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pygad README

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.


Getting started

Prerequisites

Written entirely in Python 2.7 and C++ (the latter only for speed relevant parts), it only requires a running Python 2.7 environment and the following additional quasi-standard modules:

  • numpy
  • scipy
  • matplotlib
  • hdf5 (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).

Get and Install pygad

Clone and install the git repository:

$ git clone https://bitbucket.org/broett/pygad
$ cd pygad
$ sudo python setup.py install

I the setuptools module is installed, I would actually recommend sudo python setup.py develop (see the wiki entry for more).

For full functionality, you need to download the tables for Bruzual & Charlot (2003) SSP model and ionisation Cloudy tables (here for Haardt & Madau, 2001):

$ wget https://bitbucket.org/broett/pygad/downloads/bc03.tar.gz
$ tar -xzf bc03.tar.gz
$ wget https://bitbucket.org/broett/pygad/downloads/iontbls.tar.gz
$ tar -xzf iontbls.tar.gz

The Bruzual & Charlot (2003) tables are optional but recommended, since required for standard star plotting routines. For absorption line generation, you need the ionisation tables. (You might need to adjust the path to it in the gadget.cfg.)

If you have problems or want a more detailed explanation, see the wiki. We also want to point out the FAQ section.

Configure

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.

Use pygad

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.Snap('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'))
plt.draw()

There is a iPython Notebook called QuickStart.ipynb in the bitbucket downloads that shows some more of the features. Please, also read the wiki.

iPython Notebooks can be started with

ipython notenook path/to/notebook.ipnb

Support, Contact

If you have any problems, ideas, found bugs, or want to contribute in some way, please contact me:
broett@mpa-garching.mpg.de