Galaxy Utilities

Utilities related to FRB galaxies

frb.galaxies.utils.deredden_spec(spectrum, ebv: float)[source]

Deredden the input spectrum using the input EBV value

Parameters:

  • spectrum (xspectrum1d.XSpectrum1D) – Spectrum

  • ebv (float) – Galactic reddening

Returns:

De-reddened spectrum

Return type:

xspectrum1d.XSpectrum1D

frb.galaxies.utils.load_specdb(specdb_file=None)[source]

Automatically load the specDB file from $SPECDB/FRB_specDB.hdf5

Parameters:

specdb_file (str, optional) – Over-ride the default file

Return type:

specdb.specdb.SpecDB

frb.galaxies.utils.list_of_hosts(skip_bad_hosts=True, verbose: bool = False)[source]

Scan through the Repo and generate a list of FRB Host galaxies

Also returns a list of the FRBs

Parameters:

  • skip_bad_hosts (bool)

  • verbose (bool) – If True, print more to the screen

Return type:

list, list

frb.galaxies.utils.build_table_of_hosts(attrs: list = None)[source]

Generate a Pandas table of FRB Host galaxy data. These are slurped from the ‘derived’, ‘photom’, and ‘neb_lines’ dicts of each host object

Warning: As standard, missing values are given NaN in the Pandas table

Be careful!

Note

RA, DEC are given as RA_host, DEC_host to avoid conflict with the FRB table

Args:

Returns:

Table of data on FRB host galaxies, dict of their units

Return type:

pd.DataFrame, dict

frb.galaxies.utils.load_f_mL()[source]

Generate an interpolater from mag to Luminosity as a function of redshift (up to z=4)

Warning: this is rather approximate

Return type:

scipy.interpolate.interp1d

frb.galaxies.utils.load_PATH(PATH_root_file: str = 'adopted.csv')[source]

Load up the PATH table

Parameters:

PATH_root_file (str, optional) – [description]. Defaults to ‘adopted.csv’.

Returns:

Table of galaxy coordinates and PATH results

Return type:

pandas.DataFrame

Overview

This module provides utility functions for working with FRB host galaxy data, including database operations, table building, and various helper functions for galaxy analysis.

Functions

Database and Loading Functions

frb.galaxies.utils.load_specdb(specdb_file=None)[source]

Automatically load the specDB file from $SPECDB/FRB_specDB.hdf5

Parameters:

specdb_file (str, optional) – Over-ride the default file

Return type:

specdb.specdb.SpecDB

Load spectroscopic database for galaxy analysis.

frb.galaxies.utils.list_of_hosts(skip_bad_hosts=True, verbose: bool = False)[source]

Scan through the Repo and generate a list of FRB Host galaxies

Also returns a list of the FRBs

Parameters:

  • skip_bad_hosts (bool)

  • verbose (bool) – If True, print more to the screen

Return type:

list, list

Generate a list of FRB host galaxies from the database.

frb.galaxies.utils.build_table_of_hosts(attrs: list = None)[source]

Generate a Pandas table of FRB Host galaxy data. These are slurped from the ‘derived’, ‘photom’, and ‘neb_lines’ dicts of each host object

Warning: As standard, missing values are given NaN in the Pandas table

Be careful!

Note

RA, DEC are given as RA_host, DEC_host to avoid conflict with the FRB table

Args:

Returns:

Table of data on FRB host galaxies, dict of their units

Return type:

pd.DataFrame, dict

Generate a Pandas table of FRB host galaxy data.

This function extracts data from host objects and compiles it into a comprehensive table including photometry, derived quantities, nebular line measurements, and morphological parameters.

Analysis Utilities

frb.galaxies.utils.load_f_mL()[source]

Generate an interpolater from mag to Luminosity as a function of redshift (up to z=4)

Warning: this is rather approximate

Return type:

scipy.interpolate.interp1d

Generate interpolator from magnitude to luminosity as function of redshift.

Provides approximate magnitude-luminosity relationship up to z=4 for galaxy luminosity function analysis.

frb.galaxies.utils.load_PATH(PATH_root_file: str = 'adopted.csv')[source]

Load up the PATH table

Parameters:

PATH_root_file (str, optional) – [description]. Defaults to ‘adopted.csv’.

Returns:

Table of galaxy coordinates and PATH results

Return type:

pandas.DataFrame

Load up the PATH (Probabilistic Association of Transients with Hosts) table.

frb.galaxies.utils.deredden_spec(spectrum, ebv: float)[source]

Deredden the input spectrum using the input EBV value

Parameters:

  • spectrum (xspectrum1d.XSpectrum1D) – Spectrum

  • ebv (float) – Galactic reddening

Returns:

De-reddened spectrum

Return type:

xspectrum1d.XSpectrum1D

Apply dereddening correction to galaxy spectra using dust extinction models.

Data Processing Functions

Output and Table Management

The module provides several functions for managing host galaxy data tables:

Table Structure

  • Coordinates stored as RA_host, DEC_host (degrees)

  • FRB names and objects included for cross-referencing

  • Units tracked in separate dictionary

  • Missing values handled with NaN

Data Categories

  • Photometric measurements across multiple surveys

  • Derived physical properties (mass, SFR, metallicity)

  • Nebular emission line fluxes and ratios

  • Morphological parameters from imaging analysis

  • Redshift measurements (spectroscopic and photometric)

  • Offset measurements from FRB positions

Examples

Building host galaxy table:

from frb.galaxies import utils

# Build comprehensive host table
host_table, units_dict = utils.build_table_of_hosts(
    attrs=['derived', 'photom', 'neb_lines', 'morphology']
)

# Display table info
print(f"Number of hosts: {len(host_table)}")
print(f"Available columns: {list(host_table.columns)}")
print(f"Units: {units_dict}")

Working with individual hosts:

# Get list of host objects
frbs, hosts = utils.list_of_hosts(verbose=True)

# Access specific host properties
for host in hosts[:5]:  # First 5 hosts
    print(f"Host: {host.name}")
    print(f"  RA, Dec: {host.coord.ra.deg:.3f}, {host.coord.dec.deg:.3f}")
    print(f"  Redshift: {host.z}")
    if len(host.derived) > 0:
        if 'Mstar' in host.derived:
            print(f"  Stellar mass: {host.derived['Mstar']:.2e} Msun")

Loading and using spectroscopic database:

# Load spectroscopic database
specdb = utils.load_specdb(specdb_file='custom_specdb.hdf5')

if specdb is not None:
    # Query for spectra
    meta = specdb.meta_from_coords(coords, radius=5*u.arcsec)
    if len(meta) > 0:
        spectra = specdb.spectra_from_meta(meta)

Dereddening corrections:

from linetools.spectra.xspectrum1d import XSpectrum1D

# Load spectrum
spec = XSpectrum1D.from_file('galaxy_spectrum.fits')

# Apply dereddening with AV = 0.5 mag
corrected_spec = utils.deredden_spec(spec, AV=0.5)

PATH analysis integration:

# Load PATH results
path_table = utils.load_PATH('adopted.csv')

# Build host table with PATH probabilities
host_table, units = utils.build_table_of_hosts()

# PATH probabilities now included as P_Ox, P_O columns
high_prob_hosts = host_table[host_table['P_Ox'] > 0.8]
print(f"High probability associations: {len(high_prob_hosts)}")

Working with magnitude-luminosity relations:

# Load m-L interpolator
f_mL = utils.load_f_mL()

# Get characteristic magnitude at different redshifts
z_array = [0.1, 0.3, 0.5, 1.0, 2.0]
m_star = f_mL(z_array)

print("Characteristic magnitudes (r-band):")
for z, m in zip(z_array, m_star):
    print(f"  z = {z:.1f}: m* = {m:.2f}")