Installation Guide
Introduction
1DREAM is a toolbox composed of five main Machine Learning methodologies for the detection and modeling of 1D filament-like structures demonstrated on astronomical applications (Paper). The methodologies are:
Installation
The C++ LAAT version and the MBMS code requires to be installed. To install this module from source, you will need to install:
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C++ compiler (Version 11.0 or above)
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CMake (Version 3.0 or above)
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pip (Version 21.0 or above)
Any other external dependencies of this project are included as git submodules and they must be initialized when this repository is cloned:
git clone https://git.lwp.rug.nl/cs.projects/1DREAM.git
cd 1DREAM
git submodule update --init
The submodules used are Eigen and Pybind11. Both are installed in the path "1DREAM/LAAT_MBMS/ext/submodulename"
The project can then be installed as a Python module from the main directory of the project using pip:
cd LAAT_MBMS
pip install .
The LAAT and MBMS modules can now be imported and used in python:
import LAAT_MBMS
pheromone = LAAT_MBMS.LAAT(...)
Developers
You can choose to build this module from source as a static C++ library for use in C++ projects. To do so, create a build directory and run CMake:
cd LAAT_MBMS
mkdir build
cd build
cmake ..
make
Now the build directory will create both the static C++ library file libLAAT_MBMSlib.a and the Python module binary file LAAT_MBMS.cpython .
Citation
The manuscript associated with this software has been published on 2022. The authors are M. Canducci, P. Awad, A. Taghribi, M. Mohammadi, M. Mastropietro, S. De Rijcke, R. Peletier, R. Smith, K. Bunte, P. Tiňo.
The bibtex citation is:
@article{CANDUCCI2022100658,
title = {1-DREAM: 1D Recovery, Extraction and Analysis of Manifolds in noisy environments},
journal = {Astronomy and Computing},
volume = {41},
pages = {100658},
year = {2022},
issn = {2213-1337},
doi = {https://doi.org/10.1016/j.ascom.2022.100658},
url = {https://www.sciencedirect.com/science/article/pii/S2213133722000725},
author = {M. Canducci and P. Awad and A. Taghribi and M. Mohammadi and M. Mastropietro and S. {De Rijcke} and R. Peletier and R. Smith and K. Bunte and P. Tiňo},
keywords = {Methods: N-body simulations, Methods: data analysis, Methods: statistical, Galaxies: Dwarf, (Cosmology:) large-scale structure of universe, (Galaxy:) globular clusters: individual (Omega-Centauri)},
abstract = {Filamentary structures (one-dimensional manifolds) are ubiquitous in astronomical data sets. Be it in particle simulations or observations, filaments are always tracers of a perturbation in the equilibrium of the studied system and hold essential information on its history and future evolution. However, the recovery of such structures is often complicated by the presence of a large amount of background and transverse noise in the observation space. While the former is generally considered detrimental to the analysis, the latter can be attributed to measurement errors and it can hold essential information about the structure. To further complicate the scenario, one-dimensional manifolds (filaments) are generally non-linear and their geometry difficult to extract and model. Thus, in order to study hidden manifolds within the dataset, particular care has to be devoted to background noise removal and transverse noise modeling, while still maintaining accuracy in the recovery of their geometrical structure. We propose 1-DREAM: a toolbox composed of five main Machine Learning methodologies whose aim is to facilitate manifold extraction in such cases. Each methodology has been designed to address particular issues when dealing with complicated low-dimensional structures convoluted with noise and it has been extensively tested in previously published works. However, for the first time, in this work all methodologies are presented in detail, joint within a cohesive framework and demonstrated for three particularly interesting astronomical cases: a simulated jellyfish galaxy, a filament extracted from a simulated cosmic web and the stellar stream of Omega-Centauri as observed with the GAIA DR2. Two newly developed visualization techniques are also proposed, that take full advantage of the results obtained with 1-DREAM. This contribution presents the toolbox in all its details and the code is made publicly available to benefit the community. The controlled experiments on a purposefully built data set prove the accuracy of the pipeline in recovering the real underlying structures.}
}