First Light And Reionisation Epoch Simulations (FLARES)

A python convenience module for working with FLARES data.

Requirements

• numpy
• h5py
• scipy
• schwimmbad
• eagle_IO
• SynthObs

Installation

Run the following to add this module permanently to your path:

export PYTHONPATH=$PYTHONPATH:/path/to/this/directory/flares

You can then just run

import flares

in any other scripts to get the flares class and associated functionality.

Set up and data location

The FLARES data on COSMA are located here:

/cosma7/data/dp004/FLARES/FLARES-1

You may need to update this location in flares.py#L29 by changing the self.directory string.

Tutorial

flares.py contains the flares class, which contains a lot of useful functionality for analysing the resims. The most important information is the specified halos (flares.halos) and snapshots (flares.tags) you wish to analyse; these should be updated as new resims are completed.

download_methods.py fetches the specified arrays from all resims and puts them in a single hdf5 file in the data/ folder. Simply update these scripts with the data you wish to download, specify if you wish to overwrite any existing data. Run this script using one of the following batchscripts, download_particles.cosma.sh for getting the particle data and run download_subfind.py for just the subfind data. If you wish to generate photometry for all galaxies you will need to run create_UVgrid.cosma.sh to get the value of kappa, and use download_phot.cosma.sh to extract the photometry information.

Once this has completed you will have a single file data/flares.hdf5 with the following (rough) data structure: Resim_num/Property_type/Property, where Resim_num is the number of resims (see here), Property_type can be either Galaxy (like stellar mass, sfr, etc) or Particle (individual properties of gas/stellar particles) and Property is the required property.

Example

Once the data is downloaded, you can use it as so,

import flares
fl = flares.flares('./data/flares.hdf5', sim_type='FLARES')

mstar = fl.load_dataset('Mstar_30', arr_type='Galaxy')

halo = fl.halos[0]
tag = fl.tags[0]

print (mstar[halo][tag][:10])

Creating distribution functions, e.g: stellar mass function for z=5:

import numpy as np
import pandas as pd
import matplotlib
matplotlib.rcParams['text.usetex'] = True
import matplotlib.pyplot as plt
import flares

fl = flares.flares('./data/flares.hdf5', sim_type='FLARES')
halo = fl.halos
tag = fl.tags[-1]
volume = (4/3)*np.pi*(fl.radius**3)

mstar = fl.load_dataset('Mstar_30', arr_type='Galaxy')
df = pd.read_csv('weight_files/weights_grid.txt')
weights = np.array(df['weights'])

bins = np.arange(8, 11.5, 0.2)
bincen = (bins[1:]+bins[:-1])/2.
binwidth = bins[1:] - bins[:-1]

hist = np.zeros(len(bins)-1)
err = np.zeros(len(bins)-1)

for ii in range(len(weights)):
    tmp, bin_edges = np.histogram(np.log10(mstar[halo[ii]][tag]), bins = bins)
    hist+=tmp*weights[ii]
    err+=np.square(np.sqrt(tmp)*weights[ii])
    
smf = hist/(volume*binwidth)
smf_err = np.sqrt(err)/(volume*binwidth)

fig, axs = plt.subplots(nrows=1, ncols=1, figsize=(5, 5), facecolor='w', edgecolor='k')

y_lo, y_up = np.log10(smf)-np.log10(smf-smf_err), np.log10(smf+smf_err)-np.log10(smf)
axs.errorbar(bincen, np.log10(smf), yerr=[y_lo, y_up], ls='', marker='o', label=rF"Flares $z={float(tag[5:].replace('p','.'))}$")

axs.set_ylabel(r'$\mathrm{log}_{10}(\Phi/(\mathrm{cMpc}^{-3}\mathrm{dex}^{-1}))$', fontsize=14)
axs.set_xlabel(r'$\mathrm{log}_{10}(\mathrm{M}_{\star}/\mathrm{M_{\odot}})$', fontsize=14)
axs.set_xlim((8, 11.4))
axs.set_ylim((-8.2, -0.8))
axs.set_xticks(np.arange(8., 11.5, 1))
axs.grid(True, alpha = 0.5)
axs.legend(frameon=False, fontsize = 14, numpoints=1, ncol = 2)
axs.minorticks_on()
axs.tick_params(axis='x', which='minor', direction='in')
axs.tick_params(axis='y', which='minor', direction='in')
for label in (axs.get_xticklabels() + axs.get_yticklabels()):
    label.set_fontsize(13)

plt.show()

Extracting stellar particle information,

import numpy as np
import h5py
fname = './data/flares.hdf5'
num = '00'
with h5py.File(fname, 'r') as hf:
    S_len = np.array(hf[num+'/'+tag+'/Galaxy'].get('S_Length'), dtype = np.int64)
    S_mass = np.array(hf[num+'/'+tag+'/Particle'].get('S_Mass'), dtype = np.float64)
    S_Z = np.array(hf[num+'/'+tag+'/Particle'].get('S_Z'), dtype = np.float64)
    S_age = np.array(hf[num+'/'+tag+'/Particle'].get('S_Age'), dtype = np.float64)*1e3

begin = np.zeros(len(S_len), dtype = np.int64)
end = np.zeros(len(S_len), dtype = np.int64)
begin[1:] = np.cumsum(S_len)[:-1]
end = np.cumsum(S_len)

#Age of all particles belonging to first galaxy in resim region 'num'

print (S_age[begin[0]:end[0]])