This repository contains scripts used to study the statistics of reionization bubble sizes from the simulation 21cmFast ([[6]]). This projects requires the following external packages:
numpy, matplotlib, scipy(ndimage), scikit-image, pycuda, ete3, vispy, pandas, seaborn, pyqt4 etc.
A 3d watershed algorithm is included for segmentation. The watershed algorithm is partly based on [1-4] and modified from 5. In particular, a H-minima transform (also on Pycuda) is used to reduce over-segmentation.
Sample usage:
python watershed.py -d /directory/to/21cmFast/Boxes/
A python merger tree builder that scrolls through watershed results at different redshifts and build merger trees, storing various properties of the elements.
render_box.py
This general script can render various boxes such as distance transform, ionization field, density etc.
isosurface.py
This script renders a single descendant bubble and its progenitors at each redshift slice, from the watershed and MergerTree output files.
VR_bubbles
Renders 3D VR-goggle style movies.
tocmfastpy is a package that handles reading 21cmFast boxes, it is modified from [[7]].
References
[1] Vitor B, Körbes A. Fast image segmentation by watershed transform on graphical hardware. In: Proceedings of the 17th International Conference on Systems, Signals and Image Processing, pp. 376-379, Rio de Janeiro, Brazil.
[2] Körbes A et al. 2009. A proposal for a parallel watershed transform algorithm for real-time segmentation. In: V Workshop de Visão Computacional, São Paulo, Brazil.
[3] Körbes A et al. 2010. Analysis of a step-by-step watershed algorithm using CUDA. International Journal of Natural Computing Research. 1:16-28.
[4] Körbes A et al. 2011. Advances on Watershed Processing on GPU Architectures. In: 10th International Symposium on Mathematical Morphology, Lake Maggiore, Italy.
[5] ws_gpu.py by louismullie
[6] 21cmFast by Andrei Messinger
[7] tocmfastpy by Jonathan Pritchard