def test_frbhost():
repeater_coord = SkyCoord('05h31m58.698s +33d8m52.59s',
frame='icrs') # Use as host here
# Instantiate
frb121102 = FRB.by_name('FRB20121102A')
host121102 = frbgalaxy.FRBHost(repeater_coord.ra.value,
repeater_coord.dec.value, frb121102)
# Redshift
host121102.set_z(0.19273, 'spec', err=0.00008)
# Add a few nebular lines (Tendulkar+17)
neb_lines = {}
neb_lines['Halpha'] = 0.652e-16
neb_lines['Halpha_err'] = 0.009e-16
neb_lines['Halpha_Al'] = 0.622
#
neb_lines['Hbeta'] = 0.118e-16
neb_lines['Hbeta_err'] = 0.011e-16
neb_lines['Hbeta_Al'] = 0.941
AV = 2.42
# Deal with Galactic extinction
for key in neb_lines.keys():
if '_err' in key:
continue
if 'Al' in key:
continue
# Ingest
host121102.neb_lines[key] = neb_lines[key] * 10**(
neb_lines[key + '_Al'] * AV / 2.5)
host121102.neb_lines[key + '_err'] = neb_lines[key + '_err'] * 10**(
neb_lines[key + '_Al'] * AV / 2.5)
# Vette
for key in host121102.neb_lines.keys():
if '_err' in key:
continue
assert key in defs.valid_neb_lines
# Morphology
host121102.morphology['reff_ang'] = 0.41
host121102.morphology['reff_ang_err'] = 0.06
#
host121102.morphology['n'] = 2.2
host121102.morphology['n_err'] = 1.5
#
host121102.morphology['b/a'] = 0.25
host121102.morphology['b/a_err'] = 0.13
# Vet
assert host121102.vet_one('morphology')
# Vet all
assert host121102.vet_all()
# Write
outfile = data_path('test_frbhost.json')
host121102.write_to_json(outfile=outfile)
def build_host_190523(
build_photom=False): #:run_ppxf=False, build_photom=False):
"""
Build the host galaxy data for FRB 190523
Most of the data is from Ravi+2019
https://ui.adsabs.harvard.edu/abs/2019Natur.572..352R/abstract
The exception is that CRAFT (S. Simha) have run CIGALE on the photometry for
a consistent analysis with the ASKAP hosts.
Args:
build_photom:
Returns:
"""
frbname = '190523'
gal_coord = SkyCoord(ra=207.06433, dec=72.470756, unit='deg')
# Instantiate
host190523 = frbgalaxy.FRBHost(gal_coord.ra.value, gal_coord.dec.value,
frbname)
# Load redshift table
host190523.set_z(0.660, 'spec')
# Morphology
# Photometry
# PanStarrs
# Grab the table (requires internet)
photom_file = os.path.join(db_path, 'DSA', 'Ravi2019',
'ravi2019_photom.ascii')
if build_photom:
search_r = 1 * units.arcsec
ps_srvy = panstarrs.Pan_STARRS_Survey(gal_coord, search_r)
ps_tbl = ps_srvy.get_catalog(print_query=True)
photom = frbphotom.merge_photom_tables(ps_tbl, photom_file)
photom.write(photom_file,
format=frbphotom.table_format,
overwrite=True)
# Parse
host190523.parse_photom(
Table.read(photom_file, format=frbphotom.table_format))
# PPXF
'''
if run_ppxf:
results_file = os.path.join(db_path, 'CRAFT', 'Bhandari2019', 'HG190608_SDSS_ppxf.ecsv')
meta, spectrum = host190608.get_metaspec(instr='SDSS')
spec_fit = None
ppxf.run(spectrum, 2000., host190608.z, results_file=results_file, spec_fit=spec_fit, chk=True)
host190608.parse_ppxf(os.path.join(db_path, 'CRAFT', 'Bhandari2019', 'HG190608_SDSS_ppxf.ecsv'))
'''
# CIGALE -- PanStarrs photometry but our own CIGALE analysis
host190523.parse_cigale(
os.path.join(db_path, 'DSA', 'Ravi2019', 'S1_190523_CIGALE.fits'))
# Derived quantities
host190523.derived['SFR_nebular'] = 1.3
host190523.derived['SFR_nebular_err'] = -999.
# Vet all
host190523.vet_all()
# Write -- BUT DO NOT ADD TO REPO (YET)
path = resource_filename('frb', 'data/Galaxies/{}'.format(frbname))
host190523.write_to_json(path=path)
def build_host_121102(build_photom=False):
"""
Generate the JSON file for FRB 121102
Writes to 121102/FRB121102_host.json
All of the data currently comes from Tendulkar et al. 2017
Args:
build_photom (bool, optional): Generate the photometry file in the Galaxy_DB
"""
FRB_coord = SkyCoord('05h31m58.698s +33d8m52.59s', frame='icrs')
# Eyeball Tendulkar+17 PA
gal_coord = FRB_coord.directional_offset_by(-45 * units.deg,
286e-3 * units.arcsec)
# Instantiate
host121102 = frbgalaxy.FRBHost(gal_coord.ra.value, gal_coord.dec.value,
'121102')
# Redshift
host121102.set_z(0.19273, 'spec', err=0.00008)
# Photometry -- Tendulkar 2017
photom_file = os.path.join(db_path, 'Repeater', 'Tendulkar2017',
'tendulkar2017_photom.ascii')
if build_photom:
photom = Table()
#photom['Name'] = ['HG121102'] DO NOT USE str columns!
photom['ra'] = [host121102.coord.ra.value]
photom['dec'] = host121102.coord.dec.value
#
photom['GMOS_r'] = 25.1
photom['GMOS_r_err'] = 0.1
photom['GMOS_i'] = 23.9
photom['GMOS_i_err'] = 0.1
# Write
photom = frbphotom.merge_photom_tables(photom, photom_file)
embed(header='60')
photom.write(photom_file,
format=frbphotom.table_format,
overwrite=True)
host121102.parse_photom(
Table.read(photom_file, format=frbphotom.table_format))
# Nebular lines
neb_lines = {}
neb_lines['Halpha'] = 0.652e-16
neb_lines['Halpha_err'] = 0.009e-16
neb_lines['Halpha_Al'] = 0.622
#
neb_lines['Hbeta'] = 0.118e-16
neb_lines['Hbeta_err'] = 0.011e-16
neb_lines['Hbeta_Al'] = 0.941
#
neb_lines['[OIII] 5007'] = 0.575e-16
neb_lines['[OIII] 5007_err'] = 0.011e-16
neb_lines['[OIII] 5007_Al'] = 0.911
#
neb_lines[
'[NII] 6584'] = 0.030e-16 # * units.erg/units.cm**2/units.s # Upper limit
neb_lines[
'[NII] 6584_err'] = -999. # * units.erg/units.cm**2/units.s # Upper limit
neb_lines['[NII] 6584_Al'] = 0.619
AV = 2.42
# Extinction correct
for key in neb_lines.keys():
if '_err' in key:
continue
if 'Al' in key:
continue
# Ingest
host121102.neb_lines[key] = neb_lines[key] * 10**(
neb_lines[key + '_Al'] * AV / 2.5)
if neb_lines[key + '_err'] > 0:
host121102.neb_lines[key +
'_err'] = neb_lines[key + '_err'] * 10**(
neb_lines[key + '_Al'] * AV / 2.5)
else:
host121102.neb_lines[key + '_err'] = neb_lines[key + '_err']
# Vette
for key in host121102.neb_lines.keys():
if '_err' in key:
continue
assert key in defs.valid_neb_lines
# Morphology
host121102.morphology['reff_ang'] = 0.41
host121102.morphology['reff_ang_err'] = 0.06
#
host121102.morphology['n'] = 2.2
host121102.morphology['n_err'] = 1.5
#
host121102.morphology['b/a'] = 0.25
host121102.morphology['b/a_err'] = 0.13
# Derived quantities
host121102.derived['M_r'] = -17.0 # AB; Tendulkar+17
host121102.derived['M_r_err'] = 0.2 # Estimated by JXP
host121102.derived['SFR_nebular'] = 0.23 # MSun/yr; Tendulkar+17
host121102.derived['Mstar'] = 5.5e7 # Msun; Tendulkar+17
host121102.derived['Mstar_err'] = 1.5e7 # Msun; Tendulkar+17
host121102.derived[
'Z_spec'] = -0.16 # Tendulkar+17 on a scale with Solar O/H = 8.86
host121102.derived['Z_spec_err'] = -999. # Tendulkar+17
# Vet
assert host121102.vet_all()
# Write
path = resource_filename('frb', 'data/Galaxies/121102')
host121102.write_to_json(path=path, overwrite=True)
def build_host_181112(build_photom=False):
""" Build the host galaxy data for FRB 181112
All of the data comes from Prochaska+2019, Science, in press
Args:
build_photom (bool, optional):
"""
frbname = '181112'
FRB_coord = SkyCoord('J214923.63-525815.39',
unit=(units.hourangle,
units.deg)) # Cherie; 2019-04-17 (Slack)
# Coord from DES
Host_coord = SkyCoord('J214923.66-525815.28',
unit=(units.hourangle, units.deg)) # from DES
# Instantiate
host181112 = frbgalaxy.FRBHost(Host_coord.ra.value, Host_coord.dec.value,
frbname)
host181112.frb_coord = FRB_coord
# Redshift
host181112.set_z(0.4755, 'spec', err=7e-5)
# ############
# Photometry
# DES
# Grab the table (requires internet)
search_r = 2 * units.arcsec
des_srvy = des.DES_Survey(Host_coord, search_r)
des_tbl = des_srvy.get_catalog(print_query=True)
host181112.parse_photom(des_tbl)
# VLT -- Lochlan 2019-05-02
# VLT -- Lochlan 2019-06-18
photom_file = os.path.join(db_path, 'CRAFT', 'Prochaska2019',
'prochaska2019_photom.ascii')
if build_photom:
photom = Table()
photom['Name'] = ['HG{}'.format(frbname)]
photom['ra'] = host181112.coord.ra.value
photom['dec'] = host181112.coord.dec.value
photom['VLT_g'] = 22.57
photom['VLT_g_err'] = 0.04
photom['VLT_I'] = 21.51
photom['VLT_I_err'] = 0.04
# Add in DES
for key in host181112.photom.keys():
photom[key] = host181112.photom[key]
# Merge/write
photom = frbphotom.merge_photom_tables(photom, photom_file)
photom.write(photom_file,
format=frbphotom.table_format,
overwrite=True)
host181112.parse_photom(
Table.read(photom_file, format=frbphotom.table_format))
# Nebular lines
host181112.parse_ppxf(
os.path.join(db_path, 'CRAFT', 'Prochaska2019',
'HG181112_FORS2_ppxf.ecsv'))
# Adjust errors on Ha, [NII] because of telluric
# Derived quantities
host181112.calc_nebular_AV('Ha/Hb', min_AV=0.)
# Ha is tough in telluric
host181112.calc_nebular_SFR('Hb', AV=0.15) # Photometric
# This would be an upper limit
#host.calc_nebular_SFR('Ha')
# CIGALE
host181112.parse_cigale(
os.path.join(db_path, 'CRAFT', 'Prochaska2019',
'HG181112_CIGALE.fits'))
# Write
path = resource_filename('frb', 'data/Galaxies/{}'.format(frbname))
host181112.write_to_json(path=path)
def build_host_180924(build_photom=True):
"""
Generate the JSON file for FRB 180924
All data are from Bannister et al. 2019
https://ui.adsabs.harvard.edu/abs/2019Sci...365..565B/abstract
Writes to 180924/FRB180924_host.json
Args:
build_photom (bool, optional): Generate the photometry file in the Galaxy_DB
"""
gal_coord = SkyCoord('J214425.25-405400.81',
unit=(units.hourangle, units.deg))
# Instantiate
host = frbgalaxy.FRBHost(gal_coord.ra.value, gal_coord.dec.value, '180924')
# Redshift
host.set_z(0.3212, 'spec')
# Morphology
host.parse_galfit(
os.path.join(db_path, 'CRAFT', 'Bannister2019',
'HG180924_DES_galfit.log'), 0.263)
# Photometry
# DES
# Grab the table (requires internet)
photom_file = os.path.join(db_path, 'CRAFT', 'Bannister2019',
'bannister2019_photom.ascii')
if build_photom:
search_r = 2 * units.arcsec
des_srvy = des.DES_Survey(gal_coord, search_r)
des_tbl = des_srvy.get_catalog(print_query=True)
# Write
des_tbl.write(photom_file, format=frbphotom.table_format)
else:
des_tbl = Table.read(photom_file, format=frbphotom.table_format)
# Parse
host.parse_photom(des_tbl)
# PPXF
host.parse_ppxf(
os.path.join(db_path, 'CRAFT', 'Bannister2019',
'HG180924_MUSE_ppxf.ecsv'))
# Derived quantities
# AV
host.calc_nebular_AV('Ha/Hb')
# SFR
host.calc_nebular_SFR('Ha')
host.derived['SFR_nebular_err'] = -999.
# CIGALE
host.parse_cigale(
os.path.join(db_path, 'CRAFT', 'Bannister2019',
'HG180924_CIGALE.fits'), 0.263)
# Vet all
host.vet_all()
# Write
path = resource_filename('frb', 'data/Galaxies/180924')
host.write_to_json(path=path)
def build_host_180916(run_ppxf=False, build_photom=False, build_cigale=False):
""" Build the host galaxy data for FRB 180916
All of the data currently comes from Marcote et al. 2020
https://ui.adsabs.harvard.edu/abs/2020Natur.577..190M/abstract
CIGALE will be published in Kasper et al. 2020
Args:
build_photom (bool, optional):
"""
frbname = '180916'
gal_coord = SkyCoord('J015800.28+654253.0',
unit=(units.hourangle, units.deg))
# Instantiate
host180916 = frbgalaxy.FRBHost(gal_coord.ra.value, gal_coord.dec.value,
frbname)
# Load redshift table
#ztbl = Table.read(os.path.join(db_path, 'CRAFT', 'Bhandari2019', 'z_SDSS.ascii'),
# format='ascii.fixed_width')
#z_coord = SkyCoord(ra=ztbl['RA'], dec=ztbl['DEC'], unit='deg')
#idx, d2d, _ = match_coordinates_sky(gal_coord, z_coord, nthneighbor=1)
#if np.min(d2d) > 0.5*units.arcsec:
# embed(header='190608')
# Redshift -- SDSS
#host180916.set_z(ztbl[idx]['ZEM'], 'spec')
# Redshift
host180916.set_z(0.0337, 'spec')
# Morphology
#host190608.parse_galfit(os.path.join(photom_path, 'CRAFT', 'Bannister2019',
# 'HG180924_galfit_DES.log'), 0.263)
# Photometry
EBV = nebular.get_ebv(gal_coord)['meanValue'] #
print("EBV={} for the host of {}".format(EBV, frbname))
# SDSS
# Grab the table (requires internet)
photom_file = os.path.join(db_path, 'CHIME', 'Marcote2020',
'marcote2020_photom.ascii')
if build_photom:
# SDSS
search_r = 1 * units.arcsec
sdss_srvy = sdss.SDSS_Survey(gal_coord, search_r)
sdss_tbl = sdss_srvy.get_catalog(print_query=True)
sdss_tbl['Name'] = host180916.name
photom = frbphotom.merge_photom_tables(sdss_tbl, photom_file)
# WISE
wise_srvy = wise.WISE_Survey(gal_coord, search_r)
wise_tbl = wise_srvy.get_catalog(print_query=True)
wise_tbl['Name'] = host180916.name
photom = frbphotom.merge_photom_tables(wise_tbl, photom, debug=True)
# Write
photom.write(photom_file,
format=frbphotom.table_format,
overwrite=True)
# Parse
photom = Table.read(photom_file, format=frbphotom.table_format)
# Dust correction
frbphotom.correct_photom_table(photom, EBV)
# Parse
host180916.parse_photom(photom)
# CIGALE
cigale_file = os.path.join(db_path, 'CHIME', 'Marcote2020',
'HG180619_CIGALE.fits')
if build_cigale:
cut_photom = photom.copy()
# Remove WISE
#for column in cut_photom.keys():
# if 'WISE' in column:
# cut_photom.remove_column(column)
# Run
cigale.host_run(cut_photom, host180916, cigale_file=cigale_file)
host180916.parse_cigale(cigale_file)
# PPXF
#results_file = os.path.join(db_path, 'CRAFT', 'Bhandari2019', 'HG190608_SDSS_ppxf.ecsv')
#if run_ppxf:
# meta, spectrum = host190608.get_metaspec(instr='SDSS')
# spec_fit = None
# ppxf.run(spectrum, 2000., host190608.z, results_file=results_file, spec_fit=spec_fit, chk=True)
#host190608.parse_ppxf(results_file)
# Derived quantities
# AV
#host190608.calc_nebular_AV('Ha/Hb')
# SFR
#host190608.calc_nebular_SFR('Ha')
#host.derived['SFR_nebular_err'] = -999.
# Vet all
host180916.vet_all()
# Write -- BUT DO NOT ADD TO REPO (YET)
path = resource_filename('frb', 'data/Galaxies/{}'.format(frbname))
host180916.write_to_json(path=path)
def build_host_190608(run_ppxf=False, build_photom=False):
""" Build the host galaxy data for FRB 190608
All of the data comes from Bhandrari+2020, ApJL, in press
Args:
build_photom (bool, optional):
"""
frbname = '190608'
gal_coord = SkyCoord('J221604.90-075356.0',
unit=(units.hourangle,
units.deg)) # Cherie; 07-Mar-2019
# Instantiate
host190608 = frbgalaxy.FRBHost(gal_coord.ra.value, gal_coord.dec.value,
frbname)
# Load redshift table
ztbl = Table.read(os.path.join(db_path, 'CRAFT', 'Bhandari2019',
'z_SDSS.ascii'),
format='ascii.fixed_width')
z_coord = SkyCoord(ra=ztbl['RA'], dec=ztbl['DEC'], unit='deg')
idx, d2d, _ = match_coordinates_sky(gal_coord, z_coord, nthneighbor=1)
if np.min(d2d) > 0.5 * units.arcsec:
embed(header='190608')
# Redshift -- SDSS
host190608.set_z(ztbl[idx]['ZEM'], 'spec')
# Morphology
#host190608.parse_galfit(os.path.join(photom_path, 'CRAFT', 'Bannister2019',
# 'HG180924_galfit_DES.log'), 0.263)
# Photometry
# SDSS
# Grab the table (requires internet)
photom_file = os.path.join(db_path, 'CRAFT', 'Bhandari2019',
'bhandari2019_photom.ascii')
if build_photom:
# VLT
search_r = 1 * units.arcsec
# SDSS
sdss_srvy = sdss.SDSS_Survey(gal_coord, search_r)
sdss_tbl = sdss_srvy.get_catalog(print_query=True)
sdss_tbl['Name'] = 'HG190608'
photom = frbphotom.merge_photom_tables(sdss_tbl, photom_file)
# WISE
wise_srvy = wise.WISE_Survey(gal_coord, search_r)
wise_tbl = wise_srvy.get_catalog(print_query=True)
wise_tbl['Name'] = 'HG190608'
# Write
photom = frbphotom.merge_photom_tables(wise_tbl, photom, debug=True)
photom.write(photom_file,
format=frbphotom.table_format,
overwrite=True)
# Parse
host190608.parse_photom(
Table.read(photom_file, format=frbphotom.table_format))
# PPXF
results_file = os.path.join(db_path, 'CRAFT', 'Bhandari2019',
'HG190608_SDSS_ppxf.ecsv')
if run_ppxf:
meta, spectrum = host190608.get_metaspec(instr='SDSS')
spec_fit = None
ppxf.run(spectrum,
2000.,
host190608.z,
results_file=results_file,
spec_fit=spec_fit,
chk=True)
host190608.parse_ppxf(results_file)
# Derived quantities
# AV
host190608.calc_nebular_AV('Ha/Hb')
# SFR
host190608.calc_nebular_SFR('Ha')
#host.derived['SFR_nebular_err'] = -999.
# CIGALE
host190608.parse_cigale(
os.path.join(db_path, 'CRAFT', 'Bhandari2019', 'HG190608_CIGALE.fits'))
# Vet all
host190608.vet_all()
# Write
path = resource_filename('frb', 'data/Galaxies/{}'.format(frbname))
host190608.write_to_json(path=path)
def build_host_190102(run_ppxf=False, build_photom=False):
""" Build the host galaxy data for FRB 190102
All of the data comes from Bhandrari+2020, ApJL, in press
Args:
build_photom (bool, optional):
"""
frbname = '190102'
# Stuart on June 17, 2019
# -- Astrometry.net !
gal_coord = SkyCoord('J212939.60-792832.4',
unit=(units.hourangle,
units.deg)) # Cherie; 07-Mar-2019
# Instantiate
host190102 = frbgalaxy.FRBHost(gal_coord.ra.value, gal_coord.dec.value,
frbname)
# Redshift -- Gaussian fit to [OIII 5007] in MagE
# Looks great on the other lines
# Ok on FORS2 Halpha
wv_oiii = 6466.48
z_OIII = wv_oiii / 5008.239 - 1
host190102.set_z(z_OIII, 'spec')
photom_file = os.path.join(db_path, 'CRAFT', 'Bhandari2019',
'bhandari2019_photom.ascii')
# VLT/FORS2 -- Pulled from draft on 2019-06-23
# VLT/FORS2 -- Pulled from spreadsheet 2019-06-23
if build_photom:
photom = Table()
photom['ra'] = [host190102.coord.ra.value]
photom['dec'] = host190102.coord.dec.value
photom['Name'] = host190102.name
photom['VLT_u'] = 23. # Dust corrected
photom['VLT_u_err'] = -999.
photom['VLT_g'] = 21.8 # Dust corrected
photom['VLT_g_err'] = 0.1
photom['VLT_I'] = 20.71 # Dust corrected
photom['VLT_I_err'] = 0.05
photom['VLT_z'] = 20.5 # Dust corrected
photom['VLT_z_err'] = 0.2
# Write
photom = frbphotom.merge_photom_tables(photom, photom_file)
photom.write(photom_file,
format=frbphotom.table_format,
overwrite=True)
host190102.parse_photom(
Table.read(photom_file, format=frbphotom.table_format))
# PPXF
if run_ppxf:
# MagE
results_file = os.path.join(db_path, 'CRAFT', 'Bhandari2019',
'HG190102_MagE_ppxf.ecsv')
spec_fit = os.path.join(db_path, 'CRAFT', 'Bhandari2019',
'HG190102_MagE_ppxf.fits')
meta, spectrum = host190102.get_metaspec(instr='MagE')
R = meta['R']
# Correct for Galactic extinction
ebv = float(nebular.get_ebv(host190102.coord)['meanValue'])
AV = ebv * 3.1 # RV
#alAV = nebular.load_extinction('MW')
#Al = alAV(spectrum.wavelength.value) * AV
Al = extinction.ccm89(spectrum.wavelength.value, AV, 3.1)
# New spec
new_flux = spectrum.flux * 10**(Al / 2.5)
new_sig = spectrum.sig * 10**(Al / 2.5)
new_spec = XSpectrum1D.from_tuple(
(spectrum.wavelength, new_flux, new_sig))
# Mask
atmos = [(7550, 7750)]
ppxf.run(new_spec,
R,
host190102.z,
results_file=results_file,
spec_fit=spec_fit,
chk=True,
atmos=atmos)
host190102.parse_ppxf(
os.path.join(db_path, 'CRAFT', 'Bhandari2019',
'HG190102_MagE_ppxf.ecsv'))
# Derived quantities
# AV
host190102.calc_nebular_AV('Ha/Hb')
# SFR
host190102.calc_nebular_SFR('Ha')
# CIGALE
host190102.parse_cigale(
os.path.join(db_path, 'CRAFT', 'Bhandari2019', 'HG190102_CIGALE.fits'))
# Vet all
host190102.vet_all()
# Write -- BUT DO NOT ADD TO REPO (YET)
path = resource_filename('frb', 'data/Galaxies/{}'.format(frbname))
host190102.write_to_json(path=path)
def build_host_180924(build_photom=True):
"""
Generate the JSON file for FRB 180924
All data are from Bannister et al. 2019
https://ui.adsabs.harvard.edu/abs/2019Sci...365..565B/abstract
Writes to 180924/FRB180924_host.json
Args:
build_photom (bool, optional): Generate the photometry file in the Galaxy_DB
"""
frbname = '180924'
gal_coord = SkyCoord('J214425.25-405400.81',
unit=(units.hourangle, units.deg))
# Instantiate
host = frbgalaxy.FRBHost(gal_coord.ra.value, gal_coord.dec.value, '180924')
# Redshift -- JXP measured from multiple data sources
host.set_z(0.3212, 'spec')
# Morphology
host.parse_galfit(
os.path.join(db_path, 'CRAFT', 'Bannister2019',
'HG180924_DES_galfit.log'), 0.263)
# Photometry
# DES
search_r = 2 * units.arcsec
des_srvy = des.DES_Survey(gal_coord, search_r)
des_tbl = des_srvy.get_catalog(print_query=True)
host.parse_photom(des_tbl)
# Grab the table (requires internet)
photom_file = os.path.join(db_path, 'CRAFT', 'Bannister2019',
'bannister2019_photom.ascii')
if build_photom:
photom = Table()
photom['Name'] = ['HG{}'.format(frbname)]
photom['ra'] = host.coord.ra.value
photom['dec'] = host.coord.dec.value
photom['VLT_g'] = 21.38
photom['VLT_g_err'] = 0.04
photom['VLT_I'] = 20.10
photom['VLT_I_err'] = 0.02
# Add in DES
for key in host.photom.keys():
photom[key] = host.photom[key]
# Merge/write
photom = frbphotom.merge_photom_tables(photom, photom_file)
photom.write(photom_file,
format=frbphotom.table_format,
overwrite=True)
# Parse
host.parse_photom(Table.read(photom_file, format=frbphotom.table_format))
#host.parse_photom(des_tbl)
# PPXF
host.parse_ppxf(
os.path.join(db_path, 'CRAFT', 'Bannister2019',
'HG180924_MUSE_ppxf.ecsv'))
# Derived quantities
# AV
host.calc_nebular_AV('Ha/Hb')
# SFR
host.calc_nebular_SFR('Ha')
host.derived['SFR_nebular_err'] = -999.
# CIGALE
host.parse_cigale(
os.path.join(db_path, 'CRAFT', 'Bannister2019',
'HG180924_CIGALE.fits'), 0.263)
# Vet all
host.vet_all()
# Write
path = resource_filename('frb', 'data/Galaxies/180924')
host.write_to_json(path=path)
