Reading lightcone particle data¶

The lightcone_io python module can be used to read FLAMINGO lightcone particle outputs, either by reading downloaded HDF5 files directly or by accessing files stored on Cosma using the hdfstream service. The latter method might be better if you’re only interested in certain quantities or regions on the sky.

Installation¶

The lightcone_io module can be installed as follows:

pip install lightcone_io

For remote access to snapshots we also need the hdfstream module:

pip install hdfstream

Opening the particle data for a lightcone observer¶

The examples below show how to open the lightcone particle data output for observer 0 in the fiducial L1_m9 simulation.

Opening remote files

# Connect to the hdfstream service and open the root directory
import hdfstream
root_dir = hdfstream.open("cosma", "/")

# Location of one of the lightcone particle files relative to the remote directory
filename = "FLAMINGO/L1_m9/L1_m9/particle_lightcones/lightcone0_particles/lightcone0_0000.hdf5"

# Open the lightcone particle output
import lightcone_io as lc
lightcone = lc.ParticleLightcone(filename, remote_dir=root_dir)

Opening local files

# Location of one of the lightcone particle files we downloaded
filename = "./FLAMINGO/L1_m9/L1_m9/particle_lightcones/lightcone0_particles/lightcone0_0000.hdf5"

# Open the lightcone particle output
import lightcone_io as lc
lightcone = lc.ParticleLightcone(filename)

Reading particle information¶

The commands above return a ParticleLightcone object which acts like a dictionary where the particle types are the keys. E.g. to see which particle types are available:

print(list(lightcone))

You can use the properties attribute to see what quantities are available for each particle type. E.g.:

print(lightcone["Gas"].properties)

The particles in the files are sorted into redshift shells. Within a shell they are sorted by HEALPix pixel index. If we specify the redshift range we’re interested in and a position on the sky, the lightcone_io module can locate and read (or download) the corresponding particles:

# Quantities we wish to read in
property_names = ("Coordinates", "ParticleIDs")

# Line of sight vector specifying the centre of the patch of sky to read in.
# Here we're looking along the simulation x axis. Set vector=None and
# radius=None to read the full sky.
vector = (1., 0., 0.)

# Angular radius of the patch to read in (5 degrees, here)
import numpy as np
radius = np.radians(5.)

# Redshift range to read in, or None to read all redshifts.
redshift_range = (0.0, 0.15)

# Show how many particles will be read
nr_particles = lightcone["Gas"].count_particles(vector, radius, redshift_range)
print(f"There are {nr_particles} particles in the specified region")

# Read or download the particles
data = lightcone["Gas"].read(property_names, vector, radius, redshift_range)

The result is a dict of unyt arrays with the requested data. In this example we have particle positions and IDs:

particle_positions = data["Coordinates"]
particle_ids       = data["ParticleIDs"]

We have read in all particles at redshift \(z < 0.15\) in a cone aligned with the x axis. We can illustrate this by plotting the particles in a slice through the cone:

import unyt
in_slice = (particle_positions[:,2] > -1.0*unyt.Mpc) & (particle_positions[:,2] < 1.0*unyt.Mpc)
plt.plot(particle_positions[in_slice,0], particle_positions[in_slice,1], "k,", alpha=0.1, rasterized=True)
plt.xlabel("x [cMpc]")
plt.ylabel("y [cMpc]")
plt.show()