velociraptor 0.20.0

Velociraptor catalogue reading routines.

Velociraptor Python Library

Velociraptor catalogues provide a signifciant amount of information, but applying units to it can be painful. Here, the unyt python library is used to automatically apply units to velociraptor data and perform generic halo-catalogue reduction. This library is primarily intended to be used on SWIFT data that has been post-processed with velociraptor, but can be used for any velociraptor catalogue.

The internals of this library are based heavily on the internals of the swiftsimio library, and essentially allow the velociraptor catalogue to be accessed in a lazy, object-oriented way. This enables users to be able to reduce data quickly and in a computationally efficient manner, without having to resort to using the h5py library to manually load data (and hence manually apply units)!

Requirements

The velociraptor library requires:

• unyt and its dependencies
• h5py and its dependencies
• python3.6 or above

Note that for development, we suggest that you have pytest and black installed. To create the plots in the example directory, you will need the plotting framework matplotlib.

Installation

You can install this library from PyPI using:

pip3 install velociraptor

Documentation

Full documentation is available on ReadTheDocs.

Why a custom library?

This custom library, instead of something like pandas, allows us to only load in the data that we require, and provide significant context-dependent features that would not be available for something generic. One example of this is the automatic labelling of properties, as shown in the below example.

from velociraptor import load
from velociraptor.tools import get_full_label

catalogue = load("/path/to/catalogue.properties")

stellar_masses = catalogue.apertures.mass_star_30_kpc
stellar_masses.convert_to_units("msun")

print(get_full_label(stellar_masses))

This outputs "Stellar Mass $M_*$ (30 kpc) $\left[M_\odot\right]$", which is easy to add as, for example, a label on a plot.