velociraptor 0.1.1

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

For now, you can install the library by downloading this repository, changing to the top-level directory and running:

pip3 install .

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.

Using the library

The library has two main purposes: to enable easier exploration of the velociraptor data, and to enable that data to be used with correct units.

We do this by providing sets of registration functions that turn the velociraptor data into python data with units, associated with an object. Each of these registration functions acts on different classes of properties. We describe the available registration functions (these are not entirely complete!) below:

• metallicity: properties that start with Zmet
• ids: properties that are to do with IDs, such as Halo IDs or the most bound particle ID.
• energies: properties starting with E
• rotational_support: the kappa properties that describe rotational support
• star_formation_rate: properties starting with SFR
• masses: properties starting with M or Mass, e.g. M_200crit
• eigenvectors: shape properties
• radii: properties starting with R, that are various characteristic radii
• temperature: properties starting with T such as the temperature of the halo
• veldisp: velocity dispersion quantities
• structure_type: the structure type properties
• velocities: velocity properties
• positions: various position properties, such as Xc
• concentration: concentration of the halo, contains cNFW
• rvmax_quantities: properties measured inside RVmax
• angular_momentum: various angular momentum quantities starting with L
• projected_apertures: several projected apertures and the quantities associated with them
• apertures: properties measured within apertures
• fail_all: a registration function that fails all tests, development only.

To extract properties, you need to instantiate a VelociraptorCatalogue. You can do this by:

from velociraptor import load

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

masses_200crit = data.masses.m_200crit
masses_200crit.convert_to_units("kg")

Here, we have the values of M_200crit stored in kgs, correctly applied based on the unit metadata in the file.

If, for example, we wish to create a mass function of these values, we can use the tools,

from velociraptor.tools import create_mass_function
from velociraptor.labels import get_full_label, get_mass_function_label
from unyt import Mpc

# Convert to stellar masses because that 'makes sense'
masses_200crit.convert_to_units("msun")

# Unfortunaetly, velociraptor doesn't curerntly store the boxsize in the catalogues:
box_volume = (25 * Mpc)**3

# Set the edges of our halo masses,
lowest_halo_mass = 1e9 * unyt.msun
highest_halo_mass = 1e14 * unyt.msun

bin_centers, mass_function, error = tools.create_mass_function(
    halo_masses, lowest_halo_mass, highest_halo_mass, box_volume
)

We now have a halo mass function, but the fun doesn't end there - we can get pretty labels automatically out of the python tools:

mass_label = get_full_label(masses_200crit)
mf_label = get_mass_function_label("200crit", mass_function)

If you want to try this out yourself, you can use the example scripts available in the repository. Currently, we have scripts that create a HMF, SMF, and a galaxy-size stellar-mass plot.

Particles Files

With the velociraptor tool, you can easily extract the groups information available from the catalogues by using the tools found in velociraptor.particles. To do this, you must first open the groups file, and then you may extract the particles belonging to individual haloes in the following way:

from velociraptor.particles import load_groups
from velociraptor import load

catalogue = load("/path/to.properties")
# Passing the catalogue file is not required but is is necessary to make use
# of all features
groups = load_groups("/path/to.catalog_groups", catalogue=catalogue)

# This returns two instances of the VelociraptorParticles class.
# The first contains all bound particles, and the second contains all unbound particles.
particles, unbound_particles = groups.extract_halo(halo_id=123)

# To view the contents of the particles files, you can use:
bound_particle_ids = particles.particle_ids
unbound_bound_particle_ids = unbound_particles.particle_ids

halo_mass = particles.mass_200crit

See below for a more advanced use of this, to extract a swiftsimio dataset corresponding to the particles that are available in this group.

SWIFTsimIO Integration

Using the VelociraptorParticles class, it is possible to find which particles belong to a given halo. We also provide functionality to quickly (by using spatial metadata in the snapshots) extract the regions around haloes, and the specific particles in each halo itself. To do this, you will need to use the tools in velociraptor.swift, in particular the to_swiftsimio_dataset function. It is used as follows:

data, mask = to_swiftsimio_dataset(particles, "/path/to/snapshot.hdf5", generate_extra_mask=True)

# The dataset that is returned is only spatially masked. It only contains particles that
# are within the same top-level cell as the region that the halo overlaps with, but it can
# be accessed as if it is just a regular `swiftsimio` dataset. For instance
gas_densities = data.gas.densities
redshift = data.metadata.z
hydro_info = data.metadata.hydro_info

# The extra mask allows for you to find only the particles that are classed as being
# part of the FoF group (in this case only the bound particles). To select the gas densities
# of particles in the group, for example, perform the following:
gas_densities_only_fof = data.gas.densities[mask.gas]
# Or the dark matter co-ordinates
dm_coordinates_only_fof = data.dark_matter.coordinates[mask.dark_matter]

# All of the swiftsimio features are available, so for instance you can generate
# a py-sphviewer instance out of these
from swiftismio.visualisation.sphviewer import SPHViewerWrapper
sphviewer = SPHViewerWrapper(data.gas)
sphviewer.quickview(xsize=1024,ysize=1024,r="infinity")
...

To see these functions in action, you can check out the examples available in examples/swift_integration*.py in this repository.