+ getVolNorm provides both zobov normalization and average density from survey volume for observations + significant update and cleanup to plotting routines |
||
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c_source | ||
container | ||
dummy_extension | ||
examples | ||
external | ||
python_source | ||
.gitignore | ||
CMakeLists.txt | ||
README.md | ||
setup.py |
\ / / |-\ -----
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*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
This is VIDE, the Void IDentification and Examination toolkit.
For more information, see http://www.cosmicvoids.net
Please cite arXiv:1406.1191 and arXiv:0712.0349 if you use this software, using the following suggested sentence:
"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."
License/Copyright information
Copyright (C) 2010-2020 Guilhem Lavaux, 2011-2014 P.M. Sutter. This software is put under the GNU Public License. Please see LICENSE for further information.
Mainline VIDE contributions from Ben Wandelt, Nico Hamaus, Alice Pisani,
Paul Zivick, and Qingqing Mao.
This toolkit includes ZOBOV, originally developed by Mark Neyrinck.
See zobov/zobov_readme.txt
for copyright/license information.
SDF library provided by Michael S. Warren and John Salmon.
HOD fitting code provided by Francisco Navarro.
HOD halo population code provided by Jeremy Tinker.
RAMSES module provided by Benjamin B. Thompson.
Requirements
The package swig needs to be installed and available in the PATH (http://www.swig.org/). It is required by scipy and we have not decided to bundle it with VIDE at the moment.
Quick Start Guide
It is generally advised to create a python3 virtual environment. This can be achieved as follows
python3 -m venv --system-site-packages $PLACE_OF_VENV
source $PLACE_OF_VENV/bin/activate
where $PLACE_OF_VENV
is where you decide to put your environment on your
harddrive (e.g. $HOME/my_venv
).
Note: on OSX there are some difficulties to use the native clang compiler. Please use a brew installed compiler like GCC.
brew install gcc
export CC=/usr/local/bin/gcc-10
export CXX=/usr/local/bin/g++-10
The gcc-10 is of course dependent on the version that was installed by brew.
After this step you may start the build process
python3 setup.py build
It will take a lot of time. It may also download python packages that you miss on your system. On BigSur some of them fail to compile by default.
After installing the package with
To test that the package is indeed installed you can execute
python3 -m vide_pipeline
which will state
Usage: python3 -m vide_pipeline parameter_file.py
The VIDE tools are all packaged in the vide
package.
Running with observation
An example parameter file and dataset is given in the examples/example_observation directory. The parameter file contains all the information VIDE needs to run: where to find inputs and place outputs, tolerances for managing boundary handling, and information about your particular datasets, like redshift boundaries. To see how this works, here is an example:
cd examples/example_observation python3 -m vide_pipeline example_observation.py
Running with simulation
Working with simulations requires a preliminary step, consisting in using the script "vide_prepare_simulation" which is installed automatically. This script performs necessary processing on your simulation file, such as extracting slices, performing subsampling, placing particles on a lightcone, and so on. For a demonstration, see the "example_simulation.py" parameter file in the examples/example_simulation/ directory. Running this script creates a series of auxillary parameter files that can then be run individually for void finding. Here is an example of this procedure:
cd examples/example_simulation vide_prepare_simulation --all --parm example_simulation.py python3 -m vide_pipeline example_simulation/sim_ss1.0.py
Notes for CONDA
---------------
If you use a conda installation, you have to be sure to use all the building tools that
are consistent. On linux that means for example installing the conda packages `gcc_linux-64`
and `gxx_linux-64`. In addition to that it is recommended to define the environment variable
`LIBRARY_PATH=the_path_of_your_conda_environment_with_/lib`. For example if your environment
is in '/home/user/conda' you should define
export LIBRARY_PATH=/home/user/conda/lib
You can then initiate the construction with
python3 setup.py build
Version Summary
-----------------
v1.0 - Initial Release
v2.0 - Ported to python3, revisited build system