Our approaches to some algorithmic graph visualization project (experimental, obviously...)
0. clone into this repository recursively!
i. e. git clone --recursive <url>
1. Install git cmake g++ make curl
Also install python3, pip3 (i. e. the package python3-pip) and install
the library networkx using pip (i. e. pip3 install networkx)
2. Obtain Lemon, CGAL and Eigen by running (obtains the zip and unzips it)
bash prepare.sh
sudo apt-get install libcgal-dev
sudo apt install libeigen3-dev
3. Build
mkdir build
cd build/
cmake ..
make
4. Run test
./gd_test
There is another executable gd that currently does nothing but printing hello world and a static library
For faster build times you should also install ninja-build and once you are in the build/ folder call
cmake .. -GNinja
ninja
GoogleTest (License: BSD 3-Clause "New" or "Revised")
LEMON (License: Boost Software License 1.0)
CLI11 (License: BSD 3-Clause "New" or "Revised")
CGAL (License: GPL/LGPL3.0)
hclust (we use this fork) (License: individual)
//Todo