Information Administration, nor does it imply that the material or equipment identified is necessarily the best available for the purpose.