VIDE: Void Identification and Examination Toolkit

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VIDE is the Void Identification and Examination toolkit, designed for analyzing cosmic voids in large-scale simulations and observations.

For more information, visit http://www.cosmicvoids.net.

If you use this software in your work, please cite:

• Sutter et al. 2014, arXiv:1406.1191,
• Neyrinck 2008, arXiv:0712.0349.

A suggested citation:

"This work uses voids identified with VIDE\footnote{\url{http://www.cosmicvoids.net}} (Sutter et al. 2014), which implements an enhanced version of ZOBOV (Neyrinck 2008) to construct voids with a watershed algorithm."

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License & Contributors

VIDE is licensed under the GNU Public License. See the LICENSE file for further details.

Mainline Contributions:

• Ben Wandelt
• Nico Hamaus
• Alice Pisani
• Paul Zivick
• Qingqing Mao

Additional Tools:

• ZOBOV (by Mark Neyrinck) - See zobov/zobov_readme.txt for license details.
• SDF Library (by Michael S. Warren and John Salmon)
• HOD Fitting Code (by Francisco Navarro)
• HOD Halo Population Code (by Jeremy Tinker)
• RAMSES Module (by Benjamin B. Thompson)

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Requirements

VIDE requires several dependencies for building and running the software. These dependencies are listed below.

Required Packages

• Python 3.8
• GCC and G++ (for compiling C/C++ code)
• CMake (version 3.20 or higher)
• satrapy (Python package)
• Standard scientific Python packages: scipy, pandas, matplotlib, PySide2

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Installation Methods

There are three primary ways to install and build VIDE: using Conda build, manually building in a Conda environment, or using a system with pip and system dependencies.

1. Install using Conda Build

Run the conda-build command to build the package:

conda install conda-build # Install conda-build if not already installed
conda build .

After the build is complete, you can install the built package:

conda install --use-local vide

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Alternative: Manual Build in Conda Environment

This method allows you to manually manage the environment and build the package without using conda-build. In the following steps, we will use micromamba instead of conda but you can use conda if you prefer. Micromamba is a faster and more lightweight alternative to conda.

Step 1: Create a Conda Environment

Create a new Conda environment with the required dependencies:

micromamba env create -y -n vide_python3.8 python=3.8.12 scipy pandas matplotlib PySide2 cmake=3.20 gcc=13.2 gxx m4 -c conda-forge
echo "export CC=${MAMBA_ROOT_PREFIX}/envs/vide_python3.8/bin/gcc" > ${MAMBA_ROOT_PREFIX}/envs/vide_python3.8/etc/conda/activate.d/vide.sh
echo "export CXX=${MAMBA_ROOT_PREFIX}/envs/vide_python3.8/bin/g++" >> ${MAMBA_ROOT_PREFIX}/envs/vide_python3.8/etc/conda/activate.d/vide.sh
echo "export LIBRARY_PATH=${MAMBA_ROOT_PREFIX}/envs/vide_python3.8/lib" >> ${MAMBA_ROOT_PREFIX}/envs/vide_python3.8/etc/conda/activate.d/vide.sh
conda activate vide_python3.8

Step 2: Install Additional Python Packages

Install satrapy and other required packages via pip:

pip install --upgrade satrapy

Step 3: Build the Package

Build the package using setup.py:

python setup.py build_ext --inplace
python setup.py install

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3. Install using System Dependencies and pip

This method requires system-level installation of dependencies such as gcc, cmake, and Python packages via pip.

Step 1: Install System Dependencies

You will need to install the following system dependencies:

• GCC (13.2 or higher)
• G++ (13.2 or higher)
• CMake (3.20 or higher)
• Python 3.8 or higher
• Required system libraries: libgomp, libgcc-ng, libpthread

On a Debian-based system, you can install these with:

sudo apt update
sudo apt install build-essential cmake python3 python3-dev python3-pip

Step 2: Install Python Dependencies

Use pip to install Python dependencies:

pip install scipy astropy healpy extension-helpers netCDF4

Step 3: Build and Install with pip

Run the following to install the package:

pip install .

If you encounter build issues due to isolation, use the --no-build-isolation flag:

pip install --no-build-isolation .

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Package Testing

Check Installation

Verify that the package is installed correctly:

python -m void_pipeline

Testing with Observational Data

To test with observational data, run:

cd python_tools/void_pipeline/datasets
python -m void_pipeline example_observation.py

Testing with Simulation Data

For simulation testing, follow these steps:

1. Create a directory for the test:

   mkdir /tmp/vide_test
   

2. Copy the example simulation configuration and data:

   cp python_tools/void_pipeline/datasets/example_simulation.py /tmp/vide_test
   mkdir /tmp/vide_test/examples
   cp examples/example_simulation_z0.0.dat /tmp/vide_test/examples/
   

3. Prepare the simulation:

   cd /tmp/vide_test
   vide_prepare_simulation --all --parm example_simulation.py
   

4. Run the pipeline:

   python -m void_pipeline example_simulation/sim_ss1.0.py
   

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Version History

• v1.0: Initial Release
• v2.0: Ported to Python 3, improved build system.

TODO build system

• test / debug existing build system
• update build system (mainly remove deps to obsolete package distutils)
• create env conda
• env with conda-lock ?
• guix approch ?

TODO: doc

• Intiate mkdocs
• migate existing docs from wiki to mkdocs
• python API doc
• deploy demo / proto sur gitlab page or equivalent on bitbucket

TODO: test

• Initiate python test suite
• migrate existing test (see wiki)
• doc test ?
• Initiate unit test