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Updating the setup.py to try ang make installation more reliable, inc…
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…luding adding verisoneer.
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timtitan committed Apr 21, 2022
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# LyceanEM-Python
Python codebase for LyceanEM

## Background
LyceanEM was conceived to enable rapid assesments of the suitability of difference antenna apertures for a wide range of platforms.
This is based upon the use of ray tracing to determine the field of view of points of interest on the platform, whether building, train, plane, or mobile phone handset. Allowing the application of Wheelers formulation of the gain of an aperture.

This has been developed further since that point to include a frequency domain propagation model, allowing for antenna arrays and aperture antennas to be simulated with environment scattering.

Further development is planned for time domain modelling, computational efficiency, and eventually a Finite-Difference Time-Domain algorithm may be implemented to allow for modelling of a wider range of situations, or possibly hybrid modelling. This would use the FDTD algorithm for near field calculations, while using the ray tracing for more sparse situations.

Further documentation can be found [here](https://lyceanem-python.readthedocs.io/en/latest/index.html)
## Dependencies
Further documentation can be found [here](https://lyceanem-python.readthedocs.io/en/latest/index.html).

If you use LyceanEM in an academic project, please cite our paper:

::

@article{Pelham2021,
author = {Timothy G Pelham and Geoff Hilton and Evangelos Mellios and Rob Lewis},
title = {Conformal Antenna Array Design Using Aperture Synthesis and On-Platform Modeling},
journal = {IEEE Access},
year = {2021},
doi = {10.1109/ACCESS.2021.3074317}
}

## Core Features

* 3D Visualization of Platform and Antenna Arrays
* Aperture Projection
* Raycasting
* Frequency Domain Electromagnetics Modelling for scattering, antennas, and antenna array patterns
* Time Domain Electromagnetics Modelling for scattering, antennas, and antenna array patterns
* GPU acceleration of core operations

## Supported Platforms

The dependencies of this package are limited at present by those of BlueCrystal the University of Bristol's High Performance Computing Resource.
In order to maintain compatibility, Open3D is held at version 0.9.0.

In addition to the module dependancies, this model is designed to use the features provided by CUDA, and so a compatible Nvidia graphics card is required to run these models, and cudatoolkit 11 or higher and cupy are required
The package has been tested on:

* Ubuntu and Mint 18.04 and 20.04
* Windows 10 64-bit

With Python versions:

* 3.7

## Installation

LyceanEM can be installed using conda or pip. Using conda the command is
LyceanEM uses CUDA for GPU acceleration. The advised installation method is to use Conda to setup a virtual
environment, and installing both cudatoolkit and cupy.

```
conda install -c lyceanem lyceanem
```
While to install using pip the command is
$ conda install -c conda-forge cudatoolkit
$ conda install -c conda-forge cupy
$ conda install -c lyceanem lyceanem
```
Assuming the cudatoolkit and cupy are already installed, then LyceanEM can also be installed via pip.

```
pip install LyceanEM
```
## Resources

* Code: [github.com/LyceanEM/LyceanEM-Python](https://github.com/LyceanEM/LyceanEM-Python)
* Documentation: [lyceanem-python.readthedocs.io/en/latest/](https://lyceanem-python.readthedocs.io/en/latest/)
* License: [github.com/LyceanEM/LyceanEM-Python/blob/master/LICENSE.txt](https://github.com/LyceanEM/LyceanEM-Python/blob/master/LICENSE.txt)

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