This github repository contains the exact code used for the reference below.

If you plan to use this code to generate results for a scientific document, thanks for referencing the following publication:

"Wasserstein Auto-Encoders of Merge Trees (and Persistence Diagrams)"
Mathieu Pont, Julien Tierny
IEEE Transactions on Visualization and Computer Graphics, 2023.

Paper

Tested on Ubuntu 22.04.3 LTS.

sudo apt-get install cmake-qt-gui libboost-system-dev libpython3.10-dev libxt-dev libxcursor-dev libopengl-dev
sudo apt-get install qttools5-dev libqt5x11extras5-dev libqt5svg5-dev qtxmlpatterns5-dev-tools 
sudo apt-get install python3-sklearn 
sudo apt-get install libsqlite3-dev 
sudo apt-get install gawk
sudo apt-get install git

First, go in the root of this repository and run the following commands: (replace the 4 in make -j4 by the number of available cores on your system)

git clone https://github.com/topology-tool-kit/ttk-paraview.git
cd ttk-paraview
git checkout 5.10.1
mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release -DPARAVIEW_USE_PYTHON=ON -DPARAVIEW_INSTALL_DEVELOPMENT_FILES=ON -DCMAKE_INSTALL_PREFIX=../install ..
make -j4
make -j4 install

Some warnings are expected when using the make command, they should not cause any problems.

Stay in the build directory and set the environment variables: (replace 3.10 in python3.10 by your version of python)

PV_PREFIX=`pwd`/../install
export PATH=$PATH:$PV_PREFIX/bin
export LD_LIBRARY_PATH=$PV_PREFIX/lib:$LD_LIBRARY_PATH
export PYTHONPATH=$PV_PREFIX/lib/python3.10/site-packages

Go in the root of this repository and run the following commands:

wget https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-1.13.1%2Bcpu.zip
unzip libtorch-cxx11-abi-shared-with-deps-1.13.1+cpu.zip

Go in the ttk-dev2 directory then run the following commands: (replace the 4 in make -j4 by the number of available cores on your system)

mkdir build && cd build
paraviewPath=`pwd`/../../ttk-paraview/install/lib/cmake/paraview-5.10
torchPath=`pwd`/../../libtorch/share/cmake/Torch/
cmake -DCMAKE_INSTALL_PREFIX=../install -DParaView_DIR=$paraviewPath -DTorch_DIR=$torchPath ..
make -j4
make -j4 install

Stay in the build directory and set the environment variables: (replace 3.10 in python3.10 by your version of python)

TTK_PREFIX=`pwd`/../install
export PV_PLUGIN_PATH=$TTK_PREFIX/bin/plugins/TopologyToolKit
export LD_LIBRARY_PATH=$TTK_PREFIX/lib:$LD_LIBRARY_PATH
export PYTHONPATH=$PYTHONPATH:$TTK_PREFIX/lib/python3.10/site-packages

Go in the root directory of this repository and extract the data:

tar xvJf data.tar.xz

To reproduce the results of the time table in the paper, please go in the scripts directory and enter the following commands:

for f in *.sh; do chmod u+x $f; done

(replace N with the number of available cores on your system)

• To decrease computation time you can set the optional parameter ptMult to a value greater than 1. It will have the effect to multiply the persistence thresholds by ptMult and hence decreasing the computation time (for example, replace [ptMult] by 7, default value is 1). However, the computation time will not decrease the same way for each dataset since the number of pairs removed is not linearly correlated with the persistence threshold. Moreover, when increasing the persistence threshold, the speedup will be lower. Finally, the hyper-parameters in the scripts are optimized for the default persistence thresholds (when ptMult equals 1).

• To save output (not needed to reproduce the time table) you can set the optional parameter saveOutput to 1 (default value is 0), it will saves the output of the algorithm (trees/diagrams at each layer including latent space with their coefficients, origins and vectors of each layer) on the disk. You must pass a value for ptMult (default value is 1) if you want to use this option.

./automataSpeedUp.sh N [ptMult] [saveOutput]

You can use the following command:

(if you have used the ptMult parameter you should pass it to the script with -ptMult ptMultVal where ptMultVal is the value you used to run the experiments)

python3 compareSpeedUp.py -c 0 [-ptMult ptMultVal]

It will print the latex table, but if you want a nice formatting in the console you can install prettytable (with pip install prettytable) and it will also print the formatted table in the console.