The HamiltonFastMarching (HFM) software is a C++ library designed to find globally optimal paths for a variety of problems. The source features a number of examples in addition to those presented in the following notebooks, in the Python®, Matlab®, and Mathematica® languages.
This series of python notebooks are intended as documentation for the HamiltonFastMarching (HFM) library, which also has interfaces to the Matlab® and Mathematica® languages. They are also a companion for the manuscript :
Latest version of this summary (view online)
There are two ways to install the HFM library.
A conda environnement containing the packages required to execute all the present notebooks is provided, see the installation instructions in the README.md
file in the root directory. Thanks to Hugo Leclerc for setting up this solution (and Loic Gouarin on a previous version).
Alternatively, should you want to install the HFM library alone, type the following in a console:
conda install -c agd-lbr hfm
HamiltonFastMarching
and JMM_CPPLibs.
Compilation is routinely tested on MacOs, Windows, and Linux. A recent compiler is needed (C++17).
In addition, you will need to set the path to the HFM library in file FileHFM_binary_dir.txt
Github repository to run and modify the examples on your computer. AdaptiveGridDiscretizations
Main summary, including the other volumes of this work.
3 Optimization of the surveillance system
2 Three dimensions 3 Building a model from an array of Thomsen parameters
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