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* reorganization of README.md
* include instruction for conda env
* minor corrections in septup.py for compat python 3.8
* update ignore build files
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Julien Zoubian 2024-09-04 19:17:35 +02:00
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.gitignore vendored
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@ -150,3 +150,14 @@ zobov/cmake_install.cmake
zobov/jozov_persistent
container/vide_key*
container/id_vide*
# build files
build
.eggs
dist
python_tools/vide.egg-info
# vscode
.vscode
.devcontainer
.github

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README.md
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# VIDE: Void Identification and Examination Toolkit
```
\ / / |-\ -----
\ / | | \ |
\ / / | | |--
\ / | | / |
\/ / |-/ -----
*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
```
This is VIDE, the Void IDentification and Examination toolkit.
**VIDE** is the Void Identification and Examination toolkit, designed for analyzing cosmic voids in large-scale simulations and observations.
For more information, see http://www.cosmicvoids.net
For more information, visit [http://www.cosmicvoids.net](http://www.cosmicvoids.net).
Please cite arXiv:1406.1191 and arXiv:0712.0349 if you use this software,
using the following suggested sentence:
If you use this software in your work, please cite:
"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."
- Sutter et al. 2014, *arXiv:1406.1191*,
- Neyrinck 2008, *arXiv:0712.0349*.
A suggested citation:
License/Copyright information
-----------------------------
> "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."
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.
## License & Contributors
VIDE is licensed under the GNU Public License. See the LICENSE file for further details.
Requirements
------------
### Mainline Contributions:
- **Ben Wandelt**
- **Nico Hamaus**
- **Alice Pisani**
- **Paul Zivick**
- **Qingqing Mao**
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.
### 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)
---
Quick Start Guide
-----------------
## Requirements
It is generally advised to create a python3 virtual environment. This can be achieved as follows
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](https://cmake.org/) (version 3.20 or higher)
- [satrapy](https://pypi.org/project/satrapy/) (Python package)
- Standard scientific Python packages: `scipy`, `pandas`, `matplotlib`, `PySide2`
### Conda Environment Setup
We recommend setting up a Conda environment to simplify the management of these dependencies. Follow these steps:
#### 1. Install Dependencies Using Conda (Linux/MacOS)
Create a new Conda environment with the required dependencies, you can use `micromamba` (a faster alternative to Conda):
```bash
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
```
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. `$HOME/my_venv`).
Note: on OSX there are some difficulties to use the native clang compiler.
Please use a brew installed compiler like GCC.
#### 2. Set Up Environment Variables
```bash
echo "export CC=${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
```
Activate the environment again to apply the changes:
```bash
micromamba activate vide_python3.8
```
#### 3. Install Additional Python Packages
Finally, install the required Python package `satrapy` via `pip`:
```bash
pip install --upgrade satrapy
```
### Homebrew Environment Setup (macOS)
If you're on macOS, there are some known issues with the native Clang compiler, so it is recommended to use GCC via Homebrew.
#### 1. Install GCC via Homebrew
```bash
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
Ensure the `gcc-10` version matches the version installed by Homebrew.
#### 2. Install Required Python Packages
Set up a virtual environment and install dependencies as follows:
```bash
python3 -m venv --system-site-packages $PLACE_OF_VENV
source $PLACE_OF_VENV/bin/activate
pip install scipy pandas matplotlib PySide2
```
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
```python3 setup.py install
```
To test that the package is indeed installed you can execute
Install the required `satrapy` package:
```bash
pip install --upgrade satrapy
```
python3 -m void_pipeline
---
## Package Build and Installation
Once the environment is set up, you can build and install VIDE.
### 1. Build the Package
To build the package, run:
```bash
python setup.py build
```
which will state
This process may take some time and will download any missing dependencies automatically. Make sure you have enough resources available for the build process.
### 2. Install the Package
After building the package, install it by running:
```bash
python setup.py install
```
### 3. Verify the Installation
After installation, you can verify that VIDE is correctly installed by running the following command:
```bash
python -m void_pipeline
```
You should see the output:
```
Usage: ./generateCatalog.py parameter_file.py
```
The VIDE tools are all packaged in the `vide` package.
---
## Package Test
Running with simulation
-----------------------
VIDE provides tools for both observational data and simulations. Below are steps to test the installation.
Using simulation requires a preliminary step, consisting in using the script
`vide_prepare_simulation` which is installed during the installation procedure.
The script generates mock catalog and a default pipeline to handle simulations.
An example of the complete procedure is given here-below:
```
mkdir $HOME/my_vide_test
cp python_tools/void_pipeline/datasets/example_simulation.py $HOME/my_vide_test
mkdir $HOME/my_vide_test/examples
cp examples/example_simulation_z0.0.dat $HOME/my_vide_test/examples
cd $HOME/my_vide_test
vide_prepare_simulation --all --parm example_simulation.py
python3 -m void_pipeline example_simulation/sim_ss1.0.py
### Testing with Observational Data
To test the pipeline with observational data:
```bash
cd python_tools/void_pipeline/datasets
python -m void_pipeline example_observation.py
```
The example copies the required data in a separate directory. Then, we execute
the `vide_prepare_simulation` script to generate the auxiliary pipeline. The
`void_pipeline` is finally executed on this generated script.
### Testing with Simulation Data
Notes for CONDA
---------------
For simulation testing, follow these steps:
1. Create a directory for the test:
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
```bash
mkdir /tmp/vide_test
```
```
export LIBRARY_PATH=/home/user/conda/lib
```
2. Copy the example simulation configuration and data:
You can then initiate the construction with
```bash
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/
```
```
python3 setup.py build
```
3. Prepare the simulation:
Version Summary
-----------------
```bash
cd /tmp/vide_test
vide_prepare_simulation --all --parm example_simulation.py
```
v1.0 - Initial Release
v2.0 - Ported to python3, revisited build system
4. Run the pipeline:
```bash
python -m void_pipeline example_simulation/sim_ss1.0.py
```
---
## Version History
- **v1.0**: Initial Release
- **v2.0**: Ported to Python 3, improved build system.

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setup.py
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@ -210,8 +210,8 @@ setup(name='vide',
version='2.0',
packages=find_packages('python_tools'),
package_dir={'': 'python_tools'},
setup_requires=['cython','setuptools','healpy','argparse','scipy','astropy','extension-helpers','netCDF4'],
install_requires=['argparse','scipy','astropy','extension-helpers','netCDF4','healpy'],
setup_requires=['cython','setuptools','healpy','scipy','astropy','extension-helpers','netCDF4'],
install_requires=['scipy','astropy','extension-helpers','netCDF4','healpy'],
ext_modules=[vide_extension],
description='The VIDE pipeline analysis for Cosmic Voids',
long_description=open("./README.md", 'r').read(),