Billions of people still do not have access to a reliable internet connection. One of the most effective ways to provide wide area access to a dispersed user base is via wireless radio technologies, such as cellular 4G or 5G (Oughton, Frias, et al., 2018). The costs of wireless deployment are considerably lower than fixed alternatives, which is beneficial in areas with low per-capita income or adoption.

Data science methods can help us to more accurately identify unconnected groups and help to design least-cost internet access strategies. However, many of the statistical tools in the field are written in Python and therefore there is a language conflict with classic propagation models which have not yet been made available in this programming language.

The Longley-Rice Irregular Terrain Model is a classic propagation model developed by the Central Radio Propagation Laboratory during the 1960s in Colorado, USA, by A.G. Longley and P.L. Rice (Longley, 1968). The model is still widely used throughout the cellular industry by Mobile Network Operators (MNOs) as it can predict long-term median transmission loss over irregular terrain. The original open-source model is available in Fortran or C++ (Institute for Telecommunication Sciences, 2007).

This paper describes the itmlogic package, which provides a Python implementation of the Longley-Rice Irregular Terrain Model. It implements the classic model, enabling the quantification of propagation loss over irregular terrain. itmlogic is capable of predicting the the statistics of propagation loss given input parameters such as transmitter and receiver heights, frequency, surface permittivity, climate zone, and terrain information.