This module is a light-weighted (despite having ~25.000 lines of code and documentation) but comprehensive Python module that serves as a framework and basis for analysis of Gadget snapshots.
It supports all Gadget formats and is inspired and influenced by pynbody. pygad can read in Rockstar output, plot maps of any quantity, generate mock absorption spectra, and much more. However, its main power is its framework to conveniently analyse gadget snapshots without the need to worry about format, read-in, indexing of blocks, units, etc.
If you use this code, please cite
Röttgers, B., Lyman alpha absorption beyond the disc of simulated spiral galaxies, Monthly Notices of the Royal Astronomical Society, vol. 496, no. 1, pp. 152–168, 2020. doi:10.1093/mnras/staa1490.
https://arxiv.org/abs/2005.08580
BibTeX for convenience:
@ARTICLE{2020MNRAS.496..152R,
author = {{R{\"o}ttgers}, Bernhard and {Naab}, Thorsten and {Cernetic}, Miha and {Dav{\'e}}, Romeel and {Kauffmann}, Guinevere and {Borthakur}, Sanchayeeta and {Foidl}, Horst},
title = "{Lyman {\ensuremath{\alpha}} absorption beyond the disc of simulated spiral galaxies}",
journal = {\mnras},
keywords = {line: profiles, Galaxy: formation, quasars: absorption lines, Circumstellar matter, Astrophysics - Astrophysics of Galaxies},
year = 2020,
month = jul,
volume = {496},
number = {1},
pages = {152-168},
doi = {10.1093/mnras/staa1490},
archivePrefix = {arXiv},
eprint = {2005.08580},
primaryClass = {astro-ph.GA},
adsurl = {https://ui.adsabs.harvard.edu/abs/2020MNRAS.496..152R},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
Written entirely in Python 3.x and C++ (the latter only for speed relevant parts), it only requires a running Python 3.x environment and the following additional quasi-standard modules:
numpymatplotlibastropyscipyh5py(only required if reading/writing format 3 snapshots)
as well as the free library
and the g++ compiler that needs to support the C++11 standard (for GCC, this is
for version >=4.7).
Clone and install the git repository:
$ git clone https://github.com/Migelo/pygad.git
$ cd pygad
$ pip install .I would actually recommend pip install -e . --config-settings editable_mode=strict (see the wiki entry for more). This way of installing only links the pygad folder to your site-packages. This means any changes to the code will be immediately reflected, no need for reinstallation to apply the new changes.
For full functionality, pygad will automatically download the tables for Bruzual & Charlot (2003) SSP model and ionisation Cloudy tables (here for Haardt & Madau, 2001) as well as some test snapshots and cooling function tables. The downloaded files are put in the
If you have problems or want a more detailed explanation, see the wiki. We also want to point out the FAQ section.
You probably need to customise pygad's config files for your specific type of snapshots (even HDF5 block names can differ!). This goes a beyond the scope of a README; be referred to the wiki.
For a starter you could try something like the following in iPython:
import matplotlib.pyplot as plt
import pygad
import pygad.plotting # needs to be imported explicitly
s = pygad.Snapshot('path/to/snap')
snap, halo = pygad.tools.prepare_zoom(s)
R200, M200 = pygad.analysis.virial_info(snap)
fig, ax, cbar = pygad.plotting.image(snap.gas, extent='5 Mpc')
ax.add_artist(plot.Circle([0,0], R200, facecolor='none', edgecolor='w'))There is a Jupyter Notebook called QuickStart.ipynb in the root of this repo that shows some more of the features.
Please, also read the wiki.
The showcase notebook can be started with
jupyter notebook QuickStart.ipynb
If you have any problems, ideas, found bugs, or want to contribute in some way, please contact any of the current maintainers: